Split functions — split_funcs • rtables

Split functions

Usage

remove_split_levels(excl)

keep_split_levels(only, reorder = TRUE)

drop_split_levels(df, spl, vals = NULL, labels = NULL, trim = FALSE)

drop_and_remove_levels(excl)

reorder_split_levels(neworder, newlabels = neworder, drlevels = TRUE)

trim_levels_in_group(innervar, drop_outlevs = TRUE)

Arguments

excl: (character)
levels to be excluded (they will not be reflected in the resulting table structure regardless of presence in the data).
only: (character)
levels to retain (all others will be dropped).
reorder: (flag)
whether the order of only should be used as the order of the children of the split. Defaults to TRUE.
df: (data.frame or tibble)
dataset.
spl: (Split)
a Split object defining a partitioning or analysis/tabulation of the data.
vals: (ANY)
for internal use only.
labels: (character)
labels to use for the remaining levels instead of the existing ones.
trim: (flag)
whether splits corresponding with 0 observations should be kept when tabulating.
neworder: (character)
new order of factor levels.
newlabels: (character)
labels for (new order of) factor levels.
drlevels: (flag)
whether levels in the data which do not appear in neworder should be dropped. Defaults to TRUE.
innervar: (string)
variable whose factor levels should be trimmed (e.g. empty levels dropped) separately within each grouping defined at this point in the structure.
drop_outlevs: (flag)
whether empty levels in the variable being split on (i.e. the "outer" variable, not innervar) should be dropped. Defaults to TRUE.

Value

A closure suitable for use as a splitting function (splfun) when creating a table layout.

Custom Splitting Function Details

User-defined custom split functions can perform any type of computation on the incoming data provided that they meet the requirements for generating "splits" of the incoming data based on the split object.

Split functions are functions that accept:

df: a data.frame of incoming data to be split.
spl: a Split object. This is largely an internal detail custom functions will not need to worry about, but obj_name(spl), for example, will give the name of the split as it will appear in paths in the resulting table.
vals: any pre-calculated values. If given non-NULL values, the values returned should match these. Should be NULL in most cases and can usually be ignored.
labels: any pre-calculated value labels. Same as above for values.
trim: if TRUE, resulting splits that are empty are removed.
(optional) .spl_context: a data.frame describing previously performed splits which collectively arrived at df.

The function must then output a named list with the following elements:

values: the vector of all values corresponding to the splits of df.
datasplit: a list of data.frames representing the groupings of the actual observations from df.
labels: a character vector giving a string label for each value listed in the values element above.
(optional) extras: if present, extra arguments are to be passed to summary and analysis functions whenever they are executed on the corresponding element of datasplit or a subset thereof.

One way to generate custom splitting functions is to wrap existing split functions and modify either the incoming data before they are called or their outputs.

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("COUNTRY",
    split_fun = remove_split_levels(c(
      "USA", "CAN",
      "CHE", "BRA"
    ))
  ) %>%
  analyze("AGE")

tbl <- build_table(lyt, DM)
tbl
#>          A: Drug X   B: Placebo   C: Combination
#> ————————————————————————————————————————————————
#> CHN                                             
#>   Mean     36.08       34.12          33.71     
#> PAK                                             
#>   Mean     35.38       33.12          36.75     
#> NGA                                             
#>   Mean     31.20       31.40          35.78     
#> RUS                                             
#>   Mean     33.33       34.20          33.00     
#> JPN                                             
#>   Mean     31.20       32.50          36.20     
#> GBR                                             
#>   Mean     32.00       29.00          30.00     

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("COUNTRY",
    split_fun = keep_split_levels(c("USA", "CAN", "BRA"))
  ) %>%
  analyze("AGE")

tbl <- build_table(lyt, DM)
tbl
#>          A: Drug X   B: Placebo   C: Combination
#> ————————————————————————————————————————————————
#> USA                                             
#>   Mean     36.77       32.57          36.41     
#> CAN                                             
#>   Mean     36.00       34.00          29.50     
#> BRA                                             
#>   Mean     31.78       30.62          36.14     

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX", split_fun = drop_split_levels) %>%
  analyze("AGE")

tbl <- build_table(lyt, DM)
tbl
#>          A: Drug X   B: Placebo   C: Combination
#> ————————————————————————————————————————————————
#> F                                               
#>   Mean     33.71       33.84          34.89     
#> M                                               
#>   Mean     36.55       32.10          34.28     

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX", split_fun = drop_and_remove_levels(c("M", "U"))) %>%
  analyze("AGE")

tbl <- build_table(lyt, DM)
tbl
#>          A: Drug X   B: Placebo   C: Combination
#> ————————————————————————————————————————————————
#> F                                               
#>   Mean     33.71       33.84          34.89