proportion() lets you quickly count observations (like dplyr::count())
and compute relative proportions. Proportions are computed separately by
group (see examples).
Usage
proportion(data, ...)
# S3 method for class 'data.frame'
proportion(
data,
...,
.by = NULL,
.na.rm = FALSE,
.weight = NULL,
.scale = 100,
.sort = FALSE,
.drop = FALSE,
.conf.int = FALSE,
.conf.level = 0.95,
.options = list(correct = TRUE)
)
# S3 method for class 'survey.design'
proportion(
data,
...,
.by = NULL,
.na.rm = FALSE,
.scale = 100,
.sort = FALSE,
.conf.int = FALSE,
.conf.level = 0.95,
.options = NULL
)
# Default S3 method
proportion(
data,
...,
.na.rm = FALSE,
.scale = 100,
.sort = FALSE,
.drop = FALSE,
.conf.int = FALSE,
.conf.level = 0.95,
.options = list(correct = TRUE)
)Arguments
- data
A vector, a data frame, data frame extension (e.g. a tibble), or a survey design object.
- ...
<
data-masking> Variable(s) for those computing proportions.- .by
<
tidy-select> Optional additional variables to group by (in addition to those eventually previously declared usingdplyr::group_by()).- .na.rm
Should
NAvalues be removed (from variables declared in...)?- .weight
<
data-masking> Frequency weights. Can beNULLor a variable.- .scale
A scaling factor applied to proportion. Use
1for keeping proportions unchanged.- .sort
If
TRUE, will show the highest proportions at the top.- .drop
If
TRUE, will remove empty groups from the output.- .conf.int
If
TRUE, will estimate confidence intervals.- .conf.level
Confidence level for the returned confidence intervals.
- .options
Additional arguments passed to
stats::prop.test()orsrvyr::survey_prop().
Examples
# using a vector
titanic$Class |> proportion()
#> # A tibble: 4 × 4
#> value n N prop
#> <chr> <int> <int> <dbl>
#> 1 1st 325 2201 14.8
#> 2 2nd 285 2201 12.9
#> 3 3rd 706 2201 32.1
#> 4 Crew 885 2201 40.2
# univariable table
titanic |> proportion(Class)
#> # A tibble: 4 × 4
#> Class n N prop
#> <chr> <int> <int> <dbl>
#> 1 1st 325 2201 14.8
#> 2 2nd 285 2201 12.9
#> 3 3rd 706 2201 32.1
#> 4 Crew 885 2201 40.2
titanic |> proportion(Class, .sort = TRUE)
#> # A tibble: 4 × 4
#> Class n N prop
#> <chr> <int> <int> <dbl>
#> 1 Crew 885 2201 40.2
#> 2 3rd 706 2201 32.1
#> 3 1st 325 2201 14.8
#> 4 2nd 285 2201 12.9
titanic |> proportion(Class, .conf.int = TRUE)
#> # A tibble: 4 × 6
#> Class n N prop prop_low prop_high
#> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 1st 325 2201 14.8 13.3 16.3
#> 2 2nd 285 2201 12.9 11.6 14.4
#> 3 3rd 706 2201 32.1 30.1 34.1
#> 4 Crew 885 2201 40.2 38.2 42.3
titanic |> proportion(Class, .conf.int = TRUE, .scale = 1)
#> # A tibble: 4 × 6
#> Class n N prop prop_low prop_high
#> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 1st 325 2201 0.148 0.133 0.163
