Script Syntax And Chart Functions Guide
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Script Syntax and Chart Functions
Guide
Qlik® Sense
1.1
Copyright © 1993-2015 QlikTech International AB. All rights reserved.
Copyright © 1993-2015 QlikTech International AB. All rights reserved.
Qlik®, QlikTech®, Qlik® Sense, QlikView®, Sense™ and the Qlik logo are trademarks which have been
registered in multiple countries or otherwise used as trademarks by QlikTech International AB. Other
trademarks referenced herein are the trademarks of their respective owners.
Contents
1 What is Qlik Sense?
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1.1 What can you do in Qlik Sense?
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1.2 How does Qlik Sense work?
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The app model
The associative experience
Collaboration and mobility
1.3 How can you deploy Qlik Sense?
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Qlik Sense Desktop
Qlik Sense Server
1.4 How to administer and manage a Qlik Sense site
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1.5 Extend Qlik Sense and adapt it for your own purposes
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Building extensions and mashups
Building clients
Building server tools
Connecting to other data sources
2 Script syntax
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2.1 Introduction to script syntax
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2.2 What is Backus-Naur formalism?
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2.3 Script statements and keywords
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Script control statements
Script control statements overview
Call
Do..loop
Exit script
For..next
For each..next
If..then..elseif..else..end if
Sub..end sub
Switch..case..default..end switch
Script prefixes
Script prefixes overview
Buffer
Bundle
Concatenate
Crosstable
First
Generic
Hierarchy
HierarchyBelongsTo
Image_Size
Info
Inner
IntervalMatch
Join
Keep
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Left
Mapping
NoConcatenate
Outer
Right
Sample
Semantic
Unless
When
Script regular statements
Script regular statements overview
Alias
Binary
Comment field
Comment table
Connect
Declare
Setting up a new field definition
Re-using an existing field definition
Derive
Direct Query
Direct Discovery field lists
Directory
Disconnect
Drop field
Drop table
Execute
FlushLog
Force
Load
Format specification items
Character set
Table format
Delimiter
No eof
Labels
Header is
Record is
Quotes
XML
KML
Let
Map
NullAsNull
NullAsValue
Qualify
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Rem
Rename field
Rename table
Search
Section
Select
Set
Sleep
SQL
SQLColumns
SQLTables
SQLTypes
Star
Store
Tag
Trace
Unmap
Unqualify
Untag
2.4 Working with variables in the data load editor
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Variable calculation
System variables
System variables overview
HidePrefix
HideSuffix
Include
OpenUrlTimeout
StripComments
Verbatim
Value handling variables
Value handling variables overview
NullDisplay
NullInterpret
NullValue
OtherSymbol
Number interpretation variables
Number interpretation variables overview
Currency formatting
Number formatting
Time formatting
BrokenWeeks
DateFormat
DayNames
DecimalSep
FirstWeekDay
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Contents
LongDayNames
LongMonthNames
MoneyDecimalSep
MoneyFormat
MoneyThousandSep
MonthNames
ReferenceDay
ThousandSep
TimeFormat
TimestampFormat
Direct Discovery variables
Direct Discovery system variables
Teradata query banding variables
Direct Discovery character variables
Direct Discovery number interpretation variables
Error variables
Error variables overview
ErrorMode
ScriptError
ScriptErrorCount
ScriptErrorList
2.5 Script expressions
3 Visualization expressions
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3.1 Defining the aggregation scope
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3.2 Syntax for sets
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3.3 Set modifiers
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Based on another field
Based on element sets (a field value list in the modifier)
Forced exclusion
Set modifiers with set operators
Set modifiers using assignments with implicit set operators
Set modifiers with advanced searches
Set modifiers with dollar-sign expansions
Set modifiers with implicit field value definitions
3.4 Visualization expression and aggregation syntax
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General syntax for chart expressions
General syntax for aggregations
4 Operators
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4.1 Bit operators
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4.2 Logical operators
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4.3 Numeric operators
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4.4 Relational operators
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4.5 String operators
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5 Functions in scripts and chart expressions
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Contents
5.1 Aggregation functions
Using aggregation functions in a data load script
Using aggregation functions in chart expressions
Aggr - chart function
Basic aggregation functions
Basic aggregation functions overview
Basic aggregation functions in the data load script
Basic aggregation functions in chart expressions
FirstSortedValue
FirstSortedValue - chart function
Max
Max - chart function
Min
Min - chart function
Mode
Mode - chart function
Only
Only - chart function
Sum
Sum - chart function
Counter aggregation functions
Counter aggregation functions in the data load script
Counter aggregation functions in chart expressions
Count
Count - chart function
MissingCount
MissingCount - chart function
NullCount
NullCount - chart function
NumericCount
NumericCount - chart function
TextCount
TextCount - chart function
Financial aggregation functions
Financial aggregation functions in the data load script
Financial aggregation functions in chart expressions
IRR
IRR - chart function
NPV
NPV - chart function
XIRR
XIRR - chart function
XNPV
XNPV - chart function
Statistical aggregation functions
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Statistical aggregation functions in the data load script
Statistical aggregation functions in chart expressions
Avg
Avg - chart function
Correl
Correl - chart function
Fractile
Fractile - chart function
Kurtosis
Kurtosis - chart function
LINEST_B
LINEST_B - chart function
LINEST_DF
LINEST_DF - chart function
LINEST_F
LINEST_F - chart function
LINEST_M
LINEST_M - chart function
LINEST_R2
LINEST_R2 - chart function
LINEST_SEB
LINEST_SEB - chart function
LINEST_SEM
LINEST_SEM - chart function
LINEST_SEY
LINEST_SEY - chart function
LINEST_SSREG
LINEST_SSREG - chart function
LINEST_SSRESID
LINEST_SSRESID - chart function
Median
Median - chart function
Skew
Skew - chart function
Stdev
Stdev - chart function
Sterr
Sterr - chart function
STEYX
STEYX - chart function
An example of how to use linest functions
Loading the sample data
Displaying the results from the data load script calculations
Creating the linest chart function visualizations
Statistical test functions
Chi-2 test functions
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T-test functions
Z-test functions
Chi2-test functions
Chi2Test_chi2
Chi2Test_df
Chi2Test_p - chart function
T-test functions
TTest_conf
TTest_df
TTest_dif
TTest_lower
TTest_sig
TTest_sterr
TTest_t
TTest_upper
TTestw_conf
TTestw_df
TTestw_dif
TTestw_lower
TTestw_sig
TTestw_sterr
TTestw_t
TTestw_upper
TTest1_conf
TTest1_df
TTest1_dif
TTest1_lower
TTest1_sig
TTest1_sterr
TTest1_t
TTest1_upper
TTest1w_conf
TTest1w_df
TTest1w_dif
TTest1w_lower
TTest1w_sig
TTest1w_sterr
TTest1w_t
TTest1w_upper
Z-test functions
ZTest_z
ZTest_sig
ZTest_dif
ZTest_sterr
ZTest_conf
ZTest_lower
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Contents
ZTest_upper
ZTestw_z
ZTestw_sig
ZTestw_dif
ZTestw_sterr
ZTestw_conf
ZTestw_lower
ZTestw_upper
Statistical test function examples
Examples of how to use chi2-test functions in charts
Examples of how to use chi2-test functions in the data load script
Creating a typical t-test report
Examples of how to use z-test functions
String aggregation functions
String aggregation functions in the data load script
String aggregation functions in charts
Concat
Concat - chart function
FirstValue
LastValue
MaxString
MaxString - chart function
MinString
MinString - chart function
Synthetic dimension functions
ValueList - chart function
ValueLoop - chart function
Nested aggregations
Nested aggregations with the TOTAL qualifier
5.2 Color functions
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Pre-defined color functions
ARGB
RGB
HSL
5.3 Conditional functions
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Conditional functions overview
alt
class
if
match
mixmatch
pick
wildmatch
5.4 Counter functions
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Counter functions overview
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Contents
autonumber
autonumberhash128
autonumberhash256
fieldvaluecount
IterNo
RecNo
RowNo
RowNo - chart function
5.5 Date and time functions
Date and time functions overview
Integer expressions of time
Timestamp functions
Make functions
Other date functions
Timezone functions
Set time functions
In... functions
Start ... end functions
Day numbering functions
addmonths
addyears
age
converttolocaltime
day
dayend
daylightsaving
dayname
daynumberofquarter
daynumberofyear
daystart
firstworkdate
GMT
hour
inday
indaytotime
inlunarweek
inlunarweektodate
inmonth
inmonths
inmonthstodate
inmonthtodate
inquarter
inquartertodate
inweek
inweektodate
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inyear
inyeartodate
lastworkdate
localtime
lunarweekend
lunarweekname
lunarweekstart
makedate
maketime
makeweekdate
minute
month
monthend
monthname
monthsend
monthsname
monthsstart
monthstart
networkdays
now
quarterend
quartername
quarterstart
second
setdateyear
setdateyearmonth
timezone
today
UTC
week
weekday
weekend
weekname
weekstart
weekyear
year
yearend
yearname
yearstart
yeartodate
5.6 Exponential and logarithmic functions
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Exponential and logarithmic functions
5.7 Field functions
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Count functions
Field and selection functions
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Contents
GetAlternativeCount - chart function
GetCurrentSelections - chart function
GetExcludedCount - chart function
GetFieldSelections - chart function
GetNotSelectedCount - chart function
GetPossibleCount - chart function
GetSelectedCount - chart function
5.8 File functions
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File functions overview
Attribute
ConnectString
FileBaseName
FileDir
FileExtension
FileName
FilePath
FileSize
FileTime
GetFolderPath
QvdCreateTime
QvdFieldName
QvdNoOfFields
QvdNoOfRecords
QvdTableName
5.9 Financial functions
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Financial functions overview
BlackAndSchole
FV
nPer
Pmt
PV
Rate
5.10 Formatting functions
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Formatting functions overview
Date
Dual
Interval
Num
Money
Time
Timestamp
5.11 General numeric functions
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General numeric functions overview
Combination and permutation functions
Modulo functions
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Contents
Parity functions
Rounding functions
BitCount
Ceil
Combin
Div
Even
Fabs
Fact
Floor
Fmod
Frac
Mod
Odd
Permut
Round
Sign
5.12 Geographical functions
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Geographical functions overview
Parameters used in geographical functions
5.13 Interpretation functions
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Interpretation functions overview
Date#
Interval#
Money#
Num#
Text
Time#
Timestamp#
5.14 Inter-record functions
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Row functions
Column functions
Field functions
Pivot table functions
Inter-record functions in the data load script
Above - chart function
Below - chart function
Bottom - chart function
Column - chart function
Dimensionality - chart function
Exists
FieldIndex
FieldValue
FieldValueCount
LookUp
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NoOfRows - chart function
Peek
Previous
Top - chart function
Secondarydimensionality
After - chart function
Before - chart function
First - chart function
Last - chart function
ColumnNo - chart function
NoOfColumns - chart function
5.15 Logical functions
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5.16 Mapping functions
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Mapping functions overview
ApplyMap
MapSubstring
5.17 Mathematical functions
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5.18 NULL functions
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NULL functions overview
IsNull
NULL
5.19 Range functions
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Basic range functions
Counter range functions
Statistical range functions
Financial range functions
RangeAvg
RangeCorrel
RangeCount
RangeFractile
RangeIRR
RangeKurtosis
RangeMax
RangeMaxString
RangeMin
RangeMinString
RangeMissingCount
RangeMode
RangeNPV
RangeNullCount
RangeNumericCount
RangeOnly
RangeSkew
RangeStdev
RangeSum
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RangeTextCount
RangeXIRR
RangeXNPV
5.20 Ranking functions in charts
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Rank - chart function
HRank - chart function
5.21 Statistical distribution functions
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Statistical distribution functions overview
CHIDIST
CHIINV
FDIST
FINV
NORMDIST
NORMINV
TDIST
TINV
5.22 String functions
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String functions overview
Capitalize
Chr
Evaluate
FindOneOf
Hash128
Hash160
Hash256
Index
KeepChar
Left
Len
Lower
LTrim
Mid
Ord
PurgeChar
Repeat
Replace
Right
RTrim
SubField
SubStringCount
TextBetween
Trim
Upper
5.23 System functions
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System functions overview
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Contents
GetObjectField - chart function
ProductVersion
StateName - chart function
5.24 Table functions
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Table functions overview
FieldName
FieldNumber
NoOfFields
NoOfRows
5.25 Trigonometric and hyperbolic functions
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6 File system access restriction
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Security aspects when connecting to file based ODBC and OLE DB data connections
6.1 Limitations in standard mode
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System variables
Regular script statements
Script control statements
File functions
System functions
6.2 Disabling standard mode
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Qlik Sense
Qlik Sense Desktop
7 QlikView functions and statements not supported in Qlik Sense
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7.1 Script statements not supported in Qlik Sense
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7.2 Functions not supported in Qlik Sense
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8 Functions and statements not recommended in Qlik Sense
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8.1 Script statements not recommended in Qlik Sense
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8.2 Script statement parameters not recommended in Qlik Sense
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8.3 Functions not recommended in Qlik Sense
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ALL qualifier
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1 What is Qlik Sense?
1
What is Qlik Sense?
Qlik Sense is a platform for data analysis. With Qlik Sense you can analyze data and make data discoveries
on your own. You can share knowledge and analyze data in groups and across organizations. Qlik Sense lets
you ask and answer your own questions and follow your own paths to insight. Qlik Sense enables you and
your colleagues to reach decisions collaboratively.
1.1
What can you do in Qlik Sense?
Most Business Intelligence (BI) products can help you answer questions that are understood in advance. But
what about your follow-up questions? The ones that come after someone reads your report or sees your
visualization? With the Qlik Sense associative experience, you can answer question after question after
question, moving along your own path to insight. With Qlik Sense you can explore your data freely, with just
clicks, learning at each step along the way and coming up with next steps based on earlier findings.
1.2
How does Qlik Sense work?
Qlik Sense generates views of information on the fly for you. Qlik Sense does not require predefined and
static reports or you being dependent on other users – you just click and learn. Every time you click, Qlik
Sense instantly responds, updating every Qlik Sense visualization and view in the app with a newly
calculated set of data and visualizations specific to your selections.
The app model
Instead of deploying and managing huge business applications, you can create your own Qlik Sense apps
that you can reuse, modify and share with others. The app model helps you ask and answer the next question
on your own, without having to go back to an expert for a new report or visualization.
The associative experience
Qlik Sense automatically manages all the relationships in the data and presents information to you using a
green/white/gray metaphor. Selections are highlighted in green, associated data is represented in white,
and excluded (unassociated) data appears in gray. This instant feedback enables you to think of new
questions and continue to explore and discover.
Collaboration and mobility
Qlik Sense further enables you to collaborate with colleagues no matter when and where they are located. All
Qlik Sense capabilities, including the associative experience and collaboration, are available on mobile
devices. With Qlik Sense, you can ask and answer your questions and follow-up questions, with your
colleagues, wherever you are.
1.3
How can you deploy Qlik Sense?
There are two versions of Qlik Sense to deploy, Qlik Sense Desktop and Qlik Sense Server.
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1 What is Qlik Sense?
Qlik Sense Desktop
This is an easy-to-install single user version that is typically installed on a local computer.
Qlik Sense Server
This version is used to deploy Qlik Sense sites. A site is a collection of one or more server machines
connected to a common logical repository or central node.
1.4
How to administer and manage a Qlik Sense site
With the Qlik Management Console you can configure, manage and monitor Qlik Sense sites in an easy and
intuitive way. You can manage licenses, access and security rules, configure nodes and data source
connections and synchronize content and users among many other activities and resources.
1.5
Extend Qlik Sense and adapt it for your own purposes
Qlik Sense provides you with flexible APIs and SDKs to develop your own extensions and adapt and
integrate Qlik Sense for different purposes, such as:
Building extensions and mashups
Here you can do web development using JavaScript to build extensions that are custom visualization in Qlik
Sense apps, or you use a mashups APIs to build websites with Qlik Sense content.
Building clients
You can build clients in .NET and embed Qlik Sense objects in your own applications. You can also build
native clients in any programming language that can handle WebSocket communication by using the Qlik
Sense client protocol.
Building server tools
With service and user directory APIs you can build your own tool to administer and manage Qlik Sense sites.
Connecting to other data sources
Create Qlik Sense connectors to retrieve data from custom data sources.
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2 Script syntax
2
Script syntax
2.1
Introduction to script syntax
In the script, the name of the data source, the names of the tables and the names of the fields included in the
logic are defined. Furthermore, the fields in the access rights definition are defined in the script.
The script consists of a number of statements that are executed consecutively.
The Qlik Sense command line syntax and script syntax are described in a notation called Backus-Naur
Formalism, or BNF code.
The first lines of code are already generated when a new Qlik Sense file is created. The default values of
these number interpretation variables are derived from the regional settings of the OS.
In the script the name of the data source, the names of the tables and the names of the fields included in the
logic are defined. The script consists of a number of script statements and keywords that are executed
consecutively.
For a table file with commas, tabs or semicolons as delimiter, the LOAD-statement may be used. By default
the LOAD-statement will load all fields of the file.
A general database must be accessed through Microsoft ODBC. Here standard SQL statements are used.
The SQL syntax accepted differs between different ODBC drivers.
All script statements must end with a semicolon, " ;".
A detailed description of script syntax can be accessed via the topics in this section.
2.2
What is Backus-Naur formalism?
The Qlik Sense command line syntax and script syntax are described in a notation called Backus-Naur
formalism, also known as BNF code.
The following table provides a list of symbols used in BNF code, with a description of how they are
interpreted:
|
Logical OR: the symbol on either side can be used.
()
Parentheses defining precedence: used for structuring the BNF syntax.
[]
Square brackets: enclosed items are optional.
{}
Braces: enclosed items may be repeated zero or more times.
Symbol
A non-terminal syntactic category, that: can be divided further into other symbols. For
example, compounds of the above, other non-terminal symbols, text strings, and so on.
::=
Marks the beginning of a block that defines a symbol.
LOAD
A terminal symbol consisting of a text string. Should be written as it is into the script.
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2 Script syntax
All terminal symbols are printed in a bold face font. For example, "(" should be interpreted as a parenthesis
defining precedence, whereas " (" should be interpreted as a character to be printed in the script.
Example:
The description of the alias statement is:
alias fieldname as aliasname { , fieldname as aliasname}
This should be interpreted as the text string "alias", followed by an arbitrary field name, followed by the text
string "as", followed by an arbitrary alias name. Any number of additional combinations of "fieldname as
alias" may be given, separated by commas.
The following statements are correct:
alias a as first;
alias a as first, b as second;
alias a as first, b as second, c as third;
The following statements are not correct:
alias a as first b as second;
alias a as first { , b as second };
2.3
Script statements and keywords
The Qlik Sense script consists of a number of statements. A statement can be either a regular script
statement or a script control statement. Certain statements can be preceded by prefixes.
Regular statements are typically used for manipulating data in one way or another. These statements may be
written over any number of lines in the script and must always be terminated by a semicolon, ";".
Control statements are typically used for controlling the flow of the script execution. Each clause of a control
statement must be kept inside one script line and may be terminated by a semicolon or the end-of-line.
Prefixes may be applied to applicable regular statements but never to control statements. The when and
unless prefixes can however be used as suffixes to a few specific control statement clauses.
In the next subchapter, an alphabetical listing of all script statements, control statements and prefixes, are
found.
All script keywords can be typed with any combination of lower case and upper case characters. Field and
variable names used in the statements are however case sensitive.
Script control statements
The Qlik Sense script consists of a number of statements. A statement can be either a regular script
statement or a script control statement.
Control statements are typically used for controlling the flow of the script execution. Each clause of a control
statement must be kept inside one script line and may be terminated by semicolon or end-of-line.
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Prefixes are never applied to control statements, with the exceptions of the prefixes when and unless which
may be used with a few specific control statements.
All script keywords can be typed with any combination of lower case and upper case characters.
Script control statements overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Call
The call control statement calls a subroutine which must be defined by a previous sub statement.
Call name ( [ paramlist ])
Do..loop
The do..loop control statement is a script iteration construct which executes one or several statements until
a logical condition is met.
Do..loop [ ( while | until ) condition ] [statements]
[exit do [ ( when | unless ) condition ] [statements]
loop [ ( while | until ) condition ]
Exit script
This control statement stops script execution. It may be inserted anywhere in the script.
Exit script[ (when | unless) condition ]
For each ..next
The for each..next control statement is a script iteration construct which executes one or several statements
for each value in a comma separated list. The statements inside the loop enclosed by for and next will be
executed for each value of the list.
For each..next var in list
[statements]
[exit for [ ( when | unless ) condition ]
[statements]
next [var]
For..next
The for..next control statement is a script iteration construct with a counter. The statements inside the loop
enclosed by for and next will be executed for each value of the counter variable between specified low and
high limits.
For..next counter = expr1 to expr2 [ stepexpr3 ]
[statements]
[exit for [ ( when | unless ) condition ]
[statements]
Next [counter]
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If..then
The if..then control statement is a script selection construct forcing the script execution to follow different
paths depending on one or several logical conditions.
Since the if..then statement is a control statement and as such is ended with either a
semicolon or end-of-line, each of its four possible clauses (if..then, elseif..then, else and end
if) must not cross a line boundary.
If..then..elseif..else..end if condition then
[ statements ]
{ elseif condition then
[ statements ] }
[ else
[ statements ] ]
end if
Sub
The sub..end sub control statement defines a subroutine which can be called upon from a call statement.
Sub..end sub name [ ( paramlist )] statements end sub
Switch
The switch control statement is a script selection construct forcing the script execution to follow different
paths, depending on the value of an expression.
Switch..case..default..end switch expression {case valuelist [ statements
]} [default statements] end switch
Call
The call control statement calls a subroutine which must be defined by a previous sub statement.
Syntax:
Call name ( [ paramlist ])
Arguments:
Argument
Description
name
The name of the subroutine.
paramlist
A comma separated list of the actual parameters to be sent to the
subroutine. Each item in the list may be a field name, a variable or an
arbitrary expression.
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The subroutine called by a call statement must be defined by a sub encountered earlier during script
execution.
Parameters are copied into the subroutine and, if the parameter in the call statement is a variable and not an
expression, copied back out again upon exiting the subroutine.
Limitations:
Since the call statement is a control statement and as such is ended with either a semicolon or end-of-line, it
must not cross a line boundary.
Example 1:
// Example 1
Sub INCR (I,J)
I = I + 1
Exit Sub when I < 10
J = J + 1
End Sub
Call INCR (X,Y)
Example 2:
// Example 2 - List all QV related files on disk
sub DoDir (Root)
For Each Ext in 'qvw', 'qvo', 'qvs', 'qvt', 'qvd', 'qvc', 'qvf'
For Each File in filelist (Root&'\*.' &Ext)
LOAD
'$(File)' as Name, FileSize( '$(File)' ) as
Size, FileTime( '$(File)' ) as FileTime
autogenerate 1;
Next File
Next Ext
For Each Dir in dirlist (Root&'\*' )
Call DoDir (Dir)
Next Dir
End Sub
Call DoDir ('C:')
Do..loop
The do..loop control statement is a script iteration construct which executes one or several statements until
a logical condition is met.
Syntax:
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Do [ ( while | until ) condition ] [statements]
[exit do [ ( when | unless ) condition ] [statements]
loop[ ( while | until ) condition ]
Since the do..loop statement is a control statement and as such is ended with either a
semicolon or end-of-line, each of its three possible clauses (do, exit do and loop) must not
cross a line boundary.
Arguments:
Argument
Description
condition
A logical expression evaluating to True or False.
statements
Any group of one or more Qlik Sense script statements.
while / until
The while or until conditional clause must only appear once in any do..loop statement, i.e.
either after do or after loop. Each condition is interpreted only the first time it is
encountered but is evaluated for every time it encountered in the loop.
exit do
If an exit do clause is encountered inside the loop, the execution of the script will be
transferred to the first statement after the loop clause denoting the end of the loop. An exit
do clause can be made conditional by the optional use of a when or unless suffix.
Example:
// LOAD files file1.csv..file9.csv
Set a=1;
Do while a<10
LOAD * from file$(a).csv;
Let a=a+1;
Loop
Exit script
This control statement stops script execution. It may be inserted anywhere in the script.
Syntax:
Exit Script [ (when | unless) condition ]
Since the exit script statement is a control statement and as such is ended with either a semicolon or endof-line, it must not cross a line boundary.
Arguments:
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Argument
Description
condition
A logical expression evaluating to True or False.
when
/ unless
An exit script statement can be made conditional by the optional use of
when or unless clause.
Examples:
//Exit script
Exit Script;
//Exit script when a condition is fulfilled
Exit Script when a=1
For..next
The for..next control statement is a script iteration construct with a counter. The statements inside the loop
enclosed by for and next will be executed for each value of the counter variable between specified low and
high limits.
Syntax:
For counter = expr1 to expr2 [ step expr3 ]
[statements]
[exit for [ ( when | unless ) condition ]
[statements]
Next [counter]
The expressions expr1, expr2 and expr3 are only evaluated the first time the loop is entered. The value of the
counter variable may be changed by statements inside the loop, but this is not good programming practice.
If an exit for clause is encountered inside the loop, the execution of the script will be transferred to the first
statement after the next clause denoting the end of the loop. An exit for clause can be made conditional by
the optional use of a when or unless suffix.
Since the for..next statement is a control statement and as such is ended with either a
semicolon or end-of-line, each of its three possible clauses (for..to..step, exit for and next)
must not cross a line boundary.
Arguments:
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Argument
Description
counter
A variable name. If counter is specified after next it must be the same variable name as the
one found after the corresponding for.
expr1
An expression which determines the first value of the counter variable for which the loop
should be executed.
expr2
An expression which determines the value indicating the increment of the counter variable
each time the loop has been executed.
expr3
An expression which determines the value indicating the increment of the counter variable
each time the loop has been executed.
condition
a logical expression evaluating to True or False.
statements
Any group of one or more Qlik Sense script statements.
Example 1: Loading a sequence of files
// LOAD files file1.csv..file9.csv
for a=1 to 9
LOAD * from file$(a).csv;
next
Example 2: Loading a random number of files
In this example, we assume there are data files x1.csv, x3.csv, x5.csv, x7.csv and x9.csv. Loading is
stopped at a random point using the if rand( )<0.5 then condition.
for counter=1 to 9 step 2
set filename=x$(counter).csv;
if rand( )<0.5 then
exit for unless counter=1
end if
LOAD a,b from $(filename);
next
For each..next
The for each..next control statement is a script iteration construct which executes one or several statements
for each value in a comma separated list. The statements inside the loop enclosed by for and next will be
executed for each value of the list.
Syntax:
Special syntax makes it possible to generate lists with file and directory names in the current directory.
for each var in list
[statements]
[exit for [ ( when | unless ) condition ]
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[statements]
next [var]
Arguments:
Argument
Description
var
A script variable name which will acquire a new value from list for each loop execution. If var
is specified after next it must be the same variable name as the one found after the
corresponding for each.
The value of the var variable may be changed by statements inside the loop, but this is not good
programming practice.
If an exit for clause is encountered inside the loop, the execution of the script will be transferred to the first
statement after the next clause denoting the end of the loop. An exit for clause can be made conditional by
the optional use of a when or unless suffix.
Since the for each..next statement is a control statement and as such is ended with either a
semicolon or end-of-line, each of its three possible clauses (for each, exit for and next) must
not cross a line boundary.
Syntax:
list := item { , item }
item := constant | (expression) | filelist mask | dirlist mask |
fieldvaluelist mask
Argument
Description
constant
Any number or string. Note that a string written directly in the script must be enclosed by
single quotes. A string without single quotes will be interpreted as a variable, and the value
of the variable will be used. Numbers do not need to be enclosed by single quotes.
expression
An arbitrary expression.
mask
A filename or folder name mask which may include any valid filename characters as well
as the standard wildcard characters, * and ?.
You can use absolute file paths or lib:// paths.
condition
A logical expression evaluating to True or False.
statements
Any group of one or more Qlik Sense script statements.
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Argument
Description
filelist mask
This syntax produces a comma separated list of all files in the current directory matching
the filename mask.
This argument supports only library connections in standard mode.
dirlist mask
This syntax produces a comma separated list of all folders in the current folder matching
the folder name mask.
This argument supports only library connections in standard mode.
fieldvaluelist
mask
This syntax iterates through the values of a field already loaded into Qlik Sense.
Example 1: Loading a list of files
// LOAD the files 1.csv, 3.csv, 7.csv and xyz.csv
for each a in 1,3,7,'xyz'
LOAD * from file$(a).csv;
next
Example 2: Creating a list of files on disk
This example loads a list of all Qlik Sense related files in a folder.
sub DoDir (Root)
for each Ext in 'qvw', 'qva', 'qvo', 'qvs', 'qvc', 'qvf', 'qvd'
for each File in filelist (Root&'\*.' &Ext)
LOAD
'$(File)'
FileSize(
FileTime(
autogenerate
as Name,
'$(File)' ) as Size,
'$(File)' ) as FileTime
1;
next File
next Ext
for each Dir in dirlist (Root&'\*' )
call DoDir (Dir)
next Dir
end sub
call DoDir ('lib://MyData')
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Example 3: Iterating through a the values of a field
This example iterates through the list of loaded values of FIELD and generates a new field, NEWFIELD. For
each value of FIELD, two NEWFIELD records will be created.
load * inline [
FIELD
one
two
three
];
FOR Each a in FieldValueList('FIELD')
LOAD '$(a)' &'-'&RecNo() as NEWFIELD AutoGenerate 2;
NEXT a
The resulting table looks like this:
NEWFIELD
one-1
one-2
two-1
two-2
three-1
three-2
If..then..elseif..else..end if
The if..then control statement is a script selection construct forcing the script execution to follow different
paths depending on one or several logical conditions.
See: if (page 332) (script and chart function)
Syntax:
If
condition then
[ statements ]
{ elseif condition then
[ statements ] }
[ else
[ statements ] ]
end if
Since the if..then statement is a control statement and as such is ended with either a semicolon or end-ofline, each of its four possible clauses (if..then, elseif..then, else and end if) must not cross a line boundary.
Arguments:
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Argument
Description
condition
A logical expression which can be evaluated as True or False.
statements
Any group of one or more Qlik Sense script statements.
Example 1:
if a=1 then
LOAD * from abc.csv;
SQL SELECT e, f, g from tab1;
end if
Example 2:
if a=1 then; drop table xyz; end if;
Example 3:
if x>0 then
LOAD * from pos.csv;
elseif x<0 then
LOAD * from neg.csv;
else
LOAD * from zero.txt;
end if
Sub..end sub
The sub..end sub control statement defines a subroutine which can be called upon from a call statement.
Syntax:
Sub name [ ( paramlist )] statements end sub
Arguments are copied into the subroutine and, if the corresponding actual parameter in the call statement is
a variable name, copied back out again upon exiting the subroutine.
If a subroutine has more formal parameters than actual parameters passed by a call statement, the extra
parameters will be initialized to NULL and can be used as local variables within the subroutine.
Since the sub statement is a control statement and as such is ended with either a semicolon or end-of-line,
each of its two clauses (sub and end sub) must not cross a line boundary.
Arguments:
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Argument
Description
name
The name of the subroutine.
paramlist
A comma separated list of variable names for the formal parameters of the subroutine.
These can be used as any variable inside the subroutine.
statements
Any group of one or more Qlik Sense script statements.
Example 1:
Sub INCR (I,J)
I = I + 1
Exit Sub when I < 10
J = J + 1
End Sub
Call INCR (X,Y)
Example 2: - parameter transfer
Sub ParTrans (A,B,C)
A=A+1
B=B+1
C=C+1
End Sub
A=1
X=1
C=1
Call ParTrans (A, (X+1)*2)
The result of the above will be that locally, inside the subroutine, A will be initialized to 1, B will be initialized
to 4 and C will be initialized to NULL.
When exiting the subroutine, the global variable A will get 2 as value (copied back from subroutine). The
second actual parameter “(X+1)*2” will not be copied back since it is not a variable. Finally, the global
variable C will not be affected by the subroutine call.
Switch..case..default..end switch
The switch control statement is a script selection construct forcing the script execution to follow different
paths, depending on the value of an expression.
Syntax:
Switch expression {case valuelist [ statements ]} [default statements] end
switch
Since the switch statement is a control statement and as such is ended with either a
semicolon or end-of-line, each of its four possible clauses (switch, case, default and end
switch) must not cross a line boundary.
Arguments:
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Argument
Description
expression
An arbitrary expression.
valuelist
A comma separated list of values with which the value of expression will be compared.
Execution of the script will continue with the statements in the first group encountered with
a value in valuelist equal to the value in expression. Each value in valuelist may be an
arbitrary expression. If no match is found in any case clause, the statements under the
default clause, if specified, will be executed.
statements
Any group of one or more Qlik Sense script statements.
Example:
Switch I
Case 1
LOAD '$(I): CASE 1' as case autogenerate 1;
Case 2
LOAD '$(I): CASE 2' as case autogenerate 1;
Default
LOAD '$(I): DEFAULT' as case autogenerate 1;
End Switch
Script prefixes
Prefixes may be applied to applicable regular statements but never to control statements. The when and
unless prefixes can however be used as suffixes to a few specific control statement clauses.
All script keywords can be typed with any combination of lower case and upper case characters. Field and
variable names used in the statements are however case sensitive.
Script prefixes overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Buffer
QVD files can be created and maintained automatically via the buffer prefix. This prefix can be used on most
LOAD and SELECT statements in script. It indicates that QVD files are used to cache/buffer the result of
the statement.
Buffer[(option [ , option])] ( loadstatement | selectstatement )
option::= incremental | stale [after] amount [(days | hours)]
Bundle
The Bundle prefix is used to include external files, such as image or sound files, or objects connected to a
field value, to be stored in the qvf file.
Bundle [Info] ( loadstatement | selectstatement)
Concatenate
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If two tables that are to be concatenated have different sets of fields, concatenation of two tables can still be
forced with the Concatenate prefix.
Concatenate[ (tablename ) ] ( loadstatement | selectstatement )
Crosstable
The crosstable prefix is used to turn a cross table into a straight table.
Crosstable (attribute field name, data field name [ , n ] ) ( loadstatement
| selectstatement )
First
The First prefix to a LOAD or SELECT (SQL) statement is used for loading a set maximum number of
records from a data source table.
First n( loadstatement | selectstatement )
Generic
The unpacking and loading of a generic database can be done with a generic prefix.
Generic ( loadstatement | selectstatement )
Hierarchy
The hierarchy prefix is used to transform a hierarchy table to a table that is useful in a Qlik Sense data
model. It can be put in front of a LOAD or a SELECT statement and will use the result of the loading
statement as input for a table transformation.
Hierarchy (NodeID, ParentID, NodeName, [ParentName], [PathSource],
[PathName], [PathDelimiter], [Depth])(loadstatement | selectstatement)
HierarchBelongsTo
This prefix is used to transform a hierarchy table to a table that is useful in a Qlik Sense data model. It can be
put in front of a LOAD or a SELECT statement and will use the result of the loading statement as input for a
table transformation.
HierarchyBelongsTo (NodeID, ParentID, NodeName, AncestorID, AncestorName,
[DepthDiff])(loadstatement | selectstatement)
Image_Size
This clause is used with the Info prefix to resize images from a database management system to fit in the
fields.
Info [Image_Size(width,height )] ( loadstatement | selectstatement )
Info
The info prefix is used to link external information, such as a text file, a picture or a video to a field value.
Info( loadstatement | selectstatement )
Inner
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The join and keep prefixes can be preceded by the prefix inner.If used before join it specifies that an inner
join should be used. The resulting table will thus only contain combinations of field values from the raw data
tables where the linking field values are represented in both tables. If used before keep, it specifies that both
raw data tables should be reduced to their common intersection before being stored in Qlik Sense. .
Inner ( Join | Keep) [ (tablename) ](loadstatement |selectstatement )
IntervalMatch
The IntervalMatch prefix is used to create a table matching discrete numeric values to one or more numeric
intervals, and optionally matching the values of one or several additional keys.
IntervalMatch (matchfield)(loadstatement | selectstatement )
IntervalMatch (matchfield,keyfield1 [ , keyfield2, ... keyfield5 ] )
(loadstatement | selectstatement )
Join
The join prefix joins the loaded table with an existing named table or the last previously created data table.
[Inner | Outer | Left | Right ] Join [ (tablename ) ]( loadstatement |
selectstatement )
Keep
The keep prefix is similar to the join prefix. Just as the join prefix, it compares the loaded table with an
existing named table or the last previously created data table, but instead of joining the loaded table with an
existing table, it has the effect of reducing one or both of the two tables before they are stored in Qlik Sense,
based on the intersection of table data. The comparison made is equivalent to a natural join made over all
the common fields, i.e. the same way as in a corresponding join. However, the two tables are not joined and
will be kept in Qlik Sense as two separately named tables.
(Inner | Left | Right) Keep [(tablename ) ]( loadstatement |
selectstatement )
Left
The Join and Keep prefixes can be preceded by the prefix left.
If used before join it specifies that a left join should be used. The resulting table will only contain
combinations of field values from the raw data tables where the linking field values are represented in the first
table. If used before keep, it specifies that the second raw data table should be reduced to its common
intersection with the first table, before being stored in Qlik Sense.
Left ( Join | Keep) [ (tablename) ](loadstatement |selectstatement )
Mapping
The mapping prefix is used to create a mapping table that can be used to, for example, replacing field
values and field names during script execution.
Mapping
( loadstatement | selectstatement )
NoConcatenate
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The NoConcatenate prefix forces two loaded tables with identical field sets to be treated as two separate
internal tables, when they would otherwise be automatically concatenated.
NoConcatenate( loadstatement | selectstatement )
Outer
The explicit Join prefix can be preceded by the prefix outer in order to specify an outer join. In an outer join
all combinations between the two tables are generated. The resulting table will thus contain combinations of
field values from the raw data tables where the linking field values are represented in one or both tables. The
outer keyword is optional.
Outer Join [ (tablename) ](loadstatement |selectstatement )
Right
The Join and Keep prefixes can be preceded by the prefix right.
If used before join it specifies that a right join should be used. The resulting table will only contain
combinations of field values from the raw data tables where the linking field values are represented in the
second table. If used before keep, it specifies that the first raw data table should be reduced to its common
intersection with the second table, before being stored in Qlik Sense.
Right (Join | Keep) [(tablename)](loadstatement |selectstatement )
Sample
The sample prefix to a LOAD or SELECT statement is used for loading a random sample of records from
the data source.
Sample p ( loadstatement | selectstatement )
Semantic
Tables containing relations between records can be loaded through a semantic prefix. This can for example
be self-references within a table, where one record points to another, such as parent, belongs to, or
predecessor.
Semantic ( loadstatement | selectstatement)
Unless
The unless prefix and suffix is used for creating a conditional clause which determines whether a statement
or exit clause should be evaluated or not. It may be seen as a compact alternative to the full if..end if
statement.
(Unless condition statement | exitstatement Unless condition )
When
The when prefix and suffix is used for creating a conditional clause which determines whether a statement or
exit clause should be executed or not. It may be seen as a compact alternative to the full if..end if
statement.
( When condition statement | exitstatement when condition )
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Buffer
QVD files can be created and maintained automatically via the buffer prefix. This prefix can be used on most
LOAD and SELECT statements in script. It indicates that QVD files are used to cache/buffer the result of
the statement.
Syntax:
Buffer [(option [ , option])] ( loadstatement | selectstatement )
option::= incremental | stale [after] amount [(days | hours)]
If no option is used, the QVD buffer created by the first execution of the script will be used indefinitely.
QVD buffers will normally be removed when no longer referenced anywhere throughout a complete script
execution in the app that created it or when the app that created it no longer exists.
Arguments:
Argument
Description
incremental
The incremental option enables the ability to read only part of an underlying file. Previous
size of the file is stored in the XML header in the QVD file. This is particularly useful with
log files. All records loaded at a previous occasion are read from the QVD file whereas the
following new records are read from the original source and finally an updated QVD-file is
created. Note that the incremental option can only be used with LOAD statements and
text files and that incremental load cannot be used where old data is changed or deleted!
stale [after]
amount
[(days |
hours)]
amount is a number specifying the time period. Decimals may be used. The unit is
assumed to be days if omitted.
The stale after option is typically used with DB sources where there is no simple
timestamp on the original data. Instead you specify how old the QVD snapshot can be to
be used. A stale after clause simply states a time period from the creation time of the
QVD buffer after which it will no longer be considered valid. Before that time the QVD
buffer will be used as source for data and after that the original data source will be used.
The QVD buffer file will then automatically be updated and a new period starts.
Limitations:
Numerous limitations exist, most notable is that there must be either a file LOAD or a SELECT statement at
the core of any complex statement.
Example 1:
Buffer SELECT * from MyTable;
Example 2:
Buffer (stale after 7 days) SELECT * from MyTable;
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Example 3:
Buffer (incremental) LOAD * from MyLog.log;
Bundle
The Bundle prefix is used to include external files, such as image or sound files, or objects connected to a
field value, to be stored in the qvf file.
Syntax:
Bundle [Info] ( loadstatement | selectstatement)
In order to maintain portability, it is possible to include the external files into the .qvf file itself. To this end use
the Bundle prefix. The bundled info files are compressed in the process, but will nevertheless take up
additional space both in the file and in RAM. Therefore consider both the size and the number of bundled
files before going for this solution.
The info may be referenced from the layout as normal info, via the chart info function or as an internal file via
the special syntax qmem:// fieldname / fieldvalue alternatively qmem:// fieldname / < index > where index is
the internal index of a field value.
Arguments:
Argument
Description
Info
If a piece of external information, such as an image or a sound file is to be connected to a
field value, this is done in a table that is loaded with the Info prefix.
The Info prefix may be omitted when Bundle is used.
Example:
Bundle Info LOAD * From flagoecd.csv;
Bundle SQL SELECT * from infotable;
Concatenate
If two tables that are to be concatenated have different sets of fields, concatenation of two tables can still be
forced with the Concatenate prefix. This statement forces concatenation with an existing named table or the
latest previously created logical table.
Syntax:
Concatenate[ (tablename ) ] ( loadstatement | selectstatement )
A concatenation is in principle the same as the SQL UNION statement, but with two differences:
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l
The Concatenate prefix can be used no matter if the tables have identical field names or not.
l
Identical records are not removed with the Concatenate prefix.
Arguments:
Argument
Description
tablename
The name of the existing table.
Example:
Concatenate
Concatenate
tab1:
LOAD * From
tab2:
LOAD * From
.. .. ..
Concatenate
LOAD * From file2.csv;
SELECT * From table3;
file1.csv;
file2.csv;
(tab1) LOAD * From file3.csv;
Crosstable
The crosstable prefix is used to turn a cross table into a straight table.
Syntax:
crosstable (attribute field name, data field name [ , n ] ) ( loadstatement
| selectstatement )
Arguments:
Argument
Description
attribute field
name
The field that contains the attribute values.
data field name
The field that contains the data values.
n
The number of qualifier fields preceding the table to be transformed to generic form.
Default is 1.
A crosstable is a common type of table featuring a matrix of values between two or more orthogonal lists of
header data, of which one is used as column headers. A typical example could be to have one column per
month. The result of the crosstable prefix is that the column headers (for example month names) will be
stored in one field, the attribute field, and the column data (month numbers) will be stored in a second field:
the data field.
Examples:
Crosstable (Month, Sales) LOAD * from ex1.csv;
Crosstable (Month,Sales,2) LOAD * from ex2.csv;
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Crosstable (A,B) SELECT * from table3;
First
The First prefix to a LOAD or SELECT (SQL) statement is used for loading a set maximum number of
records from a data source table.
Syntax:
First n ( loadstatement | selectstatement )
Arguments:
Argument
Description
n
An arbitrary expression that evaluates to an integer indicating the maximum number of
records to be read.
n can be enclosed in parentheses, like (n), but this is not required.
Examples:
First 10 LOAD * from abc.csv;
First (1) SQL SELECT * from Orders;
Generic
The unpacking and loading of a generic database can be done with a generic prefix.
Syntax:
Generic( loadstatement | selectstatement )
Tables loaded through a generic statement are not auto-concatenated.
Examples:
Generic LOAD * from abc.csv;
Generic SQL SELECT * from table1;
Hierarchy
The hierarchy prefix is used to transform a hierarchy table to a table that is useful in a Qlik Sense data
model. It can be put in front of a LOAD or a SELECT statement and will use the result of the loading
statement as input for a table transformation.
Syntax:
Hierarchy (NodeID, ParentID, NodeName, [ParentName], [PathSource],
[PathName], [PathDelimiter], [Depth])(loadstatement | selectstatement)
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The input table must be an adjacent nodes table. Adjacent nodes tables are tables where each record
corresponds to a node and has a field that contains a reference to the parent node. In such a table the node is
stored on one record only but the node can still have any number of children. The table may of course contain
additional fields describing attributes for the nodes.
The prefix creates an expanded nodes table, which normally has the same number of records as the input
table, but in addition each level in the hierarchy is stored in a separate field. The path field can be used in a
tree structure.
Usually the input table has exactly one record per node and in such a case the output table will contain the
same number of records. However, sometimes there are nodes with multiple parents, i.e. one node is
represented by several records in the input table. If so, the output table may have more records than the input
table.
All nodes with a parent id not found in the node id column (including nodes with missing parent id) will be
considered as roots. Also, only nodes with a connection to a root node - direct or indirect - will be loaded, thus
avoiding circular references.
Additional fields containing the name of the parent node, the path of the node and the depth of the node can
be created.
Arguments:
Argument
Description
NodeID
The name of the field that contains the node id. This field must exist in the input table.
ParentID
The name of the field that contains the node id of the parent node. This field must exist in
the input table.
NodeName
The name of the field that contains the name of the node. This field must exist in the
input table.
ParentName
A string used to name the new ParentName field. If omitted, this field will not be
created.
ParentSource
The name of the field that contains the name of the node used to build the node path.
Optional parameter. If omitted, NodeName will be used.
PathName
A string used to name the new Path field, which contains the path from the root to the
node. Optional parameter. If omitted, this field will not be created.
PathDelimiter
A string used as delimiter in the new Path field. Optional parameter. If omitted, ‘/’ will be
used.
Depth
A string used to name the new Depth field, which contains the depth of the node in the
hierarchy. Optional parameter. If omitted, this field will not be created.
Example:
Hierarchy(NodeID, ParentID, NodeName) LOAD
NodeID,
ParentID,
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NodeName,
Attribute
From data.xls (biff, embedded labels, table is [Sheet1$];
HierarchyBelongsTo
This prefix is used to transform a hierarchy table to a table that is useful in a Qlik Sense data model. It can be
put in front of a LOAD or a SELECT statement and will use the result of the loading statement as input for a
table transformation.
Syntax:
HierarchyBelongsTo (NodeID, ParentID, NodeName, AncestorID, AncestorName,
[DepthDiff])(loadstatement | selectstatement)
The input table must be an adjacent nodes table. Adjacent nodes tables are tables where each record
corresponds to a node and has a field that contains a reference to the parent node. In such a table the node is
stored on one record only but the node can still have any number of children. The table may of course contain
additional fields describing attributes for the nodes.
The prefix creates a table containing all ancestor-child relations of the hierarchy. The ancestor fields can then
be used to select entire trees in the hierarchy. The output table in most cases contains several records per
node.
An additional field containing the depth difference of the nodes can be created.
Arguments:
Argument
Description
NodeID
The name of the field that contains the node id. This field must exist in the input table.
ParentID
The name of the field that contains the node id of the parent node. This field must exist
in the input table.
NodeName
The name of the field that contains the name of the node. This field must exist in the
input table.
AncestorID
A string used to name the new ancestor id field, which contains the id of the ancestor
node.
AncestorName
A string used to name the new ancestor field, which contains the name of the ancestor
node.
DepthDiff
A string used to name the new DepthDiff field, which contains the depth of the node in
the hierarchy relative the ancestor node. Optional parameter. If omitted, this field will
not be created.
Example:
HierarchyBelongsTo (NodeID, ParentID, Node, Tree, ParentName) LOAD
NodeID,
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ParentID,
NodeName
From data.xls (biff, embedded labels, table is [Sheet1$];
Image_Size
This clause is used with the Info prefix to resize images from a database management system to fit in the
fields.
Syntax:
Info [Image_Size(width,height )] ( loadstatement | selectstatement )
Arguments:
Argument
Description
width
The width of the image specified in pixels.
height
The height of the image specified in pixels.
Example:
Info Image_Size(122,122) SQL SELECT ID, Photo From infotable;
Info
The info prefix is used to link external information, such as a text file, a picture or a video to a field value.
Syntax:
Info( loadstatement | selectstatement )
If a piece of external information, such as a text file, a picture or a video is to be linked to a field value, this is
done in a table that is loaded using an info prefix.(In some cases it will be preferable to store the information
inside the qvf file, by using the bundle prefix. The table must contain two columns only, the first one with the
field values that will form the keys to the information, the second one containing the information elements,
that is, the file names of the pictures etcetera.
The same applies to, for example, a picture from a database management system. On a binary field, a blob,
the info select statement makes an implicit bundle, i.e. the binary data will be fetched immediately and
stored in the qvf. The binary data must be the second field in a SELECT statement.
If a picture needs to be resized, the image_size clause can be used.
Example:
Info LOAD * from flagoecd.csv;
Info SQL SELECT * from infotable;
Info SQL SELECT Key, Picture From infotable;
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Inner
The join and keep prefixes can be preceded by the prefix inner.If used before join it specifies that an inner
join should be used. The resulting table will thus only contain combinations of field values from the raw data
tables where the linking field values are represented in both tables. If used before keep, it specifies that both
raw data tables should be reduced to their common intersection before being stored in Qlik Sense.
Syntax:
Inner ( Join | Keep) [ (tablename) ](loadstatement |selectstatement )
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Example 1:
Table1
A
B
1
aa
2
cc
3
ee
Table2
A
C
1
xx
4
yy
QVTable:
SQL SELECT * From table1;
inner join SQL SELECT * From table2;
QVTable
A
B
C
1
aa
xx
Example 2:
QVTab1:
SQL SELECT * From Table1;
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QVTab2:
inner keep SQL SELECT * From Table2;
QVTab1
A
B
1
aa
QVTab2
A
C
1
xx
The two tables in the keep example are, of course, associated via A.
IntervalMatch
The IntervalMatch prefix is used to create a table matching discrete numeric values to one or more numeric
intervals, and optionally matching the values of one or several additional keys.
Syntax:
IntervalMatch (matchfield)(loadstatement | selectstatement )
IntervalMatch (matchfield,keyfield1 [ , keyfield2, ... keyfield5 ] )
(loadstatement | selectstatement )
The IntervalMatch prefix must be placed before a LOAD or a SELECT statement that loads the intervals.
The field containing the discrete data points (Time in the example below) and additional keys must already
have been loaded into Qlik Sense before the statement with the IntervalMatch prefix. The prefix does not by
itself read this field from the database table. The prefix transforms the loaded table of intervals and keys to a
table that contains an additional column: the discrete numeric data points. It also expands the number of
records so that the new table has one record per possible combination of discrete data point, interval and
value of the key field(s).
The intervals may be overlapping and the discrete values will be linked to all matching intervals.
In order to avoid undefined interval limits being disregarded, it may be necessary to allow NULL values to
map to other fields that constitute the lower or upper limits to the interval. This can be handled by the
NullAsValue statement or by an explicit test that replaces NULL values with a numeric value well before or
after any of the discrete numeric data points.
Arguments:
Argument
Description
matchfield
The field containing the discrete numeric values to be linked to intervals.
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Argument
Description
keyfield
Fields that contain the additional attributes that are to be matched in the
transformation.
loadstatement
or
selectstatement
Must result in a table, where the first field contains the lower limit of each interval, the
second field contains the upper limit of each interval, and in the case of using key
matching, the third and any subsequent fields contain the keyfield(s) present in the
IntervalMatch statement. The intervals are always closed, i.e. the end points are
included in the interval. Non-numeric limits render the interval to be disregarded
(undefined).
Example 1:
In the two tables below, the first one defines the start and end times for the production of different orders.
The second one lists a number of discrete events. By means of the IntervalMatch prefix it is possible to
logically connect the two tables in order to find out e.g. which orders were affected by disturbances and which
orders were processed by which shifts.
OrderLog
Start
End
Order
01:00
03:35
A
02:30
07:58
B
03:04
10:27
C
07:23
11:43
D
EventLog
Time
Event
Comment
00:00
0
Start of shift 1
01:18
1
Line stop
02:23
2
Line restart 50%
04:15
3
Line speed 100%
08:00
4
Start of shift 2
11:43
5
End of production
First load the two tables as usual, then link the field Time to the time intervals defined by the fields Start and
End:
SELECT * from OrderLog;
SELECT * from Eventlog;
IntervalMatch ( Time ) SELECT Start, End from OrderLog;
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The following table box could now be created in Qlik Sense:
Tablebox
Time
Event
Comment
Order
Start
End
00:00
0
Start of shift 1
-
-
-
01:18
1
Line stop
A
01:00
03:35
02:23
2
Line restart 50%
A
01:00
03:35
04:15
3
Line speed 100%
B
02:30
07:58
04:15
3
Line speed 100%
C
03:04
10:27
08:00
4
Start of shift 2
C
03:04
10:27
08:00
4
Start of shift 2
D
07:23
11:43
11:43
5
End of production
D
07:23
11:43
Example 2: (using keyfield)
Inner Join IntervalMatch (Date,Key) LOAD FirstDate, LastDate, Key resident Key;
Join
The join prefix joins the loaded table with an existing named table or the last previously created data table.
Syntax:
[inner | outer | left | right ]Join [ (tablename ) ]( loadstatement |
selectstatement )
The join is a natural join made over all the common fields. The join statement may be preceded by one of the
prefixes inner, outer, left or right.
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Example:
Join LOAD * from abc.csv;
Join SELECT * from table1;
tab1:
LOAD * from file1.csv;
tab2:
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LOAD * from file2.csv;
.. .. ..
join (tab1) LOAD * from file3.csv;
Keep
The keep prefix is similar to the join prefix. Just as the join prefix, it compares the loaded table with an
existing named table or the last previously created data table, but instead of joining the loaded table with an
existing table, it has the effect of reducing one or both of the two tables before they are stored in Qlik Sense,
based on the intersection of table data. The comparison made is equivalent to a natural join made over all
the common fields, i.e. the same way as in a corresponding join. However, the two tables are not joined and
will be kept in Qlik Sense as two separately named tables.
Syntax:
(inner | left | right) keep [(tablename ) ]( loadstatement |
selectstatement )
The keep prefix must be preceded by one of the prefixes inner, left or right.
The explicit join prefix in Qlik Sense script language performs a full join of the two tables. The result is one
table. In many cases such joins will result in very large tables. One of the main features of Qlik Sense is its
ability to make associations between multiple tables instead of joining them, which greatly reduces memory
usage, increases processing speed and offers enormous flexibility. Explicit joins should therefore generally
be avoided in Qlik Sense scripts. The keep functionality was designed to reduce the number of cases where
explicit joins needs to be used.
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Example:
Inner Keep LOAD * from abc.csv;
Left Keep SELECT * from table1;
tab1:
LOAD * from file1.csv;
tab2:
LOAD * from file2.csv;
.. .. ..
Left Keep (tab1) LOAD * from file3.csv;
Left
The Join and Keep prefixes can be preceded by the prefix left.
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If used before join it specifies that a left join should be used. The resulting table will only contain
combinations of field values from the raw data tables where the linking field values are represented in the first
table. If used before keep, it specifies that the second raw data table should be reduced to its common
intersection with the first table, before being stored in Qlik Sense.
Were you looking for the string function by the same name? See: Left (page 597)
Syntax:
Left ( Join | Keep) [ (tablename) ](loadstatement | selectstatement)
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Example:
Table1
A
B
1
aa
2
cc
3
ee
Table2
A
C
1
xx
4
yy
QVTable:
SELECT * From table1;
Left Join Sselect * From table2;
QVTable
A
B
C
1
aa
xx
2
cc
3
ee
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QVTab1:
SELECT * From Table1;
QVTab2:
Left Keep SELECT * From Table2;
QVTab1
A
B
1
aa
2
cc
3
ee
A
C
1
xx
QVTab2
The two tables in the keep example are, of course, associated via A.
tab1:
LOAD * From file1.csv;
tab2:
LOAD * From file2.csv;
.. .. ..
Left Keep (tab1) LOAD * From file3.csv;
Mapping
The mapping prefix is used to create a mapping table that can be used to, for example, replacing field
values and field names during script execution.
Syntax:
Mapping( loadstatement | selectstatement )
The mapping prefix can be put in front of a LOAD or a SELECT statement and will store the result of the
loading statement as a mapping table. A mapping table consists of two columns, the first containing
comparison values and the second containing the desired mapping values. Mapping tables are stored
temporarily in memory and dropped automatically after script execution.
The content of the mapping table can be accessed using e.g. the Map … Using statement, the Rename
Field statement, the Applymap() function or the Mapsubstring() function.
Example:
Mapping LOAD * from x.csv
Mapping SQL SELECT a, b from map1
map1:
mapping LOAD * inline [
x,y
US,USA
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U.S.,USA
America,USA ];
NoConcatenate
The NoConcatenate prefix forces two loaded tables with identical field sets to be treated as two separate
internal tables, when they would otherwise be automatically concatenated.
Syntax:
NoConcatenate( loadstatement | selectstatement )
Example:
LOAD A,B from file1.csv;
NoConcatenate LOAD A,B from file2.csv;
Outer
The explicit Join prefix can be preceded by the prefix outer in order to specify an outer join. In an outer join
all combinations between the two tables are generated. The resulting table will thus contain combinations of
field values from the raw data tables where the linking field values are represented in one or both tables. The
outer keyword is optional.
Syntax:
Outer Join [ (tablename) ](loadstatement |selectstatement )
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Example:
Table1
A
B
1
aa
2
cc
3
ee
A
C
Table2
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1
xx
4
yy
SQL SELECT * from table1;
join SQL SELECT * from table2;
OR
SQL SELECT * from table1;
outer join SQL SELECT * from table2;
Joined table
A
B
C
1
aa
xx
2
cc
-
3
ee
-
4
-
yy
Right
The Join and Keep prefixes can be preceded by the prefix right.
If used before join it specifies that a right join should be used. The resulting table will only contain
combinations of field values from the raw data tables where the linking field values are represented in the
second table. If used before keep, it specifies that the first raw data table should be reduced to its common
intersection with the second table, before being stored in Qlik Sense.
Were you looking for the string function by the same name? See: Right (page 600)
Syntax:
Right (Join | Keep) [(tablename)](loadstatement |selectstatement )
Arguments:
Argument
Description
tablename
The named table to be compared to the loaded table.
loadstatement or selectstatement
The LOAD or SELECT statement for the loaded table.
Examples:
Table1
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A
B
1
aa
2
cc
3
ee
Table2
A
C
1
xx
4
yy
QVTable:
SQL SELECT * from table1;
right join SQL SELECT * from table2;
QVTable
A
B
C
1
aa
xx
4
-
yy
QVTab1:
SQL SELECT * from Table1;
QVTab2:
right keep SQL SELECT * from Table2;
QVTab1
A
B
1
aa
A
C
1
xx
4
yy
QVTab2
The two tables in the keep example are, of course, associated via A.
tab1:
LOAD * from file1.csv;
tab2:
LOAD * from file2.csv;
.. .. ..
right keep (tab1) LOAD * from file3.csv;
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Sample
The sample prefix to a LOAD or SELECT statement is used for loading a random sample of records from
the data source.
Syntax:
Sample
p ( loadstatement | selectstatement )
Arguments:
Argument
Description
p
An arbitrary expression which valuates to a number larger than 0 and lower or equal to 1.
The number indicates the probability for a given record to be read.
All records will be read but only some of them will be loaded into Qlik Sense.
Example:
Sample 0.15 SQL SELECT * from Longtable;
Sample(0.15) LOAD * from Longtab.csv;
The parentheses are allowed but not required.
Semantic
Tables containing relations between records can be loaded through a semantic prefix. This can for example
be self-references within a table, where one record points to another, such as parent, belongs to, or
predecessor.
Syntax:
Semantic( loadstatement | selectstatement)
The semantic load will create semantic fields that can be displayed in filter panes to be used for navigation in
the data.
Tables loaded through a semantic statement cannot be concatenated.
Example:
Semantic LOAD * from abc.csv;
Semantic SELECT Object1, Relation, Object2, InverseRelation from table1;
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Unless
The unless prefix and suffix is used for creating a conditional clause which determines whether a statement
or exit clause should be evaluated or not. It may be seen as a compact alternative to the full if..end if
statement.
Syntax:
(Unless condition statement | exitstatement Unless condition )
The statement or the exitstatement will only be executed if condition is evaluated to False.
The unless prefix may be used on statements which already have one or several other statements, including
additional when or unless prefixes.
Arguments:
Argument
Description
condition
A logical expression evaluating to True or False.
statement
Any Qlik Sense script statement except control statements.
exitstatement
An exit for, exit do or exit sub clause or an exit script statement.
Examples:
exit script unless A=1;
unless A=1 LOAD * from myfile.csv;
unless A=1 when B=2 drop table Tab1;
When
The when prefix and suffix is used for creating a conditional clause which determines whether a statement or
exit clause should be executed or not. It may be seen as a compact alternative to the full if..end if
statement.
Syntax:
(when condition statement | exitstatement when condition )
The statement or the exitstatement will only be executed if condition is evaluated to True.
The when prefix may be used on statements which already have one or several other statements, including
additional when or unless prefixes.
Syntax:
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Argument
Description
condition
A logical expression evaluating to True or False.
statement
Any Qlik Sense script statement except control statements.
exitstatement
An exit for, exit do or exit sub clause or an exit script statement.
Example 1:
exit script when A=1;
Example 2:
when A=1 LOAD * from myfile.csv;
Example 3:
when A=1 unless B=2 drop table Tab1;
Script regular statements
Regular statements are typically used for manipulating data in one way or another. These statements may be
written over any number of lines in the script and must always be terminated by a semicolon, ";".
All script keywords can be typed with any combination of lower case and upper case characters. Field and
variable names used in the statements are however case sensitive.
Script regular statements overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Alias
The alias statement is used for setting an alias according to which a field will be renamed whenever it occurs
in the script that follows.
Alias fieldname as aliasname {,fieldname as aliasname}
Binary
The binary statement is used for loading the data from another Qlik Sense app or QlikView 11.2 or earlier
document, including section access data.
Binary file
file ::= [ path ] filename
comment
Provides a way of displaying the field comments (metadata) from databases and spreadsheets. Field names
not present in the app will be ignored. If multiple occurrences of a field name are found, the last value is
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used.
Comment field *fieldlist using mapname
Comment field fieldname with comment
comment table
Provides a way of displaying the table comments (metadata) from databases or spreadsheets.
Comment table tablelist using mapname
Comment table tablename with comment
Connect
The CONNECT statement is used to define Qlik Sense access to a general database through the OLE
DB/ODBC interface. For ODBC, the data source first needs to be specified using the ODBC administrator.
ODBC Connect TO connect-string [ ( access_info ) ]
OLEDB CONNECT TO connect-string [ ( access_info ) ]
CUSTOM CONNECT TO connect-string [ ( access_info ) ]
LIB CONNECT TO connection
Declare
The Declare statement is used to create field and group definitions, where you can define relations between
fields or functions. A set of field definitions can be used to automatically generate derived fields, which can
be used as dimensions. For example, you can create a calendar definition, and use that to generate related
dimensions, such as year, month, week and day, from a date field.
definition_name:
Declare [Field[s]] Definition [Tagged tag_list ]
[Parameters parameter_list ]
Fields field_list
[Groups group_list ]
:
Declare [Field][s] Definition
Using
[With ]
Derive
The Derive statement is used to generate derived fields based on a field definition created with a Declare
statement. You can either specify which data fields to derive fields for, or derive them explicitly or implicitly
based on field tags.
Derive [Field[s]] From [Field[s]] field_list Using definition
Derive [Field[s]] From Explicit [Tag[s]] (tag_list) Using definition
Derive [Field[s]] From Implicit [Tag[s]] Using definition
Direct Query
The DIRECT QUERY statement allows you to access tables through an ODBC or OLE DB connection using
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the Direct Discovery function.
Direct Query [path]
Directory
The Directory statement defines which directory to look in for data files in subsequent LOAD statements,
until a new Directory statement is made.
Directory [path]
Disconnect
The Disconnect statement terminates the current ODBC/OLE DB/Custom connection. This statement is
optional.
Disconnect
drop field
One or several Qlik Sense fields can be dropped from the data model, and thus from memory, at any time
during script execution, by means of a drop field statement.
Both drop field and drop fields are allowed forms with no difference in effect. If no table is
specified, the field will be dropped from all tables where it occurs.
Drop field
fieldname [ , fieldname2 ...] [from tablename1 [ , tablename2
...]]
drop fields fieldname [ , fieldname2 ...] [from tablename1 [ , tablename2
...]]
drop table
One or several Qlik Sense internal tables can be dropped from the data model, and thus from memory, at
any time during script execution, by means of a drop table statement.
The forms drop table and drop tables are both accepted.
Drop table tablename [, tablename2 ...]
drop tables[ tablename [, tablename2 ...]
Execute
The Execute statement is used to run other programs while Qlik Sense is loading data. For example, to
make conversions that are necessary.
Execute commandline
FlushLog
The FlushLog statement forces Qlik Sense to write the content of the script buffer to the script log file.
FlushLog
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Force
The force statement forces Qlik Sense to interpret field values of subsequent LOAD and SELECT
statements as written with only upper case letters, with only lower case letters, as always capitalized or as
they appear (mixed). This statement makes it possible to associate field values from tables made according
to different conventions.
Force ( capitalization | case upper | case lower | case mixed )
LOAD
The LOAD statement loads fields from a file, from data defined in the script, from a previously loaded table,
from a web page, from the result of a subsequent SELECT statement or by generating data automatically.
Load [ distinct ] *fieldlist
[( from file [ format-spec ] |
from_field fieldassource [format-spec]
inline data [ format-spec ] |
resident table-label |
autogenerate size )]
[ where criterion | while criterion ]
[ group_by groupbyfieldlist ]
[order_by orderbyfieldlist ]
Let
The let statement is a complement to the set statement, used for defining script variables. The let
statement, in opposition to the set statement, evaluates the expression on the right side of the ' =' before it is
assigned to the variable.
Let variablename=expression
Map ... using
The map ... using statement is used for mapping a certain field value or expression to the values of a
specific mapping table. The mapping table is created through the Mapping statement.
Map
*fieldlist Using
mapname
NullAsNull
The NullAsNull statement turns off the conversion of NULL values to string values previously set by a
NullAsValue statement.
NullAsNull *fieldlist
NullAsValue
The NullAsValue statement specifies for which fields that NULL should be converted to a value.
NullAsValue *fieldlist
Qualify
The Qualify statement is used for switching on the qualification of field names, i.e. field names will get the
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table name as a prefix.
Qualify *fieldlist
Rem
The rem statement is used for inserting remarks, or comments, into the script, or to temporarily deactivate
script statements without removing them.
Rem string
Rename Field
This script function renames one or more existing Qlik Sense field(s) after they have been loaded.
Rename field (using mapname | oldname to newname{ , oldname to newname })
Rename Fields (using mapname | oldname to newname{ , oldname to newname })
Rename Table
This script function renames one or more existing Qlik Sense internal table(s) after they have been loaded.
Rename table (using mapname | oldname to newname{ , oldname to newname })
Rename Tables (using mapname | oldname to newname{ , oldname to newname })
Section
With the section statement, it is possible to define whether the subsequent LOAD and SELECT
statements should be considered as data or as a definition of the access rights.
Section (access | application)
Select
The selection of fields from an ODBC data source or OLE DB provider is made through standard SQL
SELECT statements. However, whether the SELECT statements are accepted depends on the ODBC
driver or OLE DB provider used.
Select [all | distinct | distinctrow | top n [percent] ] *fieldlist
From tablelist
[Where criterion ]
[Group by fieldlist [having criterion ] ]
[Order by fieldlist [asc | desc] ]
[ (Inner | Left | Right | Full)Join tablename on fieldref = fieldref ]
Set
The set statement is used for defining script variables. These can be used for substituting strings, paths,
drives, and so on.
Set variablename=string
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Sleep
The sleep statement pauses script execution for a specified time.
Sleep n
SQL
The SQL statement allows you to send an arbitrary SQL command through an ODBC or OLE DB
connection.
SQL sql_command
SQLColumns
The sqlcolumns statement returns a set of fields describing the columns of an ODBC or OLE DB data
source, to which a connect has been made.
SQLColumns
SQLTables
The sqltables statement returns a set of fields describing the tables of an ODBC or OLE DB data source, to
which a connect has been made.
SQLTables
SQLTypes
The sqltypes statement returns a set of fields describing the types of an ODBC or OLE DB data source, to
which a connect has been made.
SQLTypes
Star
The string used for representing the set of all the values of a field in the database can be set through the star
statement. It affects the subsequent LOAD and SELECT statements.
Star is [ string ]
Store
This script function creates a QVD or a CSV file.
Store [ *fieldlist from] table into filename [ format-spec ];
Tag
This script function provides a way of assigning tags to one or more fields. If an attempt to tag a field name
not present in the app is made, the tagging will be ignored. If conflicting occurrences of a field or tag name
are found, the last value is used.
Tag fields fieldlist using mapname
Tag field fieldname with tagname
Trace
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The trace statement writes a string to the Script Execution Progress window and to the script log file,
when used. It is very useful for debugging purposes. Using $-expansions of variables that are calculated prior
to the trace statement, you can customize the message.
Trace string
Unmap
The Unmap statement disables field value mapping specified by a previous Map … Using statement for
subsequently loaded fields.
Unmap *fieldlist
Unqualify
The Unqualify statement is used for switching off the qualification of field names that has been previously
switched on by the Qualify statement.
Unqualify *fieldlist
Untag
Provides a way of removing tags from one or more fields. If an attempt to untag a Field name not present in
the app is made, the untagging will be ignored. If conflicting occurrences of a field or tag name is found, the
last value is used.
Untag fields fieldlist using mapname
Untag field fieldname with tagname
Alias
The alias statement is used for setting an alias according to which a field will be renamed whenever it occurs
in the script that follows.
Syntax:
alias fieldname as aliasname {,fieldname as aliasname}
Arguments:
Argument
Description
fieldname
The name of the field in your source data
aliasname
An alias name you want to use instead
Examples and results:
Example
Result
Alias ID_N as
NameID;
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Example
Result
Alias A as
Name, B as
Number, C as
Date;
The name changes defined through this statement are used on all subsequent
SELECT and LOAD statements. A new alias can be defined for a field name by a new
alias statement at any subsequent position in the script.
Binary
The binary statement is used for loading the data from another Qlik Sense app or QlikView 11.2 or earlier
document, including section access data.
Syntax:
binary file
file ::= [ path ] filename
Arguments:
Argument
Description
file
The name of the file, including the file extension .qvw or .qvf.
path
The path to the file as a folder data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the app containing this script line.
Example: data\
Only one binary statement is allowed in the script and it must be the first statement of the
script.
Examples
Binary lib://MyData/customer.qvw;
Binary customer.qvw;
Binary c:\qv\customer.qvw;
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Comment field
Provides a way of displaying the field comments (metadata) from databases and spreadsheets. Field names
not present in the app will be ignored. If multiple occurrences of a field name are found, the last value is
used.
Syntax:
comment [fields] *fieldlist using mapname
comment [field] fieldname with comment
The map table used should have two columns, the first containing field names and the second the
comments.
Arguments:
Argument
Description
*fieldlist
A comma separated list of the fields to be commented. Using * as field list indicates all
fields. The wildcard characters * and ? are allowed in field names. Quoting of field
names may be necessary when wildcards are used.
mapname
The name of a mapping table previously read in a mapping LOAD or mapping SELECT
statement.
fieldname
The name of the field that should be commented.
comment
The comment that should be added to the field.
Example 1:
commentmap:
mapping LOAD * inline [
a,b
Alpha,This field contains text values
Num,This field contains numeric values
];
comment fields using commentmap;
Example 2:
comment field Alpha with AFieldContainingCharacters;
comment field Num with '*A field containing numbers';
comment Gamma with 'Mickey Mouse field';
Comment table
Provides a way of displaying the table comments (metadata) from databases or spreadsheets.
Table names not present in the app are ignored. If multiple occurrences of a table name are found, the last
value is used. The keyword can be used to read comments from a data source.
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Syntax:
comment [tables] tablelist using mapname
Arguments:
Argument
Description
tablelist
(table{,table})
mapname
The name of a mapping table previously read in a mapping LOAD or mapping SELECT
statement.
Syntax:
To set individual comments, the following syntax is used:
comment [table] tablename with comment
Arguments:
Argument
Description
tablename
The name of the table that should be commented.
comment
The comment that should be added to the table.
Example 1:
Commentmap:
mapping LOAD * inline [
a,b
Main,This is the fact table
Currencies, Currency helper table
];
comment tables using commentmap;
Example 2:
comment table Main with ‘Main fact table’;
Connect
The CONNECT statement is used to define Qlik Sense access to a general database through the OLE
DB/ODBC interface. For ODBC, the data source first needs to be specified using the ODBC administrator.
This statement supports only folder data connections in standard mode.
Syntax:
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ODBC CONNECT TO connect-string
OLEDB CONNECT TO connect-string
CUSTOM CONNECT TO connect-string
LIB CONNECT TO connection
Arguments:
Argument
Description
connectstring
connect-string ::= datasourcename { ; conn-spec-item }
The connection string is the data source name and an optional list of one or
more connection specification items. If the data source name contains
blanks, or if any connection specification items are listed, the connection
string must be enclosed by quotation marks.
datasourcename must be a defined ODBC data source or a string that
defines an OLE DB provider.
conn-spec-item ::=DBQ=database_specifier |DriverID=driver_
specifier |UID=userid |PWD=password
The possible connection specification items may differ between different
databases. For some databases, also other items than the above are
possible. For OLE DB, some of the connection specific items are
mandatory and not optional.
connection
The name of a data connection stored in the data load editor.
If the ODBC is placed before CONNECT, the ODBC interface will be used; else, OLE DB will be used.
Using LIB CONNECT TO connects to a database using a stored data connection that was created in the
data load editor.
Example 1:
ODBC CONNECT TO 'Sales
DBQ=C:\Program Files\Access\Samples\Sales.mdb';
The data source defined through this statement is used by subsequent Select (SQL) statements, until a new
CONNECT statement is made.
Example 2:
LIB CONNECT TO 'MyDataConnection';
Connect32
This statement is used the same way as the CONNECT statement, but forces a 64-bit system to use a 32-bit
ODBC/OLE DB provider. Not applicable for custom connect.
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Connect64
This statement is used the same way as the as the CONNECT statement, but forces use of a 64-bit
provider. Not applicable for custom connect.
Declare
The Declare statement is used to create field and group definitions, where you can define relations between
fields or functions. A set of field definitions can be used to automatically generate derived fields, which can
be used as dimensions. For example, you can create a calendar definition, and use that to generate related
dimensions, such as year, month, week and day, from a date field.
You can use Declare to either set up a new field definition, or to create a field definition based on an already
existing definition.
Setting up a new field definition
Syntax:
definition_name:
Declare [Field[s]] Definition [Tagged tag_list ]
[Parameters parameter_list ]
Fields field_list
Arguments:
Argument
Description
definition_
name
Name of the field definition, ended with a colon.
Example:
Calendar:
tag_list
A comma separated list of tags to apply to fields derived from the field definition. Optional.
Example:
'$date'
parameter_
list
A comma separated list of parameters. A parameter is defined in the form name=value and is
assigned a start value, which can be overridden when a field definition is re-used. Optional.
Example:
first_month_of_year = 1
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Argument
Description
field_list
A comma separated list of fields to generate when the field definition is used. A field is
defined in the form As field_name tagged tag. Use $1 to reference the data
field from which the derived fields should be generated.
Example:
Year($1) As Year tagged '$year'
Example:
In this example we create a calendar definition that can be used to generate date related fields, such as year
and month, that can be used in dimensions.
To try out the calendar we need some source data with date fields.
Load * Inline [
OrderID, Terminal, OrderDate, ShippingDate
100001, London, 2012-03-12, 2012-03-14
100002, Berlin, 2012-03-12, 2012-03-16
100003, London, 2012-03-12, 2012-03-14
100004, Paris, 2012-03-12, 2012-03-16
100005, Paris, 2012-03-12, 2012-03-14
100006, Paris, 2012-04-11, 2012-04-22
100007, London, 2012-04-12, 2012-04-14
100008, Paris, 2012-05-12, 2012-05-13
100009, London, 2013-01-12, 2013-03-14
100010, Berlin, 2013-01-12, 2013-03-16
100011, London, 2013-01-12, 2013-03-14
100012, Paris, 2013-01-12, 2013-03-16
100013, Paris, 2013-01-12, 2013-03-14
100014, Berlin, 2013-03-12, 2013-03-16
100015, London, 2013-03-12, 2013-03-14
100016, Berlin, 2013-03-12, 2013-03-16
100017, Paris, 2013-09-12, 2013-10-14
100018, Berlin, 2013-09-12, 2013-09-16
];
Now we can define a calendar that creates fields for year, month, week, weekday and day number.
Calendar:
DECLARE FIELD DEFINITION TAGGED '$date'
Parameters
first_month_of_year = 1
Fields
Year($1) As Year Tagged '$year',
Month($1) as Month Tagged '$month',
Date($1) as Date Tagged ('$date', '$day'),
Week($1) as Week Tagged '$week',
Weekday($1) as Weekday Tagged '$weekday',
DayNumberOfYear($1, first_month_of_year) as DayNumberOfYear Tagged ('$numeric');
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The calendar is now defined, and you can apply it to the date fields that have been loaded, in this case
OrderDate and ShippingDate, using a Derive clause.
DERIVE FIELDS FROM FIELDS OrderDate,ShippingDate USING Calendar;
When you have reloaded the data script, you can find the generated fields, for example
OrderDate.Calendar.Weekday under Date & time fields in the Fields asset panel. They are also available
in the expression editor and when you create dimensions, and can be used as any other field in visualizations.
