Interactive Course

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
3,308 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.

Analyzing Election and Polling Data in R

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.

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 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?

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.

1
Presidential Job Approval Polls
Free

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.
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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?
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

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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.

Collaborators

Chester Ismay
David Campos
Shon Inouye

Prerequisites

Introduction to the Tidyverse

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Analyzing Election and Polling Data in R

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Course Description

Presidential Job Approval Polls

Introduction

Selecting Columns of Data

Filtering Rows of Data

Averaging Job Approval by President

Averaging Approval Ratings by President

Averaging a column

Visualizing Trump's Approval Over Time

Averaging President Trump's Approval By Month

Calculating a Rolling Average of Polls

Visualizing Donald Trump's Approval Polls

Bonus: Visualizing Every President's Approval

Election Results and Political Demography

The 2016 US Presidential Election

County-level election results and spread()

County-level demographic data and left_join()

Merging demographic and voting data

Analyzing votes and demographic data

Making County-Level Maps in R

Mapping the Democratic vote by county

Mapping the concentration of White Americans

Analyzing Results with Linear Regression

Answering "why" questions for elections

Multivariate Regression in R

The 2016 UK Referendum to Leave the EU (Brexit)

Who was "supposed" to win?

Brexit results

UKIP and Brexit

The "why" question for Brexit

U.S. House and Senate Polling

Elections and Political Parties

Tidyverse refresher

Measuring Support for U.S. Political Parties

Mutating a new column

73 Years of "Generic Ballot" Polls

Comparing the Democratic lead by year

A time series of House and Senate polling

Visualizing a time series

Graphing trend lines with geom_smooth()

Calculating and Visualizing Error in Polls

Calculating Error in Polls

Confidence intervals

Geom_point() and geom_errorbar()

Predicting Winners with Linear Regression

Fitting a model to past polls and results

Making a prediction on new data

Predicting the Future of Politics

The House of Representatives in 2018

The "generic ballot" in 2018

Polls in August and September

Training a Model to Predict the Future with Polls

Training a model

Predictions on new data

Uncertainty

Lesson 4.3: The Presidency in 2020

A model for presidential elections

Training the model

Predicting 2020 with the model

Course Recap