Publishable Stuff

Rasmus Bååth's Blog

Hello stranger, and welcome! 👋😊
I'm Rasmus Bååth, data scientist, engineering manager, father, husband, tinkerer, tweaker, coffee brewer, tea steeper, and, occasionally, publisher of stuff I find interesting down below👇

The International Bartenders Association (IBA) cocktails in csv and json format


I find it fascinating that the International Bartenders Association (IBA) keeps a list of “official” cocktails. Like, it’s not like the World Association of Chefs’ Societies keeps a list of official dishes. But yet the IBA keeps a list of official cocktails and keeps this up to date (!), as well. For example, I have sad news for all you vodka and orange juice fans out there: As of 2020 the Screwdriver is not an official cocktail anymore.

While a list of official cocktails is a bit silly, it’s also a nice dataset that I’ve now scraped and put into an iba-cocktails repo. This includes all the International Bartenders Association (IBA) Official Cocktails in CSV and JSON format as of 2023, from two different sources: The IBA website and Wikipedia’s list of IBA cocktails. My take on the difference between these sources is that the IBA website is more “official” (it’s their list, after all), but the Wikipedia recipes are easier to follow.

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Call ChatGPT (or really any other API) from R


It’s March 2023 and right now ChatGPT, the amazing AI chatbot tool from OpenAI, is all the rage. But when OpenAI released their public web API for ChatGPT on the 1st of March you might have been a bit disappointed. If you’re an R user, that is. Because, when scrolling through the release announcement you find that there is a python package to use this new API, but no R package.

I’m here to say: Don’t be disappointed! As long as there is a web API for a service then it’s going to be easy to use this service from R, no specialized package needed. So here’s an example of how to use the new (as of March 2023) ChatGPT API from R. But know that when the next AI API hotness comes out (likely April 2023, or so) then it’s going to be easy to interface with that from R, as well.

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P-value bowling


I recently went bowling, and you know those weird 3D-animated bowling animations that all bowling alleys seemed to show whenever you made a strike? They are still alive and well! (At least at my local bowling place). And then I thought: Can I get animations like that into my daily data science workflow? With Rstudio’s built-in Viewer tab, I absolutely could! Below you find the code for a much improved t.test function that gives you different animations when you hit a strike ($p < 0.01$), a spare ($p < 0.05$), a “near miss” ($p < 0.1$) and a complete miss ($p > 0.1$).

(If you think this is silly, then I agree. Roughly as silly as using ritualized p-value cutoffs to decide whether an experiment is a “success” or not.)

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Three strategies to tackle Big Data in R and Python


While Big Data™ might not be a buzzword anymore, data that’s uncomfortably large is not going anywhere. In this 30 min. screencast I go through three strategies you can use to tackle big data in R and Python. I also briefly cover three tools: duckDB, Apache Spark, and SnowflakeDB.

Here’s the full R code and the full Python code shown in the video. The source of charts.csv is the Spotify Charts dataset on Kaggle.

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Putting the top 100 R packages into a GIF


You can say what you want about Twitter, but the way animated GIFs are presented on that platform is pretty nice. It’s not so surprising that they play and loop, as one would expect them to do, but the nice thing is that if you click them, they pause. This tiny change in GIF behavior has resulted in a small cottage industry of GIF games (like here or here) and click-the-GIF-and-see-what-you-get animations (like Mario roulette). Here I’ll go through how I made one of the latter in R with gganimate showing the top 100 downloaded R packages. But first the actual GIF! Click to pause it and learn more about a popular R-package:

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Data from the file drawer: Remembering case-sensitive and case-insensitive words


I’ve dug up an old, never published, dataset that I collected back in 2013. This dataset fairly cleanly shows that it’s harder to remember words correctly if you also have to remember the case of the letters. That is, if the shown word is Banana and the subject recalls it as Banana, then it’s correct, but banana is as wrong as if the subject had recalled bapple. It’s not very surprising that it’s harder to correctly remember words when case matters, but the result and the dataset are fairly “clean”: Two groups, simple-to-understand experimental conditions, plenty of participants (200+), the data could even be analyzed with a t-test (but then please look at the confidence interval, and not the p-value!). So maybe a dataset that could be used when teaching statistics, who knows? Well, here it is, released by me to the public domain:


