Summarize categorical data

cat_data(
  data,
  cols,
  by = ".total",
  panel = by,
  summarize_all = TRUE,
  all_name = "All",
  wide = FALSE,
  nby = NULL,
  complete = FALSE,
  denom = c("group", "total")
)

Arguments

data: the data frame to summarize; the user should filter or subset so that data contains exactly the records to be summarized; pmtables will not add or remove rows prior to summarizing data
cols: the columns to summarize; may be character vector or quosure
by: a grouping variable; may be character vector or quosure
panel: data set column name to stratify the summary
summarize_all: logical indicating whether or not to include a summary of the full data in the output.
all_name: label for full data summary.
wide: logical; if TRUE, data frame will be returned in wide format; if FALSE, it will be returned in long format.
nby: number of unique levels for the by variable.
complete: logical; if TRUE, then data the summary will be completed for missing levels of byand panel.
denom: the denominator to use when calculating percent for each level; group uses the total number in the chunk being summarized; total uses the total number in the data set; historically, group has been used as the default.

Examples


cat_data(pmt_first, cols = c(SEX = "SEXf", RF = "RFf"), by = "STUDYf")
#> # A tibble: 6 × 6
#>   name  level    `12-DEMO-001` `12-DEMO-002` `11-DEMO-005` `13-DEMO-001`
#>   <chr> <chr>    <chr>         <chr>         <chr>         <chr>        
#> 1 SEX   male     10 (33.3)     18 (36.0)     29 (72.5)     23 (57.5)    
#> 2 SEX   female   20 (66.7)     32 (64.0)     11 (27.5)     17 (42.5)    
#> 3 RF    normal   30 (100.0)    50 (100.0)    10 (25.0)     40 (100.0)   
#> 4 RF    mild     0 (0.0)       0 (0.0)       10 (25.0)     0 (0.0)      
#> 5 RF    moderate 0 (0.0)       0 (0.0)       10 (25.0)     0 (0.0)      
#> 6 RF    severe   0 (0.0)       0 (0.0)       10 (25.0)     0 (0.0)