Re-using an existing field definition
Syntax:
:
Declare [Field][s] Definition
Using
[With ]
Arguments:
Argument
Description
definition_
name
Name of the field definition, ended with a colon.
Example:
MyCalendar:
existing_
definition
The field definition to re-use when creating the new field definition. The new field definition
will function the same way as the definition it is based on, with the exception if you use
parameter_assignment to change a value used in the field expressions.
Example:
Using Calendar
parameter_
assignment
A comma separated list of parameter assignments. A parameter assignment is defined in
the form name=value and overrides the parameter value that is set in the base field
definition. Optional.
Example:
first_month_of_year = 4
Example:
In this example we re-use the calendar definition that was created in the previous example. In this case we
want to use a fiscal year that starts in April. This is achieved by assigning the value 4 to the first_month_of_
year parameter, which will affect the DayNumberOfYear field that is defined.
The example assumes that you use the sample data and field definition from the previous example.
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MyCalendar:
DECLARE FIELD DEFINITION USING Calendar WITH first_month_of_year=4;
DERIVE FIELDS FROM FIELDS OrderDate,ShippingDate USING MyCalendar;
When you have reloaded the data script, the generated fields are available in the sheet editor, with names
OrderDate.MyCalendar.* and ShippingDate.MyCalendar.*.
Derive
The Derive statement is used to generate derived fields based on a field definition created with a Declare
statement. You can either specify which data fields to derive fields for, or derive them explicitly or implicitly
based on field tags.
Syntax:
Derive [Field[s]] From [Field[s]] field_list Using definition
Derive [Field[s]] From Explicit [Tag[s]] tag_list Using definition
Derive [Field[s]] From Implicit [Tag[s]] Using definition
Arguments:
Argument
Description
definition
Name of the field definition to use when deriving fields.
Example: Calendar
field_list
A comma separated list of data fields from which the derived fields should be generated,
based on the field definition. The data fields should be fields you have already loaded in the
script.
Example: OrderDate, ShippingDate
tag_list
A comma separated list of tags. Derived fields will be generated for all data fields with any of
the listed tags.
Example: '$date'
Example:
In this example we create a calendar definition that can be used to generate date related fields, such as year
and month, that can be used in dimensions.
To try out the calendar we need some source data with date fields.
Load * Inline [
OrderID, Terminal, OrderDate, ShippingDate
100001, London, 2012-03-12, 2012-03-14
100002, Berlin, 2012-03-12, 2012-03-16
100003, London, 2012-03-12, 2012-03-14
100004, Paris, 2012-03-12, 2012-03-16
100005, Paris, 2012-03-12, 2012-03-14
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100006,
100007,
100008,
100009,
100010,
100011,
100012,
100013,
100014,
100015,
100016,
100017,
100018,
];
Paris, 2012-04-11, 2012-04-22
London, 2012-04-12, 2012-04-14
Paris, 2012-05-12, 2012-05-13
London, 2013-01-12, 2013-03-14
Berlin, 2013-01-12, 2013-03-16
London, 2013-01-12, 2013-03-14
Paris, 2013-01-12, 2013-03-16
Paris, 2013-01-12, 2013-03-14
Berlin, 2013-03-12, 2013-03-16
London, 2013-03-12, 2013-03-14
Berlin, 2013-03-12, 2013-03-16
Paris, 2013-09-12, 2013-10-14
Berlin, 2013-09-12, 2013-09-16
Now we can define a calendar that creates fields for year, month, week, weekday and day number.
Calendar:
DECLARE FIELD DEFINITION TAGGED '$date'
Parameters
first_month_of_year = 1
Fields
Year($1) As Year Tagged '$year',
Month($1) as Month Tagged '$month',
Date($1) as Date Tagged ('$date', '$day'),
Week($1) as Week Tagged '$week',
Weekday($1) as Weekday Tagged '$weekday',
DayNumberOfYear($1, first_month_of_year) as DayNumberOfYear Tagged ('$numeric');
The calendar is now defined, and you can apply it to the date fields that have been loaded, in this case
OrderDate and ShippingDate, using a Derive clause. There are three alternative ways of doing this.
l
Derive fields for specific data fields.
In this case we specify the OrderDate and ShippingDate fields.
DERIVE FIELDS FROM FIELDS OrderDate,ShippingDate USING Calendar;
l
Derive fields for all fields with a specific tag.
In this case we derive fields based on Calendar for all fields with a $date tag.
DERIVE FIELDS FROM EXPLICIT TAGS '$date' USING Calendar;
l
Derive fields for all fields with the field definition tag.
In this case we derive fields for all data fields with the same tag as the Calendar field definition, which
in this case is $date.
DERIVE FIELDS FROM IMPLICIT TAG USING Calendar;
When you have reloaded the data script, you can find the generated fields, for example
OrderDate.Calendar.Weekday under Date & time fields in the Fields asset panel. They are also available
in the expression editor and when you create dimensions, and can be used as any other field in visualizations.
Direct Query
The DIRECT QUERY statement allows you to access tables through an ODBC or OLE DB connection using
the Direct Discovery function.
Syntax:
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DIRECT QUERY DIMENSION fieldlist [MEASURE fieldlist] [DETAIL fieldlist]
FROM tablelist
[WHERE where_clause]
The DIMENSION , MEASURE, and DETAIL keywords can be used in any order.
The DIMENSION and FROM keyword clauses are required on all DIRECT QUERY statements. The
FROM keyword must appear after the DIMENSION keyword.
The fields specified directly after the DIMENSION keyword are loaded in memory and can be used to create
associations between in-memory and Direct Discovery data.
The DIRECT QUERY statement cannot contain DISTINCT or GROUP BY clauses.
Using the MEASURE keyword you can define fields that Qlik Sense is aware of on a “meta level”. The actual
data of a measure field resides only in the database during the data load process, and is retrieved on an ad
hoc basis driven by the chart expressions that are used in a visualization.
Typically, fields with discrete values that will be used as dimensions should be loaded with the DIMENSION
keyword, whereas numbers that will be used in aggregations only should be selected with the MEASURE
keyword.
DETAIL fields provide information or details, like comment fields, that a user may want to display in a drillto-details table box. DETAIL fields cannot be used in chart expressions.
By design, the DIRECT QUERY statement is data-source neutral for data sources that support SQL. For
that reason, the same DIRECT QUERY statement can be used for different SQL databases without
change. Direct Discovery generates database-appropriate queries as needed.
Native data-source syntax can be used when the user knows the database to be queried and wants to exploit
database-specific extensions to SQL. Native data-source syntax is supported:
l
As field expressions in DIMENSION and MEASURE clauses
l
As the content of the WHERE clause
Examples:
DIRECT QUERY
DIMENSION Dim1, Dim2
MEASURE
NATIVE ('X % Y') AS X_MOD_Y
FROM TableName
DIRECT QUERY
DIMENSION Dim1, Dim2
MEASURE X, Y
FROM TableName
WHERE NATIVE ('EMAIL MATCHES "\*.EDU"')
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The following terms are used as keywords and so cannot be used as column or field names
without being quoted: and, as, detach, detail, dimension, distinct, from, in, is, like, measure,
native, not, or, where
Arguments:
Argument
Description
fieldlist
A comma-separated list of field specifications, fieldname {, fieldname} . A field
specification can be a field name, in which case the same name is used for the database
column name and the Qlik Sense field name. Or a field specification can be a "field alias," in
which case a database expression or column name is given a Qlik Sense field name.
tablelist
A list of the names of tables or views in the database from which data will be loaded.
Typically, it will be views that contain a JOIN performed on the database.
where_
clause
The full syntax of database WHERE clauses is not defined here, but most SQL "relational
expressions" are allowed, including the use of function calls, the LIKE operator for strings,
IS NULL and IS NOT NULL, and IN. BETWEEN is not included.
NOT is a unary operator, as opposed to a modifier on certain keywords.
Examples:
WHERE x > 100 AND "Region Code" IN ('south', 'west')
WHERE Code IS NOT NULL and Code LIKE '%prospect'
WHERE NOT X in (1,2,3)
The last example can not be written as:
WHERE X NOT in (1,2,3)
Example:
In this example, a database table called TableName, containing fields Dim1, Dim2, Num1, Num2 and Num3,
is used.Dim1 and Dim2 will be loaded into the Qlik Sense dataset.
DIRECT QUERY DIMENSTION Dim1, Dim2 MEASURE Num1, Num2, Num3 FROM TableName ;
Dim1 and Dim2 will be available for use as dimensions. Num1, Num2 and Num3 will be available for
aggregations. Dim1 and Dim2 are also available for aggregations. The type of aggregations for which Dim1
and Dim2 can be used depends on their data types. For example, in many cases DIMENSION fields contain
string data such as names or account numbers.Those fields cannot be summed, but they can be counted:
count(Dim1).
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DIRECT QUERY statements are written directly in the script editor. To simplify construction
of DIRECT QUERY statements, you can generate a SELECT statement from a data
connection, and then edit the generated script to change it into a DIRECT QUERY statement.
For example, the SELECT statement:
SQL SELECT
SalesOrderID,
RevisionNumber,
OrderDate,
SubTotal
TaxAmt
FROM MyDB.Sales.SalesOrderHeader;
could be changed to the following DIRECT QUERY statement:
DIRECT QUERY
DIMENSION
SalesOrderID,
RevisionNumber,
MEASURE
SubTotal
TaxAmt
DETAIL
OrderDate,
FROM MyDB.Sales.SalesOrderHeader;
Direct Discovery field lists
A field list is a comma-separated list of field specifications, fieldname {, fieldname}. A field specification can
be a field name, in which case the same name is used for the database column name and the field name. Or
a field specification can be a field alias, in which case a database expression or column name is given a Qlik
Sense field name.
Field names can be either simple names or quoted names. A simple name begins with an alphabetic
Unicode character and is followed by any combination of alphabetic or numeric characters or underscores.
Quoted names begin with a double quotation mark and contain any sequence of characters. If a quoted
name contains double quotation marks, those quotation marks are represented using two adjacent double
quotation marks.
Qlik Sense field names are case-sensitive. Database field names may or may not be case-sensitive,
depending on the database. A Direct Discovery query preserves the case of all field identifiers and aliases. In
the following example, the alias "MyState" is used internally to store the data from the database column
"STATEID".
DIRECT QUERY Dimension STATEID as MyState Measure AMOUNT from SALES_TABLE;
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This differs from the result of an SQL Select statement with an alias. If the alias is not explicitly quoted, the
result contains the default case of column returned by the target database. In the following example, the
SQL Select statement to an Oracle database creates "MYSTATE," with all upper case letters, as the
internal Qlik Sense alias even though the alias is specified as mixed case. The SQL Select statement uses
the column name returned by the database, which in the case of Oracle is all upper case.
SQL Select STATEID as MyState, STATENAME from STATE_TABLE;
To avoid this behavior, use the LOAD statement to specify the alias.
Load STATEID as MyState, STATENAME;
SQL Select STATEID, STATEMENT from STATE_TABLE;
In this example, the "STATEID" column is stored internally byQlik Sense as "MyState".
Most database scalar expressions are allowed as field specifications. Function calls can also be used in field
specifications. Expressions can contain constants that are boolean, numeric, or strings contained in single
quotation marks (embedded single quotation marks are represented by adjacent single quotation marks).
Examples:
DIRECT QUERY DIMENSION SalesOrderID, RevisionNumber MEASURE SubTotal AS "Sub Total" FROM
AdventureWorks.Sales.SalesOrderHeader
DIRECT QUERY DIMENSION "SalesOrderID" AS "Sales Order ID" MEASURE SubTotal,TaxAmt,(SubTotal-TaxAmt)
AS "Net Total" FROM AdventureWorks.Sales.SalesOrderHeader
DIRECT QUERY DIMENSION (2*Radius*3.14159) AS Circumference, Molecules/6.02e23 AS Moles MEASURE Num1
AS numA FROM TableName
DIRECT QUERY DIMENSION concat(region, 'code') AS region_code MEASURE Num1 AS NumA FROM TableName
Direct Discovery does not support using aggregations in LOAD statements. If aggregations are used, the
results are unpredictable. A LOAD statement such as the following should not be used:
DIRECT QUERY DIMENSION stateid, SUM(amount*7) AS MultiFirst MEASURE amount FROM sales_table
The SUM should not be in the LOAD statement.
Direct Discovery also does not support Qlik Sense functions in Direct Query statements. For example, the
following specification for a DIMENSION field results in a failure when the "Mth" field is used as a dimension
in a visualization:
month(ModifiedDate) as Mth
Directory
The Directory statement defines which directory to look in for data files in subsequent LOAD statements,
until a new Directory statement is made.
This statement has no effect in standard scripting mode.
Syntax:
Directory[path]
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If the Directory statement is issued without a path or left out, Qlik Sense will look in the Qlik Sense working
directory.
Arguments:
Argument
path
Description
A text that can be interpreted as the path to the qvf file.
The path is the path to the file, either:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Example:
Directory c:\userfiles\data;
Disconnect
The Disconnect statement terminates the current ODBC/OLE DB/Custom connection. This statement is
optional.
Syntax:
Disconnect
The connection will be automatically terminated when a new connect statement is executed or when the
script execution is finished.
Example:
Disconnect;
Drop field
One or several Qlik Sense fields can be dropped from the data model, and thus from memory, at any time
during script execution, by means of a drop field statement.
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Both drop field and drop fields are allowed forms with no difference in effect. If no table is
specified, the field will be dropped from all tables where it occurs.
Syntax:
Drop field fieldname [ , fieldname2 ...] [from tablename1 [ , tablename2
...]]
Drop fields fieldname [ , fieldname2 ...] [from tablename1 [ , tablename2
...]]
Examples:
Drop
Drop
Drop
Drop
field A;
fields A,B;
field A from X;
fields A,B from X,Y;
Drop table
One or several Qlik Sense internal tables can be dropped from the data model, and thus from memory, at
any time during script execution, by means of a drop table statement.
Syntax:
drop table tablename [, tablename2 ...]
drop tables [ tablename [, tablename2 ...]
The forms drop table and drop tables are both accepted.
The following items will be lost as a result of this:
l
The actual table(s).
l
All fields which are not part of remaining tables.
l
Field values in remaining fields, which came exclusively from the dropped table(s).
Examples and results:
Example
Result
drop table Orders, Salesmen, T456a;
This line results in three tables being dropped
from memory.
Tab1:
As a result only the aggregates remain in the
memory. Trans data is discarded.
SQL SELECT* from Trans;
LOAD Customer, Sum( sales ) resident Tab1 group by
Month;
drop table Tab1;
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Execute
The Execute statement is used to run other programs while Qlik Sense is loading data. For example, to
make conversions that are necessary.
This statement is not supported in standard mode.
Syntax:
execute commandline
Arguments:
Argument
Description
commandline
A text that can be interpreted by the operating system as a command line.
You can refer to an absolute file path or a lib:// folder path.
If you want to use Execute the following conditions need to be met:
l
l
You must run in legacy mode (applicable for Qlik Sense and Qlik Sense Desktop).
You need to set OverrideScriptSecurity to 1 in Settings.ini (applicable for Qlik Sense).
Settings.ini is located in C:\ProgramData\Qlik\Sense\Engine\ and is generally an empty file.
Do the following:
1. Make a copy of Settings.ini and open it in a text editor.
2. Check that the file includes [Settings 7] in the first line.
3. Insert a new line and type OverrideScriptSecurity=1.
4. Insert an empty line at the end of the file.
5. Save the file.
6. Substitute Settings.ini with your edited file.
7. Restart Qlik Sense Engine Service (QES).
If Qlik Sense is running as a service, some commands may not behave as expected.
Example:
Execute C:\Program Files\Office12\Excel.exe;
Execute lib://win\notepad.exe // win is a folder connection referring to c:\windows
FlushLog
The FlushLog statement forces Qlik Sense to write the content of the script buffer to the script log file.
Syntax:
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FlushLog
The content of the buffer is written to the log file. This command can be useful for debugging purposes, as
you will receive data that otherwise may have been lost in a failed script execution.
Example:
FlushLog;
Force
The force statement forces Qlik Sense to interpret field values of subsequent LOAD and SELECT
statements as written with only upper case letters, with only lower case letters, as always capitalized or as
they appear (mixed). This statement makes it possible to associate field values from tables made according
to different conventions.
Syntax:
Force ( capitalization | case upper | case lower | case mixed )
If nothing is specified, force case mixed is assumed. The force statement is valid until a new force statement
is made.
The force statement has no effect in the access section: all field values loaded are case insensitive.
Examples:
Force
Force
Force
Force
Capitalization;
Case Upper;
Case Lower;
Case Mixed;
Load
The LOAD statement loads fields from a file, from data defined in the script, from a previously loaded table,
from a web page, from the result of a subsequent SELECT statement or by generating data automatically.
Syntax:
LOAD [ distinct ] *fieldlist
[( from file [ format-spec ] |
from_field fieldassource [format-spec]
inline data [ format-spec ] |
resident table-label |
autogenerate size )]
[ where criterion | while criterion ]
[ group_by groupbyfieldlist ]
[order_by orderbyfieldlist ]
Arguments:
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Argument
Description
distinct
distinct is a predicate used if only the first of duplicate records should be loaded.
fieldlist
*fieldlist ::= ( * | field {, field } )
A list of the fields to be loaded. Using * as a field list indicates all fields in the table.
field ::= ( fieldref | expression ) [as aliasname ]
The field definition must always contain a literal, a reference to an existing
field, or an expression.
fieldref ::= ( fieldname |@fieldnumber |@startpos:endpos [ I | U | R | B | T] )
fieldname is a text that is identical to a field name in the table. Note that the field
name must be enclosed by straight double quotation marks or square brackets if it
contains e.g. spaces. Sometimes field names are not explicitly available. Then a
different notation is used:
@fieldnumber represents the field number in a delimited table file. It must be a
positive integer preceded by "@". The numbering is always made from 1 and up to the
number of fields.
@startpos: endpos represents the start and end positions of a field in a file with fixed
length records. The positions must both be positive integers. The two numbers must
be preceded by "@" and separated by a colon. The numbering is always made from 1
and up to the number of positions. In the last field, n is used as end position.
l
If @startpos: endpos is immediately followed by the characters I or U, the
bytes read will be interpreted as a binary signed (I) or unsigned (U) integer
l
(Intel byte order). The number of positions read must be 1, 2 or 4.
If @startpos: endpos is immediately followed by the character R, the bytes
l
read will be interpreted as a binary real number (IEEE 32-bit or 64 bit floating
point). The number of positions read must be 4 or 8.
If @startpos: endpos is immediately followed by the character B, the bytes
read will be interpreted as a BCD (Binary Coded Decimal) numbers according
to the COMP-3 standard. Any number of bytes may be specified.
expression can be a numeric function or a string function based on one or several
other fields in the same table. For further information, see the syntax of expressions.
as is used for assigning a new name to the field.
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Argument
Description
from
from is used if data should be loaded from a file using a folder or a web file data
connection.
file ::= [ path ] filename
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an
intranet.
Example: http://www.qlik.com
If the path is omitted, Qlik Sense searches for the file in the directory specified by the
Directory statement. If there is no Directory statement, Qlik Sense searches in the
working directory, C:\Users\{user}\Documents\Qlik\Sense\Apps.
In a Qlik Sense server installation, the working directory is specified in
Qlik Sense Repository Service, by default it is
C:\ProgramData\Qlik\Sense\Apps. See the Qlik Management Console
help for more information.
The filename may contain the standard DOS wildcard characters ( * and ? ). This will
cause all the matching files in the specified directory to be loaded.
format-spec ::= ( fspec-item { , fspec-item } )
The format specification consists of a list of several format specification items, within
brackets.
from_field
from_field is used if data should be loaded from a previously loaded field.
fieldassource::=(tablename, fieldname)
The field is the name of the previously loaded tablename and fieldname.
format-spec ::= ( fspec-item {, fspec-item } )
The format specification consists of a list of several format specification items, within
brackets.
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Argument
Description
inline
inline is used if data should be typed within the script, and not loaded from a file.
data ::= [ text ]
Data entered through an inline clause must be enclosed by double quotation marks or
by square brackets. The text between these is interpreted in the same way as the
content of a file. Hence, where you would insert a new line in a text file, you should
also do it in the text of an inline clause, i.e. by pressing the Enter key when typing the
script.
format-spec ::= ( fspec-item {, fspec-item } )
The format specification consists of a list of several format specification items, within
brackets.
resident
resident is used if data should be loaded from a previously loaded table.
table label is a label preceding the LOAD or SELECT statement(s) that created the
original table. The label should be given with a colon at the end.
autogenerate
autogenerate is used if data should be automatically generated by Qlik Sense.
size ::= number
Number is an integer indicating the number of records to be generated. The field list
must not contain expressions which require data from a database. Only constants and
parameter-free functions (such as rand() or recno()), are allowed in the expressions.
where
where is a clause used for stating whether a record should be included in the selection
or not. The selection is included if criterion is True.
criterion is a logical expression.
while
while is a clause used for stating whether a record should be repeatedly read. The
same record is read as long as criterion is True. In order to be useful, a while clause
must typically include the IterNo( ) function.
criterion is a logical expression.
group_by
group by is a clause used for defining over which fields the data should be
aggregated (grouped). The aggregation fields should be included in some way in the
expressions loaded. No other fields than the aggregation fields may be used outside
aggregation functions in the loaded expressions.
groupbyfieldlist ::= (fieldname { ,fieldname } )
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Argument
Description
order_by
order by is a clause used for sorting the records of a resident table before they are
processed by the load statement. The resident table can be sorted by one or more
fields in ascending or descending order. The sorting is made primarily by numeric
value and secondarily by national ASCII value. This clause may only be used when the
data source is a resident table.
The ordering fields specify which field the resident table is sorted by. The field can be
specified by its name or by its number in the resident table (the first field is number 1).
orderbyfieldlist ::= fieldname [ sortorder ] { , fieldname [ sortorder ] }
sortorder is either asc for ascending or desc for descending. If no sortorder is
specified, asc is assumed.
fieldname, path, filename and aliasname are text strings representing what the
respective names imply. Any field in the source table can be used as fieldname.
However, fields created through the as clause (aliasname) are out of scope and cannot
be used inside the same load statement.
If no source of data is given by means of a from, inline, resident, from_field or autogenerate clause, data
will be loaded from the result of the immediately succeeding SELECT or LOAD statement. The succeeding
statement should not have a prefix.
Examples:
Loading different file formats
// LOAD a delimited data file with default options
LOAD * from data1.csv;
// LOAD a delimited data file from a library connection MyData
LOAD * from 'lib://MyData/data1.csv';
// LOAD a delimited file, specifying comma as delimiter and embedded labels
LOAD * from 'c:\userfiles\data1.csv' (ansi, txt, delimiter is ',', embedded labels);
// LOAD a delimited file specifying tab as delimiter and embedded labels
LOAD * from 'c:\userfiles\data2.txt' (ansi, txt, delimiter is '\t', embedded labels);
// LOAD a dif file with embedded headers
LOAD * from file2.dif (ansi, dif, embedded labels);
// LOAD three fields from a fixed record file without headers
LOAD @1:2 as ID, @3:25 as Name, @57:80 as City from data4.fix (ansi, fix, no labels, header is 0,
record is 80);
//LOAD a QVX file, specifying an absolute path
LOAD * from C:\qdssamples\xyz.qvx (qvx);
Selecting certain fields, renaming and calculating fields
// LOAD only three specific fields
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LOAD FirstName, LastName, Number from data1.csv;
// Rename first field as A and second field as B when loading a file without labels
LOAD @1 as A, @2 as B from data3.txt' (ansi, txt, delimiter is '\t', no labels);
// LOAD Name as a concatenation of FirstName, a space, and LastName
LOAD FirstName&' '&LastName as Name from data1.csv;
//LOAD Quantity, Price and Value (the product of Quantity and Price)
LOAD Quantity, Price, Quantity*Price as Value from data1.csv;
Selecting certain records
// LOAD only unique records, duplicate records will be discarded
LOAD distinct FirstName, LastName, Number from data1.csv;
// LOAD only records where the field Litres has a value above zero
LOAD * from Consumption.csv where Litres>0;
Loading data not on file and autogenerated data
// LOAD a table with inline data, fields CatID and Category
LOAD * Inline
[CatID, Category
0,Regular
1,Occasional
2,Permanent];
// LOAD a table with inline data, fields UserID, Password and Access
LOAD * Inline [UserID, Password, Access
A, ABC456, User
B, VIP789, Admin];
// LOAD a table with 10 000 rows
// Field A will contain the number of the read record (1,2,3,4,5...)
// Field B will contain a random number between 0 and 1
LOAD RecNo( ) as A, rand( ) as B autogenerate(10000);
The parenthesis after autogenerate is allowed but not required.
Loading data from previously loaded table
tab1:
SELECT A,B,C,D from transtable;
// LOAD fields from already loaded table tab1
LOAD A,B,month(C),A*B+D as E resident tab1;
// LOAD fields from already loaded table tab1 but only records where A is larger than B
LOAD A,A+B+C resident tab1 where A>B;
// LOAD fields from already loaded table tab1 ordered by A
LOAD A,B*C as E resident tab1 order by A;
// LOAD fields from already loaded table tab1, ordered by the first field, then the second field
LOAD A,B*C as E resident tab1 order by 1,2;
// LOAD fields from already loaded table tab1 ordered by C descending, then B ascending,
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// then first field descending
LOAD A,B*C as E resident tab1 order by C desc, B asc, 1 des
Loading data from previously loaded fields
// LOAD field Types from previously loaded table Characters as A
LOAD A from_field (Characters, Types)
Loading data from succeeding table
// LOAD A, B and calculated fields C and D from Table1 that is loaded in succeeding statement
LOAD A, B, if(C>0,'positive','negative') as X, weekday(D) as Y;
SELECT A,B,C,D from Table1;
Grouping data
// LOAD fields grouped (aggregated) by ArtNo
LOAD ArtNo, round(Sum(TransAmount),0.05) as ArtNoTotal from table.csv group by ArtNo;
// // LOAD fields grouped (aggregated) by Week and ArtNo
LOAD Week, ArtNo, round(Avg(TransAmount),0.05) as WeekArtNoAverages from table.csv group by Week,
ArtNo;
Reading one record repeatedly
In this example we have a input file Grades.csv containing the grades for each student condensed in one
field:
Student,Grades
Mike,5234
John,3345
Pete,1234
Paul,3352
The grades, in a 1-5 scale, represent Math, English, Science and History. We can separate the grades into
separate values by reading each record several times with a while clause, using the IterNo( ) function as a
counter. In each read, the Grade is extracted with the Mid function, and the Subject is selected using the
pick function. The final while clause contains the test to check if all grades have been read (four per student
in this case), which means next student record should be read.
MyTab:
LOAD Student,
mid(Grades,IterNo( ),1) as Grade,
pick(IterNo( ), 'Math', 'English', 'Science', 'History') as Subject from Grades.csv
while IsNum(mid(Grades,IterNo(),1));
The result is a table containing this data:
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Format specification items
Each format specification item defines a certain property of the table file:
fspec-item ::= [ ansi | oem | mac | UTF-8 | Unicode | txt | fix | dif | biff | ooxml | html | xml |
kml | qvd | qvx | delimiter is char | no eof | embedded labels | explicit labels | no labels | table is
[tablename] | header is n | header is line | header is n lines | comment is string | record is n |
record is line | record isn lines | no quotes |msq ]
Character set
Character set is a file specifier for the LOAD statement that defines the character set used in the file.
The ansi, oem and mac specifiers were used in QlikView and will still work. However, they will not be
generated when creating the LOAD statement with Qlik Sense.
Syntax:
utf8 | unicode | ansi | oem | mac | codepage is
Arguments:
Argument
Description
utf8
UTF-8 character set
unicode
Unicode character set
ansi
Windows, codepage 1252
oem
DOS, OS/2, AS400 and others
mac
Codepage 10000
codepage is
With the codepage specifier, it is possible to use any Windows codepage as N .
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Limitations:
Conversion from the oem character set is not implemented for MacOS. If nothing is specified, codepage
1252 is assumed under Windows.
Example:
LOAD * from a.txt (utf8, txt, delimiter is ',' , embedded labels)
LOAD * from a.txt (unicode, txt, delimiter is ',' , embedded labels)
LOAD * from a.txt (codepage is 10000, txt, delimiter is ',' , no labels)
See also:
p
Load (page 79)
Table format
The table format is a file specifier for the LOAD statement that defines the file type. If nothing is specified, a
.txt file is assumed.
txt
In a delimited text file, .txt, the columns in the table are separated by some character.
fix
In a fixed record length file, .fix, each column is exactly a certain number of characters
wide.
dif
In a .dif file, (Data Interchange Format) a special format for defining the table is used.
biff
Qlik Sense can also interpret data in standard Excel files by means of the biff format
(Binary Interchange File Format).
ooxml
Excel 2007 and later versions use the ooxml .xslx format.
html
If the table is part of an html page or file, html should be used.
xml
xml (Extensible Markup Language) is a common markup language that is used to
represent data structures in a textual format.
qvd
The format qvd is the proprietary QVD files format, exported from a Qlik Sense app.
qvx
qvx is a file/stream format for high performance output to Qlik Sense.
Delimiter
For delimited table files, an arbitrary delimiter can be specified through the delimiter is specifier. This
specifier is relevant only for delimited .txt files.
Syntax:
delimiter is char
Arguments:
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Argument
Description
char
Specifies a single character from the 127 ASCII characters.
Additionally, the following values can be used:
"\t"
representing a tab sign, with or without quotation marks.
"\\"
representing a backslash ( \ ) character.
"spaces"
representing all combinations of one or more spaces. Non-printable
characters with an ASCII-value below 32, with the exception of CR
and LF, will be interpreted as spaces.
If nothing is specified, delimiter is ',' is assumed.
Example:
LOAD * from a.txt (utf8, txt, delimiter is ',' , embedded labels);
See also:
p
Load (page 79)
No eof
The no eof specifier is used to disregard end-of-file character when loading delimited .txt files.
Syntax:
no eof
If the no eof specifier is used, ASCII character 26, which otherwise denotes end-of-file, is disregarded and
can be part of a field value.
It is relevant only for delimited .txt files.
Example:
LOAD * from a.txt (txt, utf8, embedded labels, delimiter is ' ', no eof);
See also:
p
Load (page 79)
Labels
Labels is a file specifier for the LOAD statement that defines where in a file the field names can be found.
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Syntax:
embedded labels|explicit labels|no labels
The field names can be found in different places of the file. If the first record contains the field names,
embedded labels should be used. If there are no field names to be found, no labels should be used. In dif
files, a separate header section with explicit field names is sometimes used. In such a case, explicit labels
should be used. If nothing is specified, embedded labels is assumed, also for dif files.
Example 1:
LOAD * from a.txt (unicode, txt, delimiter is ',' , embedded labels
Example 2:
LOAD * from a.txt (codePage is 1252, txt, delimiter is ',' , no labels)
See also:
p
Load (page 79)
Header is
Specifies the header size in table files. An arbitrary header length can be specified through the header is
specifier. A header is a text section not used by Qlik Sense.
Syntax:
header is n
header is line
header is n lines
The header length can be given in bytes (header is n), or in lines (header is line or header is n lines). n
must be a positive integer, representing the header length. If not specified, header is 0 is assumed. The
header is specifier is only relevant for table files.
Example:
This is an example of a data source table containing a header text line that should not be interpreted as data
by Qlik Sense.
*Header line
Col1,Col2
a,B
c,D
Using the header is 1 lines specifier, the first line will not be loaded as data. In the example, the
embedded labels specifier tells Qlik Sense to interpret the first non-excluded line as containing field labels.
LOAD Col1, Col2
FROM 'lib://files/header.txt'
(txt, embedded labels, delimiter is ',', msq, header is 1 lines);
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The result is a table with two fields, Col1 and Col2.
See also:
p
Load (page 79)
Record is
For fixed record length files, the record length must be specified through the record is specifier.
Syntax:
Record is n
Record is line
Record is n lines
Arguments:
Argument
Description
n
Specifies the record length in bytes.
line
Specifies the record length in one lines.
n lines
Specifies the record length in lines where n is a positive integer representing the record
length.
Limitations:
The record is specifier is only relevant for fix files.
See also:
p
Load (page 79)
Quotes
Quotes is a file specifier for the LOAD statement that defines whether quotes can be used and the
precedence between quotes and separators. For text files only.
Syntax:
no quotes
msq
If the specifier is omitted, standard quoting is used, that is, the quotes " " or ' ' can be used, but only if they
are the first and last non blank character of a field value.
Arguments:
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Argument
Description
no quotes
Used if quotation marks are not to be accepted in a text file.
msq
Used to specify modern style quoting, allowing multi-line content in fields. Fields containing
end-of-line characters must be enclosed within double quotes.
One limitation of the msq option is that single double-quote (") characters appearing as first
or last character in field content will be interpreted as start or end of multi-line content,
which may lead to unpredicted results in the data set loaded. In this case you should use
standard quoting instead, omitting the specifier.
XML
This script specifier is used when loading xml files. Valid options for the XML specifier are listed in syntax.
Syntax:
xmlsax
xmlsimple
pattern is path
xmlsax and xmlsimple are mutually exclusive, only one can be specified when using xml. When using
pattern, the file will be read from the start of the specified tag to the end of the tag. If path contains spaces,
the path must be quoted.
In order to use xmlsax, Microsoft's xml parser MSXML 3.0 or higher must be installed on the
computer. MSXML is shipped with e.g. Windows XP and MS Internet Explorer 6. It can also be
downloaded from the Microsoft home page.
See also:
p
Load (page 79)
KML
This script specifier is used when loading KML files to use in a map visualization.
Syntax:
kml
The KML file can represent either area data (for example, countries or regions) represented by polygons, or
point data (for example, cities or places) represented by points in the form [long, lat].
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Let
The let statement is a complement to the set statement, used for defining script variables. The let
statement, in opposition to the set statement, evaluates the expression on the right side of the ' =' before it is
assigned to the variable.
Syntax:
Let variablename=expression
The word let may be omitted, but the statement then becomes a control statement. Such a statement
without the keyword let must be contained within a single script row and may be terminated either with a
semicolon or end-of-line.
Examples and results:
Example
Result
Set x=3+4;
Let y=3+4
z=$(y)+1;
$(x) will be evaluated as ' 3+4 '
$(y) will be evaluated as ' 7 '
$(z) will be evaluated as ' 8 '
Let T=now( );
$(T) will be given the value of
the current time.
Map
The map ... using statement is used for mapping a certain field value or expression to the values of a
specific mapping table. The mapping table is created through the Mapping statement.
Syntax:
Map *fieldlist Using
mapname
The automatic mapping is done for fields loaded after the Map … Using statement until the end of the script
or until an Unmap statement is encountered.
The mapping is done last in the chain of events leading up to the field being stored in the internal table in Qlik
Sense. This means that mapping is not done every time a field name is encountered as part of an
expression, but rather when the value is stored under the field name in the internal table. If mapping on the
expression level is required, the Applymap() function has to be used instead.
Arguments:
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Argument
Description
*fieldlist
A comma separated list of the fields that should be mapped from this point in the script.
Using * as field list indicates all fields. The wildcard characters * and ? are allowed in field
names. Quoting of field names may be necessary when wildcards are used.
mapname
The name of a mapping table previously read in a mapping load or mapping select
statement.
Examples and results:
Example
Result
Map Country Using
Cmap;
Enables mapping of the field Country using the map Cmap.
Map A, B, C Using X;
Enables mapping of the fields A, B and C using the map X.
Map * Using GenMap;
Enables mapping of all fields using GenMap.
NullAsNull
The NullAsNull statement turns off the conversion of NULL values to string values previously set by a
NullAsValue statement.
Syntax:
NullAsNull *fieldlist
The NullAsValue statement operates as a switch and can be turned on or off several times in the script,
using either a NullAsValue or a NullAsNull statement.
Arguments:
Argument
Description
*fieldlist
A comma separated list of the fields for which NullAsNull should be turned on. Using * as
field list indicates all fields. The wildcard characters * and ? are allowed in field names.
Quoting of field names may be necessary when wildcards are used.
Example:
NullAsNull A,B;
LOAD A,B from x.csv;
NullAsValue
The NullAsValue statement specifies for which fields that NULL should be converted to a value.
Syntax:
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NullAsValue *fieldlist
By default, Qlik Sense considers NULL values to be missing or undefined entities. However, certain database
contexts imply that NULL values are to be considered as special values rather than simply missing values.
The fact that NULL values are normally not allowed to link to other NULL values can be suspended by means
of the NullAsValue statement.
The NullAsValue statement operates as a switch and will operate on subsequent loading statements. It can
be switched off again by means of the NullAsNull statement.
Arguments:
Argument
Description
*fieldlist
A comma separated list of the fields for which NullAsValue should be turned on. Using * as
field list indicates all fields. The wildcard characters * and ? are allowed in field names.
Quoting of field names may be necessary when wildcards are used.
Example:
NullAsValue A,B;
Set NullValue = 'NULL';
LOAD A,B from x.csv;
Qualify
The Qualify statement is used for switching on the qualification of field names, i.e. field names will get the
table name as a prefix.
Syntax:
Qualify *fieldlist
The automatic join between fields with the same name in different tables can be suspended by means of the
qualify statement, which qualifies the field name with its table name. If qualified, the field name(s) will be
renamed when found in a table. The new name will be in the form of tablename.fieldname. Tablename is
equivalent to the label of the current table, or, if no label exists, to the name appearing after from in LOAD
and SELECT statements.
The qualification will be made for all fields loaded after the qualify statement.
Qualification is always turned off by default at the beginning of script execution. Qualification of a field name
can be activated at any time using a qualify statement. Qualification can be turned off at any time using an
Unqualify statement.
The qualify statement should not be used in conjunction with partial reload.
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Arguments:
Argument
Description
*fieldlist
A comma separated list of the fields for which qualification should be turned on. Using * as
field list indicates all fields. The wildcard characters * and ? are allowed in field names.
Quoting of field names may be necessary when wildcards are used.
Example 1:
Qualify B;
LOAD A,B from x.csv;
LOAD A,B from y.csv;
The two tables x.csv and y.csv are associated only through A. Three fields will result: A, x.B, y.B.
Example 2:
In an unfamiliar database, it is often useful to start out by making sure that only one or a few fields are
associated, as illustrated in this example:
qualify *;
unqualify TransID;
SQL SELECT * from tab1;
SQL SELECT * from tab2;
SQL SELECT * from tab3;
Only TransID will be used for associations between the tables tab1, tab2 and tab3.
Rem
The rem statement is used for inserting remarks, or comments, into the script, or to temporarily deactivate
script statements without removing them.
Syntax:
Rem string
Everything between the rem and the next semicolon ; is considered to be a comment.
There are two alternative methods available for making comments in the script:
1. It is possible to create a comment anywhere in the script - except between two quotes - by placing the
section in question between /* and */.
2. When typing // in the script, all text that follows to the right on the same row becomes a comment.
(Note the exception //: that may be used as part of an Internet address.)
Arguments:
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Argument
Description
string
An arbitrary text.
Example:
Rem ** This is a comment **;
/* This is also a comment */
// This is a comment as well
Rename field
This script function renames one or more existing Qlik Sense field(s) after they have been loaded.
Either syntax: rename field or rename fields can be used.
Syntax:
Rename Field (using mapname | oldname to newname{ , oldname to newname })
Rename Fields (using mapname | oldname to newname{ , oldname to newname })
Arguments:
Argument
Description
mapname
The name of a previously loaded mapping table containing one or more pairs of old and new
field names.
oldname
The old field name.
newname
The new field name.
Limitations:
Two differently named fields cannot be renamed to having the same name. The script will run without errors,
but the second field will not be renamed.
Example 1:
Rename Field XAZ0007 to Sales;
Example 2:
FieldMap:
Mapping SQL SELECT oldnames, newnames from datadictionary;
Rename Fields using FieldMap;
Rename table
This script function renames one or more existing Qlik Sense internal table(s) after they have been loaded.
Either syntax: rename table or rename tables can be used.
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Syntax:
Rename Table (using mapname | oldname to newname{ , oldname to newname })
Rename Tables (using mapname | oldname to newname{ , oldname to newname })
Arguments:
Argument
Description
mapname
The name of a previously loaded mapping table containing one or more pairs of old and new
table names.
oldname
The old table name.
newname
The new table name.
Limitations:
Two differently named tables cannot be renamed to having the same name. The script will run without errors,
but the second table will not be renamed.
Example 1:
Tab1:
SELECT * from Trans;
Rename Table Tab1 to Xyz;
Example 2:
TabMap:
Mapping LOAD oldnames, newnames from tabnames.csv;
Rename Tables using TabMap;
Search
The Search statement is used for including or excluding fields in the search tool function.
Syntax:
Search Include *fieldlist
Search Exclude *fieldlist
You can use several Search statements to refine your selection of fields to include. The statements are
evaluated from top to bottom.
Arguments:
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Argument
Description
*fieldlist
A comma separated list of the fields to include or exclude from searches in the search tool.
Using * as field list indicates all fields. The wildcard characters * and ? are allowed in field
names. Quoting of field names may be necessary when wildcards are used.
Example:
Search Include *;
Include all fields in searches in the search tool.
Search Exclude *ID;
Exclude all fields ending with ID from searches in the search tool.
Search Include ProductID;
Include the field ProductID in searches in the search tool.
The combined result of these three statements, in this sequence, is that all fields ending with ID except
ProductID are excluded from searches in the search tool.
Section
With the section statement, it is possible to define whether the subsequent LOAD and SELECT
statements should be considered as data or as a definition of the access rights.
Syntax:
Section (access | application)
If nothing is specified, section application is assumed. The section definition is valid until a new section
statement is made.
Example:
Section access;
Section application;
Select
The selection of fields from an ODBC data source or OLE DB provider is made through standard SQL
SELECT statements. However, whether the SELECT statements are accepted depends on the ODBC
driver or OLE DB provider used.
Syntax:
Select [all | distinct | distinctrow | top n [percent] ] *fieldlist
From tablelist
[where criterion ]
[group by fieldlist [having criterion ] ]
[order by fieldlist [asc | desc] ]
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[ (Inner | Left | Right | Full) join tablename on fieldref = fieldref ]
Furthermore, several SELECT statements can sometimes be concatenated into one through the use of a
union operator:
selectstatement Union selectstatement
The SELECT statement is interpreted by the ODBC driver or OLE DB provider, so deviations from the
general SQL syntax might occur depending on the capabilities of the ODBC drivers or OLE DB provider, for
example:.
l
as is sometimes not allowed, i.e. aliasname must follow immediately after fieldname.
l
as is sometimes compulsory if an aliasname is used.
l
distinct, as, where, group by, order by, or union is sometimes not supported.
l
The ODBC driver sometimes does not accept all the different quotation marks listed above.
This is not a complete description of the SQL SELECT statement! E.g. SELECT statements
can be nested, several joins can be made in one SELECT statement, the number of functions
allowed in expressions is sometimes very large, etc.
Arguments:
Argument
Description
distinct
distinct is a predicate used if duplicate combinations of values in the selected fields only
should be loaded once.
distinctrow
distinctrow is a predicate used if duplicate records in the source table only should be
loaded once.
*fieldlist
*fieldlist ::= (*| field ) {, field }
A list of the fields to be selected. Using * as field list indicates all fields in the table.
fieldlist ::= field {, field }
A list of one or more fields, separated by commas.
field ::= ( fieldref | expression ) [as aliasname ]
The expression can e.g. be a numeric or string function based on one or several other
fields. Some of the operators and functions usually accepted are: +, -, *, /, & (string
concatenation), sum(fieldname), count(fieldname), avg(fieldname)(average), month
(fieldname), etc. See the documentation of the ODBC driver for more information.
fieldref ::= [ tablename. ] fieldname
The tablename and the fieldname are text strings identical to what they imply. They
must be enclosed by straight double quotation marks if they contain e.g. spaces.
The as clause is used for assigning a new name to the field.
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Argument
Description
from
tablelist ::= table {, table }
The list of tables that the fields are to be selected from.
table ::= tablename [ [as ] aliasname ]
The tablename may or may not be put within quotes.
where
where is a clause used for stating whether a record should be included in the selection or
not.
criterion is a logical expression that can sometimes be very complex. Some of the
operators accepted are: numeric operators and functions, =, <> or #(not equal), >, >=, <,
<=, and, or, not, exists, some, all, in and also new SELECT statements. See the
documentation of the ODBC driver or OLE DB providerfor more information.
group by
group by is a clause used for aggregating (group) several records into one. Within one
group, for a certain field, all the records must either have the same value, or the field can
only be used from within an expression, e.g. as a sum or an average. The expression
based on one or several fields is defined in the expression of the field symbol.
having
having is a clause used for qualifying groups in a similar manner to how the where clause
is used for qualifying records.
order by
order by is a clause used for stating the sort order of the resulting table of the SELECT
statement.
join
join is a qualifier stating if several tables are to be joined together into one. Field names
and table names must be put within quotes if they contain blank spaces or letters from the
national character sets. When the script is automatically generated by Qlik Sense, the
quotation mark used is the one preferred by the ODBC driver or OLE DB provider
specified in the data source definition of the data source in the Connect statement.
Example 1:
SELECT * FROM `Categories`;
Example 2:
SELECT `Category ID`, `Category Name` FROM `Categories`;
Example 3:
SELECT `Order ID`, `Product ID`,
`Unit Price` * Quantity * (1-Discount) as NetSales
FROM `Order Details`;
Example 4:
SELECT `Order Details`.`Order ID`,
Sum(`Order Details`.`Unit Price` * `Order Details`.Quantity) as `Result`
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FROM `Order Details`, Orders
where Orders.`Order ID` = `Order Details`.`Order ID`
group by `Order Details`.`Order ID`;
Set
The set statement is used for defining script variables. These can be used for substituting strings, paths,
drives, and so on.
Syntax:
Set variablename=string
Example 1:
Set FileToUse=Data1.csv;
Example 2:
Set Constant="My string";
Example 3:
Set BudgetYear=2012;
Sleep
The sleep statement pauses script execution for a specified time.
Syntax:
Sleep n
Arguments:
Argument
Description
n
Stated in milliseconds, where n is a positive integer no larger than 3600000 (i.e. 1 hour). The
value may be an expression.
Example 1:
Sleep 10000;
Example 2:
Sleep t*1000;
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SQL
The SQL statement allows you to send an arbitrary SQL command through an ODBC or OLE DB
connection.
Syntax:
SQL sql_command
Sending SQL statements which update the database will return an error if Qlik Sense has opened the ODBC
connection in read-only mode.
The syntax:
SQL SELECT * from tab1;
is allowed, and is the preferred syntax for SELECT, for reasons of consistency. The SQL prefix will, however,
remain optional for SELECT statements.
Arguments:
Argument
Description
sql_command
A valid SQL command.
Example 1:
SQL leave;
Example 2:
SQL Execute ;
SQLColumns
The sqlcolumns statement returns a set of fields describing the columns of an ODBC or OLE DB data
source, to which a connect has been made.
Syntax:
SQLcolumns
The fields can be combined with the fields generated by the sqltables and sqltypes commands in order to
give a good overview of a given database. The twelve standard fields are:
TABLE_QUALIFIER
TABLE_OWNER
TABLE_NAME
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COLUMN_NAME
DATA_TYPE
TYPE_NAME
PRECISION
LENGTH
SCALE
RADIX
NULLABLE
REMARKS
For a detailed description of these fields, see an ODBC reference handbook.
Example:
Connect to 'MS Access 7.0 Database; DBQ=C:\Course3\DataSrc\QWT.mbd';
SQLcolumns;
Some ODBC drivers may not support this command. Some ODBC drivers may produce
additional fields.
SQLTables
The sqltables statement returns a set of fields describing the tables of an ODBC or OLE DB data source, to
which a connect has been made.
Syntax:
SQLTables
The fields can be combined with the fields generated by the sqlcolumns and sqltypes commands in order
to give a good overview of a given database. The five standard fields are:
TABLE_QUALIFIER
TABLE_OWNER
TABLE_NAME
TABLE_TYPE
REMARKS
For a detailed description of these fields, see an ODBC reference handbook.
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Example:
Connect to 'MS Access 7.0 Database; DBQ=C:\Course3\DataSrc\QWT.mbd';
SQLTables;
Some ODBC drivers may not support this command. Some ODBC drivers may produce
additional fields.
SQLTypes
The sqltypes statement returns a set of fields describing the types of an ODBC or OLE DB data source, to
which a connect has been made.
Syntax:
SQLTypes
The fields can be combined with the fields generated by the sqlcolumns and sqltables commands in order
to give a good overview of a given database. The fifteen standard fields are:
TYPE_NAME
DATA_TYPE
PRECISION
LITERAL_PREFIX
LITERAL_SUFFIX
CREATE_PARAMS
NULLABLE
CASE_SENSITIVE
SEARCHABLE
UNSIGNED_ATTRIBUTE
MONEY
AUTO_INCREMENT
LOCAL_TYPE_NAME
MINIMUM_SCALE
MAXIMUM_SCALE
For a detailed description of these fields, see an ODBC reference handbook.
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Example:
Connect to 'MS Access 7.0 Database; DBQ=C:\Course3\DataSrc\QWT.mbd';
SQLTypes;
Some ODBC drivers may not support this command. Some ODBC drivers may produce
additional fields.
Star
The string used for representing the set of all the values of a field in the database can be set through the star
statement. It affects the subsequent LOAD and SELECT statements.
Syntax:
Star is[ string ]
Arguments:
Argument
Description
string
An arbitrary text. Note that the string must be enclosed by quotation marks if it contains
blanks.
If nothing is specified, star is; is assumed, i.e. there is no star symbol available unless
explicitly specified. This definition is valid until a new star statement is made.
Examples:
Star is *;
Star is %;
Star is;
Store
This script function creates a QVD or a CSV file.
Syntax:
Store[ *fieldlist from] table into filename [ format-spec ];
The statement will create an explicitly named QVD or CSV file. The statement can only export fields from
one data table. If fields from several tables are to be exported, an explicit join must be made previously in the
script to create the data table that should be exported.
The text values are exported to the CSV file in UTF-8 format. A delimiter can be specified, see LOAD. The
store statement to a CSV file does not support BIFF export.
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Arguments:
Argument
Description
*fieldlist::= ( * | field ) { , field } )
A list of the fields to be selected. Using * as field list indicates
all fields.
field::= fieldname [asaliasname ]
fieldname is a text that is identical to a field name in table.
(Note that the field name must be enclosed b straight double
quotation marks or square brackets if it contains spaces or
other non-standard characters.)
aliasname is an alternate name for the field to be used in the
resulting QVD or CSV file.
table
A script label representing an already loaded table to be used
as source for data.
filename
The name of the target file as a folder data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also
supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
If the path is omitted, Qlik Sense stores the file in the
directory specified by the Directory statement. If there
is no Directory statement, Qlik Sense stores the file in
the working directory, C:\Users\{user}
\Documents\Qlik\Sense\Apps.
format-spec ::=( ( txt | qvd ) )
The format specification consists of the text txt for text files, or
the text qvd for qvd files. If the format specification is omitted,
qvd is assumed.
Example:
Store mytable into xyz.qvd (qvd);
Store
*
from mytable into xyz.qvd;
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Store
Store
store
store
Name, RegNo from mytable into xyz.qvd;
Name as a, RegNo as b from mytable into xyz.qvd;
mytable into myfile.txt (txt);
* from mytable into 'lib://FolderConnection/myfile.qvd';
The two first examples have identical function.
Tag
This script function provides a way of assigning tags to one or more fields. If an attempt to tag a field name
not present in the app is made, the tagging will be ignored. If conflicting occurrences of a field or tag name
are found, the last value is used.
Syntax:
Tag fields fieldlist using mapname
Tag field fieldname with tagname
A field tagged with dimension will be displayed at the top of all field selection controls in Qlik Sense except in
the Edit Expression dialog.
A field tagged with measure will be displayed at the top of all field selection controls in the Edit Expression
dialog.
Arguments:
Argument
Description
fieldlist
A comma separated list of the fields that should be tagged from this point in the script.
mapname
The name of a mapping table previously loaded in a mapping Load or mapping Select
statement.
fieldname
The name of the field that should be tagged.
tagname
The name of the tag that should be applied to the field.
Example 1:
tagmap:
mapping LOAD * inline [
a,b
Alpha,MyTag
Num,MyTag
];
tag fields using tagmap;
Example 2:
tag field Alpha with ‘MyTag2’;
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Trace
The trace statement writes a string to the Script Execution Progress window and to the script log file,
when used. It is very useful for debugging purposes. Using $-expansions of variables that are calculated prior
to the trace statement, you can customize the message.
Syntax:
Trace string
Example 1:
Trace Main table loaded;
Example 2:
Let MyMessage = NoOfRows('MainTable') & ' rows in Main Table';
Trace $(MyMessage);
Unmap
The Unmap statement disables field value mapping specified by a previous Map … Using statement for
subsequently loaded fields.
Syntax:
Unmap *fieldlist
Arguments:
Argument
Description
*fieldlist
a comma separated list of the fields that should no longer be mapped from this point in the
script. Using * as field list indicates all fields. The wildcard characters * and ? are allowed in
field names. Quoting of field names may be necessary when wildcards are used.
Examples and results:
Example
Result
Unmap Country;
Disables mapping of field Country.
Unmap A, B, C;
Disables mapping of fields A, B and C.
Unmap * ;
Disables mapping of all fields.
Unqualify
The Unqualify statement is used for switching off the qualification of field names that has been previously
switched on by the Qualify statement.
Syntax:
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Unqualify *fieldlist
Arguments:
Argument
Description
*fieldlist
A comma separated list of the fields for which qualification should be turned on. Using * as
field list indicates all fields. The wildcard characters * and ? are allowed in field names.
Quoting of field names may be necessary when wildcards are used.
Refer to the documentation for the Qualify statement for further information.
Example 1:
Unqualify *;
Example 2:
Unqualify TransID;
Untag
Provides a way of removing tags from one or more fields. If an attempt to untag a Field name not present in
the app is made, the untagging will be ignored. If conflicting occurrences of a field or tag name is found, the
last value is used.
Syntax:
Untag fields fieldlist using mapname
Untag field fieldname with tagname
Arguments:
Argument
Description
fieldlist
A comma separated list of the fields which tags should be removed.
mapname
The name of a mapping table previously loaded in a mapping LOAD or mapping SELECT
statement.
fieldname
The name of the field that should be untagged.
tagname
The name of the tag that should be removed from the field.
Example 1:
tagmap:
mapping LOAD * inline [
a,b
Alpha,MyTag
Num,MyTag
];
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Untag fields using tagmap;
Example 2:
Untag field Alpha with MyTag2;];
2.4
Working with variables in the data load editor
A variable in Qlik Sense is a named entity, containing a single data value. A variable typically acquires its
value from a Let, Set or other control statement. The value of a variable can normally be changed by the user
at any time. Variables can contain numeric or alphanumeric data.
If the first character of a variable value is an equals sign ' = ' Qlik Sense will try to evaluate the value as a
formula (Qlik Sense expression) and then display or return the result rather than the actual formula text.
When used, the variable is substituted by its value. Variables can be used in the script for dollar sign
expansion and in various control statements. This is very useful if the same string is repeated many times in
the script, for example, a path.
Some special system variables will be set by Qlik Sense at the start of the script execution regardless of their
previous values.
When defining a variable, the syntax:
set variablename = string
or
let variable = expression
is used. The Set command assigns the text to the right of the equal sign to the variable, whereas the Let
command evaluates the expression.
Variables are case sensitive.
Examples:
set HidePrefix = $ ; // the variable will get
the character ‘$’ as value.
let vToday = Num(Today()); // returns the date serial number of
today.
Variable calculation
There are several ways to use variables with calculated values in Qlik Sense, and the result depends on how
you define it and how you call it in an expression.
In this example we load some inline data:
LOAD * INLINE [
Dim, Sales
A, 150
A, 200
B, 240
B, 230
C, 410
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C, 330
];
Let's define two variables:
Let vSales = 'Sum(Sales)' ;
Let vSales2 = '=Sum(Sales)' ;
In the second variable, we add an equal sign before the expression. This will cause the variable to be
calculated before it is expanded and the expression is evaluated.
If you use the vSales variable as it is, for example in a measure, the result will be the string Sum(Sales), that
is, no calculation is performed.
If you add a dollar-sign expansion and call $(vSales) in the expression, the variable is expanded, and the sum
of Sales is displayed.
Finally, if you call $(vSales2), the variable will be calculated before it is expanded. This means that the result
displayed is the total sum of Sales. The difference between using =$(vSales) and =$(vSales2) as measure
expressions is seen in this chart showing the results:
Dim
$(vSales)
$(vSales2)
A
350
1560
B
470
1560
C
740
1560
As you can see, $(vSales) results in the partial sum for a dimension value, while $(vSales2) results in the
total sum.
The following script variables are available:
Error variables
page 128
Number interpretation variables
page 118
System variables
page 111
Value handling variables
page 116
System variables
System variables, some of which are system-defined, provide information about the system and the Qlik
Sense app.
System variables overview
Some of the functions are described further after the overview. For those functions, you can click the function
name in the syntax to immediately access the details for that specific function.
Floppy
Returns the drive letter of the first floppy drive found, normally a:. This is a system-defined variable.
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Floppy
This variable is not supported in standard mode.
CD
Returns the drive letter of the first CD-ROM drive found. If no CD-ROM is found, then c: is returned. This is a
system-defined variable.
CD
This variable is not supported in standard mode.
Include
The include variable specifies a file that contains text that should be included in the script. The entire script
can thus be put in a file. This is a user-defined variable.
$(Include
=filename)
HidePrefix
All field names beginning with this text string will be hidden in the same manner as the system fields. This is
a user-defined variable.
HidePrefix
HideSuffix
All field names ending with this text string will be hidden in the same manner as the system fields. This is a
user-defined variable.
HideSuffix
QvPath
Returns the browse string to the Qlik Sense executable. This is a system-defined variable.
QvPath
This variable is not supported in standard mode.
QvRoot
Returns the root directory of the Qlik Sense executable. This is a system-defined variable.
QvRoot
This variable is not supported in standard mode.
QvWorkPath
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Returns the browse string to the current Qlik Sense app. This is a system-defined variable.
QvWorkPath
This variable is not supported in standard mode.
QvWorkRoot
Returns the root directory of the current Qlik Sense app. This is a system-defined variable.
QvWorkRoot
This variable is not supported in standard mode.
StripComments
If this variable is set to 0, stripping of /*..*/ and // comments in the script will be inhibited. If this variable is not
defined, stripping of comments will always be performed.
StripComments
Verbatim
Normally all field values are automatically stripped of leading and trailing blanks (ASCII 32) before being
loaded into the Qlik Sense database. Setting this variable to 1 suspends the stripping of blanks. Tab (ASCII
32) and hard space (ANSI 160) are never stripped.
Verbatim
OpenUrlTimeout
This variable defines the timeout in seconds that Qlik Sense should respect when getting data from URL
sources (e.g. HTML pages). If omitted, the timeout is about 20 minutes.
OpenUrlTimeout
WinPath
Returns the browse string to Windows. This is a system-defined variable.
WinPath
This variable is not supported in standard mode.
WinRoot
Returns the root directory of Windows. This is a system-defined variable.
WinRoot
This variable is not supported in standard mode.
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CollationLocale
Specifies which locale to use for sort order and search matching. The value is the culture name of a locale,
for example 'en-US'.This is a system-defined variable.
CollationLocale
HidePrefix
All field names beginning with this text string will be hidden in the same manner as the system fields. This is
a user-defined variable.
Syntax:
HidePrefix
Example:
set HidePrefix='_' ;
If this statement is used, the field names beginning with an underscore will not be shown in the field name
lists when the system fields are hidden.
HideSuffix
All field names ending with this text string will be hidden in the same manner as the system fields. This is a
user-defined variable.
Syntax:
HideSuffix
Example:
set HideSuffix='%';
If this statement is used, the field names ending with a percentage sign will not be shown in the field name
lists when the system fields are hidden.
Include
The include variable specifies a file that contains text that should be included in the script. The entire script
can thus be put in a file. This is a user-defined variable.
This variable supports only folder data connections in standard mode.
Syntax:
$(Include =filename)
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Example:
$(Include=abc.txt);
$(Include=lib://MyDataFiles\abc.txt);
If you don't specify a path, the filename will be relative to the Qlik Sense app working directory. You can also
specify an absolute file path, or a path to a lib:// folder connection.
The construction set Include =filename is not applicable.
OpenUrlTimeout
This variable defines the timeout in seconds that Qlik Sense should respect when getting data from URL
sources (e.g. HTML pages). If omitted, the timeout is about 20 minutes.
Syntax:
OpenUrlTimeout
Example:
set OpenUrlTimeout=10
StripComments
If this variable is set to 0, stripping of /*..*/ and // comments in the script will be inhibited. If this variable is not
defined, stripping of comments will always be performed.
Syntax:
StripComments
Certain database drivers use /*..*/ as optimization hints in SELECT statements. If this is the case, the
comments should not be stripped before sending the SELECT statement to the database driver.
It is recommended that this variable be reset to 1 immediately after the statement(s) where it is
needed.
Example:
set StripComments=0;
SQL SELECT * /* */ FROM Table ;
set StripComments=1;
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Verbatim
Normally all field values are automatically stripped of leading and trailing blanks (ASCII 32) before being
loaded into the Qlik Sense database. Setting this variable to 1 suspends the stripping of blanks. Tab (ASCII
32) and hard space (ANSI 160) are never stripped.
Syntax:
Verbatim
Example:
set Verbatim = 1;
Value handling variables
This section describes variables that are used for handling NULL and other values.
Value handling variables overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
NullDisplay
The defined symbol will substitute all NULL values from ODBC on the lowest level of data. This is a userdefined variable.
NullDisplay
NullInterpret
The defined symbol will be interpreted as NULL when it occurs in a text file, Excel file or an inline statement.
This is a user-defined variable.
NullInterpret
NullValue
If the NullAsValue statement is used, the defined symbol will substitute all NULL values in the
NullAsValue specified fields with the specified string.
NullValue
OtherSymbol
Defines a symbol to be treated as 'all other values' before a LOAD/SELECT statement. This is a userdefined variable.
OtherSymbol
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NullDisplay
The defined symbol will substitute all NULL values from ODBC on the lowest level of data. This is a userdefined variable.
Syntax:
NullDisplay
Example:
set NullDisplay='';
NullInterpret
The defined symbol will be interpreted as NULL when it occurs in a text file, Excel file or an inline statement.
This is a user-defined variable.
Syntax:
NullInterpret
Examples:
set NullInterpret=' ';
set NullInterpret =;
will NOT return NULL values for blank values in Excel (but it will for a csv text file)
set NullInterpret ='';
will return NULL values for blank values in Excel (but will NOT for a csv text files)
NullValue
If the NullAsValue statement is used, the defined symbol will substitute all NULL values in the
NullAsValue specified fields with the specified string.
Syntax:
NullValue
Example:
NullAsValue Field1, Field2;
set NullValue='';
OtherSymbol
Defines a symbol to be treated as 'all other values' before a LOAD/SELECT statement. This is a userdefined variable.
Syntax:
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OtherSymbol
Example:
set OtherSymbol='+';
LOAD * inline
[X, Y
a, a
b, b];
LOAD * inline
[X, Z
a, a
+, c];
The field value Y=’b’ will now link to Z=’c’ through the other symbol.
Number interpretation variables
Number interpretation variables are system defined, that is, they are automatically generated according to
the current regional settings of the operating system when a new app is created. In Qlik Sense Desktop, this
is according to the settings of the computer operating system, and in Qlik Sense, it is according to the
operating system of the server where Qlik Sense is installed.
The variables are included at the top of the script of the new Qlik Sense app and substitute operating system
defaults for certain number formatting settings at the time of the script execution. They can be deleted,
edited or duplicated freely.
If you want to create an app for a certain locale, the easiest way is probably to use Qlik Sense
Desktop on a computer with the desired locale setting in the operating system to create the
app. The app will then contain the appropriate regional settings of that locale, and you can
move it to a Qlik Sense server of choice for further development.
Number interpretation variables overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Currency formatting
MoneyDecimalSep
The decimal separator defined replaces the decimal symbol for currency of the operating system (regional
settings).
MoneyDecimalSep
MoneyFormat
The symbol defined replaces the currency symbol of the operating system (regional settings).
MoneyFormat
MoneyThousandSep
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The thousands separator defined replaces the digit grouping symbol for currency of the operating system
(regional settings).
MoneyThousandSep
Number formatting
DecimalSep
The decimal separator defined replaces the decimal symbol of the operating system (regional settings).
DecimalSep
ThousandSep
The thousands separator defined replaces the digit grouping symbol of the operating system (regional
settings).
ThousandSep
Time formatting
DateFormat
The format defined replaces the date format of the operating system (regional settings).
DateFormat
TimeFormat
The format defined replaces the time format of the operating system (regional settings).
TimeFormat
TimestampFormat
The format defined replaces the date and time formats of the operating system (regional settings).
TimestampFormat
MonthNames
The format defined replaces the month names convention of the operating system (regional settings).
MonthNames
LongMonthNames
The format defined replaces the long month names convention of the operating system (regional settings).
LongMonthNames
DayNames
The format defined replaces the weekday names convention of the operating system (regional settings).
DayNames
LongDayNames
The format defined replaces the long weekday names convention of the operating system (regional settings).
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LongDayNames
FirstWeekDay
The integer defines which day to use as the first day of the week.
FirstWeekDay
BrokenWeeks
The setting defines if weeks are broken or not.
BrokenWeeks
ReferenceDay
The setting defines which day in January to set as reference day to define week 1.
ReferenceDay
FirstMonthOfYear
The setting defines which month to use as first month of the year, which can be used to define financial years
that use a monthly offset, for example starting April 1.
Valid settings are 1 (January) to 12 (December). Default setting is 1.
Syntax:
FirstMonthOfYear
Example:
Set FirstMonthOfYear=4; //Sets the year to start in April
BrokenWeeks
The setting defines if weeks are broken or not.
Syntax:
BrokenWeeks
By default, Qlik Sense functions use unbroken weeks. This means that:
l
In some years, week 1 starts in December, and in other years, week 52 or 53 continues into January.
l
Week 1 always has at least 4 days in January.
The alternative is to use broken weeks.
l
Week 52 or 53 do not continue into January.
l
Week 1 starts on January 1 and is, in most cases, not a full week.
The following values can be used:
l
0 (=use unbroken weeks)
l
1 (= use broken weeks)
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Examples:
Set BrokenWeeks=0; //(use unbroken weeks)
Set BrokenWeeks=1; //(use broken weeks)
DateFormat
The format defined replaces the date format of the operating system (regional settings).
Syntax:
DateFormat
Examples:
Set DateFormat='M/D/YY'; //(US format)
Set DateFormat='DD/MM/YY'; //(UK date format)
Set DateFormat='YYYY-MM-DD'; //(ISO date format)
DayNames
The format defined replaces the weekday names convention of the operating system (regional settings).
Syntax:
DayNames
Example:
Set DayNames='Mon;Tue;Wed;Thu;Fri;Sat;Sun';
DecimalSep
The decimal separator defined replaces the decimal symbol of the operating system (regional settings).
Syntax:
DecimalSep
Examples:
Set DecimalSep='.';
Set DecimalSep=',';
FirstWeekDay
The integer defines which day to use as the first day of the week.
Syntax:
FirstWeekDay
By default, Qlik Sense functions use Monday as the first day of the week. The following values can be used:
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l
0 (= Monday)
l
1 (= Tuesday)
l
2 (= Wednesday)
l
3 (= Thursday)
l
4 (= Friday)
l
5 (= Saturday)
l
6 (= Sunday)
Examples:
Set FirstWeekDay=6; //(set Sunday as the first day of the week)
LongDayNames
The format defined replaces the long weekday names convention of the operating system (regional settings).
Syntax:
LongDayNames
Example:
Set LongDayNames='Monday;Tuesday;Wednesday;Thursday;Friday;Saturday;Sunday';
LongMonthNames
The format defined replaces the long month names convention of the operating system (regional settings).
Syntax:
LongMonthNames
Example:
Set LongMonthNames='January;February;March;April;May;June - -
MoneyDecimalSep
The decimal separator defined replaces the decimal symbol for currency of the operating system (regional
settings).
Syntax:
MoneyDecimalSep
Example:
Set MoneyDecimalSep='.';
MoneyFormat
The symbol defined replaces the currency symbol of the operating system (regional settings).
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Syntax:
MoneyFormat
Example:
Set MoneyFormat='$ #,##0.00; ($ #,##0.00)';
MoneyThousandSep
The thousands separator defined replaces the digit grouping symbol for currency of the operating system
(regional settings).
Syntax:
MoneyThousandSep
Example:
Set MoneyThousandSep=',';
MonthNames
The format defined replaces the month names convention of the operating system (regional settings).
Syntax:
MonthNames
Example:
Set MonthNames='Jan;Feb;Mar;Apr;May;Jun;Jul;Aug;Sep;Oct;Nov;Dec';
ReferenceDay
The setting defines which day in January to set as reference day to define week 1.
Syntax:
ReferenceDay
By default, Qlik Sense functions use 4 as the reference day. This means that week 1 must contain January 4,
or put differently, that week 1 must always have at least 4 days in January.
The following values can be used to set a different reference day:
l
1 (= January 1)
l
2 (= January 2)
l
3 (= January 3)
l
4 (= January 4)
l
5 (= January 5)
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l
6 (= January 6)
l
7 (= January 7)
Examples:
Set ReferenceDay=3; //(set January 3 as the reference day)
ThousandSep
The thousands separator defined replaces the digit grouping symbol of the operating system (regional
settings).
Syntax:
ThousandSep
Examples:
Set ThousandSep=','; //(for example, seven billion must be specified as: 7,000,000,000)
Set ThousandSep=' ';
TimeFormat
The format defined replaces the time format of the operating system (regional settings).
Syntax:
TimeFormat
Example:
Set TimeFormat='hh:mm:ss';
TimestampFormat
The format defined replaces the date and time formats of the operating system (regional settings).
Syntax:
TimestampFormat
Example:
Set TimestampFormat='M/D/YY hh:mm:ss[.fff]';
Direct Discovery variables
Direct Discovery system variables
DirectCacheSeconds
You can set a caching limit to the Direct Discovery query results for visualizations. Once this time limit is
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reached, Qlik Sense clears the cache when new Direct Discovery queries are made. Qlik Sense queries the
source data for the selections and creates the cache again for the designated time limit. The result for each
combination of selections is cached independently. That is, the cache is refreshed for each selection
independently, so one selection refreshes the cache only for the fields selected, and a second selection
refreshes cache for its relevant fields. If the second selection includes fields that were refreshed in the first
selection, they are not updated in cache again if the caching limit has not been reached.
The Direct Discovery cache does not apply to Table visualizations. Table selections query the data source
every time.
The limit value must be set in seconds. The default cache limit is 1800 seconds (30 minutes).
The value used for DirectCacheSeconds is the value set at the time the DIRECT QUERY statement is
executed. The value cannot be changed at runtime.
Example:
SET DirectCacheSeconds=1800
DirectConnectionMax
You can do asynchronous, parallel calls to the database by using the connection pooling capability. The load
script syntax to set up the pooling capability is as follows:
SET DirectConnectionMax=10
The numeric setting specifies the maximum number of database connections the Direct Discovery code
should use while updating a sheet. The default setting is 1.
This variable should be used with caution. Setting it to greater than 1 is known to cause
problems when connecting to Microsoft SQL Server.
DirectUnicodeStrings
Direct Discovery can support the selection of extended Unicode data by using the SQL standard format for
extended character string literals (N’’) as required by some databases (notably SQL
Server). The use of this syntax can be enabled for Direct Discovery with the script variable
DirectUnicodeStrings.
Setting this variable to 'true' will enable the use of the ANSI standard wide character marker “N” in front of the
string literals. Not all databases support this standard. The default setting is 'false'.
DirectDistinctSupport
When a DIMENSION field value is selected in a Qlik Sense object, a query is generated for the source
database. When the query requires grouping, Direct Discovery uses the DISTINCT keyword to select only
unique values. Some databases, however, require the GROUP BY keyword. Set DirectDistinctSupport to
'false' to generate GROUP BY instead of DISTINCT in queries for unique values.
SET DirectDistinctSupport='false'
If DirectDistinctSupport is set to true, then DISTINCT is used. If it is not set, the default behavior is to use
DISTINCT.
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Teradata query banding variables
Teradata query banding is a function that enables enterprise applications to collaborate with the underlying
Teradata database in order to provide for better accounting, prioritization, and workload management. Using
query banding you can wrap metadata, such as user credentials, around a query.
Two variables are available, both are strings that are evaluated and sent to the database.
SQLSessionPrefix
This string is sent when a connection to the database is created.
SET SQLSessionPrefix = 'SET QUERY_BAND = ' & Chr(39) & 'Who=' & OSuser() & ';' & Chr(39) & ' FOR
SESSION;';
If OSuser() for example returns WA\sbt, this will be evaluated to SET QUERY_BAND = 'Who=WA\sbt;' FOR
SESSION; ,
which is sent to the database when the connection is created.
SQLQueryPrefix
This string is sent for each single query.
SET SQLSessionPrefix = 'SET QUERY_BAND = ' & Chr(39) & 'Who=' & OSuser() & ';' & Chr(39) &
' FOR TRANSACTION;';
Direct Discovery character variables
DirectFieldColumnDelimiter
You can set the character used as the field delimiter in Direct Query statements for databases that require a
character other than comma as the field delimiter. The specified character must be surrounded by single
quotation marks in the SET statement.
SET DirectFieldColumnDelimiter= '|'
DirectStringQuoteChar
You can specify a character to use to quote strings in a generated query. The default is a single quotation
mark. The specified character must be surrounded by single quotation marks in the SET statement.
SET DirectStringQuoteChar= '"'
DirectIdentifierQuoteStyle
You can specify that non-ANSI quoting of identifiers be used in generated queries. At this time, the only nonANSI quoting available is GoogleBQ. The default is ANSI. Uppercase, lowercase, and mixed case can be
used (ANSI, ansi, Ansi).
SET DirectIdentifierQuoteStyle="GoogleBQ"
For example, ANSI quoting is used in the following SELECT statement:
SELECT [Quarter] FROM [qvTest].[sales] GROUP BY [Quarter]
When DirectIdentifierQuoteStyle is set to "GoogleBQ", the SELECT statement would use quoting as
follows:
SELECT [Quarter] FROM [qvTest.sales] GROUP BY [Quarter]
DirectIdentifierQuoteChar
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You can specify a character to control the quoting of identifiers in a generated query. This can be set to either
one character (such as a double quotation mark) or two (such as a pair of square brackets). The default is a
double quotation mark.
SET DirectIdentifierQuoteChar='YYYY-MM-DD'
DirectTableBoxListThreshold
When Direct Discovery fields are used in a Table visualization, a threshold is set to limit the number of rows
displayed. The default threshold is 1000 records. The default threshold setting can be changed by setting the
DirectTableBoxListThreshold variable in the load script. For example:
SET DirectTableBoxListThreshold=5000
The threshold setting applies only to Table visualizations that contain Direct Discovery fields. Table
visualizations that contain only in-memory fields are not limited by the DirectTableBoxListThreshold
setting.
No fields are displayed in the Table visualization until the selection has fewer records than the threshold
limit.
Direct Discovery number interpretation variables
DirectMoneyDecimalSep
The decimal separator defined replaces the decimal symbol for currency in the SQL statement generated to
load data using Direct Discovery. This character must match the character used in DirectMoneyFormat.
Default value is '.'
Example:
Set DirectMoneyDecimalSep='.';
DirectMoneyFormat
The symbol defined replaces the currency format in the SQL statement generated to load data using Direct
Discovery. The currency symbol for the thousands separator should not be included.
Default value is '#.0000'
Example:
Set DirectMoneyFormat='#.0000';
DirectTimeFormat
The time format defined replaces the time format in the SQL statement generated to load data using Direct
Discovery.
Example:
Set DirectTimeFormat='hh:mm:ss';
DirectDateFormat
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The date format defined replaces the date format in the SQL statement generated to load data using Direct
Discovery.
Example:
Set DirectDateFormat='MM/DD/YYYY';
DirectTimeStampFormat
The format defined replaces the date and time format in the SQL statement generated in the SQL statement
generated to load data using Direct Discovery.
Example:
Set DirectTimestampFormat='M/D/YY hh:mm:ss[.fff]';
Error variables
The values of all error variables will exist after the script execution. The first variable, ErrorMode, is input
from the user, and the last three are output from Qlik Sense with information on errors in the script.
Error variables overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
ErrorMode
Th is error variable determines what action is to be taken by Qlik Sense when an error is encountered during
script execution.
ErrorMode
ScriptError
This error variable returns the error code of the last executed script statement.
ScriptError
ScriptErrorCount
This error variable returns the total number of statements that have caused errors during the current script
execution. This variable is always reset to 0 at the start of script execution.
ScriptErrorCount
ScriptErrorList
This error variable will contain a concatenated list of all script errors that have occurred during the last script
execution. Each error is separated by a line feed.
ScriptErrorList
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ErrorMode
Th is error variable determines what action is to be taken by Qlik Sense when an error is encountered during
script execution.
Syntax:
ErrorMode
Arguments:
Argument
Description
ErrorMode=1
The default setting. The script execution will halt and the user will be prompted for action
(non-batch mode).
ErrorMode
Qlik Sense will simply ignore the failure and continue script execution at the next script
statement.
=0
ErrorMode
=2
Qlik Sense will trigger an "Execution of script failed..." error message immediately on
failure, without prompting the user for action beforehand.
Example:
set ErrorMode=0;
ScriptError
This error variable returns the error code of the last executed script statement.
Syntax:
ScriptError
This variable will be reset to 0 after each successfully executed script statement. If an error occurs it will be
set to an internal Qlik Sense error code. Error codes are dual values with a numeric and a text component.
The following error codes exist:
Error
code
Description
0
No error
1
General error
2
Syntax error
3
General ODBC error
4
General OLE DB error
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Error
code
Description
5
General custom
database error
6
General XML error
7
General HTML error
8
File not found
9
Database not found
10
Table not found
11
Field not found
12
File has wrong format
13
BIFF error
14
BIFF error encrypted
15
BIFF error unsupported
version
16
Semantic error
Example:
set ErrorMode=0;
LOAD * from abc.qvf;
if ScriptError=8 then
exit script;
//no file;
end if
ScriptErrorCount
This error variable returns the total number of statements that have caused errors during the current script
execution. This variable is always reset to 0 at the start of script execution.
