checkAllUnique: Function to check all values of a vector or data frame/tibble...
In cpsyctc/CECPfuns: Package of Utility Functions for Psychological Therapies, Mental Health and Well-being Work (Created by Chris Evans and Clara Paz)
Description
Usage
Arguments
Value
Background
History/development log
Author(s)
See Also
Examples
View source: R/checkAllUnique.R
Description
Function to check all values of a vector or data frame/tibble column are unique
Usage
1
2
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checkAllUnique(
dat,
errIfNA = TRUE,
allowJustOneNA = TRUE,
allowMultipleColumns = FALSE
)
Arguments
dat
vector, single dimensional list or columns of data frame or tibble to test to see if values/rows are all unique
errIfNA
logical: defaults to TRUE to disallow any NA values
allowJustOneNA
logical: defaults to TRUE so if errIfNA is FALSE but you have more than one NA function returns FALSE
allowMultipleColumns
logical: defaults to FALSE but if you really do want to allow all of multi-column dat, set to TRUE
Value
logical: TRUE if all unique (see examples)
Background
This is a utility function to check that all the values of a vector, or a single column of a data frame or tibble,
or a single dimensional list are unique. It can also check that all the rows of multiple columns of a data frame
or tibble, are all unique.
This is important where you expect a single row of data for each participant say, and so you expect the
values of participantID in a data set to be unique. It will cripple things later if you don't identify
duplicated ID values. Sometimes it's not just a single variable that should be unique, a typical example
of this is when you have data from multiple services and ID values for participants from each service.
Then there may be multiple values for ID but they should each come from a different service. (Mind you,
once I have established that rows are unique across serviceID and participantID I usally paste the two
variables together with something like
1
or a tidyverse way of doing it:
1
2
data %>%
mutate(superID = str_c(serviceID, ":", participantID))
Making sure things are unique also prevents all sorts of sometimes very confusing error messages if you want to pivot
data depending on an ID variable whose values ought to be unique but aren't. Checking for this is in my
Wisdom! Rblog page .
History/development log
Started before 5.iv.21
Tweaked 11.iv.21 to fix error in examples.
Author(s)
Chris Evans
See Also
Other data checking functions:
getNNA(),
getNOK(),
isOneToOne()
Examples
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## Not run:
### letters gets us the 26 lower case letters of the English alphabet so should test OK
checkAllUnique(letters)
# [1] TRUE
### good!
### the checking is case sensitive:
checkAllUnique(c("A", letters), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### but ...
checkAllUnique(c("a", letters), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] FALSE
### both good!
### by default checkAllUnique doesn't allow any NA values,
### generally sensible for my data: ID codes or table indices
checkAllUnique(c(letters, NA))
# [1] FALSE
### good!
### but we can override that:
checkAllUnique(c(letters, NA), errIfNA = FALSE)
# [1] TRUE
### good!
### but generally I wouldn't want multiple NA values
### in the typical situations I'd be using
### checkAllUnique() so I have forced you to allow
### that explicitly if you really want it ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE)
# [1] FALSE
### good!
### but you _can_ override that if you need to ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### good!
### but you _can_ override that if you need to ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### good!
### by default checkAllUnique expects vector input but it can handle data
### in multiple columns in a data frame or tibble
tmpDat <- as.data.frame(matrix(1:10, ncol = 2))
tmpDat
# V1 V2
# 1 1 6
# 2 2 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDat[, 1])
# [1] TRUE
checkAllUnique(tmpDat[, 2])
# [1] TRUE
### but remember column indexing a tibble returns a tibble not a vector
tmpTib <- as_tibble(tmpDat)
