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.

• 1. Introduction. 2. Foundations. 3. Data Management. 4. Visualizing Your Data. 5. Essential Programming. 6. Exploratory Data Analysis. 7. Essential Statistical Modeling. 8. Parting Thoughts.

"The authors do a good job of explaining why and how programmers should use R! This book is ideal for social scientists but also good for all industries since it does not assume prior knowledge of R and also addresses R learning pain points. The book examples are based on real-world applications and the R syntax is explained in easy to understand language. The book is unique because it divides exercises into three levels: Easy, Intermediate and Advanced for all levels of R programmers.The step-by-step guide helps new R programmers stay on the workflow as well as apply best practices.The R examples show various options for each function which helps R programmers understand the function better. Finally, the essential programming chapter is great since all R programmers need to learn and master these R concepts."-Sunil Gupta, SAS, CDISC and R Corporate Trainer and Author, Founder of R-Guru.com

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.

• 1. Introduction. 2. Foundations. 3. Data Management. 4. Visualizing Your Data. 5. Essential Programming. 6. Exploratory Data Analysis. 7. Essential Statistical Modeling. 8. Parting Thoughts.

"The authors do a good job of explaining why and how programmers should use R! This book is ideal for social scientists but also good for all industries since it does not assume prior knowledge of R and also addresses R learning pain points. The book examples are based on real-world applications and the R syntax is explained in easy to understand language. The book is unique because it divides exercises into three levels: Easy, Intermediate and Advanced for all levels of R programmers.The step-by-step guide helps new R programmers stay on the workflow as well as apply best practices.The R examples show various options for each function which helps R programmers understand the function better. Finally, the essential programming chapter is great since all R programmers need to learn and master these R concepts."-Sunil Gupta, SAS, CDISC and R Corporate Trainer and Author, Founder of R-Guru.com

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.

• 1. Introduction. 2. Foundations. 3. Data Management. 4. Visualizing Your Data. 5. Essential Programming. 6. Exploratory Data Analysis. 7. Essential Statistical Modeling. 8. Parting Thoughts.

"The authors do a good job of explaining why and how programmers should use R! This book is ideal for social scientists but also good for all industries since it does not assume prior knowledge of R and also addresses R learning pain points. The book examples are based on real-world applications and the R syntax is explained in easy to understand language. The book is unique because it divides exercises into three levels: Easy, Intermediate and Advanced for all levels of R programmers.The step-by-step guide helps new R programmers stay on the workflow as well as apply best practices.The R examples show various options for each function which helps R programmers understand the function better. Finally, the essential programming chapter is great since all R programmers need to learn and master these R concepts."-Sunil Gupta, SAS, CDISC and R Corporate Trainer and Author, Founder of R-Guru.com