| CSSS 569 Visualizing Data and Models Offered every Winter at the University of Washington Syllabus Readings
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Winter 2025 Class meets: MW 4:30-5:50 pm Smith Hall 205 TA: | Ramses Llobet (UW Political Science) |
Section meets: F 3:30 pm–5:20 pm Taught by Zoom |
Lectures
Click on lecture titles to view slides or the buttons to download them as PDFs. 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. (Note: These recommendations may seem dated, as many students prefer to use RStudio as an integrated design environment in combination with RMarkdown. You are free to follow that model, which minimizes start-up costs. I still prefer a combination of Emacs, the plain R console, and Latex/XeLatex for my own productivity, with occasional use of Adobe Illustrator for graphics touch-up.)
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 up to four examples in detail: - Making a scatterplot in tile: R code, data, and sample output.
- Visualizing a logit model of voting with tile’s lineplot: R code, data, and sample output for expected values, first differences, relative risks, and a combination plot.
- Making ropeladder plots to show model robustness using crime data: R code.
- Making ropeladder plots to show in-sample simulation results from an ordered probit model of preferenes over carbon taxes from a survey experiment: R code, helper function for in-sample simulation, data, and sample output.
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 this written Shiny tutorial.
We will discuss several Shiny interactives written by your instructor. Feel free to study the applications and 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. I offer three stylesheets for students looking to spruce up their documents. (Students new to Latex should read the Not So Short Introduction to Latex before embarking on any of the advanced stylesheets below.) - caxetexFreeOL (manual). A powerful XeLaTeX stylesheet using free typefaces and implemented for the popular, easy-to-use Latex platform Overleaf. You can find everything you need to get started with caxetexFreeOL at this Overleaf project. Note in particular the template for research papers.
- caxetexFree (manual). The same powerful XeLaTeX stylesheet using free typefaces, but for use on your local computer's TeX installation. You will need to download the relevant typefaces as instructed in the manual.
- caxetexBook (manual). The main XeLaTeX stylesheet I use in my own publishing. You will need to purchase the commercial typefaces listed in the manual if you wish to use this stylesheet.
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 by 27 January 2025; turn in problem 2 early if possible You will need these data.
Problem Set 2 Due 19 February 2025
Problem Set 3 Due 12 March 2025
Breakout Group Individual memo due before group meets; Group essay due by 3 March 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 3 March. Groups will answer questions from the class during the week of 3 March. See the syllabus for further details.
Final Poster Presented during the final three classes On an assigned day during the last week of the course, each poster group 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.
Labs Lab 1 Intermediate R and prediction Supplementary
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