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

Clustering data with hierarchical clustering

Hierarchical clustering adopts either an agglomerative or divisive method to build a hierarchy of clusters. Regardless of which approach is adopted, both initially use a distance similarity measure to combine clusters or split clusters. The recursive process continues until there is only one cluster left or one cannot split more clusters. Eventually, we can use a dendrogram to represent the hierarchy of clusters. In this recipe, we will demonstrate how to cluster hotel location data with hierarchical clustering.

Getting ready

In this recipe, we will perform hierarchical clustering on hotel location data to identify whether the hotels are located in the same district. You can download the data from the following GitHub link:

How to do it…

Please perform the following steps to cluster location data into a hierarchy of clusters:

  1. First, load the data from taipei_hotel.csv and save...