Create columns (or row splits) based on values (such as quartiles) of
var.
split_cols_by_cuts(
lyt,
var,
cuts,
cutlabels = NULL,
split_label = var,
nested = TRUE,
cumulative = FALSE
)split_rows_by_cuts(
lyt,
var,
cuts,
cutlabels = NULL,
split_label = var,
format = NULL,
na_str = NA_character_,
nested = TRUE,
cumulative = FALSE,
label_pos = "hidden",
section_div = NA_character_
)
split_cols_by_cutfun(
lyt,
var,
cutfun = qtile_cuts,
cutlabelfun = function(x) NULL,
split_label = var,
nested = TRUE,
extra_args = list(),
cumulative = FALSE
)
split_cols_by_quartiles(
lyt,
var,
split_label = var,
nested = TRUE,
extra_args = list(),
cumulative = FALSE
)
split_rows_by_quartiles(
lyt,
var,
split_label = var,
format = NULL,
na_str = NA_character_,
nested = TRUE,
child_labels = c("default", "visible", "hidden"),
extra_args = list(),
cumulative = FALSE,
indent_mod = 0L,
label_pos = "hidden",
section_div = NA_character_
)
split_rows_by_cutfun(
lyt,
var,
cutfun = qtile_cuts,
cutlabelfun = function(x) NULL,
split_label = var,
format = NULL,
na_str = NA_character_,
nested = TRUE,
child_labels = c("default", "visible", "hidden"),
extra_args = list(),
cumulative = FALSE,
indent_mod = 0L,
label_pos = "hidden",
section_div = NA_character_
)
A PreDataTableLayouts object suitable for passing to further
layouting functions, and to build_table.
layout object pre-data used for tabulation
string, variable name
numeric. Cuts to use
character (or NULL). Labels for the cuts
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).
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.
logical. Should the cuts be treated as cumulative. Defaults
to FALSE
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.
character(1). String that should be displayed when the value of x is missing.
Defaults to "NA".
character(1). Location the variable label should be
displayed, Accepts hidden (default for non-analyze row splits), visible,
topleft, and - for analyze splits only - default. For analyze calls,
default indicates that the variable should be visible if and only if
multiple variables are analyzed at the same level of nesting.
character(1). String which should be repeated as a section
divider after each group defined by this split instruction, or
NA_character_ (the default) for no section divider.
function. Function which accepts the full vector of var
values and returns cut points to be passed to cut.
function. Function which returns either labels for the
cuts or NULL when passed the return value of cutfun
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.
string. One of "default", "visible",
"hidden". What should the display behavior be for the labels (ie
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.
numeric. Modifier for the default indent position for the structure created by this function(subtable, content table, or row) and all of that structure's children. Defaults to 0, which corresponds to the unmodified default behavior.
Gabriel Becker
For dynamic cuts, the cut is transformed into a static cut by
build_table based on the full dataset, before
proceeding. Thus even when nested within another split in column/row space,
the resulting split will reflect the overall values (e.g., quartiles) in the
dataset, NOT the values for subset it is nested under.
library(dplyr)
# split_cols_by_cuts
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by_cuts("AGE", split_label = "Age",
cuts = c(0, 25, 35, 1000),
cutlabels = c("young", "medium", "old")) %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl <- build_table(lyt, ex_adsl)
tbl
# split_rows_by_cuts
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by_cuts("AGE", split_label = "Age",
cuts = c(0, 25, 35, 1000),
cutlabels = c("young", "medium", "old")) %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl2 <- build_table(lyt2, ex_adsl)
tbl2
# split_cols_by_quartiles
lyt3 <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by_quartiles("AGE", split_label = "Age") %>%
analyze(c("BMRKR2", "STRATA2")) %>%
append_topleft("counts")
tbl3 <- build_table(lyt3, ex_adsl)
tbl3
# split_rows_by_quartiles
lyt4 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by_quartiles("AGE", split_label = "Age") %>%
analyze("BMRKR2") %>%
append_topleft(c("Age Quartiles", " Counts BMRKR2"))
tbl4 <- build_table(lyt4, ex_adsl)
tbl4
# split_cols_by_cutfun
cutfun <- function(x) {
cutpoints <- c(
min(x),
mean(x),
max(x)
)
names(cutpoints) <- c("", "Younger", "Older")
cutpoints
}
lyt5 <- basic_table() %>%
split_cols_by_cutfun("AGE", cutfun = cutfun) %>%
analyze("SEX")
tbl5 <- build_table(lyt5, ex_adsl)
tbl5
# split_rows_by_cutfun
lyt6 <- basic_table() %>%
split_cols_by("SEX") %>%
split_rows_by_cutfun("AGE", cutfun = cutfun) %>%
analyze("BMRKR2")
tbl6 <- build_table(lyt6, ex_adsl)
tbl6
Run the code above in your browser using DataLab