rstats

I keep saying that the more programming languages you know, the more you will understand all the others you know - I’m now at the point where I want to solve every problem I see in a handful of different languages. They all offer different functionality, and some are certainly more suited to particular problems than others, but there’s a world of difference between two characters and importing from two libraries.

Continuing my theme of learning all the languages, I took the opportunity of a programming puzzle to try out the same approach in a handful of different languages to compare how they work.

In this post I describe a useful programming pattern that I implemented, and hopefully provide a gentle introduction to the idea of monads.

As October rolls around once more, the term Hacktoberfest might pop across your feeds; an effort aiming to encourage people to contribute to open-source software, particularly if they’re new to that. In this post I’ll describe what I’m offering towards that goal.

In this post I explore some differences between R, python, julia, and APL in terms of mutability, and try to make something that probably shouldn’t exist.

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!