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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 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. 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 Download instructions for the tile package can be found under the Software tab at left. We will discuss up to four 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 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.) 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 in class 21 January 2021 You will need these data. Due in class 16 February 2021 Due in class 11 March 2021 Breakout Group Individual memo due before group meets; Group essay 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 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 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 three to four classes On an assigned day during the last two weeks 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, R and RMarkdown Supplementary
material: Take a look at the section syllabus. For RMarkdown, see lab1_RMarkdownSample.Rmd (and the sample PDF output). Lab 2 Supplementary
material: Source .Rmd for section slides. Lab 3 Supplementary material: Exercise: data to reproduce this graph; also a customized theme. Lab 4 Supplementary material: Data: cyyoungFD for ropeladder, and measles for heatmap. Lab 5 Supplementary material: Inequality example's script and data; voting example 's script and data; crime example's script. Lab 6 Supplementary material: Download New York's shapefile (.shp) here. Lab 7 Visualizing network/relational data Supplementary material: Two datasets required: Florentine marriage data and global migration data. Lab 8 Interactive Visual Display with R + Shiny Supplementary material: Simple Shiny app script 1 and script 2; and simple graphs explaining reactive intermediary. Lab 9 Latest extension packages for visualization Supplementary material: The underlying .Rmd written with flipbookr + xaringan packages. |
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