Manual

User Manual:

Open the PDF directly: View PDF PDF.
Page Count: 28

Package ‘birdproofr’
February 3, 2019
Title Bird Banding Data Validator
Version 1.0.2
Description birdproofr is a package of R tools for bird banding data validation un-
der a set of rules written by Heidi Ware Carlisle, Intermountain Bird Observatory. The valida-
tor can be ran as a Shiny app for convenience, which includes utilities for viewing and download-
ing flagged data. Individual attributes can also be validated through func-
tion calls from the R console - please see IBO ruleset. The current birdproofr build has been up-
dated for Fall 2018 banding. Support for a hummingbird ruleset is planned.
Depends R (>= 3.5.0)
Imports shiny (>= 1.2.0), dplyr (>= 0.7.0), shinycssloaders (>= 0.2.0), shinythemes (>= 1.1.0), shiny-
Widgets (>= 0.4.0)
License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
Rtopics documented:
clean_df........................................... 2
run_birdproofr_app..................................... 3
validate_age......................................... 3
validate_age_bp_cp..................................... 3
validate_age_ffmlt ..................................... 4
validate_age_ffwear..................................... 4
validate_age_ha....................................... 5
validate_age_hs....................................... 5
validate_age_skull ..................................... 6
validate_all ......................................... 6
validate_bandcode ..................................... 7
validate_bandcode_species ................................. 7
validate_bandcode_status.................................. 8
validate_bandsize...................................... 8
validate_bandsize_disp ................................... 9
validate_bmlt ........................................ 9
validate_bp ......................................... 10
validate_bp_hs ....................................... 10
validate_captime ...................................... 11
1
2clean_df
validate_cp ......................................... 11
validate_cp_hs ....................................... 12
validate_day......................................... 12
validate_day_species .................................... 13
validate_disp ........................................ 13
validate_disp_status..................................... 14
validate_ey ......................................... 14
validate_fat ......................................... 15
validate_ffmolt ....................................... 15
validate_ffwear ....................................... 16
validate_ha_ffmlt ...................................... 16
validate_ha_ffwear ..................................... 17
validate_ha_ha2....................................... 17
validate_ha_skull ...................................... 18
validate_hs_hs2....................................... 18
validate_location ...................................... 19
validate_month ....................................... 19
validate_month_species................................... 20
validate_muscle....................................... 20
validate_net......................................... 21
validate_notes........................................ 21
validate_parasites...................................... 22
validate_sex......................................... 22
validate_sex_hs....................................... 23
validate_species....................................... 23
validate_status........................................ 24
validate_status_500..................................... 24
validate_tail......................................... 25
validate_weight....................................... 25
validate_wing........................................ 26
validate_year ........................................ 26
validate_year_species.................................... 27
Index 28
clean_df Cleans bird data frame before validating, e.g. for mystery whitespace
Description
Cleans bird data frame before validating, e.g. for mystery whitespace
Usage
clean_df(df)
Arguments
df bird data frame
Value
cleaned df
run_birdproofr_app 3
run_birdproofr_app Runs birdproofr Shiny app
Description
Runs birdproofr Shiny app
Usage
run_birdproofr_app()
validate_age Validate age column. Acceptable ages are: 0,1,2,4,5,6 –flag any
records with blank age
Description
Validate age column. Acceptable ages are: 0,1,2,4,5,6 –flag any records with blank age
Usage
validate_age(df)
Arguments
df bird data frame
Value
data frame of rows with age issues
validate_age_bp_cp Check that age and BP/CP match. Age 2, 4, and 0 should always have
0 for both BP and CP
Description
Check that age and BP/CP match. Age 2, 4, and 0 should always have 0 for both BP and CP
Usage
validate_age_bp_cp(df)
Arguments
df bird data frame
Value
data frame of rows with age/BP/CP issues
4validate_age_ffwear
validate_age_ffmlt Validate age-ffmolt combinations. Blanks are okay, and can match
with any age. Refer to table on rules page
Description
Validate age-ffmolt combinations. Blanks are okay, and can match with any age. Refer to table on
rules page
Usage
validate_age_ffmlt(df)
Arguments
df bird data frame
Value
data frame of rows with age/ffmolt issues
validate_age_ffwear Validate age-ffwear combinations.
