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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 a simple tile plot with ggplot2


Tiles areessentially rectangles. Actually, the documentation of ggplot2 stresses that both geom_rect() and geom_tile()  "do the same thing but are parameterized differently". Imagine seeing a roof from the top and each color of tile stands for a different value of z, this is tile plots.

Function geom_tile() draws rectangles, often the filling colors stands for some continuous variables. The usual purpose they are used with is to represent 3D surfaces in the two dimensions plane. Using cars data set, let's see how ggplot2 can pull out a tile plot from it.

How to do it...

Use stat_bin_2d() to compute a third variable and output a tile plot:

> library(ggplot2)
> ggplot(data = cars, aes(x = speed, y = dist)) +
   stat_bin_2d(aes(fill = ..count..), 
               binwidth = c(5,15),
               colour = 'green',
               size = 1.05)

A tile plot can be seen at the following illustration (Figure 8.7):

Figure 8.7 - Tile plot draw using ggplot2

Now...

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