The Ultimate Guide to the gtsummary Package in R

R Packages

In this comprehensive reference guide, you'll find all the information you need about the gtsummary package in R. We'll make it easy for you to explore and understand the package's documentation. Get ready to unlock the full potential of the gtsummary package!

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Basic details of the gtsummary package in R

At R Basics, our goal is to make it easy for you to find documentation about the gtsummary package. That’s why we’ve compiled all the key information in one place. Dive into the details below to understand how this package can improve your work.

Title: Presentation-Ready Data Summary and Analytic Result Tables
Description: Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
Author: Daniel D. Sjoberg [aut, cre] (), Joseph Larmarange [aut] (), Michael Curry [aut] (), Jessica Lavery [aut] (), Karissa Whiting [aut] (), Emily C. Zabor [aut] (), Xing Bai [ctb], Esther Drill [ctb] (), Jessica Flynn [ctb] (), Margie Hannum [ctb] (), Stephanie Lobaugh [ctb], Shannon Pileggi [ctb] (), Amy Tin [ctb] (), Gustavo Zapata Wainberg [ctb] ()
Maintainer: Daniel D. Sjoberg
License type: MIT + file LICENSE

Technical details of the gtsummary package in R

R documentation also provides technical details of a package, including its dependencies, enhancements, and compilation requirements. Learning this information will help you master the gtsummary package.

Version: In R, a version refers to a specific release of a package and can be used to track changes. To find the version number of the gtsummary package in the R console, you can use the packageVersion("gtsummary") function.
Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The gtsummary package does not have compilation requirements.
Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The gtsummary package has the following required dependencies: R (>= 3.4).
Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The gtsummary package has the following suggested dependencies: aod (>= 1.3.1), broom.mixed (>= 0.2.9), car (>= 3.0-11), cmprsk, covr, effectsize (>= 0.6.0), emmeans (>= 1.7.3), flextable (>= 0.8.1), geepack, ggstats (>= 0.2.1), Hmisc, huxtable (>= 5.4.0), insight (>= 0.15.0), kableExtra (>= 1.3.4), lme4, mgcv, mice (>= 3.10.0), nnet, officer, openxlsx, parameters (>= 0.20.2), parsnip (>= 0.1.7), rmarkdown, sandwich (>= 3.0.1), scales, smd (>= 0.6.6), spelling (>= 2.2), survey, survival (>= 3.2-11), testthat (>= 3.0.4), tidycmprsk (>= 0.1.2), workflows (>= 0.2.4).
External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The gtsummary package does not use any external sources.
Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The gtsummary package has the following imported packages: broom (>= 1.0.1), broom.helpers (>= 1.13.0), cli (>= 3.1.1), dplyr (>= 1.1.1), forcats (>= 1.0.0), glue (>= 1.6.2), gt (>= 0.9.0), knitr (>= 1.37), lifecycle (>= 1.0.1), purrr (>= 1.0.1), rlang (>= 1.0.3), stringr (>= 1.4.0), tibble (>= 3.2.1), tidyr (>= 1.1.4), vctrs (>= 0.5.2).
Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The gtsummary package has no enhancements.

Three data scientists looking at a laptop

Capabilities of the gtsummary package in R

R documentation provides comprehensive information about a package, including its functions, datasets, and vignettes. Exploring this information will help you make the most of the gtsummary package’s functionalities.

Functions: Functions play a crucial role in R packages. They allow you to perform specific tasks and computations efficiently. To identify the functions in the gtsummary package, you can use the ls("package:gtsummary") function.
Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify any built-in datasets in the gtsummary package, you can use the data(package = "gtsummary") function.
Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes you can use the vignette(package = "gtsummary") function. This code will display a list of available vignettes you can read.
Citation information: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find the citation information for the gtsummary package in the R console, you can use the citation("gtsummary") function.

Getting help with the gtsummary package in R

If you have trouble using the gtsummary package in R, there are several ways to get help.

The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("gtsummary") function to retrieve detailed information, examples, and usage instructions.
The ? operator: The ? operator is a shortcut for the help() function. You can simply type ?gtsummary to quickly find documentation.
Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community.

Additional R package documentation resources

Explore our comprehensive resource guides related to various types of R documentation. These guides are valuable resources for accessing a wide range of information, making it easier to navigate R documentation in one place.

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