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

Mining frequent itemsets with Eclat

As the Apriori algorithm performs a breadth-first search to scan the complete database, support counting is rather time-consuming. Alternatively, if the database fits into memory, one can use the Eclat algorithm, which performs a depth-first search to count supports. The Eclat algorithm, therefore, runs much more quickly than the Apriori algorithm. In this recipe, we introduce how to use the Eclat algorithm to generate a frequent itemset.

Getting ready

In this recipe, one has to have completed the previous recipe by generating rules and have these stored in a variable named rules.

How to do it…

Please perform the following steps to generate a frequent itemset using the Eclat algorithm:

  1. Similar to the Apriori method, we can use the eclat function to generate a frequent itemset:

    > frequentsets=eclat(trans,parameter=list(support=0.01,maxlen=10))
  2. We can then obtain the summary information from the generated frequent itemset:

    > summary(frequentsets)
    set of...