Manual De Tableau

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Authors: Maila Hardin, Daniel Hom, Ross Perez, & Lori Williams

Which chart or graph
is right for you?

You’ve got data and you’ve got questions. Creating a chart or graph links the two, but
sometimes you’re not sure which type of chart will get the answer you seek.

This paper answers questions about how to select the best charts for the type of data
you’re analyzing and the questions you want to answer. But it won’t stop there.

Stranding your data in isolated, static graphs limits the number of questions you can
answer. Let your data become the centerpiece of decision making by using it to tell a story.
Combine related charts. Add a map. Provide filters to dig deeper. The impact? Business
insight and answers to questions at the speed of thought.

Which chart is right for you? Transforming data into an effective visualization (any kind of
chart or graph) is the first step towards making your data work for you. In this paper you’ll
find best practice recommendations for when to create these types of visualizations:

1.
2.
3.
4.
5.
6.
7.
8.
9.

Bar chart
Line chart
Pie chart
Map
Scatter plot
Gantt chart
Bubble chart
Histogram chart
Bullet chart

10.
11.
12.
13.

Heat map
Highlight table
Treemap
Box-and-whisker plot

Bar chart
Bar charts are one of the most common ways to visualize data. Why? It’s quick
to compare information, revealing highs and lows at a glance. Bar charts are
especially effective when you have numerical data that splits nicely into different
categories so you can quickly see trends within your data.
When to use bar charts:
•
Comparing data across categories. Examples: Volume of shirts in different
sizes, website traffic by origination site, percent of spending by department.

Also consider:
•

Include multiple bar charts on a dashboard. Helps the viewer quickly
compare related information instead of flipping through a bunch of
spreadsheets or slides to answer a question.

•

Add color to bars for more impact. Showing revenue performance with
bars is informative, but overlaying color to reveal profitability provides
immediate insight.

•

Use stacked bars or side-by-side bars. Displaying related data on top of
or next to each other gives depth to your analysis and addresses multiple
questions at once.

•

Combine bar charts with maps. Set the map to act as a “filter” so when you
click on different regions the corresponding bar chart is displayed.

•

Put bars on both sides of an axis. Plotting both positive and negative data
points along a continuous axis is an effective way to spot trends.

Are Film Sequels Profitable?
Box Office Stats For Major Film Franchises
Select Movie Franchise:
All

Click to Highlight Average:
Estimated Budget
Profit

Original
Sequel
2nd Sequel
3rd Sequel
4th Sequel
5th Sequel
6th Sequel
$0M

$50M

$100M
$150M
Combined Bar Length = Avg. U.S. Gross

$200M

How much does a budget increase affect a sequel's box office?

Figure 1: Tell stories with bar charts

Are film sequels profitable? In this example of a bar chart, you quickly get a sense of how
$400M

profitable sequels are for box office franchises. Select the chart and use the drop-down
Profit

U.S. Gross

$400M
filter to
see the profit for your favorite movie franchise.
Public Pension Funding Ratios Nationwide
Funding Ratio and Unfunded Liability by State

$200M

Public
Pension Funding Ratios Nationwide
$200M

Click to filter list below

Funding Ratio and Unfunded Liability by State

$0M

Click to filter list below

$0M
$0M

$100M
$200M
Estimated Budget

48%

$300M

$0M

$100M
$200M
Estimated Budget

Data: Internet Movie Database, Box Office Mojo.
48%
About Tableau maps: www.tableausoftware.com/mapdata
Funding Ratio
29%
59%
About Tableau maps: www.tableausoftware.com/mapdata

Unfunded liability
$3B

Funding Ratio
29%

$200B
Unfunded
liability
$3B

59%

Funding Ratio and Unfunded Liability by Plan
Click to highlight state
Contra Costa County

