Adding new tidiers to broom

Thank you for your interest in contributing to broom! This document describes the conventions that you should follow when adding tidiers to broom.

General guidelines:

  • Reach a minimum 90% test coverage for new tidiers. To check your test coverage we recommend using covr::report().
  • tidy, glance and augment methods must return tibbles.
  • Update to reflect the changes you’ve made
  • Follow the tidyverse style conventions. You can use the styler package to reformat your code according to these conventions, and the lintr package to check that your code meets the conventions.
  • Use new tidyverse packages such as dplyr and tidyr over older packages such as plyr and reshape2.
  • It’s better to have a predictable number of columns and unknown number rows than an unknown number of columns and a predictable number of rows.
  • It’s better for users to need to tidyr::spreadthan tidyr::gather data after it’s been tidied.
  • Add yourself as a contributor to DESCRIPTION.
  • Pull requests must pass the AppVeyor and Travis CI builds to be merged.
  • Check that your PR contains a big picture summary of what you’ve done
  • Include an example that demonstrates the new behavior implemented in your PR
  • When in doubt, please reach out to the maintainers. We are happy to help with any questions.

If you are just getting into open source development, broom is an excellent place to get started and we are more than happy to help. We recommend you start contributing by improving the documentation, writing issues with reproducible errors, or taking on issues tagged beginner-friendly.

Which package does my tidier belong in?

Ideally, tidying methods should live in the packages of their associated modelling functions. That is, if you have some object my_object produced bymy_package, the functions tidy.my_object, glance.my_object and augment.my_object should live in my_package, provided there are sensible ways to define these tidiers for my_object. For guidance on writing tidiers that live in external packages, please see vignette("external-tidiers").

We are currently working on an appropriate way to split tidiers into several domain specific tidying packages. For now, if you don’t own my_package, you should add the tidiers to broom. There are some exceptions:

  • Mixed model tidiers belong in broom.mixed
  • Natural language related tidiers belong in tidytext
  • Tree tidiers belong in broomstick
  • Tidiers for objects from BioConductor belong in biobroom

We will keep you updated as we work towards a final solution.

Implementing new tiders

We encourage you to develop new tidiers using your favorite tidyverse tools. Pipes are welcome, as is any code that you might write for tidyverse-style interactive data manipulation.

If you are implementing a new tidier, we recommend taking a look at the internals of the tidying methods for betareg and rq objects and using those as a starting point.

You should also be aware of the following helper functions:

Documenting new tidiers

All new tidiers should be fully documented following the tidyverse code documentation guidelines. Documentation should use full sentences with appropriate punctation. Documentation should also contain at least one but potentially several examples of how the tidiers can be used. Documentation should be written in R markdown as much as possible.

Many behaviors in broom are quite repetitive. We have tried to automate the documentation process as much as possible to keep documentation as consistent as possible across the package. Beyond the standard roxygen2 tags, we also make extensive use of the @template tag.

We recommend emulating the style in the betareg tidiers as much as possible.

If you implement tidy.my_object(), glance.my_object() and augment.my_object(), each method should be documented separately. Treat the tidy method as the default method that the user will read first. We commonly write the examples for all tidying methods in the tidy() documentation, and then use @inherit tidy.my_object examples in the other tidying methods.

By such to link methods together using the @family tag. We strongly encourage heavy cross-referencing in the documentation.

Lastly, we have a slightly non-standard approach to documenting function returns. In particular, we almost never use @return and prefer @evalRd return_tidy() (or @evalRd return_glance() for glance methods, and @evalRd return_augment() and so on). Never use @evalRd alongside @return. Additionally note that @evalRd does not support Markdown documentation at this time.

Using @evalRd return_tidy("column_name") will automatically pull the documentation for column_name from the column glossary in modeltests. This allows use to consistently describe columns that are used many time throughout broom. If you need to define a new column, you do so inline:

#' @evalRd return_tidy(my_new_column = "Description of the column goes here")

Column names that will be used be many models (for example, “AIC” or “BIC”) should be defined in the column glossary in modeltests via a pull request. Please read the documentation for return_tidy(), return_glance() and return_augment().

Testing new tidiers

Any changes that you make will need to pass the existing tests. The tests can be run with


Your changes should also pass R CMD Check, which can be run with


We recommend testing broom locally with R 3.5.0 or later. To test earlier versions of R, you’ll need to see the environment variable R_MAX_NUM_DLLS to 250.

