Zut, Git ! Porting {saperlipopette} to the terminal with Claude
A while ago, I decided to learn some Rust. I bought a book, opened it months later and started a small side-project: porting my R package saperlipopette to the terminal by writing a CLI with Rust. I then lost steam and discarded the project. Now, teaching more Git with saperlipopette has me itching to do the same outside of R, to reach more learners. And in the meantime, even though I have conflicting feelings about it, I realized that using an LLM, specifically Claude Code (Opus 4.
Git commits: please mark your stitches!
In which I share a crochet analogy for Git commits… This post was featured on the R Weekly highlights podcast hosted by Eric Nantz and Mike Thomas. Don’t forget your stitch markers! I am happily continuing my crochet learning journey. One lesson I have now internalised is that I should never ever forget or neglect to use stitch markers. Stitch markers are sort of small, not spiky, safety pins. A crochet project featuring several stitch markers (beginning of a Snorlax from Lee Sartori’s Pokemon crochet book)
Parallel strands between learning crochet and learning programming
Thanks to Athanasia Mo Mowinckel for reviewing this post! One goal of mine this year is to learn how to crochet amigurumi, that is to say, cute creatures. When working on my first project, a cat toy made out of yarn left-over from knitting projects, I thought of some similarities between this activity and being a beginner in programming. Here they are! This poor bee has heart wings… mounted the wrong way so won’t be able to flee the cat it was gifted to.
How to open files, folders, websites in R
Coming to you from France, a post about Mise en place for R projects. In a less francophone phrasing: to get to work on something you have to open that thing, be it a script or a project or a website. The easier that is, the faster you get to work. In this post I’ll show a roundup of R functions and related tools for opening scripts, projects and websites for yourself or on behalf of the users of your code.
Cover and modify, some tips for R package development
I’ve recently been dealing with legacy code refactoring both in theory and in practice: while I’m continuing some work on the igraph R package, I’ve started reading Working Effectively with Legacy Code by Michael Feathers, that had been in my to-read pile for months. In this post, I’ll summarize some ideas from both the book and my work. “Cover and modify” with “characterization tests” When you start modifying your rusty code, how do you ensure you do not break existing and important behaviour inadvertently?