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: - 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 We will examine two R scripts: a script using the base R graphics to show confidence intervals around a regression line, and another script using grid R graphics to accomplish the same task. A third, more advanced grid graphics script replaces ticks with gridlines. All three scripts require 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 three 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.
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. 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 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 17 January 2017 You will need these data. Due in class 7 February 2017 Due in class 9 March 2017 Breakout Group Individual memo due before group meets; Group memo due by 21 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 1-2 page memo before the first group meeting, and each group will write a 5+ page essay for the class on what they have learned, to be distributed by 21 February. Groups will answer questions from the class during the week of 21 February. See the syllabus for further details. Final Poster Presented during the final three 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. |

Designed by |