Case Studies: Network Analysis in R
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
- 4 hours
- 11 Videos
- 47 Exercises
- 1,779 Participants
- 4,150 XP
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Course Description
Now that you're familiar with the basics of network analysis it's time to see how to apply those concepts to large real-world data sets. You'll work through three different case studies, each building on your previous work. These case studies are working with the kinds of data you'll see in both academic and industry settings. We'll explore some of the computational and visualization challenges you'll face and how to overcome them. Your knowledge of igraph will continue to grow, but we'll also leverage other visualization libraries that will help you bring your visualizations to the web.
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1
Exploring graphs through time
FreeIn this chapter you'll explore a subset of an Amazon purchase graph. You'll build on what you've already learned, finding important products and discovering what drives purchases. You'll also examine how graphs can change through time by looking at the graph during different time periods.
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Bike sharing in Chicago
In this chapter you will analyze data from a Chicago bike sharing network. We will build on the concepts already covered in the introductory course, and add a few new ones to handle graphs with weighted edges. You will also start with data in a slightly more raw form and cover how to build your graph up from a data source you might find.
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How do people talk about R on Twitter?
In this lesson you'll explore some Twitter data about R by looking at conversations using '#rstats'. First you'll look at the raw data and think about how you want to build your graph. There's a number of ways to do this, and we'll cover two ways: retweets and mentions. You'll build those graphs and then compare them on a number of metrics.
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Other ways to visualize graph data
So far everything we've done has been using plotting from igraph. It provides many powerful ways to plot your graph data. However many people prefer interacting with other plotting frameworks like ggplot2, or even interactive frameworks like d3.js. In this lesson you'll look at other plotting libraries that build on the ggplot2 framework. You'll also look at other non-"hairball" type methods like hive plots, as well as building interactive and animated plots.
-
1
Exploring graphs through time
FreeIn this chapter you'll explore a subset of an Amazon purchase graph. You'll build on what you've already learned, finding important products and discovering what drives purchases. You'll also examine how graphs can change through time by looking at the graph during different time periods.
-
How do people talk about R on Twitter?
In this lesson you'll explore some Twitter data about R by looking at conversations using '#rstats'. First you'll look at the raw data and think about how you want to build your graph. There's a number of ways to do this, and we'll cover two ways: retweets and mentions. You'll build those graphs and then compare them on a number of metrics.
-
Bike sharing in Chicago
In this chapter you will analyze data from a Chicago bike sharing network. We will build on the concepts already covered in the introductory course, and add a few new ones to handle graphs with weighted edges. You will also start with data in a slightly more raw form and cover how to build your graph up from a data source you might find.
-
Other ways to visualize graph data
So far everything we've done has been using plotting from igraph. It provides many powerful ways to plot your graph data. However many people prefer interacting with other plotting frameworks like ggplot2, or even interactive frameworks like d3.js. In this lesson you'll look at other plotting libraries that build on the ggplot2 framework. You'll also look at other non-"hairball" type methods like hive plots, as well as building interactive and animated plots.
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Ted Hart
Senior Data Scientist
Ted likes to work with interesting data to answer interesting questions. He is a Senior Data Scientist in Silicon Valley and adjunct faculty in the biology department at the University of Vermont. He received his PhD in ecology from the University of Vermont, and did his post-doc at the University of British Columbia. In his spare time he develops open source software for ecology.
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Collaborators
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Chester Ismay
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Nick Solomon
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Benjamin Feder