Irregularly Scheduled Programming

I thoroughly dislike ‘hot takes’ comparing programming languages based solely on a “feel” or differential familiarity (“I know this one better therefore it is better) so when I came across a blog post detailing a small learning project written in Gleam I wanted to understand what advantages and disadvantages that language brings to the problem. This post details a side-by-side comparison after rewriting the project in R with a goal of better understanding the approach on both sides.

At a recent APL-focussed Meetup someone posed a challenge to slice up the components of an IPv4 address with an APL language and it prompted me to learn a bit more about how that works in general and how I could do the processing in APL myself.

A while ago (2019 seems so long ago now) I started working on something I thought was interesting but which never really got any traction. It has potential once more, so it’s about time I wrote up what it does and why I think it’s a useful idea. I’m going to talk about using the {dplyr} package on some data with rows and columns, but we’re not talking about data.frames or tibbles…

I’ve been using a lot of programming languages recently and they all have their quirks, differentiating features, and unique qualities, but one thing most of them have is that they handle strings as a collection of characters. R doesn’t, it has a “character” type which is 0 or more characters, and that’s what we call a “string”, but what if it did have iterable strings?

A while ago I made some plots I really liked, but I never made a blog post about them. Then the data source stopped working and I couldn’t make them again. Now there’s a new data source, so it’s time for a post about some weather data!

I heard that learning Elm is a good way to approach learning Haskell, so I gave it a go and was surprised early on about an approach to writing abstracted HTML. In this post I compare the way that R and Elm generate HTML and the differences between their approaches.

Another day, another short riddle to be solved with several programming languages! This one is nice because solving it doesn’t need a lot of code, but it uses some interesting aspects of evaluation.

I saw a toot celebrating a short, clean implementation of a square root finding algorithm and wanted to dig a bit deeper into how it works, with a diversion into some APL.

With a serious yak shaving deviation, I have a really short “cheat” solution to one of the featured Exercism problems. It’s been a really insightful journey getting to this point, and as always I’ve learned a lot along the way. The fact that I was able to understand the required changes and propose them is thanks to the open-source nature of programming languages.

I’ve tried to get this same image transformation working at least three times now, but I can finally celebrate that it’s working! I’ve been (re-)learning Julia and I still love the language, so it was time to take my learning to the next level and actually build a package.