Add a content row of summary counts

Usage

summarize_row_groups(
  lyt,
  var = "",
  label_fstr = "%s",
  format = "xx (xx.x%)",
  na_str = "-",
  cfun = NULL,
  indent_mod = 0L,
  extra_args = list()
)

Arguments

lyt: layout object pre-data used for tabulation
var: string, variable name
label_fstr: string. An sprintf style format string containing. For non-comparison splits, it can contain up to one "%s" which takes the current split value and generates the row/column label. Comparison-based splits it can contain up to two "%s".
format: FormatSpec. Format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can character vectors or lists of functions.
na_str: character(1). String that should be displayed when the value of x is missing. Defaults to "NA".
cfun: list/function/NULL. tabulation function(s) for creating content rows. Must accept x or df as first parameter. Must accept labelstr as the second argument. Can optionally accept all optional arguments accepted by analysis functions. See analyze.
indent_mod: numeric. Modifier for the default indent position for the structure created by this function(subtable, content table, or row) and all of that structure's children. Defaults to 0, which corresponds to the unmodified default behavior.
extra_args: list. Extra arguments to be passed to the tabulation function. Element position in the list corresponds to the children of this split. Named elements in the child-specific lists are ignored if they do not match a formal argument of the tabulation function.

Value

A PreDataTableLayouts object suitable for passing to further layouting functions, and to build_table.

Details

If format expects 1 value (i.e. it is specified as a format string and xx appears for two values (i.e. xx appears twice in the format string) or is specified as a function, then both raw and percent of column total counts are calculated. If format is a format string where xx appears only one time, only raw counts are used.

cfun must accept x or df as its first argument. For the df argument cfun will receive the subset data.frame corresponding with the row- and column-splitting for the cell being calculated. Must accept labelstr as the second parameter, which accepts the label of the level of the parent split currently being summarized. Can additionally take any optional argument supported by analysis functions. (see analyze).

In addition, if complex custom functions are needed, we suggest checking the available additional_fun_params that apply here as for afun.

Author

Gabriel Becker

Examples


DM2 <- subset(DM, COUNTRY %in% c("USA", "CAN", "CHN"))

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("COUNTRY", split_fun = drop_split_levels) %>%
  summarize_row_groups(label_fstr = "%s (n)") %>%
  analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx")
lyt
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> ARM (lvls) 
#> 
#> Row-Split Structure:
#> COUNTRY (lvls) -> AGE (** analysis **) 
#> 

tbl <- build_table(lyt, DM2)
tbl
#>             A: Drug X    B: Placebo   C: Combination
#> ————————————————————————————————————————————————————
#> CHN (n)     62 (79.5%)   48 (75.0%)     69 (78.4%)  
#>   Min.        22.00        25.00          24.00     
#>   1st Qu.     29.25        30.00          30.00     
#>   Median      34.00        33.50          33.00     
#>   Mean        36.08        34.12          33.71     
#>   3rd Qu.     41.00        38.00          37.00     
#>   Max.        60.00        55.00          51.00     
#> USA (n)     13 (16.7%)   14 (21.9%)     17 (19.3%)  
#>   Min.        23.00        24.00          22.00     
#>   1st Qu.     31.00        28.00          31.00     
#>   Median      36.00        30.00          37.00     
#>   Mean        36.77        32.57          36.41     
#>   3rd Qu.     41.00        37.50          41.00     
#>   Max.        58.00        47.00          51.00     
#> CAN (n)      3 (3.8%)     2 (3.1%)       2 (2.3%)   
#>   Min.        29.00        30.00          28.00     
#>   1st Qu.     32.50        32.00          28.75     
#>   Median      36.00        34.00          29.50     
#>   Mean        36.00        34.00          29.50     
#>   3rd Qu.     39.50        36.00          30.25     
#>   Max.        43.00        38.00          31.00     

