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 Course Introduction Topic 2 Principles for the Visual Display of Scientific Information Topic 3 Cognitive Issues in Visualization Topic 4 Graphical Programming in R 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 Visualizing Inference 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.
Gallery 1 Scales and Storytelling Gallery 2 Maps as Visual Displays of Information Gallery 3 Time Series as Narrative 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 in class 19 January 2016 Supplementary material: You will need these data. Problem Set 2 Due in class 9 February 2016 Problem Set 3 Due in class 11 March 2016 Breakout Group Individual memo due before group meets; Group memo 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 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 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 five classes On an assigned day during the last two or three 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. |

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