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
(
PreDataTableLayouts)
layout object pre-data used for tabulation.- var
(
string)
variable name.- cuts
(
numeric)
cuts to use.- cutlabels
(
characterorNULL)
labels for the cuts.- split_label
(
string)
label 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
(
logical)
whether this layout instruction should 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
(
flag)
whether the cuts should be treated as cumulative. Defaults toFALSE.- format
(
string,function, orlist)
format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such asanalyzecalls, they can be character vectors or lists of functions. Seeformatters::list_valid_format_labels()for a list of all available format strings.- na_str
(
string)
string that should be displayed when the value ofxis missing. Defaults to"NA".- label_pos
(
string)
location where the variable label should be displayed. Accepts"hidden"(default for non-analyze row splits),"visible","topleft", and"default"(for analyze splits only). Foranalyzecalls,"default"indicates that the variable should be visible if and only if multiple variables are analyzed at the same level of nesting.- section_div
(
string)
string which should be repeated as a section divider after each group defined by this split instruction, orNA_character_(the default) for no section divider.- cutfun
(
function)
function which accepts the full vector ofvarvalues and returns cut points to be used (viacut) when splitting data during tabulation.- cutlabelfun
(
function)
function which returns either labels for the cuts orNULLwhen passed the return value ofcutfun.- 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)
the display behavior for the labels (i.e. label rows) of the children of this split. Accepts"default","visible", and"hidden". 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
#> A: Drug X B: Placebo C: Combination
#> counts 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
#> A: Drug X B: Placebo C: Combination
#> counts [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