Add a content row of summary counts
Usage
summarize_row_groups(
lyt,
var = "",
label_fstr = "%s",
format = "xx (xx.x%)",
na_str = "-",
cfun = NULL,
indent_mod = 0L,
extra_args = list()
)Arguments
- lyt
layout object pre-data used for tabulation
- var
string, variable name
- label_fstr
string. An sprintf style format string containing. For non-comparison splits, it can contain up to one
"%s"which takes the current split value and generates the row/column label. Comparison-based splits it can contain up to two"%s".- 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".- cfun
list/function/NULL. tabulation function(s) for creating content rows. Must accept
xordfas first parameter. Must acceptlabelstras the second argument. Can optionally accept all optional arguments accepted by analysis functions. Seeanalyze.- 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.
- 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.
Value
A PreDataTableLayouts object suitable for passing to further
layouting functions, and to build_table.
Details
If format expects 1 value (i.e. it is specified as a format string
and xx appears for two values (i.e. xx appears twice in the
format string) or is specified as a function, then both raw and percent of
column total counts are calculated. If format is a format string where
xx appears only one time, only raw counts are used.
cfun must accept x or df as its first argument. For the df argument
cfun will receive the subset data.frame corresponding with the row-
and column-splitting for the cell being calculated. Must accept labelstr as
the second parameter, which accepts the label of the level of the parent
split currently being summarized. Can additionally take any optional argument
supported by analysis functions. (see analyze).
Examples
DM2 <- subset(DM, COUNTRY %in% c("USA", "CAN", "CHN"))
lyt <- basic_table() %>% split_cols_by("ARM") %>%
split_rows_by("COUNTRY", split_fun = drop_split_levels) %>%
summarize_row_groups(label_fstr = "%s (n)") %>%
analyze("AGE", afun = list_wrap_x(summary) , format = "xx.xx")
lyt
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> ARM (lvls)
#>
#> Row-Split Structure:
#> COUNTRY (lvls) -> AGE (** analysis **)
#>
tbl <- build_table(lyt, DM2)
tbl
#> A: Drug X B: Placebo C: Combination
#> ————————————————————————————————————————————————————
#> CHN (n) 62 (79.5%) 48 (75.0%) 69 (78.4%)
#> Min. 22.00 25.00 24.00
#> 1st Qu. 29.25 30.00 30.00
#> Median 34.00 33.50 33.00
#> Mean 36.08 34.12 33.71
#> 3rd Qu. 41.00 38.00 37.00
#> Max. 60.00 55.00 51.00
#> USA (n) 13 (16.7%) 14 (21.9%) 17 (19.3%)
#> Min. 23.00 24.00 22.00
#> 1st Qu. 31.00 28.00 31.00
#> Median 36.00 30.00 37.00
#> Mean 36.77 32.57 36.41
#> 3rd Qu. 41.00 37.50 41.00
#> Max. 58.00 47.00 51.00
#> CAN (n) 3 (3.8%) 2 (3.1%) 2 (2.3%)
#> Min. 29.00 30.00 28.00
#> 1st Qu. 32.50 32.00 28.75
#> Median 36.00 34.00 29.50
#> Mean 36.00 34.00 29.50
#> 3rd Qu. 39.50 36.00 30.25
#> Max. 43.00 38.00 31.00
row_paths_summary(tbl) # summary count is a content table
#> rowname node_class path
#> ——————————————————————————————————————————————————————————
#> CHN (n) ContentRow COUNTRY, CHN, @content, CHN (n)
#> Min. DataRow COUNTRY, CHN, AGE, Min.
#> 1st Qu. DataRow COUNTRY, CHN, AGE, 1st Qu.
#> Median DataRow COUNTRY, CHN, AGE, Median
#> Mean DataRow COUNTRY, CHN, AGE, Mean
#> 3rd Qu. DataRow COUNTRY, CHN, AGE, 3rd Qu.
#> Max. DataRow COUNTRY, CHN, AGE, Max.
#> USA (n) ContentRow COUNTRY, USA, @content, USA (n)
#> Min. DataRow COUNTRY, USA, AGE, Min.
#> 1st Qu. DataRow COUNTRY, USA, AGE, 1st Qu.
#> Median DataRow COUNTRY, USA, AGE, Median
#> Mean DataRow COUNTRY, USA, AGE, Mean
#> 3rd Qu. DataRow COUNTRY, USA, AGE, 3rd Qu.
#> Max. DataRow COUNTRY, USA, AGE, Max.
#> CAN (n) ContentRow COUNTRY, CAN, @content, CAN (n)
#> Min. DataRow COUNTRY, CAN, AGE, Min.
#> 1st Qu. DataRow COUNTRY, CAN, AGE, 1st Qu.
#> Median DataRow COUNTRY, CAN, AGE, Median
#> Mean DataRow COUNTRY, CAN, AGE, Mean
#> 3rd Qu. DataRow COUNTRY, CAN, AGE, 3rd Qu.
#> Max. DataRow COUNTRY, CAN, AGE, Max.
## use a cfun and extra_args to customize summarization
## behavior
sfun <- function(x, labelstr, trim) {
in_rows(
c(mean(x, trim = trim), trim),
.formats = "xx.x (xx.x%)",
.labels = sprintf("%s (Trimmed mean and trim %%)",
labelstr)
)
}
lyt2 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
split_rows_by("COUNTRY", split_fun = drop_split_levels) %>%
summarize_row_groups("AGE", cfun = sfun,
extra_args = list(trim = .2)) %>%
analyze("AGE", afun = list_wrap_x(summary) , format = "xx.xx") %>%
append_topleft(c("Country", " Age"))
tbl2 <- build_table(lyt2, DM2)
tbl2
#> Country A: Drug X B: Placebo C: Combination
#> Age (N=78) (N=64) (N=88)
#> ————————————————————————————————————————————————————————————————————————————
#> CHN (Trimmed mean and trim %) 35.1 (20.0%) 33.4 (20.0%) 33.4 (20.0%)
#> Min. 22.00 25.00 24.00
#> 1st Qu. 29.25 30.00 30.00
#> Median 34.00 33.50 33.00
#> Mean 36.08 34.12 33.71
#> 3rd Qu. 41.00 38.00 37.00
#> Max. 60.00 55.00 51.00
#> USA (Trimmed mean and trim %) 36.1 (20.0%) 31.9 (20.0%) 36.1 (20.0%)
#> Min. 23.00 24.00 22.00
#> 1st Qu. 31.00 28.00 31.00
#> Median 36.00 30.00 37.00
#> Mean 36.77 32.57 36.41
#> 3rd Qu. 41.00 37.50 41.00
#> Max. 58.00 47.00 51.00
#> CAN (Trimmed mean and trim %) 36.0 (20.0%) 34.0 (20.0%) 29.5 (20.0%)
#> Min. 29.00 30.00 28.00
#> 1st Qu. 32.50 32.00 28.75
#> Median 36.00 34.00 29.50
#> Mean 36.00 34.00 29.50
#> 3rd Qu. 39.50 36.00 30.25
#> Max. 43.00 38.00 31.00