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R for Data Science Cookbook (n)

You're reading from   R for Data Science Cookbook (n) Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Product type Paperback
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Length 452 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Toc

Table of Contents (14) Chapters Close

Preface 1. Functions in R FREE CHAPTER 2. Data Extracting, Transforming, and Loading 3. Data Preprocessing and Preparation 4. Data Manipulation 5. Visualizing Data with ggplot2 6. Making Interactive Reports 7. Simulation from Probability Distributions 8. Statistical Inference in R 9. Rule and Pattern Mining with R 10. Time Series Mining with R 11. Supervised Machine Learning 12. Unsupervised Machine Learning Index

Adjusting scales


Besides setting aesthetic mapping for each plot or geometric object, one can use scale to control how variables are mapped to the visual property. In this recipe, we introduce how to adjust the scale of aesthetics in ggplot2.

Getting ready

Ensure you have completed the previous steps by storing sample_mean in your R environment.

How to do it…

Perform the following steps to adjust the scale of aesthetic magnitude in ggplot2:

  1. First, make a scatterplot by setting size=Total_Sales, colour=Province, y=Province, and conditional on Year_Month. Resize the point with the scale_size_continuous function:

    > g <- ggplot(data=sample_sum, mapping=aes(x=Year_Month, y=Province, size=Total_Sales, colour = Province ))
    > g + geom_point(aes(size=Total_Sales)) + scale_size_continuous(range=c(1,10)) + ggtitle('Resize The Point')
    
  2. Repaint the point in gradient color with the scale_color_gradient function:

    > g + geom_point(aes(colour=Total_Sales)) + scale_color_gradient()+ ggtitle('Repaint...
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