Description
0 or 1 FF wear is highly suspicious for age 5 and 6. Flag all these records Sometimes 0 FF wear
is normal if paired with S FF molt, but then micro-ageing is suspect, so we should flag the record
either way, maybe with a message FF wear and age combination unlikely. Check this record
Usage
validate_age_ffwear(df)
Arguments
df bird data frame
Details
2+ FF wear is suspicious for age 4–add message age and FF wear combination unlikely
4+ is suspicious for age 2–add unlikely message
Value
data frame of rows with age/ffwear issues
validate_age_ha 5
validate_age_ha Validate age-how aged combinations
Description
Validate age-how aged combinations
Usage
validate_age_ha(df)
Arguments
df bird data frame
Value
data frame of rows with age/how aged issues
validate_age_hs Validate age-how sexed combinations
Description
Validate age-how sexed combinations
Usage
validate_age_hs(df)
Arguments
df bird data frame
Value
data frame of rows with age/how sexed issues
6validate_all
validate_age_skull Validate age and skull combinations. Allowable values for skull 0-6,
8,9, blank. Flag all values in the skull column that don’t match these
Description
Validate age and skull combinations. Allowable values for skull 0-6, 8,9, blank. Flag all values in
the skull column that don’t match these
Usage
validate_age_skull(df)
Arguments
df bird data frame
Value
data frame of rows with age/skull issues
validate_all Validate all columns, then store issues as a data frame
Description
Validate all columns, then store issues as a data frame
Usage
validate_all(df)
Arguments
df bird data frame
Value
issues data frame
validate_bandcode 7
validate_bandcode Validate band code. Make sure there are no blanks. Make sure the
only values used are 1,R,4,5,8,N,U.
Description
Validate band code. Make sure there are no blanks. Make sure the only values used are 1,R,4,5,8,N,U.
Usage
validate_bandcode(df)
Arguments
df bird data frame
Value
data frame of rows with band code issues
validate_bandcode_species
Validate band code-species combinations. Make sure 4 and 8 are only
used for species codes BADE and BALO
Description
Validate band code-species combinations. Make sure 4 and 8 are only used for species codes BADE
and BALO
Usage
validate_bandcode_species(df)
Arguments
df bird data frame
Value
data frame of rows with band code/species issues
8validate_bandsize
validate_bandcode_status
Validate bandcode-status combinations. Any bird with code U has
status 000 as valid.
Description
Validate bandcode-status combinations. Any bird with code U has status 000 as valid.
Usage
validate_bandcode_status(df)
Arguments
df bird data frame
Value
data frame of rows with bandcode/status issues
validate_bandsize Validate band size. Make sure there are no blanks. Make sure the only
values used are 0A, 0, 1, 1B, 1A, 1C, 2, 3, 3A, 3B
Description
Validate band size. Make sure there are no blanks. Make sure the only values used are 0A, 0, 1, 1B,
1A, 1C, 2, 3, 3A, 3B
Usage
validate_bandsize(df)
Arguments
df bird data frame
Value
data frame of rows with band size issues
validate_bandsize_disp 9
validate_bandsize_disp
Validate band size-disp combinations
Description
Validate band size-disp combinations
Usage
validate_bandsize_disp(df)
Arguments
df bird data frame
Value
data frame of rows with band size/disp issues
validate_bmlt Validate body molt. Allowable values: 0-4, blank
Description
Validate body molt. Allowable values: 0-4, blank
Usage
validate_bmlt(df)
Arguments
df bird data frame
Value
data frame of rows with body molt issues
10 validate_bp_hs
validate_bp Validate BP (0-5, blank okay)
Description
Validate BP (0-5, blank okay)
Usage
validate_bp(df)
Arguments
df bird data frame
Value
data frame of rows with BP issues
validate_bp_hs Validate how sexed and BP for females. If sexed by BP, BP value
cannot be blank or 0
Description
Validate how sexed and BP for females. If sexed by BP, BP value cannot be blank or 0
Usage