Funding
Ratio and Unfunded
Liability by Plan
California Teachers
CA

48%

CA
CA

45%
50%

California
LA CountyTeachers
ERS

CA
CA

47%53%

California
PERFCity & County
San Francisco

CA
CA

48% 58%

San Diego County

LA County ERS

50%
60%
53%

San Francisco City & County

$457B

40%
Funding ratio

$234B
$6B
$8B

58%
20%

$400B
$6B
$165B

Contra
Costa
County
San Diego
County

$457B
$300B

47%

Click
to highlight
California
PERF state

20%

$400B

$100B
$200B
45%

$300B

$100B

40%
Funding ratio

60%

$165B

$33B

$234B

$11B
$8B
$0B
$100B
$200B
$33B Unfunded liability

$300B

$11B
$0B

Grand Total

$100B
$200B
Unfunded liability

Grand Total

48%

$457B

Grand Total

48%
Figure 2: Combine bar charts and
maps

$300B

$457B

Don’t settle for a bar chart that leaves you scrolling to find the answers you seek. By
combining a bar chart with a map, this dashboard showing public pension funding
ratios in the U.S. provides rich information at a glance. When California is selected, for
example, the bar chart filters to show state-specific information.
Check out another state to see their funding ratio.

$300M

The surgical service teams at Seattle
2 : : Some kind of Header Here

place, the person who makes sense of the data
first is going to win.

Tableau is one of the best tools out there for
creating
reallyresults
powerful and insightful visuals.
Speed: Get
We’re
usingtimes
it for faster
analytics that require great
data
10 to 100
Tableau is fast analytics. In
visuals
helpteams
us attell
the stories we’re trying
tothe person who mak
place,
The surgicalto
service
Seattle
tell
to our executive management team. first is going to win.
2 : : Some kind of Header Here
– Dana Zuber, Vice President - Strategic Planning Manager, Wells Fargo

Line chart

Line charts are right up there with bars and pies as one of the most frequently used
chart types. Line charts connect individual numeric data points. The result is a simple,
straightforward way to visualize a sequence of values. Their primary use is to display
trends over a period of time.

When to use line charts:
Viewing trends in data over time. Examples: stock price change over a five•
year period, website page views during a month, revenue growth by quarter.

Also consider:
Combine a line graph with bar charts. A bar chart indicating the volume sold
•
per day of a given stock combined with the line graph of the corresponding stock
price can provide visual queues for further investigation.
Shade the area under lines. When you have two or more line charts, fill the
space under the respective lines to create an area chart. This informs a viewer
about the relative contribution that line contributes to the whole.

Black Friday

Now Bigger than Thanksgiving
'Black Friday' & 'Thanksgiving': Comparing Search Term Popularity

Search Volume Index

Amount spent (in bil#)

•

Mouse-over for score

Since 2008, 'Black Friday'
has been a more popular
search term than 'Thanksgiving.'

$40.0B

Which coincides with the increase in total
amount spent over Black Friday weekend

$30.0B

2004

2005

Color Legend:
'Black Friday'
'Thanksgiving'
Total Amount Spent (in $bil)

2006

2007

2008

2009

2010

2011

In the beginning of Nov. 2011, 'Black Friday' was already a
more searched term on Google than 'Thanksgiving.'
Filter Years:
1/4/2004 to 11/13/2011

Data: Google Trends, National Retail Federation.

SVI score
averaged
overreveal
the 2 weeks
prior, after
and including Thanksgiving/Black Friday
Figure
3: isBasic
lines
powerful
insight

These two line charts illuminate the increasing popularity of “Black Friday” as an epic event in the
United States. It’s quick to see that Thanksgiving lost ground to the popular shopping period in 2008.

Tech Leads Capital Raised in 2011
Running Total of Capital Raised (by Industry in Descending Order)
$35,000

Running Sum of Offer Amount (in mil)

$30,000

Select an industry
to view individual companies
Click here to clear filter

$25,000

Technology

$20,000

Energy

$15,000

Health Care
Consumer

$10,000

Business Services
Real Estate

$5,000
$0
Jan 1

Mar 1

May 1

Jul 1

Sep 1

Nov 1

Jan 1

Filter by IPO Date:
1/13/2011 to 12/16/2011

Facebook

Figure 4: Transform line charts into area charts
HCA Holdings, Inc.

Often when
you
haveInc.two or more sets of data in a line chart it can be helpful to shade the area
Kinder
Morgan,
under theNielsen
line.Holdings
In thisB.V.
chart, it’s easy to tell that companies in the technology sector raised more
Industry Color Legend:
Technology

Yandex N.V.

capital than real estate in 2011.
Arcos Dorados Holdings, Inc.

Energy

Zynga Inc.

Health Care

GE Stock Trend Analysis
Consumer

Michael Kors
Air Lease Corp.

Select date range to update trend line:

Business Services

Freescale
Holdi..
6/18/2010 toSemiconductor
6/27/2011

Real Estate

Renren Inc.

