split_funcs {rtables} | R Documentation |
Split functions
Description
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 |
( |
only |
( |
reorder |
( |
df |
( |
spl |
( |
vals |
( |
labels |
( |
trim |
( |
neworder |
( |
newlabels |
( |
drlevels |
( |
innervar |
( |
drop_outlevs |
( |
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 beNULL
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 atdf
.
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.frame
s representing the groupings of the actual observations fromdf
.- 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
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
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels) %>%
analyze("AGE")
tbl <- build_table(lyt, DM)
tbl
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