Modify column headers, footnotes, and spanning headers

These functions assist with modifying the aesthetics/style of a table.

• modify_header() update column headers

• modify_spanning_header() update/add spanning headers

The functions often require users to know the underlying column names. Run show_header_names() to print the column names to the console.

Usage

modify_header(x, ..., text_interpret = c("md", "html"), quiet, update)

modify_spanning_header(
  x,
  ...,
  text_interpret = c("md", "html"),
  level = 1L,
  quiet,
  update
)

remove_spanning_header(x, columns = everything(), level = 1L)

show_header_names(x, show_hidden = FALSE, include_example, quiet)

Arguments

x

(gtsummary)
A gtsummary object

...

dynamic-dots
Used to assign updates to headers and spanning headers.

Use modify_*(colname='new header') to update a single column. Using a formula will invoke tidyselect, e.g. modify_*(all_stat_cols() ~ "**{level}**"). The dynamic dots allow syntax like modify_header(x, !!!list(label = "Variable")). See examples below.

Use the show_header_names() to see the column names that can be modified.

text_interpret

(string)
String indicates whether text will be interpreted with gt::md() or gt::html(). Must be "md" (default) or "html". Applies to tables printed with {gt}.

update, quiet

level

(integer)
An integer specifying which level to place the spanning header.

columns

(tidy-select)
Columns from which to remove spanning headers.

show_hidden

(scalar logical)
Logical indicating whether to print hidden columns as well as printed columns. Default is FALSE.

include_example

Value

Updated gtsummary object

tbl_summary(), tbl_svysummary(), and tbl_cross()

When assigning column headers and spanning headers, you may use {N} to insert the number of observations. tbl_svysummary objects additionally have {N_unweighted} available.

When there is a stratifying by= argument present, the following fields are additionally available to stratifying columns: {level}, {n}, and {p} ({n_unweighted} and {p_unweighted} for tbl_svysummary objects)

Syntax follows glue::glue(), e.g. all_stat_cols() ~ "**{level}**, N = {n}".

tbl_regression()

When assigning column headers for tbl_regression tables, you may use {N} to insert the number of observations, and {N_event} for the number of events (when applicable).

Author

Daniel D. Sjoberg

Examples

# create summary table
tbl <- trial |>
  tbl_summary(by = trt, missing = "no", include = c("age", "grade", "trt")) |>
  add_p()

# print the column names that can be modified
show_header_names(tbl)
#> Column Name   Header                    level*         N*          n*          p*             
#> label         "**Characteristic**"                     200 <int>                              
#> stat_1        "**Drug A**  \nN = 98"    Drug A <chr>   200 <int>    98 <int>   0.490 <dbl>    
#> stat_2        "**Drug B**  \nN = 102"   Drug B <chr>   200 <int>   102 <int>   0.510 <dbl>    
#> p.value       "**p-value**"                            200 <int>                              
#> 
#> * These values may be dynamically placed into headers (and other locations).
#> ℹ Review the `modify_header()` (`?gtsummary::modify_header()`) help for
#>   examples.

# Example 1 ----------------------------------
# updating column headers
tbl |>
  modify_header(label = "**Variable**", p.value = "**P**")


  
Variable
      
Drug A

N = 981
      
Drug B

N = 1021
      
P2
    
Age
46 (37, 60)
48 (39, 56)
0.7
Grade




0.9
    I
35 (36%)
33 (32%)

    II
32 (33%)
36 (35%)

    III
31 (32%)
33 (32%)

1 Median (Q1, Q3); n (%)
    
2 Wilcoxon rank sum test; Pearson’s Chi-squared test
    

# Example 2 ----------------------------------
# updating headers add spanning header
tbl |>
  modify_header(all_stat_cols() ~ "**{level}**, N = {n} ({style_percent(p)}%)") |>
  modify_spanning_header(all_stat_cols() ~ "**Treatment Received**")


  
Characteristic
      

        
Treatment Received
      
      
p-value2
    
Drug A, N = 98 (49%)1
      
Drug B, N = 102 (51%)1
    
Age
46 (37, 60)
48 (39, 56)
0.7
Grade




0.9
    I
35 (36%)
33 (32%)

    II
32 (33%)
36 (35%)

    III
31 (32%)
33 (32%)

1 Median (Q1, Q3); n (%)
    
2 Wilcoxon rank sum test; Pearson’s Chi-squared test