Tutorial on high-dimensional visualisation
Website: https://dicook.github.io/mulgar_tutorial
This is for scientists and data science practitioners who regularly work with high-dimensional data and models and are interested in learning how to better visualise them. You will learn about recognising structure in high-dimensional data, including clusters, outliers, non-linear relationships, and how this can be used with methods such as supervised classification, cluster analysis and non-linear dimension reduction.
Background: Participants should have a good working knowledge of R, and some background in multivariate statistical methods and/or data mining techniques.
Presenter:
Dianne Cook is Professor of Statistics at Monash University in Melbourne, Australia. Her research is on visualisation of high-dimensional data, and on bridging the gap between exploratory graphics and statistical inference. She is a Fellow of the American Statistical Association, past editor of the Journal of Computational and Graphical Statistics, and the R Journal, Member of the R Foundation, and elected member of the International Statistical Institute, and author of several R packages.
Structure of tutorial
Background: Participants should have a good working knowledge of R, and some background in multivariate statistical methods and/or data mining techniques.
| time | topic |
|---|---|
| 1:30-1:50 | Introduction: What is high-dimensional data, why visualise and overview of methods |
| 1:50-2:10 | Basics of linear projections, and recognising high-d structure |
| 2:10-2:30 | Effectively reducing your data dimension, in association with non-linear dimension reduction |
| 2:30-3:00 | Afternoon tea |
| 3:00-3:15 | More on dimension reduction |
| 3:15-4:00 | Understanding clusters in data using visualisation |
| 4:00-4:40 | Building better classification models with visual input |
| 4:40-5:00 | Bring your own data! |
Getting started
- You should have a reasonably up-to-date version of R and R Studio, eg RStudio 2024.09.1 and at least R version 4.4.0 (2024-04-24). Install the following packages, and their dependencies.
install.packages(c("readr", "tidyr", "dplyr", "ggplot2", "tourr",
"mulgar", "geozoo", "detourr", "palmerpenguins", "GGally",
"MASS", "randomForest", "mclust", "crosstalk", "plotly",
"viridis", "conflicted", "RColorBrewer"), dependencies=c("Depends", "Imports"))
Ideally, you install this package from GitHub:
remotes::install_github("casperhart/detourr")
-
Download the Zip file of materials to your laptop, and unzip it.
-
Open your RStudio be clicking on
tutorial.Rproj.
GitHub repo with all materials is https://dicook.github.io/mulgar_tutorial/.
Contributing
We welcome contributions. If you have a suggestion or a bug report please post this as an issue on GitHub.
The online version is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The work is written under a contributor code of conduct. By participating in this project you agree to abide by its terms and the terms of the license. Instructions for making contributions can be found in the contributing.md file.
