Overview
This article aims to provide an exhaustive list of helpful tools, packages, and resources for developers, authors, maintainers, reviewers, and stakeholders in the R Package development workflow.
Welcome to the world of R Package Development! Whether you're a seasoned R programmer looking to expand your toolkit or a newcomer eager to dive into the exciting realm of package development, you've come to the right place. In this comprehensive guide, we'll unveil the essential resources every R package developer needs to master their craft. From tutorials and books to online communities and tools, we've curated a treasure trove of resources to help you embark on your journey towards becoming a proficient R package developer.
Throughout this article, I will provide a walkthrough of the various packages, tools, resources, and axioms I have encountered over my years as an R package software engineer.
R User vs. R Developer
In the world of R programming, there are distinct roles and distinctions between an R User and an R Developer, each with its own set of responsibilities, skills, and objectives.
To distinguish, R developers develop new innovative solutions while R Users use the tools and packages that the developers created. This article is aimed towards the development side of the spectrum and attempts to provide a comprehensive, curated toolbox for the R Developer, specifically in regard to developing R Packages.
In summary, while both R users and R developers utilize R for data analysis and statistical computing, they differ in their focus and expertise. R users primarily leverage R for data analysis tasks, while R developers specialize in creating and maintaining R packages to enhance the functionality and capabilities of the R programming language for a broader audience of users.
R Users
Definition: An R user is someone who primarily utilizes R for data analysis, statistical modeling, visualization, and other tasks related to data science, research, or analytics.
-
Skills & Responsibilities:
- Proficient in using R for data manipulation, exploration, and analysis.
- Familiarity with statistical methods and techniques for interpreting data.
- Ability to create visualizations and plots to communicate insights effectively.
- Understanding of data structures, functions, and packages within R.
- May use R for tasks such as data cleaning, hypothesis testing, regression analysis, and machine learning.
-
Objectives:
- Analyze and interpret data to derive meaningful insights and make data-driven decisions.
- Communicate findings through reports, presentations, or visualizations.
- Utilize R packages and libraries to streamline analysis workflows and enhance productivity.
- Collaborate with colleagues or stakeholders to address specific analytical needs or research questions.
R Developer
Definition: An R developer is someone who focuses on creating, maintaining, and extending R packages, libraries, and tools for the broader R community.
-
Skills and Responsibilities:
- Proficiency in programming with R, including knowledge of object-oriented programming principles, functions, and package development.
- Understanding of software engineering practices such as version control, testing, and documentation.
- Ability to design, implement, and maintain R packages to address specific needs or solve particular problems.
- Familiarity with R package development tools such as
devtools
,roxygen2
, andtestthat
. - Contribution to the R ecosystem through the creation of new packages, improvement of existing packages, or participation in community discussions and collaborations.
-
Objectives:
- Develop and release high-quality R packages that provide valuable functionality to users across different domains.
- Ensure the reliability, efficiency, and usability of R packages through rigorous testing, documentation, and code review.
- Engage with the R community to gather feedback, address issues, and collaborate on package development efforts.
- Stay updated on emerging trends, best practices, and advancements in R programming and package development.
Getting Started
Now that you understand the difference between an R User ans R Developer, a question arises: "How can one go from being a user to a developer?".
The answer is simple, create something! In this case, create an R Package.
Once you have accumulates more than 2-3 common functions that operate within the same context, that is a primary indicator that you should structure your project as an R Package.
After you have more than one function it starts to get easy to lose track of what your functions do. Similarly, it can start to become difficult to track down, name, and organize the functions and you may be tempted to put all of the functions in one file and just source it. Instead, I propose the best solution is to create an R package.
What you Need
To begin creating an R Package, you need:
R
(andRStudio
)Git and GitHub
Some initial functions
-
Development Packages:
devtools
usethis
roxygen2
pak
-
knitr
andrmarkdown
From there you can continue to naming your package, scaffolding out its structure, adding functions, documentation, metadata, data, license, vignettes, etc.
Pre-Requisite Resources
To start, R package developers need to be familiar with the fundamentals of package development, and therefore should be familiar with some of the highest regarded resources available to read.
