Analyzing Election and Polling Data in R
Learn R for data science by wrangling, visualizing, and modeling political data like polls and election results.
- 4 hours
- 15 Videos
- 55 Exercises
- 3,530 Participants
- 4,650 XP
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Course Description
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.
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1
Presidential Job Approval Polls
FreeChapter 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.
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Election Results and Political Demography
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.
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U.S. House and Senate Polling
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?
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Predicting the Future of Politics
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.
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1
Presidential Job Approval Polls
FreeChapter 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.
-
U.S. House and Senate Polling
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?
-
Election Results and Political Demography
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.
-
Predicting the Future of Politics
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.
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G Elliott Morris
Data Journalist
Elliott Morris is a data journalist who uses applied statistics and data science techniques with R to analyze, visualize, and model political (and other) data. Before he wrote articles and code professionally, he studied government, history, and computer science at the University of Texas at Austin. He shares his work frequently on Twitter (@gelliottmorris) and writes about data in politics at his blog, The Crosstab.
See MoreCollaborators
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Chester Ismay
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David Campos
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Shon Inouye