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Add variable labels to top left corner in table

Source: R/utils_rtables.R
append_varlabels.Rd

[Stable]

Helper layout-creating function to append the variable labels of a given variables vector from a given dataset in the top left corner. If a variable label is not found then the variable name itself is used instead. Multiple variable labels are concatenated with slashes.

Usage

append_varlabels(lyt, df, vars, indent = 0L)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

df

(data.frame)
data set containing all analysis variables.

vars

(character)
variable names of which the labels are to be looked up in df.

indent

(integer(1))
non-negative number of nested indent space, default to 0L which means no indent. 1L means two spaces indent, 2L means four spaces indent and so on.

Value

A modified layout with the new variable label(s) added to the top-left material.

Note

This is not an optimal implementation of course, since we are using here the data set itself during the layout creation. When we have a more mature rtables implementation then this will also be improved or not necessary anymore.

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  split_rows_by("SEX") %>%
  append_varlabels(DM, "SEX") %>%
  analyze("AGE", afun = mean) %>%
  append_varlabels(DM, "AGE", indent = 1)
build_table(lyt, DM)
#> SEX                   A: Drug X          B: Placebo       C: Combination 
#>   Age                  (N=121)            (N=106)            (N=129)     
#> —————————————————————————————————————————————————————————————————————————
#> F                                                                        
#>   mean             33.7142857142857   33.8392857142857   34.8852459016393
#> M                                                                        
#>   mean             36.5490196078431         32.1         34.2794117647059
#> U                                                                        
#>   mean                    NA                 NA                 NA       
#> UNDIFFERENTIATED                                                         
#>   mean                    NA                 NA                 NA       

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze("AGE", afun = mean) %>%
  append_varlabels(DM, c("SEX", "AGE"))
build_table(lyt, DM)
#> SEX / Age             A: Drug X          B: Placebo       C: Combination 
#> —————————————————————————————————————————————————————————————————————————
#> F                                                                        
#>   mean             33.7142857142857   33.8392857142857   34.8852459016393
#> M                                                                        
#>   mean             36.5490196078431         32.1         34.2794117647059
#> U                                                                        
#>   mean                    NA                 NA                 NA       
#> UNDIFFERENTIATED                                                         
#>   mean                    NA                 NA                 NA