Manual

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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 under a set of rules written by Heidi Ware Carlisle, Intermountain Bird Observatory. The validator can be ran as a Shiny app for convenience, which includes utilities for viewing and downloading flagged data. Individual attributes can also be validated through function calls from the R console - please see IBO ruleset. The current birdproofr build has been updated 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), shinyWidgets (>= 0.4.0)
License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1

R topics documented:
clean_df . . . . . . . . . .
run_birdproofr_app . . . .
validate_age . . . . . . . .
validate_age_bp_cp . . . .
validate_age_ffmlt . . . .
validate_age_ffwear . . . .
validate_age_ha . . . . . .
validate_age_hs . . . . . .
validate_age_skull . . . .
validate_all . . . . . . . .
validate_bandcode . . . .
validate_bandcode_species
validate_bandcode_status .
validate_bandsize . . . . .
validate_bandsize_disp . .
validate_bmlt . . . . . . .
validate_bp . . . . . . . .
validate_bp_hs . . . . . .
validate_captime . . . . .

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. 2
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2

clean_df
validate_cp . . . . . .
validate_cp_hs . . . .
validate_day . . . . . .
validate_day_species .
validate_disp . . . . .
validate_disp_status . .
validate_ey . . . . . .
validate_fat . . . . . .
validate_ffmolt . . . .
validate_ffwear . . . .
validate_ha_ffmlt . . .
validate_ha_ffwear . .
validate_ha_ha2 . . . .
validate_ha_skull . . .
validate_hs_hs2 . . . .
validate_location . . .
validate_month . . . .
validate_month_species
validate_muscle . . . .
validate_net . . . . . .
validate_notes . . . . .
validate_parasites . . .
validate_sex . . . . . .
validate_sex_hs . . . .
validate_species . . . .
validate_status . . . . .
validate_status_500 . .
validate_tail . . . . . .
validate_weight . . . .
validate_wing . . . . .
validate_year . . . . .
validate_year_species .

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Index
clean_df

28
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
Value
cleaned df

11
12
12
13
13
14
14
15
15
16
16
17
17
18
18
19
19
20
20
21
21
22
22
23
23
24
24
25
25
26
26
27

bird data frame

run_birdproofr_app

3

run_birdproofr_app

Runs birdproofr Shiny app

Description
Runs birdproofr Shiny app
Usage
run_birdproofr_app()

Validate age column. Acceptable ages are: 0,1,2,4,5,6 –flag any
records with blank age

validate_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

4

validate_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-how aged combinations

validate_age_ha

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-how sexed combinations

validate_age_hs

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

6

validate_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 columns, then store issues as a data frame

validate_all

Description
Validate all columns, then store issues as a data frame
Usage
validate_all(df)
Arguments
df
Value
issues data frame

bird data frame

validate_bandcode

7

Validate band code. Make sure there are no blanks. Make sure the
only values used are 1,R,4,5,8,N,U.

validate_bandcode

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

8

validate_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 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

validate_bandsize

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 body molt. Allowable values: 0-4, blank

validate_bmlt

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 (0-5, blank okay)

validate_bp

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 how sexed and BP for females. If sexed by BP, BP value
cannot be blank or 0

validate_bp_hs

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 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

validate_captime

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 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)

validate_cp_hs

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. Valid: 1-31. no blanks except for BADE/BALO

validate_day

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.
blank

validate_disp

Allowable values include: M,O,I,S,E,D,T,W,B,L,P,

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 0-5, blank are allowed. 6 fat is okay but only if there’s a
note

validate_fat

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 flight feather molt. Allowable values: N, S, J, A, blank

validate_ffmolt

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 flight feather wear. Allowable values: 0-5, blank

validate_ffwear

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 how aged-ffmolt combinations. If "how aged" says MR, FF
molt must be S or J (can’t be blank, N, or A)

validate_ha_ffmlt

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 how aged-how aged 2 combinations

validate_ha_ha2

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 how aged and skull combinations

validate_ha_skull

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 how sexed-how sexed 2 combinations

validate_hs_hs2

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. Make sure there are no blanks

validate_location

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. Valid: 2-11. No blanks except for BADE BALO

validate_month

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. 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)

validate_muscle

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. 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

validate_net

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. Check that notes that mention either flat flies, or mites,
lice, louse, mite have a Y for parasite column

validate_notes

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 column. Acceptable values= M F U–flag all the blanks

validate_sex

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 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)

validate_sex_hs

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 column. Refer to master species list to update

validate_species

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. Allowable values for new bands: 300, 500. Blank is
NOT valid

validate_status

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. Check if tail is below 30 or above 200

validate_tail

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. Flag anything under 5 but GCKI or BCHU RUHU
CAHU okay or over 200 raptors would be a rare exception

validate_weight

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. Check if wing is below 30 or above 200

validate_wing

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. No blanks. Allowable values are any valid year between
1997 and current year except BADE BALO

validate_year

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
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

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
28
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