tmpTib
# # A tibble: 5 x 2
# V1 V2
# <int> <int>
# 1 1 6
# 2 2 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpTib[, 1])
# Error in checkAllUnique(tmpTib[, 1]) :
# You have input an object of length one to checkAllUnique(): makes no sense!
### whoops, I remember, must pull() to extract as vector
checkAllUnique(tmpTib %>% pull(1))
# [1] TRUE
checkAllUnique(tmpTib %>% pull(2))
# [1] TRUE
### you _can_ allow multiple columns
checkAllUnique(tmpDat, allowMultipleColumns = TRUE)
# [1] TRUE
# In checkAllUnique(tmpDat, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
checkAllUnique(tmpTib, allowMultipleColumns = TRUE)
# [1] TRUE
# Warning message:
# In checkAllUnique(tmpTib, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### but it is checking all content in all columns so if two rows are the
### same it will return FALSE:
tmpDat2 <- tmpDat
tmpDat2[2, ] <- tmpDat[1, ] # make row 2 same as row 1
tmpDat2
# V1 V2
# 1 1 6
# 2 1 6
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDat2, allowMultipleColumns = TRUE)
# [1] FALSE
# Warning message:
# In checkAllUnique(tmpDat2, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### what about columns of different classes, e.g. numeric and character
cbind(tmpDat2, letters[1:5]) -> tmpDatMixed
tmpDatMixed
# V1 V2 letters[1:5]
# 1 1 6 a
# 2 1 6 b
# 3 3 8 c
# 4 4 9 d
# 5 5 10 e
checkAllUnique(tmpDatMixed, allowMultipleColumns = TRUE)
# [1] TRUE
# Warning message:
# In checkAllUnique(tmpDatMixed, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### that came out as TRUE because R "promoted" the numerics to character
### and because the two different letters in rows 1 and 2 of column 3
### broke the non-unique tie of rows 1 and 2 in columns 1 and 2
### what about having NA in a multicolumn input?
tmpDatNA <- tmpDat
tmpDatNA[2, 1] <- NA
tmpDatNA
# 1 1 6
# 2 NA 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE)
# [1] FALSE
# Warning message:
# In checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### but
checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE, errIfNA = FALSE)
## End(Not run)
cpsyctc/CECPfuns documentation built on Dec. 26, 2021, 1:19 p.m.
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Description Usage Arguments Value Background History/development log Author(s) See Also Examples
View source: R/checkAllUnique.R
Description
Function to check all values of a vector or data frame/tibble column are unique
Usage
1 2 3 4 5 6 | checkAllUnique(
dat,
errIfNA = TRUE,
allowJustOneNA = TRUE,
allowMultipleColumns = FALSE
)
|
Arguments
dat |
vector, single dimensional list or columns of data frame or tibble to test to see if values/rows are all unique |
errIfNA |
logical: defaults to TRUE to disallow any NA values |
allowJustOneNA |
logical: defaults to TRUE so if errIfNA is FALSE but you have more than one NA function returns FALSE |
allowMultipleColumns |
logical: defaults to FALSE but if you really do want to allow all of multi-column dat, set to TRUE |
Value
logical: TRUE if all unique (see examples)
Background
This is a utility function to check that all the values of a vector, or a single column of a data frame or tibble, or a single dimensional list are unique. It can also check that all the rows of multiple columns of a data frame or tibble, are all unique.
This is important where you expect a single row of data for each participant say, and so you expect the values of participantID in a data set to be unique. It will cripple things later if you don't identify duplicated ID values. Sometimes it's not just a single variable that should be unique, a typical example of this is when you have data from multiple services and ID values for participants from each service. Then there may be multiple values for ID but they should each come from a different service. (Mind you, once I have established that rows are unique across serviceID and participantID I usally paste the two variables together with something like
1 |
or a tidyverse way of doing it:
1 2 | data %>%
mutate(superID = str_c(serviceID, ":", participantID))
|
Making sure things are unique also prevents all sorts of sometimes very confusing error messages if you want to pivot data depending on an ID variable whose values ought to be unique but aren't. Checking for this is in my Wisdom! Rblog page .
History/development log
Started before 5.iv.21 Tweaked 11.iv.21 to fix error in examples.
