# package-development

## How to become a better R code detective?

Huge thanks to Hannah Frick for her useful feedback on this post! Vielen Dank! This post was featured on the R Weekly podcast by Eric Nantz. When trying to fix a bug or add a feature to an R package, how do you go from viewing the code as a big messy ball of wool, to a logical diagram that you can bend to your will? In this post, I will share some resources and tips on getting better at debugging and reading code, written by someone else (or yourself but long enough ago to feel foreign!

## Lintr Bot, lintr's Hester egg

Remember my blog post about automatic tools for improving R packages? One of these tools is Jim Hester’s lintr, a package that performs static code analysis. In my experience it mostly helps identifying too long code lines and missing space, although it’s a bit more involved than that. In any case, lintr helps you maintain good code style, and as mentioned in that now old post of mine, you can add a lintr unit test to your package which will ensure you don’t get lazy over time.

Now say your package has a lintr unit test and lives on GitHub. What happens if someone makes a pull request and writes looong code lines? Continuous integration builds will fail but not only that… The contributor will get to know Lintr Bot, lintr’s Hester (Easter) egg!

## How to develop good R packages (for open science)

I was invited to an exciting ecology & R hackathon in my capacity as a co-editor for rOpenSci onboarding system of packages. It also worked well geographically since this hackathon was to take place in Ghent (Belgium) which is not too far away from my new city, Nancy (France). The idea was to have me talk about my “top tips on how to design and develop high-quality, user-friendly R software” in the context of open science, and then be a facilitator at the hackathon.

The talk topic sounded a bit daunting but as soon as I started preparing the talk I got all excited gathering resources – and as you may imagine since I was asked to talk about my tips I did not need to try & be 100% exhaustive. I was not starting from scratch obviously: we at rOpenSci already have well-formed opinions about such software, and I had given a talk about automatic tools for package improvement whose content was part of my top tips.

As I’ve done in the past with my talks, I chose to increase the impact/accessibility of my work by sharing it on this blog. I’ll also share this post on the day of the hackathon to provide my audience with a more structured document than my slides, in case they want to keep some trace of what I said (and it helped me build a good narrative for the talk!). Most of these tips will be useful for package development in general, and a few of them specific to scientific software.

## What's in our internal chaimagic package at work

At my day job I’m a data manager and statistician for an epidemiology project called CHAI lead by Cathryn Tonne. CHAI means “Cardio-vascular health effects of air pollution in Telangana, India” and you can find more about it in our recently published protocol paper . At my institute you could also find the PASTA and TAPAS projects so apparently epidemiologists are good at naming things, or obsessed with food… But back to CHAI! This week Sean Lopp from RStudio wrote an interesting blog post about internal packages. I liked reading it and feeling good because we do have an internal R package for CHAI! In this blog post, I’ll explain what’s in there, in the hope of maybe providing inspiration for your own internal package!

As posted in this tweet, this pic represents the Barcelona contingent of CHAI, a really nice group to work with! We have other colleagues in India obviously, but also in the US.

## How I became a crolute i.e. an user of the crul package

A few months ago rOpenSci’s Scott Chamberlain asked me for feedback about a new package of his called crul, an http client like httr, so basically something you use for e.g. writing a package interfacing an API. He told me that a great thing about crul was that it supports asynchronous requests. I felt utterly uncool because I had no idea what this meant although I had already written quite a few API packages (for instance ropenaq, riem and opencage).

So I googled the concept, my mind was blown and I decided that I’d trust Scott’s skills (spoiler: you can always do that) and just replace the httr dependency of ropenaq by crul. Why? First of all note that Crul is a planet in Star Wars whose male inhabitants are called crolutes which sound quite cool (there are female ones as well, called gilliands which doesn’t sound like the package name) and which I now use as a synonym for “user of the crul package”. But I had other reasons to switch… that was the subject of my lightning talk today at the French R conference in Anglet. In this blog post I’ll tell the story again, with a bit more details, in the hope to make you curious about crul!

Pic by ThinkR, thanks Colin/Diane/Vincent!

## Automatic tools for improving R packages

On Tuesday I gave a talk at a meetup of the R users group of Barcelona. I got to choose the topic of my talk, and decided I’d like to expand a bit on a recent tweet of mine. There are tools that help you improve your R packages, some of them are not famous enough yet in my opinion, so I was happy to help spread the word! I published my slides online but thought that a blog post would be nice as well.