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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

Introduction


For the ones that already know faceting, there is no need to explain why they are important or what they do. For those not yet acknowledged, faceting is separating complex relations into more simple and visible ones. Think of it like the Christopher Columbus of data exploration, launching you to places (insights) you never wondered about.

A faceting grid splits a single bivariate relation into several ones displayed with respect to the interactions of some others categories. That's incredible because as the early chapters mentioned, the bivariate relations area much easier to understand. This way insights usually comes more frequently when complex problems are tackled with facets.

It's essential to outline that though facets help understand more complex relations, they aren't a device to deploy every single time. Still, if facets are required, ggplot2 supports it in a way that is very easy to understand and create those. Although by the end of 2017, there was no sight of ggvis...

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