stat645

Hadley Wickham. hadley@rice.edu
Class: 9:35-10:50am Monday and Wednesday. DH 1046.

Course syllabus

Lectures

Week 13. Tour of visualisations

• J. Heer, M. Bostock, and V. Ogievetsky. A tour through the visualization zoo. Queue, 8(5):20-30, 2010. ISSN 1542-7730.
• J. Heer, N. Kong, and M. Agrawala. Sizing the horizon: The effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the 27th international conference on Human factors in computing systems, pages 1303-1312. ACM, 2009.

Week 12. Graphical inference

• Introduction to nullabor
• Data: threept.csv

Readings

• A. Buja, D. Cook, H. Hofmann, M. Lawrence, E.-K. Lee, D. F. Swayne, and H. Wickham. Statistical inference for exploratory data analysis and model diagnostics. Royal Society Philosophical Transactions A, 367 (1906):4361-4383, 2009.
• H. Wickham, D. Cook, H. Hofmann, A. Buja. Graphical Inference for Infovis. Transactions on Information Visualisation, 2010.

Week 11. Model visualisation.

• Slides
• Data: kyphosis, wine, iris

Readings

• A. Gelman. Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13 (4):755-779, 2004.
• (Optional): Read the discussion by Andreas Buja, and then the rejoinder.
• H. Wickham, D. Cook and H. Hofmann. Visualising statistical models: removing the blindfold.. Unpublished.

Week 10. Brushing and selection

• Linked brushing slides
• Data: places.csv, stormtracks.xml, swiss-meifly.xml

Readings/watchings

• Brushing a scatterplot matrix movie
• R. A. Becker and W. S. Cleveland. Brushing Scatterplots. Technometrics, 29(2):127-142, 1987.
• G. J. Wills. Natural selection: Interactive subset creation. Journal of Computational and Graphical Statistics, 9(3):544-557, 2000.

Week 9. The tour for clustering and classification

• Tour intro slides
• Data: olives.xml, flea.xml, laser.xml, prim7.csv, prim7.xml, expo.csv

Readings

• H. Wickham, D. Cook, H. Hofmann and A. Buja. tourr: An R package for exploring multivariate data with projections. Journal of Statistical Software, to appear.
• D. Cook, E.-K. Lee, A. Buja, and H. Wickham. Grand tours, projection pursuit guided tours and manual controls. In C. Chen, W. Hardle, and A. Unwin, editors, Handbook of Data Visualization, Springer Handbooks of Computational Statistics, chapter III.2, pages 295-314. Springer, 2008.
• (Optional) J. Symanzik, A. Braverman, and Q. Luo. New applications of the image grand tour. Computing Science and Statistics, 34:500-512, 2002.

Week 8. Introduction to GGobi

• Olives data as csv file
• Windows: Download and run installer and then install rggobi package
• Mac: Install GGobi and rggobi by following these instructions.
• Read about the ggobi toolbox
• A. Buja, D. Cook, and D. F. Swayne. Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics, 5 (1):78-99, 1996.
• Introduction to GGobi slides

Week 7. Colour

Worksheet

• M. Stone. Representing Colors as Three Numbers. IEEE Computer Graphics and Applications, 25 (4):78-85, 2005.
• A. Zeileis, K. Hornik, P. Murrell. Escaping RGBland: Selecting Colors for Statistical Graphics. Department of Statistics and Mathematics, Wirtschaftsuniversitat Wien, Research Report Series, 2007.

Week 6. Mosaic and parallel coordinate plots

• Slides
• Data: happy.txt, olives.txt

Readings

• H. Hofmann. Exploring categorical data: Interactive mosaic plots. Metrika, 51(1):11-26, 2000.
• E. J. Wegman. Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85 (411):664-675, 1990.

Week 5. Introduction to Mondrian

• Mondrian website. Install mondrian, and read through the quickstart from histograms to barcharts.
• Introduction to mondrian
• Diamonds data in tab delimited format
• More mondrian resources
• More on mondrian.
• Birthweight data (from interactivegraphics.org)

Week 4. Perception of discrete variables.

• Worksheet
• Code and data

Readings

• Preattentive vision (what's different)
• Gestalt principles (what's the same)

Week 3. Perception of continuous variables.

• Worksheet (my answers)
• Worksheet
• Code and data
• Discussion slides, by Hadley

Readings

• W. S. Cleveland and R. McGill. Graphical perception: Theory, experimentation and application to the development of graphical methods. Journal of the American Statistical Association, 79 (387):531-554, 1984.

• J. Heer and M. Bostock. Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. In CHI 2010, 2010.

Week 2: Layered grammar of graphics

In class

• Discussion slides, by Brian and Xuan

Readings

• H. Wickham. A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19 (1):3-28, 2010.

Week 1: Introduction

Revision

• Worksheet (my answers)
• Code and data
• My complete code

Clean data

• Lecture
• Billboard top 100 for 2000: raw, cleaned.

  library(lubridate)
  pop <- read.csv("pop-2000-clean.csv", stringsAsFactors = F)
  pop$date <- ymd(pop$date)

• Complete R code
• Complete tb example - code and data

Important deadlines

• Project 1:Feb 21
• Project 2:Apr 4
• Project 3:Apr 22