validate_bp_hs(df)
Arguments
df bird data frame
Value
data frame of rows with BP/how sexed issues for females
validate_captime 11
validate_captime Validate cap time.Allowed values include: 650 to 1300. Flag all other
values. Other values may happen only if there is a note, sometimes
songbirds are caught during owls, hawk trapping, etc. All values
should end in 0’s
Description
Validate cap time.Allowed values include: 650 to 1300. Flag all other values. Other values may
happen only if there is a note, sometimes songbirds are caught during owls, hawk trapping, etc. All
values should end in 0’s
Usage
validate_captime(df)
Arguments
df bird data frame
Value
data frame of rows with cap time issues
validate_cp Validate CP (0-3 allowed, blank okay)
Description
Validate CP (0-3 allowed, blank okay)
Usage
validate_cp(df)
Arguments
df bird data frame
Value
data frame of rows with CP issues
12 validate_day
validate_cp_hs Validate how sexed and CP for males. If sexed by CL, CP value cannot
be blank, 0, or 1 (i.e. CP must = 2 or 3)
Description
Validate how sexed and CP for males. If sexed by CL, CP value cannot be blank, 0, or 1 (i.e. CP
must = 2 or 3)
Usage
validate_cp_hs(df)
Arguments
df bird data frame
Value
data frame of rows with CP/how sexed issues for males
validate_day Validate day. Valid: 1-31. no blanks except for BADE/BALO
Description
Validate day. Valid: 1-31. no blanks except for BADE/BALO
Usage
validate_day(df)
Arguments
df bird data frame
Value
data frame of rows with day issues
validate_day_species 13
validate_day_species Validate day-species combinations
Description
Validate day-species combinations
Usage
validate_day_species(df)
Arguments
df bird data frame
Value
data frame of rows with day/species issues
validate_disp Validate disp. Allowable values include: M,O,I,S,E,D,T,W,B,L,P,
blank
Description
Validate disp. Allowable values include: M,O,I,S,E,D,T,W,B,L,P, blank
Usage
validate_disp(df)
Arguments
df bird data frame
Value
data frame of rows with disp issues
14 validate_ey
validate_disp_status Validate disp-status combinations. Any bird with a letter in disp should
have a note explaining why and the status should say 500
Description
Validate disp-status combinations. Any bird with a letter in disp should have a note explaining why
and the status should say 500
Usage
validate_disp_status(df)
Arguments
df bird data frame
Value
data frame of rows with disp/status issues
validate_ey Validate EY in how aged. EY in the How Aged columns should only be
used for species codes SPTO, DOWO, NOFL, RSFL, HAWO, DEJU,
ORJU, SCJU, UDEJ –flag any other species that use this with note,
Check in Pyle to confirm that this species can be aged by eye color
Description
Validate EY in how aged. EY in the How Aged columns should only be used for species codes
SPTO, DOWO, NOFL, RSFL, HAWO, DEJU, ORJU, SCJU, UDEJ –flag any other species that use
this with note, Check in Pyle to confirm that this species can be aged by eye color
Usage
validate_ey(df)
Arguments
df bird data frame
Value
data frame of rows with EY issues
validate_fat 15
validate_fat Validate fat 0-5, blank are allowed. 6 fat is okay but only if there’s a
note
Description
Validate fat 0-5, blank are allowed. 6 fat is okay but only if there’s a note
Usage
validate_fat(df)
Arguments
df bird data frame
Value
data frame of rows with fat issues
validate_ffmolt Validate flight feather molt. Allowable values: N, S, J, A, blank
Description
Validate flight feather molt. Allowable values: N, S, J, A, blank
Usage
validate_ffmolt(df)
Arguments
df bird data frame
Value
data frame of rows with ffmolt issues
16 validate_ha_ffmlt
validate_ffwear Validate flight feather wear. Allowable values: 0-5, blank
Description
Validate flight feather wear. Allowable values: 0-5, blank
Usage
validate_ffwear(df)
Arguments
df bird data frame
Value
data frame of rows with ffwear issues
validate_ha_ffmlt Validate how aged-ffmolt combinations. If "how aged" says MR, FF
