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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

Plotting a basic scatterplot


Scatterplots play a major role in the representation of two continuous variables. Making simple scatterplots is a very easy task to handle using ggplot2, ggvis, or plotly. This recipe uses a data frame called iris to draw plots, it comes with base R (datasets package).

Note

Before using data coming from a package, you may want to try entering ?<package name>::<data frame name> into your console. For this recipe, that would go as: ?datasets::iris. This is may lead you towards data documentation, this way you get to know each variable coming from the data frame.

From the various features presented by this data set, this recipe uses Petal.Width and Petal.Length. They respectively account for iris' petal widths and lengths measured in centimeters. Besides drawing the plots, this recipe also teaches how to add a title to them. So, move on to the coding!

How to do it...

  1. Initialize a ggplot and then give it the point geometry:
> library(ggplot2)
> sca1 ...
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