# New Course: Network Analysis in R

**DataCamp Community - r programming**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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.

**Take me to chapter 1!**

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.

**Learn how to analyze networks in R with this new course!**

**leave a comment**for the author, please follow the link and comment on their blog:

**DataCamp Community - r programming**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.