Irregularly Scheduled Programming

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.

Hyperlinks are great - they add value to a block of text by adding additional links out to more things to read - but they’re a distraction if you’re trying to read an in-depth piece of text and comprehend it linearly. Let’s hack the web!

I’m solving Advent of Code this year using a relaxed criteria compared to last year in that I’m allowing myself to use packages where they’re helpful, rather than strictly base R. Last year I re-solved half of the exercises using Rust which helped me learn a lot about Rust. This year I’m enamored with APL, and I wanted to share a particularly beautiful solution.

In the lead up to Christmas each year, Advent of Code offers a series of 25 puzzles which start out reasonably simple, but get progressively harder, eventually requiring knowledge of algorithms and dynamic programming techniques. Last year I solved these in (strictly) base R on the day they were released (or as close to as I could). I then (starting Dec 27) went back and re-solved (13 of) them in Rust.

This post details what I learned along the way and some fun visualisations I made.

Print debugging has its place. Sure, it’s not always the best way to debug something, but it can often be the fastest. In this post I describe a useful way to do this in Rust and how we can get similar behaviour in R.

If you’re reading this hoping that I’m done with droning on about array-languages, close the tab… it only gets worse from here. If you thought APL was unreadable, even after my earlier blog posts, again - close button is right there. In this post I try out a brand new stack-based array language and continue to advocate for learning such things.

I’m part of a local Functional Programming Meetup group which hosts talks, but also coordinates social meetings where we discuss all sorts of FP-related topics including Haskell and other languages. We’ve started running challenges where we all solve a given problem in a language of our choosing then discuss over drinks how they compare.

I keep hearing the assertion that “writing APL/Haskell/etc… makes you think differently” and I kept wondering why I agreed with the statement but at the same time didn’t think too much of it. I believe I’ve figured out that it’s because I happened to have been using Array-aware languages this whole time! It turns out R is an even better language for beginners than I thought.

One thing that has really caught my attention as I learn more programming languages is the idea of generators or infinite sequences of values. Yes, infinite. Coming from R, that seems unlikely, but in at least several other languages, it’s entirely possible thanks to iterators and lazy evaluation.

I’ve been learning at least one new programming language per month through Exercism and the #12in23 challenge. I’ve keep saying, every time you learn a new language, you learn something about all the others you know. Plus, once you know $N$ languages, the $N+1^{\rm th}$ is significantly easier. This post covers a calculation I came across in Haskell, and how I can now do the same in a lot of other languages - and perhaps can’t as easily in others.