Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2. Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. 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. 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. In the first part of the lecture, we will consider examples from ggplot2 collected in this R script, which relies on this dataset. Principles for the Visual Display of Scientific Information Materials for Session 5: R code for the crime example.R code and data for the inequality scatterplot, and sample output. 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.Detailed instructions for downloading, installing, and learning my recommended software for quantitative social science are here. R code and data from the fertility example. Materials for the R review session: A brief introduction to R for data visualization, R code and data for the GDP example.Students taking the short course will also need these additional resources: This is a 9-hour short course version of the full Data Visualization course the lectures for the full term course are below. Visualizing Model Inference and Robustness Section meets: F 10:30 am–12:20 pmF 3:30 pm–5:20 pm Taught by ZoomĬlick on lecture titles to view slides or the buttons to download them as PDFs. ![]() ![]() Offered every Winter at the University of WashingtonĬlass meets: TTh 4:30-5:50 pm Week 1 by ZoomWeek 2+ in Gould 322 Emphasis on principles of effective visualization, novel visual displays, examples from the social sciences, and implementation of recommended techniques in R. This course takes the design of graphics and tables seriously, and surveys a variety of visual techniques for exploring data and summarizing statistical models. Good visual displays uncover patterns quantitative scientists might otherwise miss, and can make or break a paper.
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