Syntax:
ScriptErrorCount
ScriptErrorList
This error variable will contain a concatenated list of all script errors that have occurred during the last script
execution. Each error is separated by a line feed.
Syntax:
ScriptErrorList
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2.5
Script expressions
Expressions can be used in both the LOAD statement and the SELECT statement. The syntax and
functions described here apply to the LOAD statement, and not to the SELECT statement, since the latter
is interpreted by the ODBC driver and not by Qlik Sense. However, most ODBC drivers are often capable of
interpreting a number of the functions described below.
Expressions consist of functions, fields and operators, combined in a syntax.
All expressions in a Qlik Sense script return a number and/or a string, whichever is appropriate. Logical
functions and operators return 0 for False and -1 for True. Number to string conversions and vice versa are
implicit. Logical operators and functions interpret 0 as False and all else as True.
The general syntax for an expression is:
expression ::= (constant
constant
|
fieldref
|
operator1 expression
|
expression operator2 expression
|
function
|
( expression )
)
where:
constant is a string (a text, a date or a time) enclosed by single straight quotation marks, or a number.
Constants are written with no thousands separator and with a decimal point as decimal separator.
fieldref is a field name of the loaded table.
operator1 is a unary operator (working on one expression, the one to the right).
operator2 is a binary operator (working on two expressions, one on each side).
function ::= functionname( parameters)
parameters ::= expression { , expression }
The number and types of parameters is not arbitrary. It depends on the function used.
Expressions and functions can thus be nested freely, and as long as the expression returns an interpretable
value, Qlik Sense will not give any error messages.
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3
Visualization expressions
An expression is a combination of functions, fields, and mathematical operators (+ * / =). Expressions are
used to process data in the app in order to produce a result that can be seen in a visualization. They are not
limited to use in measures. You can build visualizations that are more dynamic and powerful, with
expressions for titles, subtitles, footnotes, and even dimensions.
This means, for example, that instead of the title of a visualization being static text, it can be made from an
expression whose result changes depending on the selections made.
For detailed reference regarding script functions and chart functions, see the Qlik Sense online
help.
3.1
Defining the aggregation scope
There are usually two factors that together determine which records are used to define the value of
aggregation in an expression. When working in visualizations, these factors are:
l
Dimensional value (of the aggregation in a chart expression)
l
Selections
Together, these factors define the scope of the aggregation. You may come across situations where you
want your calculation to disregard the selection, the dimension or both. In chart functions, you can achieve
this by using the TOTAL qualifier, set analysis, or a combination of the two.
Method
Description
TOTAL
qualifier
Using the total qualifier inside your aggregation function disregards the dimensional value.
The aggregation will be performed on all possible field values.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets. These field names should be a subset of the chart dimension variables. In this case,
the calculation is made disregarding all chart dimension variables except those listed, that is,
one value is returned for each combination of field values in the listed dimension fields. Also,
fields that are not currently a dimension in a chart may be included in the list. This may be
useful in the case of group dimensions, where the dimension fields are not fixed. Listing all of
the variables in the group causes the function to work when the drill-down level changes.
Set
analysis
Using set analysis inside your aggregation disregards the selection. The aggregation will be
performed on all values split across the dimensions.
TOTAL
qualifier
and set
analysis
Using set analysis inside your aggregation disregards the selection and the dimensions.
This method corresponds to using the ALL qualifier.
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Example: TOTAL qualifier
The following example shows how TOTAL can be used to calculate a relative share. Assuming that Q2 has
been selected, using TOTAL calculates the sum of all values disregarding the dimensions.
Year
Quarter
Sum(Amount)
Sum(TOTAL Amount)
Sum(Amount)/Sum(TOTAL Amount)
3000
3000
100%
2012
Q2
1700
3000
56,7%
2013
Q2
1300
3000
43,3%
To show the numbers as a percentage, in the properties panel, for the measure you want to
show as a percentage value, under Number formatting, select Number, and from
Formatting, choose Simple and one of the % formats.
Example: Set analysis
The following example shows how set analysis can be used to make a comparison between data sets before
any selection was made. Assuming that Q2 has been selected, using set analysis with the set definition {1}
calculates the sum of all values disregarding any selections but split by the dimensions.
Year
Quarter
Sum(Amount)
Sum({1} Amount)
Sum(Amount)/Sum({1} Amount)
3000
10800
27,8%
2012
Q1
0
1100
0%
2012
Q3
0
1400
0%
2012
Q4
0
1800
0%
2012
Q2
1700
1700
100%
2013
Q1
0
1000
0%
2013
Q3
0
1100
0%
2013
Q4
0
1400
0%
2013
Q2
1300
1300
100%
Example: TOTAL qualifier and set analysis
The following example shows how set analysis and the TOTAL qualifier can be combined to make a
comparison between data sets before any selection was made and across all dimensions. Assuming that Q2
has been selected, using set analysis with the set definition {1] and the TOTAL qualifier calculates the sum of
all values disregarding any selections and disregarding the dimensions.
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Year
Quarter
Sum
Sum({1} TOTAL
Sum(Amount)/Sum({1} TOTAL
(Amount)
Amount)
Amount)
3000
10800
27,8%
2012
Q2
1700
10800
15,7%
2013
Q2
1300
10800
12%
Data used in examples:
AggregationScope:
LOAD * inline [
Year Quarter Amount
2012 Q1 1100
2012 Q2 1700
2012 Q3 1400
2012 Q4 1800
2013 Q1 1000
2013 Q2 1300
2013 Q3 1100
2013 Q4 1400] (delimiter is ' ');
3.2
Syntax for sets
The full syntax (not including the optional use of standard brackets to define precedence) is described using
Backus-Naur Formalism:
set_expression ::= { set_entity { set_operator set_entity } }
set_entity ::= set_identifier [ set_modifier ]
set_identifier ::= 1 | $ | $N | $_N | bookmark_id | bookmark_name
set_operator ::= + | - | * | /
set_modifier ::= < field_selection {, field_selection } >
field_selection ::= field_name [ = | += | ¬–= | *= | /= ] element_set_
expression
element_set_expression ::= element_set { set_operator element_set }
element_set ::= [ field_name ] | { element_list } | element_function
element_list ::= element { , element }
element_function ::= ( P | E ) ( [ set_expression ] [ field_name ] )
element ::= field_value | " search_mask "
3.3
Set modifiers
A set can be modified by an additional or a changed selection. Such a modification can be written in the set
expression.
The modifier consists of one or several field names, each followed by a selection that should be made on the
field, all enclosed by < and >. For example: . Field names and field values
can be quoted as usual, e.g. <[Sales Region]={’West coast’, ’South America’}>.
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A set modifier can be used on a set identifier or on its own. It cannot be used on a set expression. When used
on a set identifier, the modifier must be written immediately after the set identifier, for examples {$}. When used on its own, it is interpreted as a modification of the current selection.
There are several ways to define the selection as described in the following.
Based on another field
A simple case is a selection based on the selected values of another field, for example . This modifier will take the selected values from DeliveryDate and apply those as a selection
on OrderDate. If there are many distinct values – more than a couple of hundred – then this operation is
CPU intense and should be avoided.
Based on element sets (a field value list in the modifier)
The most common case is a selection based on a field value list enclosed in curly brackets, the values
separated by commas, for example . The curly brackets here define an element set,
where the elements can be either field values or searches of field values. A search is always defined by the
use of double quotes, for example will select all ingredients including the string
’garlic’. Searches are case-insensitive and are made also over excluded values.
Empty element sets, either explicitly for example or implicitly for example (a search with no hits) mean no product, i.e. they will result in a set of records that are
not associated with any product. Note that this set cannot be achieved through usual selections, unless a
selection is made in another field, for example, TransactionID .
Forced exclusion
Finally, for fields in and-mode, there is also the possibility of forced exclusion. If you want to force exclusion
of specific field values, you will need to use “~” in front of the field name.
Examples and results:
Examples
Results
sum(
{1} Sales
)
Returns the sales for the region USA disregarding the current selection
sum( {$} Sales )
Returns the sales for the current selection, but with the selection in 'Region' removed
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Examples
Results
sum( {} Sales )
Returns the same as the example immediately above. When the set to modify is
omitted, $ is assumed.
The syntax in the two previous examples is interpreted as “no selections”
in 'Region', that is to say all regions given other selections will be
possible. It is not equivalent to the syntax (or any other text
on the right side of the equal sign implicitly resulting in an empty element
set) which is interpreted as no region.
sum( {$}
Sales )
Returns the sales for current selection, but with new selections both in 'Year' and in
sum(
{$<~Ingredient
= {“*garlic*”}>}
Sales )
Returns the sales for current selection, but with a forced exclusion of all ingredients
containing the string 'garlic'.
sum( {$} Sales )
Returns the sales for the current selection, but with all years beginning with the digit “2”,
i.e. most likely year 2000 and onwards, selected in the field 'Year'.
sum( {$}
Sales )
As above, but now also the 1980:s are included in the selection.
sum( {$1978<2004”}
>} Sales )
As above, but now with a numeric search so that an arbitrary range can be specified.
'Region'.
Set modifiers with set operators
The selection within a field can be defined using set operators working on different element sets. For
example the modifier will select all years beginning with “20” in addition to
“1997”, except for “2000”.
Examples and results:
Examples
Results
sum( {$} Sales )
Returns the sales for the current selection, but with the product “OurProduct1”
added to the list of selected products and “OurProduct2” removed from the list
of selected products.
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Examples
Results
sum( {$} Sales )
Returns the sales for the current selection but with additional selections in the
field “Year”: 1997 and all that begin with “20” – however, not 2000.
sum( {$}
Sales )
Returns almost the same as above, but here 2000 will be excluded, also if it
initially is included in the current selection. The example shows the importance
of sometimes using brackets to define an order of precedence.
sum( {$} Sales )
Returns the sales for the current selection but with a new selection in “Year”:
all years except 2000; and only for products containing the string ‘bearing’.
Note that if 2000 is included in the current selection, it will still be included after
the modification.
Set modifiers using assignments with implicit set operators
This notation defines new selections, disregarding the current selection in the field. However, if you want to
base your selection on the current selection in the field and add field values, for example you may want a
modifier . A short and equivalent way to write this is ,
i.e. the assignment operator implicitly defines a union. Also implicit intersections, exclusions and symmetric
differences can be defined using “*=”, “–=” and “/=”.
Examples and results:
Examples
Results
sum( {$} Sales )
Returns the sales for the current selection, but using an implicit union to add
the products 'OurProduct1' and 'OurProduct2' to the list of selected products.
sum( {$}
Sales )
Returns the sales for the current selection but using an implicit union to add
a number of years in the selection: 1997 and all that begin with “20” –
however, not 2000.
Note that if 2000 is included in the current selection, it will still be included
after the modification. Same as .
sum( {$} Sales )
Returns the sales for the current selection, but only for the intersection of
currently selected products and the product OurProduct1.
Set modifiers with advanced searches
Advanced searches using wild cards and aggregations can be used to define sets.
Examples and results:
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Examples
Results
sum( {$–1}
Sales )
Returns the sales for current selection, excluding transactions
pertaining to products with the string ‘Internal’ or ‘Domestic’ in the
product name.
sum( {$} Sales ) >
1000000”}>} Sales )
Returns the sales for current selection, but with a new selection in the
'Customer' field: only customers who during 2007 had a total sales of
more than 1000000.
Set modifiers with dollar-sign expansions
Variables and other dollar-sign expansions can be used in set expressions.
Examples and results:
Examples
Results
sum( {$}
Sales )
Returns the sales for the previous year in relation to current selection. Here, a
variable vLastYear containing the relevant year is used in a dollar-sign
expansion.
sum( {$}
Sales )
Returns the sales for the previous year in relation to current selection. Here, a
dollar-sign expansion is used to calculate previous year.
Set modifiers with implicit field value definitions
The following describes how to define a set of field values using a nested set definition.
In such cases, the element functions P() and E() must be used, representing the element set of possible
values and the excluded values of a field, respectively. Inside the brackets, it is possible to specify one set
expression and one field, for example P({1} Customer). These functions cannot be used in other expressions:
Examples and results:
Examples
Results
sum(
{$}
Customer)>}
Sales )
Returns the sales for current selection, but only those customers that ever have
bought the product ‘Shoe’. The element function P( ) here returns a list of possible
customers; those that are implied by the selection ‘Shoe’ in the field Product.
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Examples
Results
sum(
{$})>}
Sales )
Same as above. If the field in the element function is omitted, the function will return
the possible values of the field specified in the outer assignment.
sum(
{$}
Supplier)>}
Sales )
Returns the sales for current selection, but only those customers that ever have
supplied the product ‘Shoe’. The element function P( ) here returns a list of possible
suppliers; those that are implied by the selection ‘Shoe’ in the field Product. The list of
suppliers is then used as a selection in the field Customer.
sum(
{$})>}
Sales )
Returns the sales for current selection, but only those customers that never bought the
product ‘Shoe’. The element function E( ) here returns the list of excluded customers;
those that are excluded by the selection ‘Shoe’ in the field Product.
3.4
Visualization expression and aggregation syntax
The syntax used for visualization (chart) expressions and aggregations is described in the following sections.
General syntax for chart expressions
expression ::= ( constant
|
expressionname
|
operator1 expression
|
expression operator2 expression
|
function
|
aggregation function
|
(expression )
)
where:
constant is a string (a text, a date or a time) enclosed by single straight quotation marks, or a number.
Constants are written without thousands separator and with a decimal point as decimal separator.
expressionname is the name (label) of another expression in the same chart.
operator1 is a unary operator (working on one expression, the one to the right).
operator2 is a binary operator (working on two expressions, one on each side).
function ::= functionname ( parameters )
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parameters ::= expression { , expression }
The number and types of parameters are not arbitrary. They depend on the function used.
aggregationfunction ::= aggregationfunctionname ( parameters2 )
parameters2 ::= aggrexpression { , aggrexpression }
The number and types of parameters are not arbitrary. They depend on the function used.
General syntax for aggregations
aggrexpression ::= ( fieldref
|
operator1 aggrexpression
|
aggrexpression operator2 aggrexpression
|
functioninaggr
|
( aggrexpression )
)
fieldref is a field name.
functionaggr ::= functionname ( parameters2 )
Expressions and functions can thus be nested freely, as long as fieldref is always enclosed by exactly one
aggregation function and provided the expression returns an interpretable value, Qlik Sense does not give
any error messages.
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4 Operators
4
Operators
This section describes the operators that can be used in Qlik Sense. There are two types of operators:
l
Unary operators (take only one operand)
l
Binary operators (take two operands)
Most operators are binary.
The following operators can be defined:
l
Bit operators
l
Logical operators
l
Numeric operators
l
Relational operators
l
String operators
4.1
Bit operators
All bit operators convert (truncate) the operands to signed integers (32 bit) and return the result in the same
way. All operations are performed bit by bit. If an operand cannot be interpreted as a number, the operation
will return NULL.
bitnot
Bit inverse.
Unary operator. The operation returns the logical inverse of the operand performed
bit by bit.
Example:
bitnot 17 returns -18
bitand
Bit and.
The operation returns the logical AND of the operands performed bit by bit.
Example:
17 bitand 7 returns 1
bitor
Bit or.
The operation returns the logical OR of the operands performed bit by bit.
Example:
17 bitor 7 returns 23
bitxor
Bit
exclusive
or.
The operation returns the logical exclusive or of the operands performed bit by bit.
Example:
17 bitxor 7 returns 22
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>>
Bit right
shift.
The operation returns the first operand shifted to the right. The number of steps is
defined in the second operand.
Example:
8 >> 2 returns 2
<<
Bit left
shift.
The operation returns the first operand shifted to the left. The number of steps is
defined in the second operand.
Example:
8 << 2 returns 32
4.2
Logical operators
All logical operators interpret the operands logically and return True (-1) or False (0) as result.
not
Logical inverse. One of the few unary operators. The operation returns the
logical inverse of the operand.
and
Logical and. The operation returns the logical and of the operands.
or
Logical or. The operation returns the logical or of the operands.
Xor
Logical exclusive or. The operation returns the logical exclusive or of the
operands. I.e. like logical or, but with the difference that the result is False if
both operands are True.
4.3
Numeric operators
All numeric operators use the numeric values of the operands and return a numeric value as result.
+
Sign for positive number (unary operator) or arithmetic addition. The binary
operation returns the sum of the two operands.
-
Sign for negative number (unary operator) or arithmetic subtraction. The unary
operation returns the operand multiplied by -1, and the binary the difference
between the two operands.
*
Arithmetic multiplication. The operation returns the product of the two
operands.
/
Arithmetic division. The operation returns the ratio between the two operands.
4.4
Relational operators
All relational operators compare the values of the operands and return True (-1) or False (0) as the result. All
relational operators are binary.
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<
Less than
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
<=
Less than
or equal
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
>
Greater
than
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
>=
Greater
than or
equal
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
=
Equals
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
<>
Not
equivalent
to
A numeric comparison is made if both operands can be interpreted
numerically. The operation returns the logical value of the
evaluation of the comparison.
precedes
ASCII less
than
Unlike the < operator no attempt is made to make a numeric
interpretation of the argument values before the comparison. The
operation returns true if the value to the left of the operator has a
text representation which, in ASCII comparison, comes before the
text representation of the value on the right.
Example:
' 11' precedes ' 2' returns True
compare this to:
' 11' < ' 2' returns False
follows
ASCII
greater
than
Unlike the > operator no attempt is made to make a numeric
interpretation of the argument values before the comparison. The
operation returns true if the value to the left of the operator has a
text representation which, in ASCII comparison, comes after the
text representation of the value on the right.
Example:
' 23' follows ' 111' returns True
compare this to:
' 23' > ' 111' returns False
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4.5
String operators
There are two string operators. One uses the string values of the operands and return a string as result. The
other one compares the operands and returns a boolean value to indicate match.
&
String concatenation. The operation returns a text string, that consists of the
two operand strings, one after another.
Example:
'abc' & 'xyz' returns 'abcxyz'
like
String comparison with wildcard characters. The operation returns a boolean
True (-1) if the string before the operator is matched by the string after the
operator. The second string may contain the wildcard characters * (any number
of arbitrary characters) or ? (one arbitrary character).
Example:
'abc' like 'a*' returns True (-1)
'abcd' like 'a?c*' returns True (-1)
'abc' like 'a??bc' returns False (0)
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5
Functions in scripts and chart expressions
This section describes functions that can be used in Qlik Sense data load scripts and chart expressions to
transform and aggregate data.
Many functions can be used in the same way in both data load scripts and chart expressions, but there are a
number of exceptions:
l
Some functions can only be used in data load scripts, denoted by - script function.
l
Some functions can only be used in chart expressions, denoted by - chart function.
l
Some functions can be used in both data load scripts and chart expressions, but with differences in
parameters and application. These are described in separate topics denoted by - script function or chart function.
5.1
Aggregation functions
An aggregation function aggregates over the set of possible records defined by the selection, and returns a
single value describing a property of several records in the data, for example a sum or a count.
Most aggregation functions can be used in both the data load script and chart expressions, but the syntax
differs.
Using aggregation functions in a data load script
Aggregation functions can only be used in field lists for LOAD statements with a group by clause.
Using aggregation functions in chart expressions
The argument expression of one aggregation function must not contain another aggregation function.
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
An aggregation function aggregates over the set of possible records defined by the selection. However, an
alternative set of records can be defined by using a set expression in set analysis.
Aggr - chart function
Aggr() returns an array of values for the expression calculated over the stated dimension or dimensions. For
example, the maximum value of sales, per customer, per region. The Aggr function is used for advanced
aggregations, in which the Aggr function is enclosed in another aggregation function, using the array of
results from the Aggr function as input to the aggregation in which it is nested.
Syntax:
Aggr({[DISTINCT] [NODISTINCT ]} expr, dim{, dimension})
Return data type: dual
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Arguments:
Argument
Description
expr
An expression usually consisting of an aggregation function. By default, the aggregation
function will aggregate over the set of possible records defined by the selection.
dim
The dimension for which the array of values in the expression is determined. This is a
single field and cannot be an expression.
dimension
Optional. One or more dimensions by which the expression can be further expanded.
DISTINCT
If the expression argument is preceded by the distinct qualifier or if no qualifier is used at
all, each distinct combination of dimension values will generate only one return value.
This is the normal way aggregations are made – each distinct combination of dimension
values will render one line in the chart.
NODISTINCT
If the expression argument is preceded by the nodistinct qualifier, each combination of
dimension values may generate more than one return value, depending on underlying
data structure. If there is only one dimension, the aggr function will return an array with
the same number of elements as there are rows in the source data.
Basic aggregation functions, such as Sum, Min, and Avg, return a single numerical value while the Aggr()
function can be compared to creating a temporary straight table that can be used in a chart. For example,
finding the maximum value by customer. We can then find the minimum value of the resulting temporary
table. You use the Aggr() function to nest the initial aggregation and place that inside a basic aggregation
function, for example, Sum, Max or Count. For example: Min(Aggr(Max(Value),Customer))
Use this function in calculated dimensions if you want to create nested chart aggregation in
multiple levels.
Limitations:
Each dimension must be a single field, and cannot be an expression (calculated dimension).
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
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Customer
Product
UnitSales
UnitPrice
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
Create a table with Customer, Product, UnitPrice, and UntiSales as dimensions.
Example
Result
Min(Aggr(Max
(UnitPrice),
Customer))
The part of the expression Aggr(Max(UnitPrice), Customer)finds the maximum
UnitPrice by Customer, and returns an array of values: 16, 19, and 25. These can be
seen in the table rows in the measure column.
The aggregation Max(UnitPrice)produces a result for each Product by Customer. By
using this expression as the expr argument in the Aggr() function and Customer as the
dim argument, we can find the result of Max(UnitPrice) by Customer.
Effectively, we have built a temporary list of values without having to create a separate
chart containing those values.
The totals row for the measure returns 15 as a result of the Aggr() function enclosed in
the Min() aggregation. Ig is the minimum value f the array returned by the Aggr()
expression.
Aggr
(NODISTINCT
Max
(UnitPrice),
Customer)
An array of values: 16, 16, 16, 25, 25, 25, 19, and 19. The nodistinct qualifier means
that the array contains one element for each row in the source data: each is the
maximum UnitPrice for each Customer and Product.
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
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See also:
p Basic aggregation functions (page 148)
Basic aggregation functions
Basic aggregation functions overview
Basic aggregation functions are a group of the most common aggregation functions.
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Basic aggregation functions in the data load script
FirstSortedValue
FirstSortedValue() returns the value of the first expression that corresponds to the first value in the sort
order of the second expression iterated over a number of records, as defined by a group by clause.
FirstSortedValue ([ distinct ] expression [, sort-weight [, n ]])
Max
Max() finds the highest numeric value of the aggregated data in the expression, as defined by a group by
clause. By specifying a rank n, the nth highest value can be found.
Max ( expression[, rank])
Min
Min() returns the lowest numeric value of the aggregated data in the expression, as defined by a group by
clause. By specifying a rank n, the nth lowest value can be found.
Min ( expression[, rank])
Mode
Mode() returns the most commonly-occurring value, the mode value, of the aggregated data in the
expression, as defined by a group by clause. The Mode() function can return numeric values as well as text
values.
Mode (expression )
Only
Only() returns a value if there is one and only one possible result from the aggregated data. If records contain
only one value then that value is returned, otherwise NULL is returned. Use the group by clause to evaluate
over multiple records. The Only() function can return numeric and text values.
Only (expression )
Sum
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Sum() calculates the total of the values aggregated in the expression, as defined by a group by clause.
Sum ([distinct]expression)
Basic aggregation functions in chart expressions
Chart aggregation functions can only be used on fields in chart expressions. The argument expression of one
aggregation function must not contain another aggregation function.
FirstSortedValue
FirstSortedValue() returns the value of one field based on the sorted values of another field. For example,
the product with the lowest unit price.
FirstSortedValue - chart function([{SetExpression}] [DISTINCT] [TOTAL []] value, sort_weight [,rank])
Max
Max() finds the highest value of the aggregated data. By specifying a rank n, the nth highest value can be
found.
Max - chart function([{SetExpression}] [DISTINCT] [TOTAL []]
expr [,rank])
Min
Min() finds the lowest value of the aggregated data. By specifying a rank n, the nth lowest value can be
found.
Min - chart function([{SetExpression}] [DISTINCT] [TOTAL []]
expr [,rank])
Mode
Mode() finds the most commonly-occurring value, the mode value, in the aggregated data. The Mode()
function can process text values as well as numeric values.
Mode - chart function({[SetExpression] [TOTAL []]} expr)
Only
Only() returns a value if there is one and only one possible result from the aggregated data. For example,
searching for the only product where the unit price =9 will return NULL if more than one product has a unit
price of 9.
Only - chart function([{SetExpression}] [DISTINCT] [TOTAL []]
expr)
Sum
Sum() calculates the total of the values given by the expression or field across the aggregated data.
Sum - chart function([{SetExpression}] [DISTINCT] [TOTAL []]
expr])
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FirstSortedValue
FirstSortedValue() returns the value of the first expression that corresponds to the first value in the sort
order of the second expression iterated over a number of records, as defined by a group by clause.
Syntax:
FirstSortedValue ([ distinct ] value, sort-weight [, rank ])
Return data type: dual
Arguments:
Argument
Description
value
Expression
The function returns the value from the field specified in value associated with the result of
sorting the sort_weight field, taking into account rank, if specified. If more than one
resulting value shares the same sort_weight for the specified rank, the function returns
NULL.
sort-weight
Expression
The expression containing the data to be sorted. The first (lowest) value of sort_weight is
found, from which the corresponding value of the value expression is determined. If you
place a minus sign in front of sort_weight, the function returns the last (highest) sorted
value instead.
rank
Expression
By stating a rank "n" larger than 1, you get the nth sorted value.
distinct
If the word DISTINCT occurs before the function arguments, duplicates resulting from the
evaluation of the function arguments are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
MyProductWithLargestOrderByCustomer
AA
BB
DD
because AA corresponds to the largest order
(value of UnitSales:18) for customer Astrida, BB
corresponds to the largest order (5) for customer
Betacab and DD corresponds to the largest order
(8) for customer Canutility.
FirstSortedValue:
LOAD Customer,FirstSortedValue(Product, UnitSales) as MyProductWithLargestOrderByCustomer
Resident Temp Group By Customer;
Given that the Temp table is loaded as in the
previous example:
LOAD Customer,FirstSortedValue(Product,
UnitSales) as
MyProductWithSmallestOrderByCustomer Resident
Temp Group By Customer;
Given that the Temp table is loaded as in first
example:
LOAD Customer, FirstSortedValue(Product, UnitSales,2) as My2ndProductOrderCustomer,
Resident Temp Group By Customer;
MyProductWithSmallestOrderByCustomer
CC
AA
DD
because CC corresponds to the smallest order (2)
for customer Astrida, AA corresponds to the
smallest order (4) for customer Betacab and DD
corresponds to the smallest order (8) for customer
Canutility (there is only one valid order for
customer Canutility so it is both the smallest and
the largest).
MySecondLargetsOrderCustomer
AA
AA
-
Note! The field will only show AA once, because it
is the second-largest order for both customers
Astrida and Betacab.
FirstSortedValue - chart function
FirstSortedValue() returns the value of one field based on the sorted values of another field. For example,
the product with the lowest unit price.
Syntax:
FirstSortedValue([{SetExpression}] [DISTINCT] [TOTAL []] value,
sort_weight [,rank])
Return data type: dual
Arguments:
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Argument
Description
value
Output field. The function returns the value from the field specified in value associated
with the result of sorting the sort_weight field, taking into account rank, if specified. If
more than one resulting value shares the same sort_weight for the specified rank, the
function returns NULL.
sort_weight
Input field. The expression containing the data to be sorted. The first (lowest) value of
sort_weight is found, from which the corresponding value of the value expression is
determined. If you place a minus sign in front of sort_weight, the function returns the
last (highest) sorted value instead.
rank
By stating a rank "n" larger than 1, you get the nth sorted value.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
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Example
Result
firstsortedvalue (Product,
UnitPrice)
BB, which is the Productwith the lowest UnitPrice(9).
firstsortedvalue (Product,
UnitPrice, 2)
BB, which is the Productwith the second-lowest UnitPrice(10).
firstsortedvalue (Customer, UnitPrice, 2)
Betacab, which is the Customerwith the Product that has secondhighest UnitPrice(20).
firstsortedvalue (Customer,
UnitPrice, 3)
NULL, because there are two values of Customer (A and C) with the
samerank (third-lowest) UnitPrice(15).
firstsortedvalue (Customer, UnitPrice*UnitSales, 2)
Astrida, which is the Customer with the second-highest sales order
value UnitPrice multiplied by UnitSales (120).
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
Max
Max() finds the highest numeric value of the aggregated data in the expression, as defined by a group by
clause. By specifying a rank n, the nth highest value can be found.
Syntax:
Max ( expression[, rank])
Return data type:numeric
Arguments:
Argument
Description
expr
Expression
The expression or field containing the data to be measured.
rank
Expression
The default value of rank is 1, which corresponds to the highest value. By specifying rank as
2, the second highest value is returned. If rank is 3, the third highest value is returned, and
so on.
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Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
Customer
MyMax
Astrida
18
Betacab
5
Canutility
8
Given that the Temp table is loaded as in the previous example:
Customer
MyMaxRank2
LOAD Customer, Max(UnitSales,2) as MyMaxRank2, Resident Temp Group By
Customer;
Astrida
10
Betacab
4
Canutility
-
Max:
LOAD Customer, Max(UnitSales) as MyMax, Resident Temp Group By
Customer;
Max - chart function
Max() finds the highest value of the aggregated data. By specifying a rank n, the nth highest value can be
found.
You might also want to look at FirstSortedValue and rangemax, which have similar
functionality to the Max function.
Syntax:
Max([{SetExpression}] [DISTINCT] [TOTAL []] expr [,rank])
Return data type: numeric
Arguments:
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Argument
Description
expr
The expression or field containing the data to be measured.
rank
The default value of rank is 1, which corresponds to the highest value. By specifying
rank as 2, the second highest value is returned. If rank is 3, the third highest value is
returned, and so on.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
Examples
Results
Max(UnitSales)
10, because this is the highest value in UnitSales.
The value of an order is
calculated from the
number of units sold in
(UnitSales) multiplied by
the unit price.
150, because this is the highest value of the result of calculating all possible
values of (UnitSales)*(UnitPrice).
Max(UnitSales*UnitPrice)
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Examples
Results
Max(UnitSales, 2)
9, which is the second highest value.
Max(TOTAL UnitSales)
10, because the TOTAL qualifier means the highest possible value is found,
disregarding the chart dimensions. For a chart with Customer as dimension,
the TOTAL qualifier will ensure the maximum value across the full dataset is
returned, instead of the maximum UnitSales for each customer.
Make the selection
Customer B.
10, independent of the selection made, because the Set Analysis expression
{1} defines the set of records to be evaluated as ALL, no matter what
selection is made.
Max({1} TOTAL UnitSales)
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
See also:
p FirstSortedValue - chart function (page 151)
p RangeMax (page 556)
Min
Min() returns the lowest numeric value of the aggregated data in the expression, as defined by a group by
clause. By specifying a rank n, the nth lowest value can be found.
Syntax:
Min ( expression[, rank])
Return data type: numeric
Arguments:
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Argument
Description
expr
Expression
The expression or field containing the data to be measured.
rank
Expression
The default value of rank is 1, which corresponds to the lowest value. By specifying rank as
2, the second lowest value is returned. If rank is 3, the third lowest value is returned, and so
on.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
Customer
MyMin
Astrida
2
Betacab
4
Canutility
8
Given that the Temp table is loaded as in the previous example:
Customer
MyMinRank2
LOAD Customer, Min(UnitSales,2) as MyMinRank2 Resident Temp Group By
Customer;
Astrida
9
Betacab
5
Canutility
-
Min:
LOAD Customer, Min(UnitSales) as MyMin Resident Temp Group By Customer;
Min - chart function
Min() finds the lowest value of the aggregated data. By specifying a rank n, the nth lowest value can be
found.
You might also want to look at FirstSortedValue and rangemin, which have similar
functionality to the Min function.
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Syntax:
Min({[SetExpression] [TOTAL []]} expr [,rank])
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
rank
The default value of rank is 1, which corresponds to the highest value. By specifying
rank as 2, the second highest value is returned. If rank is 3, the third highest value is
returned, and so on.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
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The Min() function must return a non-NULL value from the array of values given by the
expression, if there is one. So in the examples, because there are NULL values in the data, the
function returns the first non-NULL value evaluated from the expression.
Examples
Results
Min(UnitSales)
2, because this is the lowest non-NULL value in UnitSales.
The value of an order is
calculated from the
number of units sold in
(UnitSales) multiplied by
the unit price.
40, because this is the lowest non-NULL value result of calculating all
possible values of (UnitSales)*(UnitPrice).
Min(UnitSales*UnitPrice)
Min(UnitSales, 2)
4, which is the second lowest value (after the NULL values).
Min(TOTAL UnitSales)
2, because the TOTAL qualifier means the lowest possible value is found,
disregarding the chart dimensions. For a chart with Customer as dimension,
the TOTAL qualifier will ensure the minimum value across the full dataset is
returned, instead of the minimum UnitSales for each customer.
Make the selection
Customer B.
40, independent of the selection made, because the Set Analysis expression
{1} defines the set of records to be evaluated as ALL, no matter what
selection is made.
Min({1} TOTAL UnitSales)
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
See also:
p FirstSortedValue - chart function (page 151)
p RangeMin (page 559)
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Mode
Mode() returns the most commonly-occurring value, the mode value, of the aggregated data in the
expression, as defined by a group by clause. The Mode() function can return numeric values as well as text
values.
Syntax:
Mode ( expression )
Return data type:dual
Argument
Description
expr Expression
The expression or field containing the data to be measured.
Limitations:
If more than one value is equally commonly occurring, NULL is returned.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
MyMostOftenSoldProduct
AA
because AA is the only product sold
more than once.
Mode:
LOAD Customer, Mode(Product) as MyMostOftenSoldProduct
Resident Temp Group By Customer;
Mode - chart function
Mode() finds the most commonly-occurring value, the mode value, in the aggregated data. The Mode()
function can process text values as well as numeric values.
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Syntax:
Mode({[SetExpression] [TOTAL []]} expr)
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
Examples
Results
Mode(UnitPrice)
15, because this is the most commonly-occurring value in UnitSales.
Make the
selection Customer
A.
Returns NULL (-). No single value occurs more often than another.
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Examples
Results
Mode(Product)
AA, because this is the most commonly occurring value in Product.
Make the
selection Customer
Returns NULL (-). No single value occurs more often than another.
A
Mode
(TOTAL UnitPrice)
15, because the TOTAL qualifier means the most commonly occurring value is still
15, even disregarding the chart dimensions.
Make the
selection
15, independent of the selection made, because the Set Analysis expression {1}
defines the set of records to be evaluated as ALL, no matter what selection is made.
Customer B.
Mode)({1}
TOTAL UnitPrice)
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
See also:
p Avg - chart function (page 201)
p Median - chart function (page 232)
Only
Only() returns a value if there is one and only one possible result from the aggregated data. If records contain
only one value then that value is returned, otherwise NULL is returned. Use the group by clause to evaluate
over multiple records. The Only() function can return numeric and text values.
Syntax:
Only ( expression )
Return data type: dual
Argument
Description
expr Expression
The expression or field containing the data to be measured.
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Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
Customer
MyUniqIDCheck
Astrida
1
because only customer Astrida has
complete records that include
CustomerID.
Only:
LOAD Customer, Only(CustomerID) as MyUniqIDCheck
Resident Temp Group By Customer;
Only - chart function
Only() returns a value if there is one and only one possible result from the aggregated data. For example,
searching for the only product where the unit price =9 will return NULL if more than one product has a unit
price of 9.
Syntax:
Only([{SetExpression}] [TOTAL []] expr)
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Use Only() when you want a NULL result if there are multiple possible values in the sample
data.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
Examples
Results
Only({} Product)
BB, because this is the only Productthat has a UnitPrice of '9'.
Only({}
Customer)
B, because the only Customer selling a Product called 'DD'.
Only({} UnitSales)
The number of UnitSales where UnitPrice is 20 is 2, because there is only one
value of UnitSales where the UnitPrice =20.
Only({} UnitSales)
NULL, because there are two values of UnitSales where the UnitPrice =15.
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
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Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
Sum
Sum() calculates the total of the values aggregated in the expression, as defined by a group by clause.
Syntax:
sum ( [ distinct] expression)
Return data type:numeric
Arguments:
Argument
Description
distinct
If the word distinct occurs before the expression, all duplicates will be disregarded.
expr Expression
The expression or field containing the data to be measured.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD
Canutility|DD|3|8
Canutility|CC
] (delimiter is '|');
Customer
MySum
Astrida
39
Betacab
9
Canutility
8
Sum:
LOAD Customer, Sum(UnitSales) as MySum Resident Temp Group By Customer;
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Sum - chart function
Sum() calculates the total of the values given by the expression or field across the aggregated data.
Syntax:
Sum([{SetExpression}] [DISTINCT] [TOTAL []] expr])
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
Although the DISTINCT qualifier is supported, use it only with extreme
caution because it may mislead the reader into thinking a total value is
shown when some data has been omitted.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
UnitSales
UnitPrice
Astrida
AA
4
16
Astrida
AA
10
15
Astrida
BB
9
9
Betacab
BB
5
10
Betacab
CC
2
20
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Customer
Product
UnitSales
UnitPrice
Betacab
DD
-
25
Canutility
AA
8
15
Canutility
CC
-
19
Examples
Results
Sum(UnitSales)
38. The total of the values in UnitSales.
Sum(UnitSales*UnitPrice)
505. The total of UnitPrice multiplied by UnitSales aggregated.
Sum
(TOTAL UnitSales*UnitPrice)
505 for all rows in the table as well as the total, because the TOTAL
qualifier means the sum is still 505, disregarding the chart dimensions.
Make the selection Customer
B.
505, independent of the selection made, because the Set Analysis
expression {1} defines the set of records to be evaluated as ALL, no
matter what selection is made.
Sum({1}
TOTAL UnitSales*UnitPrice)
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
Counter aggregation functions
Counter aggregation functions return various types of counts of an expression over a number of records in a
data load script, or a number of values in a chart dimension.
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Counter aggregation functions in the data load script
Count
Count() returns the number of values aggregated in expression, as defined by a group by clause.
Count ([distinct ] expression | * )
MissingCount
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MissingCount() returns the number of missing values aggregated in the expression, as defined by a group
by clause.
MissingCount ([ distinct ] expression)
NullCount
NullCount() returns the number of NULL values aggregated in the expression, as defined by a group by
clause.
NullCount ([ distinct ] expression)
NumericCount
NumericCount() returns the number of numeric values found in the expression, as defined by a group by
clause.
NumericCount ([ distinct ] expression)
TextCount
TextCount() returns the number of field values that are non-numeric aggregated in the expression, as
defined by a group by clause.
TextCount ([ distinct ] expression)
Counter aggregation functions in chart expressions
The following counter aggregation functions can be used in charts:
Count
Count() is used to aggregate the number of values, text and numeric, in each chart dimension.
Count - chart function({[SetExpression] [DISTINCT] [TOTAL []]}
expr)
MissingCount
MissingCount() is used to aggregate the number of missing values in each chart dimension. Missing values
are all non-numeric values.
MissingCount - chart function({[SetExpression] [DISTINCT] [TOTAL []] expr)
NullCount
NullCount() is used to aggregate the number of NULL values in each chart dimension.
NullCount - chart function({[SetExpression][DISTINCT] [TOTAL []]} expr)
NumericCount
NumericCount() aggregates the number of numeric values in each chart dimension.
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NumericCount - chart function({[SetExpression] [DISTINCT] [TOTAL []]} expr)
TextCount
TextCount() is used to aggregate the number of field values that are non-numeric in each chart dimension.
TextCount - chart function({[SetExpression] [DISTINCT] [TOTAL []]} expr)
Count
Count() returns the number of values aggregated in expression, as defined by a group by clause.
Syntax:
Count( [distinct ] expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB|1|25| 25
Canutility|AA|3|8|15
Canutility|CC|||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
Customer OrdersByCustomer
Astrida 3
Betacab 3
Canutility 2
Divadip 2
As long as the dimension Customer is included in
the table on the sheet, otherwise the result for
OrdersByCustomer is 3, 2.
Count1:
LOAD Customer,Count(OrderNumber) as
OrdersByCustomer Resident Temp Group By
Customer;
Given that the Temp table is loaded as in the
previous example:
Given that the Temp table is loaded as in the first
example:
LOAD Count(distinct OrderNumber) as
TotalOrdersNumber Resident Temp;
TotalOrderNumber
10
TotalOrderNumber
9
Because there are two values of OrderNumber with
the same value, 1.
Count - chart function
Count() is used to aggregate the number of values, text and numeric, in each chart dimension.
Syntax:
Count({[SetExpression] [DISTINCT] [TOTAL []]} expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
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Argument
Description
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
OrderNumber
UnitSales
Unit Price
Astrida
AA
1
4
16
Astrida
AA
7
10
15
Astrida
BB
4
9
9
Betacab
BB
6
5
10
Betacab
CC
5
2
20
Betacab
DD
1
25
25
Canutility
AA
3
8
15
Canutility
CC
Divadip
AA
2
Divadip
DD
3
19
4
16
25
The following examples assume that all customers are selected, except where stated.
Example
Result
Count(OrderNumber)
10, because there are 10 fields that could have a value for OrderNumber,
and all records, even empty ones, are counted.
"0" counts as a value and not an empty cell. However if a
measure aggregates to 0 for a dimension that dimension will
not be included in charts.
Count (Customer)
10, because Count evaluates the number of occurrences in all fields.
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Example
Result
Count (DISTINCT
[Customer])
4, because using the Distinct qualifier, Count only evaluates unique
occurrences.
Given that customer
Canutility is selected
0.2, because the expression returns the number of orders from the selected
customer as a percentage of orders from all customers. In this case 2 / 10.
Count (OrderNumber)/Count
({1} TOTAL OrderNumber
Given that customers
Astrida and Canutility are
selected
5, because that is the number of orders placed on products for the selected
customers only and empty cells are counted.
Count(TOTAL
OrderNumber)
Data used in examples:
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB|1|25| 25
Canutility|AA|3|8|15
Canutility|CC|||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
MissingCount
MissingCount() returns the number of missing values aggregated in the expression, as defined by a group
by clause.
Syntax:
MissingCount ( [ distinct ] expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
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Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB||| 25
Canutility|AA|||15
Canutility|CC| ||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
MissCount1:
LOAD Customer,MissingCount(OrderNumber) as
MissingOrdersByCustomer Resident Temp Group By Customer;
Customer MissingOrdersByCustomer
Astrida 0
Betacab 1
Canutility 2
Divadip 0
The second statement gives:
TotalMissingCount
3
in a table with that dimension.
Load MissingCount(OrderNumber) as TotalMissingCount Resident
Temp;
Given that the Temp table is loaded as in the previous example:
TotalMissingCountDistinct
1
LOAD MissingCount(distinct OrderNumber) as
TotalMissingCountDistinct Resident Temp;
Because there is only
oneOrderNumber one missing
value.
MissingCount - chart function
MissingCount() is used to aggregate the number of missing values in each chart dimension. Missing values
are all non-numeric values.
Syntax:
MissingCount({[SetExpression] [DISTINCT] [TOTAL []] expr)
Return data type: integer
Arguments:
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Argument
Description
expr
The expression or field containing the data to be measured.
set_
expression
By default, the aggregation function will aggregate over the set of possible records defined
by the selection. An alternative set of records can be defined by a set analysis expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from the
evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
OrderNumber
UnitSales
Unit Price
Astrida
AA
1
4
16
Astrida
AA
7
10
15
Astrida
BB
4
9
9
Betacab
BB
6
5
10
Betacab
CC
5
2
20
Betacab
DD
25
Canutility
AA
15
Canutility
CC
19
Divadip
AA
2
Divadip
DD
3
4
16
25
Example
Result
MissingCount
([OrderNumber])
3 because 3 of the 10 OrderNumber fields are empty
"0" counts as a value and not an empty cell. However if a measure
aggregates to 0 for a dimension that dimension will not be included in
charts.
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Example
Result
MissingCount
([OrderNumber])
/MissingCount ({1}
Total
[OrderNumber])
The expression returns the number of incomplete orders from the selected
customer as a fraction of incomplete orders from all customers. There is a total of 3
missing values for OrderNumber for all customers. So, for each Customer that has
a missing value for Product the result is 1/3.
Data used in example:
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB||| 25
Canutility|AA|||15
Canutility|CC| ||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
NullCount
NullCount() returns the number of NULL values aggregated in the expression, as defined by a group by
clause.
Syntax:
NullCount ( [ distinct ] expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
Set NULLINTERPRET = NULL;
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD|||
Canutility|AA|3|8|
Canutility|CC|NULL||
] (delimiter is '|');
Set NULLINTERPRET=;
NullCount1:
LOAD Customer,NullCount(OrderNumber) as
NullOrdersByCustomer Resident Temp Group By Customer;
Customer NullOrdersByCustomer
Astrida 0
Betacab 0
Canutility 1
The second statement gives:
TotalNullCount
1
in a table with that dimension, because only
one record contains a null value.
LOAD NullCount(OrderNumber) as TotalNullCount Resident
Temp;
NullCount - chart function
NullCount() is used to aggregate the number of NULL values in each chart dimension.
Syntax:
NullCount({[SetExpression][DISTINCT] [TOTAL []]} expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
set_
expression
By default, the aggregation function will aggregate over the set of possible records defined
by the selection. An alternative set of records can be defined by a set analysis expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from the
evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
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Example
Result
NullCount
([OrderNumber])
1 because we have introduced a null value using NullInterpret in the inline
LOAD statement.
Data used in example:
Set NULLINTERPRET = NULL;
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|CustomerID
Astrida|AA|1|10|1
Astrida|AA|7|18|1
Astrida|BB|4|9|1
Astrida|CC|6|2|1
Betacab|AA|5|4|2
Betacab|BB|2|5|2
Betacab|DD|||
Canutility|AA|3|8|
Canutility|CC|NULL||
] (delimiter is '|');
Set NULLINTERPRET=;
NumericCount
NumericCount() returns the number of numeric values found in the expression, as defined by a group by
clause.
Syntax:
NumericCount ( [ distinct ] expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB||| 25
Canutility|AA|||15
Canutility|CC| ||19
Divadip|CC|2|4|16
Divadip|DD|7|1|25
] (delimiter is '|');
NumCount1:
LOAD Customer,NumericCount(OrderNumber) as
NumericCountByCustomer Resident Temp Group By
Customer;
Customer
Astrida
Betacab
Canutility
Divadip
LOAD NumericCount(OrderNumber) as
TotalNumericCount Resident Temp;
The second statement gives:
TotalNumericCount
7
in a table with that dimension.
Given that the Temp table is loaded as in the
TotalNumericCountDistinct
6
Because there is one OrderNumber that
duplicates another, so the result is 6 that are not
duplicates..
previous example:
LOAD NumericCount(distinct OrderNumber) as
TotalNumeriCCountDistinct Resident Temp;
NumericCountByCustomer
3
2
0
2
NumericCount - chart function
NumericCount() aggregates the number of numeric values in each chart dimension.
Syntax:
NumericCount({[SetExpression] [DISTINCT] [TOTAL []]} expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
set_
expression
By default, the aggregation function will aggregate over the set of possible records defined
by the selection. An alternative set of records can be defined by a set analysis expression.
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Argument
Description
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from the
evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
OrderNumber
UnitSales
Unit Price
Astrida
AA
1
4
16
Astrida
AA
7
10
15
Astrida
BB
4
9
1
Betacab
BB
6
5
10
Betacab
CC
5
2
20
Betacab
DD
25
Canutility
AA
15
Canutility
CC
19
Divadip
AA
2
Divadip
DD
3
4
16
25
The following examples assume that all customers are selected, except where stated.
Example
Result
NumericCount
([OrderNumber])
7 because three of the 10 fields in OrderNumber are empty.
"0" counts as a value and not an empty cell. However if a measure
aggregates to 0 for a dimension that dimension will not be included in
charts.
NumericCount
([Product])
0 because all product names are in text. Typically you could use this to check that no
text fields have been given numeric content.
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Example
Result
NumericCount
(DISTINCT
[OrderNumber])
/Count(DISTINCT
[OrderNumber)]
Counts all the number of distinct numeric order numbers and divides it by the number
of order numbers numeric and non-numeric. This will be 1 if all field values are numeric.
Typically you could use this to check that all field values are numeric. In the example,
there are 7 distinct numeric values for OrderNumber of 8 distinct numeric and nonnumerid, so the expression returns 0.875.
Data used in example:
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB||| 25
Canutility|AA|||15
Canutility|CC| ||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
TextCount
TextCount() returns the number of field values that are non-numeric aggregated in the expression, as
defined by a group by clause.
Syntax:
TextCount ( [ distinct ] expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB||| 25
Canutility|AA|||15
Canutility|CC| ||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
TextCount1:
LOAD Customer,TextCount(Product) as ProductTextCount
Resident Temp Group By Customer;
Customer
Astrida
Betacab
Canutility
Divadip
LOAD Customer,TextCount(OrderNumber) as
OrderNumberTextCount Resident Temp Group By Customer;
Customer
Astrida
Betacab
Canutility
Divadip
ProductTextCount
3
3
2
2
OrderNumberTextCount
0
1
2
0
TextCount - chart function
TextCount() is used to aggregate the number of field values that are non-numeric in each chart dimension.
Syntax:
TextCount({[SetExpression] [DISTINCT] [TOTAL []]} expr)
Return data type: integer
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
Customer
Product
OrderNumber
UnitSales
Unit Price
Astrida
AA
1
4
16
Astrida
AA
7
10
15
Astrida
BB
4
9
1
Betacab
BB
6
5
10
Betacab
CC
5
2
20
Betacab
DD
25
Canutility
AA
15
Canutility
CC
19
Divadip
AA
2
Divadip
DD
3
4
16
25
Example
Result
TextCount
([Product])
10 because all of the 10 fields in Product are text.
"0" counts as a value and not an empty cell. However if a measure
aggregates to 0 for a dimension that dimension will not be included in
charts. Empty cells are evaluated as being non text and are not counted
by TextCount.
TextCount
([OrderNumber])
3, because empty cells are counted. Typically, you would use this to check that no
numeric fields have been given text values or are non-zero.
TextCount
(DISTINCT
[Product])/Count
([Product)]
Counts all the number of distinct text values of Product (4), and divides it by the total
number of values in Product (10). The result is 0.4.
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Data used in example:
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|1|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB|||| 25
Canutility|AA|||15
Canutility|CC|||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
Financial aggregation functions
This section describes aggregation functions for financial operations regarding payments and cash flow.
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Financial aggregation functions in the data load script
IRR
IRR() returns the aggregated internal rate of return for a series of cash flows represented by the numbers in
the expression iterated over a number of records as defined by a group by clause.
IRR (expression)
XIRR
XIRR() returns the aggregated internal rate of return for a schedule of cash flows (that is not necessarily
periodic) represented by paired numbers in pmt and date iterated over a number of records as defined by a
group by clause. All payments are discounted based on a 365-day year.
XIRR (valueexpression, dateexpression )
NPV
NPV() returns the aggregated net present value of an investment based on a discount_rate per period and a
series of future payments (negative values) and incomes (positive values), represented by the numbers in
value, iterated over a number of records, as defined by a group by clause. The payments and incomes are
assumed to occur at the end of each period.
NPV (rate, expression)
XNPV
XNPV() returns the aggregated net present value for a schedule of cashflows (not necessarily periodic)
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represented by paired numbers in pmt and date, iterated over a number of records as defined by a group by
clause. Rate is the interest rate per period. All payments are discounted based on a 365-day year.
XNPV (rate, valueexpression, dateexpression)
Financial aggregation functions in chart expressions
These financial aggregation functions can be used in charts.
IRR
IRR() returns the aggregated internal rate of return for a series of cash flows represented by the numbers in
the expression given by value iterated over the chart dimensions.
IRR - chart function[TOTAL []] value)
NPV
NPV() returns the aggregated net present value of an investment based on a discount_rate per period and a
series of future payments (negative values) and incomes (positive values,) represented by the numbers in
value, iterated over the chart dimensions. The payments and incomes are assumed to occur at the end of
each period.
NPV - chart function([TOTAL []] discount_rate, value)
XIRR
XIRR()returns the aggregated internal rate of return for a schedule of cash flows (that is not necessarily
periodic) represented by paired numbers in the expressions given by pmt and date iterated over the chart
dimensions. All payments are discounted based on a 365-day year.
XIRR - chart function (page 190)([TOTAL []] pmt, date)
XNPV
XNPV() returns the aggregated net present value for a schedule of cash flows (not necessarily periodic)
represented by paired numbers in the expressions given by pmt and date iterated over the chart dimensions.
All payments are discounted based on a 365-day year.
XNPV - chart function([TOTAL []] discount_rate, pmt, date)
IRR
IRR() returns the aggregated internal rate of return for a series of cash flows represented by the numbers in
the expression iterated over a number of records as defined by a group by clause.
These cash flows do not have to be even, as they would be for an annuity. However, the cash flows must
occur at regular intervals, such as monthly or annually. The internal rate of return is the interest rate received
for an investment consisting of payments (negative values) and income (positive values) that occur at regular
periods.The function needs at least one positive and one negative value to calculate.
Syntax:
IRR(value)
Return data type: numeric
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Arguments:
Argument
Description
value
The expression or field containing the data to be measured.
Limitations:
Text values, NULL values and missing values are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Examples and results:
Example
Result
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
Year
IRR2013
2013
0.1634
Cashflow1:
LOAD Year,IRR(Payments) as IRR2013 Resident Cashflow Group By Year;
IRR - chart function
IRR() returns the aggregated internal rate of return for a series of cash flows represented by the numbers in
the expression given by value iterated over the chart dimensions.
These cash flows do not have to be even, as they would be for an annuity. However, the cash flows must
occur at regular intervals, such as monthly or annually. The internal rate of return is the interest rate received
for an investment consisting of payments (negative values) and income (positive values) that occur at regular
periods. The function needs at least one positive and one negative value to calculate.
Syntax:
IRR([TOTAL []] value)
Return data type: numeric
Arguments:
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Argument
Description
value
The expression or field containing the data to be measured.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values are disregarded.
Examples and results:
Example
Result
IRR
(Payments)
0.1634
The payments are assumed to be periodic in nature, for example monthly.
The Date field is used in the XIRR example where payments can be nonperiodical as long as you provide the dates on which payments were made.
Data used in examples::
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
See also:
p XIRR - chart function (page 190)
p Aggr - chart function (page 145)
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NPV
NPV() returns the aggregated net present value of an investment based on a discount_rate per period and a
series of future payments (negative values) and incomes (positive values), represented by the numbers in
value, iterated over a number of records, as defined by a group by clause. The payments and incomes are
assumed to occur at the end of each period.
Syntax:
NPV(discount_rate, value)
Return data type: numeric. The result has a default number format of money.
Arguments:
Argument
Description
discount_rate
discount_rate is the rate of discount over the length of the period.
value
The expression or field containing the data to be measured.
Limitations:
Text values, NULL values and missing values are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
Year
NPV1_2013
2013
-$540.12
Cashflow1:
LOAD Year,NPV(0.2, Payments) as NPV1_2013 Resident
Cashflow Group By Year;
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Example
Result
Given that the Cashflow table is loaded as in the
Year
Discount
NPV2_2013
2013
2013
0.1
0.2
-$3456.05
$5666.67
previous example:
LOAD Year,NPV(Discount, Payments) as NPV2_2013
Resident Cashflow Group By Year, Discount;
Note that the Group By clause sorts the results by Year
and Discount. The first argument, discount_rate, is given
as a field (Discount), rather than a specific number, and
therefore, a second sorting criterion is required. A field
can contain a different values, so the aggregated records
must be sorted to allow for different values of Year and
Discount.
NPV - chart function
NPV() returns the aggregated net present value of an investment based on a discount_rate per period and a
series of future payments (negative values) and incomes (positive values,) represented by the numbers in
value, iterated over the chart dimensions. The payments and incomes are assumed to occur at the end of
each period.
Syntax:
NPV([TOTAL []] discount_rate, value)
Return data type: numeric The result has a default number format of money.
Arguments:
Argument
Description
discount_
rate
discount_rate is the rate of discount over the length of the period.
value
The expression or field containing the data to be measured.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets. These field names should be a subset of the chart dimension variables. In this
case, the calculation is made disregarding all chart dimension variables except those listed,
that is, one value is returned for each combination of field values in the listed dimension
fields. Also, fields that are not currently a dimension in a chart may be included in the list.
This may be useful in the case of group dimensions, where the dimension fields are not
fixed. Listing all of the variables in the group causes the function to work when the drill-down
level changes.
Limitations:
discount_rate and value must not contain aggregation functions, unless these inner aggregations contain
the TOTAL qualifier. For more advanced nested aggregations, use the advanced aggregation function
Aggr, in combination with calculated dimensions.
Text values, NULL values and missing values are disregarded.
Examples and results:
Example
Result
NPV(Discount, Payments)
-$540.12
Data used in examples::
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
See also:
p XNPV - chart function (page 193)
p Aggr - chart function (page 145)
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XIRR
XIRR() returns the aggregated internal rate of return for a schedule of cash flows (that is not necessarily
periodic) represented by paired numbers in pmt and date iterated over a number of records as defined by a
group by clause. All payments are discounted based on a 365-day year.
Syntax:
XIRR(pmt, date )
Return data type: numeric
Arguments:
Argument
Description
pmt
Payments.The expression or field containing the cash flows corresponding to the payment
schedule given in date.
date
The expression or field containing the schedule of dates corresponding to the cash flow
payments given in pmt.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair will result in the entire datapair to be disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
Year
XIRR2013
2013
0.5385
Cashflow1:
LOAD Year,XIRR(Payments, Date) as XIRR2013 Resident Cashflow Group By Year;
XIRR - chart function
XIRR()returns the aggregated internal rate of return for a schedule of cash flows (that is not necessarily
periodic) represented by paired numbers in the expressions given by pmt and date iterated over the chart
dimensions. All payments are discounted based on a 365-day year.
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Syntax:
XIRR([TOTAL []] pmt, date)
Return data type: numeric
Arguments:
Argument
Description
pmt
Payments.The expression or field containing the cash flows corresponding to the payment
schedule given in date.
date
The expression or field containing the schedule of dates corresponding to the cash flow
payments given in pmt.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
pmt and date must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Example
Result
XIRR(Payments, Date)
0.5385
Data used in examples::
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
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See also:
p
IRR - chart function (page 185)
p Aggr - chart function (page 145)
XNPV
XNPV() returns the aggregated net present value for a schedule of cashflows (not necessarily periodic)
represented by paired numbers in pmt and date, iterated over a number of records as defined by a group by
clause. Rate is the interest rate per period. All payments are discounted based on a 365-day year.
Syntax:
XNPV(discount_rate, pmt, date)
Return data type: numeric. The result has a default number format of money. .
Arguments:
Argument
Description
discount_
rate
discount_rate is the rate of discount over the length of the period.
pmt
The expression or field containing the data to be measured.
date
The expression or field containing the schedule of dates corresponding to the cash flow
payments given in pmt.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair will result in the entire datapair to be disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
Year
XNPV1_2013
2013
$2104.37
Cashflow1:
LOAD Year,XNPV(0.2, Payments, Date) as XNPV1_2013
Resident Cashflow Group By Year;
Given that the Cashflow table is loaded as in the
previous example:
LOAD Year,XNPV(Discount, Payments, Date) as XNPV2_
2013 Resident Cashflow Group By Year, Discount;
Year
Discount
XNPV2_2013
2013
2013
0.1
0.2
-$3164.35
$6800.00
Note that the Group By clause sorts the results by Year
and Discount. The first argument, discount_rate, is given
as a field (Discount), rather than a specific number, and
therefore, a second sorting criterion is required. A field
can contain a different values, so the aggregated records
must be sorted to allow for different values of Year and
Discount.
XNPV - chart function
XNPV() returns the aggregated net present value for a schedule of cash flows (not necessarily periodic)
represented by paired numbers in the expressions given by pmt and date iterated over the chart dimensions.
All payments are discounted based on a 365-day year.
Syntax:
XNPV([TOTAL []] discount_rate, pmt, date)
Return data type: numeric The result has a default number format of money.
Arguments:
Argument
Description
discount_
rate
discount_rate is the rate of discount over the length of the period.
pmt
Payments.The expression or field containing the cash flows corresponding to the payment
schedule given in date.
date
The expression or field containing the schedule of dates corresponding to the cash flow
payments given in pmt.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over all
possible values given the current selections, and not just those that pertain to the current
dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
discount_rate, pmt and date must not contain aggregation functions, unless these inner aggregations
contain the TOTAL or ALL qualifiers. For more advanced nested aggregations, use the advanced
aggregation function Aggr, in combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Example
Result
XNPV(Discount, Payments, Date)
-$3164.35
Data used in examples::
Cashflow:
LOAD 2013 as Year, * inline [
Date|Discount|Payments
2013-01-01|0.1|-10000
2013-03-01|0.1|3000
2013-10-30|0.1|4200
2014-02-01|0.2|6800
] (delimiter is '|');
See also:
p NPV - chart function (page 188)
p Aggr - chart function (page 145)
Statistical aggregation functions
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Statistical aggregation functions in the data load script
The following statistical aggregation functions can be used in scripts.
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Avg
Avg() finds the average value of the aggregated data in the expression over a number of records as defined
by a group by clause.
Avg
([distinct] expression)
Correl
Correl() returns the aggregated correlation coefficient for a series of coordinates represented by paired
numbers in x-expression and y-expression iterated over a number of records as defined by a group by
clause.
Correl (x-expression, y-expression)
Fractile
Fractile() finds the value that corresponds to the fractile (quantile) of the aggregated data in the expression
over a number of records as defined by a group by clause.
Fractile (expression, fractile)
Kurtosis
Kurtosis() returns the kurtosis of the data in the expression over a number of records as defined by a group
by clause.
Kurtosis
([distinct ] expression )
LINEST_B
LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
LINEST_B (y-expression, x-expression [, y0 [, x0 ]])
LINEST_df
LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
LINEST_DF
(y-expression, x-expression [, y0 [, x0 ]])
LINEST_f
This script function returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
LINEST_F (y-expression, x-expression [, y0 [, x0 ]])
LINEST_m
LINEST_M() returns the aggregated m value (slope) of a linear regression defined by the equation y=mx+b
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for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a
number of records as defined by a group by clause.
LINEST_M
(y-expression, x-expression [, y0 [, x0 ]])
LINEST_r2
LINEST_R2() returns the aggregated r2 value (coefficient of determination) of a linear regression defined by
the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and yexpression iterated over a number of records as defined by a group by clause.
LINEST_R2 (y-expression, x-expression [, y0 [, x0 ]])
LINEST_seb
LINEST_SEB() returns the aggregated standard error of the b value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
LINEST_SEB
(y-expression, x-expression [, y0 [, x0 ]])
LINEST_sem
LINEST_SEM() returns the aggregated standard error of the m value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
LINEST_SEM
(y-expression, x-expression [, y0 [, x0 ]])
LINEST_sey
LINEST_SEY() returns the aggregated standard error of the y estimate of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
LINEST_SEY
(y-expression, x-expression [, y0 [, x0 ]])
LINEST_ssreg
LINEST_SSREG() returns the aggregated regression sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
LINEST_SSREG
(y-expression, x-expression [, y0 [, x0 ]])
Linest_ssresid
LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
LINEST_SSRESID
(y-expression, x-expression [, y0 [, x0 ]])
Median
Median() returns the aggregated median of the values in the expression over a number of records as defined
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by a group by clause.
Median (expression)
Skew
Skew() returns the skewness of expression over a number of records as defined by a group by clause.
Skew ([ distinct] expression)
Stdev
Stdev() returns the standard deviation of the values given by the expression over a number of records as
defined by a group by clause.
Stdev ([distinct] expression)
Sterr
Sterr() returns the aggregated standard error (stdev/sqrt(n)) for a series of values represented by the
expression iterated over a number of records as defined by a group by clause.
Sterr
([distinct] expression)
STEYX
STEYX() returns the aggregated standard error of the predicted y-value for each x-value in the regression for
a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a
number of records as defined by a group by clause.
STEYX
(y-expression, x-expression)
Statistical aggregation functions in chart expressions
The following statistical aggregation functions can be used in charts.
Avg
Avg() returns the aggregated average of the expression or field iterated over the chart dimensions.
Avg - chart function({[SetExpression] [DISTINCT] [TOTAL []]}
expr)
Correl
Correl() returns the aggregated correlation coefficient for two data sets. The correlation function is a
measure of the relationship between the data sets and is aggregated for (x,y) value pairs iterated over the
chart dimensions.
Correl - chart function({[SetExpression] [TOTAL []]} value1,
value2 )
Fractile
Fractile() finds the value that corresponds to the fractile (quantile) of the aggregated data in the range given
by the expression iterated over the chart dimensions.
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Fractile - chart function({[SetExpression] [TOTAL []]} expr,
fraction)
Kurtosis
Kurtosis() finds the kurtosis of the range of data aggregated in the expression or field iterated over the chart
dimensions.
Kurtosis - chart function({[SetExpression] [DISTINCT] [TOTAL []]} expr)
LINEST_b
LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by the
expressions x_value and y_value, iterated over the chart dimensions.
LINEST_R2 - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
LINEST_df
LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and
y_value, iterated over the chart dimensions.
LINEST_DF - chart function({[SetExpression] [TOTAL []]} y_
value, x_value [, y0_const [, x0_const]])
LINEST_f
LINEST_F() returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and
the y_value, iterated over the chart dimensions.
LINEST_F - chart function({[SetExpression] [TOTAL[]]} y_value,
x_value [, y0_const [, x0_const]])
LINEST_m
LINEST_M() returns the aggregated m value (slope) of a linear regression defined by the equation y=mx+b
for a series of coordinates represented by paired numbers given by the expressions x_value and y_value,
iterated over the chart dimensions.
LINEST_M - chart function({[SetExpression] [TOTAL[]]} y_value,
x_value [, y0_const [, x0_const]])
LINEST_r2
LINEST_R2() returns the aggregated r2 value (coefficient of determination) of a linear regression defined by
the equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
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LINEST_R2 - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
LINEST_seb
LINEST_SEB() returns the aggregated standard error of the b value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
LINEST_SEB - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
LINEST_sem
LINEST_SEM() returns the aggregated standard error of the m value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
LINEST_SEM - chart function([{set_expression}][ distinct ] [total [] ] y-expression, x-expression [, y0 [, x0 ]] )
LINEST_sey
LINEST_SEY() returns the aggregated standard error of the y estimate of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
LINEST_SEY - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
LINEST_ssreg
LINEST_SSREG() returns the aggregated regression sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
LINEST_SSREG - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
LINEST_ssresid
LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_
value and y_value, iterated over the chart dimensions.
LINEST_SSRESID - chart function({[SetExpression] [TOTAL []] }y_
value, x_value[, y0_const[, x0_const]])
Median
Median() returns the median value of the range of values aggregated in the expression iterated over the
chart dimensions.
Median - chart function({[SetExpression] [TOTAL []]} expr)
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Skew
Skew() returns the aggregated skewness of the expression or field iterated over the chart dimensions.
Skew - chart function{[SetExpression] [DISTINCT] [TOTAL []]}
expr)
Stdev
Stdev() finds the standard deviation of the range of data aggregated in the expression or field iterated over
the chart dimensions.
Stdev - chart function({[SetExpression] [DISTINCT] [TOTAL []]}
expr)
Sterr
Sterr() finds the value of the standard error of the mean, (stdev/sqrt(n)), for the series of values aggregated in
the expression iterated over the chart dimensions.
Sterr - chart function({[SetExpression] [DISTINCT] [TOTAL[]]}
expr)
STEYX
STEYX() returns the aggregated standard error when predicting y-values for each x-value in a linear
regression given by a series of coordinates represented by paired numbers in the expressions given by y_
value and x_value.
STEYX - chart function{[SetExpression] [TOTAL []]} y_value, x_
value)
Avg
Avg() finds the average value of the aggregated data in the expression over a number of records as defined
by a group by clause.
Syntax:
Avg([DISTINCT] expr)
Return data type:numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
DISTINCT
If the word distinct occurs before the expression, all duplicates will be disregarded.
Examples and results:
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Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Temp:
crosstable (Month, Sales) load * inline [
Customer|Jan|Feb|Mar||Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
Customer MyAverageSalesByCustomer
Astrida 48.916667
Betacab 44.916667
Canutility 56.916667
Divadip 63.083333
This can be checked in the sheet by
creating a table including the measure:
Sum(Sales)/12
Avg1:
LOAD Customer, Avg(Sales) as MyAverageSalesByCustomer
Resident Temp Group By Customer;
Given that the Temp table is loaded as in the previous
example:
LOAD Customer,Avg(DISTINCT Sales) as MyAvgSalesDistinct
Resident Temp Group By Customer;
Customer MyAverageSalesByCustomer
Astrida 43.1
Betacab 43.909091
Canutility 55.909091
Divadip 61
Only the distinct values are counted.
Divide the total by the number of nonduplicate values.
Avg - chart function
Avg() returns the aggregated average of the expression or field iterated over the chart dimensions.
Syntax:
Avg([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Customer
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Astrida
46
60
70
13
78
20
45
65
78
12
78
22
Betacab
65
56
22
79
12
56
45
24
32
78
55
15
Canutility
77
68
34
91
24
68
57
36
44
90
67
27
Divadip
57
36
44
90
67
27
57
68
47
90
80
94
Example
Result
Avg(Sales)
For a table including the dimension Customer and the measure Avg([Sales]), if Totals
are shown, the result is 2566.
Avg([TOTAL
(Sales))
53.458333 for all values of Customer, because the TOTAL qualifier means that
dimensions are disregarded.
Avg(DISTINCT
(Sales))
51.862069 for the total, because using the Distinct qualifier means only unique values in
Sales for each Customer are evaluated.
Data used in examples:
Monthnames:
LOAD * INLINE [
Month, Monthnumber
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Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
Nov, 11
Dec, 12
];
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Aggr - chart function (page 145)
Correl
Correl() returns the aggregated correlation coefficient for a series of coordinates represented by paired
numbers in x-expression and y-expression iterated over a number of records as defined by a group by
clause.
Syntax:
Correl(value1, value2)
Return data type: numeric
Arguments:
Argument
Description
value1,
value2
The expressions or fields containing the two sample sets for which the correlation
coefficient is to be measured.
Limitations:
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Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Salary:
Load *, 1 as Grp;
LOAD * inline [
"Employee name"|Gender|Age|Salary
Aiden Charles|Male|20|25000
Brenda Davies|Male|25|32000
Charlotte Edberg|Female|45|56000
Daroush Ferrara|Male|31|29000
Eunice Goldblum|Female|31|32000
Freddy Halvorsen|Male|25|26000
Gauri Indu|Female|36|46000
Harry Jones|Male|38|40000
Ian Underwood|Male|40|45000
Jackie Kingsley|Female|23|28000
] (delimiter is '|');
In a table with the dimension Correl_Salary, the result of the
Correl() calculation in the data load script will be shown: 0.9270611
Correl1:
LOAD Grp,
Correl(Age,Salary) as Correl_
Salary Resident Salary Group By
Grp;
Correl - chart function
Correl() returns the aggregated correlation coefficient for two data sets. The correlation function is a
measure of the relationship between the data sets and is aggregated for (x,y) value pairs iterated over the
chart dimensions.
Syntax:
Correl([{SetExpression}] [DISTINCT] [TOTAL []] value1, value2 )
Return data type: numeric
Arguments:
Argument
Description
value1, value2
The expressions or fields containing the two sample sets for which the correlation
coefficient is to be measured.
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Argument
Description
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Example
Result
Correl
(Age,
Salary)
For a table including the dimension Employee name and the measure Correl(Age, Salary),
the result is 0.9270611. The result is only displayed for the totals cell.
Correl
(TOTAL
Age,
Salary))
0.927. This and the following results are shown to three decimal places for readability.
Correl({1}
TOTAL Age,
Salary))
0.927. Independent of selections. This is because the set expression {1} disregards all
selections and dimensions.
Correl
(TOTAL
Age,
Salary))
0.927 in the total cell, 0.939 for all values of Male, and 0.951 for all values of Female. This
corresponds to the results from making the selections in a filter panel based on Gender.
If you create a filter panel with the dimension Gender, and make selections from it, you see
the result 0.951 when Female is selected and 0.939 if Male is selected. This is because the
selection excludes all results that do not belong to the other value of Gender.
Data used in examples:
Salary:
LOAD * inline [
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"Employee name"|Gender|Age|Salary
Aiden Charles|Male|20|25000
Brenda Davies|Male|25|32000
Charlotte Edberg|Female|45|56000
Daroush Ferrara|Male|31|29000
Eunice Goldblum|Female|31|32000
Freddy Halvorsen|Male|25|26000
Gauri Indu|Female|36|46000
Harry Jones|Male|38|40000
Ian Underwood|Male|40|45000
Jackie Kingsley|Female|23|28000
] (delimiter is '|');
See also:
p Aggr - chart function (page 145)
p Avg - chart function (page 201)
p RangeCorrel (page 549)
Fractile
Fractile() finds the value that corresponds to the fractile (quantile) of the aggregated data in the expression
over a number of records as defined by a group by clause.
Syntax:
Fractile(expr, fraction)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
fraction
A number between 0 and 1 corresponding to the fractile (quantile expressed as a fraction) to
be calculated.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Table1:
crosstable LOAD recno() as ID, *
inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In a table with the dimensions Type and MyFractile, the results
of the Fractile() calculations in the data load script are:
Type MyFractile
Comparison 27.5
Observation 36
Fractile1:
LOAD Type,
Fractile(Value,0.75) as MyFractile
Resident Table1 Group By Type;
Fractile - chart function
Fractile() finds the value that corresponds to the fractile (quantile) of the aggregated data in the range given
by the expression iterated over the chart dimensions.
Syntax:
Fractile([{SetExpression}] [DISTINCT] [TOTAL []] expr,
fraction)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
fraction
A number between 0 and 1 corresponding to the fractile (quantile expressed as a
fraction) to be calculated.
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Argument
Description
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Customer
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Astrida
46
60
70
13
78
20
45
65
78
12
78
22
Betacab
65
56
22
79
12
56
45
24
32
78
55
15
Canutility
77
68
34
91
24
68
57
36
44
90
67
27
Divadip
57
36
44
90
67
27
57
68
47
90
80
94
Example
Result
Fractile
(Sales,
0.75)
For a table including the dimension Customer and the measure Fractile([Sales]), if Totals
Fractile
(TOTAL
Sales,
0.75))
71.75 for all values of Customer, because the TOTAL qualifier means that dimensions are
disregarded.
Fractile
(DISTINCT
Sales,
0.75)
70 for the total, because using the DISTINCT qualifier means only unique values in Sales
for each Customer are evaluated.
are shown, the result is 71.75. This is the point in the distribution of values of Sales that
75% of the values fall beneath.
Data used in examples:
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Monthnames:
LOAD * INLINE [
Month, Monthnumber
Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
Nov, 11
Dec, 12
];
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Aggr - chart function (page 145)
Kurtosis
Kurtosis() returns the kurtosis of the data in the expression over a number of records as defined by a group
by clause.
Syntax:
Kurtosis([distinct ] expr )
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates will be disregarded.
Examples and results:
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Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Table1:
crosstable LOAD recno()
as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is
'|');
In a table with the dimensions Type, MyKurtosis1,and MyKurtosis2, the results
of the Kurtosis() calculations in the data load script are:
Type MyKurtosis1 MyKurtosis2
Comparison -1.1612957 -1.4982366
Observation -1.1148768 -0.93540144
Kurtosis1:
LOAD Type,
Kurtosis(Value) as
MyKurtosis1,
Kurtosis(DISTINCT
Value) as MyKurtosis2
Resident Table1 Group
By Type;
Kurtosis - chart function
Kurtosis() finds the kurtosis of the range of data aggregated in the expression or field iterated over the chart
dimensions.
Syntax:
Kurtosis([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Return data type: numeric
Arguments:
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Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Type
Valu
e
Comparis
on
2
2
7
3
8
3
1
1
1
9
1
3
4
3
1
2
3
2
1
2
1
3
2
9
3
7
2
Observati
on
35
4
0
1
2
1
5
2
1
1
4
4
6
1
0
2
8
4
8
1
6
3
0
3
2
4
8
3
1
2
2
1
2
3
9
1
9
2
5
Example
Result
Kurtosis
(Value)
For a table including the dimension Type and the measure Kurtosis(Value), if Totals are
Kurtosis
(TOTAL
Value))
1.252 for all values of Type, because the TOTAL qualifier means that dimensions are
disregarded.
shown for the table, and number formatting is set to 3 significant figures, the result is 1.252.
For Comparison it is 1.161 and for Observation it is 1.115.
Data used in examples:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
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40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
See also:
p Avg - chart function (page 201)
LINEST_B
LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
Syntax:
LINEST_B (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at
a given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
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Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_B - chart function
LINEST_B() returns the aggregated b value (y-intercept) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by the
expressions x_value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_B([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value [, y0_const [ , x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0_const, x0_
const
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
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Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_DF
LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
Syntax:
LINEST_DF (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
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See also:
p An example of how to use linest functions (page 246)
LINEST_DF - chart function
LINEST_DF() returns the aggregated degrees of freedom of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and
y_value, iterated over the chart dimensions.
Syntax:
LINEST_DF([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value [, y0_const [, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
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The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_F
This script function returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression iterated
over a number of records as defined by a group by clause.
Syntax:
LINEST_F (y-value, x-value[, y0 [, x0 ]])
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at
a given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
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LINEST_F - chart function
LINEST_F() returns the aggregated F statistic (r2/(1-r2)) of a linear regression defined by the equation
y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_value and
the y_value, iterated over the chart dimensions.
