custom_split_funs | R Documentation |
Split functions provide the work-horse for rtables
's
generalized partitioning. These functions accept a (sub)set of incoming
data, a split object, and return 'splits' of that data.
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:
data.frame of incoming data to be split
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
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
Any pre-calculated value labels. Same as above for
values
If TRUE
, resulting splits that are empty should be
removed
a data.frame describing previously performed
splits which collectively arrived at df
The function must then output a named list
with the following
elements:
The vector of all values corresponding to the splits of
df
a list of data.frames representing the groupings of the
actual observations from df
.
a character vector giving a string label for each value listed
in the values
element above
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.
make_split_fun()
for the API for creating custom split functions,
and split_funcs for a variety of pre-defined split functions.
# Example of a picky split function. The number of values in the column variable
# var decrees if we are going to print also the column with all observation
# or not.
picky_splitter <- function(var) {
# Main layout function
function(df, spl, vals, labels, trim) {
orig_vals <- vals
# Check for number of levels if all are selected
if (is.null(vals)) {
vec <- df[[var]]
vals <- unique(vec)
}
# Do a split with or without All obs
if (length(vals) == 1) {
do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim)
} else {
fnc_tmp <- add_overall_level("Overall", label = "All Obs", first = FALSE)
fnc_tmp(df = df, spl = spl, vals = orig_vals, trim = trim)
}
}
}
# Data sub-set
d1 <- subset(ex_adsl, ARM == "A: Drug X" | (ARM == "B: Placebo" & SEX == "F"))
d1 <- subset(d1, SEX %in% c("M", "F"))
d1$SEX <- factor(d1$SEX)
# This table uses the number of values in the SEX column to add the overall col or not
lyt <- basic_table() %>%
split_cols_by("ARM", split_fun = drop_split_levels) %>%
split_cols_by("SEX", split_fun = picky_splitter("SEX")) %>%
analyze("AGE", show_labels = "visible")
tbl <- build_table(lyt, d1)
tbl
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