gtsummary

Presentation-Ready Data Summary and Analytic Result Tables

https://github.com/ddsjoberg/gtsummary

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Presentation-Ready Data Summary and Analytic Result Tables

README.Rmd

---
output: github_document
---



```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```


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[![](https://cranlogs.r-pkg.org/badges/gtsummary)](https://cran.r-project.org/package=gtsummary)
[![DOI:10.32614/RJ-2021-053](https://zenodo.org/badge/DOI/10.32614/RJ-2021-053.svg)](https://doi.org/10.32614/RJ-2021-053)


## gtsummary 

The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using the **R** programming language.  The {gtsummary} package summarizes data sets, regression models, and more, using sensible defaults with highly customizable capabilities.

* [**Summarize data frames or tibbles**](https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html) easily in **R**. Perfect for presenting descriptive statistics, comparing group **demographics** (e.g creating a **Table 1** for medical journals),  and more. Automatically detects continuous, categorical, and dichotomous variables in your data set, calculates appropriate descriptive statistics, and also includes amount of missingness in each variable.

* [**Summarize regression models**](https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html) in R and include 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 (i.e. Odds Ratio and Hazard Ratio). 

* [**Customize gtsummary tables**](https://www.danieldsjoberg.com/gtsummary/reference/index.html#section-general-formatting-styling-functions) using a growing list of formatting/styling functions. **[Bold](https://www.danieldsjoberg.com/gtsummary/reference/bold_italicize_labels_levels.html)** labels, **[italicize](https://www.danieldsjoberg.com/gtsummary/reference/bold_italicize_labels_levels.html)** levels, **[add p-value](https://www.danieldsjoberg.com/gtsummary/reference/add_p.html)** to summary tables, **[style](https://www.danieldsjoberg.com/gtsummary/reference/style_percent.html)** the statistics however you choose, **[merge](https://www.danieldsjoberg.com/gtsummary/reference/tbl_merge.html)** or **[stack](https://www.danieldsjoberg.com/gtsummary/reference/tbl_stack.html)** tables to present results side by side... there are so many possibilities to create the table of your dreams! 

* **[Report statistics inline](https://www.danieldsjoberg.com/gtsummary/articles/inline_text.html)** from summary tables and regression summary tables in **R markdown**. Make your reports completely reproducible!



By leveraging [{broom}](https://broom.tidymodels.org/), [{gt}](https://gt.rstudio.com/), and [{labelled}](http://larmarange.github.io/labelled/) packages, {gtsummary} creates beautifully formatted, ready-to-share summary and result tables in a single line of R code!  

Check out the examples below, review the [vignettes](https://www.danieldsjoberg.com/gtsummary/articles/) for a detailed exploration of the output options, and view the [gallery](https://www.danieldsjoberg.com/gtsummary/articles/gallery.html) for various customization examples.


## Installation

The {gtsummary} package was written as a companion to the [{gt}](https://gt.rstudio.com/) package from RStudio.
You can install {gtsummary} with the following code.  

``` r
install.packages("gtsummary")
```

Install the development version with `pak::pkg_install("ddsjoberg/gtsummary", dependencies = TRUE)`

## Examples

### Summary Table

Use [`tbl_summary()`](https://www.danieldsjoberg.com/gtsummary/reference/tbl_summary.html) to summarize a data frame.



Example basic table:

```{r table1, include = TRUE}
library(gtsummary)

# summarize the data with our package
table1 <-
  trial |> 
  tbl_summary(include = c(age, grade, response))
```

```{r tbl_summary_print_simple, include = FALSE}
# Had to manually save images in temp file, not sure if better way.
gt::gtsave(as_gt(table1), file = "man/figures/README-tbl_summary_print_simple-1.png")
```

```{r out.width = "25%", echo = FALSE}
knitr::include_graphics("man/figures/README-tbl_summary_print_simple-1.png")
```


There are many **customization options** to **add information** (like comparing groups) and **format results** (like bold labels) in your table. See the [`tbl_summary()`](https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html) tutorial for many more options, or below for one example. 

```{r table2, include = TRUE}
table2 <-
  tbl_summary(
    trial,
    include = c(age, grade, response),
    by = trt, # split table by group
    missing = "no" # don't list missing data separately
  ) |> 
  add_n() |> # add column with total number of non-missing observations
  add_p() |> # test for a difference between groups
  modify_header(label = "**Variable**") |> # update the column header
  bold_labels()
```

```{r tbl_summary_print_extra, include = FALSE}
gt::gtsave(as_gt(table2), file = "man/figures/README-tbl_summary_print_extra-1.png")
```


```{r out.width = "55%", echo = FALSE}
knitr::include_graphics("man/figures/README-tbl_summary_print_extra-1.png")
```

### Regression Models

Use [`tbl_regression()`](https://www.danieldsjoberg.com/gtsummary/reference/tbl_regression.html) to easily and beautifully display regression model results in a table. See the [tutorial](https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html) for customization options.

