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Visualizing Data and Models

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 and Models

Offered every Winter at the
University of Washington



Winter 2019

Class meets:
TTh 4:30-5:50 pm
Mary Gates Hall 231

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  

In the first part of the lecture, we will consider examples from ggplot2 collected in this R script, which relies on this dataset.

In the second half of the lecture, time permitting, we will work through an R script that uses grid graphics to solve a basic task, showing confidence intervals around a regression line using this dataset. Finally, a more advanced grid graphics script replaces ticks with gridlines and packs the grid graphics code inside a more general and usable function, contained in this required helper file. The final graphic can be viewed here.

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 with R + Shiny

The Shiny package makes it easy to convert your R code and graphics, including those made with the tile package, into interactive displays for the web. We’ll work through the written Shiny tutorial at the bottom of this page.

We will discuss several other examples in class, including this example from your instructor using tile and Shiny to show who got the most medals in the Olympics using different medal aggregation formulas. The underlying code for the example is in this zip archive; feel free to study the code and come to class with questions.

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  

See also this excellent confection from XKCD explaining the scale of global temperature variation over the last 20,000 years.

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 22 January 2019

You will need these data.

Problem Set 2  

Due in class 12 February 2019

Problem Set 3  

Due in class 14 March 2019

Breakout Group

Individual memo due before group meets; Group memo due by 20 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 2-5 page memo before the first group meeting, and each group will write a 5-8+ page essay for the class on what they have learned, to be distributed by 25 February. Groups will answer questions from the class during the week of 25 February. See the syllabus for further details.

Final Poster

Presented during the final three to five classes

On an assigned day during the last two 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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