![[Stable]](figures/lifecycle-stable.svg)
This analyze function uses the S3 generic function s_summary() to summarize different variables
that are arranged in columns. Additional standard formatting arguments are available. It is a
minimal wrapper for rtables::analyze_colvars(). The latter function is meant to add different
analysis methods for each column variables as different rows. To have the analysis methods as
column labels, please refer to analyze_vars_in_cols().
Usage
summarize_colvars(
lyt,
...,
.stats = c("n", "mean_sd", "median", "range", "count_fraction"),
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)Arguments
- lyt
(
layout)
input layout where analyses will be added to.- ...
arguments passed to
s_summary().- .stats
(
character)
statistics to select for the table.- .formats
(named
characterorlist)
formats for the statistics.- .labels
(named
character)
labels for the statistics (without indent).- .indent_mods
(named
vectorofinteger)
indent modifiers for the labels. Each element of the vector should be a name-value pair with name corresponding to a statistic specified in.statsand value the indentation for that statistic's row label.
Value
A layout object suitable for passing to further layouting functions, or to rtables::build_table().
Adding this function to an rtable layout will summarize the given variables, arrange the output
in columns, and add it to the table layout.
Examples
dta_test <- data.frame(
USUBJID = rep(1:6, each = 3),
PARAMCD = rep("lab", 6 * 3),
AVISIT = rep(paste0("V", 1:3), 6),
ARM = rep(LETTERS[1:3], rep(6, 3)),
AVAL = c(9:1, rep(NA, 9)),
CHG = c(1:9, rep(NA, 9))
)
## Default output within a `rtables` pipeline.
basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
summarize_colvars() %>%
build_table(dta_test)
#> A B C
#> AVAL CHG AVAL CHG AVAL CHG
#> ————————————————————————————————————————————————————————————————————————
#> V1
#> n 2 2 1 1 0 0
#> Mean (SD) 7.5 (2.1) 2.5 (2.1) 3.0 (NA) 7.0 (NA) NA NA
#> Median 7.5 2.5 3.0 7.0 NA NA
#> Min - Max 6.0 - 9.0 1.0 - 4.0 3.0 - 3.0 7.0 - 7.0 NA NA
#> V2
#> n 2 2 1 1 0 0
#> Mean (SD) 6.5 (2.1) 3.5 (2.1) 2.0 (NA) 8.0 (NA) NA NA
#> Median 6.5 3.5 2.0 8.0 NA NA
#> Min - Max 5.0 - 8.0 2.0 - 5.0 2.0 - 2.0 8.0 - 8.0 NA NA
#> V3
#> n 2 2 1 1 0 0
#> Mean (SD) 5.5 (2.1) 4.5 (2.1) 1.0 (NA) 9.0 (NA) NA NA
#> Median 5.5 4.5 1.0 9.0 NA NA
#> Min - Max 4.0 - 7.0 3.0 - 6.0 1.0 - 1.0 9.0 - 9.0 NA NA
## Selection of statistics, formats and labels also work.
basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
summarize_colvars(
.stats = c("n", "mean_sd"),
.formats = c("mean_sd" = "xx.x, xx.x"),
.labels = c(n = "n", mean_sd = "Mean, SD")
) %>%
build_table(dta_test)
#> A B C
#> AVAL CHG AVAL CHG AVAL CHG
#> —————————————————————————————————————————————————————————————————
#> V1
#> n 2 2 1 1 0 0
#> Mean, SD 7.5, 2.1 2.5, 2.1 3.0, NA 7.0, NA NA NA
#> V2
#> n 2 2 1 1 0 0
#> Mean, SD 6.5, 2.1 3.5, 2.1 2.0, NA 8.0, NA NA NA
#> V3
#> n 2 2 1 1 0 0
#> Mean, SD 5.5, 2.1 4.5, 2.1 1.0, NA 9.0, NA NA NA
## Use arguments interpreted by `s_summary`.
basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("AVISIT") %>%
split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
summarize_colvars(na.rm = FALSE) %>%
build_table(dta_test)
#> A B C
#> AVAL CHG AVAL CHG AVAL CHG
#> ————————————————————————————————————————————————————————————————————————
#> V1
#> n 2 2 1 1 0 0
#> Mean (SD) 7.5 (2.1) 2.5 (2.1) 3.0 (NA) 7.0 (NA) NA NA
#> Median 7.5 2.5 3.0 7.0 NA NA
#> Min - Max 6.0 - 9.0 1.0 - 4.0 3.0 - 3.0 7.0 - 7.0 NA NA
#> V2
#> n 2 2 1 1 0 0
#> Mean (SD) 6.5 (2.1) 3.5 (2.1) 2.0 (NA) 8.0 (NA) NA NA
#> Median 6.5 3.5 2.0 8.0 NA NA
#> Min - Max 5.0 - 8.0 2.0 - 5.0 2.0 - 2.0 8.0 - 8.0 NA NA
#> V3
#> n 2 2 1 1 0 0
#> Mean (SD) 5.5 (2.1) 4.5 (2.1) 1.0 (NA) 9.0 (NA) NA NA
#> Median 5.5 4.5 1.0 9.0 NA NA
#> Min - Max 4.0 - 7.0 3.0 - 6.0 1.0 - 1.0 9.0 - 9.0 NA NA