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R for Data Science Cookbook (n)

You're reading from  R for Data Science Cookbook (n)

Product type Book
Published in Jul 2016
Publisher
ISBN-13 9781784390815
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)

Table of Contents (19) Chapters

R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Functions in R Data Extracting, Transforming, and Loading Data Preprocessing and Preparation Data Manipulation Visualizing Data with ggplot2 Making Interactive Reports Simulation from Probability Distributions Statistical Inference in R Rule and Pattern Mining with R Time Series Mining with R Supervised Machine Learning Unsupervised Machine Learning Index

Using scales


Besides changing the appearance of axes and legends, we can rescale the mapping of the data and how it should be displayed on the plot with the scale function. In this recipe, we introduce how to scale data in ggvis.

Getting ready

Ensure you have installed and loaded ggvis into your R session. Also, you need to complete the previous steps by storing house in your R environment.

How to do it …

Please perform the following steps to rescale data in ggvis:

  1. First, create a bar plot and then replace the linear scale with a power scale:

    > house %>% ggvis(~Status, ~Price, fill=~Status) %>% layer_bars() %>% scale_numeric("y", trans = "pow", exponent = 0.2)
    

    Figure 26. Adjusting the scale of the bar plot

  2. We can change the fill color with the scale_nominal function:

    > house %>% ggvis(~Status, ~Price, fill=~Status) %>% layer_bars() %>% scale_nominal("fill", range = c("pink", "green", "lightblue"))
    

    Figure 27. Changing the fill color of the bar plot

How it works…

In the previous...

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