Transportation

BankUnited, Inc.

Industrial

Groupon Inc.
$20.00

Financial

The Carlyle Group LP
PetroLogistics LP

Materials
$0

$5,000
$10,000
Offer Amount (in mil)

$15,000

200M

Communications

Data: Hoovers Inc., SEC, Renaissance Capital
$15.00

Volume

Adj Close

150M

$10.00

100M

$5.00

50M

$0.00

0M
Jun 1, 10

Aug 1, 10

Oct 1, 10

Dec 1, 10
Date

Feb 1, 11

Apr 1, 11

Jun 1, 11

Figure 5: Combine line charts with bar and trend lines
Line charts are the most effective way to show change over time. In this case, GE’s stock
performance over a one-year period is joined with trading volume during the same time frame.
At a glance you can tell there were two significant events, one resulting in a sell-off and the other
a gain for shareholders. Click the graph and use the filter to select a different date range.

Pie chart

Pie charts should be used to show relative proportions – or percentages – of
information. That’s it. Despite this narrow recommendation for when to use pies, they
are made with abandon. As a result, they are the most commonly mis-used chart type.
If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your
viewer to translate pie wedges into relevant data or compare one pie to another. Key
points from your data will be missed and the viewer has to work too hard.

When to use pie charts:
Showing proportions. Examples: percentage of budget spent on different
•
departments, response categories from a survey, breakdown of how Americans
spend their leisure time.

Also consider:
Limit pie wedges to six. If you have more than six proportions to communicate,
•
consider a bar chart. It becomes too hard to meaningfully interpret the pie pieces
when the number of wedges gets too high.

•

Overlay pies on maps. Pies can be an interesting way to highlight geographical
trends in your data. If you choose to use this technique, use pies with only a
couple of wedges to keep it easy to understand.

Worldwide Oil Rigs
Land

Offshore

Rig Locations
Select Region
Africa
Asia Pacific
Canada
Europe
Latin America
Middle East
Russia and Caspian
US

Rig Count
2
1,000
2,000
3,000
4,000
5,101

About Tableau maps: www.tableausoftware.com/mapdata

Country Trends

200

150

100

0
2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 6: Use pies only to show proportions
Pie charts give viewers a fast way to understand proportional data. Using pie
charts on this map shows the distribution of oil rigs on land vs. offshore in Europe.

2010

Map

When you have any kind of location data – whether it’s postal codes, state
abbreviations, country names, or your own custom geocoding – you’ve got to see your
data on a map. You wouldn’t leave home to find a new restaurant without a map (or a
GPS anyway), would you? So demand the same informative view from your data.

When to use maps:
Showing geocoded data. Examples: Insurance claims by state, product export
•
destinations by country, car accidents by zip code, custom sales territories.

Also consider:
Use maps as a filter for other types of charts, graphs, and tables. Combine a
•
map with other relevant data then use it as a filter to drill into your data for robust
investigation and discussion of data.

•

Layer bubble charts on top of maps. Bubble charts represent the concentration
of data and their varied size is a quick way to understand relative data. By
layering bubbles on top of a map it is easy to interpret the geographical impact of
different data points quickly.

Which U.S. State is the Greenest?
LEED Buildings by state per million people

Color Scale:
0.027

0.0

0.5

1.0

1.5

Where are the LEED buildings in your state?

1.774

Select a state:
Connecticut
Search for a city:

Filter by cert. level:
All

About Tableau maps: www.tableausoftware.com/mapdata
Data: US Green Building Council
Note: All individual addresses were geocoded using Google Maps data

Figure 7: Provide street-level data on a map
Maps are a powerful way to visualize data. In this visualization you can zero in on
every LEED certified building in the United States based on their street address.
Select any state or city to find the greenest buildings in that area.

Scatter plot

Looking to dig a little deeper into some data, but not quite sure how – or if – different
pieces of information relate? Scatter plots are an effective way to give you a sense of
trends, concentrations and outliers that will direct you to where you want to focus your
investigation efforts further.

When to use scatter plots:
Investigating the relationship between different variables. Examples: Male
•
versus female likelihood of having lung cancer at different ages, technology early
adopters’ and laggards’ purchase patterns of smart phones, shipping costs of
different product categories to different regions.

