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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 simple stacked bar graphs


Stacked bars are often settled to display how data is distributed across categories with respect to other categories. Using ggplot2, stacked bar plots can be simply handled by geom_bar() function; that would require nothing more than explicitly declaring the fill parameter. To demonstrate how ggplot2, ggvis and plotly can craft simple stacked bar plots we shall use car::Salaries data frame.

Getting ready

The data frame is Salaries from the car package. We also need the plyr package:

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

Make sure to have the internet connection before running above code.

How to do it...

After looking at the data, ggplot2 can deploy stacked bars by naming the fill argument:

  1. Call the geom_bar() function to make sure to have the bar geometry:
> library(ggplot2)
> gg2_sal <- ggplot( data = car::Salaries, aes(x = rank))
> gg2_sal + geom_bar(aes(fill = sex))

Check the following...

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