Course
Case Studies: Network Analysis in R
BasicSkill Level
Updated 08/2020RProbability & Statistics4 hr11 videos47 Exercises4,150 XP4,127Statement of Accomplishment
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Prerequisites
Network Analysis in R1
Exploring graphs through time
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.
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.
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.
Case Studies: Network Analysis in R
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FAQs
What real-world network datasets are analyzed in this course?
You explore an Amazon purchase graph, Twitter conversations using the rstats hashtag, and Chicago bike sharing network data across four applied case studies.
Does this course go beyond igraph for visualization?
Yes. Chapter 4 introduces ggplot2-based graph plotting libraries, hive plots as alternatives to hairball visualizations, and interactive and animated plot frameworks.
What R packages do I need to be familiar with?
You should know igraph from the prerequisite Network Analysis in R course. The case studies also introduce visualization libraries that extend ggplot2 for network graphs.
Does the course cover how networks change over time?
Yes. Chapter 1 examines the Amazon graph during different time periods, showing you how to analyze graph evolution and identify temporal patterns in network structure.
Is this course suitable for beginners in network analysis?
It is listed as beginner level but requires completion of Network Analysis in R and intermediate R. The case study format applies foundational concepts to complex real datasets.
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