Functions to summarise count with percentage within a dataframe

count_pct is the extenstion of dplyr::count with percent count_pct_group is the extenstion of dplyr::count with percent by group

count_pct(.data, ..., sort = FALSE, accuracy = 3)

count_pct_group(.data, ..., accuracy = 3)

Value

returns a dataframe with count and percentage columns

Examples

library(dacol, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
library(scales, warn.conflicts = FALSE)

df = data.frame(x = c(1, 2, 3, 4, 5),
                y = c("b", "a", "k", "b", "k"),
                z = c(5, 8, 8, 6, 5))

df %>% count_pct(y)
#> # A tibble: 3 x 3
#>   y         n pt   
#>   <chr> <int> <chr>
#> 1 a         1 21%  
#> 2 b         2 39%  
#> 3 k         2 39%  
df %>% count_pct(z)
#> # A tibble: 3 x 3
#>       z     n pt   
#>   <dbl> <int> <chr>
#> 1     5     2 39%  
#> 2     6     1 21%  
#> 3     8     2 39%  

mtcars %>% count_pct(cyl)
#> # A tibble: 3 x 3
#>     cyl     n pt   
#>   <dbl> <int> <chr>
#> 1     4    11 33%  
#> 2     6     7 21%  
#> 3     8    14 45%  
mtcars %>% count_pct(cyl, sort = TRUE)
#> # A tibble: 3 x 3
#>     cyl     n pt   
#>   <dbl> <int> <chr>
#> 1     8    14 45%  
#> 2     4    11 33%  
#> 3     6     7 21%  
mtcars %>% count_pct(cyl, am, sort = TRUE)
#> # A tibble: 6 x 4
#>     cyl    am     n pt   
#>   <dbl> <dbl> <int> <chr>
#> 1     8     0    12 36%  
#> 2     4     1     8 24%  
#> 3     6     0     4 12%  
#> 4     4     0     3 9%   
#> 5     6     1     3 9%   
#> 6     8     1     2 6%   

mtcars %>% count_pct_group(cyl)
#> # A tibble: 3 x 3
#>     cyl     n pt   
#>   <dbl> <int> <chr>
#> 1     4    11 33%  
#> 2     6     7 21%  
#> 3     8    14 45%  
mtcars %>% count_pct_group(cyl, am)
#> # A tibble: 6 x 4
#> # Groups:   cyl [3]
#>     cyl    am     n pt   
#>   <dbl> <dbl> <int> <chr>
#> 1     4     0     3 27%  
#> 2     4     1     8 72%  
#> 3     6     0     4 57%  
#> 4     6     1     3 42%  
#> 5     8     0    12 87%  
#> 6     8     1     2 15%  
mtcars %>% count_pct_group(cyl, am) %>% arrange(cyl, desc(n))
#> # A tibble: 6 x 4
#> # Groups:   cyl [3]
#>     cyl    am     n pt   
#>   <dbl> <dbl> <int> <chr>
#> 1     4     1     8 72%  
#> 2     4     0     3 27%  
#> 3     6     0     4 57%  
#> 4     6     1     3 42%  
#> 5     8     0    12 87%  
#> 6     8     1     2 15%