Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer

Determining the number of principal components using the Kaiser method

In addition to a screeplot, we can use the Kaiser method to determine the number of principal components. In this method, the selection criteria retain eigenvalues greater than 1. In this recipe, we demonstrate how to determine the number of principal components using the Kaiser method.

Getting ready

Ensure 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 determine the number of principal components with the Kaiser method:

  1. First, obtain the standard deviation from eco.pca:

    > eco.pca$sdev 
     [1] 2.2437007 1.3067890 0.9494543 0.7947934 0.6961356 0.6515563
     [7] 0.5674359 0.5098891 0.4015613 0.2694394
  2. Next, obtain the variance from swiss.pca:

    > eco.pca$sdev ^ 2
     [1] 5.0341927 1.7076975 0.9014634 0.6316965 0.4846048 0.4245256
     [7] 0.3219835 0.2599869 0.1612515 0.0725976
  3. Select the components with a variance above...