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Summarize Variables in Columns

Source: R/summarize_colvars.R
summarize_colvars.Rd

[Stable]

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,
  ...,
  na_level = NA_character_,
  .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().

na_level

(string)
string used to replace all NA or empty values in the output.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named vector of integer)
indent modifiers for the labels. Each element of the vector should be a name-value pair with name corresponding to a statistic specified in .stats and 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.

See also

rtables::split_cols_by_multivar() and analyze_colvars_functions.

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