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Full Description
Introduction to Political Analysis in R is a comprehensive guide for students and researchers eager to delve into the intersection of data science, statistics, and political science. Aimed at equipping readers with the essential quantitative skills to analyze political data, the book bridges practical coding techniques in R with foundational statistical concepts, emphasizing real-world applications in politics.
The text adopts a progressive structure, beginning with the basics of R and data manipulation before advancing to more complex topics such as data visualization, spatial analysis, text analysis, and modeling. Through accessible language and engaging examples—ranging from U.S. election forecasting to global development trends—it demystifies complex analytical methods. Each chapter integrates coding exercises and real-world datasets to reinforce learning, fostering independent data analysis skills.
Designed for undergraduate political science majors, this book is also a valuable resource for anyone seeking to understand data-driven political analysis, whether for academic research, professional development, or personal curiosity.
Key features include:
Integrates data science and statistics with a political science focus, offering hands-on coding practice using the R programming language.
Provides real-world datasets and step-by-step exercises, enabling students to directly apply concepts to political phenomena such as gerrymandering.
Features a progressive chapter structure, progressing from foundational data handling to advanced methods like text analysis, spatial mapping, and linear modeling.
Emphasizes accessible coding for beginners, fostering self-sufficiency in data analysis without requiring prior statistical expertise.
Bridges theory and application with examples that engage students' interest in politics while developing transferable analytical skills.
H. Whitt Kilburn is Associate Professor of Political Science, Grand Valley State University, Allendale, Michigan.
Contents
1.Introduction
2.Getting Started with R for Data Analysis
3.Importing, Cleaning, and Describing Data
4.Data Visualization
5.Data Wrangling: Cleaning and Transforming Data
6.Maps and Spatial Data
7.Scaling and Clustering for Pattern Detection
8.Patterns in Text as Data
9.Analyzing Election Studies
10.Linear Models
11.Evaluating and Extending Linear Models
12.Linear Models for Binary Outcomes
