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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

Crafting an interactive globe using plotly


Until now, this chapter explored several core points related to mapping. We saw how maps can deliver important information, how paths, polygons, and points can be used to draw maps, how to read shapefiles using nothing but R, and how to set projection types and scales.

Now we will explore the advantages of having interactive maps. Even more than that, the advantages of having interactive globes. This recipe will make a globe that you can spin at will, and it does not request shapefiles at all, though they can be used if needed.

For this particular recipe, we'll be using the plotly package to map countries affected by the 2009 banking crisis. On this course, the recipe will introduce you to a whole new way of mapping using plotly. Data here is a little different; we will be looking for some columns names instead of actually data frame observations. Now for the requirements.

Getting ready

Data comes from the Ecdat package--you know the drill:

> if(...
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