| Description | Link |
|---|---|
| Saved 2D tour path for the aflw data | <a href='https://dicook.github.io/mulgar_book/data/aflw_pct.rda'> aflw_pct.rda </a> |
| Saved clusters of the penguins data from detourr | <a href='https://dicook.github.io/mulgar_book/data/detourr_penguins.csv'> detourr_penguins.csv </a> |
| Saved clusters of the fake trees data from detourr | <a href='https://dicook.github.io/mulgar_book/data/fake_trees_sb.csv'> fake_trees_sb.csv </a> |
| Tidied penguins data | <a href='https://dicook.github.io/mulgar_book/data/penguins_sub.rda'> penguins_sub.rda </a> |
| Saved 2D tour path for penguins data | <a href='https://dicook.github.io/mulgar_book/data/penguins_tour_path.rda'> penguins_tour_path.rda </a> |
| risk survey | <a href='https://dicook.github.io/mulgar_book/data/risk_MSA.rds'> risk_MSA.rds </a> |
| penguins NN model | <a href='https://dicook.github.io/mulgar_book/data/penguins_cnn> penguins_cnn </a> |
| fashion MNST NN model | <a href='https://dicook.github.io/mulgar_book/data/fashion_cnn> fashion_cnn </a> |
| penguins SHAP values | <a href='https://dicook.github.io/mulgar_book/data/p_exp_sv.rda> p_exp_sv.rda </a> |
Appendix C — Links to Book Code and Additional Data
The code and data and an RStudio project can be downloaded from the book GitHub repo https://github.com/dicook/mulgar_book/.
C.1 Additional data
Table C.1 lists additional data available on the book web site at https://github.com/dicook/mulgar_book.
C.2 Code files
Table C.2 lists additional data available on the book web site at https://dicook.github.io/mulgar_book/code.
| Chapter | Link | Filename |
|---|---|---|
| 1 Picturing high dimensions | 1-intro.R | 1-intro.R |
| 2 Technical details | 2-notation.R | 2-notation.R |
| 3 Dimension reduction overview | 3-overview-dimred.R | 3-overview-dimred.R |
| 4 Principal component analysis | 4-pca.R | 4-pca.R |
| 5 Non-linear dimension reduction | 5-nldr.R | 5-nldr.R |
| 6 Introduction to clustering | 6-overview-clust.R | 6-overview-clust.R |
| 7 Spin-and-brush approach | 7-spin-and-brush.R | 7-spin-and-brush.R |
| 8 Hierarchical clustering | 8-hierarchical.R | 8-hierarchical.R |
| 9 k-means clustering | 9-kmeans.R | 9-kmeans.R |
| 10 Model-based clustering | 10-model-based.R | 10-model-based.R |
| 11 Self-organizing maps | 11-som.R | 11-som.R |
| 12 Summarising and comparing clustering results | 12-summary-clust.R | 12-summary-clust.R |
| 13 Introduction to supervised classification | 13-intro-class.R | 13-intro-class.R |
| 14 Linear discriminant analysis | 14-lda.R | 14-lda.R |
| 15 Trees and forests | 15-forests.R | 15-forests.R |
| 16 Support vector machines | 16-svm.R | 16-svm.R |
| 17 Neural networks and deep learning | 17-nn.R | 17-nn.R |
| 18 Diagnostics for classification models | 18-summary-class.R | 18-summary-class.R |
| A Toolbox | A-toolbox.R | A-toolbox.R |
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