broom
Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
broom 
Overview
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 componentsglance()reports information about the entire modelaugment()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.
Installation
# 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::pak("tidymodels/broom")
If you find a bug, please file a minimal reproducible example in the issues.
Usage
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)
fit <- lm(Volume ~ Girth + Height, trees)
tidy(fit)
#> # A tibble: 3 × 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 × 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.
#> # ℹ 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 × 9
#> Girth Height Volume .fitted .resid .hat .sigma .cooksd .std.resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 8.3 70 10.3 4.84 5.46 0.116 3.79 0.0978 1.50
#> 2 8.6 65 10.3 4.55 5.75 0.147 3.77 0.148 1.60
#> 3 8.8 63 10.2 4.82 5.38 0.177 3.78 0.167 1.53
#> 4 10.5 72 16.4 15.9 0.526 0.0592 3.95 0.000409 0.140
#> 5 10.7 81 18.8 19.9 -1.07 0.121 3.95 0.00394 -0.294
#> 6 10.8 83 19.7 21.0 -1.32 0.156 3.94 0.00840 -0.370
#> 7 11 66 15.6 16.2 -0.593 0.115 3.95 0.00114 -0.162
#> 8 11 75 18.2 19.2 -1.05 0.0515 3.95 0.00138 -0.277
#> 9 11.1 80 22.6 21.4 1.19 0.0920 3.95 0.00348 0.321
#> 10 11.2 75 19.9 20.2 -0.288 0.0480 3.95 0.0000968 -0.0759
#> # ℹ 21 more rows
Contributing
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.