R for Political Data Science: A Practical Guide

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R that covers the most useful and common ways to interpret and analyze political data. It is organized in three sections: the use of R, econometric models, and a data science toolbox. Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R. It is highly applied in nature and can be used in methods and data science courses, at both the undergraduate and graduate levels.

Format: Hardback
Length: 440 pages
Publication date: 18 November 2020
Publisher: Taylor & Francis Ltd

R for Political Data Science: A Practical Guide is a comprehensive handbook tailored specifically for political scientists who are new to R and seek to acquire the most valuable and commonly employed techniques for interpreting and analyzing political data. Written by esteemed political scientists, this book takes into account the diverse real-world challenges encountered in their field. Comprising 16 chapters, the book is structured into three sections. The first section, dedicated to R usage, is intended for individuals who are either learning R or transitioning from another software. The second section delves into econometric models, encompassing Ordinary Least Squares (OLS), binary and survival models, panel data, and causal inference. The third section serves as a data science toolbox, offering a selection of the most useful tools in the discipline. These tools include data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis.

Key Features:

Each chapter offers the most up-to-date and straightforward approach to each task, assuming minimal prerequisites and no prior experience in R.

Extensive utilization of the Tidyverse, a group of packages that has revolutionized the use of R.

Provides a step-by-step guide that readers can replicate using their own data.

Includes exercises in every chapter for use in methods and data science courses, at both the undergraduate and graduate levels.

Focuses on practical-based approaches to statistical inference rather than relying solely on mathematical formulae.

Supplemented by an R package, encompassing all the data used in the book.

As the title implies, this book is highly applied in nature, serving as a versatile toolbox for the reader. It finds applications in methods and data science courses, at both the undergraduate and graduate levels. Moreover, it is invaluable for university students pursuing a Ph.D., political consultants, and public officials alike, who require the transformation of their datasets into substantive insights.

Weight: 1032g
Dimension: 185 x 262 x 32 (mm)
ISBN-13: 9780367818890