R for Social Network Analysis is a hands-on guide to analyzing, visualizing, and modeling network data in R. Designed for researchers and practitioners, the book introduces the core concepts and workflows of social network analysis through practical examples and reproducible code. Rather than focusing primarily on theory, it emphasizes applied techniques and implementation, making it an accessible entry point for readers who want to work directly with network data in R.

What distinguishes the book is its integrated and modern approach to the R network-analysis ecosystem. Instead of treating visualization, descriptive analysis, and statistical modeling as separate topics, the book presents them as part of a unified analytical workflow. It brings together tools and frameworks that are often scattered across package documentation and specialized texts, giving readers a coherent roadmap through network analysis in R. The emphasis on reproducible workflows, step-by-step examples, and tidyverse-compatible practices also makes the book especially well-suited as a textbook for courses on social network analysis.

The book walks readers through the complete analytical pipeline of social network analysis. It begins with foundational descriptive tools and measures, then moves into publication-quality network visualization using the ggraph ecosystem. From there, it introduces the major families of inferential network models, including exponential random graph models (ERGMs), stochastic actor-oriented models (SAOMs), and relational event models (REMs). The final part presents a modern tidyverse-oriented workflow using tidygraph, showing how network methods can integrate seamlessly into contemporary R practices. Aimed at researchers, students, and practitioners with some familiarity with R, the book serves as both an introduction and a long-term reference for applied network analysis.

Key Features:

• Provides a complete, end-to-end introduction to social network analysis in R, covering data handling, visualization, descriptive analysis, and statistical modeling within a single resource.
• Combines conceptual explanations with practical, reproducible examples, allowing readers to immediately apply methods to their own network data.
• Introduces readers to publication-quality network visualization using the ggraph ecosystem and modern graph plotting workflows in R.
• Covers the major families of inferential network models in an accessible and applied manner.
• Integrates network analysis into contemporary tidyverse workflows through tidygraph, helping readers build scalable and readable analytical pipelines in R.

Termeh Shafie is a Professor of Computational Social Science and Data Science whose work combines statistical methodology with interdisciplinary applications in computational social science and digital humanities. With more than two decades of experience, she develops methods and models for analyzing multivariate social networks and is also the creator and maintainer of R packages in the field. Her research brings together methodological innovation and empirical analysis to better understand social systems.

David Schoch is a Data Scientist and DevOps Engineer with extensive expertise in R and the developer and maintainer of more than 30 R packages. He is a major contributor to the R network-analysis ecosystem and a member of the igraph development team. His research has focused on developing new methods for network centrality and the analysis of signed networks.