modify_table: Modify Regression Table Labels and Layout

In gtregression: Tools for Creating Publication-Ready Regression Tables

Modify Regression Table Labels and Layout

Description

Allows customization of labels, headers, and layout of regression tables created using 'gtsummary'. Designed for tables from functions like 'uni_reg()', 'multi_reg()', etc.

Usage

modify_table(
  gt_table,
  variable_labels = NULL,
  level_labels = NULL,
  header_labels = NULL,
  caption = NULL,
  bold_labels = FALSE,
  bold_levels = FALSE,
  remove_N = FALSE,
  remove_N_obs = FALSE,
  remove_abbreviations = FALSE,
  caveat = NULL
)

Arguments

gt_table	A 'gtsummary' table object.
variable_labels	A named vector for relabeling variable names.
level_labels	A named list for relabeling levels of variables. Should be structured as 'list(var1 = c(old1 = new1, old2 = new2), ...)'.
header_labels	A named vector for relabeling column headers. Names should match internal column names (e.g., '"estimate"', '"p.value"').
caption	A character string used to set the table title.
bold_labels	Logical. If 'TRUE', bolds variable labels.
bold_levels	Logical. If 'TRUE', bolds factor level labels.
remove_N	Logical. If 'TRUE', hides the 'N' column in univariate regression tables ('uni_reg', 'uni_reg_nbin'). Ignored for other tables.
remove_N_obs	Logical. If 'TRUE', removes the source note showing the no of observations in multivariable models ('multi_reg', 'multi_reg_nbin').
remove_abbreviations	Logical. If 'TRUE', removes default footnotes for estimate abbreviations.
caveat	A character string to add as a footnote (source note) below the table, e.g., "N may vary due to missing data."

Value

A customized 'gtsummary' table object with modified labels, layout, and options.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese")
      )
    )

  # Descriptive table
  desc_tbl <- descriptive_table(pima,
                                exposures = c("age", "bmi_cat"),
                                by = "diabetes")

  # Univariate logistic regression
  uni_rr <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )
}

gtregression documentation built on Aug. 18, 2025, 5:23 p.m.