Syntax:
LINEST_F([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value [, y0_const [, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
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Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_M
LINEST_M() returns the aggregated m value (slope) of a linear regression defined by the equation y=mx+b
for a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a
number of records as defined by a group by clause.
Syntax:
LINEST_M (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
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LINEST_M - chart function
LINEST_M() returns the aggregated m value (slope) of a linear regression defined by the equation y=mx+b
for a series of coordinates represented by paired numbers given by the expressions x_value and y_value,
iterated over the chart dimensions.
Syntax:
LINEST_M([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value [, y0_const [, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
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Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_R2
LINEST_R2() returns the aggregated r2 value (coefficient of determination) of a linear regression defined by
the equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and yexpression iterated over a number of records as defined by a group by clause.
Syntax:
LINEST_R2 (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
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LINEST_R2 - chart function
LINEST_R2() returns the aggregated r2 value (coefficient of determination) of a linear regression defined by
the equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_R2([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
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Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_SEB
LINEST_SEB() returns the aggregated standard error of the b value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
Syntax:
LINEST_SEB (y-value, x-value[, y0 [, x0 ]])
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_SEB - chart function
LINEST_SEB() returns the aggregated standard error of the b value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
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Syntax:
LINEST_SEB([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
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p Avg - chart function (page 201)
LINEST_SEM
LINEST_SEM() returns the aggregated standard error of the m value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
Syntax:
LINEST_SEM (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_SEM - chart function
LINEST_SEM() returns the aggregated standard error of the m value of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_SEM([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value[, y0_const[, x0_const]])
Return data type: numeric
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Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
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LINEST_SEY
LINEST_SEY() returns the aggregated standard error of the y estimate of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
Syntax:
LINEST_SEY (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_SEY - chart function
LINEST_SEY() returns the aggregated standard error of the y estimate of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_SEY([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_
value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
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Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_SSREG
LINEST_SSREG() returns the aggregated regression sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
Syntax:
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LINEST_SSREG (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at
a given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_SSREG - chart function
LINEST_SSREG() returns the aggregated regression sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers given by the expressions x_
value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_SSREG([{SetExpression}] [DISTINCT] [TOTAL []] y_value,
x_value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
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Argument
Description
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
LINEST_SSRESID
LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in x-expression and y-expression
iterated over a number of records as defined by a group by clause.
Syntax:
LINEST_SSRESID (y-value, x-value[, y0 [, x0 ]])
Return data type: numeric
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Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
y(0), x(0)
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a
given point. By stating both y0 and x0 it is possible to force the regression line to pass
through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid data-pairs to
calculate. If y0 and x0 are stated, a single data pair will do.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
LINEST_SSRESID - chart function
LINEST_SSRESID() returns the aggregated residual sum of squares of a linear regression defined by the
equation y=mx+b for a series of coordinates represented by paired numbers in the expressions given by x_
value and y_value, iterated over the chart dimensions.
Syntax:
LINEST_SSRESID([{SetExpression}] [DISTINCT] [TOTAL []] y_value,
x_value[, y0_const[, x0_const]])
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
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Argument
Description
y0, x0
An optional value y0 may be stated forcing the regression line to pass through the y-axis
at a given point. By stating both y0 and x0 it is possible to force the regression line to
pass through a single fixed coordinate.
Unless both y0 and x0 are stated, the function requires at least two valid
data-pairs to calculate. If y0 and x0 are stated, a single data pair will do.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
An optional value y0 may be stated forcing the regression line to pass through the y-axis at a given point. By
stating both y0 and x0 it is possible to force the regression line to pass through a single fixed coordinate.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
See also:
p An example of how to use linest functions (page 246)
p Avg - chart function (page 201)
Median
Median() returns the aggregated median of the values in the expression over a number of records as defined
by a group by clause.
Syntax:
Median (expr)
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Return data type:numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
Table1:
crosstable LOAD recno() as
ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In a table with the dimensions Type and MyMedian, the results of the Median
() calculation in the data load script are:
Type MyMedian
Comparison 2.5
Observation 26.5
Median1:
LOAD Type,
Median(Value) as MyMedian
Resident Table1 Group By
Type;
Median - chart function
Median() returns the median value of the range of values aggregated in the expression iterated over the
chart dimensions.
Syntax:
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Median([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Type
Valu
e
Comparis
on
2
2
7
3
8
3
1
1
1
9
1
3
4
3
1
2
3
2
1
2
1
3
2
9
3
7
2
Observati
on
35
4
0
1
2
1
5
2
1
1
4
4
6
1
0
2
8
4
8
1
6
3
0
3
2
4
8
3
1
2
2
1
2
3
9
1
9
2
5
Example
Result
Median
(Value)
For a table including the dimension Type and the measure Median(Value), if Totals are
Median
(TOTAL
Value))
19 for all values of Type, because the TOTAL qualifier means that dimensions are
disregarded.
shown, the result is 19, for Comparison it is 2.5 and for Observation it is 26.5.
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Data used in examples:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
See also:
p Avg - chart function (page 201)
Skew
Skew() returns the skewness of expression over a number of records as defined by a group by clause.
Syntax:
Skew([ distinct] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
DISTINCT
If the word distinct occurs before the expression, all duplicates will be disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Table1:
crosstable LOAD recno() as
ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In a table with the dimensions Type and MySkew, the results of the Skew()
calculation in the data load script are:
Type MySkew
Comparison 0.86414768
Observation 0.32625351
Skew1:
LOAD Type,
Skew(Value) as MySkew
Resident Table1 Group By
Type;
Skew - chart function
Skew() returns the aggregated skewness of the expression or field iterated over the chart dimensions.
Syntax:
Skew([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Type
Valu
e
Comparis
on
2
2
7
3
8
3
1
1
1
9
1
3
4
3
1
2
3
2
1
2
1
3
2
9
3
7
2
Observati
on
35
4
0
1
2
1
5
2
1
1
4
4
6
1
0
2
8
4
8
1
6
3
0
3
2
4
8
3
1
2
2
1
2
3
9
1
9
2
5
Example
Result
Skew
(Value)
For a table including the dimension Type and the measure Skew(Value), if Totals are shown,
Skew
(TOTAL
Value))
0.235 for all values of Type, because the TOTAL qualifier means that dimensions are
disregarded.
and number formatting is set to 3 significant figures, the result is 0.235. For Comparison it is
0.864, and for Observation 0.3265.
Data used in examples:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
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16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
See also:
p Avg - chart function (page 201)
Stdev
Stdev() returns the standard deviation of the values given by the expression over a number of records as
defined by a group by clause.
Syntax:
Stdev([distinct] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates will be disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Table1:
crosstable LOAD recno() as
ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In a table with the dimensions Type and MyStdev, the results of the Stdev()
calculation in the data load script are:
Type MyStdev
Comparison 14.61245
Observation 12.50799
Stdev1:
LOAD Type,
Stdev(Value) as MyStdev
Resident Table1 Group By
Type;
Stdev - chart function
Stdev() finds the standard deviation of the range of data aggregated in the expression or field iterated over
the chart dimensions.
Syntax:
Stdev([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
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Argument
Description
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Examples and results:
Type
Valu
e
Comparis
on
2
2
7
3
8
3
1
1
1
9
1
3
4
3
1
2
3
2
1
2
1
3
2
9
3
7
2
Observati
on
35
4
0
1
2
1
5
2
1
1
4
4
6
1
0
2
8
4
8
1
6
3
0
3
2
4
8
3
1
2
2
1
2
3
9
1
9
2
5
Example
Result
Stdev
(Value)
For a table including the dimension Type and the measure Stdev(Value), if Totals are shown,
Stdev
(TOTAL
Value))
is 15.475 for all values of Type, because the TOTAL qualifier means that dimensions are
disregarded.
the result is 15.475, for Comparison it is 14.612 and for Observation it is 12.508.
Data used in examples:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
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28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
See also:
p Avg - chart function (page 201)
p STEYX - chart function (page 244)
Sterr
Sterr() returns the aggregated standard error (stdev/sqrt(n)) for a series of values represented by the
expression iterated over a number of records as defined by a group by clause.
Syntax:
Sterr ([distinct] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
distinct
If the word distinct occurs before the expression, all duplicates will be disregarded.
Limitations:
Text values, NULL values and missing values are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Table1:
crosstable LOAD recno() as
ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In a table with the dimensions Type and MySterr, the results of the Sterr()
calculation in the data load script are:
Type MySterr
Comparison 3.2674431
Observation 2.7968733
Sterr1:
LOAD Type,
Sterr(Value) as MySterr
Resident Table1 Group By
Type;
Sterr - chart function
Sterr() finds the value of the standard error of the mean, (stdev/sqrt(n)), for the series of values aggregated in
the expression iterated over the chart dimensions.
Syntax:
Sterr([{SetExpression}] [DISTINCT] [TOTAL []] expr)
Return data type: numeric
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
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Argument
Description
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values are disregarded.
Examples and results:
Type
Valu
e
Comparis
on
2
2
7
3
8
3
1
1
1
9
1
3
4
3
1
2
3
2
1
2
1
3
2
9
3
7
2
Observati
on
35
4
0
1
2
1
5
2
1
1
4
4
6
1
0
2
8
4
8
1
6
3
0
3
2
4
8
3
1
2
2
1
2
3
9
1
9
2
5
Example
Result
Sterr
(Value)
For a table including the dimension Type and the measure Sterr(Value), if Totals are shown,
Sterr
(TOTAL
Value))
2.447 for all values of Type, because the TOTAL qualifier means that dimensions are
disregarded.
the result is 2.447, for Comparison it is 3.267 and for Observation it is 2.797.
Data used in examples:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
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46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
See also:
p Avg - chart function (page 201)
p STEYX - chart function (page 244)
STEYX
STEYX() returns the aggregated standard error of the predicted y-value for each x-value in the regression for
a series of coordinates represented by paired numbers in x-expression and y-expression iterated over a
number of records as defined by a group by clause.
Syntax:
STEYX (y-value, x-value)
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of y-values to be measured.
x_value
The expression or field containing the range of x-values to be measured.
Limitations:
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Trend:
Load *, 1 as Grp;
LOAD * inline [
Month|KnownY|KnownX
Jan|2|6
Feb|3|5
Mar|9|11
Apr|6|7
May|8|5
Jun|7|4
Jul|5|5
Aug|10|8
Sep|9|10
Oct|12|14
Nov|15|17
Dec|14|16
] (delimiter is '|';
In a table with the dimension MySTEYX, the result of the STEYX() calculation in
the data load script is 2.0714764.
STEYX1:
LOAD Grp,
STEYX(KnownY, KnownX)
as MySTEYX
Resident Trend Group
By Grp;
STEYX - chart function
STEYX() returns the aggregated standard error when predicting y-values for each x-value in a linear
regression given by a series of coordinates represented by paired numbers in the expressions given by y_
value and x_value.
Syntax:
STEYX([{SetExpression}] [DISTINCT] [TOTAL []] y_value, x_value)
Return data type: numeric
Arguments:
Argument
Description
y_value
The expression or field containing the range of known y-values to be measured.
x_value
The expression or field containing the range of known x-values to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
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Argument
Description
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL
qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in
combination with calculated dimensions.
Text values, NULL values and missing values in any or both pieces of a data-pair result in the entire data-pair
being disregarded.
Examples and results:
Data series
KnownX
17
16
14
11
10
8
7
6
5
5
5
4
KnownY
15
14
12
9
9
10
6
2
3
5
8
7
Example
Result
Steyx(KnownY,KnownX)
2.071 (If number formatting is set to 3 decimal places.)
Steyx(TOTAL
KnownY,KnownX))
2.071 across all dimensions, if no selections are made.
2.121 across all dimensions, if the selections, 4, 5, and 6 are made for
KnownX, for example.
Data used in examples:
Trend:
LOAD * inline [
Month|KnownY|KnownX
Jan|2|6
Feb|3|5
Mar|9|11
Apr|6|7
May|8|5
Jun|7|4
Jul|5|5
Aug|10|8
Sep|9|10
Oct|12|14
Nov|15|17
Dec|14|16
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] (delimiter is '|';
See also:
p Avg - chart function (page 201)
p Sterr - chart function (page 241)
An example of how to use linest functions
The linest functions are used to find values associated with linear regression analysis. This section describes
how to build visualizations using sample data to find the values of the linest functions available in Qlik Sense.
The linest functions can be used in the data load script and in chart expressions.
Please refer to the individual linest chart function and script function topics for descriptions of syntax and
arguments.
Loading the sample data
Do the following:
1. Create a new app.
2. In the data load editor, enter the following:
T1:
LOAD *, 1 as Grp;
LOAD * inline [
X |Y
1| 0
2|1
3|3
4| 8
5| 14
6| 20
7| 0
8| 50
9| 25
10| 60
11| 38
12| 19
13| 26
14| 143
15| 98
16| 27
17| 59
18| 78
19| 158
20| 279 ] (delimiter is '|');
R1:
LOAD
Grp,
linest_B(Y,X) as Linest_B,
linest_DF(Y,X) as Linest_DF,
linest_F(Y,X) as Linest_F,
linest_M(Y,X) as Linest_M,
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linest_R2(Y,X) as Linest_R2,
linest_SEB(Y,X,1,1) as Linest_SEB,
linest_SEM(Y,X) as Linest_SEM,
linest_SEY(Y,X) as Linest_SEY,
linest_SSREG(Y,X) as Linest_SSREG,
linest_SSRESID(Y,X) as Linest_SSRESID
resident T1 group by Grp;
3. Click l to load the data.
Displaying the results from the data load script calculations
1. Do the following:
In the data load editor, click ” to go to the app view, create a new sheet and open it.
2. Click @Edit to edit the sheet.
3. From Charts add a table, and from Fields add the following as columns:
l
Linest_B
l
Linest_DF
l
Linest_F
l
Linest_M
l
Linest_R2
l
Linest_SEB
l
Linest_SEM
l
Linest_SEY
l
Linest_SSREG
l
Linest_SSRESID
The table containing the results of the linest calcuations made in the data load script should look like this:
Linest_B
Linest_DF
Linest_F
Linest_M
Linest_R2
Linest_SEB
-35.047
18
20.788
8.605
0.536
22.607
Linest_SEM
Linest_SEY
Linest_SSREG
Linest_SSRESID
1.887
48.666
49235.014
42631.186
Creating the linest chart function visualizations
Do the following:
1. In the data load editor, click ” to go to the app view, create a new sheet and open it.
2. Click @Edit to edit the sheet.
3. From Charts add a line chart, and from Fields add X as a dimension and Sum(Y) as a measure.
A line chart is create that represents the graph of X plotted against Y, from which the linest functions
are calculated.
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4. From Charts add a table with the following as a dimension:
ValueList('Linest_b', 'Linest_df','Linest_f', 'Linest_m','Linest_r2','Linest_SEB','Linest_
SEM','Linest_SEY','Linest_SSREG','Linest_SSRESID')
This uses the synthetic dimensions function to create labels for the dimensions with the names of the
linest functions. You can change the label to Linest functions to save space.
5. Add the following expression to the table as a measure:
Pick(Match(ValueList('Linest_b', 'Linest_df','Linest_f', 'Linest_m','Linest_r2','Linest_
SEB','Linest_SEM','Linest_SEY','Linest_SSREG','Linest_SSRESID'),'Linest_b', 'Linest_
df','Linest_f', 'Linest_m','Linest_r2','Linest_SEB','Linest_SEM','Linest_SEY','Linest_
SSREG','Linest_SSRESID'),Linest_b(Y,X),Linest_df(Y,X),Linest_f(Y,X),Linest_m(Y,X),Linest_r2
(Y,X),Linest_SEB(Y,X,1,1),Linest_SEM(Y,X),Linest_SEY(Y,X),Linest_SSREG(Y,X),Linest_SSRESID
(Y,X) )
This displays the value of the result of each linest function against the corresponding name in the
synthetic dimension. The result of Linest_b(Y,X) is displayed next to linest_b, and so on.
Result
Linest functions
Linest function results
Linest_b
-35.047
Linest_df
18
Linest_f
20.788
Linest_m
8.605
Linest_r2
0.536
Linest_SEB
22.607
Linest_SEM
1.887
Linest_SEY
48.666
Linest_SSREG
49235.014
Linest_SSRESID
42631.186
Statistical test functions
This section describes functions for statistical tests, which are divided into three categories. The functions
can be used in both the data load script and chart expressions, but the syntax differs.
Chi-2 test functions
Generally used in the study of qualitative variables. One can compare observed frequencies in a one-way
frequency table with expected frequencies, or study the connection between two variables in a contingency
table.
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T-test functions
T-test functions are used for statistical examination of two population means. A two-sample t-test examines
whether two samples are different and is commonly used when two normal distributions have unknown
variances and when an experiment uses a small sample size.
Z-test functions
A statistical examination of two population means. A two sample z-test examines whether two samples are
different and is commonly used when two normal distributions have known variances and when an
experiment uses a large sample size.
Chi2-test functions
Generally used in the study of qualitative variables. One can compare observed frequencies in a one-way
frequency table with expected frequencies, or study the connection between two variables in a contingency
table.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Chi2Test_chi2
Chi2Test_chi2() returns the aggregated chi2-test value for one or two series of values.
Chi2Test_chi2(col, row, actual_value[, expected_value])
Chi2Test_df
Chi2Test_df() returns the aggregated chi2-test df value (degrees of freedom) for one or two series of values.
Chi2Test_df(col, row, actual_value[, expected_value])
Chi2Test_p
Chi2Test_p() returns the aggregated chi2-test p value (significance) for one or two series of values.
Chi2Test_p - chart function(col, row, actual_value[, expected_value])
See also:
p T-test functions (page 252)
p Z-test functions (page 286)
Chi2Test_chi2
Chi2Test_chi2() returns the aggregated chi2-test value for one or two series of values.
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If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
All Qlik Sense chi2 -test functions have the same arguments.
Syntax:
Chi2Test_chi2(col, row, actual_value[, expected_value])
Return data type: numeric
Arguments:
Argument
Description
col, row
The specified column and row in the matrix of values being tested.
actual_value
The observed value of the data at the specified col and row.
expected_value
The expected value for the distribution at the specified col and row.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
Chi2Test_chi2( Grp, Grade, Count )
Chi2Test_chi2( Gender, Description, Observed, Expected )
See also:
p Examples of how to use chi2-test functions in charts (page 300)
p Examples of how to use chi2-test functions in the data load script (page 302)
Chi2Test_df
Chi2Test_df() returns the aggregated chi2-test df value (degrees of freedom) for one or two series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
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All Qlik Sense chi2 -test functions have the same arguments.
Syntax:
Chi2Test_df(col, row, actual_value[, expected_value])
Return data type: numeric
Arguments:
Argument
Description
col, row
The specified column and row in the matrix of values being tested.
actual_value
The observed value of the data at the specified col and row.
expected_value
The expected value for the distribution at the specified col and row.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
Chi2Test_df( Grp, Grade, Count )
Chi2Test_df( Gender, Description, Observed, Expected )
See also:
p Examples of how to use chi2-test functions in charts (page 300)
p Examples of how to use chi2-test functions in the data load script (page 302)
Chi2Test_p - chart function
Chi2Test_p() returns the aggregated chi2-test p value (significance) for one or two series of values. The test
can be done either on the values in actual_value, testing for variations within the specified col and row
matrix, or by comparing values in actual_value with corresponding values in expected_value, if specified.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
All Qlik Sense chi2 -test functions have the same arguments.
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Syntax:
Chi2Test_p(col, row, actual_value[, expected_value])
Return data type: numeric
Arguments:
Argument
Description
col, row
The specified column and row in the matrix of values being tested.
actual_value
The observed value of the data at the specified col and row.
expected_value
The expected value for the distribution at the specified col and row.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
Chi2Test_p( Grp, Grade, Count )
Chi2Test_p( Gender, Description, Observed, Expected )
See also:
p Examples of how to use chi2-test functions in charts (page 300)
p Examples of how to use chi2-test functions in the data load script (page 302)
T-test functions
T-test functions are used for statistical examination of two population means. A two-sample t-test examines
whether two samples are different and is commonly used when two normal distributions have unknown
variances and when an experiment uses a small sample size.
In the following sections, the t-test statistical test functions are grouped according to the sample student test
that applies to each type of function.
See: Creating a typical t-test report (page 304)
Two independent samples t-tests
The following functions apply to two independent samples student's t-tests:
ttest_conf
TTest_conf returns the aggregated t-test confidence interval value for two independent samples.
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TTest_conf ( grp, value [, sig[, eq_var]])
ttest_df
TTest_df() returns the aggregated student's t-test value (degrees of freedom) for two independent series of
values.
TTest_df (grp, value [, eq_var)
ttest_dif
TTest_dif() is a numeric function that returns the aggregated student's t-test mean difference for two
independent series of values.
TTest_dif (grp, value)
ttest_lower
TTest_lower() returns the aggregated value for the lower end of the confidence interval for two independent
series of values.
TTest_lower (grp, value [, sig[, eq_var]])
ttest_sig
TTest_sig() returns the aggregated student's t-test 2-tailed level of significance for two independent series of
values.
TTest_sig (grp, value [, eq_var])
ttest_sterr
TTest_sterr() returns the aggregated student's t-test standard error of the mean difference for two
independent series of values.
TTest_sterr (grp, value [, eq_var])
ttest_t
TTest_t() returns the aggregated t value for two independent series of values.
TTest_t (grp, value [, eq_var])
ttest_upper
TTest_upper() returns the aggregated value for the upper end of the confidence interval for two independent
series of values.
TTest_upper (grp, value [, sig [, eq_var]])
Two independent weighted samples t-tests
The following functions to two independent samples student's t-tests where the input data series is given in
weighted two-column format:
ttestw_conf
TTestw_conf() returns the aggregated t value for two independent series of values.
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TTestw_conf (weight, grp, value [, sig[, eq_var]])
ttestw_df
TTestw_df() returns the aggregated student's t-test df value (degrees of freedom) for two independent series
of values.
TTestw_df (weight, grp, value [, eq_var])
ttestw_dif
TTestw_dif() returns the aggregated student's t-test mean difference for two independent series of values.
TTestw_dif ( weight, grp, value)
ttestw_lower
TTestw_lower() returns the aggregated value for the lower end of the confidence interval for two
independent series of values.
TTestw_lower (weight, grp, value [, sig[, eq_var]])
ttestw_sig
TTestw_sig() returns the aggregated student's t-test 2-tailed level of significance for two independent series
of values.
TTestw_sig ( weight, grp, value [, eq_var])
ttestw_sterr
TTestw_sterr() returns the aggregated student's t-test standard error of the mean difference for two
independent series of values.
TTestw_sterr (weight, grp, value [, eq_var])
ttestw_t
TTestw_t() returns the aggregated t value for two independent series of values.
TTestw_t (weight, grp, value [, eq_var])
ttestw_upper
TTestw_upper() returns the aggregated value for the upper end of the confidence interval for two
independent series of values.
TTestw_upper (weight, grp, value [, sig [, eq_var]])
One sample t-tests
The following functions apply to one-sample student's t-tests:
ttest1_conf
TTest1_conf() returns the aggregated confidence interval value for a series of values.
TTest1_conf (value [, sig])
ttest1_df
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TTest1_df() returns the aggregated student's t-test df value (degrees of freedom) for a series of values.
TTest1_df (value)
ttest1_dif
TTest1_dif() returns the aggregated student's t-test mean difference for a series of values.
TTest1_dif (value)
ttest1_lower
TTest1_lower() returns the aggregated value for the lower end of the confidence interval for a series of
values.
TTest1_lower (value [, sig])
ttest1_sig
TTest1_sig() returns the aggregated student's t-test 2-tailed level of significance for a series of values.
TTest1_sig (value)
ttest1_sterr
TTest1_sterr() returns the aggregated student's t-test standard error of the mean difference for a series of
values.
TTest1_sterr (value)
ttest1_t
TTest1_t() returns the aggregated t value for a series of values.
TTest1_t (value)
ttest1_upper
TTest1_upper() returns the aggregated value for the upper end of the confidence interval for a series of
values.
TTest1_upper (value [, sig])
One weighted sample t-tests
The following functions apply to one-sample student's t-tests where the input data series is given in weighted
two-column format:
ttest1w_conf
TTest1w_conf() is a numeric function that returns the aggregated confidence interval value for a series of
values.
TTest1w_conf (weight, value [, sig])
ttest1w_df
TTest1w_df() returns the aggregated student's t-test df value (degrees of freedom) for a series of values.
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TTest1w_df (weight, value)
ttest1w_dif
TTest1w_dif() returns the aggregated student's t-test mean difference for a series of values.
TTest1w_dif (weight, value)
ttest1w_lower
TTest1w_lower() returns the aggregated value for the lower end of the confidence interval for a series of
values.
TTest1w_lower (weight, value [, sig])
ttest1w_sig
TTest1w_sig() returns the aggregated student's t-test 2-tailed level of significance for a series of values.
TTest1w_sig (weight, value)
ttest1w_sterr
TTest1w_sterr() returns the aggregated student's t-test standard error of the mean difference for a series of
values.
TTest1w_sterr (weight, value)
ttest1w_t
TTest1w_t() returns the aggregated t value for a series of values.
TTest1w_t ( weight, value)
ttest1w_upper
TTest1w_upper() returns the aggregated value for the upper end of the confidence interval for a series of
values.
TTest1w_upper (weight, value [, sig])
TTest_conf
TTest_conf returns the aggregated t-test confidence interval value for two independent samples.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_conf ( grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_conf( Group, Value )
TTest_conf( Group, Value, Sig, false )
See also:
p Creating a typical t-test report (page 304)
TTest_df
TTest_df() returns the aggregated student's t-test value (degrees of freedom) for two independent series of
values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_df (grp, value [, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_df( Group, Value )
TTest_df( Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTest_dif
TTest_dif() is a numeric function that returns the aggregated student's t-test mean difference for two
independent series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_dif (grp, value [, eq_var] )
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_dif( Group, Value )
TTest_dif( Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTest_lower
TTest_lower() returns the aggregated value for the lower end of the confidence interval for two independent
series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_lower (grp, value [, sig [, eq_var]])
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_lower( Group, Value )
TTest_lower( Group, Value, Sig, false )
See also:
p Creating a typical t-test report (page 304)
TTest_sig
TTest_sig() returns the aggregated student's t-test 2-tailed level of significance for two independent series of
values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_sig (grp, value [, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_sig( Group, Value )
TTest_sig( Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTest_sterr
TTest_sterr() returns the aggregated student's t-test standard error of the mean difference for two
independent series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_sterr (grp, value [, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_sterr( Group, Value )
TTest_sterr( Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTest_t
TTest_t() returns the aggregated t value for two independent series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
To be able to use this function you must load sample values in script using crosstable.
Syntax:
TTest_t(grp, value[, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
p Creating a typical t-test report (page 304)
TTest_upper
TTest_upper() returns the aggregated value for the upper end of the confidence interval for two independent
series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest_upper (grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
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Argument
Description
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest_upper( Group, Value )
TTest_upper( Group, Value, sig, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_conf
TTestw_conf() returns the aggregated t value for two independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_conf (weight, grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
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Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_conf( Weight, Group, Value )
TTestw_conf( Weight, Group, Value, sig, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_df
TTestw_df() returns the aggregated student's t-test df value (degrees of freedom) for two independent series
of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_df (weight, grp, value [, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_df( Weight, Group, Value )
TTestw_df( Weight, Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_dif
TTestw_dif() returns the aggregated student's t-test mean difference for two independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_dif (weight, group, value)
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_dif( Weight, Group, Value )
TTestw_dif( Weight, Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_lower
TTestw_lower() returns the aggregated value for the lower end of the confidence interval for two
independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_lower (weight, grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
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Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_lower( Weight, Group, Value )
TTestw_lower( Weight, Group, Value, sig, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_sig
TTestw_sig() returns the aggregated student's t-test 2-tailed level of significance for two independent series
of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_sig ( weight, grp, value [, eq_var])
Return data type: numeric
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Arguments:
Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_sig( Weight, Group, Value )
TTestw_sig( Weight, Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_sterr
TTestw_sterr() returns the aggregated student's t-test standard error of the mean difference for two
independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_sterr (weight, grp, value [, eq_var])
Return data type: numeric
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Arguments:
Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_sterr( Weight, Group, Value )
TTestw_sterr( Weight, Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_t
TTestw_t() returns the aggregated t value for two independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ttestw_t (weight, grp, value [, eq_var])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_t( Weight, Group, Value )
TTestw_t( Weight, Group, Value, false )
See also:
p Creating a typical t-test report (page 304)
TTestw_upper
TTestw_upper() returns the aggregated value for the upper end of the confidence interval for two
independent series of values.
This function applies to two independent samples student's t-tests where the input data series is given in
weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTestw_upper (weight, grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
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Argument
Description
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTestw_upper( Weight, Group, Value )
TTestw_upper( Weight, Group, Value, sig, false )
See also:
p Creating a typical t-test report (page 304)
TTest1_conf
TTest1_conf() returns the aggregated confidence interval value for a series of values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_conf (value [, sig ])
Return data type: numeric
Arguments:
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Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1_conf( Value )
TTest1_conf( Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
TTest1_df
TTest1_df() returns the aggregated student's t-test df value (degrees of freedom) for a series of values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_df (value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
Limitations:
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Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1_df( Value )
See also:
p Creating a typical t-test report (page 304)
TTest1_dif
TTest1_dif() returns the aggregated student's t-test mean difference for a series of values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_dif (value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1_dif( Value )
See also:
p Creating a typical t-test report (page 304)
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TTest1_lower
TTest1_lower() returns the aggregated value for the lower end of the confidence interval for a series of
values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_lower (value [, sig])
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1_lower( Value )
TTest1_lower( Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
TTest1_sig
TTest1_sig() returns the aggregated student's t-test 2-tailed level of significance for a series of values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
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Syntax:
TTest1_sig (value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1_sig( Value )
See also:
p Creating a typical t-test report (page 304)
TTest1_sterr
TTest1_sterr() returns the aggregated student's t-test standard error of the mean difference for a series of
values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_sterr (value)
Return data type: numeric
Arguments:
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Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1_sterr( Value )
See also:
p Creating a typical t-test report (page 304)
TTest1_t
TTest1_t() returns the aggregated t value for a series of values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_t (value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
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TTest1_t( Value )
TTest1_upper
TTest1_upper() returns the aggregated value for the upper end of the confidence interval for a series of
values.
This function applies to one-sample student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1_upper (value [, sig])
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1_upper( Value )
TTest1_upper( Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
TTest1w_conf
TTest1w_conf() is a numeric function that returns the aggregated confidence interval value for a series of
values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
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If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_conf (weight, value [, sig ])
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1w_conf( Weight, Value )
TTest1w_conf( Weight, Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
TTest1w_df
TTest1w_df() returns the aggregated student's t-test df value (degrees of freedom) for a series of values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
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TTest1w_df (weight, value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1w_df( Weight, Value )
See also:
p Creating a typical t-test report (page 304)
TTest1w_dif
TTest1w_dif() returns the aggregated student's t-test mean difference for a series of values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_dif (weight, value)
Return data type: numeric
Arguments:
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Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1w_dif( Weight, Value )
See also:
p Creating a typical t-test report (page 304)
TTest1w_lower
TTest1w_lower() returns the aggregated value for the lower end of the confidence interval for a series of
values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_lower (weight, value [, sig ])
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
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Argument
Description
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1w_lower( Weight, Value )
TTest1w_lower( Weight, Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
TTest1w_sig
TTest1w_sig() returns the aggregated student's t-test 2-tailed level of significance for a series of values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_sig (weight, value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
Limitations:
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Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1w_sig( Weight, Value )
See also:
p Creating a typical t-test report (page 304)
TTest1w_sterr
TTest1w_sterr() returns the aggregated student's t-test standard error of the mean difference for a series of
values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_sterr (weight, value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1w_sterr( Weight, Value )
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See also:
p Creating a typical t-test report (page 304)
TTest1w_t
TTest1w_t() returns the aggregated t value for a series of values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_t ( weight, value)
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
TTest1w_t( Weight, Value )
See also:
p Creating a typical t-test report (page 304)
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TTest1w_upper
TTest1w_upper() returns the aggregated value for the upper end of the confidence interval for a series of
values.
This function applies to one-sample student's t-tests where the input data series is given in weighted twocolumn format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
TTest1w_upper (weight, value [, sig])
Return data type: numeric
Arguments:
Argument
Description
value
The samples to be evaluated. If a field name for the sample values is not provided in the
load script, the field will automatically be named Value.
weight
Each value in value can be counted one or more times according to a corresponding weight
value in weight.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
TTest1w_upper( Weight, Value )
TTest1w_upper( Weight, Value, 0.005 )
See also:
p Creating a typical t-test report (page 304)
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Z-test functions
A statistical examination of two population means. A two sample z-test examines whether two samples are
different and is commonly used when two normal distributions have known variances and when an
experiment uses a large sample size.
The z-test statistical test functions are grouped according the type of input data series that applies to the
function.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
See: Examples of how to use z-test functions (page 307)
One column format functions
The following functions apply to z-tests with simple input data series:
ztest_conf
ZTest_conf() returns the aggregated z value for a series of values.
ZTest_conf (value [, sigma [, sig ])
ztest_dif
ZTest_dif() returns the aggregated z-test mean difference for a series of values.
ZTest_dif (value [, sigma])
ztest_sig
ZTest_sig() returns the aggregated z-test 2-tailed level of significance for a series of values.
ZTest_sig (value [, sigma])
ztest_sterr
ZTest_sterr() returns the aggregated z-test standard error of the mean difference for a series of values.
ZTest_sterr (value [, sigma])
ztest_z
ZTest_z() returns the aggregated z value for a series of values.
ZTest_z (value [, sigma])
ztest_lower
ZTest_lower() returns the aggregated value for the lower end of the confidence interval for two independent
series of values.
ZTest_lower (grp, value [, sig [, eq_var]])
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ztest_upper
ZTest_upper() returns the aggregated value for the upper end of the confidence interval for two independent
series of values.
ZTest_upper (grp, value [, sig [, eq_var]])
Weighted two-column format functions
The following functions apply to z-tests where the input data series is given in weighted two-column format.
ztestw_conf
ZTestw_conf() returns the aggregated z confidence interval value for a series of values.
ZTestw_conf (weight, value [, sigma [, sig]])
ztestw_dif
ZTestw_dif() returns the aggregated z-test mean difference for a series of values.
ZTestw_dif (weight, value [, sigma])
ztestw_lower
ZTestw_lower() returns the aggregated value for the lower end of the confidence interval for two
independent series of values.
ZTestw_lower (weight, value [, sigma])
ztestw_sig
ZTestw_sig() returns the aggregated z-test 2-tailed level of significance for a series of values.
ZTestw_sig (weight, value [, sigma])
ztestw_sterr
ZTestw_sterr() returns the aggregated z-test standard error of the mean difference for a series of values.
ZTestw_sterr (weight, value [, sigma])
ztestw_upper
ZTestw_upper() returns the aggregated value for the upper end of the confidence interval for two
independent series of values.
ZTestw_upper (weight, value [, sigma])
ztestw_z
ZTestw_z() returns the aggregated z value for a series of values.
ZTestw_z (weight, value [, sigma])
ZTest_z
ZTest_z() returns the aggregated z value for a series of values.
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If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_z(value[, sigma])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTest_z( Value-TestValue )
See also:
p Examples of how to use z-test functions (page 307)
ZTest_sig
ZTest_sig() returns the aggregated z-test 2-tailed level of significance for a series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_sig(value[, sigma])
Return data type: numeric
Arguments:
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Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTest_sig(Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTest_dif
ZTest_dif() returns the aggregated z-test mean difference for a series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_dif(value[, sigma])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
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Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTest_dif(Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTest_sterr
ZTest_sterr() returns the aggregated z-test standard error of the mean difference for a series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_sterr(value[, sigma])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTest_sterr(Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
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ZTest_conf
ZTest_conf() returns the aggregated z value for a series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_conf(value[, sigma[, sig]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTest_conf(Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTest_lower
ZTest_lower() returns the aggregated value for the lower end of the confidence interval for two independent
series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
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Syntax:
ZTest_lower (grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
ZTest_lower( Group, Value )
ZTest_lower( Group, Value, sig, false )
See also:
p Examples of how to use z-test functions (page 307)
ZTest_upper
ZTest_upper() returns the aggregated value for the upper end of the confidence interval for two independent
series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
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ZTest_upper (grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
ZTest_upper( Group, Value )
ZTest_upper( Group, Value, sig, false )
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_z
ZTestw_z() returns the aggregated z value for a series of values.
This function applies to z-tests where the input data series is given in weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_z (weight, value [, sigma])
Return data type: numeric
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Arguments:
Argument
Description
value
The values should be returned by value. A sample mean of 0 is assumed. If you want the
test to be performed around another mean, subtract that value from the sample values.
weight
Each sample value in value can be counted one or more times according to a corresponding
weight value in weight.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTestw_z( Weight, Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_sig
ZTestw_sig() returns the aggregated z-test 2-tailed level of significance for a series of values.
This function applies to z-tests where the input data series is given in weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_sig (weight, value [, sigma])
Return data type: numeric
Arguments:
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Argument
Description
value
The values should be returned by value. A sample mean of 0 is assumed. If you want the
test to be performed around another mean, subtract that value from the sample values.
weight
Each sample value in value can be counted one or more times according to a corresponding
weight value in weight.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTestw_sig( Weight, Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_dif
ZTestw_dif() returns the aggregated z-test mean difference for a series of values.
This function applies to z-tests where the input data series is given in weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_dif ( weight, value [, sigma])
Return data type: numeric
Arguments:
Argument
Description
value
The values should be returned by value. A sample mean of 0 is assumed. If you want the
test to be performed around another mean, subtract that value from the sample values.
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Argument
Description
weight
Each sample value in value can be counted one or more times according to a corresponding
weight value in weight.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTestw_dif( Weight, Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_sterr
ZTestw_sterr() returns the aggregated z-test standard error of the mean difference for a series of values.
This function applies to z-tests where the input data series is given in weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_sterr (weight, value [, sigma])
Return data type: numeric
Arguments:
Argument
Description
value
The values should be returned by value. A sample mean of 0 is assumed. If you want the
test to be performed around another mean, subtract that value from the sample values.
weight
Each sample value in value can be counted one or more times according to a corresponding
weight value in weight.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
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Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTestw_sterr( Weight, Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_conf
ZTestw_conf() returns the aggregated z confidence interval value for a series of values.
This function applies to z-tests where the input data series is given in weighted two-column format.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTest_conf(weight, value[, sigma[, sig]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. A population mean of 0 is assumed. If you want the test
to be performed around another mean, subtract that mean from the sample values.
weight
Each sample value in value can be counted one or more times according to a corresponding
weight value in weight.
sigma
If known, the standard deviation can be stated in sigma. If sigma is omitted the actual
sample standard deviation will be used.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
Limitations:
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Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Example:
ZTestw_conf( Weight, Value-TestValue)
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_lower
ZTestw_lower() returns the aggregated value for the lower end of the confidence interval for two
independent series of values.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_lower (grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
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ZTestw_lower( Group, Value )
ZTestw_lower( Group, Value, sig, false )
See also:
p Examples of how to use z-test functions (page 307)
ZTestw_upper
ZTestw_upper() returns the aggregated value for the upper end of the confidence interval for two
independent series of values.
This function applies to independent samples student's t-tests.
If the function is used in the data load script, the values are iterated over a number of records as defined by a
group by clause.
If the function is used in a chart expression, the values are iterated over the chart dimensions.
Syntax:
ZTestw_upper (grp, value [, sig [, eq_var]])
Return data type: numeric
Arguments:
Argument
Description
value
The sample values to be evaluated. The sample values must be logically grouped as
specified by exactly two values in group. If a field name for the sample values is not
provided in the load script, the field will automatically be named Value.
grp
The field containing the names of each of the two sample groups. If a field name for the
group is not provided in the load script, the field will automatically be given the name Type.
sig
The two-tailed level of significance can be specified in sig. If omitted, sig is set to 0.025,
resulting in a 95% confidence interval.
eq_var
If eq_var is specified as False (0), separate variances of the two samples will be assumed. If
eq_var is specified as True (1), equal variances between the samples will be assumed.
Limitations:
Text values, NULL values and missing values in the expression value will result in the function returning
NULL.
Examples:
ZTestw_upper( Group, Value )
ZTestw_upper( Group, Value, sig, false )
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See also:
p Examples of how to use z-test functions (page 307)
Statistical test function examples
This section includes examples of statistical test functions as applied to charts and the data load script.
Examples of how to use chi2-test functions in charts
The chi2-test functions are used to find values associated with chi squared statistical analysis. This section
describes how to build visualizations using sample data to find the values of the chi-squared distribution test
functions available in Qlik Sense. Please refer to the individual chi2-test chart function topics for descriptions
of syntax and arguments.
Loading the data for the samples
There are three sets of sample data describing three different statistical samples to be loaded into the script.
Do the following:
1. Create a new app.
2. In the data load, enter the following:
// Sample_1 data is pre-aggregated... Note: make sure you set your DecimalSep='.' at the top
of the script.
Sample_1:
LOAD * inline [
Grp,Grade,Count
I,A,15
I,B,7
I,C,9
I,D,20
I,E,26
I,F,19
II,A,10
II,B,11
II,C,7
II,D,15
II,E,21
II,F,16
];
// Sample_2 data is pre-aggregated: If raw data is used, it must be aggregated using count
()...
Sample_2:
LOAD * inline [
Sex,Opinion,OpCount
1,2,58
1,1,11
1,0,10
2,2,35
2,1,25
2,0,23 ] (delimiter is ',');
// Sample_3a data is transformed using the crosstable statement...
Sample_3a:
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crosstable(Gender, Actual) LOAD
Description,
[Men (Actual)] as Men,
[Women (Actual)] as Women;
LOAD * inline [
Men (Actual),Women (Actual),Description
58,35,Agree
11,25,Neutral
10,23,Disagree ] (delimiter is ',');
// Sample_3b data is transformed using the crosstable statement...
Sample_3b:
crosstable(Gender, Expected) LOAD
Description,
[Men (Expected)] as Men,
[Women (Expected)] as Women;
LOAD * inline [
Men (Expected),Women (Expected),Description
45.35,47.65,Agree
17.56,18.44,Neutral
16.09,16.91,Disagree ] (delimiter is ',');
// Sample_3a and Sample_3b will result in a (fairly harmless) Synthetic Key...
3. Click l to load data.
Creating the chi2-test chart function visualizations
Example: Sample 1
Do the following:
1. In the data load editor, click ” to go to the app view and then click the sheet you created before.
The sheet view is opened.
2. Click @Edit to edit the sheet.
3. From Charts add a table, and from Fields add Grp, Grade, and Count as dimensions.
This table shows the sample data.
4. Add another table with the following expression as a dimension:
ValueList('p','df','Chi2')
This uses the synthetic dimensions function to create labels for the dimensions with the names of the
three chi2-test functions.
5. Add the following expression to the table as a measure:
IF(ValueList('p','df','Chi2')='p',Chi2Test_p(Grp,Grade,Count),
IF(ValueList('p','df','Chi2')='df',Chi2Test_df(Grp,Grade,Count),
Chi2Test_Chi2(Grp,Grade,Count)))
This has the effect of putting the resulting value of each chi2-test function in the table next to its
associated synthetic dimension.
6. Set the Number formatting of the measure to Number and 3Significant figures.
In the expression for the measure, you could use the following expression instead: Pick(Match
(ValueList('p','df','Chi2'),'p','df','Chi2'),Chi2Test_p(Grp,Grade,Count),Chi2Test_df
(Grp,Grade,Count),Chi2Test_Chi2(Grp,Grade,Count))
Result
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The resulting table for the chi2-test functions for the Sample 1 data will contain the following values:
p
df
Chi2
0.820
5
2.21
Example: Sample 2
Do the following:
1. In the sheet you were editing in the example Sample 1, from Charts add a table, and from Fields
add Sex, Opinion, and OpCount as dimensions.
2. Make a copy of the results table from Sample 1 using the Copy and Paste commands. Edit the
expression in the measure and replace the arguments in all three chi2-test functions with the names
of the fields used in the Sample 2 data, for example: Chi2Test_p(Sex,Opinion,OpCount).
Result
The resulting table for the chi2-test functions for the Sample 2 data will contain the following values:
p
df
Chi2
0.000309
2
16.2
Example: Sample 3
Do the following:
1. Create two more tables in the same way as in the examples for Sample 1 and Sample 2 data. In the
dimensions table, use the following fields as dimensions: Gender, Description, Actual, and Expected.
2. In the results table, use the names of the fields used in the Sample 3 data, for example: Chi2Test_p
(Gender,Description,Actual,Expected).
Result
The resulting table for the chi2-test functions for the Sample 3 data will contain the following values:
p
df
Chi2
0.000308
2
16.2
Examples of how to use chi2-test functions in the data load script
The chi2-test functions are used to find values associated with chi squared statistical analysis. This section
describes how to use the chi-squared distribution test functions available in Qlik Sense in the data load script.
Please refer to the individual chi2-test script function topics for descriptions of syntax and arguments.
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This example uses a table containing the number of students achieving a grade (A-F) for two groups of
students (I and II).
A
B
C
D
E
F
I
15
7
9
20
26
19
II
10
11
7
15
21
16
Loading the sample data
Do the following:
1. Create a new app.
2. In the data load editor, enter the following:
// Sample_1 data is pre-aggregated... Note: make sure you set your DecimalSep='.' at the top
of the script.
Sample_1:
LOAD * inline [
Grp,Grade,Count
I,A,15
I,B,7
I,C,9
I,D,20
I,E,26
I,F,19
II,A,10
II,B,11
II,C,7
II,D,15
II,E,21
II,F,16
];
3. Click l to load data.
You have now loaded the sample data.
Loading the chi2-test function values
Now we will load the chi2-test values based on the sample data in a new table, grouped by Grp.
Do the following:
1. In the data load editor, add the following at the end of the script:
// Sample_1 data is pre-aggregated... Note: make sure you set your DecimalSep='.' at the top
of the script.
Chi2_table:
LOAD Grp,
Chi2Test_chi2(Grp, Grade, Count) as chi2,
Chi2Test_df(Grp, Grade, Count) as df,
Chi2Test_p(Grp, Grade, Count) as p
resident Sample_1 group by Grp;
2. Click l to load data.
You have now loaded the chi2-test values in a table named Chi2_table.
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Results
You can view the resulting chi2-test values in the data model viewer under Preview, they should look like
this:
Grp
chi2
df
p
I
16.00
5
0.007
II
9.40
5
0.094
Creating a typical t-test report
A typical student t-test report can include tables with Group Statistics and Independent Samples Test
results. In the following sections we will build these tables using Qlik Senset-test functions applied to two
independent groups of samples, Observation and Comparison. The corresponding tables for these samples
would look like this:
Group Statistics
Type
N
Mean
Standard Deviation
Standard Error Mean
Comparison
20
11.95
14.61245
3.2674431
Observation
20
27.15
12.507997
2.7968933
Independent Sample Test
Sig.
t
df
(2tailed)
Mean
Difference
Standard
Error
Difference
95%
95%
Confidence
Confidence
Interval of
Interval of
the
the
Difference
Difference
(Lower)
(Upper)
Equal
Variance
not
Assumed
3.534
37.116717335823
0.001
15.2
4.30101
6.48625
23.9137
Equal
Variance
Assumed
3.534
38
0.001
15.2
4.30101
6.49306
23.9069
Loading the sample data
Do the following:
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1. Create a new app with a new sheet and open that sheet.
2. Enter the following in the data load editor:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In this load script, recno() is included because crosstable requires three arguments. So, recno()
simply provides an extra argument, in this case an ID for each row. Without it, Comparison sample
values would not be loaded.
3. Click l to load data.
Creating the Group Statistics table
Do the following:
1. In the data load editor, click ” to go to app view, and then click the sheet you created before.
This opens the sheet view.
2. Click @Edit to edit the sheet.
3. From Charts, add a table, and from Fields, add the following expressions as measures:
Label
Expression
N
Count(Value)
Mean
Avg(Value)
Standard Deviation
Stdev(Value)
Standard Error Mean
Sterr(Value)
4. Add Type as a dimension to the table.
5. Click Sorting and move Type to the top of the sorting list.
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Result
A Group Statistics table for these samples would look like this:
Type
N
Mean
Standard Deviation
Standard Error Mean
Comparison
20
11.95
14.61245
3.2674431
Observation
20
27.15
12.507997
2.7968933
Creating the Two Independent Sample Student's T-test table
Do the following:
1. Click @Edit to edit the sheet.
2. Add the following expression as a dimension to the table. =ValueList (Dual('Equal Variance not
Assumed', 0), Dual('Equal Variance Assumed', 1))
3. From Charts add a table with the following expressions as measures:
Label
Expression
conf
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_conf(Type, Value),TTest_conf(Type, Value, 0))
t
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_t(Type, Value),TTest_t(Type, Value, 0))
df
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_df(Type, Value),TTest_df(Type, Value, 0))
Sig. (2-tailed)
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_sig(Type, Value),TTest_sig(Type, Value, 0))
Mean Difference
TTest_dif(Type, Value)
Standard Error
Difference
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_sterr(Type, Value),TTest_sterr(Type, Value, 0))
95% Confidence
Interval of the
Difference (Lower)
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_lower(Type, Value,(1-(95)/100)/2),TTest_lower(Type,
Value,(1-(95)/100)/2, 0))
95% Confidence
Interval of the
Difference (Upper)
if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance
Assumed', 1)),TTest_upper(Type, Value,(1-(95)/100)/2),TTest_upper
(Type, Value,(1-(95)/100)/2, 0))
Result
An Independent Sample Test table for these samples would look like this:
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Sig.
t
df
(2taile
d)
Mean
Differenc
e
95%
95%
Standard
Confidenc
Confidenc
Error
Differenc
e Interval
e Interval
of the
of the
e
Difference
Difference
(Lower)
(Upper)
Equal
Varianc
e not
Assume
d
3.53
4
37.1167173358
23
0.001
15.2
4.30101
6.48625
23.9137
Equal
Varianc
e
Assume
d
3.53
4
38
0.001
15.2
4.30101
6.49306
23.9069
Examples of how to use z-test functions
The z-test functions are used to find values associated with z-test statistical analysis for large data samples,
usually greater than 30, and where the variance is known. This section describes how to build visualizations
using sample data to find the values of the z-test functions available in Qlik Sense. Please refer to the
individual z-test chart function topics for descriptions of syntax and arguments.
Loading the sample data
The sample data used here is the same as that used in the t-test function examples. The sample data size
would normally be considered too small for z-test analysis, but is sufficient for the purposes of illustrating the
use of the different z-test functions in Qlik Sense.
Do the following:
1. Create a new app with a new sheet and open that sheet.
If you created an app for the t-test functions, you could use that and create a new sheet
for these functions.
2. In the data load editor, enter the following:
Table1:
crosstable LOAD recno() as ID, * inline [
Observation|Comparison
35|2
40|27
12|38
15|31
21|1
14|19
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46|1
10|34
28|3
48|1
16|2
30|3
32|2
48|1
31|2
22|1
12|3
39|29
19|37
25|2 ] (delimiter is '|');
In this load script, recno() is included because crosstable requires three arguments. So, recno()
simply provides an extra argument, in this case an ID for each row. Without it, Comparison sample
values would not be loaded.
3. Click l to load data.
Creating z-test chart function visualizations
Do the following:
1. In the data load editor, click ” to go to app view, and then click the sheet you created when loading
the data.
The sheet view is opened.
2. Click @Edit to edit the sheet.
3. From Charts add a table, and from Fields add Type as a dimension.
4. Add the following expressions to the table as measures.
Label
Expression
ZTest Conf
ZTest_conf(Value)
ZTest Dif
ZTest_dif(Value)
ZTest Sig
ZTest_sig(Value)
ZTest Sterr
ZTest_sterr(Value)
ZTest Z
ZTest_z(Value)
You might wish to adjust the number formatting of the measures in order to see meaningful
values. The table will be easier to read if you set number formatting on most of the measures to
Number>Simple, instead of Auto. But for ZTest Sig, for example, use the number formatting:
Custom, and then adjust the format pattern to # ##.
Result
The resulting table for the z-test functions for the sample data will contain the following values:
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Type
ZTest Conf
ZTest Dif
ZTest Sig
ZTest Sterr
ZTest Z
Comparison
6.40
11.95
0.000123
3.27
3.66
Value
5.48
27.15
0.001
2.80
9.71
Creating z-testw chart function visualizations
The z-testw functions are for use when the input data series occurs in weighted two-column format. The
expressions require a value for the argument weight. The examples here use the value 2 throughout, but you
could use an expression, which would define a value for weight for each observation.
Examples and results:
Using the same sample data and number formatting as for the z-test functions, the resulting table for the ztestw functions will contain the following values:
Type
ZTestw Conf
ZTestw Dif
ZTestw Sig
ZTestw Sterr
ZTestw Z
Comparison
3.53
2.95
5.27e-005
1.80
3.88
Value
2.97
34.25
0
4.52
20.49
String aggregation functions
This section describes string-related aggregation functions.
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
String aggregation functions in the data load script
Concat
Concat() is used to combine string values. The script function returns the aggregated string concatenation of
all values of the expression iterated over a number of records as defined by a group by clause.
Concat ([ distinct ] expression [, delimiter [, sort-weight]])
FirstValue
FirstValue() returns the value that was loaded first from the records defined by the expression, sorted by a
group by clause.
This function is only available as a script function.
FirstValue
(expression)
LastValue
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LastValue() returns the value that was loaded last from the records defined by the expression, sorted by a
group by clause.
This function is only available as a script function.
LastValue
(expression)
MaxString
MaxString() finds string values in the expression and returns the last text value sorted over a number of
records, as defined by a group by clause.
MaxString (expression )
MinString
MaxString() finds string values in the expression and returns the first text value sorted over a number of
records, as defined by a group by clause.
MinString (expression )
String aggregation functions in charts
The following chart functions are available for aggregating strings in charts.
Concat
Concat() is used to combine string values. The function returns the aggregated string concatenation of all
the values of the expression evaluated over each dimension.
Concat - chart function({[SetExpression] [DISTINCT] [TOTAL []]
string[, delimiter[, sort_weight]])
MaxString
MaxString() finds string values in the expression or field and returns the last text value in the text sort order.
MaxString - chart function({[SetExpression] [TOTAL []]} expr)
MinString
MinString() finds string values in the expression or field and returns the first text value in the text sort order.
MinString - chart function({[SetExpression] [TOTAL []]} expr)
Concat
Concat() is used to combine string values. The script function returns the aggregated string concatenation of
all values of the expression iterated over a number of records as defined by a group by clause.
Syntax:
Concat ([ distinct ] string [, delimiter [, sort-weight]])
Return data type:string
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Arguments:
The expression or field containing the string to be processed.
Argument
Description
string
The expression or field containing the string to be processed.
delimiter
Each value may be separated by the string found in delimiter.
sort-weight
The order of concatenation may be determined by the value of the dimension sort-weight,
if present, with the string corresponding to the lowest value appearing first in the
concatenation.
distinct
If the word distinct occurs before the expression, all duplicates are disregarded.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
SalesGroup
TeamConcat1
East
AlphaBetaDeltaGammaGamma
West
EpsilonEtaThetaZeta
SalesGroup
TeamConcat2
East
Alpha-Beta-Delta-Gamma
West
Epsilon-Eta-Theta-Zeta
Concat1:
LOAD SalesGroup,Concat(Team) as TeamConcat1
Resident TeamData Group By SalesGroup;
Given that the TeamData table is loaded as in the
previous example:
LOAD SalesGroup,Concat(distinct Team,'-') as
TeamConcat2 Resident TeamData Group By SalesGroup;
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Example
Result
Given that the TeamData table is loaded as in the
Because the argument for sort-weight is
previous example:
added, the results are ordered by the value of
the dimension Amount.
LOAD SalesGroup,Concat(distinct Team,'-',Amount) as
TeamConcat2 Resident TeamData Group By SalesGroup;
SalesGroup
TeamConcat2
East
Delta-Beta-Gamma-Alpha
West
Eta-Epsilon-Zeta-Theta
Concat - chart function
Concat() is used to combine string values. The function returns the aggregated string concatenation of all
the values of the expression evaluated over each dimension.
Syntax:
Concat({[SetExpression] [DISTINCT] [TOTAL []] string[,
delimiter[, sort_weight]])
Return data type: string
Arguments:
Argument
Description
string
The expression or field containing the string to be processed.
delimiter
Each value may be separated by the string found in delimiter.
sort-weight
The order of concatenation may be determined by the value of the dimension sort-
weight, if present, with the string corresponding to the lowest value appearing first in the
concatenation.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
DISTINCT
If the word DISTINCT occurs before the function arguments, duplicates resulting from
the evaluation of the function arguments are disregarded.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
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SalesGroup
Amount
Concat(Team)
Concat(TOTAL Team)
East
25000
Alpha
AlphaBetaDeltaGammaGamma
East
20000
BetaGammaGamma
AlphaBetaDeltaGammaGamma
East
14000
Delta
AlphaBetaDeltaGammaGamma
West
17000
Epsilon
EpsilonEtaThetaZeta
West
14000
Eta
EpsilonEtaThetaZeta
West
23000
Theta
EpsilonEtaThetaZeta
West
19000
Zeta
EpsilonEtaThetaZeta
Example
Result
Concat(Team)
The table is constructed from the dimensions SalesGroup and Amount, and variations on
the measure Concat(Team). Ignoring the Totals result, note that even though there is data
for eight values of Team spread across two values of SalesGroup, the only result of the
measure Concat(Team) that concatenates more than one Team string value in the table is
the row containing the dimension Amount 20000, which gives the result
BetaGammaGamma. This is because there are three values for the Amount 20000 in the
input data. All other results remain unconcatenated when the measure is spanned across
the dimensions because there is only one value of Team for each combination of
SalesGroup and Amount.
Concat
(DISTINCT
Team,', ')
Beta, Gamma. because the DISTINCT qualifier means the duplicate Gamma result is
disregarded. Also, the delimiter argument is defined as a comma followed by a space.
Concat
(TOTAL
Team)
All the string values for all values of Team are concatenated if the TOTAL qualifier is used.
With the field selection specified, this divides the results into the two
values of the dimension SalesGroup. For the SalesGroupEast, the results are
AlphaBetaDeltaGammaGamma. For the SalesGroupWest, the results are
EpsilonEtaThetaZeta.
Concat
(TOTAL
Team,';',
Amount)
By adding the argument for sort-weight: Amount, the results are ordered by the value of
the dimension Amount. The results becomes DeltaBetaGammaGammaAlpha and
EtaEpsilonZEtaTheta.
Data used in example:
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
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East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
FirstValue
FirstValue() returns the value that was loaded first from the records defined by the expression, sorted by a
group by clause.
This function is only available as a script function.
Syntax:
FirstValue ( expr)
Return data type:dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
Limitations:
If no text value is found, NULL is returned.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
SalesGroup
FirstTeamLoaded
East
Gamma
West
Zeta
FirstValue1:
LOAD SalesGroup,FirstValue(Team) as FirstTeamLoaded Resident
TeamData Group By SalesGroup;
LastValue
LastValue() returns the value that was loaded last from the records defined by the expression, sorted by a
group by clause.
This function is only available as a script function.
Syntax:
LastValue ( expr )
Return data type:dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
Limitations:
If no text value is found, NULL is returned.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in our app to see the result.
To get the same look as in the result column below, in the properties panel, under Sorting, switch from Auto
to Custom, then deselect numerical and alphabetical sorting.
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Example
Result
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
SalesGroup
LastTeamLoaded
East
Beta
West
Theta
LastValue1:
LOAD SalesGroup,LastValue(Team) as LastTeamLoaded Resident
TeamData Group By SalesGroup;
MaxString
MaxString() finds string values in the expression and returns the last text value sorted over a number of
records, as defined by a group by clause.
Syntax:
MaxString ( expr )
Return data type:dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
Limitations:
If no text value is found, NULL is returned.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
SalesGroup
MaxString1
East
Gamma
West
Zeta
Given that the TeamData table is loaded as in the previous example, and
SalesGroup
MaxString2
your data load script has the SET statement:
SET DateFormat='DD/MM/YYYY';':
East
01/11/2013
West
01/12/2013
Concat1:
LOAD SalesGroup,MaxString(Team) as MaxString1 Resident TeamData Group
By SalesGroup;
LOAD SalesGroup,MaxString(Date) as MaxString2 Resident TeamData Group
By SalesGroup;
MaxString - chart function
MaxString() finds string values in the expression or field and returns the last text value in the text sort order.
Syntax:
MaxString({[SetExpression] [TOTAL []]} expr)
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
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Limitations:
If the expression contains no values with a string representation NULL is returned.
Examples and results:
SalesGroup
Amount
MaxString(Team)
MaxString(Date)
East
14000
Delta
2013/08/01
East
20000
Gamma
2013/11/01
East
25000
Alpha
2013/07/01
West
14000
Eta
2013/10/01
West
17000
Epsilon
2013/09/01
West
19000
Zeta
2013/06/01
West
23000
Theta
2013/12/01
This table represents all values of the dimension Customer with corresponding Product values.
In an actual table visualization on a sheet, there will be a row for each value of Customer and
Product.
Example
Result
MaxString
(Team)
There are three values of 20000 for the dimension Amount: two of Gamma (on different
dates), and one of Beta. The result of the measure MaxString (Team) is therefore Gamma,
because this is the highest value in the sorted strings.
MaxString
(Date)
2013/11/01 is the greatest Date value of the three associated with the dimension Amount.
This assumes your script has the SET statement SET DateFormat='YYYY-MM-DD';'
Data used in example:
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
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MinString
MaxString() finds string values in the expression and returns the first text value sorted over a number of
records, as defined by a group by clause.
Syntax:
MinString ( expr )
Return data type:dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
Limitations:
If no text value is found, NULL is returned.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
Example
Result
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
SalesGroup
MinString1
East
Alpha
West
Epsilon
Given that the TeamData table is loaded as in the previous example, and
SalesGroup
MinString2
your data load script has the SET statement:
SET DateFormat='DD/MM/YYYY';':
East
01/05/2013
West
01062/2013
Concat1:
LOAD SalesGroup,MinString(Team) as MinString1 Resident TeamData Group
By SalesGroup;
LOAD SalesGroup,MinString(Date) as MinString2 Resident TeamData Group
By SalesGroup;
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MinString - chart function
MinString() finds string values in the expression or field and returns the first text value in the text sort order.
Syntax:
MinString({[SetExpression] [TOTAL []]} expr)
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
SetExpression
By default, the aggregation function will aggregate over the set of possible records
defined by the selection. An alternative set of records can be defined by a set analysis
expression.
TOTAL
If the word TOTAL occurs before the function arguments, the calculation is made over
all possible values given the current selections, and not just those that pertain to the
current dimensional value, that is, it disregards the chart dimensions.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Examples and results:
SalesGroup
Amount
MinString(Team)
MinString(Date)
East
14000
Delta
2013/08/01
East
20000
Beta
2013/05/01
East
25000
Alpha
2013/07/01
West
14000
Eta
2013/10/01
West
17000
Epsilon
2013/09/01
West
19000
Zeta
2013/06/01
West
23000
Theta
2013/12/01
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Examples
Results
MinString
(Team)
There are three values of 20000 for the dimension Amount: two of Gamma (on different
dates), and one of Beta. The result of the measure MinString (Team) is therefore Beta,
because this is the first value in the sorted strings.
MinString
(Date)
2013/11/01 is the earliest Date value of the three associated with the dimension Amount.
This assumes your script has the SET statement SET DateFormat='YYYY-MM-DD';'
Data used in example:
TeamData:
LOAD * inline [
SalesGroup|Team|Date|Amount
East|Gamma|01/05/2013|20000
East|Gamma|02/05/2013|20000
West|Zeta|01/06/2013|19000
East|Alpha|01/07/2013|25000
East|Delta|01/08/2013|14000
West|Epsilon|01/09/2013|17000
West|Eta|01/10/2013|14000
East|Beta|01/11/2013|20000
West|Theta|01/12/2013|23000
] (delimiter is '|');
Synthetic dimension functions
A synthetic dimension is created in the app from values generated from the synthetic dimension functions
and not directly from fields in the data model. When values generated by a synthetic dimension function are
used in a chart as a calculated dimension, this creates a synthetic dimension. Synthetic dimensions allow you
to create, for example, charts with dimensions with values arising from your data, that is, dynamic
dimensions.
Synthetic dimensions are not affected by selections.
The following synthetic dimension functions can be used in charts.
ValueList
ValueList() returns a set of listed values, which, when used in a calculated dimension, will form a synthetic
dimension.
ValueList - chart function (v1 {, Expression})
ValueLoop
ValueLoop() returns a set of iterated values which, when used in a calculated dimension, will form a synthetic
dimension.
ValueLoop - chart function(from [, to [, step ]])
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ValueList - chart function
ValueList() returns a set of listed values, which, when used in a calculated dimension, will form a synthetic
dimension.
In charts with a synthetic dimension created with the ValueList function it is possible to
reference the dimension value corresponding to a specific expression cell by restating the
ValueList function with the same parameters in the chart expression. The function may of
course be used anywhere in the layout, but apart from when used for synthetic dimensions it
will only be meaningful inside an aggregation function.
Synthetic dimensions are not affected by selections.
Syntax:
ValueList(v1 {,...})
Return data type: dual
Arguments:
Argument
Description
v1
Static value (usually a string, but can be a number).
{,...}
Optional list of static values.
Examples and results:
Example
Result
ValueList('Number
of Orders',
'Average Order
Size', 'Total
Amount')
When used to create a dimension in a table, for example, this results in the three
string values as row labels in the table. These can then be referenced in an
expression.
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Example
Result
=IF( ValueList
('Number of
Orders', 'Average
Order Size',
'Total Amount') =
'Number of
Orders', count
(SaleID),
IF( ValueList
('Number of
Orders', 'Average
Order Size',
'Total Amount') =
'Average Order
Size', avg
(Amount), sum
(Amount) ))
This expression takes the values from the created dimension and references them
in a nested IF statement as input to three aggregation functions:
Data used in examples:
SalesPeople:
LOAD * INLINE [
SaleID|SalesPerson|Amount|Year
1|1|12|2013
2|1|23|2013
3|1|17|2013
4|2|9|2013
5|2|14|2013
6|2|29|2013
7|2|4|2013
8|1|15|2012
9|1|16|2012
10|2|11|2012
11|2|17|2012
12|2|7|2012
] (delimiter is '|');
ValueLoop - chart function
ValueLoop() returns a set of iterated values which, when used in a calculated dimension, will form a synthetic
dimension.
The values generated will start with the from value and end with the to value including intermediate values in
increments of step.
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In charts with a synthetic dimension created with the ValueLoop function it is possible to
reference the dimension value corresponding to a specific expression cell by restating the
ValueLoop function with the same parameters in the chart expression. The function may of
course be used anywhere in the layout, but apart from when used for synthetic dimensions it
will only be meaningful inside an aggregation function.
Synthetic dimensions are not affected by selections.
Syntax:
ValueLoop(from [, to [, step ]])
Return data type: dual
Arguments:
Arguments
Description
from
Start value in the set of values to be generated.
to
End value in the set of values to be generated.
step
Size of increment between values.
Examples and results:
Example
Result
ValueLoop
(1, 10)
This creates a dimension in a table, for example, that can be used for purposes such as
numbered labeling. The example here results in values numbered 1 to 10. These values can
then be referenced in an expression.
ValueLoop
(2, 10,2)
This example results in values numbered 2, 4, 6, 8, and 10 because the argument step has a
value of 2.
Nested aggregations
You may come across situations where you need to apply an aggregation to the result of another
aggregation. This is referred to as nesting aggregations.
As a general rule, it is not allowed to nest aggregations in a Qlik Sense chart expression. Nesting is only
allowed if you:
l
Use the TOTAL qualifier in the inner aggregation function.
No more than 100 levels of nesting is allowed.
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Nested aggregations with the TOTAL qualifier
Example:
You want to calculate the sum of the field Sales, but only include transactions with an OrderDate equal to
the last year. The last year can be obtained via the aggregation function Max(TOTAL Year
(OrderDate)).
The following aggregation would return the desired result:
Sum(If(Year(OrderDate)=Max(TOTAL Year(OrderDate)), Sales))
The inclusion of the TOTAL qualifier is absolutely necessary for this kind of nesting to be accepted by Qlik
Sense, but then again also necessary for the desired comparison. This type of nesting need is quite common
and is a good practice.
See also:
p Aggr - chart function (page 145)
5.2
Color functions
These functions can be used in expressions associated with setting and evaluating the color properties of
chart objects, as well as in data load scripts.
Qlik Sense supports the color functions qliktechblue and qliktechgray for backwards
compatibility reasons, but use of them is not recommended.
ARGB
ARGB() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by a red component r, a green component g, and a blue component b, with an alpha factor (opacity)
of alpha.
ARGB(alpha, r, g, b)
HSL
HSL() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by values of hue, saturation, and luminosity between 0 and 255.
HSL (hue, saturation, luminosity)
RGB
RGB() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by a red component r, a green component g, and a blue component b.
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RGB (r, g, b)
Color
Color() is used in expressions to return the color representation of color number n in the relevant chart
palette. The color representation is a dual value where the text representation comes in the form of 'RGB(r,
g, b)' where r, g and b are numbers between 0 and 255 representing the red, green and blue color value
respectively. The number representation is an integer representing the red, green and blue components.
Color (n)
Colormix1
Colormix1() is used in expressions to return an RGB color representation from a two color gradient, based
on a value between 0 and 1.
Colormix1 (Value , ColorZero , ColorOne)
Value is a real number between 0 and 1.
l
If Value = 0 the first color is returned.
l
If Value = 1 the second color is returned.
l
If 0 < Value< 1 the appropriate intermediate shading is returned.
ColorZero is a valid RGB color representation for the color to be associated with the low end of the interval.
ColorOne is a valid RGB color representation for the color to be associated with the high end of the interval.
Colormix2
Colormix2() is used in expressions to return an RGB color representation from a two color gradient, based
on a value between -1 and 1, with the possibility to specify an intermediate color for the center (0) position.
Colormix2 (Value ,ColorMinusOne , ColorOne[ , ColorZero])
Value is a real number between -1 and 1.
l
If Value = -1 the first color is returned.
l
If Value = 1 the second color is returned.
l
If -1 < Value< 1 the appropriate intermediate shading is returned.
ColorMinusOne is a valid RGB color representation for the color to be associated with the low end of the
interval.
ColorOne is a valid RGB color representation for the color to be associated with the high end of the interval.
ColorZero is an optional valid RGB color representation for the color to be associated with the center of the
interval.
SysColor
SysColor() returns the RGB color representation for the Windows system color nr, where nr corresponds to
the parameter to the Windows API function GetSysColor(nr).
SysColor (nr)
ColorMapHue
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ColorMapHue() returns the color representation of a color from a colormap that varies the hue component of
the HSV color model. The colormap starts with red, passes through yellow, green, cyan, blue, magenta, and
returns to red. x must be specified as a value between 0 and 1.
ColorMapHue (x)
ColorMapJet
ColorMapJet() returns the color representation of a color from a colormap that starts with blue, passes
through cyan, yellow and orange, and returns to red. x must be specified as a value between 0 and 1.
ColorMapJet (x)
Pre-defined color functions
The following functions can be used in expressions for pre-defined colors. Each function returns an RGB
color representation.
Optionally a parameter for alpha factor can be given, in which case an ARGB color representation is
returned. An alpha factor of 0 corresponds to full transparency, and an alpha factor of 255 corresponds to full
opacity.
Color function
RGB value
black ([alpha])
(0,0,0)
blue([alpha])
(0,0,128)
brown([alpha])
(128,128,0)
cyan([alpha])
(0,128,128)
darkgray([alpha])
(128,128,128)
green([alpha])
(0,128,0)
lightblue([alpha])
(0,0,255)
lightcyan([alpha])
(0,255,255)
lightgray([alpha])
(192,192,192)
lightgreen([alpha])
(0,255,0)
lightmagenta([alpha])
(255,0,255)
lightred([alpha])
(255,0,0)
magenta([alpha])
(128,0,128)
red([alpha])
(128,0,0)
white([alpha])
(255,255,255)
yellow([alpha])
(255,255,0)
Examples and results:
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Examples
Results
Blue()
RGB(0,0,128)
Blue(128)
ARGB(128,0,0,128)
ARGB
ARGB() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by a red component r, a green component g, and a blue component b, with an alpha factor (opacity)
of alpha.
Syntax:
ARGB(alpha, r, g, b)
Return data type: dual
Arguments:
Argument
Description
alpha
Transparency value in the range 0 - 255. 0 corresponds to full transparency and 255
corresponds to full opacity.
r, g, b
Red, green, and blue component values. A color component of 0 corresponds to no
contribution and one of 255 to full contribution.
All arguments must be expressions that resolve to integers in the range 0 to 255.
If interpreting the numeric component and formatting it in hexadecimal notation, the values of the color
components are easier to see.For example, light green has the number 4 278 255 360, which in hexadecimal
notation is FF00FF00.The first two positions ‘FF’ (255) denote the alpha factor.The next two positions ‘00’
denote the amount of red, the next two positions ‘FF’ denote the amount of green and the final two positions
‘00’ denote the amount of blue.
RGB
RGB() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by a red component r, a green component g, and a blue component b.
Syntax:
RGB (r, g, b)
Return data type: dual
Arguments:
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Argument
Description
r, g, b
Red, green, and blue component values. A color component of 0 corresponds to no
contribution and one of 255 to full contribution.
All arguments must be expressions that resolve to integers in the range 0 to 255.
If interpreting the numeric component and formatting it in hexadecimal notation, the values of the color
components are easier to see.For example, light green has the number 4 278 255 360, which in hexadecimal
notation is FF00FF00.The first two positions ‘FF’ (255) denote the alpha factor.In the functions RGB and
HSL, this is always ‘FF’ (opaque).The next two positions ‘00’ denote the amount of red, the next two
positions ‘FF’ denote the amount of green and the final two positions ‘00’ denote the amount of blue.
HSL
HSL() is used in expressions to set or evaluate the color properties of a chart object, where the color is
defined by values of hue, saturation, and luminosity between 0 and 255.
Syntax:
HSL (hue, saturation, luminosity)
Return data type: dual
Arguments:
Argument
Description
hue, saturation,
luminosity
hue, saturation, and luminosity component values. A value of 0 corresponds to no
contribution and one of 255 to full contribution.
All arguments must be expressions that resolve to integers in the range 0 to 255.
If interpreting the numeric component and formatting it in hexadecimal notation, the values of the color
components are easier to see. For example, light green has the number 4 286 080 100, which in hexadecimal
notation is FF786464. The first two positions ‘FF’ (255) denote the alpha factor.In the functions RGB and
HSL, this is always ‘FF’ (opaque). The next two positions ‘78’ denote hue component, the next two positions
‘64’ denote the saturation, and the final two positions ‘64’ denote the luminosity, component.
5.3
Conditional functions
The conditional functions all evaluate a condition and then return different answers depending on the
condition value. The functions can be used in the data load script and in chart expressions.
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Conditional functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
alt
The alt function returns the first of the parameters that has a valid number representation. If no such match is
found, the last parameter will be returned. Any number of parameters can be used.
alt (case1[ , case2 , case3 , ...] , else)
class
The class function assigns the first parameter to a class interval. The result is a dual value with a<=x= 0, 'OK', 'Alarm' )
This expression will test if the amount is a positive number (0 or
larger) and return 'OK' if it is. If the amount is less than 0, 'Alarm'
is returned.
match
The match function compares the first parameter with all the following ones and returns the number of
expression that matches. The comparison is case sensitive.
Syntax:
match( str, expr1 [ , expr2,...exprN ])
If you want to use case insensitive comparison, use the mixmatch function. If you want to use
case insensitive comparison and wildcards, use the wildmatch function.
Examples and results:
Example
Result
match( M, 'Jan','Feb','Mar')
returns 2 if M = Feb.
returns 0 if M = Aprorjan.
mixmatch
The mixmatch function compares the first parameter with all the following ones and returns the number of
expression that matches. The comparison is case insensitive.
Syntax:
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mixmatch( str, expr1 [ , expr2,...exprN ])
If you want to use case sensitive comparison, use the match function. If you want to use case
insensitive comparison and wildcards, use the wildmatch function.
Examples and results:
Example
Result
mixmatch( M, 'Jan','Feb','Mar')
returns 1 if M = jan
pick
The pick function returns the n:th expression in the list.
Syntax:
pick(n, expr1[ , expr2,...exprN])
Arguments:
Argument
Description
n
n is an integer between 1 and N.
Examples and results:
Example
Result
pick( N, 'A','B',4, 6 )
returns 'B' if N = 2
returns 4 if N = 3
wildmatch
The wildmatch function compares the first parameter with all the following ones and returns the number of
expression that matches. It permits the use of wildcard characters ( * and ?) in the comparison strings. The
comparison is case insensitive.
Syntax:
wildmatch( str, expr1 [ , expr2,...exprN ])
If you want to use comparison without wildcards, use the match or mixmatch functions.
Examples and results:
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Example
Result
wildmatch( M, 'ja*','fe?','mar')
returns 1 if M = January
returns 2 if M = fex
5.4
Counter functions
This section describes functions related to record counters during LOAD statement evaluation in the data
load script. The only function that can be used in chart expressions is RowNo().
Some counter functions do not have any parameters, but the trailing parentheses are however still required.
Counter functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
autonumber
This script function returns a unique integer value for each distinct evaluated value of expression
encountered during the script execution. This function can be used e.g. for creating a compact memory
representation of a complex key.
autonumber (expression[ , AutoID])
autonumberhash128
This script function calculates a 128-bit hash of the combined input expression values and the returns a
unique integer value for each distinct hash value encountered during the script execution. This function can
be used for example for creating a compact memory representation of a complex key.
autonumberhash128 (expression {, expression})
autonumberhash256
This script function calculates a 256-bit hash of the combined input expression values and returns a unique
integer value for each distinct hash value encountered during the script execution. This function can be used
e.g. for creating a compact memory representation of a complex key.
This function is only available as a script function.
autonumberhash256 (expression {, expression})
fieldvaluecount
This script function returns the number of distinct values in a field. fieldname must be given as a string (for
example a quoted literal).
fieldvaluecount (fieldname)
IterNo
This script function returns an integer indicating for which time one single record is evaluated in a LOAD
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statement with a while clause. The first iteration has number 1. The IterNo function is only meaningful if
used together with a while clause.
