`broom`

summarizes key information about models in tidy
`tibble()`

s. `broom`

provides three verbs to make
it convenient to interact with model objects:

`tidy()`

summarizes information about model components`glance()`

reports information about the entire model`augment()`

adds informations about observations to a dataset

For a detailed introduction, please see
`vignette("broom")`

.

`broom`

tidies 100+ models from popular modelling packages
and almost all of the model objects in the `stats`

package
that comes with base R. `vignette("available-methods")`

lists
method availability.

If you aren’t familiar with tidy data structures and want to know how they can make your life easier, we highly recommend reading Hadley Wickham’s Tidy Data.

```
# we recommend installing the entire tidyverse
# modeling set, which includes broom:
install.packages("tidymodels")
# alternatively, to install just broom:
install.packages("broom")
# to get the development version from GitHub:
install.packages("pak")
::pak("tidymodels/broom") pak
```

If you find a bug, please file a minimal reproducible example in the issues.

`tidy()`

produces a `tibble()`

where each row
contains information about an important component of the model. For
regression models, this often corresponds to regression coefficients.
This is can be useful if you want to inspect a model or create custom
visualizations.

```
library(broom)
<- lm(Volume ~ Girth + Height, trees)
fit tidy(fit)
#> # A tibble: 3 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -58.0 8.64 -6.71 2.75e- 7
#> 2 Girth 4.71 0.264 17.8 8.22e-17
#> 3 Height 0.339 0.130 2.61 1.45e- 2
```

`glance()`

returns a tibble with exactly one row of
goodness of fitness measures and related statistics. This is useful to
check for model misspecification and to compare many models.

```
glance(fit)
#> # A tibble: 1 x 12
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.948 0.944 3.88 255. 1.07e-18 2 -84.5 177. 183.
#> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
```

`augment`

adds columns to a dataset, containing
information such as fitted values, residuals or cluster assignments. All
columns added to a dataset have `.`

prefix to prevent
existing columns from being overwritten.

```
augment(fit, data = trees)
#> # A tibble: 31 x 9
#> Girth Height Volume .fitted .resid .std.resid .hat .sigma .cooksd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 8.3 70 10.3 4.84 5.46 1.50 0.116 3.79 0.0978
#> 2 8.6 65 10.3 4.55 5.75 1.60 0.147 3.77 0.148
#> 3 8.8 63 10.2 4.82 5.38 1.53 0.177 3.78 0.167
#> 4 10.5 72 16.4 15.9 0.526 0.140 0.0592 3.95 0.000409
#> 5 10.7 81 18.8 19.9 -1.07 -0.294 0.121 3.95 0.00394
#> 6 10.8 83 19.7 21.0 -1.32 -0.370 0.156 3.94 0.00840
#> 7 11 66 15.6 16.2 -0.593 -0.162 0.115 3.95 0.00114
#> 8 11 75 18.2 19.2 -1.05 -0.277 0.0515 3.95 0.00138
#> 9 11.1 80 22.6 21.4 1.19 0.321 0.0920 3.95 0.00348
#> 10 11.2 75 19.9 20.2 -0.288 -0.0759 0.0480 3.95 0.0000968
#> # … with 21 more rows
```

We welcome contributions of all types!

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on Posit Community. If you think you have encountered a bug, please submit an issue. Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code. Check out further details on contributing guidelines for tidymodels packages and how to get help.

If you have never directly contributed to an R package before,
`broom`

is an excellent place to start. Find an issue with the
**Beginner Friendly** tag and comment that you’d like to
take it on and we’ll help you get started.

Generally, too, we encourage typo corrections, bug reports, bug fixes and feature requests. Feedback on the clarity of the documentation is especially valuable!

If you are interested in adding tidier methods for new model objects, please read this article on the tidymodels website.

We have a Contributor
Code of Conduct. By participating in `broom`

you agree to
abide by its terms.