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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

Creating different maps based on different map projection types


If you go back to Figure 6.4 (recipe Crafting choropleth maps using ggplot2, How to do it... section), several aspects from the map feel right, others might feel funny.  This happened because the scale used was unusual; to be honest, scales were free and ruled by figure aspect ratio along with data. Ratio is related to the projection type--it's a source of distortion--and there are ways to set it stone still, one (not the best) already introduced at the end of the previous recipe .

This recipe's intention is to teach you how one or another projection type can be selected. Speaking about ggplot2, it has a whole function dedicated to this purpose. Projection types can widely vary and we are again taking off from the results obtained by the Crafting choropleth maps using ggplot2 recipe.

Although fully understanding maps, scales, projections, and symbolization may seem complicated (yet very important), picking (and not choosing) one...

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