Count observations with percentage — count

This variant of dplyr::count() includes a column showing percentage of total observations for each group.

Usage

count_pct(
  .data,
  ...,
  na.rm = FALSE,
  .by = NULL,
  wt = NULL,
  sort = FALSE,
  .drop = dplyr::group_by_drop_default()
)

Arguments

...

Variables to group by. Will be passed to dplyr::count().

na.rm

If TRUE, removes rows with NA values before calculations.

.by

a selection of columns to group by for just this operation, functioning as an alternative to dplyr::group_by(). Percentages will be computed within each group rather than for the grand total. See examples.

wt

<data-masking> Frequency weights. Can be NULL or a variable:

If NULL (the default), counts the number of rows in each group.
If a variable, computes sum(wt) for each group.

sort

If TRUE, will show the largest groups at the top.

.drop

Handling of factor levels that don't appear in the data, passed on to group_by().

For count(): if FALSE will include counts for empty groups (i.e. for levels of factors that don't exist in the data).

For add_count(): deprecated since it can't actually affect the output.

Value

A data frame with columns for grouping variables, n (the count of observations in each group), and pct (the percentage of total observations in each group).

Details

Percentages within subgroups can be obtained by grouping with group_by

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

## note effect of `na.rm` on percentages
dplyr::starwars %>%
  count_pct(gender)
#> # A tibble: 3 × 3
#>   gender        n    pct
#>   <chr>     <int>  <dbl>
#> 1 feminine     17 0.195 
#> 2 masculine    66 0.759 
#> 3 NA            4 0.0460

dplyr::starwars %>%
  count_pct(gender, na.rm = TRUE)
#> # A tibble: 2 × 3
#>   gender        n   pct
#>   <chr>     <int> <dbl>
#> 1 feminine     17 0.205
#> 2 masculine    66 0.795

## note effect of grouping on percentages
# no grouping: % of grand total
ggplot2::mpg %>%
 count_pct(year, cyl)
#> # A tibble: 7 × 4
#>    year   cyl     n    pct
#>   <int> <int> <int>  <dbl>
#> 1  1999     4    45 0.192 
#> 2  1999     6    45 0.192 
#> 3  1999     8    27 0.115 
#> 4  2008     4    36 0.154 
#> 5  2008     5     4 0.0171
#> 6  2008     6    34 0.145 
#> 7  2008     8    43 0.184 

# grouping w `group_by()`: % of group, output is grouped
ggplot2::mpg %>%
  dplyr::group_by(year) %>%
  count_pct(cyl)
#> # A tibble: 7 × 4
#> # Groups:   year [2]
#>    year   cyl     n    pct
#>   <int> <int> <int>  <dbl>
#> 1  1999     4    45 0.385 
#> 2  1999     6    45 0.385 
#> 3  1999     8    27 0.231 
#> 4  2008     4    36 0.308 
#> 5  2008     5     4 0.0342
#> 6  2008     6    34 0.291 
#> 7  2008     8    43 0.368 

# grouping w `.by`: % of group, output isn't grouped
ggplot2::mpg %>%
  count_pct(cyl, .by = year)
#> # A tibble: 7 × 4
#>    year   cyl     n    pct
#>   <int> <int> <int>  <dbl>
#> 1  1999     4    45 0.385 
#> 2  1999     6    45 0.385 
#> 3  1999     8    27 0.231 
#> 4  2008     4    36 0.308 
#> 5  2008     5     4 0.0342
#> 6  2008     6    34 0.291 
#> 7  2008     8    43 0.368