Anyone who is serious about developing production grade R packages needs to be familiar with most of, if not all of the following resources and guides:
Table 1: R Package Developer Essentials
Resource | Source |
---|---|
Writing R Extensions | CRAN Manuals |
R Packages | Hadley Wickham |
R Package Primer | Karl Broman |
Package Guidelines | Bioconductor |
rOpenSci Packages Developer Guide | rOpenSci |
View Citations:
(Wickham and Bryan, n.d.a, n.d.b; “Why Package and Environment Management Is Critical for Serious Data Science,” n.d.a, n.d.b; Vidoni, n.d.; Initiative, n.d.; “An Introduction to Packager,” n.d.; Gandrud 2015; Glennie 2020; “Owen-TheRGuide.pdf,” n.d.; Riederer, n.d.; Spector 2004; Team, n.d.; Zhu and Jianan, n.d.)
Writing R Extensions Manual
The Writing R Extensions Manual is perhaps the most crucial resource listed above, and has even been considered the Bible of R Package Development.
However, it is very exhaustive and not the most visually-appealing write-up.
As Hadley puts it in his book Writing R Packages: (Wickham and Bryan, n.d.c)
“The best resource for the official details of package development is always the official writing R extensions manual. However, this manual can be hard to understand if you’re not already familiar with the basics of packages. It’s also exhaustive, covering every possible package component, rather than focusing on the most common and useful components, as this book does. Writing R extensions is a useful resource once you’ve mastered the basics and want to learn what’s going on under the hood.”
— Hadley Wickham
Thanks to Colin Fay, a more elegant version of the original manual has been created as a bookdown site and published online at https://colinfay.me/writing-r-extensions.
This resource is highly encouraged for anyone taking R Package Development seriously.
Note: The other manuals listed on the CRAN Manuals website contain a lot of hidden gems that are often overlooked by R developers. These resources contain some of the most crucial, foundational knowledge that anyone using R should eventually be made aware of, therefore I highly recommend you check those out in addition to Writing R Extensions.
R Packages Book
The "R Packages" (2nd Edition) Book outlines the importance of packages in R as the fundamental unit of shareable code, combining code, data, documentation, and tests.
It highlights the vast array of over 19,000 packages on CRAN and encourages readers to develop their own packages for easier code sharing and organization. The book aims to automate package development as much as possible, utilizing the devtools
and usethis
packages and the RStudio IDE for a more efficient workflow.
For a detailed overview, visit the introduction page.
R Package Primer
The "R package primer" provides a minimal tutorial on creating R packages, emphasizing their simplicity and utility for code distribution and personal organization. The primer covers essential topics like package creation, documentation, and checking, alongside advanced topics like GitHub integration, CRAN submission, and writing vignettes. It advocates for package development as a practice for better code management and documentation, even for personal use. For more detailed guidance, visit Karl Broman's R package primer.
Bioconductor Package Guidelines
The "Bioconductor Packages: Development, Maintenance, and Peer Review" guide offers comprehensive insights into the creation, upkeep, and review process of Bioconductor packages. Authored by Kevin Rue-Albrecht, Daniela Cassol, Johannes Rainer, and Lori Shepherd, it serves as an essential resource for developers within the Bioconductor project, promoting high-quality, well-documented, and interoperable software. Contributions to the guide are welcome via GitHub, indicating a collaborative and open-source approach to improving package development practices. For more details, visit the Bioconductor contributions guide.
rOpenSci Packages Development Primer
The "rOpenSci Packages: Development, Maintenance, and Peer Review" guide is a comprehensive resource designed for developers involved in the rOpenSci project. It covers a wide range of topics from package development, continuous integration, security best practices, to the software peer review process. It also includes guidance on package maintenance, collaboration, and marketing. Authored by the rOpenSci software review editorial team, it is an essential read for anyone looking to contribute to the rOpenSci ecosystem. For detailed information, visit the rOpenSci Development Guide.
R Packages
Next, I will provide a comprehensive list of R Packages that aim to assist the development process.
Essentials
The following packages represent Core Development Libraries and should generally be utilized in all scenarios of package development.
For automating the development workflow:
For generating manual pages and documentation:
roxygen2
-knitr
&rmarkdown
-
For testing -
-
testthat
-
For package management and installation:
pak
-remotes
-renv
-
Useful Common Libraries
available
- check if a package name is available.lifecycle
- represent package and function development lifecycle stagesdesc
- Manage and edit the package'sDESCRIPTION
pkgdown
- Generate package documentation static sitetestdown
-gitdown
-
Roxygen Tools
roxygen2md
roxyglobals
rd2list
Rd2roxygen
rd2markdown
roxygen2comment
Dependency Management
attachment
renv
pak
remotes
devtools
pacman
automagic
CodeDepends
dep
sysreqs
pkgnet
functiondepends
Git and GitHub
gert
gitdown
git2r
gh
git4r
gitGPT
precommit
Configuration
config
pkgconfig
options
Credentials & Secrets
keyring
credentials
gitcreds
ssh
askpass
config
plus, base R's Sys.genenv()
and Sys.setenv()
.
Fundamental Low-Level Packages
These packages are not necessarily called directly but are built on top of as dependencies to higher level libraries (i.e.
usethis
,devtools
, etc.)
pkgload
pkgdepends
pkgbuild
pkgcache
pkgapi
pkgnet
Linting & Styling
goodpractice
lintr
strylr
formatR
stylermd
spelling
roxylint
cleanr
janitor
sanitizers
refactor
Documentation
docthis
prettydoc
-
quarto
- pkgdown
- testdown
- gitdown
- papillon
- spelling
- fusen
- badger
- badgen
- bookdown
- altdoc
- fledge
- newsmd
- autonewsmd
- docreview
- covrpage
Metadata
codemeta
codemetar
pkgstats
Checks & Tests
testthat
testdown
autotest
tinytest
shinytest2
goodpractice
rcmdcheck
spelling
testdat
validate
realtest
roxytest
autotest
checkhelper
codetools
covr
covrpage
checkglobals
rhub
Validation and Assertions
assertthat
testdat
validate
Utilities
cli
logger
snakecase
prefixer
addinit
fusen
golem
roger
patrick
janitor
qs
fs
purrr
oysteR
foghorn
cranlogs
dlstats
actions
rworkflows
tic
gpg
debugr
valtools
ssh
checkmate
assertive
assertthat
pkgcond
validate
riskmetric
represtools
containerit
dockerfiler
packer
htmltools
onetime
gitignore
dbx
RPostgres
DBI
dbplyr
fs
gh
whoami
sessioninfo
plumber
rprojroot
here
whisker
config
knitr
rmarkdown
magick
spelling
desc
waldo
goodpractice
conflicted
connections
stringr
lubridate
dm
datamodelr
vctrs
webfakes
ps
evaluate
systemfonts
later
future
promises
pillar
pretyunits
processx
callr
withr
cachem
memoise
bench
R6
ymlthis
piggyback
itdepends
debugme
dependencies
assert
pointblank
docthis
ellipsis
miniUI
rversions
profvis
digest
pingr
rhub
automagic
pacman
rcompendium
litr
leprechaun
pkgverse
metamakr
Summary
Understanding the Basics: Before delving into the intricacies of package development, it's crucial to grasp the fundamental concepts of R programming. Resources like "R for Data Science" by Hadley Wickham and Garrett Grolemund serve as an excellent starting point for beginners, providing a comprehensive overview of R programming essentials and data manipulation techniques.
Mastering Package Development: Once you've familiarized yourself with the basics, it's time to dive into the world of package development. The "R Packages" book by Hadley Wickham is the go-to resource for understanding the principles of package structure, documentation, and best practices. Additionally, online tutorials from platforms like DataCamp and RStudio provide hands-on guidance for creating your first R package from scratch.
Harnessing the Power of Version Control: Effective version control is essential for managing the development and collaboration of R packages. Platforms like GitHub offer robust version control capabilities, allowing developers to track changes, collaborate with peers, and maintain a history of their package development journey. Resources such as "Happy Git and GitHub for the useR" by Jenny Bryan provide comprehensive guides to mastering Git and GitHub workflows tailored specifically for R users.
Engaging with the Community: The R community is a vibrant ecosystem bustling with passionate developers, users, and enthusiasts. Engaging with online communities such as Stack Overflow, RStudio Community, and the R4DS Slack channel enables you to seek guidance, share insights, and collaborate with fellow developers. Conferences like useR! and rstudio::conf provide opportunities to connect with experts, attend workshops, and stay updated on the latest trends in R package development.
Streamlining Development with Tools and Packages: As you progress on your journey, leveraging tools and packages designed for R package development can significantly enhance your productivity and efficiency. Tools like devtools, roxygen2, and testthat streamline package development tasks such as building, documenting, and testing your code. Additionally, exploring specialized packages like usethis and pkgdown empowers you to automate common development workflows and create polished package documentation and websites with ease.
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