The `modelsummary`

package for `R`

produces beautiful, customizable, publication-ready tables to summarize statistical models. Results from several models are presented side-by-side, with uncertainty estimates in parentheses (or brackets) underneath coefficient estimates. Tables can be saved to HTML, LaTeX and RTF (MS Word-ready) formats, or they can be fed to a dynamic report pipeline like `knitr`

or `Sweave`

.

- Sales pitch
- Installation
- A simple example
- Customizing your tables
- A complex example
- Other useful features
- Alternative summary table packages for R

Here are a few benefits of `modelsummary`

over some alternative packages:

- Customizability
- Tables are endlessly customizable, thanks to the power of the
`gt`

package. In this README, you will find tables with colored cells, weird text, spanning column labels, row groups, titles and subtitles, global footnotes, cell-specific footnotes, significance stars, etc. This only scratches the surface of possibilities. For more, see gt.rstudio.com and the Power Users section of this README.

- Tables are endlessly customizable, thanks to the power of the
- Flexibility
- Tables can be saved to html, rtf, jpeg, png, pdf, or LaTeX files. (Coming soon: TXT/ASCII, and more.)

- Integration
`modelsummary`

is extremely well integrated with RStudio. When you type`msummary(models)`

, the summary table immediately appears in the Viewer window.

- Transparency, replicability, and automation
- By combining
`knitr`

and`modelsummary`

, you can easily produce beautiful, replicable, and automated documents and reports. Click here for details.

- By combining
- Community
`modelsummary`

does not try to do everything. It leverages the incredible work of the`R`

community by building on top of the popular`broom`

package. Thanks to the`broom`

team,`modelsummary`

already supports dozens of model types out of the box. Most importantly, as`broom`

and`gt`

improve,`modelsummary`

also improves.

- Reliability
`modelsummary`

is developed using a suite of unit tests. It (probably) won’t break.

- Simplicity
- By using the
`broom`

and`gt`

packages for key operations,`modelsummary`

has a massively simplified codebase. This should improve long term code maintainability, and allow contributors to participate through GitHub.

- By using the

CFITCRS!

At the `modelsummary`

factory, we are *serious* about customizability. Are your bored of regression tables with good ol’ “Intercept”? If so, we have a solution for you:

The `gt`

and `modelsummary`

packages are not available on CRAN yet. You can install them from github:

```
library(remotes)
remotes::install_github('rstudio/gt')
remotes::install_github('vincentarelbundock/modelsummary')
```

Make sure you also install `tidyverse`

, as `modelsummary`

depends on a lot of its packages (e.g., `stringr`

, `dplyr`

, `tidyr`

, `purrr`

):

Load packages and download some data from the RDatasets repository. Then, estimate 5 different models and store them in a named list. The name of each model in that list will be used as a column label:

```
library(MASS)
library(modelsummary)
url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
dat <- read.csv(url)
dat$Clergy <- ifelse(dat$Clergy > 40, 1, 0) # binary variable for logit model
models <- list()
models[['OLS 1']] <- lm(Literacy ~ Crime_prop + Infants, dat)
models[['NBin 1']] <- glm.nb(Literacy ~ Crime_prop + Donations, dat)
models[['OLS 2']] <- lm(Desertion ~ Crime_prop + Infants, dat)
models[['NBin 2']] <- glm.nb(Desertion ~ Crime_prop + Donations, dat)
models[['Logit 1']] <- glm(Clergy ~ Crime_prop + Infants, dat, family = binomial())
```

Produce a simple table:

Of course, `modelsummary`

can also summarize single models:

`modelsummary`

prints an uncertainty estimate in parentheses below the corresponding coefficient estimate. The `statistic`

argument must be a string which is equal to `conf.int`

or to one of the columns produced by the `broom::tidy`

function. When using `conf.int`

, users can specify a confidence level with the `conf_level`

argument.

```
msummary(models, statistic = 'std.error')
msummary(models, statistic = 'p.value')
msummary(models, statistic = 'statistic')
msummary(models, statistic = 'conf.int', conf_level = .99)
```

You can override the uncertainty estimates in a number of ways. First, you can specify a function that produces variance-covariance matrices:

You can supply a list of functions of the same length as your model list:

You can supply a list of named variance-covariance matrices:

You can supply a list of named vectors:

```
custom_stats <- list(`OLS 1` = c(`(Intercept)` = 2, Crime_prop = 3, Infants = 4),
`NBin 1` = c(`(Intercept)` = 3, Crime_prop = -5, Donations = 3),
`OLS 2` = c(`(Intercept)` = 7, Crime_prop = -6, Infants = 9),
`NBin 2` = c(`(Intercept)` = 4, Crime_prop = -7, Donations = -9),
`Logit 1` = c(`(Intercept)` = 1, Crime_prop = -5, Infants = -2))
msummary(models, statistic_override = custom_stats)
```

