Introduction to R for Social Scientists Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Series Series Editors Jeff Gill, Steven Heeringa, Wim J. van der Linden, Tom Snijders Recently Published Titles Multilevel Modelling Using Mplus Holmes Finch and Jocelyn Bolin Bayesian Psychometric Modeling Roy Levy and Robert J. Mislevy Applied Survey Data Analysis, Second Edition Steven G. Heering, Brady T. West, and Patricia A. Berglund Adaptive Survey Design Barry Schouten, Andy Peytchev, and James Wagner Handbook of Item Response Theory, Volume One: Models Wim J. van der Linden Handbook of Item Response Theory, Volume Two: Statistical Tools Wim J. van der Linden Handbook of Item Response Theory, Volume Three: Applications Wim J. van der Linden Bayesian Demographic Estimation and Forecasting John Bryant and Junni L. Zhang Multivariate Analysis in the Behavioral Sciences, Second Edition Kimmo Vehkalahti and Brian S. 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Armstrong II, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole and Howard Rosenthal Introduction to R for Social Scientists: A Tidy Programming Approach Ryan Kennedy and Philip Waggoner For more information about this series, please visit: https://www.routledge.com/Chapman--Hall- CRC-Statistics-in-the-Social-and-Behavioral-Sciences/book-series/CHSTSOBESCI Introduction to R for Social Scientists A Tidy Programming Approach by Ryan Kennedy Philip Waggoner First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC The right of Ryan Kennedy and Philip Waggoner to be identified as authors of this work has been asserted by him/her/them in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. 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Library of Congress Cataloging-in-Publication Data ISBN: 9780367460709 (hbk) ISBN: 9780367460723 (pbk) ISBN: 9781003030669 (ebk) Typeset in Computer Modern font by KnowledgeWorks Global Ltd. � � “intro_to_R” — 2021/1/11 — 19:20 — page v — #5 � � Contents Preface vii Overview of Chapters . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . ix About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction 1 1.1 Why R? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Why This Book? . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Why the Tidyverse? . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 What Tools Are Needed? . . . . . . . . . . . . . . . . . . . . 7 1.5 How This Book Can be Used in a Class . . . . . . . . . . . . 9 1.6 Plan for the Book . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Foundations 13 2.1 Scripting with R . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Understanding R . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Working Directories . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Setting Up an R Project . . . . . . . . . . . . . . . . . . . . 22 2.5 Loading and Using Packages and Libraries . . . . . . . . . . 24 2.6 Where to Get Help . . . . . . . . . . . . . . . . . . . . . . . 29 2.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 31 3 Data Management and Manipulation 33 3.1 Loading the Data . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2 Data Wrangling . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Grouping and Summarizing Your Data . . . . . . . . . . . . 45 3.4 Creating New Variables . . . . . . . . . . . . . . . . . . . . . 48 3.5 Combining Data Sets . . . . . . . . . . . . . . . . . . . . . . 55 3.6 Basic Descriptive Analysis . . . . . . . . . . . . . . . . . . . 57 3.7 Tidying a Data Set . . . . . . . . . . . . . . . . . . . . . . . 62 3.8 Saving Your Data Set for Later Use . . . . . . . . . . . . . . 64 3.9 Saving Your Data Set Details for Presentation . . . . . . . . 65 4 Visualizing Your Data 69 4.1 The Global Data Set . . . . . . . . . . . . . . . . . . . . . . 69 4.2 The Data and Preliminaries . . . . . . . . . . . . . . . . . . 70 v � � � � � � “intro_to_R” — 2021/1/11 — 19:20 — page vi — #6 � � vi Contents 4.3 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.4 Bar Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.5 Scatterplots . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.6 Combining Multiple Plots . . . . . . . . . . . . . . . . . . . . 90 4.7 Saving Your Plots . . . . . . . . . . . . . . . . . . . . . . . . 94 4.8 Advanced Visualizations . . . . . . . . . . . . . . . . . . . . 95 4.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 99 5 Essential Programming 101 5.1 Data Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.2 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3 Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4 Conditional Logic . . . . . . . . . . . . . . . . . . . . . . . . 112 5.5 User-Defined Functions . . . . . . . . . . . . . . . . . . . . . 114 5.6 Making Your Code Modular . . . . . . . . . . . . . . . . . . 119 5.7 Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.8 Mapping with purrr . . . . . . . . . . . . . . . . . . . . . . 132 5.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 135 6 Exploratory Data Analysis 137 6.1 Visual Exploration . . . . . . . . . . . . . . . . . . . . . . . . 138 6.2 Numeric Exploration . . . . . . . . . . . . . . . . . . . . . . 145 6.3 Putting it All Together: Skimming Data . . . . . . . . . . . . 149 6.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . 151 7 Essential Statistical Modeling 153 7.1 Loading and Inspecting the Data . . . . . . . . . . . . . . . . 