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

Comparing clustering methods

After fitting data into clusters using different clustering methods, you may wish to measure the accuracy of the clustering. In most cases, you can use either intra-cluster or inter-cluster metrics as measurements. We now introduce how to compare different clustering methods using custer.stat from the fpc package.

Getting ready

In order to perform the clustering method comparison, you need to have completed the previous recipe by generating the hotel location dataset.

How to do it…

Perform the following steps to compare clustering methods:

  1. First, install and load the fpc package:

    > install.packages("fpc")
    > library(fpc)
  2. You then need to use hierarchical clustering with the single method to cluster customer data and generate the object hc_single:

    > hotel.dist <- dist(hotel[,c('lat', 'lon')] , method="euclidean")
    > single_c <- hclust(hotel.dist, method="single")
    > hc_single <- cutree(single_c, k = 3)
  3. Use hierarchical clustering with the complete...