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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

Adding notches and jitters to box plots


A very useful feature/trick regarding box plots are notches. They are easily achieved by ggplot2; however, when the book was written, this was neither true for ggvis nor plotly. It adds little more information about distribution. Notches usually indicate the 95 percent confidence interval around the median, proven to be very useful in suggesting skews and making simple comparison between groups.

In addition to teaching how we can add notches to box plots, this recipe will also demonstrate how to apply jitter the outliers. Jittering is a feasible response to over-plotting that may come with outliers.

Getting ready

Having the car package is a requirement here:

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

Now that we are locked and loaded, let's code.

How to do it...

We proceed as follows to add notches and jitters to box plots:

  1. Load the packages and use boxplot.stats() to separate the outliers:
> library(ggplot2) ; library(car)
> out_data <- Salaries...
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