Course Description

Information visualization is the pictorial representation of data.

  • Successful visualizations capitalize on our capacity to recognize and understand patterns presented in information displays.
  • Conversely, they require that writers of scientific papers, software designers and other providers of visual displays understand what works and what does not work to convey their message.

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:

  • history of data visualization,
  • computer science and statistical software,
  • visual design,
  • human factors.

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.

Overview & Introduction

  • Lecture notes: 1up PDF; 4up PDF

  • 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.

    • Blogs: Explore one or two of the blogs or web resources listed in the lecture notes, Readings, or in Resources. Find a few examples of kinds of graphs you find interesting or worth exploring more.
    • Good/bad graphs Explore the literature in your area, say several issues of one journal. Find one example of a data display (graph or table) that communicates particularly well, and one example of a display that communicates badly.


  • Books, readings, blogs & web resources
  • Goals of visualization; visualization as communication
  • Roles of graphics in data analysis & presentation
  • Effective data display
  • Graphs: good/bad, excellent/evil

Varieties of information visualization

  • Lecture notes: 1up PDF; 4up PDF
  • Assignment:
    • From the readings that you have done so far, find one example of a data graph that attempts to tell an interesting story of a useful topic. How well does it succeed? How could it be improved?


  • Data graphs: 1D – 3D
  • Thematic maps
  • Network and tree visualization
  • Animation & interactive graphics


History of data visualization


  • Overview: The Milestones Project
  • The first statistical graph
  • The Big Bang: William Playfair
  • Moral statistics: the birth of social science
  • Graphs in the public interest: Nightingale, Farr and Snow
  • The Golden Age
  • Case study: Re-Visions of Minard


Graphical Perception


  • Perception & Cognition
    • Encoding, decoding
    • Top-down vs. bottom-up processing
  • Perceptual aspects
    • Illusions
    • Gestalt factors
    • Accuracy of decoding
  • Cognitive aspects
    • Memory
    • Color


The Language of Graphs: from Bertin to GoG to ggplot2


  • Early attempts at standardization of graphs
  • Bertin: Semiology of Graphics
  • Graphics programming languages
  • Wilkinson: The Grammar of Graphics
  • Wickham: ggplot2

ggplot2: Basics

The next two sessions, devoted to developing graphs with ggplot2 and related methods will take place in the Hebb lab, Rm 059 BSB.

Lecture notes & tutorial


ggplot2: Going further in the tidyverse


  • Data wrangling: getting your data into shape
  • Visualizing models: broom
  • ggplot2 extensions
  • tables in R


R examples

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).

Data Journalism


Visualizing Uncertainty


2021 Student presentations

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

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