Applied Network Science with R
Preface
Statistical methods for networked systems are present in most disciplines. Despite language differences between areas, many methods developed to study specific problems can be helpful outside their original context; this is the premise of this book. Applied Network Science with R provides examples using the R programming language to study networked systems. Although most cases deal with social network analysis, the methods presented here can be applied to contexts such as biological networks, transportation networks, and many others.
The entire book was written using quarto–a literate programming system that allows mixing text and code–meaning that all the code presented is 100% executable and, thus, reproducible. The source code is available on GitHub at https://github.com/gvegayon/appliedsnar. Readers are encouraged to download the code and execute it on their machines using either RStudio or VScode.
Besides the R programming, we will be using RStudio. For data management, we will use dplyr and data.table. The network data management and visualization packages we will use are igraph, netdiffuseR, the statnet suite, and netplot.
About the project
This project began over six years ago as a part of a series of workshops and tutorials I ran at USC’s Center for Applied Network Analysis. Today, I use it to gather and study statistical methods to analyze networks, emphasizing social and biological systems. Moreover, the book will use statistical computing methods as a core component.
AI Disclosure
Starting in mid-2023, I have been using AI to help me write this book. Mainly, I use a combination of GitHub co-pilot, which aids with code and text, and Grammarly, which aids with grammar and style. AI’s role has been to help me write faster and more accurately, but it has not been involved in the book’s conceptualization or the development of the methods presented here.