You can add titles and subtitles to your table as follows:

Add notes to the bottom of your table:

Add numbered footnotes to a column, a row, or a cell:

```
library(gt)
msummary(models) %>%
tab_footnote(
footnote = md("This is a **very** important model, so we are pointing it out in a column-specific footnote."),
locations = cells_column_labels(columns = vars(`OLS 1`))) %>%
tab_footnote(
footnote = "This is the variable of interest.",
locations = cells_stub(rows = vars(Infants))) %>%
tab_footnote(
footnote = "Most important model + most important variable = most important estimate.",
locations = cells_data(columns = vars(`OLS 1`), rows = vars(Infants)))
```

The `coef_map`

argument is a named vector which allows users to rename, reorder, and subset coefficient estimates. Values of this vector correspond to the “clean” variable name. Names of this vector correspond to the “raw” variable name. The table will be sorted in the order in which terms are presented in `coef_map`

. Coefficients which are *not* included in `coef_map`

will be excluded from the table.

```
cm <- c('Crime_prop' = 'Crime / Population',
'Donations' = 'Donations',
'(Intercept)' = 'Constant')
msummary(models, coef_map = cm)
```

An alternative mechanism to subset coefficients is to use the `coef_omit`

argument. This string is a regular expression which will be fed to `stringr::str_detect`

to detect the variable names which should be excluded from the table.

`gof_omit`

is a regular expression which will be fed to `stringr::str_detect`

to detect the names of the statistics which should be excluded from the table.

A more powerful mechanism is to supply a `data.frame`

(or `tibble`

) through the `gof_map`

argument. This data.frame must include 4 columns:

`raw`

: a string with the name of a column produced by`broom::glance(model)`

.`clean`

: a string with the “clean” name of the statistic you want to appear in your final table.`fmt`

: a string which will be used to round/format the string in question (e.g.,`"%.3f"`

). This follows the same standards as the`fmt`

argument in`?modelsummary`

.`omit`

:`TRUE`

if you want the statistic to be omitted from your final table.

You can see an example of a valid data frame by typing `modelsummary::gof_map`

. This is the default data.frame that `modelsummary`

uses to subset and reorder goodness-of-fit statistics. As you can see, `omit == TRUE`

for quite a number of statistics. You can include setting `omit == FALSE`

:

The goodness-of-fit statistics will be printed in the table in the same order as in the `gof_map`

data.frame.

Notice the subtle difference between `coef_map`

and `gof_map`

. `coef_map`

works as a “white list”: any coefficient not explicitly entered will be omitted from the table. `gof_map`

works as a “black list”: statistics need to be explicitly marked for omission.

Create spanning labels to group models (columns):

```
msummary(models) %>%
gt::tab_spanner(label = 'Literacy', columns = c('OLS 1', 'NBin 1')) %>%
gt::tab_spanner(label = 'Desertion', columns = c('OLS 2', 'NBin 2')) %>%
gt::tab_spanner(label = 'Clergy', columns = 'Logit 1')
```

Some people like to add “stars” to their model summary tables to mark statistical significance. The `stars`

argument can take three types of input:

`NULL`

omits any stars or special marks (default)`TRUE`

uses these default values:`* p < 0.1, ** p < 0.05, *** p < 0.01`

- Named numeric vector for custom stars.

Whenever `stars != NULL`

, `modelsummary`

adds a note at the bottom of the table automatically. If you would like to omit this note, just use the `stars_note`

argument:

If you want to create your own stars description, you can add custom notes with the `notes`

argument.

The `fmt`

argument defines how numeric values are rounded and presented in the table. This argument follows the `sprintf`

C-library standard. For example,

`%.3f`

will keep 3 digits after the decimal point, including trailing zeros.`%.5f`

will keep 5 digits after the decimal point, including trailing zeros.- Changing the
`f`

for an`e`

will use the exponential decimal representation.

