This is an introductory course to the R programming language as applied in the context of political data analysis. In this course students learn how to wrangle, visualize, and model data with R by applying data science techniques to real-world political data such as public opinion polling and election results. The tools that you'll use in this course, from the dplyr, ggplot2, and choroplethr packages, among others, are staples of data science and can be used to analyze almost any dataset you get your hands on. Students will learn how to mutate columns and filter datasets, graph points and lines on charts, make maps, and create models to understand relationships between variables and predict the future. This course is suitable for anyone who already has downloaded R and knows the basics, like how to install packages.
Chapter one uses a dataset of job approval polling for US presidents since Harry Truman to introduce you to data wrangling and visualization in the tidyverse.
In this chapter, you will embark on a historical analysis of "generic ballot" US House polling and use data visualization and modeling to answer two big questions: Has the country changed over time? Can polls predict elections?
This chapter teaches you how to make maps and understand linear regression in R. With election results from the United States and the United Kingdom, you'll also learn how to use regression models to analyze the relationship between two (or more!) variables.
In this ensemble of applied statistics and data analysis, you will wrangle, visualize, and model polling and prediction data for two sets of very important US elections: the 2018 House midterms and 2020 presidential election.
PrerequisitesIntroduction to the Tidyverse
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