Note that testing broom requires a lot of packages. You can install them all with:

devtools::install_github("tidymodels/broom", dependencies = TRUE)

File naming

If you are only tidying a single class from a package and have fewer than 300 or so lines of code, please put your tidiers in a file R/<package>-tidiers.R. If you are adding multiple tidiers from the same package, they should live in R/<package>-<class>-tidiers.R.

The corresponding tests should have the same prefix, and should live in tests/testthat/. For example: tests/testthat/test-<package>.R or tests/testthat/test-<package>-<class>.R.

Tests for new tidiers

Again we recommend following the basic outline of the betareg tests. At a minimum new tests should include



# be sure you've added any package your tidying methods depend on to Suggests
# you can do so with:
#   usethis::use_package("mypkg", type = "Suggests")

fit1 <- myobj(...)
fit2 <- myobj(..., option = "something different")

test_that("myobj tidier arguments", {

test_that("tidy.myobj", {
  td1 <- tidy(fit1)
  td2 <- tidy(fit2, = TRUE, option = "another case")
  check_dims(td1, 12, 8)  # optional but a good idea

test_that("glance.myobj", {
  gl1 <- glance(fit1)
  gl2 <- glance(fit2)
  check_glance_outputs(gl1, gl2)

test_that("augment.myobj", {
    augment.myobj,           # *must* pass the specific method here
    data = mydata,
    newdata = mydata
    data = mydata2,
    newdata = mydata2

For details on what gets tests, see modeltests::check_tidy_output(), modeltests::check_glance_outputs() and modeltests::check_augment_function().

These do not check for correctness. They only check for behavioral consistency with the rest of broom. You also need to write tests to check that your logic is correct and the values returned by the tidiers are the values you expect.

If you find a bug in the check_* function, please open an issue in modeltests, or even better, a PR.

In some cases the tests may simply not be appropriate for your methods. In this case you can set strict = FALSE. Be sure to document which tests fail when strict = TRUE and why this is acceptable. You’ll also want to mention this in the body of your pull request.

Testing miscellenea

  • If any of your tests use random number generation, you should call set.seed() in the body of the test.
  • We prefer informative errors to magical behaviors or untested success.

Catching edge cases

You should test new tidiers on a representative set of my_object objects. At a minimum, you should have a test for each distinct type of fit that appears in the examples for a particular model (if we working with stats::arima models, the tidiers should work for seasonal and non-seasonal models).

It’s important to test your tidiers for fits estimated with different algorithms (i.e. stats::arima tidier should be tested for method = "CSS-ML", method = "ML" and method = "ML"). As another example, good tests for glm tidying methods would test tidiers on glm objects fit for all acceptable values of family.

In short: be sure that you’ve tested your tidiers on models fit with all the major modelling options (both statistical options, and estimation options).

Defining tidiers

The big picture:

  • glance should provide a summary of model-level information as a tibble with exactly one row. This includes goodness of fit measures such as deviance, AIC, BIC, etc.
  • augment should provide a summary of observation-level information as a tibble with one row per observation. This summary should preserve the observations. Additional information might include leverage, cluster assignments or fitted values.
  • tidy should provide a summary of component-level information as a tibble with one row for each model component. Examples of model components include: regression coefficients, cluster centers, etc.

Oftentimes it doesn’t make sense to define one or more of these methods for a particular model. In this case, just implement the methods that do make sense.


The glance(x, ...) method accepts a model object x and returns a tibble with exactly one row containing model level summary information.

  • Output should not include the name of the modelling function or any arguments given to the modelling function. For example, glance(glm_object) does not contain a family column.

  • In some cases, you may wish to provide model level diagnostics not returned by the original object. If these are easy to compute, feel free to add them. However, broom is not an appropriate place to implement complex or time consuming calculations.

  • glance should always return the same columns in the same order for an object x of class my_object. If a summary metric such as AIC is not defined in certain circumstances, use NA.


The augment(x, data = NULL, ...) method accepts a model object and optionally a data frame data and adds columns of observation level information to data. augment returns a tibble with the same number of rows as data.