Most users will just modify the `3`

in `%.3f`

, but this is a very powerful system, and all users are encouraged to read the details: `?sprintf`

The power of the `gt`

package makes `modelsummary`

tables endlessly customizable. For instance, we can color columns and cells, and present values in bold or italics:

```
msummary(models) %>%
tab_style(style = cells_styles(bkgd_color = "lightcyan",
text_weight = "bold"),
locations = cells_data(columns = vars(`OLS 1`))) %>%
tab_style(style = cells_styles(bkgd_color = "#F9E3D6",
text_style = "italic"),
locations = cells_data(columns = vars(`NBin 2`),
rows = 2:6))
```

Thanks to `gt`

, `modelsummary`

accepts markdown indications for emphasis and more:

```
msummary(models,
title = md('This is a **bolded series of words.**'),
notes = list(md('And an *emphasized note*.')))
```

This will produce a table with extra large row labels and extra small coefficient estimates.

```
library(gt)
msummary(models) %>%
tab_style(style = cells_styles(text_size = 'x-large'),
locations = cells_stub()) %>%
tab_style(style = cells_styles(text_size = 'x-small'),
locations = cells_data())
```

Note that `gt`

’s `tab_style`

function is more developed for HTML output than for RTF or LaTeX, so some styling options may not be availble yet. The `gt`

package is under heavy development, so feel free to file an issue on github if you have a special request, and stay tuned for more!

Use the `add_rows`

argument to add rows manually to the bottom of the table.

```
row1 <- c('Custom row 1', 'a', 'b', 'c', 'd', 'e')
row2 <- c('Custom row 2', 5:1)
msummary(models, add_rows = list(row1, row2))
```

Insert images in your tables using the `gt::text_transform`

and `gt::local_image`

functions.

```
library(gt)
msummary(models) %>%
text_transform(
locations = cells_data(columns = 1, rows = 1),
fn = function(x) {local_image(file = "examples/squirrel.png", height = 120)}
)
```

This is the code I used to generate the “complex” table posted at the top of this README.

```
library(gt)
cm <- c('Crime_prop' = 'Crime / Population',
'Donations' = 'Donations',
'Infants' = 'Infants',
'(Intercept)' = 'Constant')
msummary(models,
coef_map = cm,
stars = TRUE,
gof_omit = "Deviance",
title = 'Summarizing 5 statistical models using the `modelsummary` package for `R`.',
subtitle = 'Models estimated using the Guerry dataset.',
notes = c('First custom note to contain text.',
'Second custom note with different content.')) %>%
# add spanning labels
tab_spanner(label = 'Literacy', columns = c('OLS 1', 'NBin 1')) %>%
tab_spanner(label = 'Desertion', columns = c('OLS 2', 'NBin 2')) %>%
tab_spanner(label = 'Clergy', columns = 'Logit 1') %>%
# footnotes
tab_footnote(
footnote = md("This is a **very** important model, so we are pointing it out in a column-specific footnote."),
locations = cells_column_labels(columns = vars(`OLS 1`))) %>%
tab_footnote(
footnote = "This is the variable of interest.",
locations = cells_stub(rows = vars(Infants))) %>%
tab_footnote(
footnote = "Most important model + most important variable = most important estimate.",
locations = cells_data(columns = vars(`OLS 1`), rows = vars(Infants))) %>%
# color and bold
tab_style(style = cells_styles(text_color = "red", text_weight = "bold"),
locations = cells_data(columns = vars(`OLS 1`), rows = vars(Infants)))
```

To save a table to file, use the `filename`

argument. `modelsummary`

guesses the output format based on the `filename`

extension. The supported extensions are: `.tex`

, `.rtf`

, `.html`

(ASCII/Text tables coming soon).

```
msummary(models, filename = 'table.tex')
msummary(models, filename = 'table.rtf')
msummary(models, filename = 'table.html')
msummary(models, filename = 'table.jpeg')
```

If `filename`

is not specified, `modelsummary`

returns a `gt`

object which can be further customized and rendered by the relevant functions in the `gt`

package, such as `as_raw_html`

, `as_latex`

, or `as_rtf`

. RStudio renders the html version of this object automatically.

*Warning*: When creating complex tables by chaining multiple `gt`

functions with the `%>%`

pipe operator, the `filename`

argument will not work. The problem is that `modelsummary`

is trying to write-to-file immediately at the main `msummary()`

call, before the rest of the functions in the chain are executed. In that case, it is better to use `gt::gtsave`

explicitly at the very end of your chain. For example,

```
msummary(models) %>%
gt::tab_spanner(label = 'Literacy', columns = c('OLS 1', 'NBin 1')) %>%
gt::tab_spanner(label = 'Desertion', columns = c('OLS 2', 'NBin 2')) %>%
gt::tab_spanner(label = 'Clergy', columns = 'Logit 1') %>%
gt::gtsave('table.tex')
```

`knitr`

You can use `knitr`

and `modelsummary`

to create dynamic documents with nice summary tables. When knitting in html format, adding a `msummary(models)`

call to a code chunk should work out of the box.

