New Course: Network Analysis in R

Hello R users! We’re happy to announce that we’ve just launched a new R course: Network Analysis in R by James Curley!

In this course, you’ll learn how to work with and visualize network data. You’ll use the igraph package to create networks from edgelists and adjacency matrices. You’ll also learn how to plot networks and their attributes. Then, you’ll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you’ll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you’ll see how to create interactive network plots using threejs.

Network Analysis in R features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you an expert in analyzing networks with R!

What you’ll learn

1. Introduction to networks

In this chapter, you will be introduced to fundamental concepts in social network analysis. You will learn how to use the igraph R package to explore and analyze social network data as well as learning how to visualize networks.

2. Identifying important vertices in a network

In this chapter you will learn about directed networks. You will also learn how to identify key relationships between vertices in a network as well as how to use these relationships to identify important or influential vertices. Throughout this chapter you will use a network of measles transmission. The data come from the German city of Hagelloch in 1861. Each directed edge of the network indicates a child becoming infected with measles after coming into contact with an infected child.

3. Characterizing network structures

This module will show how to characterize global network structures and sub-structures. It will also introduce generating random network graphs.

4. Identifying special relationships

This chapter will further explore the partitioning of networks into sub-networks and determining which vertices are more highly related to one another than others. You will also develop visualization methods by creating three-dimensional visualizations.