Reflections on building my first few R packages

1 Package development

I’ve spent much of the last six months building several R packages. It’s the first time I’d built packages outside of my work environment, and I hope the released versions are much better now that they were at initial release! This post is not a tutorial on how to build packages, but will summarise some parts I’ve had to learn, and aims to show you that package building is not magic. It’s particularly accessible now with RStudio, devtools, usethis and git in combination. This is a slight wordy post, so fair wrning… ;-)

2 Packages

I’ve built or collaborated on three packages recently:

  • FunnelPlotR: ggplot2 methods for producing funnel plots of indirectly standardised ratios with overdispersion adjustment
  • NHSRdatasets: a free, collaborative datasets package, with examples of health-related data, to provide in-context examples for NHS-R community and others learning R.
  • HEDfunctions: an HED internal package that contains methods for some of the models build in HED production, and several utility functions like confidence intervals etc.

I started learning the skills to do this by watching a couple of YouTube videos on package building. These videos used the RStudio wizards to set up the package as an RStudo Project, as well as roxygen2 to generate documentation. The videos built a basic package in five minutes. How hard could it be, right? Hmmmm…..

I’ve also relied upon Hadley Wickham’s books:

The principle, for the uninitiated, is:

  • Packages are groups of functions, their help files, and metadata
  • There are conserved package structures, but broadly, your functions go in an R folder, and help files in man
  • You have a DESRIPTION file, with the fields you see on CRAN, and a NAMESPACE (written by roxygen2 in this case) that have the details required for release, e.g. package dependencies, authors etc.
  • roxygen2 converts tagged metadata into the right format for packages. You add this to the top of function with comments: #' and tagged @ e.g.:
#' @title My function for doing a thing
#' @description This is a a function for demosntrating a very basic roxygen skeleton.
#' @param input_data A data.frame input to my function
#' @param method The method that the function uses, either "A" or "B", default is: "A"
#' @return A list containing somethings
#' @export
#' @details
#'    The function tries to do a thing, but it's a demo so it won't.
#'    Don't try and use this in the real world.
#'    Seriously  
#' @import ggplot
#' @importFrom dplyr filter %>% 
#' @examples
      my_function(LOS_model, method = "B")

my_function(input_data, method = "A"){



The nice thing here, though, is that you don’t need to remember much of that structure. Write you function then (provided you have the roxygen2 package installed), go to the Code menu of Rstudio and click on ‘Insert Roxygen Skeleton’. You may need to add more tags than this, but it’s a helpful start.

When you ‘roxygenise’ the function above, roxygen2 will add the required packages to description and namespace, build the help files into the man folder, and build vignettes. This is done using either the ‘Document’ function in the build menu, or the ’Clean and rebuild option (this build the package, but document it first). It will save you all sorts of time and headaches! Although you will probably need to Google the tags a lot. I found that the formatting is not the same as Rmarkdown, it’s more like Latex. Remember to escape characters like % with \%, as that kept messing up for when I used them in help files.


  • Use https, not http URLS for stuff. This is the way of CRAN and you’ll get error messages if you don’t an packages maybe rejected.
  • If you need to hide an internal function, you need to add the ‘@keywords internal’ tag
  • Add the \@export' tag to functions so they are available outside the package (using::`). You probably want to do this if it’s your first rodeo.

3 Git and GitHub

Git is a source control tool that essentially logs your changes within a particular folder. Once you have git installed, it integrates with RStudio very well. You need to add files to it, then ‘commit’ changes periodically to log them. You can roll back your changes or merge them, as well as making ‘branches’, which are isolated parallel copies of the code that allow you to develop things without committing them to the ‘master’ branch. This protects your code from being overwritten, but also allow parallel developments to be isolated.

Where GitHub comes in, is for collaboration and code sharing. GitHub is an online hosting platform for Git. You set up repositories, ‘remotes’ on GitHub, and ‘push’ your changes from your local machine to GitHub. GitHub allows various people to pull copies of your work and work on it, merging changes in, allowing review and collaborative development. There are a bunch of Git and GitHub related features that help the development of packages.

I started using Git and GitHub after seeing various packages on Git and wanting to learn. I didn’t know what I was missing at that point. It takes a while to get to know, but is well worth it. Many other elements have been written to integrate with Git too, and I’ll talk about Travis and Code Completion later. I can highly recommend Jenny Bryan s ‘Happy Git with R’ as a starting point. Get used to using it, then try and contribute to a package, and learn Git as you go. Most authors will welcome this, and many R packages are improved this way.