IterNo ( )
RecNo
This script functions returns an integer for the number of the currently read row of the current table. The first
record is number 1.
RecNo ( )
RowNo - script function
This function returns an integer for the position of the current row in the resulting Qlik Sense internal table.
The first row is number 1.
RowNo ( )
RowNo - chart function
RowNo() returns the number of the current row within the current column segment in a table. For bitmap
charts, RowNo() returns the number of the current row within the chart's straight table equivalent.
RowNo - chart function([TOTAL])
autonumber
This script function returns a unique integer value for each distinct evaluated value of expression
encountered during the script execution. This function can be used e.g. for creating a compact memory
representation of a complex key.
You can only connect autonumber keys that have been generated in the same data load, as
the integer is generated according to the order the table is read. If you need to use keys that are
persistent between data loads, independent of source data sorting, you should use the
hash128, hash160 or hash256 functions.
Syntax:
autonumber(expression[ , AutoID])
Arguments:
Argument
Description
AutoID
In order to create multiple counter instances if the autonumber function is used on different
keys within the script, the optional parameter AutoID can be used for naming each counter.
Example: Creating a composite key
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In this example we create a composite key using the autonumber function to conserve memory. The
example is brief for demonstration purpose, but would be meaningful with a table containing a large number
of rows.
Region
Year
Month
Sales
North
2014
May
245
North
2014
May
347
North
2014
June
127
South
2014
June
645
South
2013
May
367
South
2013
May
221
The source data is loaded using inline data. Then we add a preceding load which creates a composite key
from the Region, Year and Month fields.
RegionSales:
LOAD *,
AutoNumber(Region&Year&Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Sales
North, 2014,
May,
245
North, 2014,
May,
347
North, 2014,
June,
127
South, 2014,
June,
645
South, 2013,
May, 367
South, 2013,
May,
221
];
The resulting table looks like this:
Region
Year
Month
Sales
RYMkey
North
2014
May
245
1
North
2014
May
347
1
North
2014
June
127
2
South
2014
June
645
3
South
2013
May
367
4
South
2013
May
221
4
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In this example you can refer to the RYMkey, for example 1, instead of the string 'North2014May' if you need
to link to another table.
Now we load a source table of costs in a similar way. The Region, Year and Month fields are excluded in the
preceding load to avoid creating a synthetic key, we are already creating a composite key with the
autonumber function, linking the tables.
RegionCosts:
LOAD Costs,
AutoNumber(Region&Year&Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Costs
South, 2013,
May,
167
North, 2014,
May,
56
North, 2014,
June,
199
South, 2014,
June,
64
South, 2013,
May, 172
South, 2013,
May,
126
];
Now we can add a table visualization to a sheet, and add the Region, Year and Month fields, as well as Sum
measures for the sales and the costs. the table will look like this:
Region
Year
Month
Totals
Sum([Sales])
Sum([Costs])
1952
784
North
2014
June
127
199
North
2014
May
592
56
South
2014
June
645
64
South
2013
May
588
465
See also:
p autonumberhash128 (page 338)
p autonumberhash256 (page 341)
autonumberhash128
This script function calculates a 128-bit hash of the combined input expression values and the returns a
unique integer value for each distinct hash value encountered during the script execution. This function can
be used for example for creating a compact memory representation of a complex key.
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You can only connect autonumberhash128 keys that have been generated in the same data
load, as the integer is generated according to the order the table is read. If you need to use
keys that are persistent between data loads, independent of source data sorting, you should
use the hash128, hash160 or hash256 functions.
Syntax:
autonumberhash128(expression {, expression})
Example: Creating a composite key
In this example we create a composite key using the autonumberhash128 function to conserve memory.
The example is brief for demonstration purpose, but would be meaningful with a table containing a large
number of rows.
Region
Year
Month
Sales
North
2014
May
245
North
2014
May
347
North
2014
June
127
South
2014
June
645
South
2013
May
367
South
2013
May
221
The source data is loaded using inline data. Then we add a preceding load which creates a composite key
from the Region, Year and Month fields.
RegionSales:
LOAD *,
AutoNumberHash128(Region, Year, Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Sales
North, 2014,
May,
245
North, 2014,
May,
347
North, 2014,
June,
127
South, 2014,
June,
645
South, 2013,
May, 367
South, 2013,
May,
221
];
The resulting table looks like this:
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Region
Year
Month
Sales
RYMkey
North
2014
May
245
1
North
2014
May
347
1
North
2014
June
127
2
South
2014
June
645
3
South
2013
May
367
4
South
2013
May
221
4
In this example you can refer to the RYMkey, for example 1, instead of the string 'North2014May' if you need
to link to another table.
Now we load a source table of costs in a similar way. The Region, Year and Month fields are excluded in the
preceding load to avoid creating a synthetic key, we are already creating a composite key with the
autonumberhash128 function, linking the tables.
RegionCosts:
LOAD Costs,
AutoNumberHash128(Region, Year, Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Costs
South, 2013,
May,
167
North, 2014,
May,
56
North, 2014,
June,
199
South, 2014,
June,
64
South, 2013,
May, 172
South, 2013,
May,
126
];
Now we can add a table visualization to a sheet, and add the Region, Year and Month fields, as well as Sum
measures for the sales and the costs. the table will look like this:
Region
Year
Month
Totals
Sum([Sales])
Sum([Costs])
1952
784
North
2014
June
127
199
North
2014
May
592
56
South
2014
June
645
64
South
2013
May
588
465
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See also:
p autonumberhash256 (page 341)
p autonumber (page 336)
autonumberhash256
This script function calculates a 256-bit hash of the combined input expression values and returns a unique
integer value for each distinct hash value encountered during the script execution. This function can be used
e.g. for creating a compact memory representation of a complex key.
You can only connect autonumberhash256 keys that have been generated in the same data
load, as the integer is generated according to the order the table is read. If you need to use
keys that are persistent between data loads, independent of source data sorting, you should
use the hash128, hash160 or hash256 functions.
Syntax:
autonumberhash256(expression {, expression})
Example: Creating a composite key
In this example we create a composite key using the autonumberhash256 function to conserve memory.
The example is brief for demonstration purpose, but would be meaningful with a table containing a large
number of rows.
Region
Year
Month
Sales
North
2014
May
245
North
2014
May
347
North
2014
June
127
South
2014
June
645
South
2013
May
367
South
2013
May
221
The source data is loaded using inline data. Then we add a preceding load which creates a composite key
from the Region, Year and Month fields.
RegionSales:
LOAD *,
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AutoNumberHash256(Region, Year, Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Sales
North, 2014,
May,
245
North, 2014,
May,
347
North, 2014,
June,
127
South, 2014,
June,
645
South, 2013,
May, 367
South, 2013,
May,
221
];
The resulting table looks like this:
Region
Year
Month
Sales
RYMkey
North
2014
May
245
1
North
2014
May
347
1
North
2014
June
127
2
South
2014
June
645
3
South
2013
May
367
4
South
2013
May
221
4
In this example you can refer to the RYMkey, for example 1, instead of the string 'North2014May' if you need
to link to another table.
Now we load a source table of costs in a similar way. The Region, Year and Month fields are excluded in the
preceding load to avoid creating a synthetic key, we are already creating a composite key with the
autonumberhash256 function, linking the tables.
RegionCosts:
LOAD Costs,
AutoNumberHash256(Region, Year, Month) as RYMkey;
LOAD * INLINE
[ Region, Year, Month, Costs
South, 2013,
May,
167
North, 2014,
May,
56
North, 2014,
June,
199
South, 2014,
June,
64
South, 2013,
May, 172
South, 2013,
May,
126
];
Now we can add a table visualization to a sheet, and add the Region, Year and Month fields, as well as Sum
measures for the sales and the costs. the table will look like this:
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Region
Year
Month
Totals
Sum([Sales])
Sum([Costs])
1952
784
North
2014
June
127
199
North
2014
May
592
56
South
2014
June
645
64
South
2013
May
588
465
See also:
p autonumberhash128 (page 338)
p autonumber (page 336)
fieldvaluecount
This script function returns the number of distinct values in a field. fieldname must be given as a string (for
example a quoted literal).
Syntax:
fieldvaluecount(fieldname)
Example:
In this example the number of regions is counted and stored in the RegionCount variable. This value, 2 in this
case, can then be used for further transformations where you need to know the number of existing regions.
LOAD * INLINE
[ Region, Year, Month, Sales
South, 2014, June, 645
North, 2014, May, 245
North, 2014, May, 347
North, 2014, June, 127
South, 2013, May, 367
South, 2013, July, 221 ];
Let RegionCount = FieldValueCount('Region');
IterNo
This script function returns an integer indicating for which time one single record is evaluated in a LOAD
statement with a while clause. The first iteration has number 1. The IterNo function is only meaningful if
used together with a while clause.
Syntax:
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IterNo( )
Examples and results:
Example
Result
LOAD
IterNo() as Day,
Date( StartDate + IterNo() - 1 ) as Date
While StartDate + IterNo() - 1 <= EndDate;
This LOAD statement will generate one record per
LOAD * INLINE
[StartDate, EndDate
2014-01-22, 2014-01-26
];
In this example, the resulting table will look like this:
date within the range defined by StartDate and
EndDate.
Day
Date
1
2014-01-22
2
2014-01-23
3
2014-01-24
4
2014-01-25
5
2014-01-26
RecNo
This script functions returns an integer for the number of the currently read row of the current table. The first
record is number 1.
Syntax:
RecNo( )
In contrast to RowNo( ), which counts rows in the resulting Qlik Sense table, RecNo( ), counts the records in
the raw data table and is reset when a raw data table is concatenated to another.
Example: Data load script
Raw data table load:
Tab1:
LOAD * INLINE
[A, B
1, aa
2,cc
3,ee];
Tab2:
LOAD * INLINE
[C, D
5, xx
4,yy
6,zz];
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Loading record and row numbers for selected rows:
QTab:
LOAD *,
RecNo( ),
RowNo( )
resident Tab1 where A<>2;
LOAD
C as A,
D as B,
RecNo( ),
RowNo( )
resident Tab2 where A<>5;
//We don't need the source tables anymore, so we drop them
Drop tables Tab1, Tab2;
The resulting Qlik Sense internal table:
A
B
RecNo( )
RowNo( )
1
aa
1
1
3
ee
3
2
4
yy
2
3
6
zz
3
4
RowNo
This function returns an integer for the position of the current row in the resulting Qlik Sense internal table.
The first row is number 1.
Syntax:
RowNo( [TOTAL])
In contrast to RecNo( ), which counts the records in the raw data table, the RowNo( ) function does not
count records that are excluded by where clauses and is not reset when a raw data table is concatenated to
another.
If you use preceding load, that is, a number of stacked LOAD statements reading from the
same table, you can only use RowNo( ) in the top LOAD statement. If you use RowNo( ) in
subsequent LOAD statements, 0 is returned.
Example: Data load script
Raw data table load:
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Tab1:
LOAD * INLINE
[A, B
1, aa
2,cc
3,ee];
Tab2:
LOAD * INLINE
[C, D
5, xx
4,yy
6,zz];
Loading record and row numbers for selected rows:
QTab:
LOAD *,
RecNo( ),
RowNo( )
resident Tab1 where A<>2;
LOAD
C as A,
D as B,
RecNo( ),
RowNo( )
resident Tab2 where A<>5;
//We don't need the source tables anymore, so we drop them
Drop tables Tab1, Tab2;
The resulting Qlik Sense internal table:
A
B
RecNo( )
RowNo( )
1
aa
1
1
3
ee
3
2
4
yy
2
3
6
zz
3
4
RowNo - chart function
RowNo() returns the number of the current row within the current column segment in a table. For bitmap
charts, RowNo() returns the number of the current row within the chart's straight table equivalent.
If the table or table equivalent has multiple vertical dimensions, the current column segment will include only
rows with the same values as the current row in all dimension columns, except for the column showing the
last dimension in the inter-field sort order.
Syntax:
RowNo([TOTAL])
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Return data type: integer
Arguments:
Argument
Description
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
Limitations:
Examples and results:
Customer
UnitSales
Row in Segment
Row Number
Astrida
4
1
1
Astrida
10
2
2
Astrida
9
3
3
Betacab
5
1
4
Betacab
2
2
5
Betacab
25
3
6
Canutility
8
1
7
2
8
1
9
2
10
Canutility
Divadip
4
Divadip
Examples
Results
Create a visualization consisting of a table
with the dimensions Customer, UnitSales,
The Row in Segment column shows the results 1,2,3
and add RowNo( ) and RowNo(TOTAL) as
measures labeled Row in Segment and
Row Number.
for the column segment containing the values of
UnitSales for customer Astrida. The row numbering then
begins at 1 again for the next column segment, which is
Betacab.
The Row Number column disregards the dimensions
can be used to count the rows in the table.
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Examples
Results
Add the exression:
This expression returns 0 for the first row in each column
segment, so the column will show:
IF( RowNo( )=1, 0, UnitSales / Above(
UnitSales ))
as a measure.
0, 2.25, 1.1111111, 0, 2.5, 5, 0, 2.375, 0, and 4.
Data used in examples:
Temp:
LOAD * inline [
Customer|Product|OrderNumber|UnitSales|UnitPrice
Astrida|AA|1|4|16
Astrida|AA|7|10|15
Astrida|BB|4|9|9
Betacab|CC|6|5|10
Betacab|AA|5|2|20
Betacab|BB|1|25| 25
Canutility|AA|3|8|15
Canutility|CC|||19
Divadip|CC|2|4|16
Divadip|DD|3|1|25
] (delimiter is '|');
See also:
p Above - chart function (page 500)
5.5
Date and time functions
Qlik Sense date and time functions are used to transform and convert date and time values. All functions can
be used in both the data load script and in chart expressions.
Functions are based on a date-time serial number that equals the number of days since December 30, 1899.
The integer value represents the day and the fractional value represents the time of the day.
Qlik Sense uses the numerical value of the parameter, so a number is valid as a parameter also when it is not
formatted as a date or a time. If the parameter does not correspond to numerical value, for example,
because it is a string, then Qlik Sense attempts to interpret the string according to the date and time
environment variables.
If the time format used in the parameter does not correspond to the one set in the environment variables,
Qlik Sense will not be able to make a correct interpretation. To resolve this, either change the settings or use
an interpretation function.
In the examples for each function, the default time and date formats hh:mm:ss and YYYY-MM-DD (ISO
8601) are assumed.
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Date and time functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Integer expressions of time
second
This function returns an integer representing the second when the fraction of the expression is interpreted
as a time according to the standard number interpretation.
second (expression)
minute
This function returns an integer representing the minute when the fraction of the expression is interpreted
as a time according to the standard number interpretation.
minute (expression)
hour
This function returns an integer representing the hour when the fraction of the expression is interpreted as a
time according to the standard number interpretation.
hour (expression)
day
This function returns an integer representing the day when the fraction of the expression is interpreted as a
date according to the standard number interpretation.
day (expression)
week
This function returns an integer representing the week number according to ISO 8601. The week number is
calculated from the date interpretation of the expression, according to the standard number interpretation.
week (expression)
month
This function returns a dual value with a month name as defined in the environment variable MonthNames
and an integer between 1-12. The month is calculated from the date interpretation of the expression,
according to the standard number interpretation.
month (expression)
year
This function returns an integer representing the year when the expression is interpreted as a date
according to the standard number interpretation.
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year (expression)
weekyear
This function returns the year to which the week number belongs according to ISO 8601. The week number
ranges between 1 and approximately 52.
weekyear (expression)
weekday
This function returns a dual value with: A day name as defined in the environment variable DayNames. An
integer between 0-6 corresponding to the nominal day of the week (0-6).
weekday (date)
Timestamp functions
now
This function returns a timestamp of the current time from the system clock.
now ([ timer_mode])
today
This function returns the current date from the system clock.
today ([timer_mode])
LocalTime
This function returns a timestamp of the current time from the system clock for a specified time zone.
localtime ([timezone [, ignoreDST ]])
Make functions
makedate
This function returns a date calculated from the year YYYY, the month MM and the day DD.
makedate (YYYY [ , MM [ , DD ] ])
makeweekdate
This function returns a date calculated from the year YYYY, the week WW and the day-of-week D.
makeweekdate (YYYY [ , WW [ , D ] ])
maketime
This function returns a time calculated from the hour hh, the minute mm, and the second ss.
maketime (hh [ , mm [ , ss [ .fff ] ] ])
Other date functions
AddMonths
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This function returns the date occurring n months after startdate or, if n is negative, the date occurring n
months before startdate.
addmonths (startdate, n , [ , mode])
AddYears
This function returns the date occurring n years after startdate or, if n is negative, the date occurring n years
before startdate.
addyears (startdate, n)
yeartodate
This function finds if the input date falls within the year of the date the script was last loaded, and returns
True if it does, False if it does not.
yeartodate (date [ , yearoffset [ , firstmonth [ , todaydate] ] ])
Timezone functions
timezone
This function returns the name of the current time zone, as defined in Windows.
timezone ( )
GMT
This function returns the current Greenwich Mean Time, as derived from the system clock and Windows time
settings.
GMT ( )
UTC
Returns the current Coordinated Universal Time.
UTC ( )
daylightsaving
Returns the current adjustment for daylight saving time, as defined in Windows.
daylightsaving ( )
converttolocaltime
Converts a UTC or GMT timestamp to local time as a dual value. The place can be any of a number of cities,
places and time zones around the world.
converttolocaltime (timestamp [, place [, ignore_dst=false]])
Set time functions
setdateyear
This function returns a timestamp based on the input timestamp but with the year replaced with year.
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setdateyear
(timestamp, year)
setdateyearmonth
Returns a timestamp based on the input timestamp but with the year replaced with year and the month
replaced with month
setdateyearmonth
(timestamp, year, month)
In... functions
inyear
This function returns True if timestamp lies inside the year containing base_date.
inyear (date, basedate , shift [, first_month_of_year = 1])
inyeartodate
This function returns True if timestamp lies inside the part of year containing base_date up until and
including the last millisecond of base_date.
inyeartodate
(date, basedate , shift [, first_month_of_year = 1])
inquarter
This function returns True if timestamp lies inside the quarter containing base_date.
inquarter
(date, basedate , shift [, first_month_of_year = 1])
inquartertodate
This function returns True if timestamp lies inside the part of the quarter containing base_date up until and
including the last millisecond of base_date.
inquartertodate
(date, basedate , shift [, first_month_of_year = 1])
inmonth
This function returns True if timestamp lies inside the month containing base_date.
inmonth (date, basedate , shift)
inmonthtodate
Returns True if date lies inside the part of month containing basedate up until and including the last
millisecond of basedate.
inmonthtodate
(date, basedate , shift)
inmonths
This function finds if a timestamp falls within the same month, bi-month, quarter, tertial, or half-year as a
base date.It is also possible to find if the timestamp falls within a previous or following time period.
inmonths (n, date, basedate , shift [, first_month_of_year = 1])
inmonthstodate
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This function finds if a timestamp falls within the part a period of the month, bi-month, quarter, tertial, or halfyear up to and including the last millisecond of base_date. It is also possible to find if the timestamp falls
within a previous or following time period.
inmonthstodate
(n, date, basedate , shift [, first_month_of_year = 1])
inweek
This function returns True if timestamp lies inside the week containing base_date.
inweek (date, basedate , shift [, weekstart])
inweektodate
This function returns True if timestamp lies inside the part of week containing base_date up until and
including the last millisecond of base_date.
inweektodate
(date, basedate , shift [, weekstart])
inlunarweek
This function finds if timestamp lies inside the lunar week containing base_date. Lunar weeks in Qlik Sense
are defined by counting 1 January as the first day of the week.
inlunarweek
(date, basedate , shift [, weekstart])
inlunarweektodate
This function finds if timestamp lies inside the part of the lunar week up to and including the last millisecond
of base_date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
inlunarweektodate
(date, basedate , shift [, weekstart])
inday
This function returns True if timestamp lies inside the day containing base_timestamp.
inday (timestamp, basetimestamp , shift [, daystart])
indaytotime
This function returns True if timestamp lies inside the part of day containing base_timestamp up until and
including the exact millisecond of base_timestamp.
indaytotime (timestamp, basetimestamp , shift [, daystart])
Start ... end functions
yearstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day of the year
containing date. The default output format will be the DateFormat set in the script.
yearstart ( date [, shift = 0 [, first_month_of_year = 1]])
yearend
This function returns a value corresponding to a timestamp of the last millisecond of the last day of the year
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containing date. The default output format will be the DateFormat set in the script.
yearend ( date [, shift = 0 [, first_month_of_year = 1]])
yearname
This function returns a four-digit year as display value with an underlying numeric value corresponding to a
timestamp of the first millisecond of the first day of the year containing date.
yearname (date [, shift = 0 [, first_month_of_year = 1]] )
quarterstart
This function returns a value corresponding to a timestamp of the first millisecond of the quarter containing
date. The default output format will be the DateFormat set in the script.
quarterstart (date [, shift = 0 [, first_month_of_year = 1]])
quarterend
This function returns a value corresponding to a timestamp of the last millisecond of the quarter containing
date. The default output format will be the DateFormat set in the script.
quarterend (date [, shift = 0 [, first_month_of_year = 1]])
quartername
This function returns a display value showing the months of the quarter (formatted according to the
MonthNames script variable) and year with an underlying numeric value corresponding to a timestamp of
the first millisecond of the first day of the quarter.
quartername (date [, shift = 0 [, first_month_of_year = 1]])
monthstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day of the
month containing date. The default output format will be the DateFormat set in the script.
monthstart (date [, shift = 0])
monthend
This function returns a value corresponding to a timestamp of the last millisecond of the last day of the month
containing date. The default output format will be the DateFormat set in the script.
monthend (date [, shift = 0])
monthname
This function returns a display value showing the month (formatted according to the MonthNames script
variable) and year with an underlying numeric value corresponding to a timestamp of the first millisecond of
the first day of the month.
monthname (date [, shift = 0])
monthsstart
This function returns a value corresponding to the timestamp of the first millisecond of the month, bi-month,
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quarter, tertial, or half-year containing a base date. It is also possible to find the timestamp for a previous or
following time period.
monthsstart (n, date [, shift = 0 [, first_month_of_year = 1]])
monthsend
This function returns a value corresponding to a timestamp of the last millisecond of the month, bi-month,
quarter, tertial, or half-year containing a base date. It is also possible to find the timestamp for a previous or
following time period.
monthsend (n, date [, shift = 0 [, first_month_of_year = 1]])
monthsname
This function returns a display value representing the range of the months of the period (formatted according
to the MonthNames script variable) as well as the year. The underlying numeric value corresponds to a
timestamp of the first millisecond of the month, bi-month, quarter, tertial, or half-year containing a base date.
monthsname (n, date [, shift = 0 [, first_month_of_year = 1]])
weekstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day (Monday) of
the calendar week containing date. The default output format is the DateFormat set in the script.
weekstart (date [, shift = 0 [,weekoffset = 0]])
weekend
This function returns a value corresponding to a timestamp of the last millisecond of the last day (Sunday) of
the calendar week containing date The default output format will be the DateFormat set in the script.
weekend (date [, shift = 0 [,weekoffset = 0]])
weekname
This function returns a value showing the year and week number with an underlying numeric value
corresponding to a timestamp of the first millisecond of the first day of the week containing date.
weekname (date [, shift = 0 [,weekoffset = 0]])
lunarweekstart
This function returns a value corresponding to a timestamp of the first millisecond of the lunar week
containing date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
lunarweekstart (date [, shift = 0 [,weekoffset = 0]])
lunarweekend
This function returns a value corresponding to a timestamp of the last millisecond of the lunar week
containing date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
lunarweekend (date [, shift = 0 [,weekoffset = 0]])
lunarweekname
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This function returns a display value showing the year and lunar week number corresponding to a timestamp
of the first millisecond of the first day of the lunar week containing date. Lunar weeks in Qlik Sense are
defined by counting 1 January as the first day of the week.
lunarweekname (date [, shift = 0 [,weekoffset = 0]])
daystart
This function returns a value corresponding to a timestamp with the first millisecond of the day contained in
time. The default output format will be the TimestampFormat set in the script.
daystart (timestamp [, shift = 0 [, dayoffset = 0]])
dayend
This function returns a value corresponding to a timestamp of the final millisecond of the day contained in
time. The default output format will be the TimestampFormat set in the script.
dayend (timestamp [, shift = 0 [, dayoffset = 0]])
dayname
This function returns a value showing the date with an underlying numeric value corresponding to a
timestamp of the first millisecond of the day containing time.
dayname (timestamp [, shift = 0 [, dayoffset = 0]])
Day numbering functions
age
The age function returns the age at the time of timestamp (in completed years) of somebody born on date_
of_birth.
age (timestamp, date_of_birth)
networkdays
The networkdays function returns the number of working days (Monday-Friday) between and including
start_date and end_date taking into account any optionally listed holiday.
networkdays
(start:date, end_date {, holiday})
firstworkdate
The firstworkdate function returns the latest starting date to achieve no_of_workdays (Monday-Friday)
ending no later than end_date taking into account any optionally listed holidays. end_date and holiday
should be valid dates or timestamps.
firstworkdate (end_date, no_of_workdays {, holiday} )
lastworkdate
The lastworkdate function returns the earliest ending date to achieve no_of_workdays (Monday-Friday) if
starting at start_date taking into account any optionally listed holiday. start_date and holiday should be
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valid dates or timestamps.
lastworkdate (start_date, no_of_workdays {, holiday})
daynumberofyear
This function calculates the day number of the year in which a timestamp falls. The calculation is made from
the first millisecond of the first day of the year, but the first month can be offset.
daynumberofyear (date[,firstmonth])
daynumberofquarter
This function calculates the day number of the quarter in which a timestamp falls.
daynumberofquarter (date[,firstmonth])
addmonths
This function returns the date occurring n months after startdate or, if n is negative, the date occurring n
months before startdate.
Syntax:
AddMonths(startdate, n , [ , mode])
Arguments:
Argument
Description
startdate
The start date as a time stamp, for example '2012-10-12'.
n
Number of months as a positive or negative integer.
mode
mode specifies if the month is added relative to the beginning of the month or relative to the
end of the month. If the input date is the 28th or above and mode is set to 1, the function
will return a date which is the same distance from the end of the month as the input date.
Default mode is 0.
Examples and results:
Example
Result
addmonths ('2003-01-29',3)
returns '2003-04-29'
addmonths ('2003-01-29',3,0)
returns '2003-04-29'
addmonths ('2003-01-29',3,1)
returns '2003-04-28'
addmonths ('2003-01-29',1,0)
returns '2003-02-28'
addmonths ('2003-01-29',1,1)
returns '2003-02-26'
addmonths ('2003-02-28',1,0)
returns '2003-03-28'
addmonths ('2003-02-28',1,1)
returns '2003-03-31'
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addyears
This function returns the date occurring n years after startdate or, if n is negative, the date occurring n years
before startdate.
Syntax:
AddYears(startdate, n)
Arguments:
Argument
Description
startdate
The start date as a time stamp, for example '2012-10-12'.
n
Number of years as a positive or negative integer.
Examples and results:
Example
Result
addyears ('2010-01-29',3)
returns '2013-01-29'
addyears ('2010-01-29',-1)
returns '2009-01-29'
age
The age function returns the age at the time of timestamp (in completed years) of somebody born on date_
of_birth.
Syntax:
age(timestamp, date_of_birth)
Can be an expression.
Arguments:
Argument
Description
timestamp
The timestamp,or expression resolving to a timestamp, up to which to calculate the
completed number of years.
date_of_
Date of birth of the person whose age is being calculated. Can be an expression.
birth
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
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Example
Result
age('25/01/2014', '29/10/2012')
Returns 1.
age('29/10/2014', '29/10/2012')
Returns 2.
Add the example script to your app and run it. Then add, at least, the
fields listed in the results column to a sheet in your app to see the result.
The resulting table shows the
returned values of age for
each of the records in the
table.
Employess:
LOAD * INLINE [
Member|DateOfBirth
John|28/03/1989
Linda|10/12/1990
Steve|5/2/1992
Birg|31/3/1993
Raj|19/5/1994
Prita|15/9/1994
Su|11/12/1994
Goran|2/3/1995
Sunny|14/5/1996
Ajoa|13/6/1996
Daphne|7/7/1998
Biffy|4/8/2000
] (delimiter is |);
DoBTable:
Load *,
age(LocalTime(),DateOfBirth) As Age
Resident Employees;
Drop table Employees;
Member
DateOfBirth
Age
John
28/03/1989
25
Linda
10/12/1990
23
Steve
5/2/1992
22
Birg
31/3/1993
21
Raj
19/5/1994
20
Prita
15/9/1994
20
Su
11/12/1994
19
Goran
2/3/1995
19
Sunny
14/5/1996
18
Ajoa
13/6/1996
18
Daphne
7/7/1998
16
Biffy
4/8/2000
14
converttolocaltime
Converts a UTC or GMT timestamp to local time as a dual value. The place can be any of a number of cities,
places and time zones around the world.
Syntax:
ConvertToLocalTime(timestamp [, place [, ignore_dst=false]])
Also GMT, GMT-01:00, GMT+04:00 etc. are valid places.
The resulting time is adjusted for daylight-saving time, unless the third parameter is set to True.
Valid places and time zones
Abu Dhabi
Central America
Kabul
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Sydney
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Valid places and time zones
Adelaide
Central Time (US &
Canada)
Kamchatka
New Caledonia
Taipei
Alaska
Chennai
Karachi
New Delhi
Tallinn
Amsterdam
Chihuahua
Kathmandu
Newfoundland
Tashkent
Arizona
Chongqing
Kolkata
Novosibirsk
Tbilisi
Astana
Copenhagen
Krasnoyarsk
Nuku'alofa
Tehran
Athens
Darwin
Kuala Lumpur
Osaka
Tokyo
Atlantic Time
(Canada)
Dhaka
Kuwait
Pacific Time (US &
Canada)
Urumqi
Auckland
Eastern Time (US &
Canada)
Kyiv
Paris
Warsaw
Azores
Edinburgh
La Paz
Perth
Wellington
Baghdad
Ekaterinburg
Lima
Port Moresby
West Central
Africa
Baku
Fiji
Lisbon
Prague
Vienna
Bangkok
Georgetown
Ljubljana
Pretoria
Vilnius
Beijing
Greenland
London
Quito
Vladivostok
Belgrade
Greenwich Mean
Time : Dublin
Madrid
Riga
Volgograd
Berlin
Guadalajara
Magadan
Riyadh
Yakutsk
Bern
Guam
Mazatlan
Rome
Yerevan
Bogota
Hanoi
Melbourne
Samoa
Zagreb
Brasilia
Harare
Mexico City
Santiago
Bratislava
Hawaii
Mid-Atlantic
Sapporo
Brisbane
Helsinki
Minsk
Sarajevo
Brussels
Hobart
Karachi
Saskatchewan
Bucharest
Hong Kong
Kathmandu
Seoul
Budapest
Indiana (East)
Kolkata
Singapore
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Valid places and time zones
Buenos Aires
International Date
Line West
Monrovia
Skopje
Cairo
Irkutsk
Monterrey
Sofia
Canberra
Islamabad
Moscow
Solomon Is.
Cape Verde Is.
Istanbul
Mountain Time (US &
Canada)
Sri
Jayawardenepura
Caracas
Jakarta
Mumbai
St. Petersburg
Casablanca
Jerusalem
Muscat
Stockholm
Examples and results:
Example
Result
ConvertToLocalTime('2007-11-10 23:59:00','Paris')
Returns ’2007-11-11 00:59:00’ and the
corresponding internal timestamp
representation.
ConvertToLocalTime(UTC(), 'GMT-05:00')
Returns the time for the North American east
coast, for example, New York.
ConvertToLocalTime(UTC(), 'GMT-05:00', True)
Returns the time for the North American east
coast, for example, New York, without
daylight-saving time adjustment.
day
This function returns an integer representing the day when the fraction of the expression is interpreted as a
date according to the standard number interpretation.
Syntax:
day(expression)
Examples and results:
Example
Result
day( '1971-10-12' )
returns 12
day( '35648' )
returns 6, because 35648 = 1997-08-06
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dayend
This function returns a value corresponding to a timestamp of the final millisecond of the day contained in
time. The default output format will be the TimestampFormat set in the script.
Syntax:
DayEnd(time[, [period_no[, day_start]])
Return data type: dual
Arguments:
Argument
Description
time
The timestamp to evaluate.
period_
period_no is an integer, or expression that resolves to an integer, where the value 0
indicates the day that contains time. Negative values in period_no indicate preceding days
no
and positive values indicate succeeding days.
day_start
To specify days not starting at midnight, indicate an offset as a fraction of a day in day_
start. For example, 0.125 to denote 3 AM.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
dayend('25/01/2013 16:45')
Returns 25/01/2013 23:59:59.
dayend('25/01/2013 16:45', -1)
Returns '24/01/2013 23:59:59.
dayend('25/01/2013 16:45', 0, 0.5)
Returns 26/01/2013 11:59:59.
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Example
Result
Add the example script to your app and run it.
Then add, at least, the fields listed in the
results column to a sheet in your app to see the
result.
The resulting table contains the original dates and a
column with the return value of the dayend() function.
You can display the full timestamp by specifying the
formatting in the properties panel.
This example finds the timestamp that marks
the end of the day after each invoice date in
the table.
InvDate
DEnd
28/03/2012
29/03/2012 23:59:59
10/12/2012
11/12/2012 23:59:59
5/2/2013
07/02/2013 23:59:59
31/3/2013
01/04/2013 23:59:59
19/5/2013
20/05/2013 23:59:59
15/9/2013
16/09/2013 23:59:59
11/12/2013
12/12/2013 23:59:59
2/3/2014
03/03/2014 23:59:59
14/5/2014
15/05/2014 23:59:59
13/6/2014
14/06/2014 23:59:59
7/7/2014
08/07/2014 23:59:59
4/8/2014
05/08/2014 23:59:59
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
DayEnd(InvDate, 1) AS DEnd
Resident TempTable;
Drop table TempTable;
daylightsaving
Returns the current adjustment for daylight saving time, as defined in Windows.
Syntax:
DaylightSaving( )
Example:
daylightsaving( )
dayname
This function returns a value showing the date with an underlying numeric value corresponding to a
timestamp of the first millisecond of the day containing time.
Syntax:
DayName(time[, period_no [, day_start]])
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Return data type: dual
Arguments:
Argument
Description
time
The timestamp to evaluate.
period_
period_no is an integer, or expression that resolves to an integer, where the value 0
indicates the day that contains time. Negative values in period_no indicate preceding days
no
and positive values indicate succeeding days.
day_start
To specify days not starting at midnight, indicate an offset as a fraction of a day in day_
start. For example, 0.125 to denote 3 AM.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
dayname('25/01/2013 16:45')
Returns 25/01/2013.
dayname('25/01/2013 16:45', -1)
Returns 24/01/2013.
dayname('25/01/2013 16:45', 0, 0.5 )
Returns 25/01/2013.
Displaying the full timestamp shows the
underlying numeric value corresponds to
'25/01/2013 12:00:00.000.
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Example
Result
Add the example script to your app and run it. Then add, at
least, the fields listed in the results column to a sheet in
your app to see the result.
The resulting table contains the original
dates and a column with the return value of
the dayname() function. You can display the
full timestamp by specifying the formatting
in the properties panel.
In this example, the day name is created from the
timestamp that marks the beginning of the day after each
invoice date in the table.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
DayName(InvDate, 1) AS DName
Resident TempTable;
Drop table TempTable;
InvDate
DName
28/03/2012
29/03/2012 00:00:00
10/12/2012
11/12/2012 00:00:00
5/2/2013
07/02/2013 00:00:00
31/3/2013
01/04/2013 00:00:00
19/5/2013
20/05/2013 00:00:00
15/9/2013
16/09/2013 00:00:00
11/12/2013
12/12/2013 00:00:00
2/3/2014
03/03/2014 00:00:00
14/5/2014
15/05/2014 00:00:00
13/6/2014
14/06/2014 00:00:00
7/7/2014
08/07/2014 00:00:00
4/8/2014
05/08/2014 00:00:00
daynumberofquarter
This function calculates the day number of the quarter in which a timestamp falls.
Syntax:
DayNumberOfQuarter(timestamp[,start_tmonth])
Return data type: integer
The function always uses years based on 366 days.
Arguments:
Argument
Description
timestamp
The date to evaluate.
start_
By specifying a start_month between 2 and 12 (1, if omitted), the beginning of the year
month
may be moved forward to the first day of any month. For example, if you want to work with a
fiscal year starting March 1, specify start_month = 3.
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Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
DayNumberOfQuarter('12/09/2014')
Returns 74, the day number of the current quarter.
DayNumberOfQuarter('12/09/2014',3)
Returns 12, the day number of the current quarter.
In this case, the first quarter starts with March (because start_
month is specified as 3). This means that the current quarter is
the third quarter, which started on September 1.
Add the example script to your app
and run it. Then add, at least, the
fields listed in the results column to a
sheet in your app to see the result.
The resulting table shows the returned values of
DayNumberOfQuarter for each of the records in the table.
ProjectTable:
LOAD recno() as InvID, INLINE [
StartDate
28/03/2014
10/12/2014
5/2/2015
31/3/2015
19/5/2015
15/9/2015
] ;
NrDays:
Load *,
DayNumberOfQuarter(StartDate,4) As
DayNrQtr
Resident ProjectTable;
Drop table ProjectTable;
InvID
StartDate
DayNrQtr
1
28/03/2014
88
2
10/12/2014
71
3
5/2/2015
36
4
31/3/2015
91
5
19/5/2015
49
6
15/9/2015
77
daynumberofyear
This function calculates the day number of the year in which a timestamp falls. The calculation is made from
the first millisecond of the first day of the year, but the first month can be offset.
Syntax:
DayNumberOfYear(timestamp[,start_month])
Return data type: integer
The function always uses years based on 366 days.
Arguments:
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Argument
Description
timestamp
The date to evaluate.
start_
By specifying a start_month between 2 and 12 (1, if omitted), the beginning of the year
month
may be moved forward to the first day of any month. For example, if you want to work with a
fiscal year starting March 1, specify start_month = 3.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
DayNumberOfYear('12/09/2014')
Returns 256, the day number
counted from the first of the year.
DayNumberOfYear('12/09/2014',3)
Returns 196, the number of the
day, as counted from 1 March.
Add the example script to your app and run it. Then add, at least, the
fields listed in the results column to a sheet in your app to see the
result.
The resulting table shows the
returned values of
DayNumberOfYear for each of
the records in the table.
ProjectTable:
LOAD recno() as InvID, INLINE [
StartDate
28/03/2014
10/12/2014
5/2/2015
31/3/2015
19/5/2015
15/9/2015
] ;
NrDays:
Load *,
DayNumberOfYear(StartDate,4) As DayNrYear
Resident ProjectTable;
Drop table ProjectTable;
InvID
StartDate
DayNrYear
1
28/03/2014
363
2
10/12/2014
254
3
5/2/2015
311
4
31/3/2015
366
5
19/5/2015
49
6
15/9/2015
168
daystart
This function returns a value corresponding to a timestamp with the first millisecond of the day contained in
time. The default output format will be the TimestampFormat set in the script.
Syntax:
DayStart(time[, [period_no[, day_start]])
Return data type: dual
Arguments:
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Argument
Description
timestamp
The timestamp to evaluate.
period_no
period_no is an integer, or expression that resolves to an integer, where the value 0
indicates the day that contains time. Negative values in period_no indicate preceding days
and positive values indicate succeeding days.
day_start
To specify days not starting at midnight, indicate an offset as a fraction of a day in day_
start. For example, 0.125 to denote 3 AM.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
daystart('25/01/2013 16:45')
Returns 25/01/2013 00:00:00.
daystart('25/01/2013 16:45', -1)
Returns 24/01/2013 00:00:00.
daystart('25/01/2013 16:45', 0, 0.5 )
Returns 25/01/2013 12:00:00.
Add the example script to your app and run it.
Then add, at least, the fields listed in the
results column to a sheet in your app to see
the result.
The resulting table contains the original dates and a
column with the return value of the daystart() function.
You can display the full timestamp by specifying the
formatting in the properties panel.
This example finds the timestamp that marks
the beginning of the day after each invoice
date in the table.
InvDate
DStart
28/03/2012
29/03/2012 00:00:00
10/12/2012
11/12/2012 00:00:00
5/2/2013
07/02/2013 00:00:00
31/3/2013
01/04/2013 00:00:00
19/5/2013
20/05/2013 00:00:00
15/9/2013
16/09/2013 00:00:00
11/12/2013
12/12/2013 00:00:00
2/3/2014
03/03/2014 00:00:00
14/5/2014
15/05/2014 00:00:00
13/6/2014
14/06/2014 00:00:00
7/7/2014
08/07/2014 00:00:00
4/8/2014
05/08/2014 00:00:00
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
DayStart(InvDate, 1) AS DStart
Resident TempTable;
Drop table TempTable;
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firstworkdate
The firstworkdate function returns the latest starting date to achieve no_of_workdays (Monday-Friday)
ending no later than end_date taking into account any optionally listed holidays. end_date and holiday
should be valid dates or timestamps.
Syntax:
firstworkdate(end_date, no_of_workdays [, holiday] )
Return data type: integer
Arguments:
Argument
Description
end_date
The timestamp of end date to evaluate.
no_of_
The number of working days to achieve.
workdays
holiday
Holiday periods to exclude from working days. A holiday period is stated as a start date and
an end date, separated by commas.
Example: '25/12/2013', '26/12/2013'
You can exclude more than one holiday period, separated by commas.
Example: '25/12/2013', '26/12/2013', '31/12/2013', '01/01/2014'
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
firstworkdate ('29/12/2014', 9)
Returns '17/12/2014.
firstworkdate ('29/12/2014', 9, '25/12/2014', '26/12/2014')
Returns 15/12/2014 because a
holiday period of two days is taken
into account.
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Example
Result
Add the example script to your app and run it. Then add, at least,
the fields listed in the results column to a sheet in your app to see
the result.
The resulting table shows the
returned values of FirstWorkDate for
each of the records in the table.
ProjectTable:
LOAD *, recno() as InvID, INLINE [
EndDate
28/03/2015
10/12/2015
5/2/2016
31/3/2016
19/5/2016
15/9/2016
] ;
NrDays:
Load *,
FirstWorkDate(EndDate,120) As StartDate
Resident ProjectTable;
Drop table ProjectTable;
InvID
EndDate
StartDate
1
28/03/2015
13/10/2014
2
10/12/2015
26/06/2015
3
5/2/2016
24/08/2015
4
31/3/2016
16/10/2015
5
19/5/2016
04/12/2015
6
15/9/2016
01/04/2016
GMT
This function returns the current Greenwich Mean Time, as derived from the system clock and Windows time
settings.
Syntax:
GMT( )
Example:
gmt( )
hour
This function returns an integer representing the hour when the fraction of the expression is interpreted as a
time according to the standard number interpretation.
Syntax:
hour(expression)
Examples and results:
Example
Result
hour( '09:14:36' )
returns 9
hour( '0.5555' )
returns 13 ( Because 0.5555 = 13:19:55 )
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inday
This function returns True if timestamp lies inside the day containing base_timestamp.
Syntax:
InDay (timestamp, base_timestamp, period_no[, day_start])
Arguments:
Argument
Description
timestamp
The date and time that you want to compare with base_timestamp.
base_
Date and time that is used to evaluate the timestamp.
timestamp
period_no
The day can be offset by period_no. period_no is an integer, where the value 0 indicates
the day which contains base_timestamp. Negative values in period_no indicate
preceding days and positive values indicate succeeding days.
day_start
If you want to work with days not starting midnight, indicate an offset as a fraction of a day
in day_start, For example, 0.125 to denote 3 AM.
Examples and results:
Example
Result
inday ('12/01/2006 12:23:00', '12/01/2006 00:00:00', 0)
Returns True
inday ('12/01/2006 12:23:00', '13/01/2006 00:00', 0)
Returns False
inday ('12/01/2006 12:23:00', '12/01/2006 00:00:00', -1)
Returns False
inday ('11/01/2006 12:23:00', '12/01/2006 00:00:00', -1)
Returns True
inday ('12/01/2006 12:23:00', '12/01/2006 00:00:00', 0, 0.5)
Returns False
inday ('12/01/2006 11:23:00', '12/01/2006 00:00:00', 0, 0.5)
Returns True
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column
with the return value of
the inday() function.
This example checks if an invoice date falls at any time in the day starting with
the base_timestamp.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvTime
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InDay(InvTime, '28/03/2013 00:00:00', 0, 4) AS InDayEx
Resident TempTable;
Drop table TempTable;
InvTime
InDayEx
28/03/2012
-1 (True)
10/12/2012
0
(False)
5/2/2013
0
(False)
31/3/2013
0
(False)
19/5/2013
0
(False)
15/9/2013
0
(False)
11/12/2013
0
(False)
2/3/2014
0
(False)
14/5/2014
0
(False)
13/6/2014
0
(False)
7/7/2014
0
(False)
4/8/2014
0
(False)
indaytotime
This function returns True if timestamp lies inside the part of day containing base_timestamp up until and
including the exact millisecond of base_timestamp.
Syntax:
InDayToTime (timestamp, base_timestamp, period_no[, day_start])
Arguments:
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Argument
Description
timestamp
The date and time that you want to compare with base_timestamp.
base_
Date and time that is used to evaluate the timestamp.
timestamp
period_no
The day can be offset by period_no. period_no is an integer, where the value 0 indicates
the day which contains base_timestamp. Negative values in period_no indicate
preceding days and positive values indicate succeeding days.
day_start
If you want to work with days not starting midnight, indicate an offset as a fraction of a day
in day_start, For example, 0.125 to denote 3 AM.
Examples and results:
Example
Result
indaytotime ('12/01/2006 12:23:00', '12/01/2006 23:59:00', 0)
Returns True
indaytotime ('12/01/2006 12:23:00', '12/01/2006 00:00:00', 0)
Returns False
indaytotime ('11/01/2006 12:23:00', '12/01/2006 23:59:00', -1)
Returns True
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
indaytotime() function.
This example checks if an invoice timestamp falls before 17:00:00 on the day
starting with the base_timestamp.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvTime
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InDayToTime(InvTime, '28/03/2013 17:00:00', 0, 4) AS InDayExTT
Resident TempTable;
Drop table TempTable;
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InvTime
InDayExTT
28/03/2012
-1 (True)
10/12/2012
0 (False)
5/2/2013
0 (False)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
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inlunarweek
This function finds if timestamp lies inside the lunar week containing base_date. Lunar weeks in Qlik Sense
are defined by counting 1 January as the first day of the week.
Syntax:
InLunarWeek (timestamp, base_date, period_no[, first_week_day])
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the lunar week.
period_no
The lunar week can be offset by period_no. period_no is an integer, where the value 0
indicates the lunar week which contains base_date. Negative values in period_no indicate
preceding lunar weeks and positive values indicate succeeding lunar weeks.
first_
week_day
An offset that may be greater than or less than zero. This changes the beginning of the year
by the specified number of days and/or fractions of a day.
Examples and results:
Example
Result
inlunarweek('12/01/2013',
'14/01/2013', 0)
Returns True. Because the value of timestamp, 12/01/2013 falls
in the week 08/01/2013 to 14/01/2013.
inlunarweek('12/01/2013',
'07/01/2013', 0)
Returns False. Because the base_date 07/01/2013 is in the lunar
week defined as 01/01/2013 to 07/01/2013.
inlunarweek('12/01/2013',
'14/01/2013', -1)
Returns False. Because specifying a value of period_no as -1
shifts the week to the previous week, 01/01/2013 to 07/01/2013.
inlunarweek('07/01/2013',
'14/01/2013', -1)
Returns True. In comparison with the previous example, the
timestamp is in the week after taking into account the shift
backwards.
inlunarweek('11/01/2006',
'08/01/2006', 0, 3)
Returns False. Because specifying a value for first_week_day as 3
means the start of the year is calculated from 04/01/2013, and so
the value of base_date falls in the first week, and the value of
timestamp falls in the week 11/01/2013 to 17/01/2013.
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Example
Result
Add the example script to your app
and run it. Then add, at least, the
fields listed in the results column to a
sheet in your app to see the result.
The resulting table contains the original dates and a column with
the return value of the inlunarweek() function.
This example checks if an invoice
date falls in the week shifted from
the value of base_date by four
weeks.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InLunarWeek(InvDate, '11/01/2013',
4) AS InLWeekPlus4
Resident TempTable;
Drop table TempTable;
The function returns True for the value of InvDate5/2/2013
because the value of base_date, 11/01/2013, is shifted by four
weeks, and so falls in the week 5/02/2013 to 11/02/2013.
InvDate
InLWeekPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inlunarweektodate
This function finds if timestamp lies inside the part of the lunar week up to and including the last millisecond
of base_date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
Syntax:
InLunarWeekToDate (timestamp, base_date, period_no [, first_week_day])
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the lunar week.
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Argument
Description
peroid_no
The lunar week can be offset by period_no. period_no is an integer, where the value 0
indicates the lunar week which contains base_date. Negative values in period_no indicate
preceding lunar weeks and positive values indicate succeeding lunar weeks.
week_start
An offset that may be greater than or less than zero. This changes the beginning of the year
by the specified number of days and/or fractions of a day.
Examples and results:
Example
Result
inlunarweektodate('12/01/2013', '13/01/2013',
0)
Returns True. Because the value of timestamp,
12/01/2013 falls in the part of the week 08/01/2013 to
13/01/2013.
inlunarweektodate('12/01/2013', '11/01/2013',
0)
Returns False. Because the value of timestamp is
later than the value base_date even though the two
dates are in the same lunar week before 12/01/2012.
inlunarweektodate('12/01/2006', '05/01/2006',
1)
Returns True. Specifying a value of 1 for period_no
shifts the base_date forward one week, so the value
of timestamp falls in the part of the lunar week.
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Example
Result
Add the example script to your app and run it.
Then add, at least, the fields listed in the results
column to a sheet in your app to see the result.
The resulting table contains the original dates and a
column with the return value of the inlunarweek()
function.
This example checks if an invoice date falls in the
part of the week shifted from the value of base_
date by four weeks.
The function returns True for the value of
InvDate5/2/2013 because the value of base_date,
11/01/2013, is shifted by four weeks, and so falls in
the part of the week 5/02/2013 to 07/02/2013.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InLunarWeekToDate(InvDate, '07/01/2013', 4)
AS InLWeek2DPlus4
Resident TempTable;
Drop table TempTable;
InvDate
InLWeek2DPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inmonth
This function returns True if timestamp lies inside the month containing base_date.
Syntax:
InMonth (timestamp, base_date, period_no)
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the month.
period_no
The month can be offset by period_no. period_no is an integer, where the value 0
indicates the month which contains base_date. Negative values in period_no indicate
preceding months and positive values indicate succeeding months.
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Examples and results:
Example
Result
inmonth ('25/01/2013', '01/01/2013', 0 )
Returns True
inmonth('25/01/2013', '01/04/2013', 0)
Returns False
inmonth ('25/01/2013', '01/01/2013', -1)
Returns False
inmonth ('25/12/2012', '01/01/2013', -1)
Returns True
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
inmonth() function.
This example checks if an invoice date falls at any time in the fourth month
after the month in base_date, by specifying period_no as 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InMonth(InvDate, '31/01/2013', 0, 4) AS InMthPlus4
Resident TempTable;
Drop table TempTable;
InvDate
InMthPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
0 (False)
31/3/2013
0 (False)
19/5/2013
-1 (True)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inmonths
This function finds if a timestamp falls within the same month, bi-month, quarter, tertial, or half-year as a
base date.It is also possible to find if the timestamp falls within a previous or following time period.
Syntax:
InMonths(n_months, timestamp, base_date, period_no [, first_month_of_year])
Return data type: Boolean
Arguments:
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Argument
Description
n_months
The number of months that defines the period. An integer or expression that resolves to an
integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3
(equivalent to the inquarter() function), 4 (tertial), or 6 (half year).
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the period.
period_no
The period can be offset by period_no, an integer, or expression resolving to an integer,
where the value 0 indicates the period that contains base_date. Negative values in period_
no indicate preceding periods and positive values indicate succeeding periods.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
inmonths(4, '25/01/2013', '25/04/2013', 0)
Returns True. Because the value of
timestamp, 25/01/2013, lies within
the four-month period 01/01/2013
to 30/04/2013, in which the value of
base_date, 25/04/2013 lies.
inmonths(4, '25/04/2013', '24/04/2013', 0)
Returns False. Because
25/04/2013 is outside the same
period as the previous example.
inmonths(4, '25/11/2012', '01/02/2013', -1 )
Returns True. Because the value of
period_no, -1, shifts the search
period back one period of four
months (the value of n-months),
which makes the search period
01/09/2012 to 31/12/2012.
inmonths( 4, '25/05/2006', '01/03/2006', 0, 3)
Returns True. Because the value of
first_month_of_year is set to 3,
which makes the search period
01/03/2006 to 30/07/2006 instead
of 01/01/2006 to 30/04/2006.
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Example
Result
Add the example script to your app and run it. Then add, at least,
the fields listed in the results column to a sheet in your app to see
the result.
The resulting table contains the
original dates and a column with
the return value of the InMonths()
function.
This example checks if the invoice date in the table falls in the bimonth period that includes the base_date shifted forwards by one
bi-month period (by specifying period_no as 1).
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InMonths(2, InvDate, '11/02/2013', 1) AS InMthsPlus1
Resident TempTable;
Drop table TempTable;
The search period is 01/03/2013 to
30/04/2013, because the value of
base_date is shifted forwards two
months from the value in the
function (11/02/2013).
InvDate
InMthsPlus1
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
0 (False)
31/3/2013
-1 (True)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inmonthstodate
This function finds if a timestamp falls within the part a period of the month, bi-month, quarter, tertial, or halfyear up to and including the last millisecond of base_date. It is also possible to find if the timestamp falls
within a previous or following time period.
Syntax:
InMonths (n_months, timestamp, base_date, period_no[, first_month_of_year
])
Arguments:
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Argument
Description
n_months
The number of months that defines the period. An integer or expression that resolves to an
integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3
(equivalent to the inquarter() function), 4 (tertial), or 6 (half year).
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the period.
period_no
The period can be offset by period_no, an integer, or expression resolving to an integer,
where the value 0 indicates the period that contains base_date. Negative values in period_
no indicate preceding periods and positive values indicate succeeding periods.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
inmonthstodate(4, '25/01/2013', '25/04/2013', 0)
Returns True. Because the value of timestamp,
25/01/2013, lies within the four-month period
01/01/2013 up to the end of 25/04/2013, in which
the value of base_date, 25/04/2013 lies.
inmonthstodate(4, '26/04/2013', '25/04/2006', 0)
Returns False. Because 26/04/2013 is outside the
same period as the previous example.
inmonthstodate(4, '25/09/2005', '01/02/2006', 1)
Returns True. Because the value of period_no, -1,
shifts the search period back one period of four
months (the value of n-months), which makes the
search period 01/09/2012 to 01/02/2012.
inmonthstodate(4, '25/04/2006', '01/06/2006', 0,
3)
Returns True. Because the value of first_month_
of_year is set to 3, which makes the search period
01/03/2006 to 01/06/2006 instead of 01/05/2006
to 01/06/2006.
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Example
Result
Add the example script to your app and run it. Then
add, at least, the fields listed in the results column to
a sheet in your app to see the result.
The resulting table contains the original dates and
a column with the return value of the InMonths()
function.
This example checks if the invoice date in the table
falls in the part of the bi-month period up to and
including the base_date shifted forwards by four bimonth periods (by specifying period_no as 4).
The search period is 01/09/2013 to 15/10/2013,
because the value of base_date is shifted
forwards eight months from the value in the
function (15/02/2013).
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvDate
InMths2DPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
0 (False)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
-1 (True)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
InvoiceData:
LOAD *,
InMonthsToDate(2, InvDate, '15/02/2013', 4) AS
InMths2DPlus4
Resident TempTable;
Drop table TempTable;
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inmonthtodate
Returns True if date lies inside the part of month containing basedate up until and including the last
millisecond of basedate.
Syntax:
InMonthToDate (timestamp, base_date, period_no)
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the month.
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Argument
Description
period_no
The month can be offset by period_no. period_no is an integer, where the value 0
indicates the month which contains base_date. Negative values in period_no indicate
preceding months and positive values indicate succeeding months.
Examples and results:
Example
Result
inmonthtodate ('25/01/2013', '25/01/2013', 0)
Returns True
inmonthtodate ('25/01/2013', '24/01/2013', 0)
Returns False
inmonthtodate ('25/01/2013', '28/02/2013', -1)
Returns True
Add the example script to your app and run it. Then add, at least, the
fields listed in the results column to a sheet in your app to see the result.
The resulting table contains
the original dates and a
column with the return value
of the inmonthtodate()
function.
By specifying period_no as 4, this example checks if an invoice date falls
in the fourth month after the month in base_date but before the end of the
day specified in base_date.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InMonthToDate(InvDate, '31/01/2013', 0, 4) AS InMthPlus42D
Resident TempTable;
Drop table TempTable;
InvDate
InMthPlus42D
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
0 (False)
31/3/2013
0 (False)
19/5/2013
-1 (True)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inquarter
This function returns True if timestamp lies inside the quarter containing base_date.
Syntax:
InQuarter (timestamp, base_date, period_no[, first_month_of_year])
Arguments:
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Argument
Description
timestamps
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the quarter.
period_no
The quarter can be offset by period_no. period_no is an integer, where the value 0
indicates the quarter which contains base_date. Negative values in period_no indicate
preceding quarters and positive values indicate succeeding quarters.
first_
month_of_
If you want to work with (fiscal) years not starting in January, indicate a value between 2
and 12 in first_month_of_year.
year
Examples and results:
Example
Result
inquarter ('25/01/2013', '01/01/2013', 0)
Returns True
inquarter ('25/01/2013', '01/04/2013', 0)
Returns False
inquarter ('25/01/2013', '01/01/2013', -1)
Returns False
inquarter ('25/12/2012', '01/01/2013', -1)
Returns True
inquarter ('25/01/2013', '01/03/2013', 0, 3)
Returns False
inquarter ('25/03/2013', '01/03/2013', 0, 3)
Returns True
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original dates
and a column with the
return value of the
inquarter() function.
This example checks if an invoice date falls in the fourth quarter of the fiscal
year specified by setting the value of first_month_of_year to 4, and having
the base_date 31/01/2013.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InQuarter(InvDate, '31/01/2013', 0, 4) AS Qtr4FinYr1213
Resident TempTable;
Drop table TempTable;
InvDate
Qtr4Fin1213
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
-1 (True)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inquartertodate
This function returns True if timestamp lies inside the part of the quarter containing base_date up until and
including the last millisecond of base_date.
Syntax:
InQuarterToDate (timestamp, base_date, period_no [, first_month_of_year])
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the quarter.
period_no
The quarter can be offset by period_no. period_no is an integer, where the value 0
indicates the quarter which contains base_date. Negative values in period_no indicate
preceding quarters and positive values indicate succeeding quarters.
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Argument
Description
first_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
month_
of_year
Examples and results:
Example
Result
inquartertodate ('25/01/2013', '25/01/2013', 0)
Returns True
inquartertodate ( 25/01/2013', '24/01/2013', 0)
Returns False
inquartertodate ('25/01/2012', '01/02/2013', -1)
Returns True
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
inquartertodate()
function.
This example checks if an invoice date falls in a fiscal year specified by setting
the value of first_month_of_year to 4, and in the fourth quarter, before the
end of 28/02/2013.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InQuarterToDate(InvDate, '28/02/2013', 0, 4) AS Qtr42Date
Resident TempTable;
Drop table TempTable;
InvDate
Qtr42Date
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inweek
This function returns True if timestamp lies inside the week containing base_date.
Syntax:
InWeek (timestamp, base_date, period_no[, first_week_day])
Arguments:
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Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the week.
period_no
The week can be offset by period_no. period_no is an integer, where the value 0
indicates the week which contains base_date. Negative values in period_no indicate
preceding weeks and positive values indicate succeeding weeks.
first_
week_day
By default, the first day of the week is Monday,starting at midnight between Sunday and
Monday. To indicate the week starting on another day, specify an offset in first_week_day.
This may be given as a whole number of days and/or fractions of a day.
Examples and results:
Example
Result
inweek ('12/01/2006', '14/01/2006', 0)
Returns True
inweek ('12/01/2006', '20/01/2006', 0 )
Returns False
inweek ('12/01/2006', '14/01/2006', -1 )
Returns False
inweek ('07/01/2006', '14/01/2006', -1)
Returns True
inweek ('12/01/2006', '09/01/2006', 0, 3)
Returns False
Because first_week_day is specified as 3
(Thursday), which makes 12/01/2006 the first day of
the week following the week containing 09/01/2006.
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Example
Result
Add the example script to your app and run it.
Then add, at least, the fields listed in the results
column to a sheet in your app to see the result.
The resulting table contains the original dates and a
column with the return value of the inweek()
function.
This example checks if an invoice date falls at any
time in the fourth week after the week in base_
date, by specifying period_no as 4.
The InvDate5/2/2013 falls within the week that is
four weeks after the base_date: 11/1/2013.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InWeek(InvDate, '11/01/2013', 4) AS InWeekPlus4
Resident TempTable;
Drop table TempTable;
InvDate
InWeekPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
inweektodate
This function returns True if timestamp lies inside the part of week containing base_date up until and
including the last millisecond of base_date.
Syntax:
InWeekToDate (timestamp, base_date, period_no [, first_week_day])
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the week.
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Argument
Description
period_no
The week can be offset by period_no. period_no is an integer, where the value 0
indicates the week which contains base_date. Negative values in period_no indicate
preceding weeks and positive values indicate succeeding weeks.
first_
week_day
By default, the first day of the week is Monday,starting at midnight between Sunday and
Monday. To indicate the week starting on another day, specify an offset in first_week_day.
This may be given as a whole number of days and/or fractions of a day.
Examples and results:
Example
Result
inweektodate ('12/01/2006', '12/01/2006', 0)
Returns True
inweektodate ('12/01/2006', '11/01/2006', 0)
Returns False
inweektodate ('12/01/2006', '18/01/2006', -1)
Returns False
Because period_no is specified as -1, the
effective data that timestamp is measured
against is 11/01/2006.
inweektodate ( '11/01/2006', '12/01/2006', 0, 3 )
Returns False
Because first_week_day is specified as 3
(Thursday), which makes 12/01/2006 the first day
of the week following the week containing
12/01/2006.
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Example
Result
Add the example script to your app and run it. Then
add, at least, the fields listed in the results column to
a sheet in your app to see the result.
The resulting table contains the original dates
and a column with the return value of the inweek
() function.
This example checks if an invoice date falls during the
fourth week after the week in base_date, by
specifying period_no as 4, but before the value of
base_date.
InvDate
InWeek2DPlus4
28/03/2012
0 (False)
10/12/2012
0 (False)
5/2/2013
-1 (True)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InWeekToDate(InvDate, '11/01/2013', 4) AS
InWeek2DPlus4
Resident TempTable;
Drop table TempTable;
inyear
This function returns True if timestamp lies inside the year containing base_date.
Syntax:
InYear (timestamp, base_date, period_no [, first_month_of_year])
Return data type: Boolean
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
base_date
Date that is used to evaluate the year.
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Argument
Description
period_no
The year can be offset by period_no. period_no is an integer, where the value 0 indicates
the year that contains base_date. Negative values in period_no indicate preceding years,
and positive values indicate succeeding years.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
inyear ('25/01/2013', '01/01/2013', 0 )
Returns True
inyear ('25/01/2012', '01/01/2013', 0)
Returns False
inyear ('25/01/2013', '01/01/2013', -1)
Returns False
inyear ('25/01/2012', '01/01/2013', -1 )
Returns True
inyear ('25/01/2013', '01/01/2013', 0, 3)
Returns False The value of base_date and
first_month_of_year specify that
timestamp must fall within 01/03/2012 and
28/02/2013
inyear ('25/03/2013', '2013/07/01', 0, 3 )
Returns True
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Example
Result
Add the example script to your app and run it. Then add, at
least, the fields listed in the results column to a sheet in your
app to see the result.
The resulting table contains the original
dates and a column with the return value
of the inyear() function.
This example checks if an invoice date falls in the fiscal year
specified by setting the value of first_month_of_year to 4,
and having the base_date between 1/4/2012 and
31/03/2013.
InvDate
FinYr1213
28/03/2012
0 (False)
10/12/2012
-1 (True)
5/2/2013
-1 (True)
31/3/2013
-1 (True)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
Test if InvDate is in the financial year 1/04/2012 to
31/03/2013:
InvoiceData:
LOAD *,
InYear(InvDate, '31/01/2013', 0, 4) AS FinYr1213
Resident TempTable;
Drop table TempTable;
inyeartodate
This function returns True if timestamp lies inside the part of year containing base_date up until and
including the last millisecond of base_date.
Syntax:
InYearToDate (timestamp, base_date, period_no[, first_month_of_year])
Arguments:
Argument
Description
timestamp
The date that you want to compare with base_date.
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Argument
Description
base_date
Date that is used to evaluate the year.
period_no
The year can be offset by period_no. period_no is an integer, where the value 0 indicates
the year that contains base_date. Negative values in period_no indicate preceding years,
and positive values indicate succeeding years.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
Example
Result
inyeartodate ('2013/01/25', '2013/02/01', 0)
Returns True
inyeartodate ('2012/01/25', '2013/01/01', 0)
Returns False
inyeartodate ('2012/01/25', '2013/02/01', - )
Returns True
inyeartodate ('2012/11/25', '2013/01/31', 0, 4)
Returns True
The value of timestamp falls inside the fiscal year
beginning in the fourth month and before the
value of base_date.
inyeartodate ( '2013/3/31', '2013/01/31', 0, 4 )
Returns False
Compared with the previous example, the value of
timestamp is still inside the fiscal year, but it is
after the value of base_date, so it falls outside the
part of the year.
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Example
Result
Add the example script to your app and run it. Then
add, at least, the fields listed in the results column to
a sheet in your app to see the result.
The resulting table contains the original dates and
a column with the return value of the inyeartodate
() function.
This example checks if an invoice date falls in a fiscal
year specified by setting the value of first_month_of_
year to 4, and in the part of the year before the end
of 31/01/2013.
InvDate
FinYr2Date
28/03/2012
0 (False)
10/12/2012
-1 (True)
5/2/2013
0 (False)
31/3/2013
0 (False)
19/5/2013
0 (False)
15/9/2013
0 (False)
11/12/2013
0 (False)
2/3/2014
0 (False)
14/5/2014
0 (False)
13/6/2014
0 (False)
7/7/2014
0 (False)
4/8/2014
0 (False)
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
InYearToDate(InvDate, '31/01/2013', 0, 4) AS
FinYr2Date
Resident TempTable;
Drop table TempTable;
lastworkdate
The lastworkdate function returns the earliest ending date to achieve no_of_workdays (Monday-Friday) if
starting at start_date taking into account any optionally listed holiday. start_date and holiday should be
valid dates or timestamps.
Syntax:
lastworkdate(start_date, no_of_workdays {, holiday})
Return data type: integer
Arguments:
Argument
Description
start_date
The timestamp of start date to evaluate.
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Argument
Description
no_of_
The number of working days to achieve.
workdays
holiday
Holiday periods to exclude from working days. A holiday period is stated as a start date and
an end date, separated by commas.
Example: '25/12/2013', '26/12/2013'
You can exclude more than one holiday period, separated by commas.
Example: '25/12/2013', '26/12/2013', '31/12/2013', '01/01/2014'
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
lastworkdate ('19/12/2014', 9)
Returns '31/12/2014'
lastworkdate ('19/12/2014', 9, '2014-12-25', '2014-12-26')
Returns '02/01/2015 as a holiday
period of two days is taken into
account.
Add the example script to your app and run it. Then add, at least,
the fields listed in the results column to a sheet in your app to see
the result.
The resulting table shows the
returned values of LastWorkDate
for each of the records in the
table.
ProjectTable:
LOAD *, recno() as InvID, INLINE [
StartDate
28/03/2014
10/12/2014
5/2/2015
31/3/2015
19/5/2015
15/9/2015
] ;
NrDays:
Load *,
LastWorkDate(StartDate,120) As EndDate
Resident ProjectTable;
Drop table ProjectTable;
InvID
StartDate
EndDate
1
28/03/2014
11/09/2014
2
10/12/2014
26/05/2015
3
5/2/2015
27/07/2015
4
31/3/2015
14/09/2015
5
19/5/2015
02/11/2015
6
15/9/2015
29/02/2016
localtime
This function returns a timestamp of the current time from the system clock for a specified time zone.
Syntax:
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LocalTime([timezone [, ignoreDST ]])
Arguments:
Argument
Description
timezone
The timezone is specified as a string containing any of the geographical places listed under
Time Zone in the Windows Control Panel for Date and Time or as a string in the form
'GMT+hh:mm'.
If no time zone is specified the local time will be returned.
ignoreDST
If ignoreDST is -1 (True) daylight savings time will be ignored.
Examples and results:
The examples below are based on the function being called on 2014-10-22 12:54:47 local time, with the local
time zone being GMT+01:00.
Example
Result
localtime ()
Returns the local time 2014-10-22 12:54:47.
localtime ('London')
Returns the local time in London, 2014-10-22 11:54:47.
localtime
('GMT+02:00')
Returns the local time in the timezone of GMT+02:00, 2014-10-22 13:54:47.
localtime ('Paris',1')
Returns the local time in Paris with daylight savings time ignored, 2014-10-22
11:54:47.
lunarweekend
This function returns a value corresponding to a timestamp of the last millisecond of the lunar week
containing date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
Syntax:
LunarweekEnd(date[, period_no[, first_week_day]])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
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Argument
Description
period_
period_no is an integer or expression resolving to an integer, where the value 0 indicates
the lunar week which contains date. Negative values in period_no indicate preceding lunar
no
weeks and positive values indicate succeeding lunar weeks.
first_
week_day
An offset that may be greater than or less than zero. This changes the beginning of the year
by the specified number of days and/or fractions of a day.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
lunarweekend('12/01/2013')
Returns 14/01/2013
23:59:59.
lunarweekend('12/01/2013', -1)
Returns 7/01/2013
23:59:59.
lunarweekend('12/01/2013', 0, 1)
Returns 15/01/2013
23:59:59.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
lunarweekend() function.
You can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the final day of the lunar week of each invoice date in the
table, where the date is shifted by one week by specifying period_no as 1.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
LunarWeekEnd(InvDate, 1) AS LWkEnd
Resident TempTable;
Drop table TempTable;
InvDate
LWkEnd
28/03/2012
07/04/2012
10/12/2012
22/12/2012
5/2/2013
18/02/2013
31/3/2013
08/04/2013
19/5/2013
27/05/2013
15/9/2013
23/09/2013
11/12/2013
23/12/2013
2/3/2014
11/03/2014
14/5/2014
27/05/2014
13/6/2014
24/06/2014
7/7/2014
15/07/2014
4/8/2014
12/08/2014
lunarweekname
This function returns a display value showing the year and lunar week number corresponding to a timestamp
of the first millisecond of the first day of the lunar week containing date. Lunar weeks in Qlik Sense are
defined by counting 1 January as the first day of the week.
Syntax:
LunarWeekName(date [, period_no[, first_week_day]])
Arguments:
Argument
Description
date
The date to evaluate.
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Argument
Description
period_
period_no is an integer or expression resolving to an integer, where the value 0 indicates
the lunar week which contains date. Negative values in period_no indicate preceding lunar
no
weeks and positive values indicate succeeding lunar weeks.
first_
week_day
An offset that may be greater than or less than zero. This changes the beginning of the year
by the specified number of days and/or fractions of a day.
Examples and results:
Example
Result
lunarweekname('12/01/2013')
Returns 2006/02.
lunarweekname('12/01/2013', -1)
Returns 2006/01.
lunarweekname('12/01/2013', 0, 1)
Returns 2006/02.
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
lunarweekname()
function. You can
display the full
timestamp by specifying
the formatting in the
properties panel.
In this example, for each invoice date in the table, the lunar week name is
created from the year in which the week lies and its associated lunar week
number, shifted one week by specifying period_no as 1.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
LunarWeekName(InvDate, 1) AS LWkName
Resident TempTable;
Drop table TempTable;
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InvDate
LWkName
28/03/2012
2012/14
10/12/2012
2012/51
5/2/2013
2013/07
31/3/2013
2013/14
19/5/2013
2013/21
15/9/2013
2013/38
11/12/2013
2013/51
2/3/2014
2014/10
14/5/2014
2014/21
13/6/2014
2014/25
7/7/2014
2014/28
4/8/2014
2014/32
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lunarweekstart
This function returns a value corresponding to a timestamp of the first millisecond of the lunar week
containing date. Lunar weeks in Qlik Sense are defined by counting 1 January as the first day of the week.
Syntax:
LunarweekStart(date[, period_no[, first_week_day]])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer or expression resolving to an integer, where the value 0 indicates
the lunar week which contains date. Negative values in period_no indicate preceding lunar
no
weeks and positive values indicate succeeding lunar weeks.
first_
week_day
An offset that may be greater than or less than zero. This changes the beginning of the year
by the specified number of days and/or fractions of a day.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
lunarweekstart('12/01/2013')
Returns 08/01/2013.
lunarweekstart('12/01/2013', -1)
Returns 01/01/2013.
lunarweekstart('12/01/2013', 0, 1 )
Returns 09/01/2013.
Because the offset specified by setting first_
week_day to 1 means the beginning of the
year is changed to 02/01/2013.
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Example
Result
Add the example script to your app and run it. Then add,
at least, the fields listed in the results column to a sheet in
your app to see the result.
The resulting table contains the original
dates and a column with the return value of
the lunarweekstart() function. You can
display the full timestamp by specifying the
formatting in the properties panel.
This example finds the first day of the lunar week of each
invoice date in the table, where the date is shifted by one
week by specifying period_no as 1.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
LunarWeekStart(InvDate, 1) AS LWkStart
Resident TempTable;
Drop table TempTable;
InvDate
LWkStart
28/03/2012
01/04/2012
10/12/2012
16/12/2012
5/2/2013
12/02/2013
31/3/2013
02/04/2013
19/5/2013
21/05/2013
15/9/2013
17/09/2013
11/12/2013
17/12/2013
2/3/2014
05/03/2014
14/5/2014
21/05/2014
13/6/2014
18/06/2014
7/7/2014
09/07/2014
4/8/2014
06/08/2014
makedate
This function returns a date calculated from the year YYYY, the month MM and the day DD.
Syntax:
MakeDate(YYYY [ , MM [ , DD ] ])
Arguments:
Argument
Description
YYYY
The year as an integer.
MM
The month as an integer. If no month is stated, 1 (January) is assumed.
DD
The day as an integer.
If no day is stated, 1 (the 1:st) is assumed.
Examples and results:
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Example
Result
makedate(2012)
returns 2012-01-01
makedate(12)
returns 2012-01-01
makedate(2012,12)
returns 2012-12-01
makedate(2012,2,14)
returns 2012-02-14
maketime
This function returns a time calculated from the hour hh, the minute mm, and the second ss.
Syntax:
MakeTime(hh [ , mm [ , ss ] ])
Arguments:
Argument
Description
hh
The hour as an integer.
mm
The minute as an integer.
If no minute is stated, 00 is assumed.
ss
The second as an integer.
If no second is stated, 00 is assumed.
Examples and results:
Example
Result
maketime( 22 )
returns 22:00:00
maketime( 22, 17 )
returns 22:17:00
maketime( 22, 17, 52 )
returns 22:17:52
makeweekdate
This function returns a date calculated from the year YYYY, the week WW and the day-of-week D.
Syntax:
MakeWeekDate(YYYY [ , WW [ , D ] ])
Arguments:
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Argument
Description
YYYY
The year as an integer.
WW
The week as an integer.
D
The day-of-week as an integer.
If no day-of-week is stated, 0 (Monday) is assumed.
Examples and results:
Example
Result
makeweekdate(2014,6,6)
returns 2014-02-09
makeweekdate(2014,6,1)
returns 2014-02-04
makeweekdate(2014,6)
returns 2014-02-03 (weekday 0 is assumed)
minute
This function returns an integer representing the minute when the fraction of the expression is interpreted
as a time according to the standard number interpretation.
Syntax:
minute(expression)
Examples and results:
Example
Result
minute ( '09:14:36' )
returns 14
minute ( '0.5555' )
returns 19 ( Because 0.5555 = 13:19:55 )
month
This function returns a dual value with a month name as defined in the environment variable MonthNames
and an integer between 1-12. The month is calculated from the date interpretation of the expression,
according to the standard number interpretation.
Syntax:
month(expression)
Examples and results:
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Example
Result
month( '2012-10-12' )
returns Oct
month( '35648' )
returns Aug, because 35648 = 1997-08-06
monthend
This function returns a value corresponding to a timestamp of the last millisecond of the last day of the month
containing date. The default output format will be the DateFormat set in the script.
Syntax:
MonthEnd(date[, period_no])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, which, if 0 or omitted, indicates the month that contains date.
Negative values in period_no indicate preceding months and positive values indicate
no
succeeding months.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthend('19/02/2012')
Returns 29/02/2012
23:59:59.
monthend('19/02/2001', -1)
Returns 31/02/2012
23:59:59.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original dates
and a column with the
return value of the
monthend() function. You
can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day in the quarter of each invoice date in the
table, where the month in the base_date is offset by specifying period_no as
4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
MonthEnd(InvDate, 4) AS MthEnd
Resident TempTable;
Drop table TempTable;
InvDate
MthEnd
28/03/2012
31/07/2012
10/12/2012
30/04/2013
5/2/2013
30/06/2013
31/3/2013
31/07/2013
19/5/2013
30/09/2013
15/9/2013
31/01//2014
11/12/2013
30/04//2014
2/3/2014
31/07//2014
14/5/2014
30/09/2014
13/6/2014
31/10/2014
7/7/2014
30/11/2014
4/8/2014
31/12/2014
monthname
This function returns a display value showing the month (formatted according to the MonthNames script
variable) and year with an underlying numeric value corresponding to a timestamp of the first millisecond of
the first day of the month.