Also consider:
Add a trend line/line of best fit. By adding a trend line the correlation among
•
your data becomes more clearly defined.

•

Incorporate filters. By adding filters to your scatter plots, you can drill down into
different views and details quickly to identify patterns in your data.

•

Use informative mark types. The story behind some data can be enhanced with
a relevant shape

Demographics and Premium Forecasting
Filter by Avg. Total Paid:
$49 to $172

Avg. Total Paid

$140
Filter by Avg. Total Claim:
$93 to $228

$120

Select Age Group:
30-39

$100

Select Region:
All
-2K

4K
6K
Total Incidents

Loss Codes for None

10K

12K

None, employer cost ratio: 0.71

Figure 8: Who is most expensive to insure?
Use an informative icon or “mark type” such as the female and male icons for additional detail
in your scatter plot. Select the graph and filter to see
how demographics change insurance
Select Employer Ratio:
premium forecasting for an employer.

0.71

Claimant Correlation and Fraud Analysis
Filter Incident Count:
10 to 1,561
$120,000
Filter Total Claimed:
$0 to $245,764
$100,000
Filter Total Paid:
$0 to $163,775

Total Claim

$80,000

Select Region:
Midwest - East North Central

$60,000

Select Threshold:
0.64

$40,000

Above Threshold?
False
True
Total Payout
$180

$20,000

$20,000
$40,000
$60,000

$0
0

100

200

300
400
Distinct count of INCID

500

600

$84,587

Figure 9: Can you spot the fraud?
Using scatter plots is a quick, effective way to spot outliers that might warrant further
investigation. By creating this interactive scatter plot, an insurance investigator can
quickly evaluate where they might have fraudulent activity.

The surgical service teams at Seattle
2 : : Some kind of Header Here

place, the person who makes sense of the data

first is going to win.

Visualizing
using color, shapes, positions on
Speed: Getdata
results
X
axes,
bar faster
charts, pie charts, whatever
10and
to Y
100
times
Tableau is fast analytics. In
you
use,
makes
it
instantly
visible
and
instantly
place, the person who mak
The surgical service teams at Seattle
significant to the people who are looking atfirstit.is going to win.
2 : : Some kind of Header Here

– Jon Boeckenstedt, Associate Vice President Enrollment Policy and Planning, DePaul University

Gantt chart

Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting
deadlines is paramount to a project’s success. Seeing what needs to be accomplished –
and by when – is essential to make this happen. This is where a Gantt chart comes in.
While most associate Gantt charts with project management, they can be used to
understand how other things such as people or machines vary over time. You could
use a Gantt, for example, to do resource planning to see how long it took people to hit
specific milestones, such as a certification level, and how that was distributed over time.

When to use Gantt charts:
Displaying a project schedule. Examples: illustrating key deliverables, owners,
•
and deadlines.

•

Showing other things in use over time. Examples: duration of a machine’s use,
availability of players on a team.

Also consider:
•
Adding color. Changing the color of the bars within the Gantt chart quickly
informs viewers about key aspects of the variable.

•

Combine maps and other chart types with Gantt charts. Including Gantt
charts in a dashboard with other chart types allows filtering and drill down to
expand the insight provided.

Software Project Management
Resource Status
George S
Jill S
Sarah F
Roy D
Terry U
0

100

150

200

250

Hours Completed by Detailed Task

300

Remaining work

350

Work Completed by Start Date

Project 1

1.1

High-level task 1

1.1.1

Detailed task 1

1.1.2

Detailed task 2

1.2

High-level task 2

1.2.1

Detailed task 3

Really detailed task 1

1.2.1.2

Really detailed task 2

Project 2

1.2.1.1

Remaining hours legend
Remaining schedule hours

Roy D is in trouble: too much work for
scheduled hours.

Roy D
Roy D
George S
George S
Sarah F
Sarah F
George S
George S
Roy D

2.1

High-level task 3

2.1.1

Detailed task 4

2.1.2

Detailed task 4

2.1.3

Detailed task 4

Jill S

2.2

High-level task 4

Terry U

Project 3

Sarah F

3.1

High-level task 5

Roy D

3.1.1

Detailed task 7

3.1.2

Detailed task 8

Jill S

Detailed task 7

George S

Detailed task 8
0

Task details legend
Actual hours

Sarah F
Jill S

Aug 20

100 150
Hours

Scheduled hours

Terry U

Today

Gantt chart legend
Amount complete

Aug 30
Days [2009]

Freeze
Sep 9

Sep 19

Length of task

Figure 10: Manage project effectively
A Gantt chart is the centerpiece of this dashboard, providing a complete overview of tasks,
owners, due dates, and status. By providing a menu of tasks at the top, a project manager can
drill down as needed to make informed decisions.

