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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

Dealing with over-plotting, alpha blending


Another popular technique is known as alpha blending. It consists on making points translucent, this way the audience gets to know if points are stacked or not.  Between all the techniques demonstrated so far, alpha blending must be the most popular one. This recipe teaches how to apply alpha blending using ggplot2, plotly, and ggvis.

How to do it...

  1. Set the alpha parameter to apply alpha blending to ggplot:
> library(ggplot2)
> sca1 <- ggplot( iris, aes( x = Petal.Length, y = Petal.Width))
> sca1 + geom_point( alpha = .5 , 
                     aes(shape = Species, colour = Species))

The following figure 2.7 shows alpha blending working:

Figure 2.7 - alpha blending with ggplot2.

  1. Setting alpha parameter with plotly will also apply alpha blending:
> library(plotly)
> sca9 <- plot_ly( iris, x = ~Petal.Length, y = ~Petal.Width, 
>                  type = 'scatter', mode = 'markers', alpha = .5, symbol = ~Species)
> sca9
  1. ggvis applies...
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