Syntax:
MonthName(date[, period_no])
Return data type: dual
Arguments:
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Argument
Description
date
The date to evaluate.
period_
period_no is an integer, which, if 0 or omitted, indicates the month that contains date.
Negative values in period_no indicate preceding months and positive values indicate
no
succeeding months.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthname('19/10/2013')
Returns Oct 2013.
Because in this and the other examples,
the SET Monthnames statement is set
to Jan;Feb;Mar, and so on.
monthname('19/10/2013', -1)
Returns Sep 2013.
Add the example script to your app and run it. Then add, at
least, the fields listed in the results column to a sheet in your
app to see the result.
The resulting table contains the original
dates and a column with the return value
of the monthname() function. In
In this example, for each invoice date in the table, the month
name is created from the month name shifted four months
from base_date, and from the year.
InvDate
MthName
28/03/2012
Jul 2012
10/12/2012
Apr 2013
5/2/2013
Jun 2013
31/3/2013
Jul 2013
19/5/2013
Sep 2013
15/9/2013
Jan 2014
11/12/2013
Apr 2014
2/3/2014
Jul 2014
14/5/2014
Sep 2014
13/6/2014
Oct 2014
7/7/2014
Nov 2014
4/8/2014
Dec 2014
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
MonthName(InvDate, 4) AS MthName
Resident TempTable;
Drop table TempTable;
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monthsend
This function returns a value corresponding to a timestamp of the last millisecond of the month, bi-month,
quarter, tertial, or half-year containing a base date. It is also possible to find the timestamp for a previous or
following time period.
Syntax:
MonthsEnd(n_month, date[, period_no [, first_month_of_year]])
Return data type: dual
Arguments:
Argument
Description
n_months
The number of months that defines the period. An integer or expression that resolves to an
integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3
(equivalent to the inquarter() function), 4 (tertial), or 6 (half year).
date
The date to evaluate.
period_
The period can be offset by period_no, an integer, or expression resolving to an integer,
no
where the value 0 indicates the period that contains base_date. Negative values in period_
no indicate preceding periods and positive values indicate succeeding periods.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthsend(4, '19/07/2013')
Returns 31/08/2013.
monthsend(4, '19/10/2013', -1)
Returns 31/08/2013.
monthsend(4, '19/10/2013', 0, 2)
Returns 31/01/2014.
Because the start of the
year becomes month 2.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
MonthsEnd() function.
This example finds the end of the final day of bi-month period for each
invoice date, shifted forwards by one bi-month period.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
MonthsEnd(2, InvDate, 1) AS BiMthsEnd
Resident TempTable;
Drop table TempTable;
InvDate
BiMthsEnd
28/03/2012
30/06/2012
10/12/2012
28/02/2013
5/2/2013
30/04/2013
31/3/2013
30/04/2013
19/5/2013
31/08/2013
15/9/2013
31/12/2013
11/12/2013
28/02/2014
2/3/2014
30/06/2014
14/5/2014
31/08/2014
13/6/2014
31/08/2014
7/7/2014
31/10/2014
4/8/2014
31/10/2014
monthsname
This function returns a display value representing the range of the months of the period (formatted according
to the MonthNames script variable) as well as the year. The underlying numeric value corresponds to a
timestamp of the first millisecond of the month, bi-month, quarter, tertial, or half-year containing a base date.
Syntax:
MonthsName(n_months, date[, period_no[, first_month_of_year]])
Return data type: dual
Arguments:
Argument
Description
n_months
The number of months that defines the period. An integer or expression that resolves to an
integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3
(equivalent to the inquarter() function), 4 (tertial), or 6 (half year).
date
The date to evaluate.
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Argument
Description
period_
The period can be offset by period_no, an integer, or expression resolving to an integer,
no
where the value 0 indicates the period that contains base_date. Negative values in period_
no indicate preceding periods and positive values indicate succeeding periods.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthsname(4, '19/10/2013')
Returns 'Sep-Dec 2013.
Because in this and the other
examples, the SET Monthnames
statement is set to Jan;Feb;Mar, and
so on.
monthsname(4, '19/10/2013', -1)
Returns 'May-Aug 2013.
monthsname(4, '19/10/2013', 0, 2)
Returns Oct-Jan 2014.
Because the year is specified to begin
in month 2, therefore the four-month
period ends on the first month of the
following year.
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Example
Result
Add the example script to your app and run it. Then add, at least,
the fields listed in the results column to a sheet in your app to
see the result.
The resulting table contains the
original dates and a column with the
return value of the monthsname()
function.
In this example, for each invoice date in the table, the months
name is created from the range of months in the bi-month
period, and from the year. The range is offset by 4x2 months by
specifying period_no as 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvDate
MthsName
28/03/2012
Nov-Dec 2012
10/12/2012
Jul-Aug 2013
5/2/2013
Sep-Oct 2013
31/3/2013
Nov-Dec2013
19/5/2013
Jan-Feb 2014
15/9/2013
May-Jun 2014
11/12/2013
Jul-Aug 2014
2/3/2014
Nov-Dec 2014
14/5/2014
Jan-Feb 2015
13/6/2014
Jan-Feb 2015
7/7/2014
Mar-Apr 2015
4/8/2014
Mar-Apr 2015
InvoiceData:
LOAD *,
MonthsName(2, InvDate, 4) AS MthsName
Resident TempTable;
Drop table TempTable;
monthsstart
This function returns a value corresponding to the timestamp of the first millisecond of the month, bi-month,
quarter, tertial, or half-year containing a base date. It is also possible to find the timestamp for a previous or
following time period.
Syntax:
MonthsStart(n_months, date[, period_no [, first_month_of_year]])
Return data type: dual
Arguments:
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Argument
Description
n_months
The number of months that defines the period. An integer or expression that resolves to an
integer that must be one of: 1 (equivalent to the inmonth() function), 2 (bi-month), 3
(equivalent to the inquarter() function), 4 (tertial), or 6 (half year).
date
The date to evaluate.
period_
The period can be offset by period_no, an integer, or expression resolving to an integer,
no
where the value 0 indicates the period that contains base_date. Negative values in period_
no indicate preceding periods and positive values indicate succeeding periods.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthsstart(4, '19/10/2013')
Returns 1/09/2013.
monthsstart(4, '19/10/2013, -1)
Returns 01/05/2013.
monthsstart(4, '19/10/2013', 0, 2 )
Returns 01/10/2013.
Because the start of the
year becomes month 2.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original dates
and a column with the
return value of the
MonthsStart() function.
This example finds the first day of the bi-month period for each invoice date,
shifted forwards by one bi-month period.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
MonthsStart(2, InvDate, 1) AS BiMthsStart
Resident TempTable;
Drop table TempTable;
InvDate
BiMthsStart
28/03/2012
01/05/2012
10/12/2012
01/01/2013
5/2/2013
01/03/2013
31/3/2013
01/05/2013
19/5/2013
01/07/2013
15/9/2013
01/11/2013
11/12/2013
01/01/2014
2/3/2014
01/05/2014
14/5/2014
01/07/2014
13/6/2014
01/07/2014
7/7/2014
01/09/2014
4/8/2014
01/09/2014
monthstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day of the
month containing date. The default output format will be the DateFormat set in the script.
Syntax:
MonthStart(date[, period_no])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, which, if 0 or omitted, indicates the month that contains date.
Negative values in period_no indicate preceding months and positive values indicate
no
succeeding months.
Examples and results:
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These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
monthstart('19/10/2001')
Returns 01/10/2001.
monthstart('19/10/2001', -1)
Returns 01/09/2001.
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
monthstart() function.
You can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day in the month of each invoice date in the
table, where the base_date is shifted by four months by specifying period_no
as 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
MonthStart(InvDate, 4) AS MthStart
Resident TempTable;
Drop table TempTable;
InvDate
MthStart
28/03/2012
01/07/2012
10/12/2012
01/04/2013
5/2/2013
01/06/2013
31/3/2013
01/07/2013
19/5/2013
01/09/2013
15/9/2013
01/01/2014
11/12/2013
01/04/2014
2/3/2014
01/07/2014
14/5/2014
01/09/2014
13/6/2014
01/10/2014
7/7/2014
01/11/2014
4/8/2014
01/12/2014
networkdays
The networkdays function returns the number of working days (Monday-Friday) between and including
start_date and end_date taking into account any optionally listed holiday.
Syntax:
networkdays (start_date, end_date [, holiday])
Return data type: integer
Arguments:
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Argument
Description
start_date
The timestamp of start date to evaluate.
end_date
The timestamp of end date to evaluate.
holiday
Holiday periods to exclude from working days. A holiday period is stated as a start date and
an end date, separated by commas.
Example: '25/12/2013', '26/12/2013'
You can exclude more than one holiday period, separated by commas.
Example: '25/12/2013', '26/12/2013', '31/12/2013', '01/01/2014'
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
networkdays ('19/12/2013', '07/01/2014')
Returns 14. This example does not take
holidays into account.
networkdays ('19/12/2013', '07/01/2014',
'25/12/2013', '26/12/2013')
Returns 12. This example takes the holiday
25/12/2013 to 26/12/2013 into account.
networkdays ('19/12/2013', '07/01/2014',
'25/12/2013', '26/12/2013', '31/12/2013',
'01/01/2014')
Returns 10. This example takes two holiday
periods into account.
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Example
Result
Add the example script to your app and run it. Then add,
at least, the fields listed in the results column to a sheet
in your app to see the result.
The resulting table shows the returned values
of NetworkDays for each of the records in the
table.
PayTable:
LOAD *, recno() as InvID, INLINE [
InvRec|InvPaid
28/03/2012|28/04/2012
10/12/2012|01/01/2013
5/2/2013|5/3/2013
31/3/2013|01/5/2013
19/5/2013|12/6/2013
15/9/2013|6/10/2013
11/12/2013|12/01/2014
2/3/2014|2/4/2014
14/5/2014|14/6/2014
13/6/2014|14/7/2014
7/7/2014|14/8/2014
4/8/2014|4/9/2014
] (delimiter is '|');
NrDays:
Load *,
NetWorkDays(InvRec,InvPaid) As PaidDays
Resident PayTable;
Drop table PayTable;
InvID
InvRec
InvPaid
PaidDays
1
28/03/2012
28/04/2012
23
2
10/12/2012
01/01/2013
17
3
5/2/2013
5/3/2013
21
4
31/3/2013
01/5/2013
23
5
19/5/2013
12/6/2013
18
6
15/9/2013
6/10/2013
15
7
11/12/2013
12/01/2014
23
8
2/3/2014
2/4/2014
23
9
14/5/2014
14/6/2014
23
10
13/6/2014
14/7/2014
22
11
7/7/2014
14/8/2014
29
12
4/8/2014
4/9/2014
24
now
This function returns a timestamp of the current time from the system clock.
Syntax:
now([ timer_mode])
Arguments:
Argument
Description
timer_
mode
Can have the following values:
0 (time at last finished data load)
1 (time at function call)
2 (time when the app was opened)
If you use the function in a data load script, timer_mode=0 will result in the
time of the last finished data load, while timer_mode=1 will give the time of
the function call in the current data load.
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Examples and results:
Example
Result
now( 0)
Returns the time when the last data load completed.
now( 1)
l
l
now( 2)
When used in a visualization expression, this returns the time of the function call.
When used in a data load script, this returns the time of the function call in the current
data load.
Returns the time when the app was opened.
quarterend
This function returns a value corresponding to a timestamp of the last millisecond of the quarter containing
date. The default output format will be the DateFormat set in the script.
Syntax:
QuarterEnd(date[, period_no[, first_month_of_year]])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the quarter which contains date.
Negative values in period_no indicate preceding quarters and positive values indicate
no
succeeding quarters.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
quarterend('29/10/2005')
Returns 31/12/2005
23:59:59.
quarterend('29/10/2005', -1)
Returns 30/09/2005
23:59:59.
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Example
Result
quarterend('29/10/2005', 0, 3)
Returns 30/11/2005
23:59:59.
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
quarterend() function.
You can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day in the quarter of each invoice date in the
table, where the first month in the year is specified as month 3.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
QuarterEnd(InvDate, 0, 3) AS QtrEnd
Resident TempTable;
Drop table TempTable;
InvDate
QtrEnd
28/03/2012
31/05/2012
10/12/2012
28/02/2013
5/2/2013
28/02/2013
31/3/2013
31/05/2013
19/5/2013
31/05/2013
15/9/2013
30/11/2013
11/12/2013
28/02/2014
2/3/2014
31/05/2014
14/5/2014
31/05/2014
13/6/2014
31/08/2014
7/7/2014
31/08/2014
4/8/2014
31/08/2014
quartername
This function returns a display value showing the months of the quarter (formatted according to the
MonthNames script variable) and year with an underlying numeric value corresponding to a timestamp of
the first millisecond of the first day of the quarter.
Syntax:
QuarterName(date[, period_no[, first_month_of_year]])
Return data type: dual
Arguments:
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Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the quarter which contains date.
Negative values in period_no indicate preceding quarters and positive values indicate
no
succeeding quarters.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
Example
Result
quartername('29/10/2013')
Returns Oct-Dec 2013.
quartername('29/10/2013', -1)
Returns Jul-Sep 2013.
quartername('29/10/2013', 0, 3)
Returns Sep-Nov 2013.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original dates
and a column with the
return value of the
quartername() function.
In this example, for each invoice date in the table, the quarter name is
created from the month name taken from base_date, and from the year,
where the first month in the year is specified as month 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
QuarterName(InvDate, 0, 4) AS QtrName
Resident TempTable;
Drop table TempTable;
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InvDate
QtrName
28/03/2012
Jan-Mar
2011
Because
this reflects
the fiscal
year starting
in 2011.
10/12/2012
Oct-Dec
2012
5/2/2013
Jan-Mar
2012
31/3/2013
Jan-Mar
2012
19/5/2013
Apr-Jun
2013
15/9/2013
Jul-Sep
2013
11/12/2013
Oct-Dec
2013
2/3/2014
Jan-Mar
2013
14/5/2014
Apr-Jun
2014
13/6/2014
Apr-Jun
2014
7/7/2014
Jul-Sep
2014
4/8/2014
Jul-Sep
2014
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quarterstart
This function returns a value corresponding to a timestamp of the first millisecond of the quarter containing
date. The default output format will be the DateFormat set in the script.
Syntax:
QuarterStart(date[, period_no[, first_month_of_yea]])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the quarter which contains date.
Negative values in period_no indicate preceding quarters and positive values indicate
no
succeeding quarters.
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
quarterstart('29/10/2005')
Returns 01/10/2005.
quarterstart('29/10/2005', -1 )
Returns 01/07/2005.
quarterstart('29/10/2005', 0, 3)
Returns 01/09/2005.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
quarterstart() function.
You can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day in the quarter of each invoice date in the
table, where the first month in the year is specified as month 3.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
QuarterStart(InvDate, 0, 3) AS QtrStart
Resident TempTable;
Drop table TempTable;
InvDate
QtrStart
28/03/2012
01/03/2012
10/12/2012
01/12/2012
5/2/2013
01/12/2012
31/3/2013
01/03/2013
19/5/2013
01/03/2013
15/9/2013
01/09/2013
11/12/2013
01/12/2013
2/3/2014
01/03/2014
14/5/2014
01/03/2014
13/6/2014
01/06/2014
7/7/2014
01/06/2014
4/8/2014
01/06/2014
second
This function returns an integer representing the second when the fraction of the expression is interpreted
as a time according to the standard number interpretation.
Syntax:
second (expression)
Examples and results:
Example
Result
second( '09:14:36' )
returns 36
second( '0.5555' )
returns 55 ( Because 0.5555 = 13:19:55 )
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setdateyear
This function returns a timestamp based on the input timestamp but with the year replaced with year.
Syntax:
setdateyear (timestamp, year)
Arguments:
Argument
Description
timestamp
A standard Qlik Sense timestamp (often just a date).
year
A four-digit year.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
setdateyear ('29/10/2005', 2013)
Returns '29/102013
setdateyear ('29/10/2005 04:26', 20013)
Returns '29//10/20013 04:26'
To see the time part of the timestamp in a
visualization, you must set the number formatting to
Date and choose a value for Formatting that display
time values.
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Example
Result
Add the example script to your app and run it.
Then add, at least, the fields listed in the results
column to a sheet in your app to see the result.
The resulting table contains the original dates and a
column in which the year has be set to 2013.
SetYear:
Load *,
SetDateYear(testdates, 2013) as NewYear
Inline [
testdates
1/11/2012
10/12/2012
1/5/2013
2/1/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
testdates
NewYear
1/11/2012
1//11/2013
10/12/2012
10/12/2013
2/1/2012
2/1/2013
1/5/2013
1/5/2013
19/5/2013
19/5/2013
15/9/2013
15/9/2013
11/12/2013
11/12/2013
2/3/2014
2/3/2013
14/5/2014
14/5/2013
13/6/2014
13/6/2013
7/7/2014
7/7/2013
4/8/2014
4/8/2013
setdateyearmonth
Returns a timestamp based on the input timestamp but with the year replaced with year and the month
replaced with month.
Syntax:
SetDateYearMonth (timestamp, year, month)
Arguments:
Argument
Description
timestamp
A standard Qlik Sense timestamp (often just a date).
year
A four-digit year.
month
A one- or two-digit month.
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
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Example
Result
setdateyearmonth ('29/10/2005', 2013, 3)
Returns '29/03/2013
setdateyearmonth ('29/10/2005 04:26', 2013,
3)
Returns '29/03/2013 04:26'
To see the time part of the timestamp in a
visualization, you must set the number formatting to
Date and choose a value for Formatting that display
time values.
Add the example script to your app and run it.
Then add, at least, the fields listed in the results
column to a sheet in your app to see the result.
The resulting table contains the original dates and a
column in which the year has be set to 2013.
SetYearMonth:
Load *,
SetDateYearMonth(testdates, 2013) as
NewYearMonth
Inline [
testdates
1/11/2012
10/12/2012
1/5/2013
2/1/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
testdates
NewYearMonth
1/11/2012
1//3/2013
10/12/2012
10/3/2013
2/1/2012
2/3/2013
1/5/2013
1/3/2013
19/5/2013
19/3/2013
15/9/2013
15/3/2013
11/12/2013
11/3/2013
2/3/2014
2/3/2013
14/5/2014
14/3/2013
13/6/2014
13/3/2013
7/7/2014
7/3/2013
4/8/2014
4/3/2013
timezone
This function returns the name of the current time zone, as defined in Windows.
Syntax:
TimeZone( )
Example:
timezone( )
today
This function returns the current date from the system clock.
Syntax:
today([ timer_mode])
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Arguments:
Argument
Description
timer_
mode
Can have the following values:
0 (day of last finished data load)
1 (day of function call)
2 (day when the app was opened)
If you use the function in a data load script, timer_mode=0 will result in the
day of the last finished data load, while timer_mode=1 will give the day of the
current data load.
Examples and results:
Example
Result
Today( 0)
Returns the day of the last finished data load.
Today( 1)
When used in a visualization expression, this returns the day of the function call.
When used in a data load script, this returns the day when the current data load started.
Today( 2)
Returns the day when the app was opened.
UTC
Returns the current Coordinated Universal Time.
Syntax:
UTC( )
Example:
utc( )
week
This function returns an integer representing the week number according to ISO 8601. The week number is
calculated from the date interpretation of the expression, according to the standard number interpretation.
Syntax:
week(expression)
Examples and results:
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Example
Result
week( '2012-10-12' )
returns 44
week( '35648' )
returns 32, because 35648 = 1997-08-06
weekday
This function returns a dual value with:
l
A day name as defined in the environment variable DayNames.
l
An integer between 0-6 corresponding to the nominal day of the week (0-6).
Syntax:
weekday(date [,weekstart=0])
Arguments:
Argument
Description
date
The date to evaluate.
weekstart
If you don't specify weekstart, the value of variable FirstWeekDay will be used as the first
day of the week.
If you want to use another day as the first day of the week, set weekstart to:
l
l
l
l
l
l
l
0 for Monday
1 for Tuesday
2 for Wednesday
3 for Thursday
4 for Friday
5 for Saturday
6 for Sunday
The integer returned by the function will now use the first day of the week that you set with
weekstart as base (0).
See: FirstWeekDay (page 121)
Examples and results:
Unless it is stated specifically, FirstWeekDay is set to 0 in these examples.
Example
Result
weekday( '1971-10-12' )
returns 'Tue' and 1
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Example
Result
weekday( '1971-10-12' , 6)
returns 'Tue' and 2.
In this example we use Sunday (6) as the first day of the week.
SET FirstWeekDay = 6;
returns 'Tue' and 2.
...
weekday( '1971-10-12')
weekend
This function returns a value corresponding to a timestamp of the last millisecond of the last day (Sunday) of
the calendar week containing date The default output format will be the DateFormat set in the script.
Syntax:
WeekEnd(date [, period_no[, first_week_day]])
Arguments:
Argument
Description
date
The date to evaluate.
period_
no
shift is an integer, where the value 0 indicates the week which contains date. Negative
values in shift indicate preceding weeks and positive values indicate succeeding weeks.
first_
Specifies the day on which the week starts. If omitted, the value of variable FirstWeekDay
week_day
is used.
The possible values first_week_day are:
l
l
l
l
l
l
l
0 for Monday
1 for Tuesday
2 for Wednesday
3 for Thursday
4 for Friday
5 for Saturday
6 for Sunday
See: FirstWeekDay (page 121)
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
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Example
Result
weekend('10/01/2013')
Returns 12/01/2013
23:59:59.
weekend('10/01/2013', -1)
Returns 06/01/2013
23:59:59.
weekend('10/01/2013', 0, 1)
Returns 14/01/2013
23:59:59.
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
weekend() function. You
can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the final day in the week following the week of each
invoice date in the table.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
WeekEnd(InvDate, 1) AS WkEnd
Resident TempTable;
Drop table TempTable;
InvDate
WkEnd
28/03/2012
08/04/2012
10/12/2012
23/12/2012
5/2/2013
17/02/2013
31/3/2013
07/04/2013
19/5/2013
26/05/2013
15/9/2013
22/09/2013
11/12/2013
22/12/2013
2/3/2014
09/03/2014
14/5/2014
25/05/2014
13/6/2014
22/06/2014
7/7/2014
20/07/2014
4/8/2014
17/08/2014
weekname
This function returns a value showing the year and week number with an underlying numeric value
corresponding to a timestamp of the first millisecond of the first day of the week containing date.
Syntax:
WeekName(date[, period_no[,first_week_day]])
Arguments:
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Argument
Description
date
The date to evaluate.
period_
no
shift is an integer, where the value 0 indicates the week which contains date. Negative
values in shift indicate preceding weeks and positive values indicate succeeding weeks.
first_
Specifies the day on which the week starts. If omitted, the value of variable FirstWeekDay
week_day
is used.
The possible values first_week_day are:
l
l
l
l
l
l
l
0 for Monday
1 for Tuesday
2 for Wednesday
3 for Thursday
4 for Friday
5 for Saturday
6 for Sunday
See: FirstWeekDay (page 121)
Examples and results:
Example
Result
weekname('12/01/20013')
Returns 2013/02.
weekname('12/01/20013', -1)
Returns 2013/01.
weekname('12/01/20013', 0, 1)
Returns '2013/02.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column
with the return value of
the weekname()
function. You can
display the full
timestamp by
specifying the
formatting in the
properties panel.
In this example, for each invoice date in the table, the week name is created
from the year in which the week lies and its associated week number, shifted
one week by specifying period_no as 1.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
WeekName(InvDate, 1) AS WkName
Resident TempTable;
Drop table TempTable;
InvDate
WkName
28/03/2012
2012/14
10/12/2012
2012/51
5/2/2013
2013/07
31/3/2013
2013/14
19/5/2013
2013/21
15/9/2013
2013/38
11/12/2013
2013/51
2/3/2014
2014/10
14/5/2014
2014/21
13/6/2014
2014/25
7/7/2014
2014/29
4/8/2014
2014/33
weekstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day (Monday) of
the calendar week containing date. The default output format is the DateFormat set in the script.
Syntax:
WeekStart(date [, period_no[, first_week_day]])
Return data type: dual
Arguments:
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Argument
Description
date
The date to evaluate.
period_
no
shift is an integer, where the value 0 indicates the week which contains date. Negative
values in shift indicate preceding weeks and positive values indicate succeeding weeks.
first_
Specifies the day on which the week starts. If omitted, the value of variable FirstWeekDay
week_day
is used.
The possible values first_week_day are:
l
l
l
l
l
l
l
0 for Monday
1 for Tuesday
2 for Wednesday
3 for Thursday
4 for Friday
5 for Saturday
6 for Sunday
See: FirstWeekDay (page 121)
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
weekstart('12/01/2013')
Returns 07/01/2013.
weekstart('12/01/2013', -1 )
Returns 31/11/2012.
weekstart('12/01/2013', 0, 1)
Returns 08/01/2013.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
weekstart() function. You
can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day of the week following the week of each
invoice date in the table.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
WeekStart(InvDate, 1) AS WkStart
Resident TempTable;
Drop table TempTable;
InvDate
WkStart
28/03/2012
02/04/2012
10/12/2012
17/12/2012
5/2/2013
11/02/2013
31/3/2013
01/04/2013
19/5/2013
20/05/2013
15/9/2013
16/09/2013
11/12/2013
16/12/2013
2/3/2014
03/03/2014
14/5/2014
19/05/2014
13/6/2014
16/06/2014
7/7/2014
14/07/2014
4/8/2014
11/08/2014
weekyear
This function returns the year to which the week number belongs according to ISO 8601. The week number
ranges between 1 and approximately 52.
Syntax:
weekyear(expression)
Examples and results:
Example
Result
weekyear( '1996-12-30' )
returns 1997, because week 1 of 1998 starts on 1996-12-30
weekyear( '1997-01-02' )
returns 1997
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Example
Result
weekyear( '1997-12-28' )
returns 1997
weekyear( '1997-12-30' )
returns 1998, because week 1 of 1998 starts on 1997-12-29
weekyear( '1999-01-02' )
returns 1998, because week 53 of 1998 ends on 1999-01-03
Limitations:
Some years, week #1 starts in December, e.g. December 1997. Other years start with week #53 of previous
year, e.g. January 1999. For those few days when the week number belongs to another year, the functions
year and weekyear will return different values.
year
This function returns an integer representing the year when the expression is interpreted as a date
according to the standard number interpretation.
Syntax:
year(expression)
Examples and results:
Example
Result
year( '2012-10-12' )
returns 2012
year( '35648' )
returns 1997, because 35648 = 1997-08-06
yearend
This function returns a value corresponding to a timestamp of the last millisecond of the last day of the year
containing date. The default output format will be the DateFormat set in the script.
Syntax:
YearEnd( date[, period_no[, first_month_of_year = 1]])
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the year which contains date. Negative
values in period_no indicate preceding years and positive values indicate succeeding years.
no
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Argument
Description
first_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
month_
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
yearend ( '19/10/2001' )
Returns 31/12/2001
23:59:59.
yearend ( '19/10/2001', -1 )
Returns 31/12/2000
23:59:59.
yearend ( '19/10/2001', 0, 4)
Returns 31/03/2002
23:59:59.
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
yearend() function. You
can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the final day in the year of each invoice date in the table,
where the first month in the year is specified as month 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
YearEnd(InvDate, 0, 4) AS YrEnd
Resident TempTable;
Drop table TempTable;
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InvDate
YrEnd
28/03/2012
31/03/2011
10/12/2012
31/03/2012
5/2/2013
31/03/2013
31/3/2013
31/03/2013
19/5/2013
31/03/2014
15/9/2013
31/03/2014
11/12/2013
31/03/2014
2/3/2014
31/03/2014
14/5/2014
31/03/2015
13/6/2014
31/03/2015
7/7/2014
31/03/2015
4/8/2014
31/03/2015
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yearname
This function returns a four-digit year as display value with an underlying numeric value corresponding to a
timestamp of the first millisecond of the first day of the year containing date.
Syntax:
YearName(date[, period_no[, first_month_of_year]] )
Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the year which contains date. Negative
values in period_no indicate preceding years and positive values indicate succeeding years.
no
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year. The display value will then be a string showing two years.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
yearname ( '19/10/2001')
Returns 2001.
yearname ( '19/10/2001', -1 )
Returns '2000.
yearname ( '19/10/2001', 0, 4)
Returns '2001-2002.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column
with the return value of
the yearname()
function.
This example creates a four-plus-four digit name for the years in which each
invoice date in the table is found. This is because the first month in the year is
specified as month 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
YearName(InvDate, 0, 4) AS YrName
Resident TempTable;
Drop table TempTable;
InvDate
YrName
28/03/2012
20112012
10/12/2012
20122013
5/2/2013
20122013
31/3/2013
20122013
19/5/2013
20132014
15/9/2013
20132014
11/12/2013
20132014
2/3/2014
20132014
14/5/2014
20142015
13/6/2014
20142015
7/7/2014
20142015
4/8/2014
20142015
yearstart
This function returns a value corresponding to a timestamp of the first millisecond of the first day of the year
containing date. The default output format will be the DateFormat set in the script.
Syntax:
YearStart(date[, period_no[, first_month_of_year]])
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Return data type: dual
Arguments:
Argument
Description
date
The date to evaluate.
period_
period_no is an integer, where the value 0 indicates the year which contains date. Negative
values in period_no indicate preceding years and positive values indicate succeeding years.
no
first_
month_
If you want to work with (fiscal) years not starting in January, indicate a value between 2 and
12 in first_month_of_year.
of_year
Examples and results:
These examples use the date format DD/MM/YYYY. The date format is specified in the SET DateFormat
statement at the top of your data load script. Change the format in the examples to suit your requirements.
Example
Result
yearstart ('19/10/2001')
Returns 01/01/2001.
yearstart ('19/10/2001', -1)
Returns 01/01/2000.
yearstart ('19/10/2001', 0, 4)
Returns 01/04/2001.
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Example
Result
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
contains the original
dates and a column with
the return value of the
yearstart() function. You
can display the full
timestamp by specifying
the formatting in the
properties panel.
This example finds the first day in the year of each invoice date in the table,
where the first month in the year is specified as month 4.
TempTable:
LOAD RecNo() as InvID, * Inline [
InvDate
28/03/2012
10/12/2012
5/2/2013
31/3/2013
19/5/2013
15/9/2013
11/12/2013
2/3/2014
14/5/2014
13/6/2014
7/7/2014
4/8/2014
];
InvoiceData:
LOAD *,
YearStart(InvDate, 0, 4) AS YrStart
Resident TempTable;
Drop table TempTable;
InvDate
YrStart
28/03/2012
01/04/2011
10/12/2012
01/04/2012
5/2/2013
01/04/2012
31/3/2013
01/04/2012
19/5/2013
01/04/2013
15/9/2013
01/04/2013
11/12/2013
01/04/2013
2/3/2014
01/04/2013
14/5/2014
01/04/2014
13/6/2014
01/04/2014
7/7/2014
01/04/2014
4/8/2014
01/04/2014
yeartodate
This function finds if the input date falls within the year of the date the script was last loaded, and returns
True if it does, False if it does not.
Syntax:
YearToDate(date [ , yearoffset [ , firstmonth [ , todaydate] ] ])
If none of the optional parameters are used, the year to date means any date within one calendar year from
January 1 up to and including the date of the last script execution.
Arguments:
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Argument
Description
date
The date to evaluate as a timestamp, for example '2012-10-12'.
yearoffset
By specifying a yearoffset, yeartodate returns True for the same period in another year. A
negative yearoffset indicates a previous year, a positive offset a future year. The most
recent year-to-date is achieved by specifying yearoffset = -1. If omitted, 0 is assumed.
firstmonth
By specifying a firstmonth between 1 and 12 (1 if omitted) the beginning of the year may be
moved forward to the first day of any month. For example, if you want to work with a fiscal
year beginning on May 1, specify firstmonth = 5.
todaydate
By specifying a todaydate (timestamp of the last script execution if omitted) it is possible to
move the day used as the upper boundary of the period.
Examples and results:
The following examples assume last reload time = 2011-11-18
Example
Result
yeartodate( '2010-11-18')
returns False
yeartodate( '2011-02-01')
returns True
yeartodate( '2011-11-18')
returns True
yeartodate( '2011-11-19')
returns False
yeartodate( '2011-11-19', 0, 1, '2011-12-31')
returns True
yeartodate( '2010-11-18', -1)
returns True
yeartodate( '2011-11-18', -1)
returns False
yeartodate( '2011-04-30', 0, 5)
returns False
yeartodate( '2011-05-01', 0, 5)
returns True
5.6
Exponential and logarithmic functions
This section describes functions related to exponential and logarithmic calculations. All functions can be used
in both the data load script and in chart expressions.
In the functions below, the parameters are expressions where x and y should be interpreted as real valued
numbers.
exp
The natural exponential function, e^x, using the natural logarithm e as base. The result is a positive number.
exp(x )
Examples and results:
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exp(3) returns 20.085.
log
The natural logarithm of x. The function is only defined if x> 0. The result is a number.
log(x )
Examples and results:
log(3) returns 1.0986
log10
The common logarithm (base 10) of x. The function is only defined if x> 0. The result is a number.
log10(x )
Examples and results:
log10(3) returns 0.4771
pow
Returns x to the power of y. The result is a number.
pow(x,y )
Examples and results:
pow(3, 3) returns 27
sqr
x squared (x to the power of 2). The result is a number.
sqr (x )
Examples and results:
sqr(3) returns 9
sqrt
Square root of x. The function is only defined if x >= 0. The result is a positive number.
sqrt(x )
Examples and results:
sqrt(3) returns 1.732
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Exponential and logarithmic functions
This section describes functions related to exponential and logarithmic calculations. All functions can be used
in both the data load script and in chart expressions.
In the functions below, the parameters are expressions where x and y should be interpreted as real valued
numbers.
exp
The natural exponential function, e^x, using the natural logarithm e as base. The result is a positive number.
exp(x )
Examples and results:
exp(3) returns 20.085.
log
The natural logarithm of x. The function is only defined if x> 0. The result is a number.
log(x )
Examples and results:
log(3) returns 1.0986
log10
The common logarithm (base 10) of x. The function is only defined if x> 0. The result is a number.
log10(x )
Examples and results:
log10(3) returns 0.4771
pow
Returns x to the power of y. The result is a number.
pow(x,y )
Examples and results:
pow(3, 3) returns 27
sqr
x squared (x to the power of 2). The result is a number.
sqr (x )
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Examples and results:
sqr(3) returns 9
sqrt
Square root of x. The function is only defined if x >= 0. The result is a positive number.
sqrt(x )
Examples and results:
sqrt(3) returns 1.732
5.7
Field functions
These functions can only be used in chart expressions.
Field functions either return integers or strings identifying different aspects of field selections.
Count functions
GetSelectedCount
GetSelectedCount() finds the number of selected (green) values in a field.
GetSelectedCount - chart function (field_name [, include_excluded])
GetAlternativeCount
GetAlternativeCount()is used to find the number of alternative (light gray) values in the identified field.
GetAlternativeCount - chart function (field_name)
GetPossibleCount
GetPossibleCount() is used to find the number of possible values in the identified field. If the identified field
includes selections, then the selected (green) fields are counted. Otherwise associated (white) values are
counted.
GetPossibleCount - chart function(field_name)
GetExcludedCount
GetExcludedCount() finds the number of excluded (dark gray) values in the identified field.
GetExcludedCount - chart function (page 445)(field_name)
GetNotSelectedCount
This chart function returns the number of not-selected values in the field named fieldname. The field must
be in and-mode for this function to be relevant.
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GetNotSelectedCount - chart function(fieldname [, includeexcluded=false])
Field and selection functions
GetCurrentSelections
GetCurrentSelections() returns the current selections in the app.
GetCurrentSelections - chart function([record_sep [,tag_sep [,value_sep
[,max_values]]]])
GetFieldSelections
GetFieldSelections() returns a string with the current selections in a field.
GetFieldSelections - chart function ( field_name [, value_sep [, max_
values]])
GetAlternativeCount - chart function
GetAlternativeCount()is used to find the number of alternative (light gray) values in the identified field.
Syntax:
GetAlternativeCount (field_name)
Return data type: integer
Arguments:
Argument
Description
field_name
The field containing the range of data to be measured.
Examples and results:
The following example uses two fields loaded to different filter panes, one for First name name and one for
Initials.
Examples
Results
Given that John is selected in First name.
4 as there are 4 unique and excluded (gray) values in First
GetAlternativeCount ([First name])
Given that John and Peter are selected.
GetAlternativeCount ([First name])
Given that no values are selected in First
name.
3 as there are 3 unique and excluded (gray) values in First
name.
0 as there are no selections.
name.
GetAlternativeCount ([First name])
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Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
GetCurrentSelections - chart function
GetCurrentSelections() returns the current selections in the app.
If options are used you will need to specify record_sep. To specify a new line set record_sep to chr(13)&chr
(10).
If all but two, or all but one, values, are selected, the format 'NOT x,y' or 'NOT y' will be used respectively. If
you select all values and the count of all values is greater than max_values, the text ALL will be returned.
Syntax:
GetCurrentSelections ([record_sep [,tag_sep [,value_sep [,max_values]]]])
Return data type: string
Arguments:
Arguments
Description
record_sep
Separator to be put between field records. The default is meaning a new line.
tag_sep
Separator to be put between the field name tag and the field values. The default is ': '.
value_sep
The separator to be put between field values. The default is ', '.
max_values
The maximum number of field values to be individually listed. When a larger number of
values is selected, the format 'x of y values' will be used instead. The default is 6.
Examples and results:
The following example uses two fields loaded to different filter panes, one for First name name and one for
Initials.
Examples
Results
Given that John is selected in First name.
'First name: John'
GetCurrentSelections ()
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5 Functions in scripts and chart expressions
Examples
Results
Given that John and Peter are selected in First name.
'First name: John,
Peter'
GetCurrentSelections ()
Given that John is selected in First name and JA is selected in Initials.
GetCurrentSelections ()
'First name: John;
Peter
Initials: JA'
Given that John is selected in First name and JA is selected in Initials.
'First name = John
GetCurrentSelections ( chr(13)&chr(10) , ' = ' )
Initials = JA'
Given that you have selected all names except Sue in First name and no
'First
name=NOT Sue'
selections in Initials.
=GetCurrentSelections(chr(13)&chr(10),'=',',',3)
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
GetExcludedCount - chart function
GetExcludedCount() finds the number of excluded (dark gray) values in the identified field.
Syntax:
GetExcludedCount (field_name)
Return data type: string
Limitations:
GetExcludedCount() only evaluates for fields with associated values, that is, fields without selections. For
fields with selections GetExcludedCount() will return 0.
Arguments:
Arguments
Description
field_name
The field containing the range of data to be measured.
Examples and results:
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The following example uses two fields loaded to different filter panes, one for First name name and one for
Initials.
Examples
Results
Given that John is
5 as there are 5 excluded (gray) values in Initials. The sixth cell (JA) will be
selected in First name.
white as it is associated with the selection John in First name.
GetExcludedCount
([Initials])
Given that John and
3 as Peter is associated with 2 values in Initials.
Peter are selected.
GetExcludedCount
([Initials])
Given that no values are
selected in First name.
0 as there are no selections.
GetExcludedCount
([Initials])
Given that John is
0 as GetExcludedCount() only evaluates for fields with associated values,
selected in First name.
that is, fields without selections.
GetExcludedCount ([First
name])
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
GetFieldSelections - chart function
GetFieldSelections() returns a string with the current selections in a field.
If all but two, or all but one of the values are selected, the format 'NOT x,y' or 'NOT y' will be used
respectively. If you select all values and the count of all values is greater than max_values, the text ALL will
be returned.
Syntax:
GetFieldSelections ( field_name [, value_sep [, max_values]])
Return data type: string
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Arguments:
Arguments
Description
field_name
The field containing the range of data to be measured.
value_sep
The separator to be put between field values. The default is ', '.
max_values
The maximum number of field values to be individually listed. When a larger number of
values is selected, the format 'x of y values' will be used instead. The default is 6.
Examples and results:
The following example uses two fields loaded to different filter panes, one for First name name and one for
Initials.
Examples
Results
Given that John is selected
'John'
in First name.
GetFieldSelections ([First
name])
Given that John and Peter
'John,Peter'
are selected.
GetFieldSelections ([First
name])
Given that John and Peter
'John; Peter'
are selected.
GetFieldSelections ([First
name],'; ')
Given that John, Sue,
Mark are selected in First
'NOT Jane;Peter', because the value 2 is stated as the value of the max_
values argument. Otherwise, the result would have been John; Sue; Mark.
name.
GetFieldSelections ([First
name],';',2)
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
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Peter|PF|Yes ] (delimiter is '|');
GetNotSelectedCount - chart function
This chart function returns the number of not-selected values in the field named fieldname. The field must
be in and-mode for this function to be relevant.
Syntax:
GetNotSelectedCount(fieldname [, includeexcluded=false])
Arguments:
Argument
Description
fieldname
The name of the field to be evaluated.
includeexcluded
If includeexcluded is stated as True, the count will include selected values which are
excluded by selections in another field.
Examples:
GetNotSelectedCount( Country )
GetNotSelectedCount( Country, true )
GetPossibleCount - chart function
GetPossibleCount() is used to find the number of possible values in the identified field. If the identified field
includes selections, then the selected (green) fields are counted. Otherwise associated (white) values are
counted. .
For fields with selections, GetPossibleCount() returns the number of selected (green) fields.
Return data type: integer
Syntax:
GetPossibleCount (field_name)
Arguments:
Arguments
Description
field_name
The field containing the range of data to be measured.
Examples and results:
The following example uses two fields loaded to different filter panes, one for First name name and one for
Initials.
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Examples
Results
Given that John is selected in First
1 as there is 1 value in Initials associated with the selection,
John, in First name.
name.
GetPossibleCount ([Initials])
Given that John is selected in First
1 as there is 1 selection, John, in First name.
name.
GetPossibleCount ([First name])
Given that Peteris selected in First
2 as Peter is associated with 2 values in Initials.
name.
GetPossibleCount ([Initials])
Given that no values are selected in
First name.
5 as there are no selections and there are 5 unique values in
First name.
GetPossibleCount ([First name])
Given that no values are selected in
First name.
6 as there are no selections and there are 6 unique values in
Initials.
GetPossibleCount ([Initials])
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
GetSelectedCount - chart function
GetSelectedCount() finds the number of selected (green) values in a field.
Syntax:
GetSelectedCount (field_name [, include_excluded])
Return data type: integer
Arguments:
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Arguments
Description
field_name
The field containing the range of data to be measured.
include_
excluded
If set to True(), the count will include selected values, which are currently excluded by
selections in other fields. If False or omitted, these values will not be included.
Examples and results:
The following example uses three fields loaded to different filter panes, one for First name name, one for
Initials and one for Has cellphone.
Examples
Results
Given that John is selected in First name.
1 as one value is selected in First name.
GetSelectedCount ([First name])
Given that John is selected in First name.
0 as no values are selected in Initials.
GetSelectedCount ([Initials])
With no selections in . First name, select all
6. Although selections with InitialsMC and PD have Has
values in Initials and after that select the
cellphone set to No, the result is still 6, because the
argument include_excluded is set to True().
value Yes in Has cellphone.
GetSelectedCount ([Initials])
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
5.8
File functions
The file functions (only available in script expressions) return information about the table file which is
currently being read. These functions will return NULL for all data sources except table files (exception:
ConnectString( )).
File functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Attribute
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This script function returns the value of the meta tags of different media files as text. The following file
formats are supported: MP3, WMA, WMV, PNG and JPG. If the file filename does not exist, is not a
supported file format or does not contain a meta tag named attributename, NULL will be returned.
Attribute (filename, attributename)
ConnectString
The ConnectString() function returns the name of the active data connection for ODBC or
OLE DB connections. The function returns an empty string if no connect statement has been executed, or
after a disconnect statement.
ConnectString ()
FileBaseName
The FileBaseName function returns a string containing the name of the table file currently being read,
without path or extension.
FileBaseName ()
FileDir
The FileDir function returns a string containing the path to the directory of the table file currently being read.
FileDir ()
FileExtension
The FileExtension function returns a string containing the extension of the table file currently being read.
FileExtension ()
FileName
The FileName function returns a string containing the name of the table file currently being read, without
path but including the extension.
FileName ()
FilePath
The FilePath function returns a string containing the full path to the table file currently being read.
FilePath ()
FileSize
The FileSize function returns an integer containing the size in bytes of the file filename or, if no filename is
specified, of the table file currently being read.
FileSize ()
FileTime
The FileTime function returns a timestamp for the date and time of the last modification of the file filename.
If no filename is specified, the function will refer to the currently read table file.
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FileTime ([ filename ])
GetFolderPath
The GetFolderPath function returns the value of the Microsoft WindowsSHGetFolderPath function and
returns the path. For example, MyMusic. Note that the function does not use the spaces seen in Windows
Explorer.
GetFolderPath ()
QvdCreateTime
This script function returns the XML-header time stamp from a QVD file, if any is present, otherwise it returns
NULL.
QvdCreateTime (filename)
QvdFieldName
This script function returns the name of field number fieldno, if it exists in a QVD file (otherwise NULL).
QvdFieldName (filename , fieldno)
QvdNoOfFields
This script function returns the number of fields in a QVD file.
QvdNoOfFields (filename)
QvdNoOfRecords
This script function returns the number of records currently in a QVD file.
QvdNoOfRecords (filename)
QvdTableName
This script function returns the name of the table stored in a QVD file.
QvdTableName (filename)
Attribute
This script function returns the value of the meta tags of different media files as text. The following file
formats are supported: MP3, WMA, WMV, PNG and JPG. If the file filename does not exist, is not a
supported file format or does not contain a meta tag named attributename, NULL will be returned.
Syntax:
Attribute(filename, attributename)
A large number of meta tags can be read. The examples in this topic show which tags can be read for the
respective supported file types.
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You can only read meta tags saved in the file according to the relevant specification, for
example ID2v3 for MP3 files or EXIF for JPG files, not meta information saved in the
Windows File Explorer.
Arguments:
Argument
Description
filename
The name of a media file including path, if needed, as a folder data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
attributename
The name of a meta tag.
The examples use the GetFolderPath function to find the paths to media files. As GetFolderPath is only
supported in legacy mode, you need to replace the references to GetFolderPath with a lib:// data connection
path.
See: File system access restriction (page 612)
Example 1: MP3 files
This script reads all possible MP3 meta tags in folder MyMusic.
// Script to read MP3 meta tags
for each vExt in 'mp3'
for each vFoundFile in filelist( GetFolderPath('MyMusic') & '\*.'& vExt )
FileList:
LOAD FileLongName,
subfield(FileLongName,'\',-1) as FileShortName,
num(FileSize(FileLongName),'# ### ### ###',',',' ') as FileSize,
FileTime(FileLongName) as FileTime,
// ID3v1.0 and ID3v1.1 tags
Attribute(FileLongName, 'Title') as Title,
Attribute(FileLongName, 'Artist') as Artist,
Attribute(FileLongName, 'Album') as Album,
Attribute(FileLongName, 'Year') as Year,
Attribute(FileLongName, 'Comment') as Comment,
Attribute(FileLongName, 'Track') as Track,
Attribute(FileLongName, 'Genre') as Genre,
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// ID3v2.3 tags
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
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Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
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Attribute(FileLongName,
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Attribute(FileLongName,
Attribute(FileLongName,
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Attribute(FileLongName,
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Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
Attribute(FileLongName,
'AENC')
'APIC')
'COMM')
'COMR')
'ENCR')
'EQUA')
'ETCO')
'GEOB')
'GRID')
'IPLS')
'LINK')
'MCDI')
'MLLT')
'OWNE')
'PRIV')
'PCNT')
'POPM')
'POSS')
'RBUF')
'RVAD')
'RVRB')
'SYLT')
'SYTC')
'TALB')
'TBPM')
'TCOM')
'TCON')
'TCOP')
'TDAT')
'TDLY')
'TENC')
'TEXT')
'TFLT')
'TIME')
'TIT1')
'TIT2')
'TIT3')
'TKEY')
'TLAN')
'TLEN')
'TMED')
'TOAL')
'TOFN')
'TOLY')
'TOPE')
'TORY')
'TOWN')
'TPE1')
'TPE2')
'TPE3')
'TPE4')
'TPOS')
'TPUB')
'TRCK')
'TRDA')
as
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AENC,
APIC,
COMM,
COMR,
ENCR,
EQUA,
ETCO,
GEOB,
GRID,
IPLS,
LINK,
MCDI,
MLLT,
OWNE,
PRIV,
PCNT,
POPM,
POSS,
RBUF,
RVAD,
RVRB,
SYLT,
SYTC,
TALB,
TBPM,
TCOM,
TCON,
TCOP,
TDAT,
TDLY,
TENC,
TEXT,
TFLT,
TIME,
TIT1,
TIT2,
TIT3,
TKEY,
TLAN,
TLEN,
TMED,
TOAL,
TOFN,
TOLY,
TOPE,
TORY,
TOWN,
TPE1,
TPE2,
TPE3,
TPE4,
TPOS,
TPUB,
TRCK,
TRDA,
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
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//
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//
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//
//
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//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
//
Audio encryption
Attached picture
Comments
Commercial frame
Encryption method registration
Equalization
Event timing codes
General encapsulated object
Group identification registration
Involved people list
Linked information
Music CD identifier
MPEG location lookup table
Ownership frame
Private frame
Play counter
Popularimeter
Position synchronisation frame
Recommended buffer size
Relative volume adjustment
Reverb
Synchronized lyric/text
Synchronized tempo codes
Album/Movie/Show title
BPM (beats per minute)
Composer
Content type
Copyright message
Date
Playlist delay
Encoded by
Lyricist/Text writer
File type
Time
Content group description
Title/songname/content description
Subtitle/Description refinement
Initial key
Language(s)
Length
Media type
Original album/movie/show title
Original filename
Original lyricist(s)/text writer(s)
Original artist(s)/performer(s)
Original release year
File owner/licensee
Lead performer(s)/Soloist(s)
Band/orchestra/accompaniment
Conductor/performer refinement
Interpreted, remixed, or otherwise modified by
Part of a set
Publisher
Track number/Position in set
Recording dates
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Attribute(FileLongName, 'TRSN') as TRSN, // Internet radio station name
Attribute(FileLongName, 'TRSO') as TRSO, // Internet radio station owner
Attribute(FileLongName, 'TSIZ') as TSIZ, // Size
Attribute(FileLongName, 'TSRC') as TSRC, // ISRC (international standard recording code)
Attribute(FileLongName, 'TSSE') as TSSE, // Software/Hardware and settings used for encoding
Attribute(FileLongName, 'TYER') as TYER, // Year
Attribute(FileLongName, 'TXXX') as TXXX, // User defined text information frame
Attribute(FileLongName, 'UFID') as UFID, // Unique file identifier
Attribute(FileLongName, 'USER') as USER, // Terms of use
Attribute(FileLongName, 'USLT') as USLT, // Unsychronized lyric/text transcription
Attribute(FileLongName, 'WCOM') as WCOM, // Commercial information
Attribute(FileLongName, 'WCOP') as WCOP, // Copyright/Legal information
Attribute(FileLongName, 'WOAF') as WOAF, // Official audio file webpage
Attribute(FileLongName, 'WOAR') as WOAR, // Official artist/performer webpage
Attribute(FileLongName, 'WOAS') as WOAS, // Official audio source webpage
Attribute(FileLongName, 'WORS') as WORS, // Official internet radio station homepage
Attribute(FileLongName, 'WPAY') as WPAY, // Payment
Attribute(FileLongName, 'WPUB') as WPUB, // Publishers official webpage
Attribute(FileLongName, 'WXXX') as WXXX; // User defined URL link frame
LOAD @1:n as FileLongName Inline "$(vFoundFile)" (fix, no labels);
Next vFoundFile
Next vExt
Example 2: JPEG
This script reads all possible EXIF meta tags from JPG files in folder MyPictures.
// Script to read Jpeg Exif meta tags
for each vExt in 'jpg', 'jpeg', 'jpe', 'jfif', 'jif', 'jfi'
for each vFoundFile in filelist( GetFolderPath('MyPictures') & '\*.'& vExt )
FileList:
LOAD FileLongName,
subfield(FileLongName,'\',-1) as FileShortName,
num(FileSize(FileLongName),'# ### ### ###',',',' ') as FileSize,
FileTime(FileLongName) as FileTime,
// ************
Exif Main (IFD0) Attributes
************
Attribute(FileLongName, 'ImageWidth') as ImageWidth,
Attribute(FileLongName, 'ImageLength') as ImageLength,
Attribute(FileLongName, 'BitsPerSample') as BitsPerSample,
Attribute(FileLongName, 'Compression') as Compression,
// examples: 1=uncompressed, 2=CCITT, 3=CCITT 3, 4=CCITT 4,
//5=LZW, 6=JPEG (old style), 7=JPEG, 8=Deflate, 32773=PackBits RLE,
Attribute(FileLongName, 'PhotometricInterpretation') as PhotometricInterpretation,
// examples: 0=WhiteIsZero, 1=BlackIsZero, 2=RGB, 3=Palette, 5=CMYK, 6=YCbCr,
Attribute(FileLongName, 'ImageDescription') as ImageDescription,
Attribute(FileLongName, 'Make') as Make,
Attribute(FileLongName, 'Model') as Model,
Attribute(FileLongName, 'StripOffsets') as StripOffsets,
Attribute(FileLongName, 'Orientation') as Orientation,
// examples: 1=TopLeft, 2=TopRight, 3=BottomRight, 4=BottomLeft,
// 5=LeftTop, 6=RightTop, 7=RightBottom, 8=LeftBottom,
Attribute(FileLongName, 'SamplesPerPixel') as SamplesPerPixel,
Attribute(FileLongName, 'RowsPerStrip') as RowsPerStrip,
Attribute(FileLongName, 'StripByteCounts') as StripByteCounts,
Attribute(FileLongName, 'XResolution') as XResolution,
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Attribute(FileLongName, 'YResolution') as YResolution,
Attribute(FileLongName, 'PlanarConfiguration') as PlanarConfiguration,
// examples: 1=chunky format, 2=planar format,
Attribute(FileLongName, 'ResolutionUnit') as ResolutionUnit,
// examples: 1=none, 2=inches, 3=centimeters,
Attribute(FileLongName, 'TransferFunction') as TransferFunction,
Attribute(FileLongName, 'Software') as Software,
Attribute(FileLongName, 'DateTime') as DateTime,
Attribute(FileLongName, 'Artist') as Artist,
Attribute(FileLongName, 'HostComputer') as HostComputer,
Attribute(FileLongName, 'WhitePoint') as WhitePoint,
Attribute(FileLongName, 'PrimaryChromaticities') as PrimaryChromaticities,
Attribute(FileLongName, 'YCbCrCoefficients') as YCbCrCoefficients,
Attribute(FileLongName, 'YCbCrSubSampling') as YCbCrSubSampling,
Attribute(FileLongName, 'YCbCrPositioning') as YCbCrPositioning,
// examples: 1=centered, 2=co-sited,
Attribute(FileLongName, 'ReferenceBlackWhite') as ReferenceBlackWhite,
Attribute(FileLongName, 'Rating') as Rating,
Attribute(FileLongName, 'RatingPercent') as RatingPercent,
Attribute(FileLongName, 'ThumbnailFormat') as ThumbnailFormat,
// examples: 0=Raw Rgb, 1=Jpeg,
Attribute(FileLongName, 'Copyright') as Copyright,
Attribute(FileLongName, 'ExposureTime') as ExposureTime,
Attribute(FileLongName, 'FNumber') as FNumber,
Attribute(FileLongName, 'ExposureProgram') as ExposureProgram,
// examples: 0=Not defined, 1=Manual, 2=Normal program, 3=Aperture priority, 4=Shutter
priority,
// 5=Creative program, 6=Action program, 7=Portrait mode, 8=Landscape mode, 9=Bulb,
Attribute(FileLongName, 'ISOSpeedRatings') as ISOSpeedRatings,
Attribute(FileLongName, 'TimeZoneOffset') as TimeZoneOffset,
Attribute(FileLongName, 'SensitivityType') as SensitivityType,
// examples: 0=Unknown, 1=Standard output sensitivity (SOS), 2=Recommended exposure index
(REI),
// 3=ISO speed, 4=Standard output sensitivity (SOS) and Recommended exposure index (REI),
//5=Standard output sensitivity (SOS) and ISO Speed, 6=Recommended exposure index (REI) and ISO
Speed,
// 7=Standard output sensitivity (SOS) and Recommended exposure index (REI) and ISO speed,
Attribute(FileLongName, 'ExifVersion') as ExifVersion,
Attribute(FileLongName, 'DateTimeOriginal') as DateTimeOriginal,
Attribute(FileLongName, 'DateTimeDigitized') as DateTimeDigitized,
Attribute(FileLongName, 'ComponentsConfiguration') as ComponentsConfiguration,
// examples: 1=Y, 2=Cb, 3=Cr, 4=R, 5=G, 6=B,
Attribute(FileLongName, 'CompressedBitsPerPixel') as CompressedBitsPerPixel,
Attribute(FileLongName, 'ShutterSpeedValue') as ShutterSpeedValue,
Attribute(FileLongName, 'ApertureValue') as ApertureValue,
Attribute(FileLongName, 'BrightnessValue') as BrightnessValue, // examples: -1=Unknown,
Attribute(FileLongName, 'ExposureBiasValue') as ExposureBiasValue,
Attribute(FileLongName, 'MaxApertureValue') as MaxApertureValue,
Attribute(FileLongName, 'SubjectDistance') as SubjectDistance,
// examples: 0=Unknown, -1=Infinity,
Attribute(FileLongName, 'MeteringMode') as MeteringMode,
// examples: 0=Unknown, 1=Average, 2=CenterWeightedAverage, 3=Spot,
// 4=MultiSpot, 5=Pattern, 6=Partial, 255=Other,
Attribute(FileLongName, 'LightSource') as LightSource,
// examples: 0=Unknown, 1=Daylight, 2=Fluorescent, 3=Tungsten, 4=Flash, 9=Fine weather,
// 10=Cloudy weather, 11=Shade, 12=Daylight fluorescent,
// 13=Day white fluorescent, 14=Cool white fluorescent,
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// 15=White fluorescent, 17=Standard light A, 18=Standard light B, 19=Standard light C,
// 20=D55, 21=D65, 22=D75, 23=D50, 24=ISO studio tungsten, 255=other light source,
Attribute(FileLongName, 'Flash') as Flash,
Attribute(FileLongName, 'FocalLength') as FocalLength,
Attribute(FileLongName, 'SubjectArea') as SubjectArea,
Attribute(FileLongName, 'MakerNote') as MakerNote,
Attribute(FileLongName, 'UserComment') as UserComment,
Attribute(FileLongName, 'SubSecTime') as SubSecTime,
Attribute(FileLongName, 'SubsecTimeOriginal') as SubsecTimeOriginal,
Attribute(FileLongName, 'SubsecTimeDigitized') as SubsecTimeDigitized,
Attribute(FileLongName, 'XPTitle') as XPTitle,
Attribute(FileLongName, 'XPComment') as XPComment,
Attribute(FileLongName, 'XPAuthor') as XPAuthor,
Attribute(FileLongName, 'XPKeywords') as XPKeywords,
Attribute(FileLongName, 'XPSubject') as XPSubject,
Attribute(FileLongName, 'FlashpixVersion') as FlashpixVersion,
Attribute(FileLongName, 'ColorSpace') as ColorSpace, // examples: 1=sRGB, 65535=Uncalibrated,
Attribute(FileLongName, 'PixelXDimension') as PixelXDimension,
Attribute(FileLongName, 'PixelYDimension') as PixelYDimension,
Attribute(FileLongName, 'RelatedSoundFile') as RelatedSoundFile,
Attribute(FileLongName, 'FocalPlaneXResolution') as FocalPlaneXResolution,
Attribute(FileLongName, 'FocalPlaneYResolution') as FocalPlaneYResolution,
Attribute(FileLongName, 'FocalPlaneResolutionUnit') as FocalPlaneResolutionUnit,
// examples: 1=None, 2=Inch, 3=Centimeter,
Attribute(FileLongName, 'ExposureIndex') as ExposureIndex,
Attribute(FileLongName, 'SensingMethod') as SensingMethod,
// examples: 1=Not defined, 2=One-chip color area sensor, 3=Two-chip color area sensor,
// 4=Three-chip color area sensor, 5=Color sequential area sensor,
// 7=Trilinear sensor, 8=Color sequential linear sensor,
Attribute(FileLongName, 'FileSource') as FileSource,
// examples: 0=Other, 1=Scanner of transparent type,
// 2=Scanner of reflex type, 3=Digital still camera,
Attribute(FileLongName, 'SceneType') as SceneType,
// examples: 1=A directly photographed image,
Attribute(FileLongName, 'CFAPattern') as CFAPattern,
Attribute(FileLongName, 'CustomRendered') as CustomRendered,
// examples: 0=Normal process, 1=Custom process,
Attribute(FileLongName, 'ExposureMode') as ExposureMode,
// examples: 0=Auto exposure, 1=Manual exposure, 2=Auto bracket,
Attribute(FileLongName, 'WhiteBalance') as WhiteBalance,
// examples: 0=Auto white balance, 1=Manual white balance,
Attribute(FileLongName, 'DigitalZoomRatio') as DigitalZoomRatio,
Attribute(FileLongName, 'FocalLengthIn35mmFilm') as FocalLengthIn35mmFilm,
Attribute(FileLongName, 'SceneCaptureType') as SceneCaptureType,
// examples: 0=Standard, 1=Landscape, 2=Portrait, 3=Night scene,
Attribute(FileLongName, 'GainControl') as GainControl,
// examples: 0=None, 1=Low gain up, 2=High gain up, 3=Low gain down, 4=High gain down,
Attribute(FileLongName, 'Contrast') as Contrast,
// examples: 0=Normal, 1=Soft, 2=Hard,
Attribute(FileLongName, 'Saturation') as Saturation,
// examples: 0=Normal, 1=Low saturation, 2=High saturation,
Attribute(FileLongName, 'Sharpness') as Sharpness,
// examples: 0=Normal, 1=Soft, 2=Hard,
Attribute(FileLongName, 'SubjectDistanceRange') as SubjectDistanceRange,
// examples: 0=Unknown, 1=Macro, 2=Close view, 3=Distant view,
Attribute(FileLongName, 'ImageUniqueID') as ImageUniqueID,
Attribute(FileLongName, 'BodySerialNumber') as BodySerialNumber,
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Attribute(FileLongName, 'CMNT_GAMMA') as CMNT_GAMMA,
Attribute(FileLongName, 'PrintImageMatching') as PrintImageMatching,
Attribute(FileLongName, 'OffsetSchema') as OffsetSchema,
// ************
Interoperability Attributes
************
Attribute(FileLongName, 'InteroperabilityIndex') as InteroperabilityIndex,
Attribute(FileLongName, 'InteroperabilityVersion') as InteroperabilityVersion,
Attribute(FileLongName, 'InteroperabilityRelatedImageFileFormat') as
InteroperabilityRelatedImageFileFormat,
Attribute(FileLongName, 'InteroperabilityRelatedImageWidth') as
InteroperabilityRelatedImageWidth,
Attribute(FileLongName, 'InteroperabilityRelatedImageLength') as
InteroperabilityRelatedImageLength,
Attribute(FileLongName, 'InteroperabilityColorSpace') as InteroperabilityColorSpace,
// examples: 1=sRGB, 65535=Uncalibrated,
Attribute(FileLongName, 'InteroperabilityPrintImageMatching') as
InteroperabilityPrintImageMatching,
// ************
GPS Attributes
************
Attribute(FileLongName, 'GPSVersionID') as GPSVersionID,
Attribute(FileLongName, 'GPSLatitudeRef') as GPSLatitudeRef,
Attribute(FileLongName, 'GPSLatitude') as GPSLatitude,
Attribute(FileLongName, 'GPSLongitudeRef') as GPSLongitudeRef,
Attribute(FileLongName, 'GPSLongitude') as GPSLongitude,
Attribute(FileLongName, 'GPSAltitudeRef') as GPSAltitudeRef,
// examples: 0=Above sea level, 1=Below sea level,
Attribute(FileLongName, 'GPSAltitude') as GPSAltitude,
Attribute(FileLongName, 'GPSTimeStamp') as GPSTimeStamp,
Attribute(FileLongName, 'GPSSatellites') as GPSSatellites,
Attribute(FileLongName, 'GPSStatus') as GPSStatus,
Attribute(FileLongName, 'GPSMeasureMode') as GPSMeasureMode,
Attribute(FileLongName, 'GPSDOP') as GPSDOP,
Attribute(FileLongName, 'GPSSpeedRef') as GPSSpeedRef,
Attribute(FileLongName, 'GPSSpeed') as GPSSpeed,
Attribute(FileLongName, 'GPSTrackRef') as GPSTrackRef,
Attribute(FileLongName, 'GPSTrack') as GPSTrack,
Attribute(FileLongName, 'GPSImgDirectionRef') as GPSImgDirectionRef,
Attribute(FileLongName, 'GPSImgDirection') as GPSImgDirection,
Attribute(FileLongName, 'GPSMapDatum') as GPSMapDatum,
Attribute(FileLongName, 'GPSDestLatitudeRef') as GPSDestLatitudeRef,
Attribute(FileLongName, 'GPSDestLatitude') as GPSDestLatitude,
Attribute(FileLongName, 'GPSDestLongitudeRef') as GPSDestLongitudeRef,
Attribute(FileLongName, 'GPSDestLongitude') as GPSDestLongitude,
Attribute(FileLongName, 'GPSDestBearingRef') as GPSDestBearingRef,
Attribute(FileLongName, 'GPSDestBearing') as GPSDestBearing,
Attribute(FileLongName, 'GPSDestDistanceRef') as GPSDestDistanceRef,
Attribute(FileLongName, 'GPSDestDistance') as GPSDestDistance,
Attribute(FileLongName, 'GPSProcessingMethod') as GPSProcessingMethod,
Attribute(FileLongName, 'GPSAreaInformation') as GPSAreaInformation,
Attribute(FileLongName, 'GPSDateStamp') as GPSDateStamp,
Attribute(FileLongName, 'GPSDifferential') as GPSDifferential;
// examples: 0=No correction, 1=Differential correction,
LOAD @1:n as FileLongName Inline "$(vFoundFile)" (fix, no labels);
Next vFoundFile
Next vExt
Example 3: Windows media files
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This script reads all possible WMA/WMV ASF meta tags in folder MyMusic.
/ Script to read WMA/WMV ASF meta tags
for each vExt in 'asf', 'wma', 'wmv'
for each vFoundFile in filelist( GetFolderPath('MyMusic') & '\*.'& vExt )
FileList:
LOAD FileLongName,
subfield(FileLongName,'\',-1) as FileShortName,
num(FileSize(FileLongName),'# ### ### ###',',',' ') as FileSize,
FileTime(FileLongName) as FileTime,
Attribute(FileLongName, 'Title') as Title,
Attribute(FileLongName, 'Author') as Author,
Attribute(FileLongName, 'Copyright') as Copyright,
Attribute(FileLongName, 'Description') as Description,
Attribute(FileLongName, 'Rating') as Rating,
Attribute(FileLongName, 'PlayDuration') as PlayDuration,
Attribute(FileLongName, 'MaximumBitrate') as MaximumBitrate,
Attribute(FileLongName, 'WMFSDKVersion') as WMFSDKVersion,
Attribute(FileLongName, 'WMFSDKNeeded') as WMFSDKNeeded,
Attribute(FileLongName, 'IsVBR') as IsVBR,
Attribute(FileLongName, 'ASFLeakyBucketPairs') as ASFLeakyBucketPairs,
Attribute(FileLongName, 'PeakValue') as PeakValue,
Attribute(FileLongName, 'AverageLevel') as AverageLevel;
LOAD @1:n as FileLongName Inline "$(vFoundFile)" (fix, no labels);
Next vFoundFile
Next vExt
Example 4: PNG
This script reads all possible PNG meta tags in folder MyPictures.
// Script to read PNG meta tags
for each vExt in 'png'
for each vFoundFile in filelist( GetFolderPath('MyPictures') & '\*.'& vExt )
FileList:
LOAD FileLongName,
subfield(FileLongName,'\',-1) as FileShortName,
num(FileSize(FileLongName),'# ### ### ###',',',' ') as FileSize,
FileTime(FileLongName) as FileTime,
Attribute(FileLongName, 'Comment') as Comment,
Attribute(FileLongName, 'Creation Time') as Creation_Time,
Attribute(FileLongName, 'Source') as Source,
Attribute(FileLongName, 'Title') as Title,
Attribute(FileLongName, 'Software') as Software,
Attribute(FileLongName, 'Author') as Author,
Attribute(FileLongName, 'Description') as Description,
Attribute(FileLongName, 'Copyright') as Copyright;
LOAD @1:n as FileLongName Inline "$(vFoundFile)" (fix, no labels);
Next vFoundFile
Next vExt
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ConnectString
The ConnectString() function returns the name of the active data connection for ODBC or
OLE DB connections. The function returns an empty string if no connect statement has been executed, or
after a disconnect statement.
Syntax:
ConnectString()
Examples and results:
Example
Result
LIB CONNECT TO 'Tutorial ODBC';
ConnectString:
Load ConnectString() as ConnectString
AutoGenerate 1;
Returns 'Tutorial ODBC' in field ConnectString.
This examples assumes that you have an available data
connection called Tutorial ODBC.
FileBaseName
The FileBaseName function returns a string containing the name of the table file currently being read,
without path or extension.
Syntax:
FileBaseName()
Examples and results:
Example
Result
LOAD *, filebasename( ) as X from
C:\UserFiles\abc.txt
Will return 'abc' in field X in each record read.
FileDir
The FileDir function returns a string containing the path to the directory of the table file currently being read.
Syntax:
FileDir()
This function supports only folder data connections in standard mode.
Examples and results:
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Example
Result
Load *, filedir( ) as X from
C:\UserFiles\abc.txt
Will return 'C:\UserFiles' in field X in each record read.
FileExtension
The FileExtension function returns a string containing the extension of the table file currently being read.
Syntax:
FileExtension()
Examples and results:
Example
Result
LOAD *, FileExtension( ) as X from
C:\UserFiles\abc.txt
Will return 'txt' in field X in each record read.
FileName
The FileName function returns a string containing the name of the table file currently being read, without
path but including the extension.
Syntax:
FileName()
Examples and results:
Example
Result
LOAD *, FileName( ) as X from
C:\UserFiles\abc.txt
Will return 'abc.txt' in field X in each record read.
FilePath
The FilePath function returns a string containing the full path to the table file currently being read.
Syntax:
FilePath()
This function supports only folder data connections in standard mode.
Examples and results:
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Example
Result
Load *, FilePath( ) as X from
C:\UserFiles\abc.txt
Will return 'C:\UserFiles\abc.txt' in field X in each record read.
FileSize
The FileSize function returns an integer containing the size in bytes of the file filename or, if no filename is
specified, of the table file currently being read.
Syntax:
FileSize([filename])
Arguments:
Argument
Description
filename
The name of a file, if necessary including path, as a folder or web file data connection. If you
don't specify a file name, the table file currently being read is used.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Examples and results:
Example
Result
LOAD *, FileSize( ) as X from
abc.txt;
Will return the size of the specified file (abc.txt) as an integer in field X
in each record read.
FileSize(
'lib://MyData/xyz.xls' )
Will return the size of the file xyz.xls.
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FileTime
The FileTime function returns a timestamp for the date and time of the last modification of the file filename.
If no filename is specified, the function will refer to the currently read table file.
Syntax:
FileTime([ filename ])
Arguments:
Argument
Description
filename
The name of a file, if necessary including path, as a folder or web file data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Examples and results:
Example
Result
LOAD *, FileTime( ) as X
from abc.txt;
Will return the date and time of the last modification of the file (abc.txt) as a
timestamp in field X in each record read.
FileTime( 'xyz.xls' )
Will return the timestamp of the last modification of the file xyz.xls.
GetFolderPath
The GetFolderPath function returns the value of the Microsoft WindowsSHGetFolderPath function and
returns the path. For example, MyMusic. Note that the function does not use the spaces seen in Windows
Explorer.
This function is not supported in standard mode.
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Syntax:
GetFolderPath()
Examples:
This script loads three common folder paths in a table.
LOAD
GetFolderPath('Music') as MyMusic,
GetFolderPath('MyPictures') as MyPictures,
GetFolderPath('Windows') as Windows
AutoGenerate 1;
QvdCreateTime
This script function returns the XML-header time stamp from a QVD file, if any is present, otherwise it returns
NULL.
Syntax:
QvdCreateTime(filename)
Arguments:
Argument
Description
filename
The name of a QVD file, if necessary including path, as a folder or web data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Example:
QvdCreateTime('MyFile.qvd')
QvdCreateTime('C:\MyDir\MyFile.qvd')
QvdCreateTime('lib://data\MyFile.qvd')
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QvdFieldName
This script function returns the name of field number fieldno, if it exists in a QVD file (otherwise NULL).
Syntax:
QvdFieldName(filename , fieldno)
Arguments:
Argument
Description
filename
The name of a QVD file, if necessary including path, as a folder or web data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
fieldno
The number of the field (0 based) within the table contained in the QVD file.
Examples:
QvdFieldName ('MyFile.qvd', 3)
QvdFieldName ('C:\MyDir\MyFile.qvd', 5)
QvdFieldName ('lib://data\MyFile.qvd', 5)
QvdNoOfFields
This script function returns the number of fields in a QVD file.
Syntax:
QvdNoOfFields(filename)
Arguments:
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Argument
Description
filename
The name of a QVD file, if necessary including path, as a folder or web data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Examples:
QvdNoOfFields ('MyFile.qvd')
QvdNoOfFields ('C:\MyDir\MyFile.qvd')
QvdNoOfFields ('lib://data\MyFile.qvd')
QvdNoOfRecords
This script function returns the number of records currently in a QVD file.
Syntax:
QvdNoOfRecords(filename)
Arguments:
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Argument
Description
filename
The name of a QVD file, if necessary including path, as a folder or web data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Examples:
QvdNoOfRecords ('MyFile.qvd')
QvdNoOfRecords ('C:\MyDir\MyFile.qvd')
QvdNoOfRecords ('lib://data\MyFile.qvd')
QvdTableName
This script function returns the name of the table stored in a QVD file.
Syntax:
QvdTableName(filename)
Arguments:
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Argument
Description
filename
The name of a QVD file, if necessary including path, as a folder or web data connection.
Example: 'lib://Table Files/'
In legacy scripting mode, the following path formats are also supported:
l
absolute
Example: c:\data\
l
relative to the Qlik Sense app working directory.
Example: data\
l
URL address (HTTP or FTP), pointing to a location on the Internet or an intranet.
Example: http://www.qlik.com
Examples:
QvdTableName ('MyFile.qvd')
QvdTableName ('C:\MyDir\MyFile.qvd')
QvdTableName ('lib://data\MyFile.qvd')
5.9
Financial functions
Financial functions can be used in the data load script and in chart expressions to calculate payments and
interest rates.
For all the arguments, cash that is paid out is represented by negative numbers. Cash received is
represented by positive numbers.
Listed here are the arguments that are used in the financial functions (excepting the ones beginning with
range-).
For all financial functions it is vital that you are consistent when specifying units for rate and
nper. If monthly payments are made on a five-year loan at 6% annual interest, use 0.005
(6%/12) for rate and 60 (5*12) for nper. If annual payments are made on the same loan, use 6%
for rate and 5 for nper.
Financial functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
FV
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This function returns the future value of an investment based on periodic, constant payments and a constant
interest rate. The result has a default number format of money.
FV (rate, nper, pmt [ ,pv [ , type ] ])
nPer
This function returns the number of periods for an investment based on periodic, constant payments and a
constant interest rate.
nPer (rate, pmt, pv [ ,fv [ , type ] ])
Pmt
This function returns the payment for a loan based on periodic, constant payments and a constant interest
rate. The result has a default number format of money.
Pmt (rate, nper, pv [ ,fv [ , type ] ] )
PV
This function returns the present value of an investment. The result has a default number format of money.
PV (rate, nper, pmt [ ,fv [ , type ] ])
Rate
This function returns the interest rate per period on annuity. The result has a default number format of Fix
two decimals and %.
Rate (nper, pmt , pv [ ,fv [ , type ] ])
BlackAndSchole
The Black and Scholes model is a mathematical model for financial market derivative instruments. The
formula calculates the theoretical value of an option. In Qlik Sense, the BlackAndSchole function returns
the value according to the Black and Scholes unmodified formula (European style options).
BlackAndSchole(strike , time_left , underlying_price , vol , risk_free_rate
, type)
Return data type: numeric
Arguments:
Argument
Description
strike
The future purchase price of the stock.
time_left
The number of time periods remaining.
underlying_price
The current value of the stock.
vol
The volatility in % per time period.
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Argument
Description
risk_free_rate
The risk -free rate in % per time period.
type
The type of option:
'c', 'call' or any non-zero numeric value for call options
'p', 'put' or 0 for put options.
Examples and results:
Example
Result
BlackAndSchole(130, 4, 68.5, 0.4, 0.04, 'call')
Returns
11.245
This calculates the theoretical price of an option to buy a share that is worth 68.5 today, at a
value of 130 in 4 years. A volatility of 40% per year and a risk-free interest rate of 4% is
assumed.
FV
This function returns the future value of an investment based on periodic, constant payments and a constant
interest rate. The result has a default number format of money.
Syntax:
FV(rate, nper, pmt [ ,pv [ , type ] ])
Return data type: numeric. The result has a default number format of money. .
Arguments:
Argument
Description
rate
The interest rate per period.
nper
The total number of payment periods in an annuity.
pmt
The payment made each period. It cannot change over the life of the annuity. If pmt is
omitted, the pv argument must be included. A payment is stated as a negative number, for
example, -20.
pv
The present value, or lump-sum amount, that a series of future payments is worth right now.
If pv is omitted, it is assumed to be 0 (zero) and the pmt argument must be included.
fv
The future value, or cash balance, you want to attain after the last payment is made. If fv is
omitted, it is assumed to be 0.
type
Should be 0 if payments are due at the end of the period and 1 if payments are due at the
beginning of the period. If type is omitted, it is assumed to be 0.