molt must be S or J (can’t be blank, N, or A)
Description
Validate how aged-ffmolt combinations. If "how aged" says MR, FF molt must be S or J (can’t be
blank, N, or A)
Usage
validate_ha_ffmlt(df)
Arguments
df bird data frame
Value
data frame of rows with how aged/ffmolt issues
validate_ha_ffwear 17
validate_ha_ffwear Validate how aged-ffwear combinations. If "how aged" says FF then
FF Wear cannot be blank
Description
Validate how aged-ffwear combinations. If "how aged" says FF then FF Wear cannot be blank
Usage
validate_ha_ffwear(df)
Arguments
df bird data frame
Value
data frame of rows with how aged/ffwear issues
validate_ha_ha2 Validate how aged-how aged 2 combinations
Description
Validate how aged-how aged 2 combinations
Usage
validate_ha_ha2(df)
Arguments
df bird data frame
Value
data frame of rows with ha/ha2 issues
18 validate_hs_hs2
validate_ha_skull Validate how aged and skull combinations
Description
Validate how aged and skull combinations
Usage
validate_ha_skull(df)
Arguments
df bird data frame
Value
data frame of rows with how aged/skull issues
validate_hs_hs2 Validate how sexed-how sexed 2 combinations
Description
Validate how sexed-how sexed 2 combinations
Usage
validate_hs_hs2(df)
Arguments
df bird data frame
Value
data frame of rows with hs/hs2 issues
validate_location 19
validate_location Validate location. Make sure there are no blanks
Description
Validate location. Make sure there are no blanks
Usage
validate_location(df)
Arguments
df bird data frame
Value
data frame of rows with location issues
validate_month Validate month. Valid: 2-11. No blanks except for BADE BALO
Description
Validate month. Valid: 2-11. No blanks except for BADE BALO
Usage
validate_month(df)
Arguments
df bird data frame
Value
data frame of rows with month issues
20 validate_muscle
validate_month_species
Validate month-species combinations
Description
Validate month-species combinations
Usage
validate_month_species(df)
Arguments
df bird data frame
Value
data frame of rows with month/species issues
validate_muscle Validate muscle. 2.5,3,4,5, blank allowed. 1 or 2 are allowed but
MUST have a note, otherwise it’s likely a type-o (check hard copy)
Description
Validate muscle. 2.5,3,4,5, blank allowed. 1 or 2 are allowed but MUST have a note, otherwise it’s
likely a type-o (check hard copy)
Usage
validate_muscle(df)
Arguments
df bird data frame
Value
data frame of rows with muscle issues
validate_net 21
validate_net Validate net. Allowable values: 1-12, blank. Some exceptions allowed
with a note, e.g. owl nets but we should flag those exceptions anyway
to make sure someone checks them
Description
Validate net. Allowable values: 1-12, blank. Some exceptions allowed with a note, e.g. owl nets
but we should flag those exceptions anyway to make sure someone checks them
Usage
validate_net(df)
Arguments
df bird data frame
Value
data frame of rows with net issues
validate_notes Validate notes. Check that notes that mention either flat flies, or mites,
lice, louse, mite have a Y for parasite column
Description
Validate notes. Check that notes that mention either flat flies, or mites, lice, louse, mite have a Y
for parasite column
Usage
validate_notes(df)
Arguments
df bird data frame
Value
data frame of rows with notes issues
22 validate_sex
validate_parasites Validate parasites. If there is a Y in the parasites column there needs
to be a note
Description
Validate parasites. If there is a Y in the parasites column there needs to be a note
Usage
validate_parasites(df)
Arguments
df bird data frame
Value
data frame of rows with parasite column issues
validate_sex Validate sex column. Acceptable values= M F U–flag all the blanks
Description
Validate sex column. Acceptable values= M F U–flag all the blanks
Usage
validate_sex(df)
Arguments
df bird data frame
Value
data frame of rows with sex issues
validate_sex_hs 23
validate_sex_hs Validate how sexed and sex combinations. Allowable values include:
