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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

Creating a simple histogram using geom_histogram()


Histograms are simple graphical representations on continuous variables distributions. Brewing them using ggplot2 is actually very easy, mainly done by geom_histogram(). Making interactive histograms is equally easy and can be made using both plotly and ggvis. For the first few examples we're heading back to year 1890. By this time the early efforts to measure light speed at air were paying off.

Getting ready

This first example carries data from HistData package. The frame chosen is called Michelson, after the brilliant scientist Albert A. Michelson. The following code makes sure that this data set is available, while it also introduces you to the data:

> if( !require(HistData)){ install.packages('HistData')}
> ?HistData::Michelson

Reaching data documentation by typing ?Michelson will tell you that the data frame does not hold the actual light speed measures, but the measures subtracted by 299,000 kilometers/s. This data frame is an optimal...

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