Examples and results:
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Example
Result
You are paying a new household appliance by 36 monthly installments of $20. The interest rate
is 6% per annum. The bill comes at the end of every month. What is the total invested, when
the last bill has been paid?
Returns
$786.72
FV(0.005,36,-20)
nPer
This function returns the number of periods for an investment based on periodic, constant payments and a
constant interest rate.
Syntax:
nPer(rate, pmt, pv [ ,fv [ , type ] ])
Return data type: numeric
Arguments:
Argument
Description
rate
The interest rate per period.
nper
The total number of payment periods in an annuity.
pmt
The payment made each period. It cannot change over the life of the annuity. If pmt is
omitted, the pv argument must be included. A payment is stated as a negative number, for
example, -20.
pv
The present value, or lump-sum amount, that a series of future payments is worth right now.
If pv is omitted, it is assumed to be 0 (zero) and the pmt argument must be included.
fv
The future value, or cash balance, you want to attain after the last payment is made. If fv is
omitted, it is assumed to be 0.
type
Should be 0 if payments are due at the end of the period and 1 if payments are due at the
beginning of the period. If type is omitted, it is assumed to be 0.
Examples and results:
Example
Result
You want to sell a household appliance by monthly installments of $20. The interest rate is 6%
per annum. The bill comes at the end of every month. How many periods are required if the
value of the money received after the last bill has been paid should equal $800?
Returns
36.56
nPer(0.005,-20,0,800)
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Pmt
This function returns the payment for a loan based on periodic, constant payments and a constant interest
rate. The result has a default number format of money.
Pmt(rate, nper, pv [ ,fv [ , type ] ] )
Return data type: numeric. The result has a default number format of money. .
To find the total amount paid over the duration of the loan, multiply the returned pmt value by nper.
Arguments:
Argument
Description
rate
The interest rate per period.
nper
The total number of payment periods in an annuity.
pmt
The payment made each period. It cannot change over the life of the annuity. If pmt is
omitted, the pv argument must be included. A payment is stated as a negative number, for
example, -20.
pv
The present value, or lump-sum amount, that a series of future payments is worth right now.
If pv is omitted, it is assumed to be 0 (zero) and the pmt argument must be included.
fv
The future value, or cash balance, you want to attain after the last payment is made. If fv is
omitted, it is assumed to be 0.
type
Should be 0 if payments are due at the end of the period and 1 if payments are due at the
beginning of the period. If type is omitted, it is assumed to be 0.
Examples and results:
Example
Result
The following formula returns the monthly payment on a $20,000 loan at an annual rate of
10 percent, that must be paid off in 8 months:
Returns $2,594.66
Pmt(0.1/12,8,20000)
For the same loan, if payment is due at the beginning of the period, the payment is:
Pmt(0.1/12,8,20000,0,1)
Returns $2,573.21
PV
This function returns the present value of an investment. The result has a default number format of money.
PV(rate, nper, pmt [ ,fv [ , type ] ])
Return data type: numeric. The result has a default number format of money. .
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The present value is the total amount that a series of future payments is worth right now. For example, when
borrowing money, the loan amount is the present value to the lender.
Arguments:
Argument
Description
rate
The interest rate per period.
nper
The total number of payment periods in an annuity.
pmt
The payment made each period. It cannot change over the life of the annuity. If pmt is
omitted, the pv argument must be included. A payment is stated as a negative number, for
example, -20.
pv
The present value, or lump-sum amount, that a series of future payments is worth right now.
If pv is omitted, it is assumed to be 0 (zero) and the pmt argument must be included.
fv
The future value, or cash balance, you want to attain after the last payment is made. If fv is
omitted, it is assumed to be 0.
type
Should be 0 if payments are due at the end of the period and 1 if payments are due at the
beginning of the period. If type is omitted, it is assumed to be 0.
Examples and results:
Example
Result
What is the present value of a debt, when you have to pay $100 at the end of each month
during a five-year period, given an interest rate of 7%?
Returns
$5,050.20
PV(0.07/12,12*5,-100,0,0)
Rate
This function returns the interest rate per period on annuity. The result has a default number format of Fix
two decimals and %.
Syntax:
Rate(nper, pmt , pv [ ,fv [ , type ] ])
Return data type: numeric.
The rate is calculated by iteration and can have zero or more solutions. If the successive results of rate do not
converge, a NULL value will be returned.
Arguments:
Argument
Description
rate
The interest rate per period.
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Argument
Description
nper
The total number of payment periods in an annuity.
pmt
The payment made each period. It cannot change over the life of the annuity. If pmt is
omitted, the pv argument must be included. A payment is stated as a negative number, for
example, -20.
pv
The present value, or lump-sum amount, that a series of future payments is worth right now.
If pv is omitted, it is assumed to be 0 (zero) and the pmt argument must be included.
fv
The future value, or cash balance, you want to attain after the last payment is made. If fv is
omitted, it is assumed to be 0.
type
Should be 0 if payments are due at the end of the period and 1 if payments are due at the
beginning of the period. If type is omitted, it is assumed to be 0.
Examples and results:
Example
Result
What is the interest rate of a five-year $10,000 annuity loan with monthly payments of
$300?
Returns
2.00%
Rate(60,-300,10000)
5.10 Formatting functions
The formatting functions determine the display format of fields or expressions. With these functions it is
possible to set decimal separator, thousands separator, and so on. The functions can be used both in data
load scripts and chart expressions.
For reasons of clarity, all number representations are given with decimal point as decimal
separator.
Formatting functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Date
The date function formats the expression as a date according to the given format-code string.
Date (expression [ , format-code ])
Dual
With the dual function it is possible to create arbitrary combinations of a number and a string.
Dual ( s , x )
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Interval
This script function formats the expression as a time interval according to the string given as a format-
code. If the format code is omitted, the time format set in the operating system is used.
Interval (expression [ , format-code ])
Money
The money function formats the expression numerically according to the string given as format-code.
Decimal separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4
are omitted, the number format that is set in the operating system will be used.
Money (expression [ , format-code [ , decimal-sep [ , thousands-sep ] ] ])
Num
This script function formats the expression numerically according to the string given as format-code.
Decimal separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4
are omitted, the number format set in the operating system is used.
Num (expression [ , format-code [ , decimal-sep [ , thousands-sep ] ] ] )
Time
The time function formats the expression as time according to the string given as format-code. If the format
code is omitted, the time format set in the operating system is used.
Time (expression [ , format-code ])
Timestamp
This function formats the expression as a date and time according to the string given as format-code. If the
format code is omitted, the date and time formats set in the operating system are used.
Timestamp (expression [ , format-code ])
Date
The date function formats the expression as a date according to the given format-code string.
Syntax:
Date(expression [ , format-code ])
Another way to describe it, is to look at it as a number to string conversion. The function takes the numeric
value of the expression and generates a string representing the date according to the format code. The
function returns a dual value with both the string and the number.
Arguments:
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Argument
Description
formatcode
String describing the format of the resulting string. If the format code is omitted, the date
format set in the operating system is used.
Examples and results:
The examples below assume the two following operating system settings:
Default setting 1
Default setting 2
YY-MM-DD
M/D/YY
Date format
Example
Results
Setting 1
Setting 2
Date( A ) where A=35648
String:
97-08-06
8/6/97
Number:
35648
35648
String:
97-08-06
8/6/97
Number:
35648
35648
String:
06.08.1997
06.08.1997
Number:
35648.375
35648.375
String:
NULL (nothing)
97.08.06
Number:
NULL
35648
Date( A, 'YY.MM.DD' ) where A=35648
Date( A, 'DD.MM.YY' ) where A=35648.375
Date( A, 'YY.MM.DD' ) where A=8/6/97
Dual
With the dual function it is possible to create arbitrary combinations of a number and a string.
Syntax:
Dual( s , x )
Forced association of an arbitrary string representation s with a given number representation x.
In Qlik Sense, all field values are potentially dual values. This means that the field values can have both a
numeric value and a textual value. An example is a date that could have a numeric value of 40908 and the
textual representation ‘2011-12-31’.
When several data items read into one field have different string representations but the same valid number
representation, they will all share the first string representation encountered.
The dual function is typically used early in the script, before other data is read into the field
concerned, in order to create that first string representation, which will be shown in filter panes.
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Examples and results:
Example
Description
Dual('Q' & Ceil(Month
(Date)/3), Ceil(Month
(Date)/3)) as Quarter
This field definition will create a field Quarter with the textual values 'Q1' to
'Q4' and at the same time assign the numeric values 1 to 4.
Dual(WeekYear(Date) & 'W' & Week(Date),
WeekStart(Date)) as
YearWeek
This field definition will create a field YearWeek with textual values of the
form '2012-W22' and at the same time assign a numeric value corresponding
to the date number of the first day of the week, e.g. 41057.
Interval
This script function formats the expression as a time interval according to the string given as a format-
code. If the format code is omitted, the time format set in the operating system is used.
Syntax:
Interval(expression [ , format-code ])
Intervals may be formatted as a time, as days or as a combination of days, hours, minutes, seconds and
fractions of seconds.
Examples and results:
The examples below assume the following operating system settings:
Short date format:
YY-MM-DD
Time format:
hh:mm:ss
Number decimal separator:
.
Example
String
Number
Interval( A ) where A=0.37
09:00:00
0.375
Interval( A ) where A=1.375
33:00:00
1.375
Interval( A, 'D hh:mm' ) where A=1.375
1 09:00
1.375
Interval( A-B, 'D hh:mm' ) where A=97-08-06 09:00:00 and B=96-08-06
365
09:00
365.375
00:00:00
Num
This script function formats the expression numerically according to the string given as format-code.
Decimal separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4
are omitted, the number format set in the operating system is used.
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Syntax:
Num(expression [ , format-code [ , decimal-sep [ , thousands-sep ] ] ] )
Examples and results:
The examples below assume the following operating system settings:
Default setting 1
Default setting 2
# ##0,#
#,##0.#
Number format
Example
Results
Num( A, '0.0' ) where A=35648.375
String:
Num( A, '#,##0.##', '.' , ',' ) where A=35648
Setting 1
35 648 375
35648.375
Number:
35648375
35648.375
String:
35,648.00
35,648.00
35648
35648
3,14
003
3.141592653
3.141592653
Number:
Num( pi( ), '0,00' )
Setting 2
String:
Number:
Money
The money function formats the expression numerically according to the string given as format-code.
Decimal separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4
are omitted, the number format that is set in the operating system will be used.
Syntax:
Money(expression [ , format-code [ , decimal-sep [ , thousands-sep ] ] ])
Examples and results:
The examples below assume the following operating system settings:
Money format
Default setting 1
Default setting 2
kr # ##0,00
$ #,##0.00
Example
Results
Setting 1
Setting 2
Money( A ) where A=35648
String:
kr 35 648,00
$ 35,648.00
Number:
35648.00
35648.00
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Example
Results
Setting 1
Setting 2
Money( A, '#,##0 ¥', '.' , ',' ) where A=3564800
String:
3,564,800 ¥
3,564,800 ¥
Number:
3564800
3564800
Time
The time function formats the expression as time according to the string given as format-code. If the format
code is omitted, the time format set in the operating system is used.
Syntax:
Time( expression [ , format-code ])
Examples and results:
The examples below assume the following operating system settings:
Default setting 1
Default setting 2
hh:mm:ss
hh.mm.ss
Time format
Example
Results
Time( A ) where A=0.375
String:
Number:
Time( A ) where A=35648.375
String:
Number:
Time( A, 'hh-mm' ) where A=0.99999
String:
Number:
Setting 1
Setting 2
09:00:00
09.00.00
0.375
0.375
09:00:00
09.00.00
35648.375
35648.375
23-59
23-59
0.99999
0.99999
Timestamp
This function formats the expression as a date and time according to the string given as format-code. If the
format code is omitted, the date and time formats set in the operating system are used.
Syntax:
Timestamp(expression [ , format-code ])
Examples and results:
The examples below assume the following operating system settings:
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Timestamp format
Default setting 1
Default setting 2
YY-MM-DD
M/D/YY
Example
Results
Setting 1
Timestamp( A ) where A=35648.375
String:
97-08-06 09:00:00
8/6/97 09:00:00
35648.375
35648.375
1997-08-06 00.00
1997-08-06 00.00
35648
35648
Number:
Timestamp( A,'YYYY-MM-DD hh.mm') where A=35648
String:
Number:
Setting 2
5.11 General numeric functions
In these general numeric functions, the arguments are expressions where x should be interpreted as a real
valued number. All functions can be used in both data load scripts and chart expressions.
General numeric functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
bitcount
BitCount() finds how many bits in the binary equivalent of a number are set to 1. That is, the function returns
the number of set bits in integer_number, where integer_number is interpreted as a signed 32-bit integer.
BitCount(integer_number)
div
Div() returns the integer part of the arithmetic division of the first argument by the second argument. Both
parameters are interpreted as real numbers, that is, they do not have to be integers.
Div(integer_number1, integer_number2)
fabs
Fabs() returns the absolute value of x. The result is a positive number.
Fabs(x)
fact
Fact() returns the factorial of a positive integer x.
Fact(x)
frac
Frac() returns the fraction part of x.
Frac(x)
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sign
Sign() returns 1, 0 or -1 depending on whether x is a positive number, 0, or a negative number.
Sign(x)
Combination and permutation functions
combin
Combin() returns the number of combinations of q elements that can be picked from a set of p items. As
represented by the formula: Combin(p,q) = p! / q!(p-q)! The order in which the items are selected is
insignificant.
Combinp, q)
permut
Permut() returns the number of permutations of q elements that can be selected from a set of p items. As
represented by the formula: Permut(p,q) = (p)! / (p - q)! The order in which the items are selected is
significant.
Permut(p, q)
Modulo functions
fmod
fmod() is a generalized modulo function that returns the remainder part of the integer division of the first
argument (the dividend) by the second argument (the divisor). The result is a real number. Both arguments
are interpreted as real numbers, that is, they do not have to be integers.
Fmod(a, b)
mod
Mod() is a mathematical modulo function that returns the non-negative remainder of an integer division. The
first argument is the dividend, the second argument is the divisor, Both arguments must be integer values.
Mod(integer_number1, integer_number2)
Parity functions
even
Even() returns True (-1), if integer_number is an even integer or zero. It returns False (0), if integer_
number is an odd integer, and NULL if integer_number is not an integer.
Even(integer_number)
odd
Odd() returns True (-1), if integer_number is an odd integer or zero. It returns False (0), if integer_number
is an even integer, and NULL if integer_number is not an integer.
Odd(integer_number)
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Rounding functions
ceil
Ceil() rounds up a number to the nearest multiple of the specified step interval. The result is increased by the
value of offset, if one is specified, or reduced if offset is negative.
Ceil(x[, step[, offset]])
floor
Floor() rounds down a number to the nearest multiple of the specified step interval. The result is increased
by the value of offset, if one is specified, or reduced if offset is negative.
Floor(x[, step[, offset]])
round
Round() returns the result of rounding x up or down to the nearest multiple of step. The result is increased
by the value of offset, if one is specified, or reduced if offset is negative.
Round( x [ , base [ , offset ]])
BitCount
BitCount() finds how many bits in the binary equivalent of a number are set to 1. That is, the function returns
the number of set bits in integer_number, where integer_number is interpreted as a signed 32-bit integer.
Syntax:
BitCount(integer_number)
Return data type: integer
Examples and results:
Examples
Results
BitCount ( 3 )
3 is binary 101, therefore this returns 2
BitCount ( -1 )
-1 is 64 ones in binary, therefore this returns 64
Ceil
Ceil() rounds up a number to the nearest multiple of the specified step interval. The result is increased by the
value of offset, if one is specified, or reduced if offset is negative.
Compare with the floor function, which rounds input numbers down.
Syntax:
Ceil(x[, step[, offset]])
Return data type: integer
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Examples and results:
Examples
Results
Ceil( 2.4 )
Returns 3
Ceil( 2.6 )
Returns 3
Ceil( 3.88 , 0.1 )
Returns 3.9
Ceil( 3.88 , 5 )
Returns 5
Ceil( 1.1 , 1 )
Returns 2
Ceil( 1.1 , 1 , 0.5 )
Returns 1.5
Ceil( 1.1 , 1 , -0.01 )
Returns 1.99
Combin
Combin() returns the number of combinations of q elements that can be picked from a set of p items. As
represented by the formula: Combin(p,q) = p! / q!(p-q)! The order in which the items are selected is
insignificant.
Syntax:
Combin(p, q)
Return data type: integer
Limitations:
Non-integer items will be truncated.
Examples and results:
Examples
Results
How many combinations of 7 numbers can be picked from a total of 35 lottery
numbers?
Returns
6,724,520
Combin( 35,7 )
Div
Div() returns the integer part of the arithmetic division of the first argument by the second argument. Both
parameters are interpreted as real numbers, that is, they do not have to be integers.
Syntax:
Div(integer_number1, integer_number2)
Return data type: integer
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Examples and results:
Examples
Results
Div( 7,2 )
Returns 3
Div( 7.1,2.3 )
Returns 3
Div( 9,3 )
Returns 3
Div( -4,3 )
Returns -1
Div( 4,-3 )
Returns -1
Div( -4,-3 )
Returns 1
Even
Even() returns True (-1), if integer_number is an even integer or zero. It returns False (0), if integer_
number is an odd integer, and NULL if integer_number is not an integer.
Syntax:
Even(integer_number)
Return data type: Boolean
Examples and results:
Examples
Results
Even( 3 )
Returns 0, False
Even( 2 * 10 )
Returns -1, True
Even( 3.14 )
Returns NULL
Fabs
Fabs() returns the absolute value of x. The result is a positive number.
Syntax:
fabs(x)
Return data type: numeric
Examples and results:
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Examples
Results
fabs( 2.4 )
Returns 2.4
fabs( -3.8 )
Returns 3.8
Fact
Fact() returns the factorial of a positive integer x.
Syntax:
Fact(x)
Return data type: integer
Limitations:
If the number x is not an integer, it will be truncated. Non-positive numbers will return NULL.
Examples and results:
Examples
Results
Fact( 1 )
Returns 1
Fact( 5 )
Returns 120 ( 1 * 2 * 3 * 4 * 5 = 120 )
Fact( -5 )
Returns NULL
Floor
Floor() rounds down a number to the nearest multiple of the specified step interval. The result is decreased
by the value of offset, if one is specified, or increased if offset is negative.
Compare with the ceil function, which rounds input numbers up.
Syntax:
Floor(x[, step[, offset]])
Return data type: numeric
Examples and results:
Examples
Results
Floor( 2,4 )
Returns 0
Floor( 4,2 )
Returns 4
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Examples
Results
Floor( 3.88 , 0.1 )
Returns 3.8
Floor( 3.88 , 5 )
Returns 0
Floor( 1.1 , 1 )
Returns 1
Floor( 1.1 , 1 , 0.5 )
Returns 0.5
Fmod
fmod() is a generalized modulo function that returns the remainder part of the integer division of the first
argument (the dividend) by the second argument (the divisor). The result is a real number. Both arguments
are interpreted as real numbers, that is, they do not have to be integers.
Syntax:
fmod(a, b)
Return data type: numeric
Examples and results:
Examples
Results
fmod( 7,2 )
Returns 1
fmod( 7.5,2 )
Returns 1.5
fmod( 9,3 )
Returns 0
fmod( -4,3 )
Returns -1
fmod( 4,-3 )
Returns 1
fmod( -4,-3 )
Returns -1
Frac
Frac() returns the fraction part of x.
The fraction is defined in such a way that Frac(x ) + Floor(x ) = x. In simple terms this means that the
fractional part of a positive number is the difference between the number (x) and the integer that precedes it.
For example: The fractional part of 11.43 = 11.43 - 11 = 0.43
For a negative number, say -1.4, Floor(-1.4) = -2, which produces the following result:
The fractional part of -1.4 = 1.4 - (-2) = -1.4 + 2 = 0.6
Syntax:
Frac(x)
Return data type: numeric
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Examples and results:
Examples
Results
Frac( 11.43 )
Returns 0.43
Frac( -1.4 )
Returns 0.6
Mod
Mod() is a mathematical modulo function that returns the non-negative remainder of an integer division. The
first argument is the dividend, the second argument is the divisor, Both arguments must be integer values.
Syntax:
Mod(integer_number1, integer_number2)
Return data type: integer
Limitations:
integer_number2 must be greater than 0.
Examples and results:
Examples
Results
Mod( 7,2 )
Returns 1
Mod( 7.5,2 )
Returns NULL
Mod( 9,3 )
Returns 0
Mod( -4,3 )
Returns 2
Mod( 4,-3 )
Returns NULL
Mod( -4,-3 )
Returns NULL
Odd
Odd() returns True (-1), if integer_number is an odd integer or zero. It returns False (0), if integer_number
is an even integer, and NULL if integer_number is not an integer.
Syntax:
Odd(integer_number)
Return data type: Boolean
Examples and results:
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Examples
Results
Odd( 3 )
Returns -1, True
Odd( 2 * 10 )
Returns 0, False
Odd( 3.14 )
Returns NULL
Permut
Permut() returns the number of permutations of q elements that can be selected from a set of p items. As
represented by the formula: Permut(p,q) = (p)! / (p - q)! The order in which the items are selected is
significant.
Syntax:
Permut(p, q)
Return data type: integer
Limitations:
Non-integer arguments will be truncated.
Examples and results:
Examples
Results
In how many ways could the gold, silver and bronze medals be distributed after a 100 m final
with 8 participants?
Returns
336
Permut( 8,3 )
Round
Round() returns the result of rounding x up or down to the nearest multiple of step. The result is increased
by the value of offset, if one is specified, or reduced if offset is negative. The default value of step is 1.
If x is exactly in the middle of an interval, it is rounded upwards.
Syntax:
Round(x[, step[, offset]])
Return data type: numeric
Examples and results:
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Examples
Results
Round( 3.8 )
Returns 4
Round( 3.8, 4 )
Returns 4
Round( 2.5 )
Returns 3. Rounded up because 2.5 is exactly half of the default step interval.
Round( 2, 4 )
Returns 4. Rounded up because 2 is exactly half of the step interval of 4.
Round( 2, 6 )
Returns 0. Rounded down because 2 is less than half of the step interval of 6.
Round( 3.88 , 0.1 )
Returns 3.9
Round( 3.88 , 5 )
Returns 5
Round( 1.1 , 1 , 0.5 )
Returns 1.5
Sign
Sign() returns 1, 0 or -1 depending on whether x is a positive number, 0, or a negative number.
Syntax:
Sign(x)
Return data type: numeric
Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
Examples
Results
Sign( 66 )
Returns 1
Sign( 0 )
Returns 0
Sign( - 234 )
Returns -1
5.12 Geographical functions
These functions are used to handle geographical data in map visualizations.
Geographical functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
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Parameters used in geographical functions
Geometry
This can be any of the following:
l
l
a point (latitude, longitude)
an area
The reference should be to a field containing the geometry representation.
Projection
With Mercator projection you can represent maps in square format, correcting for the
distortion created by stretching.
This can be any of the following:
l
l
'unit' (default) - projection is 1:1
'mercator'
GeoAggrGeometry
This function can be used to aggregate a number of areas into a larger area, for example aggregating a
number of sub-regions to a region.
GeoAggrGeometry(geometry)
GeoBoundingBox
This function can be used in scripts to aggregate a geometry into an area and calculate the smallest
GeoBoundingBox that contain all coordinates.
A GeoBoundingBox is represented as a list of four values, left, right, top, bottom.
GeoBoundingBox(geometry)
GeoCountVertex
This function can be used to get a count of how many vertices a polygon geometry contains.
GeoCountVertex(geometry)
GeoGetBoundingBox
This function can be used in scripts and chart expressions to calculate the smallest GeoBoundingBox that
contain all coordinates of a geometry.
A GeoBoundingBox is represented as a list of four values, left, right, top, bottom.
GeoGetBoundingBox(geometry)
GeoGetPolygonCenter
This function can be used in scripts and chart expressions to calculate and return the center point of a
geometry.
GeoGetPolygonCenter(geometry)
GeoInvProjectGeometry
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This function can be used in scripts to aggregate a geometry into an area and apply the inverse of a
projection.
GeoInvProjectGeometry(projection, geometry)
GeoMakePoint
This function can be used in scripts and chart expressions to create and tag a Point with latitude and
longitude.
GeoMakePoint(latitude, longitude )
GeoProject
This function can be used in scripts and chart expressions to apply a projection to a geometry.
GeoProject(projection, geometry)
GeoProjectGeometry
This function can be used in scripts to aggregate a geometry into an area and apply a projection.
GeoProjectGeometry(projection, geometry)
GeoReduceGeometry
This function can be used in scripts to aggregate a geometry into an area.
GeoReduceGeometry(geometry)
5.13 Interpretation functions
The interpretation functions interpret the contents of fields or expressions. With these functions it is possible
to state the data type, decimal separator, thousands separator etc. used. All functions can be used in both
data load scripts and chart expressions.
If no interpretation functions are used, Qlik Sense interprets the data as a mix of numbers, dates, times, time
stamps and strings, using the default settings for number format, date format and time format defined by
script variables and by the operating system.
For reasons of clarity, all number representations are given with decimal point as decimal
separator.
Interpretation functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Date#
This function evaluates the expression as a date according to the string given as format-code. If the format
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code is omitted, the default date format set in the operating system is used.
Date# (page 492) (expression [ , format-code ])
Interval#
This function evaluates the expression as a time interval according to the string given as a format-code. If
the format code is omitted, the time format set in the operating system is used.
Interval# (page 493)(expression [ , format-code ])
Money#
This function evaluates the expression numerically according to the string given as format-code. Decimal
separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4 are
omitted, the default number format set by script variables or in the operating system is used.
Money# (page 494)(expression [ , format-code [ , decimal-sep [ , thousandssep ] ] ])
Num#
This function evaluates the expression numerically according to the string given as format-code. Decimal
separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4 are
omitted, the default number format set by script variables or in the operating system is used.
Num# (page 494)(expression [ , format-code[ , decimal-sep [ , thousandssep] ] ])
Text
The text function forces the expression to be treated as text, even if a numeric interpretation is possible.
Text (page 495)(expression )
Time#
The time# function evaluates the expression as time according to the string given as format-code. If the
format code is omitted, the default time format set in the operating system is used.
Time# (page 495)(expression [ , format-code ])
Timestamp#
The timestamp# function evaluates the expression as a date and time according to the string given as
format-code. If the format code is omitted, the default date and time formats set in the operating system
are used.
Timestamp# (page 496)(expression [ , format-code ])
Date#
This function evaluates the expression as a date according to the string given as format-code. If the format
code is omitted, the default date format set in the operating system is used.
Syntax:
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Date#(expression [ , format-code ])
Arguments:
Argument
Description
formatcode
String describing the format of the resulting string. If the format code is omitted, the date
format set in the operating system is used.
Examples and results:
The examples below assume the two following operating system settings:
Date format
Default setting 1
Default setting 2
YY-MM-DD
M/D/YY
Result with Setting
Result with Setting
1
2
String:
8/6/97
8/6/97
Number:
-
35648
String:
1997.08.08
1997.08.06
Number:
35648
35648
Example
Results
Date#( A ) where A=8/6/97
Date#( A, 'YYYY.MM.DD' ) where
A=1997.08.06
Interval#
This function evaluates the expression as a time interval according to the string given as a format-code. If
the format code is omitted, the time format set in the operating system is used.
Syntax:
Interval#(expression [ , format-code ])
The interval# function generally behaves just like the time# function but while times can never be greater
than 23:59:59 (numeric value 0.99999) or smaller than 00:00:00 (numeric value 0.00000) an interval may
have any value.
Examples and results:
The examples below assume the following operating system settings:
Short date format:
Time format:
Number decimal separator:
YY-MM-DD
hh:mm:ss
.
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Example
Result
Interval#( A, 'D hh:mm' ) where A=1 09:00
String:
Interval#( A-B ) where A=97-08-06 09:00:00 and B=97-08-05 00:00:00
1 09:00
Number:
1.375
String:
1.375
Number:
1.375
Money#
This function evaluates the expression numerically according to the string given as format-code. Decimal
separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4 are
omitted, the default number format set by script variables or in the operating system is used.
Syntax:
Money#(expression [ , format-code [ , decimal-sep [ , thousands-sep ] ] ])
The money# function generally behaves just like the num# function but takes its default values for decimal
and thousand separator from the script variables for money format or the system settings for currency.
Examples and results:
The examples below assume the two following operating system settings:
Money format
Default setting 1
Default setting 2
kr # ##0,00
$ #,##0.00
Example
Results
Money#( , '# ##0,00 kr' ) where A=35 648,37 kr
String:
Number:
Money#( A, ' $#', '.', ',' ) where A=
$35,648.37
String:
Number:
Setting 1
Setting 2
35 648.37 kr
35 648.37 kr
35648.37
3564837
$35,648.37
$35,648.37
35648.37
35648.37
Num#
This function evaluates the expression numerically according to the string given as format-code. Decimal
separator and thousands separator can be set as third and fourth parameters. If the parameters 2-4 are
omitted, the default number format set by script variables or in the operating system is used.
Syntax:
Num#(expression [ , format-code[ , decimal-sep [ , thousands-sep] ] ])
Examples and results:
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The examples below assume the two following operating system settings:
Default setting 1
Default setting 2
# ##0,#
#,##0.#
Number format
Example
Results
Num#( A, '#' ) where A=35,648.375
String:
Setting 1
35,648.375
Number:
Num#( A, '#.#', '.' , ',') where A=35,648.375
Num#( A, '#.#',',','.' ) where A=35648.375
String:
-
35648.375
35648.375
35,648.375
35,648.375
Number:
35648.375
35648.375
String:
35648.375
35648.375
35648375
35648375
abc123,4
abc123,4
123.4
1234
Number:
Num#( A, 'abc#,#' ) where A=abc123,4
Setting 2
String:
Number:
Text
The text function forces the expression to be treated as text, even if a numeric interpretation is possible.
Syntax:
Text (expression )
Examples and results:
Example
Text( A ) where A=1234
Result
String:
1234
Number:
Text( pi( ) )
String:
3.1415926535898
Number:
-
Time#
The time# function evaluates the expression as time according to the string given as format-code. If the
format code is omitted, the default time format set in the operating system is used.
Syntax:
time#(expression [ , format-code ])
Examples and results:
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The examples below assume the two following operating system settings:
Default setting 1
Default setting 2
hh:mm:ss
hh.mm.ss
Time format
Example
Results
time#( A ) where A=09:00:00
String:
time#( A, 'hh.mm' ) where A=09.00
Setting 1
09:00:00
Setting 2
09:00:00
Number:
0.375
-
String:
09.00
09.00
Number:
0.375
0.375
Timestamp#
The timestamp# function evaluates the expression as a date and time according to the string given as
format-code. If the format code is omitted, the default date and time formats set in the operating system
are used.
Syntax:
timestamp#(expression [ , format-code ])
Examples and results:
The examples below assume the two following operating system settings:
Default setting 1
Default setting 2
Date format
YY-MM-DD
M/D/YY
Time format
hh:mm:ss
hh:mm:ss
Example
Results
Setting 1
Setting 2
timestamp#( A ) where A=8/6/97 09:00:00
String:
8/6/97 09:00:00
8/6/97 09:00:00
Number:
timestamp#( A, 'YYYY-MM-DD hh_mm' ) where A=8/6/97
String:
09_00
Number:
-
35648.375
1997-08-06 09_
00
1997-08-06 09_
00
35648.375
35648.375
5.14 Inter-record functions
Inter-record functions are used:
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l
l
In the data load script, when a value from previously loaded records of data is needed for the
evaluation of the current record.
In a chart expression, when another value from the data set of a visualization is needed.
Sorting on y-values in charts or sorting by expression columns in straight tables is not allowed
when chart inter-record functions are used in any of the chart's expressions. These sort
alternatives are therefore automatically disabled.
Suppression of zero values is automatically disabled when these functions are used.
Row functions
These functions can only be used in chart expressions.
Above
Above() evaluates an expression at a row above the current row within a column segment in a table. The row
for which it is calculated depends on the value of offset, if present, the default being the row directly above.
For charts other than tables, Above() evaluates for the row above the current row in the chart's straight table
equivalent.
Above - chart function([TOTAL []] expr [ , offset [,count]])
Below
Below() evaluates an expression at a row below the current row within a column segment in a table. The row
for which it is calculated depends on the value of offset, if present, the default being the row directly below.
For charts other than tables, Below() evaluates for the row below the current column in the chart's straight
table equivalent.
Below - chart function([TOTAL[]] expression [ , offset [,count
]])
Bottom
Bottom() evaluates an expression at the last (bottom) row of a column segment in a table. The row for which
it is calculated depends on the value of offset, if present, the default being the bottom row. For charts other
than tables, the evaluation is made on the last row of the current column in the chart's straight table
equivalent.
Bottom - chart function([TOTAL[]] expr [ , offset [,count ]])
Top
Top() evaluates an expression at the first (top) row of a column segment in a table. The row for which it is
calculated depends on the value of offset, if present, the default being the top row. For charts other than
tables, theTop() evaluation is made on the first row of the current column in the chart's straight table
equivalent.
Top - chart function([TOTAL []] expr [ , offset [,count ]])
NoOfRows
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NoOfRows() returns the number of rows in the current column segment in a table. For bitmap charts,
NoOfRows() returns the number of rows in the chart's straight table equivalent.
NoOfRows - chart function([TOTAL])
Column functions
These functions can only be used in chart expressions.
Column
Column() returns the value found in the column corresponding to ColumnNo in a straight table,
disregarding dimensions. For example Column(2) returns the value of the second measure column.
Column - chart function(ColumnNo)
Dimensionality
Dimensionality() returns the number of dimensions for the current row. In the case of pivot tables, the
function returns the total number of dimension columns that have non-aggregation content, that is, do not
contain partial sums or collapsed aggregates.
Dimensionality - chart function ( )
Secondarydimensionality
Secondarydimensionality() returns the number of dimension pivot table rows that have non-aggregation
content, that is, do not contain partial sums or collapsed aggregates. This function is the equivalent of the
dimensionality() function for horizontal pivot table dimensions.
Secondarydimensionality ( )
Field functions
FieldIndex
FieldIndex() returns the position of the field value value in the field field_name (by load order).
FieldIndex(field_name , value)
FieldValue
FieldValue() returns the value found in position elem_no of the field field_name (by load order).
FieldValue(field_name , elem_no)
FieldValueCount
FieldValueCount() is an integer function that finds the number of distinct values in a field.
FieldValueCount(field_name)
Pivot table functions
These functions can only be used in chart expressions.
After
After() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
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the column after the current column within a row segment in the pivot table.
After - chart function([TOTAL] expression [ , offset [,n]])
Before
Before() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the column before the current column within a row segment in the pivot table.
Before - chart function([TOTAL] expression [ , offset [,n]])
First
First() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the first column of the current row segment in the pivot table. This function returns NULL in all chart types
except pivot tables.
First - chart function([TOTAL] expression [ , offset [,n]])
Last
Last() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the last column of the current row segment in the pivot table. This function returns NULL in all chart types
except pivot tables.
Last - chart function([TOTAL] expression [ , offset [,n]])
ColumnNo
ColumnNo() returns the number of the current column within the current row segment in a pivot table. The
first column is number 1.
ColumnNo - chart function([TOTAL])
NoOfColumns
NoOfColumns() returns the number of columns in the current row segment in a pivot table.
NoOfColumns - chart function([TOTAL])
Inter-record functions in the data load script
Exists
Exists() determines whether a specific field value has already been loaded into the field in the data load
script. The function returns TRUE or FALSE, so can be used in the where clause of a LOAD statement or an
IF statement.
Exists (field [ , expression ]
LookUp
Lookup() looks into a table that is already loaded and returns the value of field_name corresponding to the
first occurrence of the value match_field_value in the field match_field_name. The table can be the
current table or another table previously loaded.
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LookUp (fieldname, matchfieldname, matchfieldvalue [, tablename])
Peek
Peek() finds the value of a field in a table for a row that has already been loaded or that exists in internal
memory. The row number can be specified, as can the table.
Peek (fieldname [ , row [ , tablename ] ]
Previous
Previous() finds the value of the expr expression using data from the previous input record that has not been
discarded because of a where clause. In the first record of an internal table, the function will return NULL.
Previous (expression )
See also:
p Range functions (page 544)
Above - chart function
Above() evaluates an expression at a row above the current row within a column segment in a table. The row
for which it is calculated depends on the value of offset, if present, the default being the row directly above.
For charts other than tables, Above() evaluates for the row above the current row in the chart's straight table
equivalent.
Syntax:
Above([TOTAL] expr [ , offset [,count]])
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
offset
Specifying an offsetn, greater than 0, moves the evaluation of the expression n rows further
up from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the Above function work like the Below
function with the corresponding positive offset number.
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Argument
Description
count
By specifying a third argument count greater than 1, the function will return a range of
count values, one for each of count table rows counting upwards from the original cell.
In this form, the function can be used as an argument to any of the special range functions.
Range functions (page 544)
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
On the first row of a column segment, a NULL value is returned, as there is no row above it.
A column segment is defined as a consecutive subset of cells having the same values for the
dimensions in the current sort order. Inter-record chart functions are computed in the column
segment excluding the right-most dimension in the equivalent straight table chart. If there is
only one dimension in the chart, or if the TOTAL qualifier is specified, the expression evaluates
across full table.
If the table or table equivalent has multiple vertical dimensions, the current column segment
will include only rows with the same values as the current row in all dimension columns,
except for the column showing the last dimension in the inter-field sort order.
Limitations:
Recursive calls will return NULL.
Examples and results:
Example 1:
The table visualization for Example 1.
In the screenshot of the table shown in this example, the table visualization is created from the dimension
Customer and the measures: Sum(Sales) and Above(Sum(Sales)).
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The column Above(Sum(Sales)) returns NULL for the Customer row containing Astrida, because there is
no row above it. The result for the row Betacab shows the value of Sum(Sales) for Astrida, the result for
Canutility shows the value for Sum(Sales) for Betacab, and so on.
For the column labeled Sum(Sales)+Above(Sum(Sales)), the row for Betacab shows the result of the
addition of the Sum(Sales) values for the rows Betacab + Astrida (539+587). The result for the
row Canutility shows the result of the addition of Sum(Sales) values for Canutility + Betacab (683+539).
The measure labeled Above offset 3 created using the expression Sum(Sales)+Above(Sum(Sales), 3) has the
argument offset, set to 3, and has the effect of taking the value in the row three rows above the current row.
It adds the Sum(Sales) value for the current Customer to the value for the Customerthree rows above.
The values returned for the first three Customer rows are null.
The table also shows more complex measures: one created from Sum(Sales)+Above(Sum(Sales)) and one
labeled Higher?, which is created from IF(Sum(Sales)>Above(Sum(Sales)), 'Higher').
This function can also be used in charts other than tables, for example bar charts.
For other chart types, convert the chart to the straight table equivalent so you can easily
interpret which row the function relates to.
Example 2:
In the screenshots of tables shown in this example, more dimensions have been added to the visualizations:
Month and Product. For charts with more than one dimension, the results of expressions containing the
Above, Below, Top, and Bottom functions depend on the order in which the column dimensions are sorted
by Qlik Sense. Qlik Sense evaluates the functions based on the column segments that result from the
dimension that is sorted last. The column sort order is controlled in the properties panel under Sorting and is
not necessarily the order in which the columns appear in a table.
In the following screenshot of table visualization for Example 2, the last-sorted dimension is Month, so the
Above function evaluates based on months. There is a series of results for each Product value for each
month (Jan to Aug) - a column segment. This is followed by a series for the next column segment: for each
Month for the next Product. There will be a column segment for each Customer value for each Product.
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The table visualization for Example 2.
Example 3:
In the screenshot of table visualization for Example 3, the last sorted dimension is Product. This is done by
moving the dimension Product to position 3 in the Sorting tab in the properties panel. The Above function is
evaluated for each Product, and because there are only two products, AA and BB, there is only one nonnull result in each series. In row BB for the month Jan, the value for Above(Sum(Sales)), is 46. For row
AA, the value is null. The value in each row AA for any month will always be null, as there is no value of
Product above AA. The second series is evaluated on AA and BB for the month Feb, for the Customer
value, Astrida. When all the months have been evaluated for Astrida, the sequence is repeated for the
second CustomerBetacab, and so on.
The table visualization for Example 3.
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Example 4:
Result
The Above function can be used as input to
In the arguments for the Above() function, offset is set
the range functions. For example: RangeAvg
(Above(Sum(Sales),1,3)).
to 1 and count is set to 3. The function finds the results of
the expressionSum(Sales) on the three rows
immediately above the current row in the column
segment (where there is a row). These three values are
used as input to the RangeAvg() function, which finds the
average of the values in the supplied range of numbers.
A table with Customer as dimension gives the following
results for the RangeAvg() expression.
Astrida
Betacab
Canutility
Divadip:
587
563
603
Data used in examples:
Monthnames:
LOAD * INLINE [
Month, Monthnumber
Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
Nov, 11
Dec, 12
];
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Below - chart function (page 505)
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p Bottom - chart function (page 508)
p Top - chart function (page 528)
p RangeAvg (page 547)
Below - chart function
Below() evaluates an expression at a row below the current row within a column segment in a table. The row
for which it is calculated depends on the value of offset, if present, the default being the row directly below.
For charts other than tables, Below() evaluates for the row below the current column in the chart's straight
table equivalent.
Syntax:
Below([TOTAL] expression [ , offset [,count ]])
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
offset
Specifying an offsetn, greater than 1 moves the evaluation of the expression n rows further
down from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the Below function work like the Above
function with the corresponding positive offset number.
count
By specifying a third parameter count greater than 1, the function will return a range of
count values, one for each of count table rows counting downwards from the original cell.
In this form, the function can be used as an argument to any of the special range functions.
Range functions (page 544)
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
On the last row of a column segment, a NULL value is returned, as there is no row below it.
A column segment is defined as a consecutive subset of cells having the same values for the
dimensions in the current sort order. Inter-record chart functions are computed in the column
segment excluding the right-most dimension in the equivalent straight table chart. If there is
only one dimension in the chart, or if the TOTAL qualifier is specified, the expression evaluates
across full table.
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If the table or table equivalent has multiple vertical dimensions, the current column segment
will include only rows with the same values as the current row in all dimension columns,
except for the column showing the last dimension in the inter-field sort order.
Limitations:
Recursive calls will return NULL.
Examples and results:
Example 1:
The table visualization for Example 1.
In the table shown in screenshot for Example 1, the table visualization is created from the dimension
Customer and the measures: Sum(Sales) and Below(Sum(Sales)).
The column Below(Sum(Sales)) returns NULL for the Customer row containing Divadip, because there is
no row below it. The result for the row Canutility shows the value of Sum(Sales) for Divadip, the result for
Betacab shows the value for Sum(Sales) for Canutility, and so on.
The table also shows more complex measures, which you can see in the columns labeled: Sum(Sales)+Below
(Sum(Sales)), Below +Offset 3, and Higher?. These expressions work as described in the following
paragraphs.
For the column labeled Sum(Sales)+Below(Sum(Sales)), the row for Astrida shows the result of the
addition of the Sum(Sales) values for the rows Betacab + Astrida (539+587). The result for the
row Betacab shows the result of the addition of Sum(Sales) values for Canutility + Betacab (539+683).
The measure labeled Below +Offset 3 created using the expression Sum(Sales)+Below(Sum(Sales), 3) has
the argument offset, set to 3, and has the effect of taking the value in the row three rows below the current
row. It adds the Sum(Sales) value for the current Customer to the value from the Customer three rows
below. The values for the lowest three Customer rows are null.
The measure labeled Higher? is created from the expression: IF(Sum(Sales)>Below(Sum(Sales)), 'Higher').
This compares the values of the current row in the measure Sum(Sales) with the row below it. If the current
row is a greater value, the text "Higher" is output.
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This function can also be used in charts other than tables, for example bar charts.
For other chart types, convert the chart to the straight table equivalent so you can easily
interpret which row the function relates to.
For charts with more than one dimension, the results of expressions containing the Above, Below, Top, and
Bottom functions depend on the order in which the column dimensions are sorted by Qlik Sense. Qlik Sense
evaluates the functions based on the column segments that result from the dimension that is sorted last. The
column sort order is controlled in the properties panel under Sorting and is not necessarily the order in which
the columns appear in a table.Please refer to Example: 2 in the Above function for further details.
Example 2:
Result
The Below function can be used as input to the
In the arguments for the Below() function, offset is
range functions. For example: RangeAvg (Below(Sum
(Sales),1,3)).
set to 1 and count is set to 3. The function finds the
results of the expressionSum(Sales) on the three
rows immediately below the current row in the
column segment (where there is a row). These
three values are used as input to the RangeAvg()
function, which finds the average of the values in
the supplied range of numbers.
A table with Customer as dimension gives the
following results for the RangeAvg() expression.
Astrida
Betacab
Canutility
Divadip:
659.67
720
757
-
Data used in examples:
Monthnames:
LOAD * INLINE [
Month, Monthnumber
Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
Nov, 11
Dec, 12
];
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Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Above - chart function (page 500)
p Bottom - chart function (page 508)
p Top - chart function (page 528)
p RangeAvg (page 547)
Bottom - chart function
Bottom() evaluates an expression at the last (bottom) row of a column segment in a table. The row for which
it is calculated depends on the value of offset, if present, the default being the bottom row. For charts other
than tables, the evaluation is made on the last row of the current column in the chart's straight table
equivalent.
Syntax:
Bottom([TOTAL] expr [ , offset [,count ]])
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
offset
Specifying an offsetn greater than 1 moves the evaluation of the expression up n rows
above the bottom row.
Specifying a negative offset number makes the Bottom function work like the Top function
with the corresponding positive offset number.
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Argument
Description
count
By specifying a third parameter count greater than 1, the function will return not one but a
range of count values, one for each of the last count rows of the current column segment.
In this form, the function can be used as an argument to any of the special range functions.
Range functions (page 544)
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
A column segment is defined as a consecutive subset of cells having the same values for the
dimensions in the current sort order. Inter-record chart functions are computed in the column
segment excluding the right-most dimension in the equivalent straight table chart. If there is
only one dimension in the chart, or if the TOTAL qualifier is specified, the expression evaluates
across full table.
If the table or table equivalent has multiple vertical dimensions, the current column segment
will include only rows with the same values as the current row in all dimension columns,
except for the column showing the last dimension in the inter-field sort order.
Limitations:
Recursive calls will return NULL.
Examples and results:
Example: 1
The table visualization for Example 1.
In the screenshot of the table shown in this example, the table visualization is created from the dimension
Customer and the measures: Sum(Sales) and Bottom(Sum(Sales)).
The column Bottom(Sum(Sales)) returns 757 for the all rows because this is the value of the bottom row:
Divadip.
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The table also shows more complex measures: one created from Sum(Sales)+Bottom(Sum(Sales)) and one
labeled Bottom offset 3, which is created using the expression Sum(Sales)+Bottom(Sum(Sales), 3) and has
the argument offset set to 3. It adds the Sum(Sales) value for the current row to the value from the third row
from the bottom row, that is, the current row plus the value for Betacab.
Example: 2
In the screenshots of tables shown in this example, more dimensions have been added to the visualizations:
Month and Product. For charts with more than one dimension, the results of expressions containing the
Above, Below, Top, and Bottom functions depend on the order in which the column dimensions are sorted
by Qlik Sense. Qlik Sense evaluates the functions based on the column segments that result from the
dimension that is sorted last. The column sort order is controlled in the properties panel under Sorting and is
not necessarily the order in which the columns appear in a table.
In the first table, the expression is evaluated based on Month, and in the second table it is evaluated based
on Product. The measure End value contains the expression Bottom(Sum(Sales)). The bottom row for
Month is Dec, and the value for Dec both the values of Product shown in the screenshot is 22. (Some rows
have been edited out of the screenshot to save space.)
First table for Example 2. The value of Bottom for the End value measure based on Month (Dec).
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Second table for Example 2. The value of Bottom for the End value measure based on Product (BB for Astrida).
Please refer to Example: 2 in the Above function for further details.
Example: 3
Result
The Bottom function can be used as input to the
In the arguments for the Bottom() function, offset
range functions. For example: RangeAvg (Bottom
(Sum(Sales),1,3)).
is set to 1 and count is set to 3. The function finds
the results of the expressionSum(Sales) on the
three rows starting with the row above the bottom
row in the column segment (because offset=1), and
the two rows above that (where there is a row).
These three values are used as input to the
RangeAvg() function, which finds the average of the
values in the supplied range of numbers.
A table with Customer as dimension gives the
following results for the RangeAvg() expression.
Astrida
Betacab
Canutility
Divadip:
659.67
659.67
659.67
659.67
Monthnames:
LOAD * INLINE [
Month, Monthnumber
Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
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Nov, 11
Dec, 12
];
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Top - chart function (page 528)
Column - chart function
Column() returns the value found in the column corresponding to ColumnNo in a straight table,
disregarding dimensions. For example Column(2) returns the value of the second measure column.
Syntax:
Column(ColumnNo)
Return data type: dual
Arguments:
Argument
Description
ColumnNo
Column number of a column in the table containing a measure.
The Column() function disregards dimension columns.
Limitations:
If ColumnNo references a column for which there is no measure, a NULL value is returned.
Recursive calls will return NULL.
Examples and results:
Example: Percentage total sales
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Customer
Product
UnitPrice
UnitSales
Order Value
Total Sales Value
% Sales
A
AA
15
10
150
505
29.70
A
AA
16
4
64
505
12.67
A
BB
9
9
81
505
16.04
B
BB
10
5
50
505
9.90
B
CC
20
2
40
505
7.92
B
DD
25
-
0
505
0.00
C
AA
15
8
120
505
23.76
C
CC
19
-
0
505
0.00
Example: Percentage of sales for selected customer
Customer
Product
UnitPrice
UnitSales
Order Value
Total Sales Value
% Sales
A
AA
15
10
150
295
50.85
A
AA
16
4
64
295
21.69
A
BB
9
9
81
295
27.46
Examples
Results
Order Value is added to the table as a
measure with the expression: Sum
(UnitPrice*UnitSales).
The result of Column(1) is taken from the column Order Value,
because this is the first measure column.
Total Sales Value is added as a
measure with the expression: Sum(TOTAL
UnitPrice*UnitSales)
The result of Column(2) is taken from Total Sales Value,
because this is the second measure column.
See the results in the column % Sales in the example
Percentage total sales (page 512).
% Sales is added as a measure with the
expression 100*Column(1)/Column(2)
Make the selection Customer A.
The selection changes the Total Sales Value, and therefore
the %Sales. See the example Percentage of sales for
selected customer (page 513).
Data used in examples:
ProductData:
LOAD * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
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Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD||25
Canutility|AA|8|15
Canutility|CC||19
] (delimiter is '|');
Dimensionality - chart function
Dimensionality() returns the number of dimensions for the current row. In the case of pivot tables, the
function returns the total number of dimension columns that have non-aggregation content, that is, do not
contain partial sums or collapsed aggregates.
Syntax:
Dimensionality ( )
Return data type: integer
Limitations:
This function is only available in charts. The number of dimensions in all rows, except the total which will be
0, will be returned. For all chart types, except pivot table it will return the number of dimensions in all rows
except the total, which will be 0.
Example:
A typical use for dimensionality is when you want to make a calculation only if there is a value present for a
dimension.
Example
Result
For a table containing the dimension UnitSales, you might only want to indicate an invoice is
sent:
IF(Dimensionality()=3, "Invoiced").
Exists
Exists() determines whether a specific field value has already been loaded into the field in the data load
script. The function returns TRUE or FALSE, so can be used in the where clause of a LOAD statement or an
IF statement.
Syntax:
Exists(field_name [, expr] )
Return data type: Boolean
Arguments:
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Argument
Description
field_
name
A name or a string expression evaluating to a field name to be searched for. The field must
exist in the data loaded so far by the script.
expr
An expression evaluating to the field value to look for in the field specified in field-name. If
omitted, the current record’s value in the specified field is assumed.
Examples and results:
Example
Result
Exists (Employee)
Returns -1 (True) if the value of the field Employee in the
current record already exists in any previously read record
containing that field.
Exists(Employee, 'Bill')
Returns -1 (True) if the field value 'Bill' is found in the
current content of the field Employee.
The statements Exists (Employee, Employee) and Exists
(Employee) are equivalent.
Employees:
LOAD * inline [
Employee|ID|Salary
Bill|001|20000
John|002|30000
Steve|003|35000
] (delimiter is '|');
Citizens:
Load * inline [
Name|Address
Bill|New York
Mary|London
Steve|Chicago
Lucy|Paris
John|Miami
] (delimiter is '|');
This results in a table called EmployeeAddresses in the
data model, which can be viewed as a table visualization
using the dimensions Employee and Address.
The where clause: where Exists (Employee, Name), means
only the names from the tableCitizens that are also in
Employees are loaded into the new table. The Drop
statement removes the temporary tables Employees and
Citizens to avoid confusion.
Employee
Address
Bill
New York
John
Miami
Steve
Chicago
EmployeeAddresses:
Load Name as Employee, Address Resident
Citizens where Exists (Employee, Name);
Drop Tables Employees, Citizens;
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Example
Result
Replacing the statement in the sample data
in the previous example that builds the table
EmployeeAddresses with the following,
using where not Exists.
The where clause includes not: where not Exists
(Employee, Name), means only the names from the table
Citizens that are not in Employees are loaded into the new
table.
NonEmployee:
Load Name as Employee, Address Resident
Citizens where not Exists (Employee,
Name);
Employee
Address
Mary
London
Lucy
Paris
Data used in example:
Employees:
LOAD * inline [
Employee|ID|Salary
Bill|001|20000
John|002|30000
Steve|003|35000
] (delimiter is '|');
Citizens:
Load * inline [
Name|Address
Bill|New York
Mary|London
Steve|Chicago
Lucy|Paris
John|Miami
] (delimiter is '|');
EmployeeAddresses:
Load Name as Employee, Address Resident Citizens where Exists (Employee, Name);
Drop Tables Employees, Citizens;
FieldIndex
FieldIndex() returns the position of the field value value in the field field_name (by load order).
Syntax:
FieldIndex(field_name , value)
Return data type: integer
Arguments:
Argument
Description
field_
name
Name of the field for which the index is required. Must be given as a string value. This
means that the field name must be enclosed by single quotes.
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Argument
Description
value
The value of the field field_name.
Limitations:
If value cannot be found among the field values of the field field_name, 0 is returned.
Examples and results:
The following example uses two fields: First name and Initials.
First name
Initials
John
JA
Sue
SB
Mark
MC
Peter
PD
Jane
JE
Peter
PF
Examples
Results
Chart function, as measure in a
table
FieldIndex ('First name','John')
1, because 'John' appears first in the load order of the First name
field. Note that in a filter pane John would appear as number 2
from the top as it's sorted alphabetically and not as in the load
order.
FieldIndex ('First name','Peter')
4, because FieldIndex() returns only one value, that is the first
occurrence in the load order.
Script function, as dimension in a
table
Given the table Initials is loaded,
as in the sample data:
John1:
Load FieldIndex('First
name','John') as MyJohnPos
Resident Initials;
MyJohnPos=1,
because 'John' appears first in the load order of the
First name field. Note that in a filter pane John would appear as
number 2 from the top as it's sorted alphabetically and not as in the
load order.
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Examples
Results
Given the tableInitials is loaded, as
MyPeterPos=4,
in the sample data:
the first occurrence in the load order.
because FieldIndex() returns only one value, that is
Peter1:
Load FieldIndex('First
name','Peter') as MyPeterPos
Resident Initials;
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
John1:
Load FieldIndex('First name','John') as MyJohnPos
Resident Initials;
Peter1:
Load FieldIndex('First name','Peter') as MyPeterPos
Resident Initials;
FieldValue
FieldValue() returns the value found in position elem_no of the field field_name (by load order).
Syntax:
FieldValue(field_name , elem_no)
Return data type: dual
Arguments:
Argument
Description
field_
name
Name of the field for which the value is required. Must be given as a string value. This
means that the field name must be enclosed by single quotes.
elem_no
The position (element) number of the field, following the load order, that the value is
returned for.
Limitations:
If elem_no is larger than the number of field values, NULL is returned.
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First name
Initials
John
JA
Sue
SB
Mark
MC
Peter
PD
Jane
JE
Peter
PF
Examples and results:
The following example uses two fields: First name and Initials.
Examples
Results
Chart function, as measure in a
table
FieldValue('First name','1')
John, because John appears first in the load order of the First
name field. Note that in a filter pane John would appear as
number 2 from the top, after Jane, as it's sorted alphabetically and
not as in the load order.
FieldValue('First name','7')
NULL, because there are only 6 values in the First name field.
Script function, as dimension in a
table
Given the table Initials is loaded,
as in the sample data:
MyPos1=John,
because 'John' appears first in the load order of the
First name field.
John1:
Load FieldValue('First name',1)
as MyPos1
Resident Initials;
Given the tableInitials is loaded, as
MyPo2s= - (Null),
in the sample data:
namefield.
because there are only 6 values in the First
Peter1:
Load FieldValue('First name',7)
as MyPos2
Resident Initials;
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
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John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
John1:
Load FieldValue('First name',1) as MyPos1
Resident Initials;
Peter1:
Load FieldValue('First name',7) as MyPos2
Resident Initials;
FieldValueCount
FieldValueCount() is an integer function that finds the number of distinct values in a field.
Syntax:
FieldValueCount(field_name)
Arguments:
Argument
Description
field_
name
Name of the field for which the value is required. Must be given as a string value. This
means that the field name must be enclosed by single quotes.
Examples and results:
The following example uses two fields: First name and Initials.
Examples
Results
Chart function, as measure in a table
FieldValueCount('First name')
5 as Peter appears twice.
FieldValueCount('Initials')
6 as Initials only has distinct values.
Script function, as dimension in a table
Given the table Initials is loaded, as in the sample
MyFieldCount1=5,
because 'John' appears twice.
data:
John1:
Load FieldValueCount('First name') as
MyFieldCount1
Resident Initials;
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Examples
Results
Given the table Initials is loaded, as in the sample
MyFieldCount1=6,
data:
values.
because 'Initials' only has distinct
John1:
Load FieldValueCount('Initials') as
MyInitialsCount1
Resident Initials;
Data used in example:
First name
Initials
John
JA
Sue
SB
Mark
MC
Peter
PD
Jane
JE
Peter
PF
Data used in example:
Initials:
LOAD * inline [
"First name"|Initials|"Has cellphone"
John|JA|Yes
Sue|SB|Yes
Mark|MC |No
Peter|PD|No
Jane|JE|Yes
Peter|PF|Yes ] (delimiter is '|');
FieldCount1:
Load FieldValueCount('First name') as MyFieldCount1
Resident Initials;
FieldCount2:
Load FieldValueCount('Initials') as MyInitialsCount1
Resident Initials;
LookUp
Lookup() looks into a table that is already loaded and returns the value of field_name corresponding to the
first occurrence of the value match_field_value in the field match_field_name. The table can be the
current table or another table previously loaded.
Syntax:
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lookup(field_name, match_field_name, match_field_value [, table_name])
Return data type: dual
Arguments:
Argument
Description
field_name
Name of the field for which the return value is required.Input value must be given as a
string (for example, quoted literals).
match_field_
name
Name of the field to look up match_field_value in. Input value must be given as a string
match_field_
value
Value to look up in match_field_name field.
table_name
Name of the table in which to look up the value. Input value must be given as a string (for
example quoted literals).
(for example, quoted literals).
If table_name is omitted the current table is assumed.
Arguments without quotes refer to the current table. To refer to other tables, enclose an
argument in single quotes.
Limitations:
The order in which the search is made is the load order, unless the table is the result of complex operations
such as joins, in which case, the order is not well defined. Both field_name and match_field_name must be
fields in the same table, specified by table_name.
If no match is found, NULL is returned.
Examples and results:
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Example
Result
The sample data uses the Lookup()
The ProductList table is loaded first.
function in the following form:
The Lookup() function is used to build the OrderData
Lookup('Category', 'ProductID',
ProductID, 'ProductList')
Add the example script to your app and run
it. Then add, at least, the fields listed in
the results column to a sheet in your app to
see the result.
ProductList:
Load * Inline [
ProductID|Product|Category|Price
1|AA|1|1
2|BB|1|3
3|CC|2|8
4|DD|3|2
] (delimiter is '|');
OrderData:
Load *, Lookup('Category', 'ProductID',
ProductID, 'ProductList') as CategoryID
Inline [
InvoiceID|CustomerID|ProductID|Units
1|Astrida|1|8
1|Astrida|2|6
2|Betacab|3|10
3|Divadip|3|5
4|Divadip|4|10
] (delimiter is '|');
table. It specifies the third argument as ProductID. This is
the field for which the value is to be looked up in the second
argument 'ProductID' in the ProductList, as denoted by
the enclosing single quotes.
The function returns the value for 'Category' (in the
ProductList table), loaded as CategoryID.
The drop statement deletes the ProductList table from
the data model, because it is not required, which leaves the
OrderData table with the following result:
ProductID
InvoiceID
CustomerID
Units
CategoryID
1
1
Astrida
8
1
2
1
Astrida
6
1
3
2
Betacab
10
2
3
3
Divadip
5
2
4
4
Divadip
10
3
Drop Table ProductList
The Lookup() function is flexible and can access any previously loaded table. However, it is
slow compared with the Applymap() function.
See also:
p ApplyMap (page 538)
NoOfRows - chart function
NoOfRows() returns the number of rows in the current column segment in a table. For bitmap charts,
NoOfRows() returns the number of rows in the chart's straight table equivalent.
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If the table or table equivalent has multiple vertical dimensions, the current column segment will include only
rows with the same values as the current row in all dimension columns, except for the column showing the
last dimension in the inter-field sort order.
Syntax:
NoOfRows([TOTAL])
Return data type: integer
Arguments:
Argument
Description
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
Example:
if( RowNo( )= NoOfRows( ), 0, Above( sum( Sales )))
See also:
p RowNo - chart function (page 346)
Peek
Peek() finds the value of a field in a table for a row that has already been loaded or that exists in internal
memory. The row number can be specified, as can the table.
Syntax:
Peek(field_name[, row_no[, table_name ] ] )
Return data type: dual
Arguments:
Argument
Description
field_
name
Name of the field for which the return value is required.Input value must be given as a string
(for example, quoted literals).
row_no
The row in the table that specifies the field required. Can be an expression, but must resolve
to an integer. 0 denotes the first record, 1 the second, and so on. Negative numbers indicate
order from the end of the table. -1 denotes the last record read.
If no row is stated, -1 is assumed.
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Argument
Description
table_
name
A table label without the ending colon. If no table_name is stated, the current table is
assumed. If used outside the LOAD statement or referring to another table, the table_
name must be included.
Limitations:
In the first record of an internal table, the function returns NULL.
Examples and results:
Example
Result
Add the example script to your app and run
it. Then add, at least, the fields listed in the
results column to a sheet in your app to see
the result.
EmpCode = 101, because Peek(EmployeeCode,0) returns
the first value of EmployeeCode in the table
EmployeeDates.
Substituting the value of the argument row_no returns
EmployeeDates:
Load * Inline [
EmployeeCode|StartDate|EndDate
101|02/11/2010|23/06/2012
102|01/11/2011|30/11/2013
103|02/01/2012|
104|02/01/2012|31/03/2012
105|01/04/2012|31/01/2013
106|02/11/2013|
] (delimiter is '|');
the values of other rows in the table, as follows:
FirstEmployee:
Load EmployeeCode, Peek(EmployeeCode,0)
As EmpCode
Resident EmployeeDates;
EmployeeCode
101
102
103
104
105
106
FirstEmployee:
Load EmployeeCode, Peek(EmployeeCode,2,'EmployeeDates') As EmpCode
Resident EmployeeDates;
Peek(EmployeeCode,2) returns the third value in the table:
102.
However, note that without specifying the table as the
third argument table_no, the function references the
current (in this case, internal) table. The result of Peek
(EmployeeCode,-2) is multiple values:
EmpCode
101
102
103
104
By specifying the argument table_no as 'EmployeeDates',
the function returns the second-to-last value of
EmployeeCode in the table EmployeeDates: 105.
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Example
Result
The Peek() function can be used to
Create a table in a sheet in your app with ID, List, and
reference data that is not yet loaded.
Value as the dimensions.
Add the example script to your app and run
it. Then add, at least, the fields listed in the
results column to a sheet in your app to see
the result.
ID
List
Value
1
6
6
1
6,3
3
1
6,3,4
4
2
11
11
2
11,10
10
2
11,10,1
1
3
8
8
3
8,7
7
5
13
13
5
13,2
2
5
13,2,78
78
T1:
LOAD * inline [
ID, Value
1|3
1|4
1|6
3|7
3|8
2|1
2|11
5|2
5|78
5|13
] (delimiter is '|');
T2:
LOAD
*,
IF(ID=Peek(ID), Peek(List)
&','&Value,Value) AS List
RESIDENT T1
ORDER BY ID5 ASC;
DROP TABLE T1;
The IF() statement is built from the temporary table T1.
Peek(ID) references the field ID in the previous row in
the current table T2.
Peek(List) references the field List in the previous row
in the table T2, currently being built as the expression is
evaluated.
The statement is evaluated as follows:
If the current value of ID is the same as the previous
value of ID, then write the value of Peek(List)
concatenated with the current value of Value.
Otherwise, write the current value of Value only.
If Peek(List) already contains a concatenated result, the
new result of Peek(List) will be concatenated to it.
Note the Order by clause. This specifies
how the table is ordered (by ID in
ascending order). Without this, the Peek()
function will use whatever arbitrary
ordering the internal table has, which can
lead to unpredictable results.
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Previous
Previous() finds the value of the expr expression using data from the previous input record that has not been
discarded because of a where clause. In the first record of an internal table, the function will return NULL.
Syntax:
Previous(expr)
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
The expression can contain nested previous() functions in order to access records further
back. Data are fetched directly from the input source, making it possible to refer also to
fields that have not been loaded into Qlik Sense, that is,even if they have not been stored in
its associative database.
Limitations:
In the first record of an internal table, the function returns NULL.
Examples and results:
Add the example script to your app and run it. Then add, at least, the fields listed in the results column to a
sheet in your app to see the result.
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Example
Result
Sales2013:
Load *, (Sales - Previous(Sales) )as Increase Inline [
Month|Sales
1|12
2|13
3|15
4|17
5|21
6|21
7|22
8|23
9|32
10|35
11|40
12|41
] (delimiter is '|');
By using the Previous() function in
the Load statement, we can compare
the current value of Sales with the
preceding value, and use it in a third
field, Increase.
Month
1
2
3
4
5
6
7
8
9
10
11
12
Sales
12
13
15
17
21
21
22
23
32
35
40
41
Increase
1
2
2
4
0
1
1
9
3
5
1
Top - chart function
Top() evaluates an expression at the first (top) row of a column segment in a table. The row for which it is
calculated depends on the value of offset, if present, the default being the top row. For charts other than
tables, theTop() evaluation is made on the first row of the current column in the chart's straight table
equivalent.
Syntax:
Top([TOTAL] expr [ , offset [,count ]])
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
offset
Specifying an offsetof n, greater than 1, moves the evaluation of the expression down n
rows below the top row.
Specifying a negative offset number makes the Top function work like the Bottom function
with the corresponding positive offset number.
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Argument
Description
count
By specifying a third parameter count greater than 1, the function will return a range of
count values, one for each of the last count rows of the current column segment. In this
form, the function can be used as an argument to any of the special range functions. Range
functions (page 544)
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
A column segment is defined as a consecutive subset of cells having the same values for the
dimensions in the current sort order. Inter-record chart functions are computed in the column
segment excluding the right-most dimension in the equivalent straight table chart. If there is
only one dimension in the chart, or if the TOTAL qualifier is specified, the expression evaluates
across full table.
If the table or table equivalent has multiple vertical dimensions, the current column segment
will include only rows with the same values as the current row in all dimension columns,
except for the column showing the last dimension in the inter-field sort order.
Limitations:
Recursive calls will return NULL.
Examples and results:
Example: 1
In the screenshot of the table shown in this example, the table visualization is created from the dimension
Customer and the measures: Sum(Sales) and Top(Sum(Sales)).
The column Top(Sum(Sales)) returns 587 for the all rows because this is the value of the top row: Astrida.
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The table also shows more complex measures: one created from Sum(Sales)+Top(Sum(Sales)) and one
labeled Top offset 3, which is created using the expression Sum(Sales)+Top(Sum(Sales), 3) and has the
argument offset set to 3. It adds the Sum(Sales) value for the current row to the value from the third row
from the top row, that is, the current row plus the value for Canutility.
Example: 2
In the screenshots of tables shown in this example, more dimensions have been added to the visualizations:
Month and Product. For charts with more than one dimension, the results of expressions containing the
Above, Below, Top, and Bottom functions depend on the order in which the column dimensions are sorted
by Qlik Sense. Qlik Sense evaluates the functions based on the column segments that result from the
dimension that is sorted last. The column sort order is controlled in the properties panel under Sorting and is
not necessarily the order in which the columns appear in a table.
First table for Example 2. The value of Top for the First value measure based on Month (Jan).
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Second table for Example 2. The value of Top for the First value measure based on Product (AA for Astrida).
Please refer to Example: 2 in the Above function for further details.
Example: 3
Result
The Top function can be used as input to the range
In the arguments for the Top() function, offset is set
functions. For example: RangeAvg (Top(Sum
(Sales),1,3)).
to 1 and count is set to 3. The function finds the
results of the expressionSum(Sales) on the three
rows starting with the row below the bottom row in
the column segment (because the offset=1), and
the two rows below that (where there is a row).
These three values are used as input to the
RangeAvg() function, which finds the average of the
values in the supplied range of numbers.
A table with Customer as dimension gives the
following results for the RangeAvg() expression.
Astrida
Betacab
Canutility
Divadip:
603
603
603
603
Monthnames:
LOAD * INLINE [
Month, Monthnumber
Jan, 1
Feb, 2
Mar, 3
Apr, 4
May, 5
Jun, 6
Jul, 7
Aug, 8
Sep, 9
Oct, 10
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Nov, 11
Dec, 12
];
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
To get the months to sort in the correct order, when you create your visualizations, go to the Sorting section
of the properties panel, select Month and mark the checkbox Sort by expression. In the expression box
write Monthnumber.
See also:
p Bottom - chart function (page 508)
p Above - chart function (page 500)
p Sum - chart function (page 166)
p RangeAvg (page 547)
p Range functions (page 544)
Secondarydimensionality
Secondarydimensionality() returns the number of dimension pivot table rows that have non-aggregation
content, that is, do not contain partial sums or collapsed aggregates. This function is the equivalent of the
dimensionality() function for horizontal pivot table dimensions.
Syntax:
secondarydimensionality ( )
The secondarydimensionality function always returns 0 when used outside of pivot tables.
After - chart function
After() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the column after the current column within a row segment in the pivot table.
Syntax:
after([ total ] expression [ , offset [,n ]])
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This function returns NULL in all chart types except pivot tables.
Arguments:
Argument
Description
expression
The expression or field containing the data to be measured.
offset
Specifying an offsetn, greater than 1 moves the evaluation of the expression n rows further
to the right from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the After function work like the Before function
with the corresponding positive offset number.
n
By specifying a third parameter n greater than 1, the function will return a range of n values,
one for each of n table rows counting to the right from the original cell.
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
On the last column of a row segment a NULL value will be returned, as there is no column after this one.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last horizontal
dimension of the inter-field sort order. The inter-field sort order for horizontal dimensions in pivot tables is
defined simply by the order of the dimensions from top to bottom.
Example:
after( sum( Sales ))
after( sum( Sales ), 2 )
after( total sum( Sales ))
rangeavg (after(sum(x),1,3)) returns an average of
the three results of the sum(x) function evaluated in the
three columns immediately to the right of the current column.
Before - chart function
Before() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the column before the current column within a row segment in the pivot table.
Syntax:
before([ total ] expression [ , offset [,n ]])
This function returns NULL in all chart types except pivot tables.
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Arguments:
Argument
Description
expression
The expression or field containing the data to be measured.
offset
Specifying an offsetn, greater than 1 moves the evaluation of the expression n rows further
to the left from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the Before function work like the After function
with the corresponding positive offset number.
n
By specifying a third parameter n greater than 1, the function will return a range of n values,
one for each of n table rows counting to the left from the original cell.
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
On the first column of a row segment a NULL value will be returned, as there is no column before this one.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last horizontal
dimension of the inter-field sort order.The inter-field sort order for horizontal dimensions in pivot tables is
defined simply by the order of the dimensions from top to bottom.
Examples:
before( sum( Sales ))
before( sum( Sales ), 2 )
before( total sum( Sales ))
rangeavg (before(sum(x),1,3)) returns an average of
the three results of the sum(x) function evaluated in
the three columns immediately to the left of the current column.
First - chart function
First() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the first column of the current row segment in the pivot table. This function returns NULL in all chart types
except pivot tables.
Syntax:
first([ total ] expression [ , offset [,n ]])
Arguments:
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Argument
Description
expression
The expression or field containing the data to be measured.
offset
Specifying an offsetn, greater than 1 moves the evaluation of the expression n rows further
to the right from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the First function work like the Last function
with the corresponding positive offset number.
n
By specifying a third parameter n greater than 1, the function will return a range of n values,
one for each of n table rows counting to the right from the original cell.
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last horizontal
dimension of the inter-field sort order.The inter-field sort order for horizontal dimensions in pivot tables is
defined simply by the order of the dimensions from top to bottom.
Examples:
first( sum( Sales ))
first( sum( Sales ), 2 )
first( total sum( Sales )
rangeavg (first(sum(x),1,5)) returns an average of the results of the sum(x) function evaluated
on the five leftmost columns of the current row segment.
Last - chart function
Last() returns the value of an expression evaluated with a pivot table's dimension values as they appear in
the last column of the current row segment in the pivot table. This function returns NULL in all chart types
except pivot tables.
Syntax:
last([ total ] expression [ , offset [,n ]])
Arguments:
Argument
Description
expression
The expression or field containing the data to be measured.
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Argument
Description
offset
Specifying an offsetn, greater than 1 moves the evaluation of the expression n rows further
to the left from the current row.
Specifying an offset of 0 will evaluate the expression on the current row.
Specifying a negative offset number makes the First function work like the Last function
with the corresponding positive offset number.
n
By specifying a third parameter n greater than 1, the function will return a range of n values,
one for each of n table rows counting to the left from the original cell.
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last horizontal
dimension of the inter-field sort order.The inter-field sort order for horizontal dimensions in pivot tables is
defined simply by the order of the dimensions from top to bottom.
Example:
last( sum( Sales ))
last( sum( Sales ), 2 )
last( total sum( Sales )
rangeavg (last(sum(x),1,5)) returns an average of
the results of the sum(x) function evaluated on the five
rightmost columns of the current row segment.
ColumnNo - chart function
ColumnNo() returns the number of the current column within the current row segment in a pivot table. The
first column is number 1.
Syntax:
ColumnNo([total])
Arguments:
Argument
Description
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last horizontal
dimension of the inter-field sort order.The inter-field sort order for horizontal dimensions in pivot tables is
defined simply by the order of the dimensions from top to bottom.
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Example:
if( ColumnNo( )=1, 0, sum( Sales ) / before( sum( Sales )))
NoOfColumns - chart function
NoOfColumns() returns the number of columns in the current row segment in a pivot table.
Syntax:
NoOfColumns([total])
Arguments:
Argument
Description
TOTAL
If the table is one-dimensional or if the qualifier TOTAL is used as argument, the current
column segment is always equal to the entire column.
If the pivot table has multiple horizontal dimensions, the current row segment will include only columns with
the same values as the current column in all dimension rows except for the row showing the last dimension in
the inter-field sort order.The inter-field sort order for horizontal dimensions in pivot tables is defined simply by
the order of the dimensions from top to bottom.
Example:
if( ColumnNo( )=NoOfColumns( ), 0, after( sum( Sales )))
5.15 Logical functions
This section describes functions handling logical operations. All functions can be used in both the data load
script and in chart expressions.
IsNum
Returns -1 (True) if the expression can be interpreted as a number, otherwise 0 (False).
IsNum( expr )
IsText
Returns -1 (True) if the expression has a text representation, otherwise 0 (False).
IsText( expr )
Both IsNum and IsText return 0 if the expression is NULL.
Example:
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The following example loads an inline table with mixed text and numerical values, and adds two fields to
check if the value is a numerical value, respectively a text value.
Load *, IsNum(Value), IsText(Value)
Inline [
Value
23
Green
Blue
12
33Red];
The resulting table looks like this:
Value
IsNum(Value)
IsText(Value)
23
-1
0
Green
0
-1
Blue
0
-1
12
-1
0
33Red
0
-1
5.16 Mapping functions
This section describes functions for handling mapping tables. A mapping table can be used to replace field
values or field names during script execution.
Mapping functions can only be used in the data load script.
Mapping functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
ApplyMap
The ApplyMap script function is used for mapping any expression to a previously loaded mapping table.
ApplyMap ('mapname', expr [ , defaultexpr ] )
MapSubstring
The MapSubstring script function is used to map parts of any expression to a previously loaded mapping
table. The mapping is case sensitive and non-iterative, and substrings are mapped from left to right.
MapSubstring ('mapname', expr)
ApplyMap
The ApplyMap script function is used for mapping any expression to a previously loaded mapping table.
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Syntax:
ApplyMap('map_name', expression [ , default_mapping ] )
Arguments:
Argument
Description
map_
name
The name of a mapping table that has previously been created through the mapping load
or the mapping select statement. Its name must be enclosed by single, straight quotation
marks.
expression
The expression, the result of which should be mapped.
default_
mapping
If stated, this value will be used as a default value if the mapping table does not contain a
matching value for expression. If not stated, the value of expression will be returned as is.
Example:
In this example we load a list of salespersons with a country code representing their country of residence. We
use a table mapping a country code to a country to replace the country code with the country name. Only
three countries are defined in the mapping table, other country codes are mapped to 'Rest of the world'.
