So you (don’t) think you can review a package

[This article was first published on rOpenSci Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Contributing to an open-source community without contributing code is an oft-vaunted idea that can seem nebulous. Luckily, putting vague ideas into action is one of the strengths of the rOpenSci Community, and their package onboarding system offers a chance to do just that.

This was my first time reviewing a package, and, as with so many things in life, I went into it worried that I'd somehow ruin the package-reviewing process— not just the package itself, but the actual onboarding infrastructure…maybe even rOpenSci on the whole.

Barring the destruction of someone else's hard work and/or an entire organization, I was fairly confident that I'd have little to offer in the way of useful advice. What if I have absolutely nothing to say other than, yes, this is, in fact, a package?!

rOpenSci package review: what I imagined

So, step one (for me) was: confess my inadequacies and seek advice. It turns out that much of the advice vis-à-vis how to review a package is baked right into the documents. The reviewer template is a great trail map, the utility of which is fleshed out in the rOpenSci Package Reviewing Guide. Giving these a thorough read, and perusing a recommended review or two (links in the reviewing guide) will probably have you raring to go. But, if you're feeling particularly neurotic (as I almost always am), the rOpenSci onboarding editors and larger community are endless founts of wisdom and resources.

visdat ??

I knew nothing about Nicholas Tierney's visdat package prior to receiving my invitation to review it. So the first (coding-y) thing I did was play around with it in the same way I do for other cool R packages I encounter. This is a totally unstructured mish-mash of running examples, putting my own data in, and seeing what happens. In addition to being amusing, it's a good way to sort of “ground-truth” the package's mission, and make sure there isn't some super helpful feature that's going unsung.

If you're not familiar with visdat, it “provides a quick way for the user to visually examine the structure of their data set, and, more specifically, where and what kinds of data are missing.”1 With early-stage EDA (exploratory data analysis), you're really trying to get a feel of your data. So, knowing that I couldn't be much help in the “here's how you could make this faster with C++” department, I decided to fully embrace my role as “naïve user”.2

Questions I kept in mind as ~myself~ resident naïf:

  • What did I think this thing would do? Did it do it?
  • What are things that scare me off?

The latter question is key, and, while I don't have data to back this up, can be a sort of “silent” usability failure when left unexamined. Someone who tinkers with a package, but finds it confusing doesn't necessarily stop to give feedback. There's also a pseudo curse-of-knowledge component. While messages and warnings are easily parsed, suppressed, dealt with, and/or dismissed by the veteran R user/programmer, unexpected, brightly-coloured text can easily scream Oh my gosh you broke it all!! to those with less experience.

Myriad lessons learned ?

I can't speak for Nick per the utility or lack thereof of my review (you can see his take here, but I can vouch for the package-reviewing experience as a means of methodically inspecting the innards of an R package. Methodical is really the operative word here. Though “read the docs,” or “look at the code” sounds straight-forward enough, it's not always easy to coax oneself into going through the task piece-by-piece without an end goal in mind. While a desire to contribute to open-source software is noble enough (and is how I personaly ended up involved in this process– with some help/coaxing from Noam Ross), it's also an abstraction that can leave one feeling overwhelmed, and not knowing where to begin.3

There are also self-serving bonus points that one simply can't avoid, should you go the rOpenSci-package-reviewing route– especially if package development is new to you.4 Heck, the package reviewing guide alone was illuminating.

Furthermore, the wise-sage ? rOpenSci onboarding editors5 are excellent matchmakers, and ensure that you're actually reviewing a package authored by someone who wants their package to be reviewed. This sounds simple enough, but it's a comforting thought to know that your feedback isn't totally unsolicited.

  1. Yes, I'm quoting my own review. 

  2. So, basically just playing myself… Also I knew that, if nothing more, I can proofread and copy edit. 

  3. There are lots of good resources out there re. overcoming this obstacle, though (e.g. First Timers Only; or Charlotte Wickham's Collaborative Coding from useR!2017 is esp. ? for the R-user). 

  4. OK, so I don't have a parallel world wherein a very experienced package-developer version of me is running around getting less out of the process, but if you already deeply understand package structure, you're unlikely to stumble upon quite so many basic “a-ha” moments. 

  5. ? Noam Ross, Scott Chamberlain, Karthik Ram, & Maëlle Salmon 

To leave a comment for the author, please follow the link and comment on their blog: rOpenSci Blog. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)