rstats

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’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.