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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 faceted histograms


The previous recipe had a discrete variable displayed by the x axis. In order to turn ourselves toward a single continuous variable, one option is to draw histograms. Facets are also available for histograms and are a good way to investigate how a variable is distributed across some categories.

For this recipe, we will dig into how the hourly wages are distributed among men and women, married and single. Data comes from the 1976 Current Population Survey. Data was collected by Henry Farber and Justin M. Shea in 1988. Both categories are represented by binaries so this recipe brings a new way to label the facets. The requirements department lies ahead.

Getting ready

In order to make it happen, we need the wooldridge::wage1 data frame, which is another way to say, "wooldridge package has to be installed". Following code takes care of that:

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

As is usual for this chapter, the packages ggplot2 and plotly are...

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