#> 2 2nd 285 2201 0.129 0.116 0.144
#> 3 3rd 706 2201 0.321 0.301 0.341
#> 4 Crew 885 2201 0.402 0.382 0.423
# bivariable table
titanic |> proportion(Class, Survived) # proportions of the total
#> # A tibble: 8 × 5
#> Class Survived n N prop
#> <chr> <chr> <int> <int> <dbl>
#> 1 1st No 122 2201 5.54
#> 2 1st Yes 203 2201 9.22
#> 3 2nd No 167 2201 7.59
#> 4 2nd Yes 118 2201 5.36
#> 5 3rd No 528 2201 24.0
#> 6 3rd Yes 178 2201 8.09
#> 7 Crew No 673 2201 30.6
#> 8 Crew Yes 212 2201 9.63
titanic |> proportion(Survived, .by = Class) # row proportions
#> # A tibble: 8 × 5
#> # Groups: Class [4]
#> Class Survived n N prop
#> <chr> <chr> <int> <int> <dbl>
#> 1 1st No 122 325 37.5
#> 2 1st Yes 203 325 62.5
#> 3 2nd No 167 285 58.6
#> 4 2nd Yes 118 285 41.4
#> 5 3rd No 528 706 74.8
#> 6 3rd Yes 178 706 25.2
#> 7 Crew No 673 885 76.0
#> 8 Crew Yes 212 885 24.0
titanic |> # equivalent syntax
dplyr::group_by(Class) |>
proportion(Survived)
#> # A tibble: 8 × 5
#> # Groups: Class [4]
#> Class Survived n N prop
#> <chr> <chr> <int> <int> <dbl>
#> 1 1st No 122 325 37.5
#> 2 1st Yes 203 325 62.5
#> 3 2nd No 167 285 58.6
#> 4 2nd Yes 118 285 41.4
#> 5 3rd No 528 706 74.8
#> 6 3rd Yes 178 706 25.2
#> 7 Crew No 673 885 76.0
#> 8 Crew Yes 212 885 24.0
# combining 3 variables or more
titanic |> proportion(Class, Sex, Survived)
#> # A tibble: 16 × 6
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 1st Female No 4 2201 0.182
#> 2 1st Female Yes 141 2201 6.41
#> 3 1st Male No 118 2201 5.36
#> 4 1st Male Yes 62 2201 2.82
#> 5 2nd Female No 13 2201 0.591
#> 6 2nd Female Yes 93 2201 4.23
#> 7 2nd Male No 154 2201 7.00
#> 8 2nd Male Yes 25 2201 1.14
#> 9 3rd Female No 106 2201 4.82
#> 10 3rd Female Yes 90 2201 4.09
#> 11 3rd Male No 422 2201 19.2
#> 12 3rd Male Yes 88 2201 4.00
#> 13 Crew Female No 3 2201 0.136
#> 14 Crew Female Yes 20 2201 0.909
#> 15 Crew Male No 670 2201 30.4
#> 16 Crew Male Yes 192 2201 8.72
titanic |> proportion(Sex, Survived, .by = Class)
#> # A tibble: 16 × 6
#> # Groups: Class [4]
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 1st Female No 4 325 1.23
#> 2 1st Female Yes 141 325 43.4
#> 3 1st Male No 118 325 36.3
#> 4 1st Male Yes 62 325 19.1
#> 5 2nd Female No 13 285 4.56
#> 6 2nd Female Yes 93 285 32.6
#> 7 2nd Male No 154 285 54.0
#> 8 2nd Male Yes 25 285 8.77
#> 9 3rd Female No 106 706 15.0
#> 10 3rd Female Yes 90 706 12.7
#> 11 3rd Male No 422 706 59.8
#> 12 3rd Male Yes 88 706 12.5
#> 13 Crew Female No 3 885 0.339
#> 14 Crew Female Yes 20 885 2.26
#> 15 Crew Male No 670 885 75.7
#> 16 Crew Male Yes 192 885 21.7
titanic |> proportion(Survived, .by = c(Class, Sex))
#> # A tibble: 16 × 6
#> # Groups: Class, Sex [8]
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <int> <int> <dbl>
