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Produce a data frame to summarise counts of events

count_table.Rd

Takes a data frame of events and produces the number of events and number and percentage of individuals with at least one event.

Usage

count_table(df, ..., ID, N, group, accuracy = 0.1, total = FALSE, all = FALSE)

Arguments

df

Data Frame

...

Variables to be summarised

ID

Variable that defines the individual identifier (e.g. screening number)

N

a data frame with the group counts (typically produced using dplyr::count())

group

optional, variable that defines the grouping

accuracy

see details of scales::percent()

total

Logical indicating whether a total column should be created, default is FALSE

all

logical indicating whether a row summarising all events should be created, default is FALSE

Examples

  N <- dplyr::count(outcome, group, name = "N")
  count_table(outcome_aes, serious, related, ID = screening, N = N,
              group = group)
#> # A tibble: 5 × 6
#>   variable scoring A_events A_individuals B_events B_individuals
#>   <chr>    <chr>   <chr>    <chr>         <chr>    <chr>        
#> 1 NA       NA      N = 276  N = 276       N = 324  N = 324      
#> 2 serious  No      45       37 (13.4%)    45       36 (11.1%)   
#> 3 serious  Yes     4        4 (1.4%)      6        6 (1.9%)     
#> 4 related  No      47       39 (14.1%)    50       41 (12.7%)   
#> 5 related  Yes     2        2 (0.7%)      1        1 (0.3%)     

  N <- dplyr::count(outcome, name = "N")
  count_table(outcome_aes, serious, related, ID = screening, N = N)
#> # A tibble: 5 × 4
#>   variable scoring events  individuals
#>   <chr>    <chr>   <chr>   <chr>      
#> 1 NA       NA      N = 600 N = 600    
#> 2 serious  No      90      73 (12.2%) 
#> 3 serious  Yes     10      10 (1.7%)  
#> 4 related  No      97      80 (13.3%) 
#> 5 related  Yes     3       3 (0.5%)   
  count_table(outcome_aes, serious, ID = screening, N = N, all = TRUE)
#> # A tibble: 4 × 4
#>   variable scoring events  individuals
#>   <chr>    <chr>   <chr>   <chr>      
#> 1 NA       NA      N = 600 N = 600    
#> 2 All      n       100     82 (13.7%) 
#> 3 serious  No      90      73 (12.2%) 
#> 4 serious  Yes     10      10 (1.7%)