As part of the 30 Day Chart Challenge in April 2025, I made a (deliberately) bad chart, which didn’t follow any data visualisation best practices. In this blog post, we’ll go step-by-step through the process of making it better.

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.

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!

{PrettyCols} is an R package containing aesthetically pleasing colour palettes that are compatible with {ggplot2}. Find out about new features and palettes contained in the latest release!

An alternative to a traditional legend is using coloured text in a subtitle. In {ggplot2}, we can do this using the {ggtext} package. You can also do it using the new {marquee} package. How do they compare?

Combining parameterised reporting with conditional content in Quarto, makes it easy to create multiple versions of R tutorial worksheets - a questions worksheet for students, and a solutions worksheet for students to take home.

Waffle charts can be used to visualise counts or percentages of categorical data. This blog post describes a slightly unusual solution to creating sketchy looking waffle charts in R using the {sf} and {roughsf} packages.

Shinylive, GitHub Actions, and R - the magic combination to create an app that updates itself every week. This blog post gives a walk-through of scheduled data collection, building a Shiny app to display it, and deploying with Shinylive.

After running the ‘Forecasting with generalised additive models (GAMS) in R’ workshop with Forecasting for Social Good, there were a few questions that we didn’t get the chance to answer. This blog post aims to answer some of them.

With the latest 1.4 release of Quarto, it’s now possible to create PDF documents with Quarto using Typst. How does it compare to LaTeX, and is it actually easier to learn and use?

Finding ways to reduce manual tasks when programming, like copying and pasting files or code, can save you time and minimise the risk of errors. This blog post guides you through a few small changes to your R workflow to help reduce manual tasks and streamline your programming workflows in R.

After running the ‘Introduction to machine learning with {tidymodels}’ workshop as part of the R/Pharma 2023 Conference, there were a few questions that we didn’t get the chance to answer. This blog post aims to answer some of them.

Inspired by RJ Andrews, I created a typewriter-styled map of Scotland using {ggplot2} in R. This blog post explains the process of gathering elevation data, selecting a suitable typewriter font, and coding up a map!

If you find yourself regularly copying and pasting content between files, you can use R to do it for you! For repetitive tasks you can’t fully automate, using template files is a great way to save time and this blog post will show you how to make them in R.

Adding social media icons to your data visualisation is a great, concise way to put your name on your work, and make it easy for people to find your profile from your work. This blog post explains how to add social media icons to {ggplot2} charts.

Tidier.jl is a Julia implementation of the {tidyverse}, and after 10 weeks of data wrangling and plotting #TidyTuesday data in Julia, I wanted to share what I’ve learnt about Julia as an R user.

Flowcharts can be a useful way to visualise complex processes, and the new R package {ggflowchart} makes them easy to create in R. This blog post shows you how.

Inspired by a Visual Capitalist chart, this blog post will show you how to utilise spatial data R packages in a slightly unusual way to create an infographic in the shape of a cracked egg in R.

Time series analysis can uncover hidden structures in data collected over time. In this blog post, I’ll use {tsfeatures} to extract time series features and {tidymodels} to predict which sound recordings of heartbeats contain heart murmurs, using those time series features.

{rvest} is an R package within the {tidyverse} which helps you scrape data from web pages. This blog post will showcase an example of scraping data from Wikipedia on London Marathon races and winners.

Adding custom styling to documents makes them look more professional. This blog post will take you through the process of making a Quarto extension to create a reusable custom template for good-looking PDFs.

{PrettyCols} is an R package containing aesthetically pleasing colour palettes that are compatible with {ggplot2}. Find out about new features and palettes contained in the latest release!

Back in July 2022 at rstudio::conf(2022), Posit (formerly RStudio) announced the release of Shiny for Python. I wanted to see how the two compared - so I built the same Shiny app twice! This blog post highlights a few of the differences, and things that were a little tricky switching to Shiny for Python.

Sometimes you might need to use R. Sometimes you might need to use Python. Sometimes you need to use both at the same time. This blog post shows you how to combine R and Python code using {reticulate} and output the results using Quarto.

After another 52 data visualisations created for #TidyTuesday, it’s time for the annual round-up! Read this blog post for some interesting R packages discovered, a few new I’ve tricks learnt, and the data visualisations I’d like to do again.

Throughout the #30DayChartChallenge I made most of my maps with R. This blog post details the R packages I find myself using most often when visualising spatial data.

Forget about Spotify Wrapped and make your own #RStats Wrapped instead! This blog post will show you how to find your most used functions and make a graphic with {ggplot2}!

The #30DayMapChallenge is a month-long mapping, cartography, and data visualization challenge aimed at the spatial community. Here are the things I’ve learnt from participating in the challenge for a second time.

Let’s say you need to understand how your data changes within a day, and between different days. Functional analysis is one approach of doing just that so here’s how I applied functional analysis to some air pollution data using R!

This tutorial blog will show you how to use GitHub actions to automatically update a data source and re-deploy a Shiny app, with an example Shiny application which uses {rtweet} to browse R-related tweets.

An introduction to {PrettyCols} - a new R package containing aesthetically pleasing colour palettes that are compatible with {ggplot2}.

If, like me, you mostly scroll through Twitter on your phone, you might want to consider designing your content specifically aimed at people who look at Twitter on their phone. Here’s how to do it in R, with a little help from Quarto.

This tutorial blog will walk through the process of getting data from Strava using {rStrava}, making a map of it, and animating the map with {gganimate}.

Flowcharts can be a useful way to visualise complex processes. This tutorial blog will explain how to create one using {igraph} and {ggplot2}.

One of my goals for 2021 was to participate in the #TidyTuesday challenge on a regular basis. This blog post reflects on the past year of data visualisations.

Earlier this week, whilst curating for @WeAreRLadies, I tweeted a thread on my thought process for this week’s #TidyTuesday challenge. This blog post expands on the thoughts in that thread.

The #30DayMapChallenge is a daily mapping, cartography, and data visualization challenge aimed at the spatial community.