<p>Visualising high-dimensional data</p> <p>This workshop will focus on parts A and B, and if time allows we will venture into high dimensions, part C:</p> <p>A. Review basic visualisation and inference with graphics: This part covers making plots using the grammar of graphics and how this fits into statistical inference. We will use the packages ggplot2 and nullabor.</p> <p>B. Plotting multiple dimensions in a single static plot, adding interaction: The building blocks to viewing high-dimensions are generalised pairs plots and parallel coordinate plots, available in the R package GGally. There are many variations and options that will be discussed, along with making these interactive with the plotly package.</p> <p>C. Using dynamic plots (tours) to examine models in the data space, beyond 3D: This part will cover the use of tours to examine multivariate spaces, in relation to dimension reduction techniques like principal component analysis and t-SNE, supervised and unsupervised classification models. We will also examine high-dimension, low-sample size problems. The tourr and spinifex packages will be used.</p> <p>The workshop is interactive, bring your laptop and work along with the instructor, and do challenge exercises.</p> <p>Materials are designed for an intermediate audience, users who are familiar with R, basic visualisation and tidyverse tools, and who would like to improve their knowledge about data visualisation.</p> <p>Biography</p> <p>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 focussing on bridging the gap between exploratory graphics an statistical inference. Di is a Fellow of the American Statistical Association, was recently the editor of the Journal of Computational and Graphical Statistics, and has been elected as an Ordinary Member of the R Foundation. Several of her students have won the prestigious American Statistical Association John Chambers Software Award, including Hadley Wickham, Yihui Xie, Carson Sievert, and most recently, Monash student Earo Wang.</p>