23 Social Science

23.1 Introduction to R for Social Scientists:A Tidy Programming Approach

Ryan Kennedy, Philip Waggoner

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.


23.2 Public Policy Analytics: Code & Context for Data Science in Government

Ken Steif, Ph.D

The goal of this book is to make data science accessible to social scientists and City Planners, in particular. I hope to convince readers that one with strong domain expertise plus intermediate data skills can have a greater impact in government than the sharpest computer scientist who has never studied economics, sociology, public health, political science, criminology etc.


23.3 Social Data Science with R

Daniel Anderson, Brendan Cullen, Ouafaa Hmaddi

Here’s an intro about why R is great and the cool things you can do with it and new problems you can address.


23.4 The Plain Person’s Guide to Plain Text Social Science

Kieran Healy

As a beginning graduate student in the social sciences, what sort of software should you use to do your work?1 More importantly, what principles should guide your choices? I offer some general considerations and specific answers.


23.5 Using R for Data Analysis in Social Sciences: A Research Project-Oriented Approach

Quan Li

This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations.

Paid ~$40 and listing of library availability