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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
Languages
Tools
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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


Often youmay find yourself in a spot where you are willing to investigate the relationship between two variables, just like the Chapter 2, Plotting Two Continuous Variables did. The tools covered there are more suited for the continuous versus continuous paradigma, but sometimes you are stuck with a discrete versus continuous paradigma. If that is the case, this chapter may have what you're looking for.

This chapter introduces box, dot, and violin plots. Besides the mainly used functions to build them under ggplot2, ggvis, and plotly supervision, it also addresses some nuts and bolts that are usually related to this kinds of visualizations. Little tweaks that greatly improve the outcomes are also advised.

Note

Two little disclaimers here. Sometimes, I may use categories as a pronoun for discrete values; I'm aware that they are not the same thing. Second disclaimer: by the time this chapter was written, plotly did not had functions to draw violins, nor did ggvis.

For ggvis and plotly...

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