The GGobi Book

Order from: Springer, Amazon. Available now. Instructors should note that solutions for the exercises at the end of each chapter are available from the publisher.

Contributions from Andreas Buja, Duncan Temple Lang, Heike Hofmann, Hadley Wickham, and Michael Lawrence

Licensing

The R code on this page is licensed under the MIT license, which basically means you can do whatever you want with it. The lecture notes and slides are licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 License, which means you can modify and redistribute these slides, but you need to acknowledge the original source, and you can't make money off of them.

Course notes

Infovis 2007:

• Introduction (PDF, 160k)
• Toolbox (PDF, 3.1 meg)
• Missing values (PDF, 1.3 meg)
• Classification (PDF, 2.4 meg)
• Clustering (PDF, 1.4 meg)
• Inference (PDF, 580k)

Introduction

Free sample chapter: Introduction

R code

• GGobi figures output
• R Code to make figures

Toolbox

Movies accompanying figures (in quicktime format)

• 2.9: 2D projection pursuit
• 2.10: 1D projection pursuit
• 2.12: One-to-one linking
• 2.13, 2.14: Categorical brushing
• 2.15: Point to line linking
• 2.16: Transient vs persistent brushing
• 2.17: Identifying points
• 2.18: Scaling, changing the aspect ratio

Missing values

Free sample chapter: Missing values.

Movies accompanying figures (in quicktime format)

• 3.2, 3.3: Setting missings 10% below - effect on tour and par coords
• 3.4: Using the shadow matrix to locate missings
• 3.7, 3.8: Multiple imputation

R code

• R Code

Supervised classification

Movies accompanying figures (in quicktime format)

• 4.3, 4.4: Finding variables which separate regions
• 4.6: Separating northern oils
• 4.7: Separating southern oils
• 4.8, 4.9: Checking assumptions for LDA, and misclassifications from the model
• 4.10: Improving the tree model using a manual tour
• 4.11, 4.12: Examing the random forest model
• 4.13: Examing the neural network model
• 4.14, 4.15: Examing the Support vector machine model
• 4.16: Looking at boundaries between classes

R code

• LDA
• Trees
• Random forests
• Neural nets
• Support vector machines
• Boundaries

Errata

Cluster analysis

Movies accompanying figures (in quicktime format)

• 5.3: Spin and brush
• 5.7: Hierarchical clustering
• 5.9: Model-based clustering
• 5.10: Self-organizing maps
• 5.11, 5.12: Comparing results and characterizing clusters

R code

• Hierarchical clustering
• Model-based clustering
• Self-organizing maps

Errata

Miscellaneous Topics

Movies accompanying figures (in quicktime format)

• 6.4, 6.5: Exploring longitudinal data
• 6.11, 6.12, 6.13, 6.14: Multidimensional scaling

R code

• Inference
• Longitudinal data
• Networks
• MDS

Data

Data Descriptions(Feb 2007, PDF, 1.5Mb)

• Tips: csv, xml
• Australian crabs: csv, xml
• Olive oils: csv, xml
• Flea beetles: csv, xml
• PRIM7: csv, xml
• TAO: csv, xml
• PBC: csv
• Spam: csv, xml
• Wages: xml
• Rat gene expression: csv, xml
• Arabidopsis gene expression: xml
• Music: Full data csv, xml; Smaller set of variables csv, xml; Clustering results csv, xml; SOM poor fit, better fit;
• Cluster challenge: csv, csv The first challenge data has standard types of clusters, the second is more difficult.
• Adjacent Transposition Graph: 4D, 5D,
• Florentine Families: xml
• Morse Code Confusion Rates: xml
• Personal Social Network: xml

Additional material

• More complete case study on Wages data (18 meg)
• Inference for data visualisation Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F, Wickham, H. (2009) Statistical Inference for Exploratory Data Analysis and Model Diagnostics, Royal Society Philosophical Transactions A, 367:4361-4383.

Software

• GGobi
• R
• Utility routines in R
• R packages used in the book: rggobi, DescribeDisplay, norm, Hmisc, MASS, rpart, randomForest, nnet, e1071, classifly, mclust, som, graph, SNAData