Skip to contents

Count Pruner

Source: R/pruning_functions.R
count_pruner.Rd

This is a pruning constructor function which identifies records to be pruned based on the count (assumed to be the first statistic displayed when a compound statistic (e.g., ## / ## (XX.X percent) is presented).

Usage

count_pruner(
  count = 0,
  cat_include = NULL,
  cat_exclude = NULL,
  cols = c("TRT01A")
)

Arguments

count

count threshold. Function will keep all records strictly greater than this threshold.

cat_include

Category to be considered for pruning

cat_exclude

logical Category to be excluded from pruning

cols

column path (character or integer (column indices))

Value

function that can be utilized as pruning function in prune_table

Examples


ADSL <- data.frame(
  USUBJID = c(
    'XXXXX01', 'XXXXX02', 'XXXXX03', 'XXXXX04', 'XXXXX05',
    'XXXXX06', 'XXXXX07', 'XXXXX08', 'XXXXX09', 'XXXXX10'
  ),
  TRT01P = factor(
    c(
      'ARMA', 'ARMB', 'ARMA', 'ARMB', 'ARMB',
      'Placebo', 'Placebo', 'Placebo', 'ARMA', 'ARMB'
     )
  ),
  FASFL = c('Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y'),
  SAFFL = c('N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N'),
  PKFL = c('N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N', 'N')
)

lyt <- basic_table() |>
  split_cols_by('TRT01P') |>
  add_overall_col('Total') |>
  analyze('FASFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    extra_args = list(label = 'Full', val = 'Y'),
    show_labels = 'visible'
  ) |>
  analyze('SAFFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    extra_args = list(label = 'Safety', val = 'Y'),
    show_labels = 'visible'
  ) |>
  analyze('PKFL',
    var_labels = 'Analysis set:',
    afun = a_freq_j,
    extra_args = list(label = 'PK', val = 'Y'),
    show_labels = 'visible'
  )

result <- build_table(lyt, ADSL)

result
#>                     ARMA          ARMB         Placebo         Total    
#> ————————————————————————————————————————————————————————————————————————
#> Analysis set:                                                           
#>   Full          3/3 (100.0%)   3/4 (75.0%)   3/3 (100.0%)   9/10 (90.0%)
#> Analysis set:                                                           
#>   Safety         0/3 (0.0%)    0/4 (0.0%)     0/3 (0.0%)    0/10 (0.0%) 
#> Analysis set:                                                           
#>   PK             0/3 (0.0%)    0/4 (0.0%)     0/3 (0.0%)    0/10 (0.0%) 

# use pruning function to prune where total column has >0 count,
# but exclude pruning safety set row
result <- prune_table(
  result,
  prune_func = count_pruner(cat_exclude = c('Safety'), cols = 'Total')
)

result
#>                     ARMA          ARMB         Placebo         Total    
#> ————————————————————————————————————————————————————————————————————————
#> Analysis set:                                                           
#>   Full          3/3 (100.0%)   3/4 (75.0%)   3/3 (100.0%)   9/10 (90.0%)
#> Analysis set:                                                           
#>   Safety         0/3 (0.0%)    0/4 (0.0%)     0/3 (0.0%)    0/10 (0.0%)