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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis FREE CHAPTER 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Creating adjacency matrices and edge lists

We can represent social network data in different formats. We cover two common representations:

  • Sparse adjacency matrices
  • Edge lists

Taking data from the Meetup.com social networking site (from the previous recipe in this chapter--Downloading social network data using public APIs), this recipe shows how you can convert a data frame with membership information to a sparse adjacency matrix and then to an edge list.

In this application, nodes represent users of Meetup.com and an edge connects two nodes if they are members of at least one common group. The number of common groups for a pair of people will represent the weight of the connection.

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