Network Analysis in R

In this course you'll learn to analyze and visualize network data with the igraph package.
Start Course for Free
Clock4 HoursPlay12 VideosCode50 ExercisesGroup11,510 Learners
Database4000 XP

Create Your Free Account

Google LinkedInFacebook
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of companies


Course Description

In this course you'll learn how to work with and visualize network data. You'll use the <code>igraph</code> 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.

  1. 1

    Introduction to networks

    Free
    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.
    Play Chapter Now
  2. 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.
    Play Chapter Now
  3. 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.
    Play Chapter Now
  4. 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.
    Play Chapter Now
In the following tracks
Network Analysis
Collaborators
Richie CottonNick Solomon
Prerequisites
Intermediate R
JAMES CURLEY Headshot

JAMES CURLEY

Associate Professor at UT Austin
James P. Curley is an Associate Professor in the Department of Psychology at University of Texas Austin in the Behavioral Neuroscience division. He received a Ph.D. from Cambridge University in 2003 and completed post-doctoral research at Cambridge University and Columbia University before becoming an Assistant Professor of Psychology at Columbia University in 2012. Dr. Curley’s current research interests are in studying the social dynamics of animals. Dr. Curley teaches several undergraduate and graduate courses, including R programming for Behavioral Scientists and Statistics & Research Design.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA

Join over 6 million learners and start Network Analysis in R today!

Create Your Free Account

Google LinkedInFacebook
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.