When knitting to PDF output, things are slightly different. Indeed, the `gt`

output functionality for LaTeX is still in development and it is somewhat limited. To avoid common sources of compilation errors, and to allow users to use `\label{}`

, `modelsummary`

includes the `knit_latex`

function. To knit to PDF, simply use:

My goal is to deprecate `knit_latex`

when `gt`

LaTeX export features improve.

Here are two minimal working examples of markdown files which can be converted to HTML or PDF using the `knitr`

package. Just open one the `.Rmd`

files in RStudio and click the “Knit” button:

`modelsummary`

relies on two functions from the `broom`

package to extract model information: `tidy`

and `glance`

. If `broom`

doesn’t support the type of model you are trying to summarize, `modelsummary`

won’t support it out of the box. Thankfully, it is extremely easy to add support for most models using custom methods.

For example, models produced by the `MCMCglmm`

package are not currently supported by `broom`

. To add support, you simply need to create a `tidy`

and a `glance`

method:

```
# load packages and data
library(modelsummary)
library(MCMCglmm)
data(PlodiaPO)
# add custom functions to extract estimates (tidy) and goodness-of-fit (glance) information
tidy.MCMCglmm <- function(object, ...) {
s <- summary(object, ...)
ret <- tibble::tibble(term = row.names(s$solutions),
estimate = s$solutions[, 1],
conf.low = s$solutions[, 2],
conf.high = s$solutions[, 3])
ret
}
glance.MCMCglmm <- function(object, ...) {
ret <- tibble::tibble(dic = object$DIC,
n = nrow(object$X))
ret
}
# estimate a simple model
model <- MCMCglmm(PO ~ 1 + plate, random = ~ FSfamily, data = PlodiaPO, verbose=FALSE, pr=TRUE)
# summarize the model
msummary(model, statistic = 'conf.int')
```

Two important things to note. First, the methods are named `tidy.MCMCglmm`

and `glance.MCMCglmm`

because the model object I am trying to summarize is of class `MCMCglmm`

. You can find the class of a model by running: `class(model)`

.

Second, in the example above, we used the `statistic = 'conf.int'`

argument. This is because the `tidy`

method produces `conf.low`

and `conf.high`

columns. In most cases, users will define `std.error`

column in their custom `tidy`

methods, so the `statistic`

argument will need to be adjusted.

If you create new `tidy`

and `glance`

methods, please consider contributing them to `broom`

so that the rest of the community can benefit from your work: https://github.com/tidymodels/broom

`modelsummary`

can pool and display analyses on several datasets imputed using the `mice`

package. For example:

```
library(mice)
# Create a new dataset with missing values
url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
tmp <- read.csv(url)[, c('Clergy', 'Donations', 'Literacy')]
tmp$Clergy[sample(1:nrow(tmp), 3)] <- NA
tmp$Donations[sample(1:nrow(tmp), 3)] <- NA
tmp$Literacy[sample(1:nrow(tmp), 3)] <- NA
# Impute dataset 5 times
tmp <- mice(tmp, m = 5, printFlag = FALSE, seed = 1024)
# Estimate models
mod <- list()
mod[[1]] <- with(tmp, lm(Clergy ~ Donations))
mod[[2]] <- with(tmp, lm(Clergy ~ Donations + Literacy))
# Summarize
msummary(mod, statistic = 't')
msummary(mod, statistic = 'ubar')
```

The `statistic`

argument can take any column name in the tidy data frame obtained by:

The `gt`

package allows a bunch more customization and styling. Power users can use `modelsummary`

’s `extract`

function to produce a tibble which can easily be fed into `gt`

.

```
> modelsummary::extract(models)
# A tibble: 21 x 8
group term statistic `OLS 1` `NBin 1` `OLS 2` `NBin 2` `Logit 1`
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 estimates (Intercept) estimate 64.114 4.218 57.331 4.384 1.006
2 estimates (Intercept) statistic (5.247) (0.144) (8.315) (0.233) (0.710)
3 estimates Crime_prop estimate -0.002 -0.000 -0.002 -0.000 -0.000
4 estimates Crime_prop statistic (0.001) (0.000) (0.001) (0.000) (0.000)
5 estimates Infants estimate -0.001 "" 0.000 "" -0.000
6 estimates Infants statistic (0.000) "" (0.000) "" (0.000)
7 estimates Donations estimate "" -0.000 "" -0.000 ""
8 estimates Donations statistic "" (0.000) "" (0.000) ""
9 gof R2 "" 0.237 "" 0.073 "" ""
10 gof Adj.R2 "" 0.218 "" 0.051 "" ""
# … with 11 more rows
```

There are several excellent alternative summary table packages for R: