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Title: Extra Functions for 'gtsummary' Table Styling
Version: 0.2.0
Description: Provides additional convenience functions for 'gtsummary' (Sjoberg et al. (2021) <doi:10.32614/RJ-2021-053>) & 'gt' tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.1.0)
Suggests: broom (≥ 1.0.5), broom.helpers (≥ 1.20.0), ggplot2, knitr, labelled, quarto, scales, survey, testthat (≥ 3.0.0), tibble
VignetteBuilder: knitr
Config/testthat/edition: 3
Imports: dplyr, gt (≥ 0.9.0), gtsummary (≥ 1.7.0), purrr, rlang
URL: https://github.com/kyleGrealis/sumExtras
BugReports: https://github.com/kyleGrealis/sumExtras/issues
NeedsCompilation: no
Packaged: 2026-01-13 19:18:25 UTC; kyle
Author: Kyle Grealis ORCID iD [aut, cre], Raymond Balise ORCID iD [ctb]
Maintainer: Kyle Grealis <kyleGrealis@proton.me>
Repository: CRAN
Date/Publication: 2026-01-19 18:20:07 UTC

sumExtras: Extra Functions for 'gtsummary' Table Styling

Description

logo

Provides additional convenience functions for 'gtsummary' (Sjoberg et al. (2021) doi:10.32614/RJ-2021-053) & 'gt' tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.

Provides additional convenience functions for 'gtsummary' (Sjoberg et al. (2021) doi:10.32614/RJ-2021-053) & 'gt' tables, including automatic variable labeling from dictionaries, standardized missing value display, and consistent formatting helpers for streamlined table styling workflows.

Main Functions

Important Notes on Package Dependencies

gtsummary Internals: This package depends on internal structures of the gtsummary package (specifically tbl$call_list, tbl$inputs, and tbl$table_body). While we make every effort to maintain compatibility, major updates to gtsummary may require corresponding updates to sumExtras.

Minimum Versions: Requires gtsummary >= 1.7.0 and gt >= 0.9.0. These minimum versions ensure the necessary internal structures are available.

Testing: We recommend testing your workflows after any gtsummary updates, especially major version changes.

Author(s)

Maintainer: Kyle Grealis kyleGrealis@proton.me (ORCID)

Other contributors:

See Also

Useful links:

Examples


# Basic workflow with extras()
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras()

# Complete workflow with styling
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker, grade, stage)) |>
  extras() |>
  gtsummary::add_variable_group_header(
    header = "Patient Characteristics",
    variables = age:stage
  ) |>
  add_group_styling() |>
  add_group_colors()

Add automatic labels from dictionary to a gtsummary table

Description

Automatically apply variable labels from a dictionary or label attributes to tbl_summary, tbl_svysummary, or tbl_regression objects. Intelligently preserves manual label overrides set in the original table call while applying dictionary labels or reading label attributes from data. The dictionary can be passed explicitly or will be searched for in the calling environment. If no dictionary is found, the function will attempt to read label attributes from the underlying data.

Usage

add_auto_labels(tbl, dictionary)

Arguments

tbl

A gtsummary table object created by tbl_summary(), tbl_svysummary(), or tbl_regression().

dictionary

A data frame or tibble with Variable and Description columns. If not provided (missing), the function will search for a dictionary object in the calling environment. If no dictionary is found, the function will attempt to read label attributes from the data. Set to NULL explicitly to skip dictionary search and only use attributes.

Details

Label Priority Hierarchy

The function applies labels according to this priority (highest to lowest):

  1. Manual labels - Labels set via label = list(...) in tbl_summary() etc. are always preserved

  2. Dictionary vs Attributes - Controlled by options(sumExtras.preferDictionary):

    • If TRUE: Dictionary labels take precedence over attribute labels

    • If FALSE (default): Attribute labels take precedence over dictionary labels

  3. Default - If no label source is available, uses variable name

Dictionary Format

The dictionary must be a data frame with columns:

Label Attributes

The function reads label attributes from data using attr(data$var, "label"), following the same label convention used by haven, Hmisc, and ggplot2 4.0+.

Your data may already have labels from various sources - imported from statistical software packages, set by other R packages, added manually, or from collaborative projects. This function discovers and applies them seamlessly within gtsummary tables.

Because sumExtras uses native R's attribute storage, labels work across any package that respects the "label" attribute convention, including:

This approach requires zero package dependencies and is fully compatible with the labelled package if you choose to use it, but does not require it.

Implementation Note

This function relies on internal gtsummary structures (tbl$call_list, tbl$inputs, tbl$table_body) to detect manually set labels. While robust error handling is implemented, major updates to gtsummary may require corresponding updates to sumExtras. Requires gtsummary >= 1.7.0.

Value

A gtsummary table object with labels applied. Manual labels set via label = list(...) in the original table call are always preserved.

Options

Set options(sumExtras.preferDictionary = TRUE) to prioritize dictionary labels over label attributes when both are available. Default is FALSE, which prioritizes attributes over dictionary labels.

See Also

Other labeling functions: apply_labels_from_dictionary()

Examples


# Create a dictionary
my_dict <- tibble::tribble(
  ~Variable, ~Description,
  "age", "Age at Enrollment",
  "trt", "Treatment Group",
  "grade", "Tumor Grade"
)

# Basic usage: pass dictionary explicitly
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, grade)) |>
  add_auto_labels(dictionary = my_dict)

# Automatic dictionary search (dictionary in environment)
dictionary <- my_dict
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, grade)) |>
  add_auto_labels()  # Finds dictionary automatically

# Working with pre-labeled data (no dictionary needed)
labeled_data <- gtsummary::trial
attr(labeled_data$age, "label") <- "Patient Age (years)"
attr(labeled_data$marker, "label") <- "Marker Level (ng/mL)"

labeled_data |>
  gtsummary::tbl_summary(include = c(age, marker)) |>
  add_auto_labels()  # Reads from label attributes

# Manual overrides always win
gtsummary::trial |>
  gtsummary::tbl_summary(
    by = trt,
    include = c(age, grade),
    label = list(age ~ "Custom Age Label")  # Manual override
  ) |>
  add_auto_labels(dictionary = my_dict)  # grade gets dict label, age keeps manual

# Control priority with options
options(sumExtras.preferDictionary = TRUE)  # Dictionary over attributes

# Data has both dictionary and attributes
labeled_trial <- gtsummary::trial
attr(labeled_trial$age, "label") <- "Age from Attribute"
dictionary <- tibble::tribble(
  ~Variable, ~Description,
  "age", "Age from Dictionary"
)

labeled_trial |>
  gtsummary::tbl_summary(include = age) |>
  add_auto_labels()  # Uses "Age from Dictionary" (option = TRUE)

Add background colors to group headers with automatic gt conversion

Description

Convenience function that adds background colors to variable group headers and converts the table to gt. This is a terminal operation that combines get_group_rows(), gtsummary::as_gt(), and gt::tab_style() into a single pipeable function.

For text formatting (bold/italic), use add_group_styling() before calling this function. This composable design keeps each function focused on doing one thing well.

Usage

add_group_colors(tbl, color = "#E8E8E8")

Arguments

tbl

A gtsummary table object with variable group headers created by gtsummary::add_variable_group_header()

color

Background color for group headers. Default "#E8E8E8" (light gray). Can be any valid CSS color (hex code, color name, rgb(), etc.).

Details

This function:

  1. Identifies group header rows with get_group_rows()

  2. Converts the table to gt with gtsummary::as_gt()

  3. Applies background color using gt::tab_style()

Since this function converts to gt, it should be used as the final styling step in your pipeline. Apply all gtsummary functions (like modify_caption(), modify_footnote(), etc.) and text formatting with add_group_styling() before calling add_group_colors().

Value

A gt table object with colored group headers. Note: This is a terminal operation that converts to gt. You cannot pipe to additional gtsummary functions after calling this function.

See Also

Examples


# Basic usage - text formatting then color
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras() |>
  gtsummary::add_variable_group_header(
    header = "Patient Characteristics",
    variables = age:stage
  ) |>
  add_group_styling() |>
  add_group_colors()

# Custom color - light blue
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras() |>
  gtsummary::add_variable_group_header(
    header = "Baseline Characteristics",
    variables = age:marker
  ) |>
  add_group_styling() |>
  add_group_colors(color = "#E3F2FD")

# Bold only formatting with custom color
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras() |>
  gtsummary::add_variable_group_header(
    header = "Clinical Measures",
    variables = marker:stage
  ) |>
  add_group_styling(format = "bold") |>
  add_group_colors(color = "#FFF9E6")

# Multiple group headers
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras() |>
  gtsummary::add_variable_group_header(
    header = "Demographics",
    variables = age
  ) |>
  gtsummary::add_variable_group_header(
    header = "Disease Measures",
    variables = marker:response
  ) |>
  add_group_styling() |>
  add_group_colors(color = "#E8E8E8")

Apply styling to variable group headers in gtsummary tables

Description

Adds customizable formatting to variable group headers in gtsummary tables. Variable groups are created using gtsummary::add_variable_group_header() to organize variables into sections. This function enhances table readability by making group headers visually distinct from individual variable labels.

Usage

add_group_styling(tbl, format = c("bold", "italic"), indent_labels = 0L)

Arguments

tbl

A gtsummary table object (e.g., from tbl_summary(), tbl_regression())

format

Character vector specifying text formatting. Options include "bold", "italic", or both. Default is c("bold", "italic").

indent_labels

Integer specifying indentation level (in spaces) for variable labels under group headers. Default is 0L (left-aligned). Set to 4L to preserve gtsummary's default group indentation, or use any non-negative integer for custom spacing.

Details

The function targets rows where row_type == 'variable_group' and applies the specified text formatting to the label column. This is particularly useful for tables with multiple sections or stratified analyses where clear visual hierarchy improves interpretation.

By default, variable labels are left-aligned (indent_labels = 0L) to distinguish them from categorical levels and statistics. Use indent_labels = 4L to preserve the default gtsummary behavior where grouped variables are indented under their group headers.

Value

A gtsummary table object with specified formatting applied to variable group headers

See Also

Examples


# Default formatting (bold and italic)
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker, grade)) |>
  gtsummary::add_variable_group_header(
    header = "Patient Characteristics",
    variables = age:grade
  ) |>
  add_group_styling()

# Bold only
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker)) |>
  gtsummary::add_variable_group_header(
    header = "Demographics",
    variables = age:marker
  ) |>
  add_group_styling(format = "bold")

# Multiple group headers
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  gtsummary::add_variable_group_header(
    header = "Demographics",
    variables = age
  ) |>
  gtsummary::add_variable_group_header(
    header = "Clinical Measures",
    variables = marker:response
  ) |>
  add_group_styling()

# Custom indentation for grouped variables
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker)) |>
  gtsummary::add_variable_group_header(
    header = "Patient Measures",
    variables = age:marker
  ) |>
  add_group_styling(indent_labels = 4L)  # Variables indented under header

Apply variable labels from dictionary to data as attributes

Description

Sets variable label attributes on data columns using a dictionary. This enables cross-package integration with tools that read label attributes, including ggplot2 4.0+ (automatic axis labels), gt (label support), and Hmisc. Labels are stored as the 'label' attribute on each column, following the informal convention used across the R ecosystem.

This function is designed for workflows where you need labels to persist with your data for use in plots, descriptive tables, or other visualizations beyond gtsummary tables.

Usage

apply_labels_from_dictionary(data, dictionary, overwrite = TRUE)

Arguments

data

A data frame or tibble to add label attributes to

dictionary

A data frame or tibble with Variable and Description columns matching the format used by add_auto_labels()

overwrite

Logical. If TRUE (default), overwrites existing label attributes. If FALSE, preserves existing labels and only adds new ones.

Details

This function provides a bridge from sumExtras' dictionary-based labeling system to the broader R ecosystem. Key use cases:

Only variables present in both the data and dictionary will receive label attributes. Dictionary entries for non-existent variables are silently ignored.

Implementation: The R Ecosystem Label Convention

This function uses native R's attr() function to store labels in the "label" attribute, following the same approach as haven, Hmisc, and ggplot2 4.0+. This standardized convention enables seamless integration across the R ecosystem.

Because labels are stored as simple base R attributes (not in a special package-specific format), they work transparently with any package that respects the "label" attribute:

The benefits of this approach are that labels remain portable with your data, work across multiple R packages without version constraints, and integrate naturally with the broader R ecosystem's labeling conventions.

Value

The input data with label attributes attached to matching columns. Original data is returned unmodified except for added/updated attributes.

See Also

Other labeling functions: add_auto_labels()

Examples


# Create a dictionary
my_dict <- tibble::tribble(
  ~Variable, ~Description,
  "age", "Age at Enrollment (years)",
  "marker", "Marker Level (ng/mL)",
  "trt", "Treatment Group",
  "grade", "Tumor Grade"
)

# Apply labels to data
trial_labeled <- gtsummary::trial |>
  apply_labels_from_dictionary(my_dict)

# Now labels work automatically in gtsummary
trial_labeled |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker, grade))

# And in ggplot2 4.0+ (automatic axis labels!)
if (requireNamespace("ggplot2", quietly = TRUE) &&
    utils::packageVersion("ggplot2") >= "4.0.0") {
  library(ggplot2)
  trial_labeled |>
    ggplot(aes(x = age, y = marker, color = factor(trt))) +
    geom_point()  # Axes and legend automatically labeled!
}

# Check that labels were applied
attr(trial_labeled$age, "label")  # "Age at Enrollment (years)"

# Preserve existing labels
trial_partial <- gtsummary::trial
attr(trial_partial$age, "label") <- "Existing Age Label"

trial_partial |>
  apply_labels_from_dictionary(my_dict, overwrite = FALSE)

attr(trial_partial$age, "label")  # Still "Existing Age Label"
attr(trial_partial$marker, "label")  # "Marker Level (ng/mL)" (was added)

Standardize missing value display across all gtsummary table types

Description

Improves table readability by replacing various missing value representations with a consistent "–" symbol. This makes it easier to distinguish between actual data and missing/undefined values in summary tables, creating a cleaner and more professional appearance.

Works seamlessly with all gtsummary table types, including stacked tables (tbl_strata) and survey-weighted summaries (tbl_svysummary). Automatically handles tables with or without the standard var_type column.

Usage

clean_table(tbl)

Arguments

tbl

A gtsummary table object (e.g., from tbl_summary(), tbl_svysummary(), tbl_regression(), or tbl_strata())

Details

The function uses gtsummary::modify_table_body() to transform character columns and replace common missing value patterns with "–":

This standardization makes tables more scannable and reduces visual clutter from various "empty" data representations.

Note: The function checks for the presence of var_type column before applying modify_missing_symbol(). This allows it to work seamlessly with tbl_strata objects which use var_type_1, var_type_2, etc. instead of var_type.

Value

A gtsummary table object with standardized missing value display

See Also

Examples


# Basic usage - clean missing values in summary table
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  clean_table()

# Often used as part of a styling pipeline
# Create a test dictionary for add_auto_labels():
dictionary <- tibble::tribble(
  ~Variable, ~Description,
  'age', 'Age at enrollment',
  'stage', 'T Stage',
  'grade', 'Grade',
  'response', 'Tumor Response'
)
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  add_auto_labels() |>
  extras() |>
  clean_table()

# Works with regression tables too
lm(age ~ trt + grade, data = gtsummary::trial) |>
  gtsummary::tbl_regression() |>
  clean_table()

Add standard styling and formatting to gtsummary tables

Description

Applies a consistent set of formatting options to gtsummary tables including overall column, bold labels, clean headers, and optional p-values. Streamlines the common workflow of adding multiple formatting functions. The function always succeeds by applying what works and warning about unsupported features.

Usage

extras(tbl, pval = TRUE, overall = TRUE, last = FALSE, .args = NULL)

Arguments

tbl

A gtsummary table object (e.g., from tbl_summary(), tbl_regression())

pval

Logical indicating whether to add p-values. Default is TRUE. When TRUE, uses gtsummary's default statistical tests (Kruskal-Wallis for continuous variables with 3+ groups, chi-square for categorical variables).

overall

Logical indicating whether to add overall column

last

Logical indicating if Overall column should be last. Aligns with default from gtsummary::add_overall().

.args

Optional list of arguments to use instead of individual parameters. When provided, overrides pval, overall, and last arguments.

Details

The function applies the following modifications:

The function automatically detects whether the input table is stratified (has a by argument) and what type of table it is (tbl_summary, tbl_regression, tbl_strata, etc.).

For tables that don't support overall columns or p-values (non-stratified tables, regression tables, or stacked tables), the function will issue a warning and continue by applying only the universally supported features (bold_labels and modify_header). This ensures the function always succeeds rather than failing midway through the pipeline.

If any individual formatting step fails (e.g., due to unexpected table structure), the function will issue a warning and continue without that feature. This provides robustness while keeping you informed of what was skipped.

Value

A gtsummary table object with standard formatting applied

Table Type Support

The function applies features based on table type and stratification:

Full feature support: tbl_summary and tbl_svysummary with by argument

Partial support (basic formatting only): tbl_regression, tbl_strata, and non-stratified tables. When applied to these table types and overall/pval = TRUE, the function warns about unsupported features but applies the formatting that works.

See Also

Examples


# With p-values (default)
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras()

# Using .args list
extra_args <- list(pval = TRUE, overall = TRUE, last = FALSE)
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras(.args = extra_args)

# Without p-values
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  extras(pval = FALSE)

# Chain with other functions
# Create required dictionary first
dictionary <- tibble::tribble(
  ~Variable, ~Description,
  'record_id', 'Participant ID',
  'age', 'Age at enrollment',
  'sex', 'Biological sex'
)
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt) |>
  add_auto_labels() |>
  extras(pval = TRUE) |>
  add_group_styling()

Get row numbers of variable group headers for gt styling

Description

Extracts the row indices of variable group headers from a gtsummary table. This is useful for applying background colors or other gt-specific styling after converting a gtsummary table to gt with as_gt().

