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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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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

Visualizing multivariate data using a biplot

In order to find how data and variables are mapped in regard to the principal component, you can use biplot, which plots data and projections of original features on to the first two components. In this recipe, we will demonstrate how to use biplot to plot both variables and data on the same figure.

Getting ready

Ensure that you have completed the previous recipe by generating a principal component object and saving it in variable eco.pca.

How to do it…

Perform the following steps to create a biplot:

  1. Create a scatter plot using component 1 and component 2:
    > plot(eco.pca$x[,1], eco.pca$x[,2], xlim=c(-6,6), ylim = c(-4,3))
    > text(eco.pca$x[,1], eco.pca$x[,2], eco.freedom[,2], cex=0.7, pos=4, col="red")
    
    How to do it…

    Figure 20: The scatterplot of the first two components from the PCA result

  2. In addition, if you would like to add features on the plot, you can create the biplot using a generated principal component object:
    > rownames(eco.pca$x...
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