split_funcs | R Documentation |
Split functions
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)
excl |
( |
only |
( |
reorder |
( |
df |
( |
spl |
( |
vals |
( |
labels |
( |
trim |
( |
neworder |
( |
newlabels |
( |
drlevels |
( |
innervar |
( |
drop_outlevs |
( |
A closure suitable for use as a splitting function (splfun
) when creating a table layout.
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:
a 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 usually be ignored.
any pre-calculated value labels. Same as above for values
.
if TRUE
, resulting splits that are empty are 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.frame
s 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.
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
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