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R Data Visualization Recipes

You're reading from   R Data Visualization Recipes A cookbook with 65+ data visualization recipes for smarter decision-making

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788398312
Length 366 pages
Edition 1st Edition
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Author (1):
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Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
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Table of Contents (13) Chapters Close

Preface 1. Installation and Introduction FREE CHAPTER 2. Plotting Two Continuous Variables 3. Plotting a Discrete Predictor and a Continuous Response 4. Plotting One Variable 5. Making Other Bivariate Plots 6. Creating Maps 7. Faceting 8. Designing Three-Dimensional Plots 9. Using Theming Packages 10. Designing More Specialized Plots 11. Making Interactive Plots 12. Building Shiny Dashboards

Visualizing relational data structures with ggraph


There are several ways of getting you free time to help other people. In his free time, Thomas L. Pedersen made this amazing R package called ggraph, which is built on top of ggplot2. This extension aims to handle relational data structures such as networks, trees, and graphs.

Most of the packages dealing with relational data focus only on one type of representation using different APIs; to handle many types would require you to learn several different packages. The advantage of using ggraph is that this package covers a wide variety of relational representations, all under the same ggplot2 API.

ggraph covers lots of different types of data objects such as hclust, network, dendogram, and igraph. It can design plots in a very interactive way based on three basic concepts: layouts, nodes, and edges. I really hope you look for it the next time you need a relational data visualization.

This recipe will use Canadian migration data to demonstrate...

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