Usage

get_group_rows(tbl)

Arguments

tbl

A gtsummary table object with variable group headers created by gtsummary::add_variable_group_header()

Details

Variable group headers are identified by row_type == 'variable_group' in the table body. The returned row numbers can be used with gt::tab_style() to apply styling like background colors after converting to a gt table.

This function should be called BEFORE converting the table with as_gt(), as the row type information is only available in gtsummary table objects.

Value

An integer vector of row numbers where variable_group headers are located

See Also

Examples


# Create table with variable groups
my_tbl <- gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker, grade, stage)) |>
  gtsummary::add_variable_group_header(
    header = "Demographics",
    variables = age
  ) |>
  gtsummary::add_variable_group_header(
    header = "Clinical",
    variables = marker:stage
  ) |>
  add_group_styling()

# Get group row numbers before conversion
group_rows <- get_group_rows(my_tbl)

# Convert to gt and apply gray background
my_tbl |>
  gtsummary::as_gt() |>
  gt::tab_style(
    style = gt::cell_fill(color = "#E8E8E8"),
    locations = gt::cells_body(rows = group_rows)
  )

Apply compact JAMA-style theme to gt tables

Description

Applies a compact table theme to gt tables that matches the 'jama' theme from gtsummary. This ensures visual consistency when mixing gtsummary tables (using theme_gtsummary_compact("jama")) with regular gt tables in the same document. The theme reduces padding, adjusts font sizes, and applies JAMA journal styling conventions.

Usage

theme_gt_compact(tbl)

Arguments

tbl

A gt table object created with gt::gt()

Details

This function replicates the visual appearance of gtsummary::theme_gtsummary_compact("jama") for use with regular gt tables. Key styling includes:

Value

A gt table object with compact JAMA-style formatting applied

See Also

Examples

# Basic usage with a data frame
mtcars |>
  head() |>
  gt::gt() |>
  theme_gt_compact()

# Combine with other gt functions
mtcars |>
  head() |>
  gt::gt() |>
  gt::tab_header(title = "Vehicle Data") |>
  theme_gt_compact()

# Use alongside gtsummary tables for consistency
# Set gtsummary theme first
gtsummary::set_gtsummary_theme(gtsummary::theme_gtsummary_compact("jama"))

# Then both tables will have matching appearance
summary_table <- gtsummary::trial |>
  gtsummary::tbl_summary()

data_table <- gtsummary::trial |>
  head() |>
  gt::gt() |>
  theme_gt_compact()

Apply JAMA Compact Theme to gtsummary Tables

Description

Sets the global gtsummary theme to the JAMA (Journal of the American Medical Association) compact style. This is the recommended theme for use with sumExtras functions, providing professional medical journal formatting with reduced padding and consistent styling. The theme remains active for the entire R session or until changed with another theme.

Usage

use_jama_theme()

Details

The JAMA compact theme implements formatting standards used by the Journal of the American Medical Association, making it ideal for:

Key formatting features include:

The function checks for the gtsummary package and will stop with an informative error if it is not installed. The theme is applied globally and will affect all gtsummary tables created after calling this function, including tbl_summary(), tbl_regression(), tbl_cross(), tbl_strata(), and related functions.

For visual consistency with regular gt tables, use theme_gt_compact() which replicates the same styling for non-gtsummary tables.

Value

Invisibly returns the theme list object from gtsummary::theme_gtsummary_compact("jama"). The theme is applied globally via gtsummary::set_gtsummary_theme(), affecting all subsequent gtsummary tables created in the session. A message is printed confirming the theme application.

See Also

Examples


# Apply theme at the start of your analysis
use_jama_theme()

# All subsequent gtsummary tables will use JAMA formatting
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt)

# Works with all gtsummary table types
lm(age ~ trt + grade, data = gtsummary::trial) |>
  gtsummary::tbl_regression()

# Combine with sumExtras styling functions
use_jama_theme()
gtsummary::trial |>
  gtsummary::tbl_summary(by = trt, include = c(age, marker, stage)) |>
  extras() |>
  add_group_styling()

# Reset to default theme if needed
gtsummary::reset_gtsummary_theme()

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They may not be fully stable and should be used with caution. We make no claims about them.
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