// Load mapping table of country codes:
map1:
mapping LOAD *
Inline [
CCode, Country
Sw, Sweden
Dk, Denmark
No, Norway
] ;
// Load list of salesmen, mapping country code to country
// If the country code is not in the mapping table, put Rest of the world
Salespersons:
LOAD *,
ApplyMap('map1', CCode,'Rest of the world') As Country
Inline [
CCode, Salesperson
Sw, John
Sw, Mary
Sw, Per
Dk, Preben
Dk, Olle
No, Ole
Sf, Risttu] ;
// We don't need the CCode anymore
Drop Field 'CCode';
The resulting table looks like this:
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Salesperson
Country
John
Sweden
Mary
Sweden
Per
Sweden
Preben
Denmark
Olle
Denmark
Ole
Norway
Risttu
Rest of the world
MapSubstring
The MapSubstring script function is used to map parts of any expression to a previously loaded mapping
table. The mapping is case sensitive and non-iterative, and substrings are mapped from left to right.
Syntax:
MapSubstring('map_name', expression)
Arguments:
Argument
Description
map_
name
The name of a mapping table previously read by a mapping load or a mapping select
expression
The expression whose result is to be mapped by substrings.
statement. The name must be enclosed by single straight quotation marks.
Example:
In this example we load a list of product models. Each model has a set of attributes that are described by a
composite code. Using the mapping table with MapSubstring, we can expand the attribute codes to a
description.
map2:
mapping LOAD *
Inline [
AttCode, Attribute
R, Red
Y, Yellow
B, Blue
C, Cotton
P, Polyester
S, Small
M, Medium
L, Large
] ;
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Productmodels:
LOAD *,
MapSubString('map2', AttCode) as Description
Inline [
Model, AttCode
Twixie, R C S
Boomer, B P L
Raven, Y P M
Seedling, R C L
SeedlingPlus, R C L with hood
Younger, B C with patch
MultiStripe, R Y B C S/M/L
] ;
// We don't need the AttCode anymore
Drop Field 'AttCode';
The resulting table looks like this:
Model
Description
Twixie
Red Cotton Small
Boomer
Blue Polyester Large
Raven
Yellow Polyester Medium
Seedling
Red Cotton Large
SeedlingPlus
Red Cotton Large with hood
Younger
Blue Cotton with patch
MultiStripe
Red Yellow Blue Cotton Small/Medium/Large
5.17 Mathematical functions
This section describes functions for mathematical constants and Boolean values. These functions do not
have any parameters, but the parentheses are still required.
All functions can be used in both the data load script and in chart expressions.
e
The function returns the base of the natural logarithms, e ( 2.71828...).
e( )
false
The function returns a dual value with text value 'False' and numeric value 0, which can be used as logical
false in expressions.
false( )
pi
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The function returns the value of π (3.14159...).
pi( )
rand
The function returns a random number between 0 and 1. This can be used to create sample data.
rand( )
Example:
This example script creates a table of 1000 records with randomly selected upper case characters, that is,
ASCII characters in the range 65 to 91 (65+26).
Load
Chr( Floor(rand() * 26) + 65) as UCaseChar,
RecNo() as ID
Autogenerate 1000;
true
The function returns a dual value with text value 'True' and numeric value -1, which can be used as logical
true in expressions.
true( )
5.18 NULL functions
This section describes functions for returning or detecting NULL values.
All functions can be used in both the data load script and in chart expressions.
NULL functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
Null
The Null function returns a NULL value.
NULL( )
IsNull
The IsNull function tests if the value of an expression is NULL and if so, returns -1 (True), otherwise 0
(False).
IsNull (expr )
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IsNull
The IsNull function tests if the value of an expression is NULL and if so, returns -1 (True), otherwise 0
(False).
Syntax:
IsNull(expr )
A string with length zero is not considered as a NULL and will cause IsNull to return False.
Example: Data load script
In this example, an inline table with four rows is loaded, where the first three lines contain either nothing, - or
'NULL' in the Value column. We convert these values to true NULL value representations with the middle
preceding LOAD using the Null function.
The first preceding LOAD adds a field checking if the value is NULL,using the IsNull function.
NullsDetectedAndConverted:
LOAD *,
If(IsNull(ValueNullConv), 'T', 'F') as IsItNull;
LOAD *,
If(len(trim(Value))= 0 or Value='NULL' or Value='-', Null(), Value ) as ValueNullConv;
LOAD * Inline
[ID, Value
0,
1,NULL
2,3,Value];
This is the resulting table. In the ValueNullConv column, the NULL values are represented by -.
ID
Value
0
ValueNullConv
IsItNull
-
T
1
NULL
-
T
2
-
-
T
3
Value
Value
F
NULL
The Null function returns a NULL value.
Syntax:
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Null( )
Example: Data load script
In this example, an inline table with four rows is loaded, where the first three lines contain either nothing, - or
'NULL' in the Value column. We want to convert these values to true NULL value representations.
The middle preceding LOAD performs the conversion using the Null function.
The first preceding LOAD adds a field checking if the value is NULL, just for illustration purposes in this
example.
NullsDetectedAndConverted:
LOAD *,
If(IsNull(ValueNullConv), 'T', 'F') as IsItNull;
LOAD *,
If(len(trim(Value))= 0 or Value='NULL' or Value='-', Null(), Value ) as ValueNullConv;
LOAD * Inline
[ID, Value
0,
1,NULL
2,3,Value];
This is the resulting table. In the ValueNullConv column, the NULL values are represented by -.
ID
Value
0
ValueNullConv
IsItNull
-
T
1
NULL
-
T
2
-
-
T
3
Value
Value
F
5.19 Range functions
The range functions are functions that take an array of values and produce a single value as a result. All
range functions can be used in both the data load script and in chart expressions.
For example, in a visualization, a range function can calculate a single value from an inter-record array. In the
data load script, a range function can calculate a single value from an array of values in an internal table.
Range functions replace the following general numeric functions: numsum, numavg,
numcount, nummin and nummax, which should now be regarded as obsolete.
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Basic range functions
RangeMax
RangeMax() returns the highest numeric values found within the expression or field.
RangeMax(first_expr[, Expression])
RangeMaxString
RangeMaxString() returns the last value in the text sort order that it finds in the expression or field.
RangeMaxString(first_expr[, Expression])
RangeMin
RangeMin() returns the lowest numeric values found within the expression or field.
RangeMin(first_expr[, Expression])
RangeMinString
RangeMinString() returns the first value in the text sort order that it finds in the expression or field.
RangeMinString(first_expr[, Expression])
RangeMode
RangeMode() finds the most commonly occurring value (mode value) in the expression or field.
RangeMode(first_expr[, Expression])
RangeOnly
RangeOnly() is a dual function that returns a value if the expression evaluates to one unique value. If this is
not the case then NULL is returned.
RangeOnly(first_expr[, Expression])
RangeSum
RangeSum() returns the sum of a range of values.All non-numeric values are treated as 0, unlike the +
operator.
RangeSum(first_expr[, Expression])
Counter range functions
RangeCount
RangeCount() returns the number of values, text and numeric, found within the specified range or
expression.
RangeCount(first_expr[, Expression])
RangeMissingCount
RangeMissingCount() finds the number of non-numeric values (including NULL) in the expression or field.
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RangeMissingCount(first_expr[, Expression])
RangeNullCount
RangeNullCount() finds the number of NULL values in the expression or field.
RangeNullCount(first_expr[, Expression])
RangeNumericCount
RangeNumericCount() finds the number of numeric values in an expression or field.
RangeNumericCount(first_expr[, Expression])
RangeTextCount
RangeTextCount() returns the number of text values in an expression or field.
RangeTextCount(first_expr[, Expression])
Statistical range functions
RangeAvg
RangeAvg() returns the average of a range. Input to the function can be either a range of values or an
expression.
RangeAvg(first_expr[, Expression])
RangeCorrel
RangeCorrel() returns the correlation coefficient for two sets of data. The correlation coefficient is a
measure of the relationship between the data sets.
RangeCorrel(x_values , y_values[, Expression])
RangeFractile
RangeFractile() returns the value that corresponds to the n-th fractile (quantile) of a range of numbers.
RangeFractile(fractile, first_expr[ ,Expression])
RangeKurtosis
RangeKurtosis() returns the value that corresponds to the kurtosis of a range of numbers.
RangeKurtosis(first_expr[, Expression])
RangeSkew
RangeSkew() returns the value corresponding to the skewness of a range of numbers.
RangeSkew(first_expr[, Expression])
RangeStdev
RangeStdev() finds the standard deviation of a range of numbers.
RangeStdev(expr1[, Expression])
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Financial range functions
RangeIRR
RangeIRR() returns the internal rate of return for a series of cash flows represented by the numbers in
values.
RangeIRR (value[, value][, Expression])
RangeNPV
RangeNPV() returns the net present value of an investment based on a discount rate and a series of future
payments (negative values) and incomes (positive values). The result has a default number format of
money.
RangeNPV (discount_rate, value[, value][, Expression])
RangeXIRR
RangeXIRR() returns the internal rate of return for a schedule of cash flows that is not necessarily periodic.
To calculate the internal rate of return for a series of periodic cash flows, use the RangeIRR function.
RangeXIRR (values, dates[, Expression])
RangeXNPV
RangeXNPV() returns the net present value for a schedule of cash flows that is not necessarily periodic. The
result has a default number format of money. To calculate the net present value for a series of periodic cash
flows, use the RangeNPV function.
RangeXNPV (discount_rate, values, dates[, Expression])
See also:
p Inter-record functions (page 496)
RangeAvg
RangeAvg() returns the average of a range. Input to the function can be either a range of values or an
expression.
Syntax:
RangeAvg(first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
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Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
Examples
Results
RangeAvg (1,2,4)
Returns 2.33333333
RangeAvg (1,'xyz')
Returns 1
RangeAvg (null( ), 'abc')
Returns NULL
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table shows
the returned values of
MyRangeAvg for each of
the records in the table.
RangeTab3:
LOAD recno() as RangeID, RangeAvg(Field1,Field2,Field3) as MyRangeAvg
INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeAvg
1
7
2
4
3
6
4
12.666
5
6.333
6
5
Example with expression:
RangeAvg (Above(MyField),0,3))
Returns a sliding average of the result of the range of three values of MyField calculated on the current row
and two rows above the current row. By specifying the third argument as 3, the Above() function returns
three values, where there are sufficient rows above, which are taken as input to the RangeAvg() function.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
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MyField
RangeAvg (Above
(MyField,0,3))
10
10
Because this is the top row, the range consists of one value
only.
2
6
There is only one row above this row, so the range is: 10,2.
8
6.6666666667
The equivalent to RangeAvg(10,2,8)
18
9.333333333
5
10. 333333333
9
10.6666666667
RangeTab:
LOAD * INLINE [
MyField
10
2
8
18
5
9
] ;
See also:
p Avg - chart function (page 201)
p Count - chart function (page 170)
RangeCorrel
RangeCorrel() returns the correlation coefficient for two sets of data. The correlation coefficient is a
measure of the relationship between the data sets.
Syntax:
RangeCorrel(x_values , y_values[, Expression])
Return data type: numeric
If you provide values manually, enter them as (x,y) pairs. For example, to evaluate two series of data, array 1
and array 2, where the array 1 = 2,6,9 and array 2 = 3,8,4 you would write RangeCorrel (2,3,6,8,9,4) which
returns 0.269.
Arguments:
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Argument
Description
x-value, yvalue
Each value represents a single value or a range of values as returned by an inter-record
functions with a third optional parameter. Each value or range of values must correspond to
an x-value or a range of y-values.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
The function needs at least two pairs of coordinates to be calculated.
Text values, NULL values and missing values are disregarded.
Examples and results:
Examples
Results
RangeCorrel (2,3,6,8,9,4)
Returns 0.269
See also:
p Correl - chart function (page 204)
RangeCount
RangeCount() returns the number of values, text and numeric, found within the specified range or
expression.
Syntax:
RangeCount(first_expr[, Expression])
Return data type: integer
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
NULL values are not counted.
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Examples and results:
Examples
Results
RangeCount (1,2,4)
Returns 3
RangeCount (2,'xyz')
Returns 2
RangeCount (null( ))
Returns 0
RangeCount (2,'xyz', null())
Returns 2
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table shows
the returned values of
MyRangeCount for each of
the records in the table.
RangeTab3:
LOAD recno() as RangeID, RangeCount(Field1,Field2,Field3) as
MyRangeCount INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeCount
1
3
2
3
3
3
4
3
5
3
6
3
Example with expression:
RangeCount (Above(MyField,1,3))
Returns the number of values contained in the three results of MyField. By specifying the second and third
arguments of the Above() function as 3, it returns the values from the three fields above the current row,
where there are sufficient rows, which are taken as input to the RangeSum() function.
Data used in examples:
MyField
RangeCount(Above(MyField,1,3))
10
0
2
1
8
2
18
3
5
3
9
3
Data used in examples:
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RangeTab:
LOAD * INLINE [
MyField
10
2
8
18
5
9
] ;
See also:
p Count - chart function (page 170)
RangeFractile
RangeFractile() returns the value that corresponds to the n-th fractile (quantile) of a range of numbers.
RangeFractile() uses linear interpolation between closest ranks when calculating the fractile.
Syntax:
RangeFractile(fractile, first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
fractile
A number between 0 and 1 corresponding
to the fractile (quantile expressed as a
fraction) to be calculated.
first_expr
The expression or field containing the data
to be measured.
Expression
Optional expressions or fields containing
the range of data to be measured.
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Argument
Description
Add the example script to your app and run it. Then add, at
least, the fields listed in the results column to a sheet in
your app to see the result.
The resulting table shows the returned
values of MyRangeFrac for each of the
records in the table.
RangeTab:
LOAD recno() as RangeID, RangeFractile
(0.5,Field1,Field2,Field3) as MyRangeFrac INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeFrac
1
6
2
4.5
3
8
4
11
5
5
6
4
Examples and results:
Examples
Results
RangeFractile (0.24,1,2,4,6)
Returns 1.72
RangeFractile(0.5,1,2,3,4,6)
Returns 3
RangeFractile (0.5,1,2,5,6)
Returns 3.5
Example with expression:
RangeFractile (0.5, Above(Sum(MyField),0,3))
In this example, the inter-record function Above() contains the optional offset and count arguments. This
produces a range of results that can be used as input to the any of the range functions. In this case, Above(Sum
(MyField),0,3) returns the values of MyField for the current row and the two rows above. These values
provide the input to the RangeFractile() function. So, for the bottom row in the table below, this is the
equivalent of RangeFractile(0.5, 3,4,6), that is, calculating the 0.5 fractile for the series 3, 4, and 6. The
first two rows in the table below, the number of values in the range is reduced accordingly, where there no
rows above the current row. Similar results are produced for other inter-record functions.
Data used in examples:
MyField
RangeFractile(0.5, Above(Sum(MyField),0,3))
1
1
2
1.5
3
2
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MyField
RangeFractile(0.5, Above(Sum(MyField),0,3))
4
3
6
4
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
1
2
3
4
5
6
] ;
See also:
p Above - chart function (page 500)
p Fractile - chart function (page 207)
RangeIRR
RangeIRR() returns the internal rate of return for a series of cash flows represented by the numbers in
values.
These cash flows do not have to be even, as they would be for an annuity. However, the cash flows must
occur at regular intervals, such as monthly or annually. The internal rate of return is the interest rate received
for an investment consisting of payments (negative values) and income (positive values) that occur at regular
periods.
Syntax:
RangeIRR(value[, value][, Expression])
Return data type: numeric
Arguments:
Argument
Description
value
A single value or a range of values as returned by an inter record function with a third
optional parameter. The function needs at least one positive and one negative value to be
calculated.
Expression
Optional expressions or fields containing the range of data to be measured.
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Limitations:
Text values, NULL values and missing values are disregarded.
Examples
Results
RangeIRR(-70000,12000,15000,18000,21000,26000)
Returns 0,0866
Add the example script to your app and run it. Then add, at least, the fields listed
in the results column to a sheet in your app to see the result.
The resulting table
shows the returned
values of RangeIRR
for each of the records
in the table.
RangeTab3:
LOAD *,
recno() as RangeID,
RangeIRR(Field1,Field2,Field3) as RangeIRR;
LOAD * INLINE [
Field1|Field2|Field3
-10000|5000|6000
-2000|NULL|7000
-8000|'abc'|8000
-1800|11000|9000
-5000|5000|9000
-9000|4000|2000
] (delimiter is '|');
RangeID
RangeIRR
1
5.000
2
0.8708
3
-
4
5.8419
5
0.9318
6
-0.2566
See also:
p Inter-record functions (page 496)
RangeKurtosis
RangeKurtosis() returns the value that corresponds to the kurtosis of a range of numbers.
Syntax:
RangeKurtosis(first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
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Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
Examples
Results
RangeKurtosis (1,2,4,7)
Returns -0.28571428571429
See also:
p Kurtosis - chart function (page 210)
RangeMax
RangeMax() returns the highest numeric values found within the expression or field.
Syntax:
RangeMax(first_expr[, Expression])
Return data type: numeric
Arguments:
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
Examples
Results
RangeMax (1,2,4)
Returns 4
RangeMax (1,'xyz')
Returns 1
RangeMax (null( ), 'abc')
Returns NULL
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Examples
Results
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table shows
the returned values of
MyRangeMax for each of
the records in the table.
RangeTab3:
LOAD recno() as RangeID, RangeMax(Field1,Field2,Field3) as MyRangeMax
INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeMax
1
10
2
7
3
8
4
18
5
9
6
9
Example with expression:
RangeMax (Above(MyField,0,3))
Returns the maximum value in the range of three values of MyField calculated on the current row and two
rows above the current row. By specifying the third argument as 3, the Above() function returns three values,
where there are sufficient rows above, which are taken as input to the RangeMax() function.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeMax (Above(Sum(MyField),1,3))
10
10
2
10
8
10
18
18
5
18
9
18
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
2
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8
18
5
9
] ;
RangeMaxString
RangeMaxString() returns the last value in the text sort order that it finds in the expression or field.
Syntax:
RangeMaxString(first_expr[, Expression])
Return data type: string
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeMaxString (1,2,4)
Returns 4
RangeMaxString ('xyz','abc')
Returns 'xyz'
RangeMaxString (5,'abc')
Returns 'abc'
RangeMaxString (null( ))
Returns NULL
Example with expression:
RangeMaxString (Above(MaxString(MyField),0,3))
Returns the last (in text sort order) of the three results of the MaxString(MyField) function evaluated on the
current row and two rows above the current row.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
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MyField
RangeMaxString(Above(MaxString(MyField),0,3))
10
10
abc
abc
8
abc
def
def
xyz
xyz
9
xyz
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
'abc'
8
'def'
'xyz'
9
] ;
See also:
p MaxString - chart function (page 317)
RangeMin
RangeMin() returns the lowest numeric values found within the expression or field.
Syntax:
RangeMin(first_expr[, Expression])
Return data type: numeric
Arguments:
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If no numeric value is found, NULL is returned.
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Examples and results:
Examples
Results
RangeMin (1,2,4)
Returns 1
RangeMin (1,'xyz')
Returns 1
RangeMin (null( ), 'abc')
Returns NULL
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
shows the returned
values of MyRangeMin
for each of the records in
the table.
RangeTab3:
LOAD recno() as RangeID, RangeMin(Field1,Field2,Field3) as MyRangeMin
INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeMin
1
5
2
2
3
2
4
9
5
5
6
2
Example with expression:
RangeMin (Above(MyField,0,3)
Returns the minimum value in the range of three values of MyField calculated on the current row and two
rows above the current row. By specifying the third argument as 3, the Above() function returns three values,
where there are sufficient rows above, which are taken as input to the RangeMin() function.
Data used in examples:
MyField
RangeMin(Above(MyField,0,3))
10
10
2
2
8
2
18
2
5
5
9
5
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Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
2
8
18
5
9
] ;
See also:
p Min - chart function (page 157)
RangeMinString
RangeMinString() returns the first value in the text sort order that it finds in the expression or field.
Syntax:
RangeMinString(first_expr[, Expression])
Return data type: string
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeMinString (1,2,4)
Returns 1
RangeMinString ('xyz','abc')
Returns 'abc'
RangeMinString (5,'abc')
Returns 5
RangeMinString (null( ))
Returns NULL
Example with expression:
RangeMinString (Above(MinString(MyField),0,3))
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Returns the first (in text sort order) of the three results of the MinString(MyField) function evaluated on the
current row and two rows above the current row.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeMinString(Above(MinString(MyField),0,3))
10
10
abc
10
8
8
def
8
xyz
8
9
9
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
'abc'
8
'def'
'xyz'
9
] ;
See also:
p MinString - chart function (page 320)
RangeMissingCount
RangeMissingCount() finds the number of non-numeric values (including NULL) in the expression or field.
Syntax:
RangeMissingCount(first_expr[, Expression])
Return data type: integer
Arguments:
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The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeMissingCount (1,2,4)
Returns 0
RangeMissingCount (5,'abc')
Returns 1
RangeMissingCount (null( ))
Returns 1
Example with expression:
RangeMissingCount (Above(MinString(MyField),0,3))
Returns the number of non-numeric values in the three results of the MinString(MyField) function evaluated
on the current row and two rows above the current row.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeMissingCount(Above(MinString(MyField),0,3))
2
Returns 2 because there are no rows above this row so 2 of the 3 values are missing.
2
Returns 2 because there is only 1 row above the current row and the current row is non-numeric
('abc').
1
Returns 1 because 1 of the 3 rows includes a non-numeric ('abc').
2
Returns 2 because 2 of the 3 rows include non-numeric values ('def' and 'abc').
2
Returns 2 because 2 of the 3 rows include non-numeric values (' xyz' and 'def').
2
Returns 2 because 2 of the 3 rows include non-numeric values (' xyz' and 'def').
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
'abc'
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8
'def'
'xyz'
9
] ;
See also:
p MissingCount - chart function (page 173)
RangeMode
RangeMode() finds the most commonly occurring value (mode value) in the expression or field.
Syntax:
RangeMode(first_expr {, Expression})
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If more than one value shares the highest frequency, NULL is returned.
Examples and results:
Examples
Results
RangeMode (1,2,9,2,4)
Returns 2
RangeMode ('a',4,'a',4)
Returns NULL
RangeMode (null( ))
Returns NULL
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Examples
Results
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table shows
the returned values of
MyRangeMode for each of
the records in the table.
RangeTab3:
LOAD recno() as RangeID, RangeMax(Field1,Field2,Field3) as MyRangeMode
INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeoden
1
-
2
-
3
8
4
-
5
5
6
-
Example with expression:
RangeMode (Above(MyField,0,3))
Returns the most commonly occurring value in the three results of MyField evaluated on the current row and
two rows above the current row. By specifying the third argument as 3, the Above() function returns three
values, where there are sufficient rows above, which are taken as input to the RangeMode() function.
Data used in example:
RangeTab:
LOAD * INLINE [
MyField
10
2
8
18
5
9
] ;
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeMode(Above(MyField,0,3))
10
Returns 10 because there are no rows above so the single value is the most commonly
occurring.
2
-
8
-
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MyField
RangeMode(Above(MyField,0,3))
18
-
5
-
18
18
7
-
9
-
See also:
p Mode - chart function (page 160)
RangeNPV
RangeNPV() returns the net present value of an investment based on a discount rate and a series of future
payments (negative values) and incomes (positive values). The result has a default number format of
money.
Syntax:
RangeNPV(discount_rate, value[,value][, Expression])
Return data type: numeric
Arguments:
Argument
Description
discount_
rate
The interest rate per period.
value
A payment or income occurring at the end of each period. Each value may be a single value
or a range of values as returned by an inter-record function with a third optional parameter.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
Text values, NULL values and missing values are disregarded.
Examples
Results
RangeNPV(0.1,-10000,3000,4200,6800)
Returns 1188.44
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Examples
Results
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
shows the returned
values of RangeNPV
for each of the records
in the table.
RangeTab3:
LOAD *,
recno() as RangeID,
RangeNPV(Field1,Field2,Field3) as RangeNPV;
LOAD * INLINE [
Field1|Field2|Field3
10|5|-6000
2|NULL|7000
8|'abc'|8000
18|11|9000
5|5|9000
9|4|2000
] (delimiter is '|');
RangeID
RangeNPV
1
$-49.13
2
$777.78
3
$98.77
4
$25.51
5
$250.83
6
$20.40
See also:
p Inter-record functions (page 496)
RangeNullCount
RangeNullCount() finds the number of NULL values in the expression or field.
Syntax:
RangeNullCount(firstexpr [, Expression])
Return data type: integer
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
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Examples
Results
RangeNullCount (1,2,4)
Returns 0
RangeNullCount (5,'abc')
Returns 0
RangeNullCount (null( ), null( ))
Returns 2
Example with expression:
RangeNullCount (Above(Sum(MyField),0,3))
Returns the number of NULL values in the three results of the Sum(MyField) function evaluated on the
current row and two rows above the current row.
Data used in examples:
Copying MyField in example below will not result in NULL value.
MyField
RangeNullCount(Above(Sum(MyField),0,3))
10
Returns 2 because there are no rows above this row so 2 of the 3 values are missing (=NULL).
'abc'
Returns 1 because there is only one row above the current row, so one of the three values is
missing (=NULL).
8
Returns 0 because none of the three rows is a NULL value.
null
Returns 1 because the current row is a NULL value.
'xyz'
Returns 1 because the row above is a NULL value.
9
Returns 1 because the value two rows above the current row is a NULL value.
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
'abc'
8
null()
'xyz'
9
] ;
See also:
p NullCount - chart function (page 176)
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RangeNumericCount
RangeNumericCount() finds the number of numeric values in an expression or field.
Syntax:
RangeNumericCount(first_expr[, Expression])
Return data type: integer
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeNumericCount (1,2,4)
Returns 3
RangeNumericCount (5,'abc')
Returns 1
RangeNumericCount (null( ))
Returns 0
Example with expression:
RangeNumericCount (Above(MaxString(MyField),0,3))
Returns the number of numeric values in the three results of the MaxString(MyField) function evaluated on
the current row and two rows above the current row.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeNumericCount(Above(MaxString(MyField),0,3))
10
1
abc
1
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MyField
RangeNumericCount(Above(MaxString(MyField),0,3))
8
2
def
1
xyz
1
9
1
See also:
p NumericCount - chart function (page 178)
RangeOnly
RangeOnly() is a dual function that returns a value if the expression evaluates to one unique value. If this is
not the case then NULL is returned.
Syntax:
RangeOnly(first_expr[, Expression])
Return data type: dual
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeOnly (1,2,4)
Returns NULL
RangeOnly (5,'abc')
Returns NULL
RangeOnly (null( ), 'abc')
Returns 'abc'
RangeOnly(10,10,10)
Returns 10
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See also:
p Only - chart function (page 163)
RangeSkew
RangeSkew() returns the value corresponding to the skewness of a range of numbers.
Syntax:
RangeSkew(first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
Examples
Results
rangeskew (1,2,4)
Returns 0.93521952958283
rangeskew (above
(SalesValue,0,3))
Returns a sliding skewness of the range of three values returned from the above()
function calculated on the current row and the two rows above the current row.
Data used in example:
CustID
RangeSkew(Above(SalesValue,0,3))
1-20
-, -, 0.5676, 0.8455, 1.0127, -0.8741, 1.7243, -1.7186, 1.5518, 1.4332, 0,
1.1066, 1.3458, 1.5636, 1.5439, 0.6952, -0.3766
SalesTable:
LOAD recno() as CustID, * inline [
SalesValue
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101
163
126
139
167
86
83
22
32
70
108
124
176
113
95
32
42
92
61
21
] ;
See also:
p Skew - chart function (page 235)
RangeStdev
RangeStdev() finds the standard deviation of a range of numbers.
Syntax:
RangeStdev(first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
If no numeric value is found, NULL is returned.
Examples and results:
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Examples
Results
RangeStdev (1,2,4)
Returns 1.5275252316519
RangeStdev (null(
))
Returns NULL
RangeStdev (above
(SalesValue),0,3))
Returns a sliding standard of the range of three values returned from the above()
function calculated on the current row and the two rows above the current row.
Data used in example:
CustID
RangeStdev(SalesValue, 0,3))
1-20
-,43.841, 34.192, 18.771, 20.953, 41.138, 47.655, 36.116, 32.716, 25.325,
38,000, 27.737, 35.553, 33.650, 42.532, 33.858, 32.146, 25.239, 35.595
SalesTable:
LOAD recno() as CustID, * inline [
SalesValue
101
163
126
139
167
86
83
22
32
70
108
124
176
113
95
32
42
92
61
21
] ;
See also:
p Stdev - chart function (page 238)
RangeSum
RangeSum() returns the sum of a range of values.All non-numeric values are treated as 0, unlike the +
operator.
Syntax:
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RangeSum(first_expr[, Expression])
Return data type: numeric
Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
The RangeSumfunction treats all non-numeric values as 0, unlike the + operator.
Examples and results:
Examples
Results
RangeSum (1,2,4)
Returns 7
RangeSum (5,'abc')
Returns 5
RangeSum (null( ))
Returns 0
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table shows
the returned values of
MyRangeSum for each of
the records in the table.
RangeTab3:
LOAD recno() as RangeID, Rangesum(Field1,Field2,Field3) as MyRangeSum
INLINE [
Field1, Field2, Field3
10,5,6
2,3,7
8,2,8
18,11,9
5,5,9
9,4,2
];
RangeID
MyRangeSum
1
21
2
12
3
18
4
38
5
19
6
15
Example with expression:
RangeSum (Above(MyField,0,3))
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Returns the sum of the three values of MyField): from the current row and two rows above the current row.
By specifying the third argument as 3, the Above() function returns three values, where there are sufficient
rows above, which are taken as input to the RangeSum() function.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
RangeSum(Above(MyField,0,3))
10
10
2
12
8
20
18
28
5
31
9
32
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
10
2
8
18
5
9
] ;
See also:
p Sum - chart function (page 166)
p Above - chart function (page 500)
RangeTextCount
RangeTextCount() returns the number of text values in an expression or field.
Syntax:
RangeTextCount(first_expr[, Expression])
Return data type: integer
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Arguments:
The argument expressions of this function may contain inter-record functions with a third optional parameter,
which in themselves return a range of values.
Argument
Description
first_expr
The expression or field containing the data to be measured.
Expression
Optional expressions or fields containing the range of data to be measured.
Examples and results:
Examples
Results
RangeTextCount (1,2,4)
Returns 0
RangeTextCount (5,'abc')
Returns 1
RangeTextCount (null( ))
Returns 0
Example with expression:
RangeTextCount (Above(MaxString(MyField),0,3))
Returns the number of text values within the three results of the MaxString(MyField) function evaluated
over the current row and two rows above the current row.
Data used in examples:
Disable sorting of MyField to ensure that example works as expected.
MyField
MaxString(MyField)
RangeTextCount(Above(Sum(MyField),0,3))
10
10
0
abc
abc
1
8
8
1
def
def
2
xyz
xyz
2
9
9
2
Data used in examples:
RangeTab:
LOAD * INLINE [
MyField
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10
'abc'
8
null()
'xyz'
9
] ;
See also:
p TextCount - chart function (page 181)
RangeXIRR
RangeXIRR() returns the internal rate of return for a schedule of cash flows that is not necessarily periodic.
To calculate the internal rate of return for a series of periodic cash flows, use the RangeIRR function.
Syntax:
RangeXIRR(values, dates[, Expression])
Return data type: numeric
Arguments:
Argument
Description
values
A cash flow or a series of cash flows that correspond to a schedule of payments in dates.
Each value may be a single value or a range of values as returned by an inter-record function
with a third optional parameter. The series of values must contain at least one positive and
one negative value.
dates
A payment date or a schedule of payment dates that corresponds to the cash flow
payments.
Expression
Optional expressions or fields containing the range of data to be measured.
Limitations:
Text values, NULL values and missing values are disregarded.
All payments are discounted based on a 365-day year.
Examples
Results
RangeXIRR(-2500,'2008-01-01',2750,'2008-09-01')
Returns 0.1532
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Examples
Results
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
shows the returned
values of RangeXIRR
for each of the records
in the table.
RangeTab3:
LOAD *,
recno() as RangeID,
RangeXIRR(Field1,Field2,Field3) as RangeXIRR;
LOAD * INLINE [
Field1|Field2|Field3
10|5|-6000
2|NULL|7000
8|'abc'|8000
18|11|9000
5|5|9000
9|4|2000
] (delimiter is '|');
RangeID
RangeXIRR
1
-
2
0.5893
3
0.5089
4
0.4476
5
0.4476
6
2.5886
See also:
p RangeIRR (page 554)
RangeXNPV
RangeXNPV() returns the net present value for a schedule of cash flows that is not necessarily periodic. The
result has a default number format of money. To calculate the net present value for a series of periodic cash
flows, use the RangeNPV function.
Syntax:
RangeXNPV(discount_rate, values, dates[, Expression])
Return data type: numeric
Arguments:
Argument
Description
discount_
rate
The interest rate per period.
values
A cash flow or a series of cash flows that corresponds to a schedule of payments in dates.
Each value may be a single value or a range of values as returned by an inter-record function
with a third optional parameter. The series of values must contain at least one positive and
one negative value.
dates
A payment date or a schedule of payment dates that corresponds to the cash flow
payments.
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Limitations:
Text values, NULL values and missing values are disregarded.
All payments are discounted based on a 365-day year.
Examples
Results
RangeXNPV(0.1, -2500,'2008-01-01',2750,'2008-09-01')
Returns 80.25
Add the example script to your app and run it. Then add, at least, the fields
listed in the results column to a sheet in your app to see the result.
The resulting table
shows the returned
values of RangeXNPV
for each of the records in
the table.
RangeTab3:
LOAD *,
recno() as RangeID,
RangeXNPV(Field1,Field2,Field3) as RangeNPV;
LOAD * INLINE [
Field1|Field2|Field3
10|5|-6000
2|NULL|7000
8|'abc'|8000
18|11|9000
5|5|9000
9|4|2000
] (delimiter is '|');
RangeID
RangeXNPV
1
$-49.13
2
$777.78
3
$98.77
4
$25.51
5
$250.83
6
$20.40
5.20 Ranking functions in charts
These functions can only be used in chart expressions.
Suppression of zero values is automatically disabled when these functions are used. NULL
values are disregarded.
Rank
Rank() evaluates the rows of the chart in the expression, and for each row, displays the relative position of
the value of the dimension evaluated in the expression. When evaluating the expression, the function
compares the result with the result of the other rows containing the current column segment and returns the
ranking of the current row within the segment.
Rank - chart function([TOTAL []] expr[, mode[, fmt]])
HRank
HRank() evaluates the expression, and compares the result with the result of the other columns containing
the current row segment of a pivot table. The function then returns the ranking of the current column within
the segment.
HRank - chart function([TOTAL] expr[, mode[, fmt]])
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Rank - chart function
Rank() evaluates the rows of the chart in the expression, and for each row, displays the relative position of
the value of the dimension evaluated in the expression. When evaluating the expression, the function
compares the result with the result of the other rows containing the current column segment and returns the
ranking of the current row within the segment.
For charts other than tables, the current column segment is defined as it appears in the chart's straight table
equivalent.
Syntax:
Rank([TOTAL []] expr[, mode[, fmt]])
Return data type: dual
Arguments:
Argument
Description
expr
The expression or field containing the data to be measured.
mode
Specifies the number representation of the function result.
fmt
Specifies the text representation of the function result.
TOTAL
If the chart is one-dimensional, or if the expression is preceded by the TOTAL qualifier,the
function is evaluated across the entire column. If the table or table equivalent has multiple
vertical dimensions, the current column segment will include only rows with the same values
as the current row in all dimension columns except for the column showing the last
dimension in the inter-field sort order.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
The ranking is returned as a dual value, which in the case when each row has a unique ranking, is an integer
between 1 and the number of rows in the current column segment.
In the case where several rows share the same ranking, the text and number representation can be controlled
with the mode and fmt parameters.
mode
The second argument, mode, can take the following values:
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Value
Description
0 (default)
If all ranks within the sharing group fall on the low side of the middle value of
the entire ranking, all rows get the lowest rank within the sharing group.
If all ranks within the sharing group fall on the high side of the middle value of
the entire ranking, all rows get the highest rank within the sharing group.
If ranks within the sharing group span over the middle value of the entire
ranking, all rows get the value corresponding to the average of the top and
bottom ranking in the entire column segment.
1
Lowest rank on all rows.
2
Average rank on all rows.
3
Highest rank on all rows.
4
Lowest rank on first row, then incremented by one for each row.
fmt
The third argument, fmt, can take the following values:
Value
Description
0 (default)
Low value - high value on all rows (for example 3 - 4).
1
Low value on all rows.
2
Low value on first row, blank on the following rows.
The order of rows for mode 4 and fmt 2 is determined by the sort order of the chart dimensions.
Examples and results:
Create two visualizations from the dimensions Product and Sales and another from Product and UnitSales.
Add measures as shown in the following table.
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Examples
Results
Create a
table with
the
dimensions
The result depends on the sort order of the dimensions. If the tables is sorted on Customer,
the table lists all the values of Sales for Astrida, then Betacab, and so on. The results for
Rank(Sales) will show 10 for the Sales value 12, 9 for the Sales value 13, and so on, with
the rank value of 1 returned for the Sales value 78. Note that even though there are 12
Sales values, only 11 rows are shown because two values of Sales are the same (78). The
next column segment begins with Betacab, for which the first value of Sales in the segment
is 12. The rank value of Rank(Sales) for this is given as 11.
Customer
and Sales
and the
measure
Rank
(Sales))
Replace
the
dimension
Customer
with
Product
and add the
measure
If the table is sorted on Sales, the column segments consist of the values of Sales and the
corresponding Customer. Because there are two Sales values of 12 (for Astrida and
Betacab), the value of Rank(Sales) for that column segment is 1-2, for each value of
Customer. This is because there are two values of Customer for the Sales value 12. If there
had been 4 values, the result would be 1-4, for all rows. This shows what the result looks
like for the default value (0) of the argument fmt.
This returns 1 on the first row on each column segment and leaves all other rows blank,
because arguments mode and fmt are set to 1 and 2 respectively.
Rank
(Sales,1,2)
Data used in examples:
ProductData:
Load * inline [
Customer|Product|UnitSales|UnitPrice
Astrida|AA|4|16
Astrida|AA|10|15
Astrida|BB|9|9
Betacab|BB|5|10
Betacab|CC|2|20
Betacab|DD|0|25
Canutility|AA|8|15
Canutility|CC|0|19
] (delimiter is '|');
Sales2013:
crosstable (Month, Sales) LOAD * inline [
Customer|Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec
Astrida|46|60|70|13|78|20|45|65|78|12|78|22
Betacab|65|56|22|79|12|56|45|24|32|78|55|15
Canutility|77|68|34|91|24|68|57|36|44|90|67|27
Divadip|57|36|44|90|67|27|57|68|47|90|80|94
] (delimiter is '|');
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See also:
p Sum - chart function (page 166)
HRank - chart function
HRank() evaluates the expression, and compares the result with the result of the other columns containing
the current row segment of a pivot table. The function then returns the ranking of the current column within
the segment.
Syntax:
HRank([ total ] expression [ , mode [, format ] ])
Return data type: dual
This function only works in pivot tables. In all other chart types it returns NULL.
Arguments:
Argument
Description
expression
The expression or field containing the data to be measured.
mode
Specifies the number representation of the function result.
format
Specifies the text representation of the function result.
TOTAL
If the chart is one-dimensional, or if the expression is preceded by the TOTAL qualifier,the
function is evaluated across the entire column. If the table or table equivalent has multiple
vertical dimensions, the current column segment will include only rows with the same values
as the current row in all dimension columns except for the column showing the last
dimension in the inter-field sort order.
The TOTAL qualifier may be followed by a list of one or more field names within angle
brackets . These field names should be a subset of the chart dimension variables.
If the pivot table is one-dimensional or if the expression is preceded by the total qualifier, the current row
segment is always equal to the entire row. If the pivot table has multiple horizontal dimensions, the current
row segment will include only columns with the same values as the current column in all dimension rows
except for the row showing the last horizontal dimension of the inter-field sort order.
The ranking is returned as a dual value, which in the case when each column has a unique ranking will be an
integer between 1 and the number of columns in the current row segment.
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In the case where several columns share the same ranking, the text and number representation can be
controlled with the mode and format arguments.
The second argument, mode, specifies the number representation of the function result:
Value
Description
0 (default)
If all ranks within the sharing group fall on the low side of the middle value of
the entire ranking, all columns get the lowest rank within the sharing group.
If all ranks within the sharing group fall on the high side of the middle value of
the entire ranking, all columns get the highest rank within the sharing group.
If ranks within the sharing group span over the middle value of the entire
ranking, all rows get the value corresponding to the average of the top and
bottom ranking in the entire column segment.
1
Lowest rank on all columns in the group.
2
Average rank on all columns in the group.
3
Highest rank on all columns in the group.
4
Lowest rank on first column, then incremented by one for each column in the
group.
The third argument, format, specifies the text representation of the function result:
Value
Description
0 (default)
Low value&' - '&high value on all columns in the group (for example 3 - 4).
1
Low value on all columns in the group.
2
Low value on first column, blank on the following columns in the group.
The order of columns for mode 4 and format 2 is determined by the sort order of the chart dimensions.
Examples:
HRank( sum( Sales ))
HRank( sum( Sales ), 2 )
HRank( sum( Sales ), 0, 1 )
5.21 Statistical distribution functions
The statistical distribution functions described below are all implemented in Qlik Sense using the Cephes
library. For references and details on algorithms used, accuracy etc, see http://www.netlib.org/cephes/. The
Cephes function library is used by permission.
All functions can be used in both the data load script and in chart expressions.
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Statistical distribution functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
CHIIDIST
This function returns the one-tailed probability of the chi2 distribution. The chi2distribution is associated with
a chi2 test.
CHIDIST (value, degrees_freedom)
CHIINV
This function returns the inverse of the one-tailed probability of the chi2 distribution.
CHIINV (prob, degrees_freedom)
NORMDIST
This function returns the cumulative normal distribution for the specified mean and standard deviation. If
mean = 0 and standard_dev = 1, the function returns the standard normal distribution.
NORMDIST (value, mean, standard_dev)
NORMINV
This function returns the inverse of the normal cumulative distribution for the specified mean and standard
deviation.
NORMINV (prob, mean, standard_dev)
TDIST
This function returns the probability for the student t-distribution where a numeric value is a calculated value
of t for which the probability is to be computed.
TDIST (value, degrees_freedom, tails)
TINV
This function returns the t-value of the student's t-distribution as a function of the probability and the degrees
of freedom.
TINV (prob, degrees_freedom)
FDIST
This function returns the F-distribution.
FDIST (value, degrees_freedom1, degrees_freedom2)
FINV
This function returns the inverse of the F-distribution.
FINV (prob, degrees_freedom1, degrees_freedom2)
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CHIDIST
This function returns the one-tailed probability of the chi2 distribution. The chi2distribution is associated with
a chi2 test.
Syntax:
CHIDIST (value, degrees_freedom)
This function is related to the CHIINV function in the following way:
If prob = CHIDIST(value,df), then CHIINV(prob, df) = value.
Arguments:
Argument
Description
value
The value at which you want to evaluate the distribution. The value must not be negative.
degrees_
freedom
A positive integer stating the number of degrees of freedom. Both arguments must be
numeric, else NULL will be returned.
Examples and results:
Example
Result
CHIDIST( 8, 15 )
Returns 0.9237827
CHIINV
This function returns the inverse of the one-tailed probability of the chi2 distribution.
Syntax:
CHIINV (prob, degrees_freedom)
This function is related to the CHIDIST function in the following way:
If prob = chidist(value,df), then chiinv(prob, df) = value.
Arguments:
Argument
Description
prob
A probability associated with the chi2 distribution. It must be a number between 0 and
1.
degrees_
freedom
An integer stating the number of degrees of freedom.
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Limitations:
All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
CHIINV(0.9237827, 15 )
Returns 8.0000001
FDIST
This function returns the F-distribution.
Syntax:
FDIST(value, degrees_freedom1, degrees_freedom2)
This function is related to the FINV function in the following way:
If prob = FDIST(value, df1, df2), then FINV(prob, df1, df2) = value.
Arguments:
Argument
Description
value
The value at which you want to evaluate the distribution. Value must not be
negative.
degrees_
freedom1
A positive integer stating the number of numerator degrees of freedom.
degrees_
freedom2
A positive integer stating the number of denominator degrees of freedom.
Limitations:
All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
FDIST( 15, 8, 6 )
Returns 0.0019369
FINV
This function returns the inverse of the F-distribution.
Syntax:
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FINV (prob, degrees_freedom1, degrees_freedom2)
This function is related to the FDIST function in the following way:
If prob = fdist(value, df1, df2), then finv(prob, df1, df2) = value.
Arguments:
Argument
Description
prob
A probability associated with the F-distribution and must be a number between 0 and
1.
degrees_
freedom
An integer stating the number of degrees of freedom.
Limitations:
All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
FINV( 0.0019369, 8, 5 )
Returns 15.0000197
NORMDIST
This function returns the cumulative normal distribution for the specified mean and standard deviation. If
mean = 0 and standard_dev = 1, the function returns the standard normal distribution.
Syntax:
NORMDIST(value, mean, standard_dev)
This function is related to the NORMINV function in the following way:
If prob = normdist(value, m, sd), then norminv(prob, m, sd) = value.
Arguments:
Argument
Description
value
The value at which you want to evaluate the distribution.
mean
A value stating the arithmetic mean for the distribution.
standard_dev
A positive value stating the standard deviation of the distribution.
Limitations:
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All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
NORMDIST( 0.5, 0, 1 )
Returns 0.691462
NORMINV
This function returns the inverse of the normal cumulative distribution for the specified mean and standard
deviation.
Syntax:
NORMINV (prob, mean, standard_dev)
This function is related to the NORMDIST function in the following way:
If prob = NORMDIST(value, m, sd), then NORMINV(prob, m, sd) = value.
Arguments:
Argument
Description
prob
A probability associated with the normal distribution. It must be a number between 0 and
1.
mean
A value stating the arithmetic mean for the distribution.
standard_
dev
A positive value stating the standard deviation of the distribution.
Limitations:
All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
NORMINV( 0.6914625, 0, 1 )
Returns 0.4999717
TDIST
This function returns the probability for the student t-distribution where a numeric value is a calculated value
of t for which the probability is to be computed.
Syntax:
TDIST (value, degrees_freedom, tails)
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This function is related to the TINV function in the following way:
If prob = tdist(value, df ,2), then tinv(prob, df) = value.
Arguments:
Argument
Description
value
The value at which you want to evaluate the distribution and must not be negative.
degrees_freedom
A positive integer stating the number of degrees of freedom.
tails
Must be either 1 (one-tailed distribution) or 2 (two-tailed distribution).
Limitations:
All arguments must be numeric, else NULL will be returned.
Examples and results:
Example
Result
tdist( 1, 30, 2 )
Returns 0.3253086
TINV
This function returns the t-value of the student's t-distribution as a function of the probability and the degrees
of freedom.
Syntax:
TINV (prob, degrees_freedom)
This function is related to the TDIST function in the following way:
If prob = tdist(value, df ,2), then tinv(prob, df) = value.
Arguments:
Argument
Description
prob
A two-tailed probability associated with the t-distribution. It must be a number between
0 and 1.
degrees_
freedom
An integer stating the number of degrees of freedom.
Limitations:
All arguments must be numeric, else NULL will be returned.
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Examples and results:
Example
Result
TINV(0.3253086, 30 )
Returns 1
5.22 String functions
This section describes functions for handling and manipulating strings. In the functions below, the
parameters are expressions where s should be interpreted as a string.
All functions can be used in both the data load script and in chart expressions, except for Evaluate which can
only be used in the data load script.
String functions overview
Each function is described further after the overview. You can also click the function name in the syntax to
immediately access the details for that specific function.
ApplyCodepage
Applies a different codepage to the field or text stated in the expression. The codepage must be in number
format.
ApplyCodepage(text, codepage)
Capitalize
This function returns the string s with all words capitalized.
Capitalize ( s )
Chr
This function returns the ASCII character corresponding to number n. The result is a string.
Chr ( n
)
Evaluate
This script function returns the evaluated result of the expression if the text string s can be evaluated as a
valid Qlik Sense expression. If s is not a valid expression, NULL is returned.
Evaluate ( s )
FindOneOf
This function returns the position of the n:the occurrence in the string text of any of the characters found in
the string characterset. If n is omitted, the position of the first occurrence is returned. If there no matching
string is found, 0 is returned.
FindOneOf (text , characterset [ , n])
Hash128
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This function returns a 128-bit hash of the combined input expression values. The result is a string.
Hash128 (expression {, expression})
Hash160
This function returns a 160-bit hash of the combined input expression values. The result is a string.
Hash160 (expression {, expression})
Hash256
This function returns a 256-bit hash of the combined input expression values. The result is a string.
Hash256 (expression {, expression} )
Index
This function returns the starting position of the n:th occurrence of substring s2 in string s1. If n is omitted,
the first occurrence is assumed. If n is negative, the search is made starting from the end of string s1. The
result is an integer. The positions in the string are numbered from 1 and up.
Index
( s1 , s2[ , n] )
KeepChar
This function returns the string s1 less all characters not contained in string s2.
KeepChar (s1 , s2)
Left
This function returns a string consisting of the first n characters of s.
Left ( s , n
)
Len
This function returns the length of string s. The result is an integer.
Len ( s
)
Lower
This function forces lower case for all data in the expression.
Lower ( textexpression )
LTrim
This function returns the string s trimmed of any leading spaces.
LTrim (s)
Mid
This function returns the string starting at character n1 with the length of n2 characters. If n2 is omitted, the
function returns the rightmost part of the string starting at character n1. The positions in the string are
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numbered from 1 and up.
Mid (s, n1[, n2 ])
Ord
This function returns the ASCII number of first character of string s. The result is an integer.
Ord ( s
)
PurgeChar
This function returns the string s1 less all characters contained in string s2.
PurgeChar (s1, s2)
Repeat
This function forms a string consisting of the string s repeated n times.
Repeat ( s, n )
Replace
This function returns a string after replacing all occurrences of a given substring within the string s with
another substring. The function is non-recursive and works from left to right.
Replace (s, fromstring ,tostring)
Right
This function returns a string consisting of the last n characters of s.
Right ( s , n
)
RTrim
This function returns the string s trimmed of any trailing spaces.
RTrim ( s )
SubField
In its three-parameter version, this function returns a given substring from a larger string s with delimiter
'delimiter'. Index is an optional integer denoting which of the substrings should be returned. If index is
omitted when subfield is used in a field expression in a LOAD statement, the subfield function will cause
the LOAD statement to automatically generate one full record of input data for each sub-string that can be
found in s.
In its two-parameter version, the subfield function generates one record for each substring that can be taken
from a larger string s with the delimiter 'delimiter'. If several subfield functions are used in the same LOAD
statement, the Cartesian product of all combinations will be generated.
SubField ( s, 'delimiter' [ , index ] )
SubStringCount
This function returns the number of times the string substring appears within the string text. The result is an
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integer. If there is no match, 0 is returned.
SubStringCount ( text , substring)
TextBetween
This function returns the text between the n:th occurrence of beforetext and the immediately following
occurrence of aftertext within the string s.
TextBetween (s , beforetext , aftertext [, n ])
Trim
This function returns the string s trimmed of any leading and trailing spaces.
Trim ( s )
Upper
This function forces upper case for all data in the expression.
Upper ( textexpression )
Capitalize
This function returns the string s with all words capitalized.
Syntax:
Capitalize( s )
Examples and results:
Example
Result
Capitalize ( 'my little pony' )
Returns 'My Little Pony'
Capitalize ( 'AA bb cC Dd')
Returns 'Aa Bb Cc Dc'
Chr
This function returns the ASCII character corresponding to number n. The result is a string.
Syntax:
Chr( n
)
Examples and results:
Example
Result
Chr(65)
Returns the string 'A'
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Evaluate
This script function returns the evaluated result of the expression if the text string s can be evaluated as a
valid Qlik Sense expression. If s is not a valid expression, NULL is returned.
Syntax:
Evaluate( s )
This string function can not be used in chart expressions.
Examples and results:
Example
Result
Evaluate ( 5 * 8 )
Returns '40'
FindOneOf
This function returns the position of the n:the occurrence in the string text of any of the characters found in
the string characterset. If n is omitted, the position of the first occurrence is returned. If there no matching
string is found, 0 is returned.
Syntax:
FindOneOf(text , characterset [ , n])
Argument
Description
text
The original string.
characterset
A set of characters to evaluate.
n
The number of occurrences of the character to evaluate.
Examples and results:
Example
Result
FindOneOf( 'my example text string', 'et%s')
Returns '4'
FindOneOf( 'my example text string', 'et%s', 3)
Returns '12'
FindOneOf( 'my example text string', '¤%&')
Returns '0'
Hash128
This function returns a 128-bit hash of the combined input expression values. The result is a string.
Syntax:
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Hash128(expression {, expression})
Example:
Hash128 ( 'abc', 'xyz', '123' )
Hash128 ( Region, Year, Month )
Hash160
This function returns a 160-bit hash of the combined input expression values. The result is a string.
Syntax:
Hash160(expression {, expression})
Example:
Hash160 ( Region, Year, Month )
Hash256
This function returns a 256-bit hash of the combined input expression values. The result is a string.
Syntax:
Hash256(expression {, expression} )
Example:
Hash256 ( Region, Year, Month )
Index
This function returns the starting position of the n:th occurrence of substring s2 in string s1. If n is omitted,
the first occurrence is assumed. If n is negative, the search is made starting from the end of string s1. The
result is an integer. The positions in the string are numbered from 1 and up.
Syntax:
Index( s1 , s2[ , n] )
Examples and results:
For a more complicated example, see the index function below.
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Example
Result
Index( 'abcdefg', 'cd' )
Returns 3
Index( 'abcdefg', 'b', 2)
Returns 6
Index( 'abcdefg', 'b', -2)
Returns 2
Left( Date, Index( Date,'-') -1 ) where Date =
1997-07-14
Mid( Date, Index( Date, '-', 2 ) -2, 2 ) where Date =
Returns 1997
1997-07-14
Returns 07
KeepChar
This function returns the string s1 less all characters not contained in string s2.
Syntax:
KeepChar(s1 , s2)
Examples and results:
Example
Result
KeepChar ( 'a1b2c3','123' )
Returns '123'
Left
This function returns a string consisting of the first n characters of s.
Syntax:
Left( s , n
)
Examples and results:
For a more complicated example, see the index function.
Example
Result
Left('abcdef', 3)
Returns 'abc'
Left(Date, 4) where Date =
1997-07-14
Returns '1997'
See also:
p Index (page 596)
Len
This function returns the length of string s. The result is an integer.
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Syntax:
Len( s
)
Examples and results:
Example
Len(Name) where Name =
Result
'Peter'
Returns '5'
Lower
This function forces lower case for all data in the expression.
Syntax:
Lower( textexpression )
Examples and results:
Example
Result
Lower('abcD')
Returns 'abcd'
LTrim
This function returns the string s trimmed of any leading spaces.
Syntax:
LTrim( s )
Examples and results:
Example
Result
LTrim( ' abc' )
Returns 'abc'
LTrim( 'abc ' )
Returns 'abc '
Mid
This function returns the string starting at character n1 with the length of n2 characters. If n2 is omitted, the
function returns the rightmost part of the string starting at character n1. The positions in the string are
numbered from 1 and up.
Syntax:
Mid(s, n1[, n2 ])
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Examples and results:
For a more complicated example, see the index function.
Example
Result
Mid('abcdef',3 )
Returns 'cdef'
Mid('abcdef',3, 2 )
Returns 'cd'
Mid( Date,3 ) where Date =
970714
Mid( Date,3,2 ) where Date =
970714
Returns '0714'
Returns '07'
See also:
p Index (page 596)
Ord
This function returns the ASCII number of first character of string s. The result is an integer.
Syntax:
Ord( s
)
Examples and results:
Example
Result
Ord('A')
Returns the number '65'
PurgeChar
This function returns the string s1 less all characters contained in string s2.
Syntax:
PurgeChar(s1, s2)
Examples and results:
Example
Result
PurgeChar ( 'a1b2c3','123' )
Returns 'abc'
Repeat
This function forms a string consisting of the string s repeated n times.
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Syntax:
Repeat( s, n )
Examples and results:
Example
Result
Repeat( ' * ', rating ) when rating =
4
Returns '****'
Replace
This function returns a string after replacing all occurrences of a given substring within the string s with
another substring. The function is non-recursive and works from left to right.
Syntax:
Replace(s, fromstring ,tostring)
Arguments:
Argument
Description
s
The original string.
fromstring
A string which may occur one or more times within string.
tostring
The string which will replace all occurrences of fromstring within the string.
Examples and results:
Example
Result
Replace('abccde','cc','xyz')
Returns 'abxyzde'
Right
This function returns a string consisting of the last n characters of s.
Syntax:
Right( s , n
)
Examples and results:
Example
Result
Right('abcdef', 3)
Returns 'def'
Right( Date,2 ) where Date =
1997-07-14
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5 Functions in scripts and chart expressions
RTrim
This function returns the string s trimmed of any trailing spaces.
Syntax:
RTrim( s )
Examples and results:
Example
Result
RTrim( ' abc' )
Returns ' abc'
RTrim( 'abc ' )
Returns 'abc'
SubField
In its three-parameter version, this function returns a given substring from a larger string s with delimiter
'delimiter'. Index is an optional integer denoting which of the substrings should be returned. If index is
omitted when subfield is used in a field expression in a LOAD statement, the subfield function will cause
the LOAD statement to automatically generate one full record of input data for each sub-string that can be
found in s.
In its two-parameter version, the subfield function generates one record for each substring that can be taken
from a larger string s with the delimiter 'delimiter'. If several subfield functions are used in the same LOAD
statement, the Cartesian product of all combinations will be generated.
Syntax:
SubField ( s, 'delimiter' [ , index ] )
Examples and results:
Example
Result
SubField(S, ';' ,2)
Returns 'cde' if S is 'abc;cde;efg'
SubField(S, ';' ,1)
Returns NULL if S is an empty string
SubField(S, ';' ,1)
Returns an empty string if S is ';'
SubStringCount
This function returns the number of times the string substring appears within the string text. The result is an
integer. If there is no match, 0 is returned.
Syntax:
SubStringCount( text , substring)
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Examples and results:
Example
Result
SubStringCount ( 'abcdefgcdxyz', 'cd' )
Returns '2'
TextBetween
This function returns the text between the n:th occurrence of beforetext and the immediately following
occurrence of aftertext within the string s.
Syntax:
TextBetween(s , beforetext , aftertext [, n ])
Examples and results:
Example
Result
TextBetween('', '<', '>')
Returns 'abc'
TextBetween('', '<', '>',2)
Returns 'de'
Trim
This function returns the string s trimmed of any leading and trailing spaces.
Syntax:
Trim( s )
Examples and results:
Example
Result
Trim( ' abc' )
Returns ' abc'
Trim( 'abc ' )
Returns 'abc'
Trim( ' abc ' )
Returns 'abc'
Upper
This function forces upper case for all data in the expression.
Syntax:
Upper( textexpression )
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Examples and results:
Example
Result
Upper(' abcD')
Returns 'ABCD'
5.23 System functions
System functions provide functions for accessing system, device and Qlik Sense app properties.
System functions overview
Some of the functions are described further after the overview. For those functions, you can click the function
name in the syntax to immediately access the details for that specific function.
Author()
This function returns a string containing the author property of the current app. It can be used in both the data
load script and in a chart expression.
Author property can not be set in the current version of Qlik Sense. If you migrate a QlikView
document, the author property will be retained.
ClientPlatform()
This function returns the user agent string of the client browser. It can be used in both the data load script and
in a chart expression.
Example:
Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.114
Safari/537.36
ComputerName
This function returns a string containing the name of the computer as returned by the operating system. It
can be used in both the data load script and in a chart expression.
ComputerName( )
DocumentName
This function returns a string containing the name of the current Qlik Sense app, without path but with
extension. It can be used in both the data load script and in a chart expression.
DocumentName( )
DocumentPath
This function returns a string containing the full path to the current Qlik Sense app. It can be used in both the
data load script and in a chart expression.
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DocumentPath( )
This function is not supported in standard mode.
DocumentTitle
This function returns a string containing the title of the current Qlik Sense app. It can be used in both the data
load script and in a chart expression.
DocumentTitle( )
GetCollationLocale
This script function returns the culture name of the collation locale that is used. If the variable CollationLocale
has not been set, the actual user machine locale is returned.
GetCollationLocale( )
GetObjectField
This function returns the name of the dimension. Index is an optional integer denoting which of the used
dimensions that should be returned.
GetObjectField - chart function([index])
GetRegistryString
This function returns the value of a key in the Windows registry. It can be used in both the data load script and
in a chart expression.
GetRegistryString(path, key)
OSUser
This function returns a string containing the name of the current user as returned by the operating system. It
can be used in both the data load script and in a chart expression.
OSUser( )
ProductVersion
This function returns the full Qlik Sense version and build number as a string.
ProductVersion ()
ReloadTime
This function returns a timestamp for when the last data load finished. It can be used in both the data load
script and in a chart expression.
ReloadTime( )
StateName
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This chart function returns the state name of the object in which it is used. The Qlik Sense developer can use
this function to have dynamic text and colors when an object’s state is changed. It is important to note that
this function works on objects only. It cannot be used in a chart expression to determine the state that the
expression refers to.
StateName - chart function()
See also:
p GetFolderPath (page 463)
GetObjectField - chart function
This function returns the name of the dimension. Index is an optional integer denoting which of the used
dimensions that should be returned.
Syntax:
GetObjectField ([index])
Example:
GetObjectField(2)
ProductVersion
This function returns the full Qlik Sense version and build number as a string.
Syntax:
ProductVersion()
StateName - chart function
This chart function returns the state name of the object in which it is used. The Qlik Sense developer can use
this function to have dynamic text and colors when an object’s state is changed. It is important to note that
this function works on objects only. It cannot be used in a chart expression to determine the state that the
expression refers to.
Syntax:
StateName ()
Alternate states can only be defined and assigned using the Qlik Engine API.
Example 1:
Dynamic Text
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='Region - ' & if(StateName() = '$', 'Default', StateName())
Example 2:
Dynamic Colors
if(StateName() = 'Group 1', rgb(152, 171, 206),
if(StateName() = 'Group 2', rgb(187, 200, 179),
rgb(210, 210, 210)
)
)
5.24 Table functions
The table functions return information about the data table which is currently being read. If no table name is
specified and the function is used within a LOAD statement, the current table is assumed.
All functions can be used in the data load script, while only NoOfRows can be used in a chart expression.
Table functions overview
Some of the functions are described further after the overview. For those functions, you can click the function
name in the syntax to immediately access the details for that specific function.
FieldName
The FieldName script function returns the name of the field with the specified number within a previously
loaded table. If the function is used within a LOAD statement, it must not reference the table currently being
loaded.
FieldName (field_number ,table_name)
FieldNumber
The FieldNumber script function returns the number of a specified field within a previously loaded table. If
the function is used within a LOAD statement, it must not reference the table currently being loaded.
FieldNumber (field_name ,table_name)
NoOfFields
The NoOfFields script function returns the number of fields in a previously loaded table. If the function is
used within a LOAD statement, it must not reference the table currently being loaded.
NoOfFields (table_name)
NoOfRows
The NoOfRows function returns the number of rows (records) in a previously loaded table. If the function is
used within a LOAD statement, it must not reference the table currently being loaded.
NoOfRows (table_name)
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NoOfTables
This script function returns the number of tables previously loaded.
NoOfTables()
TableName
This script function returns the name of the table with the specified number.
TableName(table_number)
TableNumber
This script function returns the number of the specified table. The first table has number 0.
If table_name does not exist, NULL is returned.
TableNumber(table_name)
Example:
In this example, we want to create a table with information about the tables and fields that have been loaded.
First we load some sample data. This creates the two tables that will be used to illustrate the table functions
described in this section.
Characters:
Load Chr(RecNo()+Ord('A')-1) as Alpha, RecNo() as Num autogenerate 26;
ASCII:
Load
if(RecNo()>=65 and RecNo()<=90,RecNo()-64) as Num,
Chr(RecNo()) as AsciiAlpha,
RecNo() as AsciiNum
autogenerate 255
Where (RecNo()>=32 and RecNo()<=126) or RecNo()>=160 ;
Next, we iterate through the tables that have been loaded, using the NoOfTables function, and then through
the fields of each table, using the NoOfFields function, and load information using the table functions.
//Iterate through the loaded tables
For t = 0 to NoOfTables() - 1
//Iterate through the fields of table
For f = 1 to NoOfFields(TableName($(t)))
Tables:
Load
TableName($(t)) as Table,
TableNumber(TableName($(t))) as TableNo,
NoOfRows(TableName($(t))) as TableRows,
FieldName($(f),TableName($(t))) as Field,
FieldNumber(FieldName($(f),TableName($(t))),TableName($(t))) as FieldNo
Autogenerate 1;
Next f
Next t;
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The resulting table Tables will look like this:
Table
TableNo
TableRows
Field
FieldNo
Characters
0
26
Alpha
1
Characters
0
26
Num
2
ASCII
1
191
Num
1
ASCII
1
191
AsciiAlpha
2
ASCII
1
191
AsciiNum
3
FieldName
The FieldName script function returns the name of the field with the specified number within a previously
loaded table. If the function is used within a LOAD statement, it must not reference the table currently being
loaded.
Syntax:
FieldName(field_number ,table_name)
Arguments:
Argument
Description
field_number
The field number of the field you want to reference.
table_name
The table containing the field you want to reference.
Example:
LET a = FieldName(4,'tab1');
FieldNumber
The FieldNumber script function returns the number of a specified field within a previously loaded table. If
the function is used within a LOAD statement, it must not reference the table currently being loaded.
Syntax:
FieldNumber(field_name ,table_name)
Argument
Description
field_name
The name of the field.
table_name
The name of the table containing the field.
If the field field_name does not exist in table_name, or table_name does not exist, the function returns 0.
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Example:
LET a = FieldNumber('Customer','tab1');
NoOfFields
The NoOfFields script function returns the number of fields in a previously loaded table. If the function is
used within a LOAD statement, it must not reference the table currently being loaded.
Syntax:
NoOfFields(table_name)
Arguments:
Argument
Description
table_name
The name of the table.
Example:
LET a = NoOfFields('tab1');
NoOfRows
The NoOfRows function returns the number of rows (records) in a previously loaded table. If the function is
used within a LOAD statement, it must not reference the table currently being loaded.
Syntax:
NoOfRows(table_name)
Arguments:
Argument
Description
table_name
The name of the table.
Example:
LET a = NoOfRows('tab1');
5.25 Trigonometric and hyperbolic functions
This section describes functions for performing trigonometric and hyperbolic operations. In all of the
functions, the arguments are expressions resolving to angles measured in radians, where x should be
interpreted as a real number.
All angles are measured in radians.
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All functions can be used in both the data load script and in chart expressions.
cos
Cosine of x. The result is a number between -1 and 1.
cos( x )
acos
Inverse cosine of x. The function is only defined if -1≤x≤1. The result is a number between 0 and p.
acos( x )
sin
Sine of x. The result is a number between -1 and 1.
sin( x )
asin
Inverse sine of x. The function is only defined if -1≤x≤1. The result is a number between - p/2 and p/2.
asin( x )
tan
Tangent of x. The result is a real number.
tan( x )
atan
Inverse tangent of x. The result is a number between - p/2 and p/2.
atan( x )
atan2
Two-dimensional generalization of the inverse tangent function. Returns the angle between the origin and
the point represented by the coordinates x and y. The result is a number between - p and + p.
atan2( y,x )
cosh
Hyperbolic cosine of x. The result is a positive real number.
cosh( x )
sinh
Hyperbolic sine of x. The result is a real number.
sinh( x )
tanh
Hyperbolic tangent of x. The result is a real number.
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tanh( x )
Examples:
The following script code loads a sample table, and then loads a table containing the calculated trigonometric
and hyperbolic operations on the values.
SampleData:
LOAD * Inline
[Value
-1
0
1];
Results:
Load *,
cos(Value),
acos(Value),
sin(Value),
asin(Value),
tan(Value),
atan(Value),
atan2(Value, Value),
cosh(Value),
sinh(Value),
tanh(Value)
RESIDENT SampleData;
Drop Table SampleData;
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6 File system access restriction
6
File system access restriction
For security reasons, Qlik Sense in standard mode does not support absolute or relative paths in the data
load script or functions and variables that expose the file system.
However, since absolute and relative paths were supported in QlikView, it is possible to disable standard
mode and use legacy mode in order to reuse QlikView load scripts.
Disabling standard mode can create a security risk by exposing the file system.
Security aspects when connecting to file based ODBC and OLE DB
data connections
ODBC and OLE DB data connections using file based drivers will expose the path to the connected data file
in the connection string. The path can be exposed when the connection is edited, in the data selection dialog,
or in certain SQL queries. This is the case both in standard mode and legacy mode.
If exposing the path to the data file is a concern, it is recommended to connect to the data file
using a folder data connection if it is possible.
6.1
Limitations in standard mode
Several statements, variables and functions cannot be used or have limitations in standard mode. Using
unsupported statements in the data load script produces an error when the load script runs. Error messages
can be found in the script log file. Using unsupported variables and functions does not produce error
messages or log file entries, the function returns NULL.
There is no indication that a variable, statement or function is unsupported when you are editing the data
load script.
System variables
Variable
Standard mode
Legacy mode
Definition
Floppy
Not supported
Supported
Returns the drive letter
of the first floppy drive
found, normally a:.
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Variable
Standard mode
Legacy mode
Definition
CD
Not supported
Supported
Returns the drive letter
of the first CD-ROM
drive found. If no CDROM is found, then c: is
returned.
QvPath
Not supported
Supported
Returns the browse
string to the Qlik Sense
executable.
QvRoot
Not supported
Supported
Returns the root
directory of the Qlik
Sense executable.
QvWorkPath
Not supported
Supported
Returns the browse
string to the current Qlik
Sense app.
QvWorkRoot
Not supported
Supported
Returns the root
directory of the current
Qlik Sense app.
WinPath
Not supported
Supported
Returns the browse
string to Windows.
WinRoot
Not supported
Supported
Returns the root
directory of Windows.
$(include=...)
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The include variable
specifies a file that
contains text that should
be included in the script.
The entire script can
thus be put in a file. This
is a user-defined
variable.
Regular script statements
Statement
Standard mode
Legacy mode
Definition
Binary
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The binary statement is
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used for loading data
from another app.
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6 File system access restriction
Statement
Standard mode
Legacy mode
Definition
Connect
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The CONNECT
With a library
connection, this
statement has no effect
on subsequent script.
Supported input: Library
connection or
absolute/relative path
The Directory
Directory
statement is used to
define Qlik Sense
access to a general
database through the
OLE DB/ODBC
interface. For ODBC,
the data source first
needs to be specified
using the ODBC
administrator.
statement defines which
directory to look in for
data files in subsequent
LOAD statements, until
a new Directory
statement is made.
Execute
Not supported
Supported input: Library
connection or
absolute/relative path
The Execute statement
is used to run other
programs while Qlik
Sense is loading data.
For example, to make
conversions that are
necessary.
LOAD from ...
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
Returns the browse
string to the Qlik Sense
executable.
Store into ...
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
Returns the root
directory of the Qlik
Sense executable.
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6 File system access restriction
Script control statements
Statement
Standard mode
Legacy mode
Definition
For each...
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The filelist mask
filelist mask/dirlist
mask
Returned output: Library
connection
Returned output: Library
connection or absolute
path, depending on
input
syntax produces a
comma separated list of
all files in the current
directory matching the
filelist mask. The
dirlist mask syntax
produces a comma
separated list of all
directories in the current
directory matching the
directory name mask.
File functions
Function
Standard mode
Legacy mode
Definition
Attribute()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
Returns the value of the
meta tags of different
media files as text.
ConnectString()
Returned output: Library
connection name
Library connection name
or actual connection,
depending on input
Returns the active
connect string for ODBC
or OLE DB connections.
FileDir()
Returned output: Library
connection
Returned output: Library
connection or absolute
path, depending on
input
The FileDir function
Returned output: Library
connection
Returned output: Library
connection or absolute
path, depending on
input
The FilePath function
FilePath()
Script Syntax and Chart Functions Guide - Qlik Sense, 1.1
returns a string
containing the path to
the directory of the table
file currently being read.
returns a string
containing the full path
to the table file currently
being read.
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6 File system access restriction
Function
Standard mode
Legacy mode
Definition
FileSize()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The FileSize function
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
The FileTime function
Not supported
Returned output:
Absolute path
The GetFolderPath
FileTime()
GetFolderPath()
returns an integer
containing the size in
bytes of the file
filename or, if no
filename is specified, of
the table file currently
being read.
returns a timestamp for
the date and time of the
last modification of the
file filename. If no
filename is specified,
the function will refer to
the currently read table
file.
function returns the
value of the Microsoft
Windows
SHGetFolderPath
function and returns the
path. For example,
MyMusic. Note that the
function does not use
the spaces seen in
Windows Explorer.
QvdCreateTime()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
This script function
returns the XML-header
time stamp from a QVD
file, if any is present,
otherwise it returns
NULL.
QvdFieldName()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
This script function
returns the name of field
number fieldno, if it
exists in a QVD file
(otherwise NULL).
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6 File system access restriction
Function
Standard mode
Legacy mode
Definition
QvdNoOfFields()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
This script function
returns the number of
fields in a QVD file.
QvdNoOfRecords()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
This script function
returns the number of
records currently in a
QVD file.
QvdTableName()
Supported input: Library
connection
Supported input: Library
connection or
absolute/relative path
This script function
returns the name of the
table stored in a QVD
file.
Function
Standard mode
Legacy mode
Definition
DocumentPath()
Not supported
Returned output:
Absolute path
This function returns a
string containing the full
path to the current Qlik
Sense app.
GetRegistryString()
Not supported
Supported
Returns the value of a
named registry key with
a given registry path.
This function can be
used in chart and script
alike.
System functions
6.2
Disabling standard mode
You can disable standard mode, or in other words, set legacy mode, in order to reuse QlikView load scripts
that refer to absolute or relative file paths as well as library connections.
Disabling standard mode can create a security risk by exposing the file system.
Qlik Sense
For Qlik Sense, standard mode can be disabled in QMC using the Standard mode property.
Qlik Sense Desktop
In Qlik Sense Desktop, you can set standard/legacy mode in Settings.ini.
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6 File system access restriction
Do the following:
1. Open C:\Users\{user}\Documents\Qlik\Sense\Settings.ini in a text editor.
2. Change StandardReload=1 to StandardReload=0.
3. Save the file and start Qlik Sense Desktop, which will run in legacy mode.
The available settings for StandardReload are:
l
1 (standard mode)
l
0 (legacy mode)
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7 QlikView functions and statements not supported in Qlik Sense
7
QlikView functions and statements not
supported in Qlik Sense
Most functions and statements that can be used in QlikView load scripts and chart expressions are also
supported in Qlik Sense, but there are some exceptions, as described here.
7.1
Script statements not supported in Qlik Sense
This list describes QlikView script statements that are not supported in Qlik Sense.
Statement
Comments
Add
This command is only relevant during partial reload, which is not supported in Qlik Sense.
Command
Use SQL instead.
InputField
This command is only relevant during partial reload, which is not supported in Qlik Sense.
Replace
7.2
Functions not supported in Qlik Sense
This list describes QlikView script and chart functions that are not supported in Qlik Sense.
l
GetCurrentField
l
GetExtendedProperty
l
Input
l
InputAvg
l
InputSum
l
IsPartialReload
l
MsgBox
l
NoOfReports
l
ReportComment
l
ReportId
l
ReportName
l
ReportNumber
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8 Functions and statements not recommended in Qlik Sense
8
Functions and statements not recommended
in Qlik Sense
Most functions and statements that can be used in QlikView load scripts and chart expressions are also
supported in Qlik Sense, but some of them are not recommended for use in Qlik Sense. For compatibility
reasons they will still work as intended, but it is advisable to update the code according to the
recommendations in this section, as they may be removed in coming versions.
8.1
Script statements not recommended in Qlik Sense
This list describes QlikView script statements that are not recommended for use in Qlik Sense.
Statement
Recommendation
Command
Use SQL instead.
CustomConnect
Use Custom Connect instead.
8.2
Script statement parameters not recommended in
Qlik Sense
This list describes QlikView script statement parameters that are not recommended for use in Qlik Sense.
Statement
Parameters
Buffer
Use Incremental instead of:
l
l
Inc (not recommended)
Incr (not recommended)
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8 Functions and statements not recommended in Qlik Sense
LOAD
The following parameter keywords are generated by QlikView file transformation wizards.
Functionality is retained when data is reloaded, but Qlik Sense does not provide guided
support/wizards for generating the statement with these parameters:
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
l
8.3
Bottom
Cellvalue
Col
Colmatch
Colsplit
Colxtr
Compound
Contain
Equal
Every
Expand
Filters
Intarray
Interpret
Length
Longer
Numerical
Pos
Remove
Rotate
Row
Rowcnd
Shorter
Start
Strcnd
Top
Transpose
Unwrap
Functions not recommended in Qlik Sense
This list describes QlikView script and chart functions that are not recommended for use in Qlik Sense.
Function
Recommendation
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8 Functions and statements not recommended in Qlik Sense
NumAvg
Use Range functions instead.
NumCount
See: Range functions (page 544)
NumMax
NumMin
NumSum
QliktechBlue
Use other color functions instead. QliktechBlue() can be replaced by RGB(8, 18,
QliktechGray
90) and QliktechGray can be replaced by RGB(158, 148, 137) to get the same
colors.
See: Color functions (page 325)
QlikViewVersion
Use ProductVersion instead.
See: ProductVersion (page 605)
QVUser
Year2Date
Use YearToDate instead.
Vrank
Use Rank instead.
WildMatch5
Use WildMatch instead.
ALL qualifier
In QlikView, the ALL qualifier may occur before an expression. This is equivalent to using {1} TOTAL. In
such a case the calculation will be made over all the values of the field in the document, disregarding the
chart dimensions and current selections. The same value is always returned regardless of the logical state in
the document. If the ALL qualifier is used, a set expression cannot be used, since the ALL qualifier defines a
set by itself. For legacy reasons, the ALL qualifier will still work in this version of Qlik Sense, but may be
removed in coming versions.
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