Take a data plot and make it better
Website: https://dicook.github.io/tutorial_make_better_data_plots/
This tutorial is for data analysts and statisticians who work regularly with data and want to improve their ability to make effective data visualisations. You will learn general principles that can get you off to a good start in making a data plot, and how to use these to layer up to a more effective communication. It will be hands-on, so we will code together and make the plots and improve them. There will be an opportunity to bring your own plot and data, and work together on making it better.
Presenter: Dianne Cook, a Professor of Statistics at Monash University in Melbourne, Australia, is a global leader in data visualisation. She has delivered over 100 invited talks internationally and published extensively on various aspects of data visualisation. Dr. Cook is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, past editor of the Journal of Computational and Graphical Statistics, and the R Journal. She has served as a Board Member of the R Foundation and is currently the co-chair of the Statistical Computing and Visualisation Section of the Statistical Society of Australia.
Background: You should have a basic understanding of R, be familiar with tidy data, and know how to use ggplot2. It’s also helpful if you’ve read the material in R4DS and taken a first-year statistics course.
Structure of tutorial
| length (mins) | topic | description |
|---|---|---|
| 5 | Overview | What we will cover |
| 10 | Tidy data | Make the data do the work to more easily create a plot |
| 20 | Grammar of graphics | Defining a plot succinctly and clearly |
| 15 | Guided exercises | |
| 15 | Cognitive principles | Equipping your toolbox to work for many purposes |
| 15 | Guided exercises | |
| 10 | Identifying poor elements | Develop skills in identifying what can be improved in a plot |
| 30 | BREAK | |
| 15 | Fixing the plot design | Practicing improving a plot based on cognitive principles, tidy data and the grammar of graphics |
| 10 | Guided exercises | |
| 15 | Styling and theming | Common ways to polish a plot or ensemble of plots |
| 10 | Guided exercises | |
| 10 | Is the pattern visible and real | Check what you are trying to communicate, using statistical thinking |
| 10 | Guided exercises | |
| 20 | Making your own plot better | Your turn to have a guided practice at improving a plot from your own work |
Zip file of materials (COMING)
Getting started
- (This is a head start, but final list available before tutorial) You should have a reasonably up to date version of R and R Studio, eg RStudio 2024.09.0 +375 and R version 4.5.0 (2025-04-11). Install the following packages, and their dependencies.
install.packages(c("ggplot2", "tidyr", "dplyr", "readr", "stringr", "nullabor", "colorspace", "palmerpenguins", "broom", "ggbeeswarm", "vcd", "MASS", "conflicted"), dependencies=c("Depends", "Imports"))
-
Download the Zip file of materials (COMING) to your laptop, and unzip it.
-
Download just the R scripts, (COMING)
-
Open your RStudio be clicking on
tutorial.Rproj.
GitHub repo with all materials is https://dicook.github.io/tutorial_make_better_data_plots/.
Resources:
- Healy (2018) Data Visualization: A practical introduction
- Wilke (2019) Fundamentals of Data Visualization
Copyright: Dianne Cook 2025
These materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
