SSA SCV Tutorial: Visualising Highdimensional Data with R
Website: https://StatSocAus.github.io/tutorial_highd_vis
This is for scientists and data science practitioners who regularly work with highdimensional data and models and are interested in learning how to better visualise them. You will learn about recognising structure in highdimensional data, including clusters, outliers, nonlinear relationships, and how this can be used with methods such as supervised classification, cluster analysis and nonlinear 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. She is a world leader in data visualisation, especially the visualisation of highdimensional data using tours with lowdimensional projections, and projection pursuit. She also works 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, and the R Journal, elected Ordinary Member of the R Foundation, and elected member of the International Statistical Institute.
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 

1:001:20  Introduction: What is highdimensional data, why visualise and overview of methods 
1:201:45  Basics of linear projections, and recognising highd structure 
1:452:30  Effectively reducing your data dimension, in association with nonlinear dimension reduction 
2:303:00  BREAK 
3:003:45  Understanding clusters in data using visualisation 
3:454:30  Building better classification models with visual input 
Getting started
 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 (20230616). 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"), 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://statsocaus.github.io/tutorial_highd_vis/.