Introduction to R for Social Scientists : A Tidy Programming Approach book cover
1st Edition

Introduction to R for Social Scientists
A Tidy Programming Approach

  • Available for pre-order. Item will ship after February 15, 2021
ISBN 9780367460723
February 15, 2021 Forthcoming by Chapman and Hall/CRC
192 Pages

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Book Description

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.


Table of Contents


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                            

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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.