1st Edition

Introduction to R for Social Scientists A Tidy Programming Approach

By Ryan Kennedy, Philip D. Waggoner Copyright 2021
    208 Pages
    by Chapman & Hall

    208 Pages
    by Chapman & Hall

    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.



    1. Introduction
     Why R?                                
     Why This Book?                           
     Why the Tidyverse?                         
     What tools are needed?                       
     How This Book Can be Used in a Class              
     Plan for the Book                           

    2. Foundations
     Scripting with R                           
     Understanding R                           
     Working directories                          
     Setting Up an R Project                       
     Loading and Using Packages and Libraries             
     Where to Get Help                          
     Moving Forward                           

    3. Data Management and Manipulation
     Loading Our Data                          
     Data Wrangling                            
     Grouping and Summarizing Your Data (and Using “the Pipe”) 
     Creating New Variables                       
     Combining Data sets                         
     Basic Descriptive Analysis                      
     Tidying a Data Set                          
     Saving Your Data Set for Later Use                
     Saving Your Data Set Details for Presentation           

    4. Visualizing Your Data
     The Global Data Set                         
     The Data and Preliminaries                     
     Bar Plots                               
     Combining Multiple Plots                      
     Saving Your Plots                          
     Advanced Visualizations                       
     Parting Thoughts                           
     More Resources                            

    5. Essential Programming
     Data Classes                             
     Data Structures                            
     Conditional Logic                           
     User-Defined Functions                       
     Making your Code Modular                     
     The map_*() Family from purrr                  
     Concluding Remarks                         

    6. Exploratory Data Analysis
     Visual Exploration                          
     Numeric Exploration                         
     Putting it All Together: Skimming Data              
     Concluding Remarks                         

    7. Essential Statistical Modeling
     Loading and Inspecting the Data                  
     Chi-square Test for Contingency Tables              
     Ordinary Least Squares Regression                 
     Binary Response Models                       
     Parting Thoughts                           

    8. Parting Thoughts
     Continuing to Learn with R                     
     Where To Go From Here                       
     Final Thought                            


    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.