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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
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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

Drawing simple box plots


Box plots are simple, yet a very popular way to plot continuous versus discrete variables. Information covered by these plots usually account for median, first, and third quartile intervals, plus outliers. Among other visuals displayed here, it may be considered a simple one, once distribution format remains hidden. Sometimes less means more and simple means better.

If there is neither the need to highlight the distribution format nor investigate it, there is no reason to go further than box plot, otherwise you may send mixed signals to your audience. That would be like trying to hit a fly with a .50 bullet-you would probably miss it badly. This recipe teaches how to draw simple box plots using ggplot2, ggvis, and plotly. 

Getting ready

Previous recipe had car package installed. If you did not run the previous recipe, you can simply run the following code:

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

Now our data set will be available.

How to do it...

Here is how we...

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