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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

Crafting simple violin plots


To visualize data and its distribution format, violin plots may be the way to go. Some data scientists love them, others hate. Still they play a role of major importance in data visualization and it's good to know how to brew them and the whole utility belt that comes along. There are alternatives though, as we are going to discuss later.

Again, until first half of 2017, both ggvis and plotly R's libraries does not dispose of a dedicated functions and types to draw violin plots, thus making relatively difficult to obtain such a viz using those packages. ggplot2 has a fully dedicated function, geom_violin(). Current recipe is teaching how to use this function. Make sure to have car package installed.

How to do it...

Let's get started with the recipe:

  1. Draw the base aesthetics under an object:
> library(ggplot2)
> base <-ggplot(car::Salaries, 
                aes(x = rank, y = salary))
  1. Call geom_violin() to draw a simple violin plot:
> base + geom_violin()

Following...

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