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

Transforming data into transactions

Before using any rule mining algorithm, we need to transform data from the data frame format into transactions. In this example, we demonstrate how to transform a purchase order dataset into transactions with the arules package.

Getting ready

Download the purchase_order.RData dataset from the GitHub link.

How to do it…

Perform the following steps to create transactions:

  1. First, install and load the arules package:

    > install.packages("arules")
    > library(arules)
  2. Use the load function to load purchase orders by user into an R session:

    > load("product_by_user.RData")
  3. Last, convert the data.table (or data.frame) into transactions with the as function:

    > trans = as(product_by_user $Product, "transactions")
    > trans
    transactions in sparse format with
     32539 transactions (rows) and
     20054 items (columns)

How it works…

Before mining a frequent item set or association rule, it is...