Information visualization is the pictorial representation of data.
This course will examine a variety of issues related to data visualization from a largely psychological perspective, but will also touch upon other related communities of research and practice related to this topic:
We will consider visualization methods for a wide range of types of data from the points of view of both the viewer and designer/producer of graphic displays.
Assignment: Please prepare a 1-2 page summary of something(s) you found on the topics below. Not for grading; I’ll ask a few of you to speak on this next class.
Howard Wainer (1984) How to Display Data Badly. American Statistician 38 137-147
Check out Additional resources for Session 1
Pros and Cons of Chart Taxonomies. Are these chart taxonomies good or evil?
TED talks: Hans Rosling, The Best Stats …
Check out Additional resources for Session 2
Friendly, M. A Brief History of Data Visualization
Friendly, M. & Denis, D. The early origins and development of the scatterplot
Jeff Heer, A Brief History of Data Visualization, gives a lecture on his take on this history, interpreting and extending my work from a computer science perspective.
Check out Additional resources for Session 3
The next two sessions, devoted to developing graphs with
ggplot2 and related methods will take place in the Hebb lab, Rm 059 BSB.
getting started with ggplot This web page describes installing
ggplot2 and the
tidyverse of related packages. It also contains some useful links for learning to use
The online chapter, Data Visualization of the book, R for Data Science is an excellent brief introduction to
ggplot2. Another chapter in this book, Graphics for Communication takes up some more advanced topics.
A free online book, An Introduction to Statistical and Data Sciences via R. The focus is on the
tidyverse of R packages for data manipulation and
ggplot2 for graphics. Also covers data modeling (regression), hypothesis testing, etc.
David Robinson. broom: An R Package for Converting Statistical Analysis Objects Into Tidy Data Frames. See also this broom presentation
Software Carpentry. Dataframe Manipulation with dplyr. A very nice interactive tutorial on manipulating data frames using
dplyr and other tidy tools. Contains some Challenge questions and nice diagrams showing the effects of
group_by and other tidy verbs. This is part of a larger series, R for Reproducible Scientific Analysis.
A collection of other R examples is available as R scripts, with some markup so that you can run them with Compile Report (Ctrl+Shift+K).
broomfor tidy model visualization. Shows the tools used to fit a collection of models for
lifeExpand visualize various model summaries.
These will take place June 15 & 17. Details will be posted to eClass Students page.
Copyright © 2018 Michael Friendly. All rights reserved.
friendly AT yorku DOT ca