`modelsummary`

creates tables and plots to present
*descriptive statistics* and to summarize *statistical
models* in `R`

.

modelsummary is a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as “Table 1”). The appearance of the tables produced by modelsummary can be customized using external packages such as kableExtra, gt, flextable, or huxtable; the plots can be customized using ggplot2. Tables can be exported to many output formats, including HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, Excel, RTF, PDF, and image files. Tables and plots can be embedded seamlessly in rmarkdown, knitr, or Sweave dynamic documents. The modelsummary package is designed to be simple, robust, modular, and extensible (Arel-Bundock, 2022).

`modelsummary`

includes two families of functions:

- Model Summary
`modelsummary`

: Regression tables with side-by-side models.`modelplot`

: Coefficient plots.

- Data Summary
`datasummary`

: Powerful tool to create (multi-level) cross-tabs and data summaries.`datasummary_crosstab`

: Cross-tabulations.`datasummary_balance`

: Balance tables with subgroup statistics and difference in means (aka “Table 1”).`datasummary_correlation`

: Correlation tables.`datasummary_skim`

: Quick overview (“skim”) of a dataset.`datasummary_df`

: Turn dataframes into nice tables with titles, notes, etc.

With these functions, you can create tables and plots like these:

The `modelsummary`

website hosts a *ton* of
examples. Make sure you click on the links at the top of this page:
https://vincentarelbundock.github.io/modelsummary

- Introduction
`modelsummary`

: Model summaries`modelplot`

: Model plots`datasummary`

: Data summaries`Customizing the look of your tables`

`modelsummary`

?Here are a few benefits of `modelsummary`

over some alternative packages:

`modelsummary`

is very easy to use. This simple call often
suffices:

```
library(modelsummary)
<- lm(y ~ x, dat)
mod modelsummary(mod)
```

The command above will automatically display a summary table in the
`Rstudio`

Viewer or in a web browser. All you need is one
word to change the output format. For example, a text-only version of
the table can be printed to the Console by typing:

`modelsummary(mod, output = "markdown")`

Tables in Microsoft Word and LaTeX formats can be saved to file by typing:

```
modelsummary(mod, output = "table.docx")
modelsummary(mod, output = "table.tex")
```

*Information*: The package offers many intuitive and powerful
utilities to customize
the information reported in a summary table. You can rename,
reorder, subset or omit parameter estimates; choose the set of
goodness-of-fit statistics to include; display various “robust” standard
errors or confidence intervals; add titles, footnotes, or source notes;
insert stars or custom characters to indicate levels of statistical
significance; or add rows with supplemental information about your
models.

*Appearance*: Thanks to the `gt`

, `kableExtra`

,
`huxtable`

,
`flextable`

,
and `DT`

packages, the appearance of `modelsummary`

tables is
endlessly customizable. The appearance
customization page shows tables with colored cells, weird text,
spanning column labels, row groups, titles, source notes, footnotes,
significance stars, and more. This only scratches the surface of
possibilities.

*Supported models*: Thanks to the `broom`

and `parameters`

,
`modelsummary`

supports *hundreds* of statistical
models out-of-the-box. Installing other packages can extend the
capabilities further (e.g., `broom.mixed`

).
It is also very easy to add
or customize your own models.

*Output formats*: `modelsummary`

tables can be
saved to HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, RTF,
JPG, or PNG formats. They can also be inserted seamlessly in Rmarkdown
documents to produce automated
documents and reports in PDF, HTML, RTF, or Microsoft Word
formats.

`modelsummary`

is dangerous! It allows users to do stupid
stuff like replacing
their intercepts by squirrels.

`modelsummary`

is *reliably* dangerous! The package
is developed using a suite
of unit tests with about 95% coverage, so it (probably) won’t
break.

`modelsummary`

does not try to do everything. Instead, it
leverages the incredible work of the `R`

community. By
building on top of the `broom`

and `parameters`

packages, `modelsummary`

already supports hundreds of model
types out-of-the-box. `modelsummary`

also supports five of
the most popular table-building and customization packages:
`gt`

, `kableExtra`

, `huxtable`

,
`flextable`

, and `DT`

packages. By using those
packages, `modelsummary`

allows users to produce beautiful,
endlessly customizable tables in a wide variety of formats, including
HTML, PDF, LaTeX, Markdown, and MS Word.

One benefit of this community-focused approach is that when external
packages improve, `modelsummary`

improves as well. Another
benefit is that leveraging external packages allows
`modelsummary`

to have a massively simplified codebase
(relative to other similar packages). This should improve long term code
maintainability, and allow contributors to participate through
GitHub.

You can install `modelsummary`

from CRAN:

`install.packages('modelsummary')`

You can install the development version of `modelsummary`

(and its dependency `insight`

) from R-Universe:

```
install.packages(
c("modelsummary", "insight", "performance", "parameters"),
repos = c(
"https://vincentarelbundock.r-universe.dev",
"https://easystats.r-universe.dev"))
```

**Restart R completely before moving
on.**

There are a million ways to customize the tables and plots produced
by `modelsummary`

. In this Getting Started section we will
only scratch the surface. For details, see the vignettes:

`modelsummary`

: https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html`modelplot`

: https://vincentarelbundock.github.io/modelsummary/articles/modelplot.html`datasummary`

: https://vincentarelbundock.github.io/modelsummary/articles/datasummary.html- Appearance: https://vincentarelbundock.github.io/modelsummary/articles/appearance.html

To begin, load the `modelsummary`

package and download
data from the Rdatasets
archive:

```
library(modelsummary)
<- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
url <- read.csv(url)
dat $Small <- dat$Pop1831 > median(dat$Pop1831) dat
```

Quick overview of the data:

`datasummary_skim(dat)`

Balance table (aka “Table 1”) with differences in means by subgroups:

`datasummary_balance(~Small, dat)`

Correlation table:

`datasummary_correlation(dat)`

Two variables and two statistics, nested in subgroups:

`datasummary(Literacy + Commerce ~ Small * (mean + sd), dat)`

Estimate a linear model and display the results:

```
<- lm(Donations ~ Crime_prop, data = dat)
mod
modelsummary(mod)
```

Estimate five regression models, display the results side-by-side, and save them to a Microsoft Word document:

```
<- list(
models "OLS 1" = lm(Donations ~ Literacy + Clergy, data = dat),
"Poisson 1" = glm(Donations ~ Literacy + Commerce, family = poisson, data = dat),
"OLS 2" = lm(Crime_pers ~ Literacy + Clergy, data = dat),
"Poisson 2" = glm(Crime_pers ~ Literacy + Commerce, family = poisson, data = dat),
"OLS 3" = lm(Crime_prop ~ Literacy + Clergy, data = dat)
)
modelsummary(models, output = "table.docx")
```

There are several excellent alternatives to draw model summary tables
in `R`

: