Roman Tsegelskyi's blog    About    Atom    RSS

Good Practices for Writing R Packages

Recently I have finished working on my first R package - yummlyr, and decided to write a short post on best practices for creating R packages that I have encountered. There are many different manuals on creating R packages that can be found over the internet, with great R packages book by Hadley Wickham for in depth study of the topic, while this post aims to give a short overview of best practices and tools for writing R packages, drawing some inspiration from similar post for Rust.

What is an R package?

R packages was one of the most fundamental things of the language. Essentially, it's a well defined format for distributing R code typically through CRAN and Bioconductor. CRAN took it's idea from CPAN for Perl and now holds over 7000 packages.

Best Practices

Simplify development process with devtools

Hadley Wickham wrote an amazing devtools package, which simplifies all the process around development with R. For example, creating a package is as easy as calling


Generally, I feel that the best piece of advice you can take from this article, is to maximize the usage of devtools. Whatever task you need to perform, first look into devtools, it's very likely that there is a function for that already.

Use Rcpp for compiled code

One of the things that distinguishes R from other languages is it's interfaces. It's deeply embedded into how language was designed thus providing great methods for interacting with other systems, in particular compiled code. R is great in executing compiled C/C++ code, especially with Rcpp. Unless you have some code that is already written and you just want to hook it up to R, strongly recommend using Rcpp for writing optimized code that is used in R. The simples way to start using Rcpp in your package is through devtools:


Which will create src/ directory and first file in it and update DESCRIPTION for you. Great overview on Rcpp can be found in Advanced R book.

Test code with testthat/runit

There are two main packages used for testing throughout R community - testthat and runit. testthat draws inspiration from the xUnit family of testing packages, as well from many of the innovative ruby testing libraries, while runit aims to provide similar testing experience to junit. Also runit seems to be one of the first testing frameworks for R, but recently testhat have been more popular and I rarely see new packages using runit.

With testthat being a tool from HadleyVerse, it is also very easy to set it up with devtools


A test file lives in tests/testthat/. Its name must start with test. Good example of using testthat for testing is devtools test suite. More detailed information can be found on testthat here.

Put the package on GitHub with useful

Putting the package on GitHub will allow the user of your package to get the development version with devtools, thus enabling you to deliver bug fixes/new features faster than going through formal process of submitting a package to CRAN/Bitbucket. Additionally I find it much easier to manage bug reports though GitHub issues. Also consider creating a detailed, which in a lot of cases will serve the first introduction to your package. More and more now I find myself just going to the GitHub page of the package to find out how it works. And judging from these comments it is a general trend in R community.

Use Travis and AppVeyor for Continious Integration

During last year, I have seen a significant improvement of CI systems supporting R. Even though most of the tools are written by community, they are robust and make it relatively easy to setup. There are 2 systems, which I have seen primarily used in many packages (which are free for open source projects):

  • Travis - arguably most popular CI system. Supports R almost as first class citizen, configuration as simple as adding
language: r
sudo: required

to travis.yml. As with many tools here, this is automated with devtools::use_travis().

  • AppVeyor - CI systems for Windows and a must to have if you are using compiled code, otherwise there not that many cross-platform issues with R. Can be setup with devtools::use_appveyov().

Measure code coverage with covr

Last year, Jim Hester created covr package for measuring test coverage. Use


to get quick statistics for code coverage of your package and


to inspect line by line code coverage inside shiny app.

Also covr is supported by and with allows a nice integration of code coverage into reporting into CI pipeline. In essence, to measure coverage with CI, you will only need to add covr::coveralls as part of your CI build. Just call,


which will do the job of updating .travis.yml. Replace coveralls with codecov if you want to use it instead.

Enforce coding style with lintr or formatR

The formatR is a package created by Yihui Xie which aims at reformating R code to improve readability; if you are familiar with gofmt or rustfmt, you will easily understand the appeal of the package.

Most obvious usage is


which, will format all the files in the package.

The lintr package by Jim Haster takes a different approach. Rather than trying to fix the error by itself it simply reports them to the user. It is easy to use inside major editors. I prefer lintr since it allows to write custom lints and is a bit more configurable in my opinion.

Using lintr is also very simple, just call


For persistent project configuration create .lintr file as explained here.

Document objects with roxygen2

Standard way of documenting the objects and functions inside the packages is to create .Rd files and store them in man/ directory. However, Roxygen2 packages provides a more preferred solution to that. Roxygen2 dynamically inspects the objects that it documents, and generates the .Rd files for you. Moreover, the package also generates the NAMESPACE file, which is mainly responsible for defining which functions should be exported from the package (more can be found here). It draws inspiration from doxygen, and it will be simple to pick it up for people familiar with C/C++.

A simple example of function documentation looks like this:

#' Documentation title
#' More description
#' @param x parameter documentation.
#' @param y parameter documentation.
#' @return Description of the return value.
#' @examples
#' Some usage example
func <- function(x, y) {

After documenting the objects in this way, call




which will generate the .Rd files for you.

For more information on possible tags and examples, take a look at Object Documentation chapter of R packages book.

Create Vignettes with knitr

Vignettes are the form of long-term documentation for R packages. They somewhat resemble usage manuals, papers or book chapters. Typically they are aimed at in-depth description of a particular use case or implementation details of the package. The easiest way to create vignettes now is to use knitr packages.

To create your first vignette run


Which creates, vignettes folder and the my-vignette.Rmd file inside it. Rmd a file extension for R markdown, which is a flavor of markdown, designed to create reports with embedded R code. I won't go into much detail about markdown, which can be found here for example, so I think it is more useful to give a short primer knitr usage instead.

knitr is an R package created by Yihui Xie which allows intermingling of code, results and text to create documents of different types. knitr takes R code, runs it, captures the output, and translates it into formatted Markdown. For example, this snippet


and covered by knitr rendered like

## [1]  1  2  3  4  5  6  7  8  9 10

After you are done with creating vignettes, run devtools::build_vignettes(), which will build the vignette in different formats and copy them to inst\doc.

For more information and great demos refer to knitr's web-page.


Thanks for reading this post. I hope it was useful and practices listed here will simplify creating R package for you. If you have any comments/suggestions, please reach out to me in twitter, this reddit thread or this HackerNews thread.

comments powered by Disqus