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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

Drawing univariate colored dot plots with geom_dotplot()


Univariate dot plots account for very simple and very objective visualizations. They can represent either continuous or discrete variables and are a good way to display how small samples are distributed. These plots are also an alternative to histograms, specially when there is only very few observations.

For the this recipe, we're going to use continuous data in order to explain how to manage the binwidth parameter whenever calling for a ggplot2 dot plot. There are two things that are good to know about dot plots breed using ggplot2::geom_dotplot(). First thing is that these plots are very sensible to plot measures (height and width). Second is that the y-axis hardly is meaningful. Now let's get to know our data.

Getting ready

To demonstrate how to plot univariate dot plots using geom_dotplot(), the data to be used will come from the DAAG package. Data set name is anesthetic and it's telling if a patient moved (move) or not when an incision...

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