I found the Github pages, a web-server published version of code in your repository, can be really helpful to give a nice package website using pkgdown with minimal effort for you.

4 Vignettes

Vignette are the term used for ‘worked examples.’ They range from a few lines to long tutorials. Some packages contain several vignettes, demonstrating special cases or variants of use. I think they are really helpful for demonstrating the motivation and application of functions in package, and I believe all packages released on CRAN should include at least one.

You can use rmarkdown to create vignettes. In doing so, you need to include a few things:

  1. Specific YAML to tell the package build what it is and how to process it
  2. Add rmarkdown and knitr to the DESCRIPION, in Suggests (and the namespace).
  3. Consider where images for plots are being store

The easiest way to solve all three is to call usethis::use_vignette(). This sets up the YAML, picture location and the required packages. I didn’t realize this at first, and had copied the YAML in. If you don’t specify the right picture location, you can get warnings at build time about unexpected files because they are in the wrong location. The other issue with this is that, if you use ‘pkgdown’ to build a package documentation website, it won’t pick up the pictures properly (see next section).

5 README file

Include a file in the top level of your package if you are using GitHub! This is the ‘landing page’ people will see when they arrive at your repository. You can write them as github markdown, or using Rmarkdown, then specify knit to github_document in your YAML. Either way, let RStudio and co. help you! You will get the right structure, YAML and the right knitr setup chunk, with usethis::use_readme_md or usethis::use_readme_rmd (the second of which is in Rmarkdown).

My biggest issue with the was when it came to pkgdown. pkgdown is a fantastic package that transforms your package metadata into a package documentation website. There are various control elements, but I simply published it using GitHub pages (gh-pages branches in the repos).

The biggest problem I had was that, again, I had manually created the README, and I hadn’t added the following line to my setup code chunk: fig.path = "man/figures/README-" . My README file was still fine, but as FunnelPlotR is a plotting package, the README had example plots that render as png files. When building the pkgdown site, it couldn’t find my image files.

TLDR: Use a README, but create it using the functions in the usethis package.

6 Unit testing and Code coverage

Like many of us, I’d seen the cool little badges that better R programmers had on the repositories, saying things like “code coverage: 80%”. These badges are normally reporting something from another site, in this case, the unit testing results for your package and how much of your code they cover.

Unit testing, in this application, is the process of testing your code. Many of us do this informally, as we know what we want out of a section of code/function. This gets harder when you are working over several functions at once, and sometimes bugs induced at one point aren’t always apparent until they hit other processes. Formalising your unit testing allows you to trust that a set of tests, that you’ve written, that relate to the behavior you expect, are run against your code. You can run these tests in RStudio, and they are also run on other builder platforms (and CRAN submission). The testthis package has a framework for building these test. Go and look it up and it will make you more productive, and hopefully, catch issues as you work on your code.

You can use the covr package to check how much of your code is tested. Once you push your code to GitHub, you can also link it to Codecov. Codecov runs a report and analyses your code. The percentage represents how much of your code it ‘covered’ by the tests you wrote. This is a useful piece of info for improving your tests and seeing how much of your new code is not yet covered.

You can register with Codecov using your GitHub details, and it integrates with Travis builds. You may need to set up authentication, allowing access to the repository. Register for an account, and again, usethis come to the rescue with usethis::use_coverage(). This adds the necessary element to a package already building with Travis. Speaking of which:

7 Travis CI

‘Build passing’ is another one of those cool badges you see on repositories, and it relates to Travis CI. Travis is a continuous integration tool. This means that (unless you set it otherwise), each time you push code to your repository, Travis will build it for you and report whether there are error messages, where the process fails, and run tests and trigger reports with Codecov if you want them.

Travis is controlled with a .travis.yml YAML file. Again, once you’ve registered for a Travis account, and set it up with access to your repo, trust the usethis package. Run usethis::use_travis, and this will set up your file. Push this to your repository and you should be good to go.

You can also use Travis to build your pkgdown site, again with usethis:use_pkgdown_travis will add the the relevant extra instructions to tell Travis to load pkgdown and build the site, after it has built the package.

I got in a real pickle with this, and I can advocate the ‘nuclear option’ for solving this one. I deleted the whole pkgdown site several times and removed everything from my ‘travis.yml’ and deleted the gh-pages branch on github. You can at least rebuild it as if it is new in this case. The issue in the NHSRdatasets package was that I hadn’t set up the authentication for Travis (that usethis would have done for me) because I did it manually.