```{r regress, include = TRUE}
mod1 <- glm(response ~ trt + age + grade, trial, family = binomial)

t1 <- tbl_regression(mod1, exponentiate = TRUE)
```

```{r tbl_regression_printa, include = FALSE}
gt::gtsave(as_gt(t1), file = "man/figures/README-tbl_regression_printa-1.png")
```


```{r out.width = "40%", echo = FALSE}
knitr::include_graphics("man/figures/README-tbl_regression_printa-1.png")
```

### Side-by-side Regression Models

You can also present side-by-side regression model results using `tbl_merge()`

```{r}
library(survival)

# build survival model table
t2 <-
  coxph(Surv(ttdeath, death) ~ trt + grade + age, trial) |> 
  tbl_regression(exponentiate = TRUE)

# merge tables
tbl_merge_ex1 <-
  tbl_merge(
    tbls = list(t1, t2),
    tab_spanner = c("**Tumor Response**", "**Time to Death**")
  )
```

```{r tbl_merge_ex1, include = FALSE}
gt::gtsave(as_gt(tbl_merge_ex1), file = "man/figures/README-tbl_merge_ex1-1.png")
```


```{r out.width = "60%", echo = FALSE}
knitr::include_graphics("man/figures/README-tbl_merge_ex1-1.png")
```

Review even more output options in the **[table gallery](https://www.danieldsjoberg.com/gtsummary/articles/gallery.html)**.

## gtsummary + R Markdown

The **{gtsummary}** package was written to be a companion to the **{gt}** package from RStudio.
But not all output types are supported by the **{gt}** package.
Therefore, we have made it possible to print **{gtsummary}** tables with various engines.

Review the **[gtsummary + R Markdown](https://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html)** vignette for details.


```{r out.width = "55%", echo = FALSE}
knitr::include_graphics("man/figures/gt_output_formats.PNG")
```


## Save Individual Tables

{gtsummary} tables can also be saved directly to file as an image, HTML, Word, RTF, and LaTeX file.

```r
tbl |> 
  as_gt() |> 
  gt::gtsave(filename = ".") # use extensions .png, .html, .docx, .rtf, .tex, .ltx
```

## Additional Resources

- The best resources are the gtsummary vignettes: [table gallery](https://www.danieldsjoberg.com/gtsummary/articles/gallery.html), [`tbl_summary()` tutorial](https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html), [`tbl_regression()` tutorial](https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html), [`inline_text()` tutorial](https://www.danieldsjoberg.com/gtsummary/articles/inline_text.html), [gtsummary themes](https://www.danieldsjoberg.com/gtsummary/articles/themes.html), [gtsummary+R markdown](https://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html).
- The R Journal Article [*Reproducible Summary Tables with the gtsummary Package*](https://github.com/ddsjoberg/gtsummary/raw/main/data-raw/RJ-2021-053.pdf).
- The [RStudio Education Blog](https://education.rstudio.com/blog/2020/07/gtsummary/) includes a post with a brief introduction to the package. 
- A [recording of a presentation](https://www.youtube.com/watch?v=tANo9E1SYJE) given to the Weill Cornell Biostatistics Department and the Memorial Sloan Kettering R Users Group.

    

## Cite gtsummary

```{text, comment="", eval = FALSE}
> citation("gtsummary")

To cite gtsummary in publications use:

  Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible summary tables with the gtsummary package.
  The R Journal 2021;13:570–80. https://doi.org/10.32614/RJ-2021-053.

A BibTeX entry for LaTeX users is

  @Article{gtsummary,
    author = {Daniel D. Sjoberg and Karissa Whiting and Michael Curry and Jessica A. Lavery and Joseph Larmarange},
    title = {Reproducible Summary Tables with the gtsummary Package},
    journal = {{The R Journal}},
    year = {2021},
    url = {https://doi.org/10.32614/RJ-2021-053},
    doi = {10.32614/RJ-2021-053},
    volume = {13},
    issue = {1},
    pages = {570-580},
  }
```

## Contributing

Big thank you to `@jeffreybears` for the hex sticker!

Please note that the {gtsummary} project is released with a [Contributor Code of Conduct](https://www.danieldsjoberg.com/gtsummary/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms. Thank you to all contributors!  

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