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,
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_
)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 cuts
- 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- format
FormatSpec. Format associated with this split. Formats can be declared via strings (
"xx.x") or function. In cases such asanalyzecalls, they can character vectors or lists of functions.- na_str
character(1). String that should be displayed when the value of
xis missing. Defaults to"NA".- 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,
defaultindicates that the variable should be visible if and only if multiple variables are analyzed at the same level of nesting.- section_div
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.- cutfun
function. Function which accepts the full vector of
varvalues and returns cut points to be passed tocut.- cutlabelfun
function. Function which returns either labels for the cuts or NULL when passed the return value of
cutfun- extra_args
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.
- 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.
Value
A PreDataTableLayouts object suitable for passing to further
layouting functions, and to build_table.
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 values (e.g., quartiles) in the
dataset, NOT the values for subset it is nested under.
Examples
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
#> counts A: Drug X B: Placebo C: Combination
#> young medium old young medium old young medium old
#> —————————————————————————————————————————————————————————————————————————————
#> BMRKR2
#> LOW 4 30 16 4 17 24 5 19 16
#> MEDIUM 6 12 19 2 28 26 4 25 13
#> HIGH 4 24 19 2 17 14 1 21 28
#> STRATA2
#> S1 8 33 32 2 27 38 5 25 26
#> S2 6 33 22 6 35 26 5 40 31
# 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
#> counts A: Drug X B: Placebo C: Combination
#> ————————————————————————————————————————————————————
#> young
#> BMRKR2
#> LOW 4 4 5
#> MEDIUM 6 2 4
#> HIGH 4 2 1
#> STRATA2
#> S1 8 2 5
#> S2 6 6 5
#> medium
#> BMRKR2
#> LOW 30 17 19
#> MEDIUM 12 28 25
#> HIGH 24 17 21
#> STRATA2
#> S1 33 27 25
#> S2 33 35 40
#> old
#> BMRKR2
#> LOW 16 24 16
#> MEDIUM 19 26 13
#> HIGH 19 14 28
#> STRATA2
#> S1 32 38 26
#> S2 22 26 31
# 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
#> counts A: Drug X B: Placebo C: Combination
#> [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max]
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> BMRKR2
#> LOW 18 16 7 9 12 8 10 15 8 11 13 8
#> MEDIUM 11 7 9 10 14 15 14 13 12 13 7 10
#> HIGH 14 11 14 8 6 10 9 8 7 12 13 18
#> STRATA2
#> S1 22 18 18 15 15 11 22 19 11 14 12 19
#> S2 21 16 12 12 17 22 11 17 16 22 21 17
# 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
#> Age Quartiles A: Drug X B: Placebo C: Combination
#> Counts BMRKR2 (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————
#> Age
#> [min, Q1]
#> LOW 18 12 8
#> MEDIUM 11 14 12
#> HIGH 14 6 7
#> (Q1, Q2]
#> LOW 16 8 11
#> MEDIUM 7 15 13
#> HIGH 11 10 12
#> (Q2, Q3]
#> LOW 7 10 13
#> MEDIUM 9 14 7
#> HIGH 14 9 13
#> (Q3, max]
#> LOW 9 15 8
#> MEDIUM 10 13 10
#> HIGH 8 8 18
# 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
#> Younger Older
#> ——————————————————————————————————
#> F 124 98
#> M 75 91
#> U 5 4
#> UNDIFFERENTIATED 1 2
# 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
#> F M U UNDIFFERENTIATED
#> ———————————————————————————————————————————
#> AGE
#> Younger
#> LOW 43 26 3 1
#> MEDIUM 47 23 2 0
#> HIGH 34 26 0 0
#> Older
#> LOW 30 29 1 2
#> MEDIUM 29 33 1 0
#> HIGH 39 29 2 0