Top 25 Longest Serving Senators

Click a State to
see Senators

About Tableau maps: www.tableausoftware.com/mapdata

Sort by

Length of Service

Robert C. Byrd
Strom Thurmond
Edward M. Kennedy
Carl T. Hayden
John Stennis
Ted Stevens
Ernest F. Hollings
Richard B. Russell
Russell Long
1906

1946

1986

Data source: http://www.senate.gov/senators/Biographical/longest_serving.htm

Figure 11: Who served the longest?

With a quick glance, this Gantt chart lets you know which U.S. senator held office the longest
and which side of the aisle they represented. Select the visualization and use the drop down
menu to see criteria such as party.

Bubble chart

Bubbles are not their own type of visualization but instead should be viewed as a
technique to accentuate data on scatter plots or maps. Bubbles are not their own type
of visualization but instead should be viewed as a technique to accentuate data on
scatter plots or maps. People are drawn to using bubbles because the varied size of
circles provides meaning about the data.

When to use bubbles:
Showing the concentration of data along two axes. Examples: sales
•
concentration by product and geography, class attendance by department and
time of day.

Also consider:
Accentuate data on scatter plots: By varying the size and color of data points,
•
a scatterplot can be transformed into a rich visualization that answers many
questions at once.

•

Overlay on maps: Bubbles quickly inform a viewer about relative concentration
of data. Using these as an overlay on map puts geographically-related data in
context quickly and effectively for a viewer.

Game Play Analysis
Character Types
Assassins & Fighters

Highlight

Damagers & Tanks

Tier A
Tier B
Tier C

KDA

Tier D

Choose C

4 Game Average

Aldon

Alekim
Angok

Angust
Arir
Atril

Hybrid Characters
0.4

0.5

0.6

Healers

Game Average
0.7

0.8

0.9
1.0
Avg. Win-Loss Ratio

Brybur

1.1

1.2

1.3

1.4

Cereck
Chyden

1.5

Drasayo
Eldwori

Summary Statistics
Figure 12: Add data

Enur

Win-Loss
Popularity
Ratio
depth
with bubbles

Matches

KDA

Avg Kills

Avg Deaths

Faor

Avg Assists

Garler

1.41
50,542
1.52
3.77
In thisSacha
scatter plot accentuated
with2.12%
bubbles, the
varied size3.18
and color of
circles make
it quick 10.86

Geess

Joen

1.40

2.07%

49,493

3.695

2.5

3.91

10.21

Ghaia

Hoet

1.39

1.89%

45,042

3.13

4.04

4.54

7.26

Hoet

3.08

4.37

9.51

6.04

5.71

7.07 Year

Tier A

to see how the game’s players compare. Click this dashboard then scroll over the bubbles to get
instant
access to more detailed
information
about
each character.
Turden
1.29
1.84%
43,876
3.465
Warhis
Tier B

Angok
Sagha

1.24

1.89%

45,200

3.865

Crude1.22Net Balance
by Country
for 4.67
2009,
1.58%
37,699
4.54
Normalized
by
None
1.19
1.00%
23,975
3.955
4.81

5.27
5.49

Jitin

10.28 2009
9.27

Joen
Kalldel
Kelech
Kelque

Region
All

Net Exporte

Net Importe
Type
Crude
Normalization
None

Imports, Export
Net Balance

Figure 13: Oil imports and exports at a glance
It’s easy
to tell
whowww.tableausoftware.com/mapdata
buys and sells the most oil with green bubbles for net exporters and red
About
Tableau
maps:
details
about consumption
history.
United States
9,609

-8.1%

Saudi Arabia

6,824

-12.2%

Russia

4,545

6.2%

Japan

4,031

-13.3%

o.. Do.. Do.. Do..

forThousands
net importers
overlaid on this map. Select
a country on the Last
mapDecade
and the dashboard reveals
of Barrels
Latest to Prior
Reserves
0
0
0

Histogram chart

Use histograms when you want to see how your data are distributed across groups.
Say, for example, that you’ve got 100 pumpkins and you want to know how many
weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into
these categories then plotting them with vertical bars along an axis, you will see the
distribution of your pumpkins according to weight. And, in the process, you’ve created
a histogram.
At times you won’t necessarily know which categorization approach makes sense for
your data. You can use histograms to try different approaches to make sure you create
groups that are balanced in size and relevant for your analysis.

