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Visualizing Data

CSSS 569

Good visual displays uncover patterns quantitative scientists might otherwise miss, and can make or break a paper. This course takes the design of graphics and tables seriously, and surveys a variety of visual techniques for exploring data and summarizing statistical models. Emphasis on principles of effective visualization, novel visual displays, examples from the social sciences, and implementation of recommended techniques in R.

CSSS 569

Visualizing Data

Offered every Winter at the
University of Washington



Winter 2016

Class meets:
TTh 4:30-5:50 pm
Loew Hall 102

Lectures           PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser.

Short Course

Visualizing Model Inference and Robustness  

This is a 9-hour short course version of the full Data Visualization course; the lectures for the full term course are below. Students taking the short course will also need these additional resources:

Topic 1

Course Introduction  

Topic 2

Principles for the Visual Display of Scientific Information  

Topic 3

Cognitive Issues in Visualization  

Topic 4

Graphical Programming in R    

We will examine two R scripts: a script using the base R graphics to show confidence intervals around a regression line, and another script using grid R graphics to accomplish the same task. A third, more advanced grid graphics script replaces ticks with gridlines. All three scripts require this dataset.

Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.

Topic 5

Exploratory Data Analysis: Between Data & Model    

Topic 6

Visualizing Inference  

Download instructions for the tile package can be found under the Software tab at left. We will discuss three examples in detail:

Topic 7

Interactive Visual Displays

The Shiny package makes it easy to convert your R code and graphics into interactive displays for the web. We’ll work through this Shiny tutorial and discuss several other examples in class.

Topic 8

Advanced Latex for Scientific Typesetting

Time permitting, we will consider the use of modern Latex typesetting tools, especially Xetex and the fontspec package. We’ll discuss my caxetexFree stylesheet (manual; .sty file). Students new to Latex should read the Not So Short Introduction to Latex first.

Gallery 1

Scales and Storytelling  

Gallery 2

Maps as Visual Displays of Information  

Gallery 3

Time Series as Narrative  

Gallery 4

Grayscale Images of Continuous Data  

Gallery 5

Turning Tables into Graphs  

Gallery 6

Heatmaps for Visualizing Continuous Dyadic Data    

Gallery 7

Ternary Plots for Compositional Data Analysis  

Student Assignments

Problem Set 1  

Due in class 19 January 2016

You will need these data.

Problem Set 2  

Due in class 9 February 2016

Problem Set 3  

Due in class 10 March 2016

Breakout Group

Individual memo due before group meets; Group memo due by 22 February

Students will join a small group to discuss a visual display problem of common interest; creation and organization of these groups to be coordinated through the web. Students will write a 1-2 page memo before the first group meeting, and each group will write a 5+ page essay for the class on what they have learned, to be distributed by 22 February. Groups will answer questions from the class during the week of 22 February. See the syllabus for further details.

Final Poster

Presented during the final five classes

On an assigned day during the last two or three weeks of the course, each student will present a poster applying the tools learned in class to their own research. Alternatively, students can take an article published in their field and show how better visuals would either more clearly convey the findings or cast doubt on them. The final presentation may address problems raised in the breakout session or problem sets, but it is usually more fruitful for students to tackle a new problem.

University of Washington link

CSSS Center for Statistics and the Social Sciences link

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Chris Adolph & Erika Steiskal

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