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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

Creating simple raster plots with ggplot2


Raster plots can be seen as optimized tile plots. By adopting this geometry, there is no need to pick the binwidth argument. This makes the plot brewing process easier sometimes. It may be faster than brewing tiles while it also produces a smaller output when saved to PDF.

The ggplot2documentation considers raster geometry as a high performance special case when all tiles are the same size. This recipe demonstrates how to craft a simple raster plot with ggplot2. Using the car data set, a third variable will be computed by the stat_density_2d() function and then used to fill the raster. Explanations are highlighting alternative functions.

How to do it...

Here is how we proceed with the recipe:

  1. To simultaneously compute ..density.. and plot a raster, use stat_density_2d():
> library(ggplot2)
> ggplot(data = cars, aes(x = speed, y = dist)) + 
   stat_density_2d(aes(fill = ..density..),
                   geom = 'raster', contour = F)

Result looks like...

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