This is a repository for the syllabus and materials for a course taught at Johns Hopkins University in the Aitchison Program in Fall 2016, on Tuesdays at 5:30-8 P.M.
Lee Drutman, Ph.D. @ldrutman
Georgia Bullen, @georgiamoon
You will be expected to purchase:
- The Functional Art, by Alberto Cairo.
- The Craft of Political Research, by W. Phillips Shively (earlier editions are fine)
- 1-2 month Adobe Illustrator license. The software will not be required for assignments until late October. Adobe offers single month subscriptions, which will be reimbursed by JHU.
If you want a good reference book to keep handy as you learn to program in R, we recommend The Art of R Programming, by Norman Matloff.
In addition, there are a number of free online readings and resources that you may find helpful. As the course progresses, we will share recommended resources with you.
Your grade will be based on:
- Class participation (10%)
- Visualization critique presentation (10%)
- Visualization brainstorm paper (10%)
- Homework assignments (20%)
- Midterm report (15%)
- Final presentation and report (35%)
Early in the semester, you will sign up for presentation dates, which will begin in week 6. You will prepare a 5-10 minute presentation in which you find and critique two data visualizations or information graphics. You will:
- Describe both graphics and the data they represent.
- Focus on the strengths of one graphic. Describe how it conveys information effectively.
- Focus on the weaknesses of a second graphic. Describe how it is misleading, or does not convey information effectively.
You will be assigned a paper for which we will give you a journalistic article with no accompanying data visualizations, and you will be asked to:
- Create an accompanying visualization.
- Explain how and why it is effective.
You will receive a more detailed assignment sheet closer to the due date.
You will be assigned weekly problem sets, on weeks where there is not another graded assignment due. The majority of these problems sets will require you to write functional R code to manipulate and visualize your data. You will be graded based on whether or not we can run your code with the desired results, as well as qualitative discussions of your results. Assignments will be due, by e-mail to both of us, at 5:30 p.m. sharp on Tuesdays – the start of class. No late assignments will be accepted (see the late assignment policy below).
For these two assignments (which make up 50% of your course grade), you will be asked to produce a series of charts that collectively tell a story, and write a narrative description of your findings.
We urge you to use data from the American National Election Studies, a large-scale public opinion survey that goes back to 1948.
For the mid-term, you will come up with a targeted research question, describe the variables in the data set that will help you to answer this questions, develop some hypotheses about your question, and produce some initial descriptive data analysis.
Your final paper will describe your answer to your research question, and will focus on the graphical presentation of your findings.
You will present your final papers in 15-minute presentations during the last week of class.
You will receive a more detailed assignment sheets closer to the due dates.
This class depends on your engagement. We expect all of you to be full participants.
Missing a class without contacting us ahead of time, or missing more than one class under any circumstance, automatically precludes you from receiving full credit for class participation.
We have a zero tolerance policy for late homework assignments. Assignments not turned in by e-mail at the start of each class will be marked as a zero.
For the midterm, final, and visual brainstorm paper our late policy is that for each 8 hours a paper is late, we drop a third of a letter grade. So, using the example of the final paper (due on December 16 at 5 p.m.) a paper that comes in between 5:01 p.m. on December 10 and 1:30 a.m. on December 11 would be discounted 1/3 a letter grade. A paper that comes in between 1:01 a.m. and 9:01 a.m. on December 11 would be discounted 2/3 a letter grade. And so on...
INTRODUCTION AND OVERVIEW
THE BASICS OF DATA I
- Readings due: The Craft of Research, Chapters 3 & 4
- Assignments due: None
THE BASICS OF DATA II
- Readings due: The Craft of Research, Chapters 7 & 8
- Assignments due: Problem set 1: R, the basics
HOW TO ASK GOOD QUESTIONS OF YOUR DATA
- Readings due: The Craft of Political Research, Chapters 2 & 6
- Assignments due: Problem set 2: R, data analysis
INTRODUCTION TO DESIGN THEORY
- Readings due: The Functional Art, Introduction and Chapter 1: Why Visualize?
- Assignments due: Problem set 3: R, data analysis II
A GRAMMAR OF GRAPHICS
- Readings due:
- Hadley Wickham,“A Layered Grammar of Graphics,” 2010, Journal of Computational and Graphical Statistics
- The Functional Art: Chapter 2: Forms and Functions
- Assignments due: Problem set 4: Design hierarchy critique
CATEGORIAL DATA VISUALIZATIONS
- Readings due: The Functional Art: Chapter 3: The Beauty Paradox
- Assignments due: Midterm report (15% of final grade)
ADOBE ILLUSTRATOR WORKSHOP
- Readings due: The Functional Art: Chapter 4: The Complexity Challenge
- Assignments due: Problem set 5: R, categorical data visualization
VISUALIZING DATA OVER TIME
- Readings due: The Functional Art: Chapter 5: The Eye and the Visual Brain
- Assignments due: Problem set 6: Adobe Illustrator, the basics
VISUALIZING RELATIONSHIPS
- Readings due: The Functional Art: Chapter 6: Visualizing for the Mind
- Assignments due: Problem set 7: R, time series visualizations
GEOSPATIAL RELATIONSHIPS
- Readings due: Matthew Ericson, “When Maps Shouldn’t be Maps,” 2011, ericson.net
- Assignments due: Visualization brainstorm paper (10% of final grade)
FLEX WEEK FOR ADVANCED TECHNIQUES
- Readings due: None
- Assignments due: Problem set 8: Map critique
FINAL PROJECT WORKSHOPS
- Readings due: None
- Assignments due: None
FINAL PRESENTATIONS
In-class, 15 minute presentations of final projects. Final papers due Friday, December 9, 4 p.m.