Data Visualization

Quantitative Methods in the Social Sciences (QMSS)
Institute for Social and Economic Research and Policy (ISERP)
Columbia University

Instructor

Elliot Cohen, PhD | Earth Institute | Sustainable Engineering Lab | QMSS
email: [email protected]
phone: 212.854.7993
office: Mudd 134F

Course Description

This course offers a rigorous introduction to data visualization from theory to implementation. Drawing on a combination of lectures, readings, discussions and coding, we will translate the timeless concepts of Minard, Playfair, Tufte and Wilkinson to new and diverse fields of study. Students will receive a coding crash-course in R, JavaScript, CSS, HTML and D3. The goal is not to become computer scientists, but to build the requisite foundation for modern implementation of exploratory and explanatory data visualizations. Students will have the opportunity to work in small teams to create interactive data visualizations worthy of their portfolios. The final deliverable will be a research-driven data visualization with accompanying prose in the form of a conference paper submission. A working knowledge of R from at least one previous class is highly recommended.

Required Reading

• Joint Committee on Standards for Graphic Presentation. 1915. American Statistical Association, 14 (112): 790-797.
• Edward R. Tufte. 2001. The visual display of quantitative information. Cheshire, Conn.: Graphics Press, c2001.
• Leland Wilkinson. 2005. The grammar of graphics. New York: Springer, 2005.
• Hadley Wickham. 2009. ggplot2: elegant graphics for data analysis
• Norman Matloff. 2011. The art of R programming. San Francisco : No Starch Press, c2011.
• Scott Murray. 2013. Interactive data visualization for the web. Sebastopol, CA: O'Reilly Media, 2013.

Learning Objectives

... and resources to help you get there

• Tufte's Rules. Above all else, show the data.
• Grammar of Graphics. Wilkinson's theory and Wickham's implementation.
• Working with data in R
  • Basic training
  • Data analysis with plyr
  • Data visualization with ggplot
  • Scripting, debugging and writing functions
• Communicating & sharing your results in the browser
  • github.io
  • HTML, CSS, JavaScript
  • Interactive visualization with D3
• Version control and collaboration with github
  • Github Help
  • Don't get scared, get started!
  • Git intro for readers
  • Git intro for visual and auditory learners
  • Git workflow for beginners
  • Forking
  • Sharing and Collaborating

Get Started!

• Install R and RStudio

• Read about RMarkdown

• Compute 2+2

• Install git

• Create a github account if you don't already have one

• Fork the class repo. Your assignments will be submitted as pull requests!

    git clone https://github.com/YOUR-NAME/qmssviz.git
    cd data-viz
    git remote add upstream https://github.com/ecohen4/data-viz.git