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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

Plotting a bar graphic with aggregated data using geom_col()


This recipe is taking one step further into bar plots. We are sticking with the side-by-side grouped bars, but now we shall plot aggregated data on the y-axis (it could fit continuous data as well). Plotting aggregated data can feel a little more complicated than plotting continuous data once it requires aggregation. So it's a great opportunity to learn how to aggregate data and build data frames from it.

About the visuals this graph brings in, they are very good to show exactly how the variables perform across diverse categories and groups. For example, economists could use bar graphics with aggregated data to make stands about salary inequalities between U.S. college professors from different ranks and genders. Let us check what are the requirements to bring this example alive.

Getting ready

Besides ggplot2, the car package is needed in order to reproduce this recipe:

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

Once this is...

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