This page lists various resources for the course in general, as well as additional links or topics related to individual lectures beyond what is listed on the main course page. If you find a link that doesn’t work, or could be replaced by something better or more recent, please let me know by filing an issue.

General, tutorials

  • Royal Statistical Society Best Practices for Data Visualisation: Insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful.

  • Financial Times Visual Vocabulary. A large cheatsheet poster for fining types of graphs to use for different purposes.

  • Checklist For Good Graphical Practice. From From: Gordon & Finch (2015), a useful set of questions and suggestions to apply to graphs designed for presentation purposes.

  • Twenty rules for good graphics. This post by Rob Hyndman, describes some best practices for producing graphs for journal publication.

  • British Ecological Society’s Guide to Reproducible Science. The guide proposes a simple reproducible project workflow, and a guide to organizing projects for reproducibility. The Programming section provides concrete tips and traps to avoid (example: use relative, not absolute pathnames), and the Reproducible Reports section provides a step-by-step guide for generating reports with R Markdown.

Books

Main texts

Colin Ware, Information Visualization, 3rd Ed. . What perceptual science has to say about data visualization, from a bottom-up perspective.
Course notes at http://ccom.unh.edu/vislab/VisCourse/index.html
Alberto Cairo, The Truthful Art . Information graphics from a communication perspective.
Blog: http://www.thefunctionalart.com/
Claus Wilke, Fundamentals of Data Visualization . Well thought out, a wide range of topics, good practical advice, lots of examples. It was written entirely using R Studio and the `bookdown` package, and now online.

More books I recommend

Michael Friendly & Howard Wainer, A History of Data Visualization and Graphic Communicantion . Everything you've ever wanted to know about the history of data visualization: Who did what, when and why..
Jen Christiansen, Building Science Graphics . An illustrated guide to communicating science through diagrams and visualizations. Beautifully illustrated itself, it outlines a process for creating graphics using evidence-based design strategies.
Tamara Munzner, Visualization Analysis & Design . An attractive recent book combining computer science and design perspectives.
Web page at http://www.cs.ubc.ca/~tmm/vadbook/ with lots of illustrations and lectures
Koponen & Hilden Handbook of Data Visualization . A practical guide for creating compelling graphics to explain and explore data, primarily aimed at designers, journalists, researchers, analysts, and other professionals who want to learn the basics of visualization. Lovely illustrations!
Some content on Google books
Antony Unwin Getting (More out of) Graphics . A lovely collection of 25 case studies using ggplot2, followed by discussion of factors affecting understanding graphics.
The {GmooG} package contains datasets, and code for all figures available on GitHub

Tufte Stufte

Four books by Edward Tufte largely defined the landscape for data visualization and information design.

Blogs & Web Resources

The following blogs are a rich source of information on visual design, data graphics and the history of data visualization.

My web site, http://datavis.ca . Contains the Milestone Project on the history of data vis, the Data Visualization gallery links to books, papers, courses, and software.
Nathan Yau, http://flowingdata.com/ A large number of blog posts illustrating data visualization methods with tutorials on how do do these with R and other software.
Kaiser Fung, http://junkcharts.typepad.com/ Fung discusses a variety of data displays and how they can be improved.
A podcast on data visualization with Enrico Bertini and Moritz Stefaner, http://datastori.es/ . Interviews with over 100 graphic designers & developers.
Kantar Information is Beautiful Awards https://www.informationisbeautifulawards.com Celebrates excellence and beauty in data visualizations,infographics, interactives & information art.
Raymond Andrews, http://infowetrust.com/ A visual storyteller delights with graphic stories from the history of data visualization.
Andy Kirk, https://www.visualisingdata.com/blog/ Among other things, Kirk provides a monthly digest of his picks for the Best in Data Visualization.

Newsletters

Want to get an occasional inbox dataviz fix? Here are some newsletters I enjoy.

  • RJ Andrews Chartography. Information-design insights and inspiration. Featuring charts and more, from yesteryear to today.

  • Alberto Cairo - The Art of Insight. A highly personal newsletter about data visualization, information design, data literacy — and many other topics.

  • Giuseppe Sollazzo - Quantum of Salazzo

Additional session resources

These are additional resources related to weekly topics, many of which were originally suggested by Borzu Talaie (Thx!). Feel free to suggest other topics that can be added to these lists.

