split_cols_by: Declaring a column-split based on levels of a variable

In rtables: Reporting Tables

Declaring a column-split based on levels of a variable

Description

Will generate children for each subset of a categorical variable

Usage

split_cols_by(
  lyt,
  var,
  labels_var = var,
  split_label = var,
  split_fun = NULL,
  format = NULL,
  nested = TRUE,
  child_labels = c("default", "visible", "hidden"),
  extra_args = list(),
  ref_group = NULL
)

Arguments

lyt	layout object pre-data used for tabulation
var	string, variable name
labels_var	string, name of variable containing labels to be displayed for the values of var
split_label	string. Label string to be associated with the table generated by the split. Not to be confused with labels assigned to each child (which are based on the data and type of split during tabulation).
split_fun	function/NULL. custom splitting function See custom_split_funs
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.
nested	boolean. Should this layout instruction be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element ('FALSE). Ignored if it would nest a split underneath analyses, which is not allowed.
child_labels	string. One of "default", "visible", "hidden". What should the display behavior be for the labels (i.e. label rows) of the children of this split. Defaults to "default" which flags the label row as visible only if the child has 0 content rows.
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.
ref_group	character(1) or NULL. Level of var which should be considered ref_group/reference

Value

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

Custom Splitting Function Details

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

Split functions are functions that accept:

df

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 likely be ignored

labels

Any pre-calculated value labels. Same as above for values

trim

If TRUE, resulting splits that are empty should be 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.

Author(s)

Gabriel Becker

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(c("AGE", "BMRKR2"))

tbl <- build_table(lyt, ex_adsl)
tbl

# Let's look at the splits in more detail

lyt1 <- basic_table() %>% split_cols_by("ARM")
lyt1

# add an analysis (summary)
lyt2 <- lyt1 %>%
    analyze(c("AGE", "COUNTRY"), afun = list_wrap_x(summary) ,
            format = "xx.xx")
lyt2

tbl2 <- build_table(lyt2, DM)
tbl2

# By default sequentially adding layouts results in nesting
library(dplyr)
DM_MF <- DM %>% filter(SEX %in% c("M", "F")) %>%
  mutate(SEX = droplevels(SEX))

lyt3 <- basic_table() %>% split_cols_by("ARM") %>%
  split_cols_by("SEX") %>%
  analyze(c("AGE", "COUNTRY"), afun = list_wrap_x(summary),
          format = "xx.xx")
lyt3

tbl3 <- build_table(lyt3, DM_MF)
tbl3

# nested=TRUE vs not
lyt4 <- basic_table() %>% split_cols_by("ARM") %>%
 split_rows_by("SEX", split_fun = drop_split_levels) %>%
 split_rows_by("RACE", split_fun = drop_split_levels) %>%
 analyze("AGE")
lyt4

tbl4 <- build_table(lyt4, DM)
tbl4

lyt5 <- basic_table() %>% split_cols_by("ARM") %>%
 split_rows_by("SEX", split_fun= drop_split_levels) %>%
 analyze("AGE") %>%
 split_rows_by("RACE", nested=FALSE, split_fun = drop_split_levels) %>%
 analyze("AGE")
lyt5

tbl5 <- build_table(lyt5, DM)
tbl5

rtables documentation built on Aug. 30, 2023, 5:07 p.m.