In the rest of this post, I’ll explain what’s in this dataset and how it was collected, and I’ll end with a short example analysis of the data. First up, here’s how the memory task was presented to the participants (click here if you want to try it out yourself):

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A simple maze generator for Bitsy


Bitsy is a wonderfully constrained little game maker for making tiny story-driven game-like experiences. Like, picture a Zelda game, but with a minimal color palette and the only thing you can do is walk around and talk to other characters. Thanks to this simplicity there’s a huge community around Bitsy and many many Bitsy-made games. Another simple thing is Bitsy’s game file format, which is just plain text. This makes it easy to have Bitsy as a “compilation target” and write programs that create Bitsy games. This is exactly what I’ve done!

Over the summer I put together a simple one-page web app that generates random mazes and code for the Bitsy game maker so that these mazes can be directly copy-n-pasted to Bitsy. Why? Maybe you want to have a maze as part of your game, then this gives you a place to start! Or maybe you just like to play autogenerated maze games. But mostly I was just fascinated with Bitsy and wanted to try something out. You can try out the maze generator here:

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Setting up plain markdown blogging in Hugo


I recently spent a lot of time migrating this blog from being generated by Octopress (RIP) to the Hugo static site generator. This was fairly painful. Not because any of these frameworks are bad, but just because I also had to migrate all of Octopress’s quirks and special cases to Hugo (slightly different RSS formats, markdown engines, file name conventions, etc.). So, when migrating to Hugo I had two things in mind:

  1. To go back in time to tell young Rasmus to never jump on the static site generator train and just get a bog-standard WordPress blog.
  2. Lacking a working time machine, to rely on as few Hugo-specific features as possible to make any inevitable future migration less painful.

Specifically, I wanted to write my blog posts in plain markdown only, and not rely on Hugo shortcodes (a Hugo-specific syntax for generating custom html content in markdown). I also wanted each markdown post and its related resources (images, linked files, etc.) to live together in the same folder and not spread out with posts being in content/blog and images being over in static/images, as is the default. The benefit of a setup like this is that I can write markdown posts in anything (say in Rstudio, which works great as a markdown editor) without having to change any image paths or add short codes to get it to work in Hugo later. Here I’ll go through the problems that I needed to solve to get to this setup.

The Hugo and  Markdown logos

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Can AI save us from the perils of P-values? at Bayes@Lund 2023


After a three year hiatus, the Bayes@Lund mini-conference was back in 2023, this year arranged by Dmytro Perepolkin and Ullrika Sahlin. A day packed with interesting talks and good discussions, three highlights being the two keynote speakers, Aubrey Clayton (author of Bernoulli’s Fallacy: Statistical Illogic and the Crisis of Modern Science) and Mine Dogucu (co-author of Bayes Rules!), and the priorsense package presented by Noa Kallioinen. This package implements diagnostics showing how influential the prior and likelihood is in a Bayesian model telling you, for example, that what you thought was an uninformative prior isn’t that uninformative, at all.

I also presented the short, silly talk: Can AI save us from the perils of P-values? (Spoiler alert… No)

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The Tidyverse in a Table


This was my submission to the 2020 RStudio Table Contest. For many good reasons it didn’t qualify, you can check out all those good reasons here: Winners of the 2020 RStudio Table Contest.

Some tables are beautiful. And yes, I’m talking about the stats-and-numbers kind of tables and not the ones you get at IKEA. Some tables show carefully selected statistics, with headers in bold and spacious yet austere design; the numbers rounded to just the right number of decimal places.

But here we’re not going to make a beautiful table, instead we’re making a useful table. In this tutorial, I’m going show you how to take all the documentation, for all the functions in the tidyverse core packages, and condense it into one single table. Why is this useful? As we’re going to use the excellent DT package the result is going to be an interactive table that makes it easy to search, sort, and explore the functions of the tidyverse.

Actually, let’s start with the finished table and then I’ll show you how it’s made. Or a screenshot of it, at least. To read on and to try out the interactive table check out my full submission here.

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