The 30 Day Chart Challenge is a data visualisation challenge where participants create a chart for each daily prompt. In this blog post, I’ll recap the charts I made during the 2025 challenge and discuss the data and tools I used, alongside what I learned during the process.

Observable is a JavaScript-based programming framework for data exploration and visualisation, which is popular for creating interactive charts and dashboards. This blog post demonstrates why and how R users can integrate Observable into their existing R workflows.

If you’ve ever wondered what data-driven art is, or why people make it, then reading this blog post should help to answer those questions. It also includes an example of data-driven art, which can be created in Python or R, and talks through the process of getting started with your own art.

In data visualisations, colours are often used to show values or categories of data. However, sometimes you might not be able to or want to use colour. This blog post discusses some tips for designing better visualisations when you’re restricted to a monochrome palette.

Whether you’re building data visualisations or generative art, at some point you will likely need to consider which colours to use in R. This blog post describes different ways to define colours, how to make good choices about colour palettes, and ways to generate your own colour schemes.

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.