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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

Using point geometry to work as dots using ggvis, plotly and ggplot2


Dot plots can be seen as binned scatterplots. Once you realize it, you also realize that the point geometry coming from all three packages (ggplot2, plotly, and ggvis) can be used to draw sort of dot plots (not actual dot plots). ggplot2 can do this very easily. For ggvis and plotly, there are few key steps to follow:

  1. Coerce the categories into numbers, so x will behave as continuous.
  2. Add some little noise to x but not to y.
  3. Draw a scatterplot using x and y.
  4. Relabel the x-axis in order to reference categories.

This recipe is using runif() to create the noise and some tricks to re-label the ticks. Next section tells the requirements.

Getting ready...

Skip to the next section if you are sure about having car package installed. If you don't please run the following code:

> if( !require(car)){ install.packages('car')}

Having the car package installed is still a requirement.

How it works...

Following steps are crafting alternative visualization...

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