row_paths_summary(tbl) # summary count is a content table
#> rowname      node_class    path                           
#> ——————————————————————————————————————————————————————————
#> CHN (n)      ContentRow    COUNTRY, CHN, @content, CHN (n)
#>   Min.       DataRow       COUNTRY, CHN, AGE, Min.        
#>   1st Qu.    DataRow       COUNTRY, CHN, AGE, 1st Qu.     
#>   Median     DataRow       COUNTRY, CHN, AGE, Median      
#>   Mean       DataRow       COUNTRY, CHN, AGE, Mean        
#>   3rd Qu.    DataRow       COUNTRY, CHN, AGE, 3rd Qu.     
#>   Max.       DataRow       COUNTRY, CHN, AGE, Max.        
#> USA (n)      ContentRow    COUNTRY, USA, @content, USA (n)
#>   Min.       DataRow       COUNTRY, USA, AGE, Min.        
#>   1st Qu.    DataRow       COUNTRY, USA, AGE, 1st Qu.     
#>   Median     DataRow       COUNTRY, USA, AGE, Median      
#>   Mean       DataRow       COUNTRY, USA, AGE, Mean        
#>   3rd Qu.    DataRow       COUNTRY, USA, AGE, 3rd Qu.     
#>   Max.       DataRow       COUNTRY, USA, AGE, Max.        
#> CAN (n)      ContentRow    COUNTRY, CAN, @content, CAN (n)
#>   Min.       DataRow       COUNTRY, CAN, AGE, Min.        
#>   1st Qu.    DataRow       COUNTRY, CAN, AGE, 1st Qu.     
#>   Median     DataRow       COUNTRY, CAN, AGE, Median      
#>   Mean       DataRow       COUNTRY, CAN, AGE, Mean        
#>   3rd Qu.    DataRow       COUNTRY, CAN, AGE, 3rd Qu.     
#>   Max.       DataRow       COUNTRY, CAN, AGE, Max.        


## use a cfun and extra_args to customize summarization
## behavior
sfun <- function(x, labelstr, trim) {
  in_rows(
    c(mean(x, trim = trim), trim),
    .formats = "xx.x (xx.x%)",
    .labels = sprintf(
      "%s (Trimmed mean and trim %%)",
      labelstr
    )
  )
}

lyt2 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("COUNTRY", split_fun = drop_split_levels) %>%
  summarize_row_groups("AGE",
    cfun = sfun,
    extra_args = list(trim = .2)
  ) %>%
  analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%
  append_topleft(c("Country", "  Age"))

tbl2 <- build_table(lyt2, DM2)
tbl2
#> Country                          A: Drug X      B: Placebo    C: Combination
#>   Age                              (N=78)         (N=64)          (N=88)    
#> ————————————————————————————————————————————————————————————————————————————
#> CHN (Trimmed mean and trim %)   35.1 (20.0%)   33.4 (20.0%)    33.4 (20.0%) 
#>   Min.                             22.00          25.00           24.00     
#>   1st Qu.                          29.25          30.00           30.00     
#>   Median                           34.00          33.50           33.00     
#>   Mean                             36.08          34.12           33.71     
#>   3rd Qu.                          41.00          38.00           37.00     
#>   Max.                             60.00          55.00           51.00     
#> USA (Trimmed mean and trim %)   36.1 (20.0%)   31.9 (20.0%)    36.1 (20.0%) 
#>   Min.                             23.00          24.00           22.00     
#>   1st Qu.                          31.00          28.00           31.00     
#>   Median                           36.00          30.00           37.00     
#>   Mean                             36.77          32.57           36.41     
#>   3rd Qu.                          41.00          37.50           41.00     
#>   Max.                             58.00          47.00           51.00     
#> CAN (Trimmed mean and trim %)   36.0 (20.0%)   34.0 (20.0%)    29.5 (20.0%) 
#>   Min.                             29.00          30.00           28.00     
#>   1st Qu.                          32.50          32.00           28.75     
#>   Median                           36.00          34.00           29.50     
#>   Mean                             36.00          34.00           29.50     
#>   3rd Qu.                          39.50          36.00           30.25     
#>   Max.                             43.00          38.00           31.00