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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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 Bianchi Lanzetta Bianchi Lanzetta
Author Profile Icon Bianchi Lanzetta
Bianchi Lanzetta
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Table of Contents (19) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
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 publish-quality scatterplots


Drawing a publication quality scatterplot doesn't require stacking up all that we've seen until now. It's usually the other way round. Telling a good history means sticking with the right tools and not deploying unnecessary ones. Unnecessary usually is synonymous to mixed signals. The history you need to tell with your plot may be a short or long one, may request few or many devices. This decision is up to you, but there are general things to look for that improves almost any scatterplot. 

All graphics brought until now by this chapter may be considered good results if those were made only for exploratory purposes. However, on the other hand, they can be considered unfinished work when it comes to publish quality standards-there is still a pretty run to make.

Jeff Leek stresses that defaults in ggplot2 are pretty enough that might trick you into thinking the graph is production ready by using only defaults. Each context will request a different amount of...

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