Produce a dataframe to summarise discrete variables — discrete

Takes a dataframe and produces the number and percentage for discrete variables.

Usage

discrete_table(
  df = .,
  ...,
  group = .,
  time = .,
  total = TRUE,
  n = FALSE,
  missing = "Missing",
  accuracy = 0.1,
  condense = FALSE
)

Arguments

df: Data Frame
...: Variables to be summarised
group: Optional variable that defines the grouping
time: Optional variable for repeated measures (currenlty must me used with group)
total: Logical indicating whether a total column should be created
n: Logical indicating whether percentages should be out of n (n = TRUE) or N (n = FALSE)
missing: String determining what missing data will be called (if n = TRUE). Default is "Missing".
accuracy: see details of scales::percent
condense: should the variable and scoring columns in the output be condensed to one column?

Value

A tibble data frame summarising the data

Examples

    library(ggplot2) # for the data
    discrete_table(df = mpg, drv, group = manufacturer)
#> # A tibble: 4 × 18
#>   variable scoring Total       audi    chevrolet dodge ford  honda hyundai jeep 
#>   <chr>    <chr>   <chr>       <chr>   <chr>     <chr> <chr> <chr> <chr>   <chr>
#> 1 NA       NA      N = 234     N = 18  N = 19    N = … N = … N = 9 N = 14  N = 8
#> 2 drv      4       103 (44.0%) 11 (61… 4 (21.1%) 26 (… 13 (… 0 (0… 0 (0.0… 8 (1…
#> 3 drv      f       106 (45.3%) 7 (38.… 5 (26.3%) 11 (… 0 (0… 9 (1… 14 (10… 0 (0…
#> 4 drv      r       25 (10.7%)  0 (0.0… 10 (52.6… 0 (0… 12 (… 0 (0… 0 (0.0… 0 (0…
#> # ℹ 8 more variables: `land rover` <chr>, lincoln <chr>, mercury <chr>,
#> #   nissan <chr>, pontiac <chr>, subaru <chr>, toyota <chr>, volkswagen <chr>
    discrete_table(df = mpg, drv)
#> # A tibble: 4 × 3
#>   variable scoring value      
#>   <chr>    <chr>   <chr>      
#> 1 NA       NA      N = 234    
#> 2 drv      4       103 (44.0%)
#> 3 drv      f       106 (45.3%)
#> 4 drv      r       25 (10.7%)