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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, jittering points


Size reduction is never an option when there are too many points sharing the exact same coordinates; it simply is not the right tool for the job. A clear option therefore is to jitter the data, that is, add a little noise to the data so that the points move around a little bit and the over-plotting kind of wears off.

Two points must be highlighted here. Jittering may be a good way to adjust the plot but not to adjust the data, so do not use jittered data for modeling and always be honest when transformations of that nature take place. Second point is that as long it may work pretty well when many points share coordinates. Although, if too many points are only close enough but do no share same coordinates there is a chance that jittering will work very badly.

Now let's go back to the iris data set and demonstrate how this technique can be applied using ggplot2, ggvis and plotly.

How to do it...

  1. With ggplot2, set potion = 'jitter' in order to obtain...
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