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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 12. Unsupervised Machine Learning

This chapter covers the following topics:

  • Clustering data with hierarchical clustering

  • Cutting trees into clusters

  • Clustering data with the k-means method

  • Clustering data with the density-based method

  • Extracting silhouette information from clustering

  • Comparing clustering methods

  • Recognizing digits using density-based clustering methods

  • Grouping similar text documents with k-means clustering methods

  • Performing dimension reduction with Principal Component Analysis (PCA)

  • Determining the number of principal components using a scree plot

  • Determining the number of principal components using the Kaiser method

  • Visualizing multivariate data using a biplot