153 7.2 t-statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 7.3 Chi-square Test for Contingency Tables . . . . . . . . . . . . 158 7.4 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 7.5 Ordinary Least Squares Regression . . . . . . . . . . . . . . . 161 7.6 Binary Response Models . . . . . . . . . . . . . . . . . . . . . 171 7.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . 183 8 Parting Thoughts 185 8.1 Continuing to Learn with R . . . . . . . . . . . . . . . . . . 185 8.2 Where To Go from Here . . . . . . . . . . . . . . . . . . . . 186 8.3 A Final Word . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Bibliography 189 Index 193 � � � � � � “intro_to_R” — 2021/1/11 — 19:01 — page vii — #7 � � Preface This book is a distillation of our approach to programming in R for exploring and explaining a variety of social science behavior. It is the product of our own notes from teaching R to many groups of people, from undergraduates and graduates to faculty members and practitioners. Indeed, this book would be impossible without the support from and engagement by many students, colleagues, and folks generally interested in our work. We are deeply grateful to these people and are excited to share our work in this form. In this book, we have two primary goals: 1. To introduce social scientists, both in and out of academia, to R. R is at the same time a programming language as well as an environment to do statistics and data science. As R is open source (meaning open contribution of packages via the Comprehensive R Archive Network (CRAN)), there are many powerful tools available to users in virtually any discipline or domain to accomplish virtually any statistical or data science task. Our goal, then, is to cover the tools we find most helpful in our research as social scientists. 2. As the subtitle of the book suggests, we are interested in exposing social scientists to the “tidy” approach to coding, which is also referred to as the Tidyverse. Though we expound on this in much greater detail throughout, the Tidyverse is a collection of packages all built around consistency and making tasks in R streamlined, with the product being a clean, clear rendering of the quantity or object of interest. And as this is an introductory text, we suggest it is most valuable to start from the Tidyverse framework, rather than base R, to reduce the steepness of the learning curve as much as possible. vii � � � � � � “intro_to_R” — 2021/1/11 — 19:01 — page viii — #8 � � viii Preface Overview of Chapters In the book, we cover the following topics for a full introduction to tidy R programming for social scientists: 1. Introduction: Motivation for the book, getting and using R 2. Foundations: Packages, libraries, and object-oriented programming 3. Data Management: Getting your data into workable, tidy form 4. Visualization: Visual presentations using ggplot2 and the gram­ mar of graphics 5. Essential Programming: Interacting with base R to learn func­ tional programming 6. Exploratory Data Analysis: Exploring relationships and data in the Tidyverse 7. Essential Statistical Modeling: Fitting and diagnosing widely used models in the Tidyverse 8. Parting Thoughts: Conclusion and wrap-up � � � � � � “intro_to_R” — 2021/1/11 — 19:01 — page ix — #9 � � Preface ix Acknowledgements Though the final product we present in this book is our own (wherein we accept full responsibility for any errors), we could not have produced this book without the help and influence of many other excellent social scientists and programmers. Thus, in the same open-source spirit, we would like to acknowledge the following people for sharing and/or making code available: Ling Zhu, Scott Basinger, Thomas Leeper, Max Kuhn, and Hadley Wickham. About the Authors Dr. 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 in computational social science has dealt with issues of forecasting elections, political participation, international conflicts, protests, state failure, and disease spread. He has published in Science, the American Political Science Review, Journal of Politics, and International Studies Quarterly, among others. These articles have won several awards, including best paper in the American Political Science Review, and have been cited over 1,700 times since 2013. They have also drawn attention from media outlets like Time, The New York Times, and Smithsonian Magazine, among others. For more information, visit https://ryanpkennedy.weebly.com/. Dr. Philip Waggoner is an assistant instructional professor of computational social science at the University of Chicago and a visiting research scholar at the Institute for Social and Economic Research and Policy at Columbia University. He is also an Editorial Board Member at the Journal of Mathematical Sociology and an Associate Editor at the Journal of Open Research Software, as well as a member of easystats (a software group that tends to an ecosystem of packages making statistics in R easy). He is the author of the recent book, Unsupervised Machine Learning for Clustering in Political and Social Research (Cambridge University Press, 2020). And in addition to authoring and co-authoring several R packages, his work has appeared or is forthcoming in numerous peer-reviewed journals including the Journal of Politics, Journal of Mathematical Sociology, Journal of Statistical Theory and Practice, Journal of Open Source Software, among others. For more information, visit https://pdwaggoner.github.io/. � � � �