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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

Creating a facet box plot


With faceting tools at hand, we will now give the car::Salaries data frame a better look. There are many aspects of this particular data set that stood out in the analysis made at previous chapters. In this recipe, a facet box plot will be crafted. We shall use ranks and discipline variables to create facets. Variables sex and salary are going to fit respectively the x and y axes.

Variable discipline is assigned with A for theoretical departments and B for applied ones, so this recipe will also teach how to relabel these in order to make it intuitive.

How to do it...

Let us now create a facet box plot:

  1. Store data into a new object and factor discipline properly:
> library(car)
> data_box <- Salaries
> data_box$discipline <- factor(data_box$discipline, labels = c('theoretical','applied'))
  1. Craft a box plot and call facet_grid():
> library(ggplot2)
> boxplot <- ggplot(data = data_box) + 
   geom_boxplot(aes( x = sex, y = salary), position = 'identity...
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