About the Book
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.


Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.

Ordering the Book

About the Authors
I2RSS is currently available for pre-order through the publisher or on Amazon.com.

Support Resources

On the Resources page, you will find a slate of resources we have used for teaching using the material in this book, as well as the R scripts and data sources used in the book. If you have requests for additional materials, please feel free to Contact us.

Ryan Kennedy also teaches an annual summer course using material from this book at the ICPSR summer program. Further information about registering for ICPSR courses can be found here.​

Other Recommended Resources

  • CRAN Task Views -- a curated list of packages available for all kinds of analyses.
  • R Bloggers -- an aggregation site for hundreds of people using R and posting what they have done online. We highly recommend subscribing to their emails.
  • R Project Books -- a compendium of books written about R.
  • R For Data Science -- Hadley Wickam's eponymous introduction to the Tidyverse. A comprehensive introduction to the Tidyverse, but may be more valuable after you have already gained some familiarity with R and the basics of the Tidyverse.
  • Using R for Data Analysis in Social Sciences -- Quan Li's excellent introduction to Base R, with much more detail on specific statistical models and replication.
  • Data Visualization: A Practical Introduction -- Kieran Healy provides an epic overview of the graphical capabilities of R using practical examples in ggplot2.
  • Statistical Rethinking -- for those interested in Bayesian statistics using R, we cannot recommend Richard McElreath's book highly enough.
Ryan Kennedy is an associate professor of political science at the University of Houston and a research associate for the Hobby Center for Public Policy. His work has appeared in top journals including Science, the American Political Science Review, and Journal of Politics. These articles have won several awards, including best paper in the American Political Science Review, and have been cited over 1,700 times. They have also drawn attention from media outlets like Time, the New York Times, and Smithsonian Magazine.
Philip Waggoner is an assistant instructional professor of computational social science at the University of Chicago and a visiting research scholar at ISERP at Columbia University. He is an Associate Editor at the Journal of Mathematical Sociology and the Journal of Open Research Software, and author of the forthcoming book, Unsupervised Machine Learning for Clustering in Political and Social Research (Cambridge University Press). His work has appeared or is forthcoming in many journals including the Journal of Politics, Journal of Mathematical Sociology, and Journal of Statistical Theory and Practice. 
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