The {messy} R package takes a clean dataset, and randomly adds mess to create data more similar to that which you’d find in the real world. This is an easy way for educators to create data sets that give students the opportunity to practice their data wrangling skills without having to change all of their examples.

A blog post about some of the things that have helped me in getting comfortable talking about tech (although hopefully some of the advice is also useful for non-tech talks!).

After running the ‘Parameterized plots and reports with R and Quarto’ workshop as part of the R/Pharma 2024 Conference, there were a few questions that we didn’t get the chance to answer. This blog post aims to answer some of them.

There are many reasons why learning about generative art can be beneficial for data scientists. This blog post will explain what generative art is, where to find some inspiration, and how to start creating your own generative art systems.

A poor choice of axes for your chart can make it more difficult to understand, and in some cases, suggest misleading conclusions. In this blog post, we’ll look at five ways to make better choices about your axes and stop relying on default settings.

This blog post explains the process of manipulating images using {imager} in R, processing pixel data, and using it to create a new version of an image that looks like it was printed with a typewriter!