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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👇

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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Image Dithering in R


This January I played the most intriguing computer game I’ve played in ages: The Return of the Obra Dinn. Except for being a masterpiece of murder-mystery storytelling it also has the most unique art-style as it only uses black and white pixels. To pull this off Obra Dinn makes use of image dithering: the arrangement of pixels of low color resolution to emulate the color shades in between. Since the game was over all too quickly I thought I instead would explore how basic image dithering can be implemented in R. If old school graphics piques your interest, read on! There will be some grainy looking ggplot charts at the end.

(The image above is copyright Lucas Pope and is the title screen of The Return of the Obra Dinn)

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Yet another visualization of the Bayesian Beta-Binomial model


The Beta-Binomial model is the “hello world” of Bayesian statistics. That is, it’s the first model you get to run, often before you even know what you are doing. There are many reasons for this:

That’s why I also introduced the Beta-Binomial model as the first model in my DataCamp course Fundamentals of Bayesian Data Analysis in R and quite a lot of people have asked me for the code I used to visualize the Beta-Binomial. Scroll to the bottom of this post if that’s what you want, otherwise, here is how I visualized the Beta-Binomial in my course given two successes and four failures:

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My introductory course on Bayesian statistics


So, after having held workshops introducing Bayes for a couple of years now, I finally pulled myself together and completed my DataCamp course: Fundamentals of Bayesian Data Analysis in R! 😁

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A Stan case study, sort of: The probability my son will be stung by a bumblebee


The Stan project for statistical computation has a great collection of curated case studies which anybody can contribute to, maybe even me, I was thinking. But I don’t have time to worry about that right now because I’m on vacation, being on the yearly visit to my old family home in the north of Sweden.

What I do worry about is that my son will be stung by a bumblebee. His name is Torsten, he’s almost two years old, and he loves running around barefoot on the big lawn. Which has its fair share of bumblebees. Maybe I should put shoes on him so he wont step on one, but what are the chances, really.

Well, what are the chances? I guess if I only had

I could figure that out. “How hard can it be?”, I thought. And so I made an attempt.

Getting the data

To get some data on bumblebee density I marked out a 1 m² square on a representative part of the lawn. During the course of the day, now and then, I counted up how many bumblebees sat in the square.

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Video Introduction to Bayesian Data Analysis, Part 3: How to do Bayes?


This is the last video of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched the other parts yet, I really recommend you do that first: Part 1 & Part 2.

This third video covers the how? of Bayesian data analysis: How to do it efficiently and how to do it in practice. But covers is really a big word, briefly introduces is really more appropriate. Along the way I will then briefly introduce Markov chain Monte Carlo, parameter spaces and the computational framework Stan:

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Video Introduction to Bayesian Data Analysis, Part 2: Why use Bayes?


This is video two of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. If you haven’t watched part one yet, I really recommend you do that first, here it is. This second video covers the why? of Bayesian data analysis: Why (and when) use it instead of some other method of analyzing data?

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Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?


This is video one of a three part introduction to Bayesian data analysis aimed at you who isn’t necessarily that well-versed in probability theory but that do know a little bit of programming. I gave a version of this tutorial at the UseR 2015 conference, but I didn’t get around doing a screencast of it. Until now, that is! I should warn you that this tutorial is quite handwavey (but it’s also pretty short), and if you want a more rigorous video tutorial I can really recommend Richard McElreath’s YouTube lectures.

This first video covers the what? of Bayesian data analysis with part two and three covering the why? and the how?. I expect to be able to record part two and three over the next couple of weeks but, for now, here is part one:

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