Irregularly Scheduled Programming

When things run slower than we’d like in R we tend to reach for another, usually compiled, language, and move our code there. What if it “just happened”? What started out as a silly exploration of how to count digits ended up with a race to see which language does it fastest. Maybe some surprises here for some, maybe some bad implementations on my part - let’s find out.

How well do you know your fundamental operators in different languages? ‘Easy’ examples help to fortify that knowledge, and comparing across languages makes for some neat implementation detail discoveries.

In which I confront the way I read code in different languages, and end up wishing that R had a feature that it doesn’t.

Python seems to be everywhere these days, and I’m really into learning languages, so it should come as no surprise that I’m learning a lot of Python. This post serves as a review of Pandas Workout as well as a ‘first impression’ review of the Pandas approach to data.

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.