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: - Materials for the R review session: A brief introduction to R for data visualization, R code and data for the GDP example. R code and data from the fertility example. Detailed instructions for downloading, installing, and learning my recommended software for quantitative social science are here.
- Materials for Session 3: R code and data for the voting example, and sample output for expected values, first differences, relative risks, and a combination plot. R code and data for the inequality scatterplot, and sample output.
- Materials for Session 5: R code for the crime example.
Topic 1 Topic 2 Principles for the Visual Display of Scientific Information Topic 3 Cognitive Issues in Visualization Topic 4 In the first part of the lecture, we will consider examples from ggplot2 collected in this R script, which relies on this dataset. Topic 5 Exploratory Data Analysis: Between Data & Model Topic 6 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 the written Shiny tutorial at the bottom of this page. - An interactive scatterplot combining Shiny and ggplot2. This example builds on the running example from the first half of Topic 4 exploring the relationship between life expectancy and GDP per capita in cross-national data. You can download the interactive R code to run locally.
- A simple text-based Shiny interactive with a html-based user interface. This online example tests whether a user-provided sentence is a pangram (a sentence containing every letter in the alphabet). You can also download the underlying R code.
- A more elaborate interactive using Shiny and tile 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.
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 Gallery 2 Maps as Visual Displays of Information Gallery 3 Gallery 4 Grayscale Images of Continuous Data Gallery 5 Gallery 6 Heatmaps for Visualizing Continuous Dyadic Data Gallery 7 Ternary Plots for Compositional Data Analysis Student Assignments Due by 25 January 2023; turn in problem 2 early if possible You will need these data. Due 15 February 2023 Due 8 March 2023 Breakout Group Individual memo due before group meets; Group essay due by 27 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 27 February. Groups will answer questions from the class during the week of 27 February. See the syllabus for further details. Final Poster Presented during the final two 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 Intro to labs; intermediate R and prediction Supplementary
material: We will go through this R Markdown file centered on the O-ring example and different methods to compute and visualize confidence intervals. If you want to follow the section while writing your own code, you can use this clean R Markdown file with no code but just notes. See this PDF report directly "knitted" from the R Markdown file, which is one of the recommended ways to produce and submit your homework. Lab 2 R Markdown, LaTeX and Overleaf Supplementary
material: We will go through this R Markdown Sample file, which cover most of its basic functionalities; and you need this JPG to knit the final PDF output. Lab 3 Supplementary material: Two datasets you need to reproduce the figures for the electric vehicles in Washington State example: ev_data.csv and county_data.csv, with our customized ggplot2 theme. A supplementary .Rmd file containing the main code chunks for reproducing the graphs is available for reference. Lab 4 Supplementary material: Four datasets you need to reproduce the figures for the exercises: Nobel prize winners data, Measles data, 92 Presidential election data and Cy Young award data. A supplementary .Rmd file containing the main code chunks for reproducing the graphs is available for reference. |

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