PL, EY,FF,MB,PC,LP,NL,MR,SK,TS, (blank only in second field, or for
age 0)
Description
F: PL,BP,WL–first HS field can NOT be blank
Usage
validate_sex_hs(df)
Arguments
df bird data frame
Details
M: PL,CL,WL–first HS field can NOT be blank
U: always blank, or IC, If not blank, check hard copy for errors or white-out. If sex is whited out,
leave as U. Check fields above and below to make sure there’s not a data entry error
Value
data frame of rows with hs/sex issues
validate_species Validate species column. Refer to master species list to update
Description
Validate species column. Refer to master species list to update
Usage
validate_species(df)
Arguments
df bird data frame
Value
data frame of rows with species issues
24 validate_status_500
validate_status Validate status. Allowable values for new bands: 300, 500. Blank is
NOT valid
Description
Validate status. Allowable values for new bands: 300, 500. Blank is NOT valid
Usage
validate_status(df)
Arguments
df bird data frame
Value
data frame of rows with status issues
validate_status_500 Validate status 500s. ALL status 500’s MUST have text in the note
column and a letter in the disp column i.e. Note and Disp columns
cannot be blank
Description
Validate status 500s. ALL status 500’s MUST have text in the note column and a letter in the disp
column i.e. Note and Disp columns cannot be blank
Usage
validate_status_500(df)
Arguments
df bird data frame
Value
data frame of rows with status 500 issues
validate_tail 25
validate_tail Validate tail. Check if tail is below 30 or above 200
Description
Validate tail. Check if tail is below 30 or above 200
Usage
validate_tail(df)
Arguments
df bird data frame
Value
data frame of rows with tail issues
validate_weight Validate weight. Flag anything under 5 but GCKI or BCHU RUHU
CAHU okay or over 200 raptors would be a rare exception
Description
Validate weight. Flag anything under 5 but GCKI or BCHU RUHU CAHU okay or over 200 raptors
would be a rare exception
Usage
validate_weight(df)
Arguments
df bird data frame
Value
data frame of rows with weight issues
26 validate_year
validate_wing Validate wing. Check if wing is below 30 or above 200
Description
Validate wing. Check if wing is below 30 or above 200
Usage
validate_wing(df)
Arguments
df bird data frame
Value
data frame of rows with wing issues
validate_year Validate year. No blanks. Allowable values are any valid year between
1997 and current year except BADE BALO
Description
Validate year. No blanks. Allowable values are any valid year between 1997 and current year except
BADE BALO
Usage
validate_year(df)
Arguments
df bird data frame
Value
data frame of rows with year issues
validate_year_species 27
validate_year_species Validate year-species combinations
Description
Validate year-species combinations
Usage
validate_year_species(df)
Arguments
df bird data frame
Value
data frame of rows with year/species issues
Index
clean_df,2
run_birdproofr_app,3
validate_age,3
validate_age_bp_cp,3
validate_age_ffmlt,4
validate_age_ffwear,4
validate_age_ha,5
validate_age_hs,5
validate_age_skull,6
validate_all,6
validate_bandcode,7
validate_bandcode_species,7
validate_bandcode_status,8
validate_bandsize,8
validate_bandsize_disp,9
validate_bmlt,9
validate_bp,10
validate_bp_hs,10
validate_captime,11
validate_cp,11
validate_cp_hs,12
validate_day,12
validate_day_species,13
validate_disp,13
validate_disp_status,14
validate_ey,14
validate_fat,15
validate_ffmolt,15
validate_ffwear,16
validate_ha_ffmlt,16
validate_ha_ffwear,17
validate_ha_ha2,17
validate_ha_skull,18
validate_hs_hs2,18
validate_location,19
validate_month,19
validate_month_species,20
validate_muscle,20
validate_net,21
validate_notes,21
validate_parasites,22
validate_sex,22
validate_sex_hs,23
validate_species,23
validate_status,24
validate_status_500,24
validate_tail,25
validate_weight,25
validate_wing,26
validate_year,26
validate_year_species,27
28

Navigation menu