When to use histograms:
Understanding the distribution of your data. Examples: Number of customers
•
by company size, student performance on an exam, frequency of a product defect.

Also consider:
•
Test different groupings of data. When you are exploring your data and looking
for groupings or “bins” that make sense, creating a variety of histograms can help
you determine the most useful sets of data.
Add a filter. By offering a way for the viewer to drill down into different categories
of data, the histogram becomes a useful tool to explore a lot of data views quickly.
King Co. SFH Sales Histogram [Sold 2012-06]
300
275
250
225
Number of Sales

200
175
150
125
100
75
50

Sale Month:
Sold 2012-06

County:
King

Distress:
All

Pierce
Snohomish

Over $1,000k

$900k to <$950k

$950k to <$1,000k

$850k to <$900k

$800k to <$850k

$750k to <$800k

$700k to <$750k

$650k to <$700k

$600k to <$650k

$550k to <$600k

$500k to <$550k

$450k to <$500k

$400k to <$450k

$350k to <$400k

$300k to <$350k

$250k to <$300k

$200k to <$250k

$0 to <$150k

25
0
$150k to <$200k

•

Distress Status
Short Sale
Bank Owned
Non-Distressed

Figure 14: Which houses are selling?
This histogram shows which houses are seeing the most sales in a month. Explore for yourself
how the histogram changes when you select a different month, county, or distress level.

Bullet chart

When you’ve got a goal and want to track progress against it, bullet charts are for
you. At its heart, a bullet graph is a variation of a bar chart. It was designed to replace
dashboard gauges, meters and thermometers. Why? Because those images typically
don’t display sufficient information and require valuable dashboard real estate.
Bullet graphs compare a primary measure (let’s say, year-to-date revenue) to one or
more other measures (such as annual revenue target) and presents this in the context
of defined performance metrics (sales quota, for example). Looking at a bullet graph
tells you instantly how the primary measure is performing against overall goals (such
as how close a sales rep is to achieving her annual quota).

When to use bullet graphs:
Evaluating performance of a metric against a goal. Examples: sales quota
•
assessment, actual spending vs. budget, performance spectrum (great/good/poor).

Also consider:
Use color to illustrate achievement thresholds. Including color, such as
•
red, yellow, green as a backdrop to the primary measure lets the viewer quickly
understand how performance measures against goals.

•

Add bullets to dashboards for summary insights. Combining bullets with
other chart types into a dashboard supports productive discussions about where
attention is needed to accomplish objectives.

Quota Dashboard
Company Total
$0

$2,000,000

$4,000,000

$6,000,000

$8,000,000

$10,000,000

Regional Total (click to see salespeople in region)

$12,000,000

$14,000,000 $16,000,000

Stats- All

Central
East
West
$0K $1,000K $2,000K $3,000K $4,000K $5,000K $6,000K $7,000K $8,000K
Sales

# of Sales People
# Hitting Quota
% Hitting Quota
% of Sales by Quota Hitters
Quota $
Sales $
Avg. Quota
Avg. Sales per Person

41
27
65.9%
86.6%
$11,825K
$15,603K
$275K
$380,558

Salespeople in All Region: Sales ($)
Barbara Davis
Betty Clark
Carol Allen
Charles Lee
Christopher Wright
Daniel Gonzalez
David Thompson
Deborah Adams
Donald Mitchell
Donna Walker
Dorothy Harris
Elizabeth Miller
Helen Rodriguez
James Williams
Jennifer Anderson
Jessica Baker
John Jones

View by Quota (%) or Sales ($)
%
$

Hit Quota
No
Yes

200K

400K
600K
800K
Achievement: Quota (%) or Sales ($)

1000K

1200K

Figure 15: Have you hit your quota?
Tracking a sales team’s progression to hitting its quota is a critical element to managing
success. In this quota dashboard, a sales manager can quickly select to view her team’s
performance by quota percentage or sales amount as well as zero in on regional achievement.

