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 density-based method

As an alternative to distance measurement, we can use density-based measurement to cluster data. This method finds area with a higher density than the remaining area. One of the most famous methods is DBSCAN. In the following recipe, we demonstrate how to use DBSCAN to perform density-based clustering.

Getting ready

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

How to do it…

Please perform the following steps to perform density-based clustering:

  1. First, install and load the dbscan packages:

    > install.packages("dbscan")
    > library(dbscan)
  2. Cluster data in regard to its density measurement:

    > fit <- dbscan(hotel.dist, eps = 0.01, minPts = 3)
    > fit
    DBSCAN clustering for 102 objects.
    Parameters: eps = 0.01, minPts = 3
    The clustering contains 4 cluster(s) and 3 noise points.
     0  1  2  3  4 
     3 17 65 12  5 
    Available fields: cluster, eps, minPts
  3. Plot the data in a...