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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

Plotting side-by-side bar graph


Instead of stacking the categories, we could have chosen to display them side-by-side. You might recall one side-by-side bar graph coming from a blog, magazine or newspaper. These plots can be easily made with ggplot2 and plotly, but  ggvis was not there when this book was written. Check the See also section for more reference.

This kind of viz is good to show comparisons between categories across some other categories (or time). If you choose side-by-side bars instead of stacked ones you're probably choosing to highlight the variable displayed by colors over the one displayed by the x-axis. Let's see how ggplot2 and plotly handle it.

Getting ready

Besides the usual ggplot2 and plotly packages, car package is required once data frame is coming from it. We also need plyr to do some computation:

> if( !require(car)){ install.pacakges('car')}
> if( !require(plyr)){ install.packages('plyr')}

Running the preceding code is going to check local availability of such...

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