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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

Plotting a scatterplot with shapes and colors


There are several aesthetics coming out from geom_points() that can be changed. Typing ?geom_point into the R console will take you to the function documentation, which comes with a complete list of aesthetics understood by the function. The mandatory ones come in bold.

Names given are nothing but self-explanatory. Besides the mandatory x and y values, optional values range from alpha to stroke. For this particular recipe, we're settling for changes in the shape and colours arguments. Recipe  also aims for similar results using both ggvis and plotly. 

How to do it...

  1. Change the shape and colour arguments to get a better result:
> library(ggplot2)
> sca1 <- ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width))
> sca1 + geom_point(aes(shape = Species, colour = Species))

Now each iris species is designated by a unique combination of shapes and colors:

Figure 2.3 - Adding shapes and colors to a scatter plot.

  1. plotly can also handle such...
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