Author(s)
Chris Evans
See Also
Other data checking functions:
getNNA(),
getNOK(),
isOneToOne()
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | ## Not run:
### letters gets us the 26 lower case letters of the English alphabet so should test OK
checkAllUnique(letters)
# [1] TRUE
### good!
### the checking is case sensitive:
checkAllUnique(c("A", letters), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### but ...
checkAllUnique(c("a", letters), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] FALSE
### both good!
### by default checkAllUnique doesn't allow any NA values,
### generally sensible for my data: ID codes or table indices
checkAllUnique(c(letters, NA))
# [1] FALSE
### good!
### but we can override that:
checkAllUnique(c(letters, NA), errIfNA = FALSE)
# [1] TRUE
### good!
### but generally I wouldn't want multiple NA values
### in the typical situations I'd be using
### checkAllUnique() so I have forced you to allow
### that explicitly if you really want it ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE)
# [1] FALSE
### good!
### but you _can_ override that if you need to ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### good!
### but you _can_ override that if you need to ...
checkAllUnique(c(NA, letters, NA), errIfNA = FALSE, allowJustOneNA = FALSE)
# [1] TRUE
### good!
### by default checkAllUnique expects vector input but it can handle data
### in multiple columns in a data frame or tibble
tmpDat <- as.data.frame(matrix(1:10, ncol = 2))
tmpDat
# V1 V2
# 1 1 6
# 2 2 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDat[, 1])
# [1] TRUE
checkAllUnique(tmpDat[, 2])
# [1] TRUE
### but remember column indexing a tibble returns a tibble not a vector
tmpTib <- as_tibble(tmpDat)
tmpTib
# # A tibble: 5 x 2
# V1 V2
# <int> <int>
# 1 1 6
# 2 2 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpTib[, 1])
# Error in checkAllUnique(tmpTib[, 1]) :
# You have input an object of length one to checkAllUnique(): makes no sense!
### whoops, I remember, must pull() to extract as vector
checkAllUnique(tmpTib %>% pull(1))
# [1] TRUE
checkAllUnique(tmpTib %>% pull(2))
# [1] TRUE
### you _can_ allow multiple columns
checkAllUnique(tmpDat, allowMultipleColumns = TRUE)
# [1] TRUE
# In checkAllUnique(tmpDat, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
checkAllUnique(tmpTib, allowMultipleColumns = TRUE)
# [1] TRUE
# Warning message:
# In checkAllUnique(tmpTib, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### but it is checking all content in all columns so if two rows are the
### same it will return FALSE:
tmpDat2 <- tmpDat
tmpDat2[2, ] <- tmpDat[1, ] # make row 2 same as row 1
tmpDat2
# V1 V2
# 1 1 6
# 2 1 6
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDat2, allowMultipleColumns = TRUE)
# [1] FALSE
# Warning message:
# In checkAllUnique(tmpDat2, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### what about columns of different classes, e.g. numeric and character
cbind(tmpDat2, letters[1:5]) -> tmpDatMixed
tmpDatMixed
# V1 V2 letters[1:5]
# 1 1 6 a
# 2 1 6 b
# 3 3 8 c
# 4 4 9 d
# 5 5 10 e
checkAllUnique(tmpDatMixed, allowMultipleColumns = TRUE)
# [1] TRUE
# Warning message:
# In checkAllUnique(tmpDatMixed, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### that came out as TRUE because R "promoted" the numerics to character
### and because the two different letters in rows 1 and 2 of column 3
### broke the non-unique tie of rows 1 and 2 in columns 1 and 2
### what about having NA in a multicolumn input?
tmpDatNA <- tmpDat
tmpDatNA[2, 1] <- NA
tmpDatNA
# 1 1 6
# 2 NA 7
# 3 3 8
# 4 4 9
# 5 5 10
checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE)
# [1] FALSE
# Warning message:
# In checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE) :
# Input of dat to checkAllUnique was not a vector: be careful please!
### but
checkAllUnique(tmpDatNA, allowMultipleColumns = TRUE, errIfNA = FALSE)
## End(Not run)
|
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