rtables (version 0.5.1)

split_cols_by_cuts: Split on static or dynamic cuts of the data

Description

Create columns (or row splits) based on values (such as quartiles) of var.

Usage

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,
  nested = TRUE,
  cumulative = FALSE,
  label_pos = "hidden"
)
split_cols_by_cutfun(
  lyt,
  var,
  cutfun = qtile_cuts,
  cutlabelfun = function(x) NULL,
  split_label = var,
  format = NULL,
  nested = TRUE,
  extra_args = list(),
  cumulative = FALSE
)
split_cols_by_quartiles(
  lyt,
  var,
  split_label = var,
  format = NULL,
  nested = TRUE,
  extra_args = list(),
  cumulative = FALSE
)
split_rows_by_quartiles(
  lyt,
  var,
  split_label = var,
  format = NULL,
  nested = TRUE,
  child_labels = c("default", "visible", "hidden"),
  extra_args = list(),
  cumulative = FALSE,
  indent_mod = 0L,
  label_pos = "hidden"
)
split_rows_by_cutfun(
  lyt,
  var,
  cutfun = qtile_cuts,
  cutlabelfun = function(x) NULL,
  split_label = var,
  format = NULL,
  nested = TRUE,
  child_labels = c("default", "visible", "hidden"),
  extra_args = list(),
  cumulative = FALSE,
  indent_mod = 0L,
  label_pos = "hidden"
)

Value

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

Arguments

lyt

layout object pre-data used for tabulation

var

string, variable name

cuts

numeric. Cuts to use

cutlabels

character (or NULL). Labels for the cutst

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).

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.

cumulative

logical. Should the cuts be treated as cumulative. Defaults to FALSE

label_pos

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.

cutfun

function. Function which accepts the full vector of var values and returns cut points to be passed to cut.

cutlabelfun

function. Function which returns either labels for the cuts or NULL when passed the return value of cutfun

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.

extra_args

list. Extra arguments to be passed to the tabulation function. Element position in thte 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 ttabulation function.

child_labels

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.

indent_mod

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.

Author

Gabriel Becker

Details

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 vaalues (e.g., quartiles) in the dataset, NOT the values for subset it is nested under.

Examples

library(dplyr)

# split_cols_by_cuts
l <- 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")

build_table(l, ex_adsl)


# split_rows_by_cuts
l <- 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")


build_table(l, ex_adsl)


# split_cols_by_quartiles

l <- basic_table() %>%
    split_cols_by("ARM") %>%
    split_cols_by_quartiles("AGE", split_label = "Age") %>%
    analyze(c("BMRKR2", "STRATA2")) %>%
    append_topleft("counts")

build_table(l, ex_adsl)

# split_rows_by_quartiles
l <- basic_table() %>%
    split_cols_by("ARM") %>%
    add_colcounts() %>%
    split_rows_by_quartiles("AGE", split_label = "Age") %>%
    analyze("BMRKR2") %>%
    append_topleft(c("Age Quartiles", " Counts BMRKR2"))

build_table(l, ex_adsl)