The data argument can be any of the following:

  • a data.frame containing both the original predictors and the original responses
  • a tibble containing both the the original predictors and the original responses
  • if not data argument is specified, augment should try to reconstruct the original data as much as possible from the model object. This may not always be possible, and often it will not be possible to recover columns not used by the model.

Any other inputs should result in an error.

Many augment methods will also provide an optional newdata argument that should also default to NULL. newdata should always precedence over data (you can check if newdata has been specified by checking whether not newdata still has the default NULL value).

Data given to the data argument must have both the original predictors and the original response. Data given to the newdata argument only needs to have the original predictors. This is important because there may be important information associated with training data that is not associated with test data, for example, leverages (.hat below) in the case in linear regression:

model <- lm(speed ~ dist, data = cars)
augment(model, data = cars)
#> # A tibble: 50 x 9
#>    speed  dist .fitted  .resid   .hat .sigma   .cooksd .std.resid
#>  * <dbl> <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>     <dbl>      <dbl>
#>  1     4     2    8.62   0.844 -4.62   0.0716   3.11 0.0888       -1.52  
#>  2     4    10    9.94   0.729 -5.94   0.0534   3.06 0.106        -1.93  
#>  3     7     4    8.95   0.815 -1.95   0.0667   3.18 0.0146       -0.638 
#>  4     7    22   11.9    0.578 -4.93   0.0335   3.10 0.0437       -1.59  
#>  5     8    16   10.9    0.650 -2.93   0.0424   3.16 0.0200       -0.950 
#>  6     9    10    9.94   0.729 -0.940  0.0534   3.19 0.00264      -0.306 
#>  7    10    18   11.3    0.625 -1.26   0.0392   3.18 0.00340      -0.409 
#>  8    10    26   12.6    0.536 -2.59   0.0289   3.17 0.0103       -0.832 
#>  9    10    34   13.9    0.473 -3.91   0.0225   3.14 0.0181       -1.25  
#> 10    11    17   11.1    0.637 -0.0986 0.0407   3.19 0.0000216    -0.0319
#> # ... with 40 more rows
augment(model, newdata = cars)
#> # A tibble: 50 x 4
#>    speed  dist .fitted
#>  * <dbl> <dbl>   <dbl>   <dbl>
#>  1     4     2    8.62   0.844
#>  2     4    10    9.94   0.729
#>  3     7     4    8.95   0.815
#>  4     7    22   11.9    0.578
#>  5     8    16   10.9    0.650
#>  6     9    10    9.94   0.729
#>  7    10    18   11.3    0.625
#>  8    10    26   12.6    0.536
#>  9    10    34   13.9    0.473
#> 10    11    17   11.1    0.637
#> # ... with 40 more rows

This means that many augment(model, data = original_data) should provide .fitted and .resid columns in most cases. augment(model, data = test_data) only needs to add a .fitted column, but if you can add .resid column we when the response is present in test_data, we encourage it.

If the data or newdata is specified as a data.frame with rownames, augment should return them in a column called .rownames. Use the helper as_broom_tibble() for this.

For observations where no fitted values or summaries are available (where there’s missing data, for example) return NA. Rows with missing data should never silently disappear. That is nrow(input) should equal nrow(output) with rare exceptions.

Added column names should begin with . to avoid overwriting columns in the original data.

Please do not use augment_columns(), which we are slowly phasing out.


The tidy(x, ...) method accepts a model object x and returns a tibble with one row per model component. A model component might be a single term in a regression, a single test, or one cluster/class. Exactly what a component is varies across models but is usually self-evident.

Sometimes a model will have different types of components. For example, in mixed models, there is different information associated with fixed effects and random effects, since this information doesn’t have the same interpretation, it doesn’t make sense to summarize the fixed and random effects in the same table. In cases like this you should add an argument that allows the user to specify which type of information they want. For example, you might implement an interface along the lines of:

model <- mixed_model(...)
tidy(model, effects = "fixed")
tidy(model, effects = "random")

Common arguments to tidy methods:

  • Logical indicating whether or not to calculate confidence/credible intervals. Should default to FALSE.
  • conf.level: The confidence level to use for the interval when = TRUE. If a tidying has a conf.level argument it must have a argument.
  • exponentiate: Logical indicating whether or not model terms should be presented on an exponential scale (typical for logistic regression).
  • quick: Logical indicating whether to use a faster tidy method that returns less information about each component, typically only term and estimate columns.