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


The majority of readers will be familiar with Wal-Mart moving beer next to diapers in its stores because it found that the purchase of both products is highly correlated. This is one example of what data mining is about; it can help us find how items are associated in a transaction dataset. Using this skill, a business can explore the relationship between items, allowing it to sell correlated items together to increase sales.

As an alternative to identifying correlated items with association mining, another popular application of data mining is to discover frequent sequential patterns from transaction datasets with temporal information. This can be used in a number of applications, including predicting customer shopping sequence order, web click streams and biological sequences.

The recipes in this chapter cover creating and inspecting transaction datasets, performing association analysis with the Apriori algorithm, visualizing associations in various graph formats, and finding...