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SSA SCV Tutorial: Creating data plots for effective decision-making using statistical inference with R
Website: https://StatSocAus.github.io/tutorial_effective_data_plots
This is for statisticians and data science practitioners who are interested in improving their data visualisation skills.
Presenter: Dianne Cook is Professor of Business Analytics at Monash University in Melbourne, Australia. She is a world leader in data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit. She is currently focusing on bridging the gap between exploratory graphics and statistical inference. Di is a Fellow of the American Statistical Association, past editor of the Journal of Computational and Graphical Statistics, current editor of the R Journal, elected Ordinary Member of the R Foundation, and elected member of the International Statistical Institute.
- Review of making effective plots using ggplot2's grammar of graphics:
- Organising your data to enable mapping variables to graphical elements,
- Common plot descriptions as scripts,
- Do's and don'ts following cognitive perception principles.
- Making decisions and inferential statements based on data plots
- What is your plot testing? Determining the hypothesis based on the type of plot.
- Creating null samples to build lineups for comparison and testing.
- Conducting a lineup test using your friends to determine whether what you see is real or spurious, and to determine the best design for your plot.
Background: Participants should have a good working knowledge of R, and tidy verse, and some experience with ggplot2. Familiarity with the material in R4DS (https://r4ds.hadley.nz) is helpful.
| time | topic |
|---|---|
| 1:00-1:15 | Why, philosophy and benefits |
| 1:15-1:35 | Organising data to map variables to plots |
| 1:35-2:05 | Making a variety of plots |
| 2:05-2:30 | Do but don’t, and cognitive principles |
| 2:30-3:00 | BREAK |
| 3:00-3:20 | What is your plot testing? |
| 3:20-3:35 | Creating null samples |
| 3:35-4:00 | Conducting a lineup test |
| 4:00-4:30 | Testing for best plot design |
- You should have a reasonably up to date version of R and R Studio, eg RStudio RStudio 2023.06.2 +561 and R version 4.3.1 (2023-06-16). Install the following packages, and their dependencies.
install.packages(c("ggplot2", "tidyr", "dplyr", "readr", "stringr", "nullabor", "colorspace", "palmerpenguins", "broom", "ggbeeswarm"), dependencies=c("Depends", "Imports"))
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Download the Zip file of materials to your laptop, and unzip it.
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Open your RStudio be clicking on tutorial.Rproj.
GitHub repo with all materials is https://statsocaus.github.io/tutorial_effective_data_plots/.