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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 a faceted line plot


As long as there is a categorical variable to make facets from it, ggplot2 might able to pull it. That said, line plots are also contemplated. Lines can come from geom_lines(), geom_path(), and also geom_smooth(). For this particular example, we will use the last one to craft a faceted line plot.

This recipe digs the relation between education (expressed in years) and wages (represented by hourly earnings). To enhance analysis, facets are deployed to contrast these aspects across single and married males and females. This result is achieved by combining our beloved ggplot2 along with data coming from the wooldridge package.

Getting ready

Make sure to have the wooldridge package installed:

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

Data frame is named wage1; a good way to get to know it is by typing ?wage1 in the console once the package is properly loaded. Now you're ready to plot.

How to do it...

Let us now start with crafting a faceted line plot...

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