8 Checking and building your R package

How do you know your R package is stable across different installations, and builds correctly in each case? To do this, we need to ‘check’ it. You can find checking options in RStudio, in the ‘Build’ menu. It is worth going to the ‘Configure Build Tools’ option int the tools menu, and in the ‘Check package’ box, add --as-cran. This will run the same set of checks that CRAN runs automatically on your project, so it is sensible to run locally first.

If you are new to this (or even if you are not), it is likely that you will find errors and warnings. DON’T PANIC! This is what this stage is for. In most cases, you can follow a combination of the error messages and Googling to figure out the errors, but some are trickier and I’ve consulted #rstats twitter many times. You cannot distribute a package (or load to CRAN) with errors, but warnings are a grayer area.

In my FunnelPlotR package, checking revealed several warnings about variables not being in-scope, or defined. This ended up being because I’d used dplyr, and dplyr uses ‘tidy’, or non-standard evaluation. I hadn’t taken time to learn about this because we don’t usually use it in the HEDfunctions package that I’d built first. I didn’t realise that I needed the rlang package as a dependency, and to use the argument; .data in a piped dplyr ‘sentence.’ You don’t need this in normally use for EDA, but if you include it in a function within a package, you do, or the function thinks it’s missing arguments on build/check. e.g.

# Normal use
mydata %>%
  filter(columnA > 10)%>%

# Fully qualified version for function/package use
mydata %>%
  filter(.data$columnA > 10)%>%

Once you’ve checked locally, you should also check other systems, as R is used on a variety of platforms. Unless you are fortunate enough to have multiple environments available to you locally, you are probably going to need the automated checking services.

R-hub builds your package in several environments, including a couple of Linux distributions and a Windows server, reporting results to an email address, with logs available online. A similar process is followed by Win-Builder, that builds the windows distributions on either the current or development versions ofR under Windows. Both can be accessed using devtools:


Remember that, if you’ve used Travis, you also have a build on Linux and you should consider it a build test as well.

Finally, summarise all of this in a file called in the root directory of the package. This allows the CRAN moderators to easily see what you’ve tested and any outstanding points. You can check out my cran-comments file for FunnelPlotR here:

9 Process for release

Prior to release, you should have + Tested any hyperlinks in the documents (using https, not https) + Spell-checked your files + file explaining what you’ve published or updated since the last time + Built your package successful on several systems + Checked and corrected any warnings or errors etc. from the tests + Added a ‘’ file to explain what the package is, what environments you tested it on and any outstanding warnings/errors etc. + Committed everything to github (if using it)

If you can’t remember to do this, when you try and release it using devtools you’ll be reminded and asked to confirm that you’ve done all the expected/helpful things for the CRAN maintainers. When you are ready, run: devtools::release(), and follow the prompts to submit to CRAN.

You’ll then get a couple of automatic emails with links for you to confirm your package submission. Once you’ve don that, it’s submitted!

…and expect it to be knocked by the CRAN maintainers. It’s not a problem. Every time I’ve had this, they give clear (if brief) feedback and tell you what to correct for re-submission.

10 Licencing

My final point is: think about licencing. This may seem boring and/or unnecessary to you, but it really isn’t. As a minimum, you want a licence that limits your liability for the code. The main things to consider are whether it is open source, or not. If it’s not, it won’t be going on CRAN, but open source licences vary slightly. Hadley Wickham’s package development material discusses them in more details, but I used a CC0 licence for the NHSRdatasets package, as there is no code per se, just open source data. For FunnelPlotR I used an MIT licence, which is a minimal open source licence, but allows code to be included in propriety systems if reused. Some of the other licences like GPL insist that any reuse of the code must be also be in open source setting. Using a licence like this would not allow me to reuse the code in propriety code for HED work.

11 Summary

Package development has taken a lot of hours, but I’m proud of what I’ve published so far. I’ve learnt a lot about R, Git and GitHub, writing functions, and I’ve had my head lifted up to see the subject more broadly. I’ve got tonnes left to do with FunnelPlotR, and I’m excited to see more data added to NHSRdatasets, but they’ve made a good start. If you’d like to try contributing to open source R packages, I’d be delighted to receive contributions on my work, as would many other authors.

Chris Mainey
Chris Mainey
Deputy Director of Specialist Analytics

NHS Data Scientist and analytical leader with interests in statistical modelling and machine learning in healthcare data.