# Case Studies: Network Analysis in R

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

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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.

- 1
### Exploring graphs through time

**Free**In 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.

- 2
### 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.

Creating your retweet graph50 xpVisualize the graph100 xpVisualize nodes by degree100 xpWhat's the distribution of centrality?100 xpWho is important in the conversation?100 xpPlotting important vertices100 xpBuilding a mentions graph50 xpComparing mention and retweet graph100 xpAssortativity and reciprocity100 xpFinding who is talking to whom100 xpFinding communities50 xpComparing community algorithms100 xpVisualizing the communities100 xp - 3
### 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.

Creating our graph from raw data50 xpMaking Graphs of Different User Types100 xpCompare Graphs of Different User Types100 xpCompare graph distance vs. geographic distance50 xpCompare Subscriber vs. Non-Subscriber Distances100 xpMost Traveled To and From Stations100 xpMost Traveled To and From Stations with Weights100 xpVisualize central vertices100 xpWeighted Measures of Centrality100 xpConnectivity50 xpFind the minimum cut 1100 xpFind the minimum cut 2100 xpUnweighted Clustering Randomizations100 xpWeighted Clustering Randomizations100 xp - 4
### 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.

Other packages for plotting graphs!50 xpggnet Basics100 xpggnetwork Basics100 xpMore ggnet Plotting Options100 xpMore ggnetwork Plotting Options100 xpInteractive visualizations50 xpInteractive plots with ggiraph100 xpInteractive javascript plots100 xpAlternative visualizations50 xpAlternative ways to visualize a graph: Hive plots100 xpBioFabric as an HTML widget100 xpPlotting graphs on a map100 xp

In the following tracks

Network AnalysisDatasets

Amazon graphAmazon purchase graph over timeTwitter retweet graphTwitter mention graphBike sharing dataPrerequisites

Network Analysis in R#### 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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