Declaring a column-split based on levels of a variable
Source:R/colby_constructors.R
split_cols_by.RdWill generate children for each subset of a categorical variable
Arguments
- lyt
layout object pre-data used for tabulation
- var
string, variable name
- labels_var
string, name of variable containing labels to be displayed for the values of
var- 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).
- split_fun
function/NULL. custom splitting function See
custom_split_funs- 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.- 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.- child_labels
string. One of
"default","visible","hidden". What should the display behavior be for the labels (i.e. 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.- 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.
- ref_group
character(1) or
NULL. Level ofvarwhich should be considered ref_group/reference
Value
A PreDataTableLayouts object suitable for passing to further
layouting functions, and to build_table.
Custom Splitting Function Details
User-defined custom split functions can perform any type of computation on the incoming data provided that they meet the contract for generating 'splits' of the incoming data 'based on' the split object.
Split functions are functions that accept:
- df
data.frame of incoming data to be split
- spl
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- vals
Any pre-calculated values. If given non-null values, the values returned should match these. Should be NULL in most cases and can likely be ignored
- labels
Any pre-calculated value labels. Same as above for
values- trim
If
TRUE, resulting splits that are empty should be removed- (Optional) .spl_context
a data.frame describing previously performed splits which collectively arrived at
df
The function must then output a named list with the following
elements:
- values
The vector of all values corresponding to the splits of
df- datasplit
a list of data.frames representing the groupings of the actual observations from
df.- labels
a character vector giving a string label for each value listed in the
valueselement above- (Optional) extras
If present, extra arguments are to be passed to summary and analysis functions whenever they are executed on the corresponding element of
datasplitor 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.
Examples
lyt <- 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