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

Creating transactions with temporal information

In addition to mining interesting associations within the transaction database, we can mine interesting sequential patterns using transactions with temporal information. In the following recipe, we demonstrate how to create transactions with temporal information from a web traffic dataset.

Getting ready

Download the web_traffic.csv dataset from the GitHub link.

We can then generate transactions from the loaded dataset for frequent sequential pattern mining.

How to do it…

Perform the following steps to create transactions with temporal information:

  1. First, install and load the arulesSequences package:

    > install.packages("arulesSequences")
    > library(arulesSequences)
  2. Load web traffic data into an R session:

    > load('traffic.RData')
  3. Create the transaction data with temporal information:

    > traffic_data<-data.frame(item=traffic$Page)
    > traffic.tran<-as(traffic_data...