#> 1 1st Female No 4 145 2.76
#> 2 1st Female Yes 141 145 97.2
#> 3 1st Male No 118 180 65.6
#> 4 1st Male Yes 62 180 34.4
#> 5 2nd Female No 13 106 12.3
#> 6 2nd Female Yes 93 106 87.7
#> 7 2nd Male No 154 179 86.0
#> 8 2nd Male Yes 25 179 14.0
#> 9 3rd Female No 106 196 54.1
#> 10 3rd Female Yes 90 196 45.9
#> 11 3rd Male No 422 510 82.7
#> 12 3rd Male Yes 88 510 17.3
#> 13 Crew Female No 3 23 13.0
#> 14 Crew Female Yes 20 23 87.0
#> 15 Crew Male No 670 862 77.7
#> 16 Crew Male Yes 192 862 22.3
# missing values
dna <- titanic
dna$Survived[c(1:20, 500:530)] <- NA
dna |> proportion(Survived)
#> # A tibble: 3 × 4
#> Survived n N prop
#> <chr> <int> <int> <dbl>
#> 1 No 1439 2201 65.4
#> 2 Yes 711 2201 32.3
#> 3 NA 51 2201 2.32
dna |> proportion(Survived, .na.rm = TRUE)
#> # A tibble: 2 × 4
#> Survived n N prop
#> <chr> <int> <int> <dbl>
#> 1 No 1439 2150 66.9
#> 2 Yes 711 2150 33.1
# \donttest{
## SURVEY DATA ------------------------------------------------------
ds <- srvyr::as_survey(titanic)
# univariable table
ds |> proportion(Class)
#> # A tibble: 4 × 4
#> Class n N prop
#> <chr> <dbl> <dbl> <dbl>
#> 1 1st 325 2201 14.8
#> 2 2nd 285 2201 12.9
#> 3 3rd 706 2201 32.1
#> 4 Crew 885 2201 40.2
ds |> proportion(Class, .sort = TRUE)
#> # A tibble: 4 × 4
#> Class n N prop
#> <chr> <dbl> <dbl> <dbl>
#> 1 Crew 885 2201 40.2
#> 2 3rd 706 2201 32.1
#> 3 1st 325 2201 14.8
#> 4 2nd 285 2201 12.9
ds |> proportion(Class, .conf.int = TRUE)
#> # A tibble: 4 × 6
#> Class n N prop prop_low prop_high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1st 325 2201 14.8 13.3 16.3
#> 2 2nd 285 2201 12.9 11.6 14.4
#> 3 3rd 706 2201 32.1 30.2 34.1
#> 4 Crew 885 2201 40.2 38.2 42.3
ds |> proportion(Class, .conf.int = TRUE, .scale = 1)
#> # A tibble: 4 × 6
#> Class n N prop prop_low prop_high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1st 325 2201 0.148 0.133 0.163
#> 2 2nd 285 2201 0.129 0.116 0.144
#> 3 3rd 706 2201 0.321 0.302 0.341
#> 4 Crew 885 2201 0.402 0.382 0.423
# bivariable table
ds |> proportion(Class, Survived) # proportions of the total
#> # A tibble: 8 × 5
#> Class Survived n N prop
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st No 122 2201 5.54
#> 2 1st Yes 203 2201 9.22
#> 3 2nd No 167 2201 7.59
#> 4 2nd Yes 118 2201 5.36
#> 5 3rd No 528 2201 24.0
#> 6 3rd Yes 178 2201 8.09
#> 7 Crew No 673 2201 30.6
#> 8 Crew Yes 212 2201 9.63
ds |> proportion(Survived, .by = Class) # row proportions
#> # A tibble: 8 × 5
#> # Groups: Class [4]
#> Class Survived n N prop
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st No 122 325 37.5
#> 2 1st Yes 203 325 62.5
#> 3 2nd No 167 285 58.6
#> 4 2nd Yes 118 285 41.4
#> 5 3rd No 528 706 74.8
#> 6 3rd Yes 178 706 25.2
#> 7 Crew No 673 885 76.0
#> 8 Crew Yes 212 885 24.0
