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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
Languages
Tools
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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

Dealing with over-plotting, reducing points


There are mainly three techniques used to deal with over-plot. They are: (i) adopting smaller points,(ii) jittering data, and (iii) alpha blending. These are useful tools, not only to deal with over-plot but also to check if there is over-plotting.

However, these are not the only options; for example, alternative geometries can also be implemented. No matter how troublesome over-plotting may be there are good solutions available.There is not a single solution that is better for all the situations, so you must know a bunch of them. 

This recipe advises how to apply a technique based on point size reduction using ggplot2, ggvis and plotly. In order to do so, we are trusting the ggplot2::diamonds data frame. Keep in mind that reducing points works better for cases where points are very close to each other but do not actually occupy the same coordinates.

How to do it...

  1. Set shape to '.' in order to reduce points using ggplot2:
> library(ggplot2)
&gt...
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