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

Grouping similar text documents with k-means clustering methods

Computer programs face limitations in interpreting the meaning of given sentences, and therefore do not know how to group documents based on their similarities. However, if we can convert sentences into a mathematical matrix (document term matrix), a program can compute the distance between each document and group similar ones together.

In this recipe, we demonstrate how to compute the distance between text documents and how we can cluster similar text documents with the k-means method.

Getting ready

In this recipe, we use news titles as clustering input. You can find the data on the author's GitHub page at

How to do it…

Perform the following steps to cluster text document with k-means clustering techniques:

  1. First, install and load the tm and SnowballC packages:

    > install.packages('tm')
    > library(tm)
    > install.packages('SnowballC')
    > library(SnowballC...