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 the k-means method

K-means clustering is a method of partitioning clustering. The goal of the algorithm is to partition n objects into k clusters, in which each object belongs to the cluster with the nearest mean. Unlike hierarchical clustering, which does not require a user to determine the number of clusters at the beginning, the k-means method does require this to be determined first. However, k-means clustering is much faster than hierarchical clustering as the construction of a hierarchical tree is very time-consuming. In this recipe, we will demonstrate how to perform k-means clustering on the hotel location dataset.

Getting ready

In this recipe, we will continue to use the hotel location dataset as the input data source to perform k-means clustering.

How to do it…

Please perform the following steps to cluster the hotel location dataset with the k-means method:

  1. First, use kmeans to cluster the customer data:

    > set.seed(22)
    > fit <- kmeans(hotel[,c("lon", "lat...