Thank you for your interest in contributing to broom! This document is a work in progress describing the conventions that you should follow when adding tidiers to broom.
augmentmethods must return tibbles.
NEWS.mdto reflect the changes you’ve made
stylerpackage to reformat your code according to these conventions, and the
lintrpackage to check that your code meets the conventions.
tidyrover older packages such as
tidyr::gatherdata after it’s been tidied.
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
Ideally, tidying methods should live in the packages of their associated modelling functions. That is, if you have some object
my_object produced by
my_package, the functions
augment.my_object should live in
my_package, provided there are sensible ways to define these tidiers for
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:
We will keep you updated as we work towards a final solution.
NOTE: okay to write
tidyverse code to tidy and wrap it in a function. encouraged, in fact.
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
rq objects and using those as a starting point.
You should also be aware of the following helper functions:
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.
There’ll be a major overhaul of documentation later this summer, at which point this portion of the vignette will also get some major updates.
Your tests should include:
The tests for
augment are rapidly evolving at the moment, and we’ll follow up with more details on them soon.
If any of your tests use random number generation, you should call
set.seed() in the body of the test.
In general, we prefer informative errors to magical behaviors or untested success.
your_packageto the Suggests section of broom’s DESCRIPTION.
skip_if_not_installed("my_package")at the beginning of any test that uses
devtools::install_github("tidyverse/broom", dependencies = TRUE).
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
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).
broom doesn’t currently pass all of these. If you are adding new tidiers at the moment, it’s enough for these to throw no warnings for the files you’ve changed.
The big picture:
glanceshould provide a summary of model-level information as a
tibblewith exactly one row. This includes goodness of fit measures such as deviance, AIC, BIC, etc.
augmentshould provide a summary of observation-level information as a
tibblewith one row per observation. This summary should preserve the observations. Additional information might include leverage, cluster assignments or fitted values.
tidyshould provide a summary of component-level information as a
tibblewith 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.
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
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
augment(x, data = NULL, ...) method accepts a model object and optionally a data frame
data and adds columns of observation level information to
augment returns a
tibble with the same number of rows as
data argument can be any of the following:
data.framecontaining both the original predictors and the original responses
tibblecontaining both the the original predictors and the original responses
dataargument is specified,
augmentshould 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. This will eventually be checked by the
augment methods will also provide an optional
newdata argument that should also default to
NULL. Users should only ever specify one of
newdata. Providing both
newdata should result in an error.
newdata should accept both
tibbles and should be tested with both.
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 .se.fit .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 .se.fit #> <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
.resid columns in most cases, whereas
augment(model, data = test_data) only needs to a
.fitted column, even if the response is present in
newdata is specified as a
data.frame with rownames,
augment should return them in a column called
For observations where no fitted values or summaries are available (where there’s missing data, for example) return
Added column names should begin with
. to avoid overwriting columns in the original data.
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:
Common arguments to tidy methods:
conf.int: logical indicating whether or not to calculate confidence/credible intervals. should default to
conf.level: the confidence level to use for the interval when
conf.int = TRUE
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
tidymethod that returns less information about each component, typically only