Will generate children for each subset of a categorical variable.
Usagesplit_cols_by(
lyt,
var,
labels_var = var,
split_label = var,
split_fun = NULL,
format = NULL,
nested = TRUE,
child_labels = c("default", "visible", "hidden"),
extra_args = list(),
ref_group = NULL,
show_colcounts = FALSE,
colcount_format = NULL
)(PreDataTableLayouts)
layout object pre-data used for tabulation.
(string)
variable name.
(string)
name of variable containing labels to be displayed for the values of var.
(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).
(function or NULL)
custom splitting function. See custom_split_funs.
(string, function, or list)
format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can be character vectors or lists of functions. See formatters::list_valid_format_labels() for a list of all available format strings.
(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.
(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.
(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 or NULL)
level of var that should be considered ref_group/reference.
(logical(1))
should column counts be displayed at the level facets created by this split. Defaults to FALSE.
(character(1))
if show_colcounts is TRUE, the format which should be used to display column counts for facets generated by this split. Defaults to "(N=xx)".
A PreDataTableLayouts object suitable for passing to further layouting functions, and to build_table().
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.frames 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.
Exampleslyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(c("AGE", "BMRKR2"))
tbl <- build_table(lyt, ex_adsl)
tbl
#> A: Drug X B: Placebo C: Combination
#> ——————————————————————————————————————————————————
#> AGE
#> Mean 33.77 35.43 35.43
#> BMRKR2
#> LOW 50 45 40
#> MEDIUM 37 56 42
#> HIGH 47 33 50
# Let's look at the splits in more detail
lyt1 <- basic_table() %>% split_cols_by("ARM")
lyt1
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls)
#>
#> Row-Split Structure:
#> ()
#>
# add an analysis (summary)
lyt2 <- lyt1 %>%
analyze(c("AGE", "COUNTRY"),
afun = list_wrap_x(summary),
format = "xx.xx"
)
lyt2
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls)
#>
#> Row-Split Structure:
#> AGE:COUNTRY (** multivar analysis **)
#>
tbl2 <- build_table(lyt2, DM)
tbl2
#> A: Drug X B: Placebo C: Combination
#> ———————————————————————————————————————————————————
#> AGE
#> Min. 20.00 21.00 22.00
#> 1st Qu. 29.00 29.00 30.00
#> Median 33.00 32.00 33.00
#> Mean 34.91 33.02 34.57
#> 3rd Qu. 39.00 37.00 38.00
#> Max. 60.00 55.00 53.00
#> COUNTRY
#> CHN 62.00 48.00 69.00
#> USA 13.00 14.00 17.00
#> BRA 9.00 13.00 7.00
#> PAK 8.00 8.00 12.00
#> NGA 10.00 5.00 9.00
#> RUS 9.00 5.00 6.00
#> JPN 5.00 8.00 5.00
#> GBR 2.00 3.00 2.00
#> CAN 3.00 2.00 2.00
#> CHE 0.00 0.00 0.00
# By default sequentially adding layouts results in nesting
library(dplyr)
DM_MF <- DM %>%
filter(SEX %in% c("M", "F")) %>%
mutate(SEX = droplevels(SEX))
lyt3 <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by("SEX") %>%
analyze(c("AGE", "COUNTRY"),
afun = list_wrap_x(summary),
format = "xx.xx"
)
lyt3
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls) -> SEX (lvls)
#>
#> Row-Split Structure:
#> AGE:COUNTRY (** multivar analysis **)
#>
tbl3 <- build_table(lyt3, DM_MF)
tbl3
#> A: Drug X B: Placebo C: Combination
#> F M F M F M
#> —————————————————————————————————————————————————————————————
#> AGE
#> Min. 20.00 24.00 21.00 21.00 22.00 25.00
#> 1st Qu. 29.00 31.00 29.00 28.00 30.00 29.00
#> Median 32.00 35.00 33.00 31.00 35.00 32.00
#> Mean 33.71 36.55 33.84 32.10 34.89 34.28
#> 3rd Qu. 38.00 41.50 38.00 35.75 39.00 38.00
#> Max. 58.00 60.00 55.00 47.00 53.00 53.00
#> COUNTRY
#> CHN 34.00 28.00 29.00 19.00 31.00 38.00
#> USA 8.00 5.00 6.00 8.00 10.00 7.00
#> BRA 6.00 3.00 6.00 7.00 3.00 4.00
#> PAK 2.00 6.00 5.00 3.00 5.00 7.00
#> NGA 6.00 4.00 2.00 3.00 5.00 4.00
#> RUS 7.00 2.00 1.00 4.00 2.00 4.00
#> JPN 2.00 3.00 3.00 5.00 4.00 1.00
#> GBR 2.00 0.00 3.00 0.00 1.00 1.00
#> CAN 3.00 0.00 1.00 1.00 0.00 2.00
#> CHE 0.00 0.00 0.00 0.00 0.00 0.00
# nested=TRUE vs not
lyt4 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels) %>%
split_rows_by("RACE", split_fun = drop_split_levels) %>%
analyze("AGE")
lyt4
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls)
#>
#> Row-Split Structure:
#> SEX (lvls) -> RACE (lvls) -> AGE (** analysis **)
#>
tbl4 <- build_table(lyt4, DM)
tbl4
#> A: Drug X B: Placebo C: Combination
#> —————————————————————————————————————————————————————————————————————
#> F
#> ASIAN
#> Mean 33.55 34.00 34.90
#> BLACK OR AFRICAN AMERICAN
#> Mean 33.17 30.58 33.85
#> WHITE
#> Mean 35.88 38.57 36.50
#> M
#> ASIAN
#> Mean 35.03 31.10 34.39
#> BLACK OR AFRICAN AMERICAN
#> Mean 37.40 32.83 34.14
#> WHITE
#> Mean 44.00 35.29 34.00
lyt5 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels) %>%
analyze("AGE") %>%
split_rows_by("RACE", nested = FALSE, split_fun = drop_split_levels) %>%
analyze("AGE")
lyt5
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls)
#>
#> Row-Split Structure:
#> SEX (lvls) -> AGE (** analysis **)
#> RACE (lvls) -> AGE (** analysis **)
#>
tbl5 <- build_table(lyt5, DM)
tbl5
#> A: Drug X B: Placebo C: Combination
#> ———————————————————————————————————————————————————————————————————
#> F
#> Mean 33.71 33.84 34.89
#> M
#> Mean 36.55 32.10 34.28
#> ASIAN
#> Mean 34.20 32.68 34.63
#> BLACK OR AFRICAN AMERICAN
#> Mean 34.68 31.71 34.00
#> WHITE
#> Mean 39.36 36.93 35.11
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