tidy-deprecate-function

Tools to make an R developer's life easier

Instalación
CLI
npx skills add https://github.com/r-lib/devtools --skill tidy-deprecate-function

Instala esta habilidad con la CLI y comienza a usar el flujo de trabajo SKILL.md en tu espacio de trabajo.

Última actualización 4/30/2026

devtools

R-CMD-check
CRAN_Status_Badge
Codecov test coverage

The aim of devtools is to make package development easier by providing R
functions that simplify and expedite common tasks. R
Packages
is a book based around this workflow.

Installation

# Install devtools from CRAN
install.packages("devtools")

# Or the development version from GitHub:
# install.packages("pak")
pak::pak("r-lib/devtools")

Cheatsheet

thumbnail of package development cheatsheet

Usage

All devtools functions accept a path as an argument, e.g.
load_all("path/to/mypkg"). If you don't specify a path, devtools will
look in the current working directory - this is a recommended practice.

Frequent development tasks:

  • load_all() simulates installing and reloading your package, loading R code
    in R/, compiled shared objects in src/ and data files in data/. During
    development you would usually want to access all functions (even un-exported
    internal ones) so load_all() works as if all functions were exported in the
    package NAMESPACE.

  • document() updates generated documentation in man/, file collation and
    NAMESPACE.

  • test() reloads your code with load_all(), then runs all testthat tests.

  • test_coverage() runs test coverage on your package with
    covr. This makes it easy to see what parts of your
    package could use more tests!

Building and installing:

  • install() reinstalls the package, detaches the currently loaded version
    then reloads the new version with library(). Reloading a package is not
    guaranteed to work: see the documentation for unload() for caveats.

  • build() builds a package file from package sources. You can use it to build
    a binary version of your package.

  • install_* functions install an R package:

    • install_github() from GitHub
    • install_gitlab() from GitLab
    • install_bitbucket() from Bitbucket
    • install_url() from an arbitrary url
    • install_git() and install_svn() from an arbitrary git or SVN repository
    • install_local() from a local file on disk
    • install_version() from a specific version on CRAN
  • update_packages() updates a package to the latest version. This works
    both on packages installed from CRAN as well as those installed from any of
    the install_* functions.

Check and release:

  • check() updates the documentation, then builds and checks the package locally.
  • check_win_release(), check_win_devel(), and check_mac_release() check
    a package using win-builder or
    https://mac.r-project.org/macbuilder/submit.html.
  • release() and submit_cran() handle the mechanics of CRAN submission with
    or without, respectively, (re)-running lots of local checks.

Learning more

R package development can be intimidating, however there are now a number of
valuable resources to help!

Cover image of R Packages book

  1. R Packages is a book that gives a comprehensive treatment of all common parts
    of package development and uses devtools throughout.

    • The first edition is no longer available online, but it is still in print. Note that it has grown somewhat out of sync with the current version of devtools.
    • A second edition that reflects the current state of devtools, plus new topics such as package websites and GitHub Actions, is available at https://r-pkgs.org and in paperback format.
    • The Whole Game and
      Package structure chapters
      make great places to start.
  2. Posit Community - package
    development

    is a great place to ask specific questions related to package development.

  3. rOpenSci packages has
    extensive documentation on best practices for R packages looking to be
    contributed to rOpenSci, but also very useful general recommendations
    for package authors.

  4. There are a number of fantastic blog posts on writing your first package, including

  5. Writing R
    Extensions
    is
    the exhaustive, canonical reference for writing R packages, maintained by
    the R core developers.

Conscious uncoupling

devtools started off as a lean-and-mean package to facilitate local package
development, but over the years it accumulated more and more functionality.
devtools has undergone a conscious
uncoupling

to split out functionality into smaller, more tightly focussed packages. This
includes:

  • testthat: Writing and running tests
    (i.e. test()).

  • roxygen2: Function and package documentation
    (i.e. document()).

  • pak: Installing packages (i.e. pak::pak()).

  • pkgbuild: Building binary packages
    (including checking if build tools are available) (i.e. build()).

  • pkgload: Simulating package loading (i.e.
    load_all()).

  • rcmdcheck: Running R CMD check and
    reporting the results (i.e. check()).

  • revdepcheck: Running R CMD check on
    all reverse dependencies, and figuring out what's changed since the last CRAN
    release (i.e. revdep_check()).

  • sessioninfo: R session info (i.e.
    session_info()).

  • usethis: Automating package setup (i.e.
    use_test()).

Generally, you would not need to worry about these different packages, because
devtools installs all of them automatically. You will need to care, however, if
you're filing a bug because reporting it at the correct place will lead to a
speedier resolution.

You may also need to care if you are trying to use some devtools functionality
in your own package or deployed application. Generally in these cases it
is better to depend on the particular package directly rather than depend on devtools,
e.g. use sessioninfo::session_info() rather than devtools::session_info(),
or pak::pak("user/repo") vs devtools::install_github("user/repo").

However for day to day development we recommend you continue to use
library(devtools) to quickly load all needed development tools, just like
library(tidyverse) quickly loads all the tools necessary for data exploration
and visualization.

Code of conduct

Please note that the devtools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.