Session 1: Overview

Session 2: Varieties Data Visualization

  • Financial Times Visual Vocabulary poster, showing examples of different chart types depending on what you want to show.

  • 30 Day Chart Challenge This collection, currated by Cédric Scherer, contains examples of graphs and charts designed to show visualizations classified by type and topic. But, more generally, follow #30DayChartChallenge on Twitter.

  • Out of Sight, Out of Mind A great example in storytelling with data: history of drone strikes in Pakistan

Session 3: History of Data Visualization

Session 4: Graphical perception

Session 5: Human Factors

Session 6: The Language of Graphs

Session 7: ggplot2 basics

Session 8: Going further in the tidyverse

  • Cédric Scherer Engaging and Beautiful Data Visualizations with ggplot2. A one-day workshop for those who already know a bit of ggplot2, but want to take their graphs to the next level.

  • Frank Harrell’s R Workflow, intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting. Very complete, but relies heavily on Harrell’s Hmisc package.

Session 10: Data Journalism

Software for graphics in R

At http://visiphilia.org/ statisticians Di Cook and Heike Hofmann from Iowa State University blog about data visualization topics, using R
https://www.r-bloggers.com/ A large collection of posts on R news and tutorials by over 750 R bloggers.
Kieran Healy, http://socviz.co/ Data Visualization for Social Science: A practical introduction with R and ggplot2.
DataVis Catalog, https://datavizcatalogue.com/blog/ Extended discussions of variations of a given chart type.

R

Presentation/project ideas

Here are a few ideas (in no particular order) for the course presentation or project:

  • Winners of the ASA Police Data Challenge student visualization contest. The American Statistical Association teamed up with the Police Data Initiative, which provides open data from local law enforcement agencies in the US, to create a competition for high school and college students to analyze crime data from Baltimore, Seattle and Cincinnati. Explore some data and create your own visualization.

  • Somewhat related, OpenIntro maintains a large set of datasets, one of which details Fatal police shootings by on-duty officers since Jan. 2015. Collected by the Washington Post. This includes 6400 incidents, with many variables on the nature of the incident and the age, race, sex of the victim.

  • The 50 Most Influential Living Psychologists. This page gives brief biographies of a collection of 50 living psychologists called the most influential in the world. How can this collection be visualized in terms of their attributes (area of psychology, age, gender, …)? You’ll need to scrape the data from the web page. Here is a start on that:

library(rvest)
library(stringr)
url <- "https://thebestschools.org/features/most-influential-psychologists-world/"
page <- read_html(url)
items <- page %>% html_nodes("h3") %>% html_text() 
items <- items[grep("More", items, invert=TRUE)]
items <- sub("^\\d+\\. ", "", items, perl=TRUE)

  • Graphical inference for Info Vis. Wickham et al. (2010) describe a graphical “lineup” technique for doing statistical inference with graphs and human judges. Do a literature review of this topic and design a possible experimental use of this technique. There is an R package, nullabor with this vignette for contstructing such graphical displays.

  • Best practices for research presentations using data graphics: How to best communicate your results in a conference presentation or in a poster?

  • Redesigning maps for usability: One of the best early examples is Harry Beck’s London Tube Map, which has become iconic. But there have been many other additions to the literature on this topic. Some local examples of maps that could use a design-overhaul are:

    • The Toronto PATH map, showing the 30 km of walkways underneath a large part of the dpowntown core.
    • The York Campus Map, showing all buildings on the Keele Campus.

  • Language of Graphs: Characteristics of different computer languages for graphics, including aspects of syntax, semantics and expressive power. See me for some pointers and references.

  • Online experiments: More and more psychologists are using computer-controlled methods of data collection, either with specific software for personal computers or with web-based or cloud-based application. Some of these use graphics, either for stimulus presentation or for displaying results.

  • Measures of graph literacy: In many studies of graph perception, it is useful to measure a construct of ability to understand graphic information (“graph literacy”). What is the state of the art on this topic?

  • Accessibility: Making graphics accessible to the visually impaired. A simple example is ALT text, now increasingly used. Are there standards or tools available? See the links under Human factors above.

  • Other dataviz & graphic software:

  • Data physicalization: representation of data by physical objects

 

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