Designing better data plots at the Burnet Institute
Website: https://dicook.github.io/burnet-workshop/
This workshop is for data analysts at the Burnet Institute, designed to provide skills for improving plot design. It covers these topics:
- Data visualisation principles, including accessibility, colour choices, proximity, appropriate mapping of variables to visual elements
- Identifying poor elements in a plot
- Fixing a plot design
- Styling and theming plots
- Quantitatively assessing the best plot design, using lineups
- Incorporating uncertainty
- Writing alt-text for data plots
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 tidy data, wrangling and plotting data using a grammar. It’s also helpful if you’ve read the material in R4DS and taken a first-year statistics course.
Structure of tutorial
| time | topic | description |
|---|---|---|
| 1:00 | Data visualisation principles | Accessibility, colour choices, proximity, appropriate mapping of variables to visual elements |
| 1:30 | Identifying poor elements in a plot | With examples from the institute we will develop a list of what is good and what is not for each. |
| 2:00 | Fixing a plot design | We will work with the examples from the institute to improve the designs in steps (if we have data and code available). |
| 2:30 | BREAK | |
| 3:00 | Quantitatively assessing the best plot design, and incorporating uncertainty | A lineup helps to assess whether the structure in a data plot is different from noise. We will show how this can be used to determine if one design makes it easier to read the structure than another. A second aspect of this session will be ways to incorporate representation of uncertainty in a plot to better communicate if patterns are important or not. |
| 3:30 | Styling and theming plots, and writing alt-text descriptions | Making plots have the same look and feel is easier if a template design is available. Here we will discuss ways to construct a template that can be used repeatedly. We will also discuss writing effective text descriptions of data visualisations to make them more accessible for vision-impaired readers, an important (and often required) aspect of report writing today. |
| 4:00 | Polishing your plots | This will be a hands-on session taking the previous designs, using lineups to determine which is better for communication, writing alt-text descriptions and developing themes. |
Software used for slides
R 4.5.0 (2025-04-11) – “How About a Twenty-Six” and RStudio 2025.05.0+496 “Mariposa Orchid” Release (f0b76cc00df96fe7f0ee687d4bed0423bc3de1f8, 2025-05-04)
install.packages(c("tibble",
"ggplot2",
"tidyr",
"dplyr",
"readr",
"readxl",
"stringr",
"forcats",
"colorspace",
"patchwork",
"broom",
"ggbeeswarm",
"ggmosaic",
"nullabor",
"gapminder",
"sf",
"cartogram",
"ggthemes",
"conflicted",
"ggstats",
"ggdist",
"brolgar",
"lme4"), dependencies=c("Depends", "Imports"))
Source material
GitHub repo with all materials is https://github.com/dicook/burnet-workshop/.
Attribution
Copyright: Dianne Cook 2025
These materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
