Introduction to R for Social Scientists A Tidy Programming Approach

Preface

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                     
 Histograms                              
 Bar Plots                               
 Scatterplots                              
 Combining Multiple Plots                      
 Saving Your Plots                          
 Advanced Visualizations                       
 Parting Thoughts                           
 More Resources                            

5. Essential Programming
 Data Classes                             
 Data Structures                            
 Operators                               
 Conditional Logic                           
 User-Defined Functions                       
 Making your Code Modular                     
 Loops                                 
 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                  
 t-statistics                               
 Chi-square Test for Contingency Tables              
 Correlation                              
 Ordinary Least Squares Regression                 
 Binary Response Models                       
 Parting Thoughts                           

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

Biography

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.

"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