Produce a data frame to summarise discrete variables — discrete

Takes a data frame and produces the number and percentage for discrete variables.

Usage

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

Arguments

df: Data Frame
...: Variables to be summarised
group: Optional variable that defines the grouping
time: Optional variable for repeated measures (currently 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::label_percent()
drop.levels: logical indicating whether unused levels in the factors should be dropped. Default is FALSE.
condense: condense = TRUE is deprecated, use condense() instead.

Value

A tibble data frame summarising the data

Examples

    discrete_table(outcome, sex, group = group)
#> # A tibble: 5 × 5
#>   variable scoring               A           B           Total      
#>   <chr>    <chr>                 <chr>       <chr>       <chr>      
#> 1 NA       NA                    N = 276     N = 324     N = 600    
#> 2 sex      Female                144 (52.2%) 159 (49.1%) 303 (50.5%)
#> 3 sex      Male                  129 (46.7%) 162 (50.0%) 291 (48.5%)
#> 4 sex      Prefer not to specify 0 (0.0%)    0 (0.0%)    0 (0.0%)   
#> 5 sex      Missing               3 (1.1%)    3 (0.9%)    6 (1.0%)   
    discrete_table(outcome, sex, drop.levels = TRUE)
#> # A tibble: 4 × 3
#>   variable scoring value      
#>   <chr>    <chr>   <chr>      
#> 1 NA       NA      N = 600    
#> 2 sex      Female  303 (50.5%)
#> 3 sex      Male    291 (48.5%)
#> 4 sex      Missing 6 (1.0%)   
    discrete_table(outcome, sex, group = group, time = event_name, n = TRUE,
                   total = FALSE)
#> # A tibble: 13 × 5
#>    event_name variable scoring               A          B         
#>    <chr>      <chr>    <chr>                 <chr>      <chr>     
#>  1 NA         NA       NA                    N = 92     N = 108   
#>  2 Baseline   sex      n                     91         107       
#>  3 Baseline   sex      Female                48 (52.7%) 53 (49.5%)
#>  4 Baseline   sex      Male                  43 (47.3%) 54 (50.5%)
#>  5 Baseline   sex      Prefer not to specify 0 (0.0%)   0 (0.0%)  
#>  6 6 Weeks    sex      n                     91         107       
#>  7 6 Weeks    sex      Female                48 (52.7%) 53 (49.5%)
#>  8 6 Weeks    sex      Male                  43 (47.3%) 54 (50.5%)
#>  9 6 Weeks    sex      Prefer not to specify 0 (0.0%)   0 (0.0%)  
#> 10 12 Weeks   sex      n                     91         107       
#> 11 12 Weeks   sex      Female                48 (52.7%) 53 (49.5%)
#> 12 12 Weeks   sex      Male                  43 (47.3%) 54 (50.5%)
#> 13 12 Weeks   sex      Prefer not to specify 0 (0.0%)   0 (0.0%)