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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 2. Plotting Two Continuous Variables FREE CHAPTER 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 variability estimates to plots with geom_errrorbar()


The previous recipe can be improved. One must request a sort of a variability analysis in addition the bar chart. We came to the conclusion 9-month salary was higher for men of every rank but what if maximum women salary were much higher than men or the minimum much lower.

ggplot2 certainly has functions draw variability intervals; one of them is the geom_errorbar(). The device drawn by such function demands ymin and ymax aesthetics arguments. Variability intervals can fit information of any kind: standard deviations, minimum and maximum values. Limitations are given only by creativity.

Getting ready

To make it happen, our departure point is going to be the new_data object. Thus, we need to make sure that the car package is installed and run step 1 from Recipe Plotting a bar graphic with aggregated data using geom_col(). Next code block is doing both things:

> if( !require(car)){ install.packages('car')}
> new_data <- aggregate...
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