The surgical service teams at Seattle
2 : : Some kind of Header Here

place, the person who makes sense of the data
first is going to win.

Tableau has many great visualization capabilities.
We
useGet
a lotresults
of mapping, not only to show
Speed:
the
geographnical
location, but also to do aTableau
lot is fast analytics. In
10 to
100 times faster
of
andatwe
map relationships with
place, the person who mak
Thegeocoding
surgical service teams
Seattle
first is going to win.
geocoding
the distances.
2 : : Some kind of Header Here
– Marta Magnuszewska, Intelligence Data Analyst, Allstate Insurance

10.

Heat maps

Heat maps are a great way to compare data across two categories using color. The
effect is to quickly see where the intersection of the categories is strongest and weakest.

When to use heat maps:
Showing the relationship between two factors. Examples: segmentation
•
analysis of target market, product adoption across regions, sales leads by
individual rep.

Also consider:
Vary the size of squares. By adding a size variation for your squares, heat
•
maps let you know the concentration of two intersecting factors, but add a third
element. For example, a heat map could reveal a survey respondent’s sports
activity preference and the frequency with which they attend the event based on
color, and the size of the square could reflect the number of respondents in that
category.
Using something other than squares. There are times when other types of

Book
Survey
marks help convey your data
in a Preference
more impactful
way.
Favorite Type of Book by Age

Favorite Type of Book by Income Category

% of Total Sum of Response

•

4%
2%
0%

40%

20%

0%
30

Select book type:
Children's

<$50K

<$100K

<$250K

<$500K

<$750K

$750K+

Highlight book type:
Children's

% of Total Weight by Age and Assets
24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86
<$50K
<$100K
<$250K
<$500K
<$750K
$750K+

Figure 16: Who buys the most books?
In this market segmentation analysis, the heat map reveals a new campaign idea. Highincome households of people in their sixties buy children’s books. Perhaps it’s time for a new
grandparent-oriented campaign?

11.

Highlight table

Highlight tables take heat maps one step further. In addition to showing how data
intersects by using color, highlight tables add a number on top to provide additional detail.

When to use highlight tables:
Providing detailed information on heat maps. Examples: the percent of a
•
market for different segments, sales numbers by a reps in a particular region,
population of cities in different years.

Also consider:
Combine highlight tables with other chart types: Combining a line chart with
•
a highlight table, for example, lets a viewer understand overall trends as well as
quickly drill down into a specific cross section of data.

Comparing the 2012 Budget Proposals
Select Item to Compare:
Interest

Highlight Budget:
Obama

Ryan

$0.8T

$0.6T

$0.4T

$0.2T
$0.0T
Program

2011

2012

Medicaid

2013 2014 2015
27

2016

106

151

179

Medicare

-75

-92

-100

Interest

-7

-16

Security

104

110

144

159

163

199

-107

-112

-119

-131

Non Sec.

-69

-23

Other

2017 2018 2019

2020

228

248

272

2021
301

-139

-147

-151

-161

103

131

157

193

173

184

185

193

201

104

115

124

129
521

198

209

194

276

351

392

413

440

455

483

Soc Sec

Revenue

-56

211

250

302

353

414

447

442

466

460

483

513

545

640

725

2320 2755

3244

Deficit

209

148

272

410

Nat Debt

486

434

560

797

1067 1312 1615 1950

Ryan more
-21.8%

What is the Spending Difference?

Obama more
65.9%

Figure 17: Highlight table shows spending difference
This highlight table compares two 2012 budget proposals for the U.S. Click the table to learn more.

12.

Treemap

Looking to see your data at a glance and discover how the different pieces relate
to the whole? Then treemaps are for you. These charts use a series of rectangles,
nested within other rectangles, to show hierarchical data as a proportion to the whole.
As the name of the chart suggests, think of your data as related like a tree: each
branch is given a rectangle which represents how much data it comprises. Each
rectangle is then sub-divided into smaller rectangles, or sub-branches, again based
on its proportion to the whole. Through each rectangle’s size and color, you can often
see patterns across parts of your data, such as whether a particular item is relevant,
even across categories. They also make efficient use of space, allowing you to see
your entire data set at once.

