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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- badges: start -->
[](https://codecov.io/gh/ddsjoberg/gtsummary?branch=master)
[](https://github.com/ddsjoberg/gtsummary/actions)
[](https://cran.r-project.org/package=gtsummary)
[](https://cran.r-project.org/package=gtsummary)
[](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->
## gtsummary <a href='https://github.com/ddsjoberg/gtsummary'><img src='man/figures/logo.png' align="right" height="120" /></a>
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**](http://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**](http://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**](http://www.danieldsjoberg.com/gtsummary/reference/index.html#section-general-formatting-styling-functions) using a growing list of formatting/styling functions. **[Bold](http://www.danieldsjoberg.com/gtsummary/reference/bold_italicize_labels_levels.html)** labels, **[italicize](http://www.danieldsjoberg.com/gtsummary/reference/bold_italicize_labels_levels.html)** levels, **[add p-value](http://www.danieldsjoberg.com/gtsummary/reference/add_p.html)** to summary tables, **[style](http://www.danieldsjoberg.com/gtsummary/reference/style_percent.html)** the statistics however you choose, **[merge](http://www.danieldsjoberg.com/gtsummary/reference/tbl_merge.html)** or **[stack](http://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](http://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](http://www.danieldsjoberg.com/gtsummary/articles/) for a detailed exploration of the output options, and view the [gallery](http://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 of {gtsummary} with:
``` r
remotes::install_github("ddsjoberg/gtsummary")
```
## Examples
### Summary Table
Use [`tbl_summary()`](http://www.danieldsjoberg.com/gtsummary/reference/tbl_summary.html) to summarize a data frame.
<img src = "https://github.com/ddsjoberg/gtsummary/raw/master/data-raw/misc_files/tbl_summary_demo1.gif" alt = "animated" width = "100%">
Example basic table:
```{r table1, include = TRUE}
library(gtsummary)
# make dataset with a few variables to summarize
trial2 <- trial %>% select(age, grade, response, trt)
# summarize the data with our package
table1 <- tbl_summary(trial2)
```
```{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 = file.path(tempdir(), "temp.png"))
```
```{r out.width = "30%", echo = FALSE}
# Have to do this workaround since the README needs markdown format for GitHub page but a different format for the website.
if (pkgdown::in_pkgdown()) {
table1
} else {
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()`](http://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(
trial2,
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 = file.path(tempdir(), "temp.png"))
```
```{r out.width = "60%", echo = FALSE}
if (pkgdown::in_pkgdown()) {
table2
} else {
knitr::include_graphics("man/figures/README-tbl_summary_print_extra-1.png")
}
```
### Regression Models
Use [`tbl_regression()`](http://www.danieldsjoberg.com/gtsummary/reference/tbl_regression.html) to easily and beautifully display regression model results in a table. See the [tutorial](http://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 = file.path(tempdir(), "temp.png"))
```
```{r out.width = "40%", echo = FALSE}
if (pkgdown::in_pkgdown()) {
t1
} else {
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 = file.path(tempdir(), "temp.png"))
```
```{r out.width = "60%", echo = FALSE}
if (pkgdown::in_pkgdown()) {
t3
} else {
knitr::include_graphics("man/figures/README-tbl_merge_ex1-1.png")
}
```
Review even more output options in the **[table gallery](http://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](http://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html)** vignette for details.
<a href="http://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html">
```{r out.width = "55%", echo = FALSE}
knitr::include_graphics("man/figures/gt_output_formats.PNG")
```
</a>
## Save Individual Tables
{gtsummary} tables can also be saved directly to file as an image, RTF, LaTeX, and Word file.
```r
tbl %>%
as_gt() %>%
gt::gtsave(filename = ".") # use extensions .html .tex .ltx .rtf
```
For a Word file, use
```r
tbl %>%
as_flex_table() %>%
flextable::save_as_docx()
```
## Contributing
Big thank you to [@jeffreybears](https://www.jeffreybears.com/) for the hex sticker!
Please note that the {gtsummary} project is released with a [Contributor Code of Conduct](http://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!
`r usethis::use_tidy_thanks("ddsjoberg/gtsummary", from = "2018-01-01") %>% {glue::glue("[@{.}](https://github.com/{.})")} %>% glue::glue_collapse(sep = ", ", last = ", and ")`