ds |> dplyr::group_by(Class) |> proportion(Survived)
#> # A tibble: 8 × 5
#> # Groups: Class [4]
#> Class Survived n N prop
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st No 122 325 37.5
#> 2 1st Yes 203 325 62.5
#> 3 2nd No 167 285 58.6
#> 4 2nd Yes 118 285 41.4
#> 5 3rd No 528 706 74.8
#> 6 3rd Yes 178 706 25.2
#> 7 Crew No 673 885 76.0
#> 8 Crew Yes 212 885 24.0
# combining 3 variables or more
ds |> proportion(Class, Sex, Survived)
#> # A tibble: 16 × 6
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st Female No 4 2201 0.182
#> 2 1st Female Yes 141 2201 6.41
#> 3 1st Male No 118 2201 5.36
#> 4 1st Male Yes 62 2201 2.82
#> 5 2nd Female No 13 2201 0.591
#> 6 2nd Female Yes 93 2201 4.23
#> 7 2nd Male No 154 2201 7.00
#> 8 2nd Male Yes 25 2201 1.14
#> 9 3rd Female No 106 2201 4.82
#> 10 3rd Female Yes 90 2201 4.09
#> 11 3rd Male No 422 2201 19.2
#> 12 3rd Male Yes 88 2201 4.00
#> 13 Crew Female No 3 2201 0.136
#> 14 Crew Female Yes 20 2201 0.909
#> 15 Crew Male No 670 2201 30.4
#> 16 Crew Male Yes 192 2201 8.72
ds |> proportion(Sex, Survived, .by = Class)
#> # A tibble: 16 × 6
#> # Groups: Class [4]
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st Female No 4 325 1.23
#> 2 1st Female Yes 141 325 43.4
#> 3 1st Male No 118 325 36.3
#> 4 1st Male Yes 62 325 19.1
#> 5 2nd Female No 13 285 4.56
#> 6 2nd Female Yes 93 285 32.6
#> 7 2nd Male No 154 285 54.0
#> 8 2nd Male Yes 25 285 8.77
#> 9 3rd Female No 106 706 15.0
#> 10 3rd Female Yes 90 706 12.7
#> 11 3rd Male No 422 706 59.8
#> 12 3rd Male Yes 88 706 12.5
#> 13 Crew Female No 3 885 0.339
#> 14 Crew Female Yes 20 885 2.26
#> 15 Crew Male No 670 885 75.7
#> 16 Crew Male Yes 192 885 21.7
ds |> proportion(Survived, .by = c(Class, Sex))
#> # A tibble: 16 × 6
#> # Groups: Class, Sex [8]
#> Class Sex Survived n N prop
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 1st Female No 4 145 2.76
#> 2 1st Female Yes 141 145 97.2
#> 3 1st Male No 118 180 65.6
#> 4 1st Male Yes 62 180 34.4
#> 5 2nd Female No 13 106 12.3
#> 6 2nd Female Yes 93 106 87.7
#> 7 2nd Male No 154 179 86.0
#> 8 2nd Male Yes 25 179 14.0
#> 9 3rd Female No 106 196 54.1
#> 10 3rd Female Yes 90 196 45.9
#> 11 3rd Male No 422 510 82.7
#> 12 3rd Male Yes 88 510 17.3
#> 13 Crew Female No 3 23 13.0
#> 14 Crew Female Yes 20 23 87.0
#> 15 Crew Male No 670 862 77.7
#> 16 Crew Male Yes 192 862 22.3
# missing values
dsna <- srvyr::as_survey(dna)
dsna |> proportion(Survived)
#> # A tibble: 3 × 4
#> Survived n N prop
#> <chr> <dbl> <dbl> <dbl>
#> 1 No 1439 2201 65.4
#> 2 Yes 711 2201 32.3
#> 3 NA 51 2201 2.32
dsna |> proportion(Survived, .na.rm = TRUE)
#> # A tibble: 2 × 4
#> Survived n N prop
#> <chr> <dbl> <dbl> <dbl>
#> 1 No 1439 2150 66.9
#> 2 Yes 711 2150 33.1
# }