When to use treemaps:
Showing hierarchical data as a proportion of a whole: Examples: storage
•
usage across computer machines, managing the number and priority of technical
support cases, comparing fiscal budgets between years

Also consider:
Coloring the rectangles by a category different from how they are
•
hierarchically structured

•

Combining treemaps with bar charts. In Tableau, place another dimension
on Rows so that each bar in a bar chart is also a treemap. This lets you quickly
compare items through the bar’s length, while allowing you to see the proportional
relationships within each bar.

Support Case Overview
Document
Priority: P4
10,963

Document
Priority: P3
3,987

Usability
Priority:
P4
2,721

Maintenance
Priority: P4
7,845

Usability
Priority:
P3
2,680

Priority
P1
P2
P3
P4
P5
Pre-Support

Document
Priority: P1
1,433
Document
Priority: P2
Feedbacks
Priority: P1
5,693

Feedbacks
Priority: P4
2,854

Feedbacks
Priority: P3
2,647

Support
Priority: P1
4,261

Usability
Priority: P2

Maintenance
Feedbacks
Priority: Feature
Priority: P5
P2
4,680
2,230

Help Request
Feedbacks Priority: P4
2,105
Support
Support
Priority: P3 Priority: Help Request
Priority: P3
2,770
P2
1,995
2,132

Support
Priority: P4
3,952

Other
Priority: P5
4,870

#N/A
Priority:
P5
1,788

#N/A

Help
Request
Priority:
P2
1,144

Customer
Services
Priority:

Setup
Priority: P5
897

Figure 18: Support Cases at a Glance
This treemap shows all of a company’s support cases, broken by case type, and also priority
level. You can see that Document, Feedback, Support and Maintenance make up the lion share
of support cases. However, in Feedback and Support, P1 cases make up the most number of
cases, whereas most other categories are dominated by relatively mild P4 cases.
World GDP Through Time
2001

2002

2003

Select Region
All
Highlight Region
The Americas

2004

Europe
Asia
Middle East

2005

Oceania
Africa

2006

Other
Click Bar for Details

2007

2008

2009

2010

Figure 19: Visualizing World GDP
In this treemap-bar chart combination chart, we can see how overall GDP has grown over time
(with the exception of 2009, when GDP fell), but also which regions and countries comprised
most of the world’s GDP. Since 2001, the region ‘The Americas’ made up most of the world’s
GDP, until 2007 for three years. You can also see that GDP for ‘The Americas’ is made up of
largely one rectangle (one country), whereas ‘Europe’ is made up of rectangles that are more
similar in size. Click a rectangle to see which country it represents and how much GDP was
produced (and how much per capita).

13.

Box-and-whisker Plot

Box-and-whisker plots, or boxplots, are an important way to show distributions of data.
The name refers to the two parts of the plot: the box, which contains the median of the
data along with the 1st and 3rd quartiles (25% greater and less than the median), and
the whiskers, which typically represents data within 1.5 times the Inter-quartile Range
(the difference between the 1st and 3rd quartiles). The whiskers can also be used to
also show the maximum and minimum points within the data.

When to use box-and-whisker plots:
Showing the distribution of a set of a data: Examples: understanding your
•
data at a glance, seeing how data is skewed towards one end, identifying outliers
in your data.

Also consider:
Hiding the points within the box. This helps a viewer focus on the outliers.
•
•

Comparing boxplots across categorical dimensions. Boxplots are great at
allowing you to quickly compare distributions between data sets.

Two Weeks of Home Sales
$4,500,000

Filter Date Range
9/16/13 to 10/1/13

$4,000,000

Filter by Home Type
Condo/Coop
Multi-Family (2-4 Unit)
Multi-Family (5+ Unit)
Parking
Single Family Residential
Townhouse
Vacant Land

$3,500,000
$3,000,000
$2,500,000

Homes Sold by City

$2,000,000

Chicago
$1,500,000

Los Angeles

$1,000,000

Seattle

$500,000

Washington DC

San Francisco
Chicago

Los
Angeles

San
Francisco

Seattle

Washington
DC

100

200

300 400

# of Homes Sold

Figure 20: Comparing the sales prices of homes
For this time period, the median prices of homes sold were highest in San Francisco, but
the distribution was wider for Los Angeles. In fact, the most expensive home in Los Angeles
was sold at several times greater than the median. Hover over a point to see its geographic
location and how much it sold for.

About Tableau
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visualize and share information. More than 15,000 customer accounts get rapid results with Tableau
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their blogs and websites. See how Tableau can help you by downloading the free trial at
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Additional Resources
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