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  • Book Overview & Buying R Data Science Essentials
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R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
3 (3)
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
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9
Index

Sequential dataset


So far, we generated various rules based on the transactions and events as well as the co-occurrence of various events. Now, we will consider the data along with the sequence in which the events happen. Sequence analysis is very popular to predict the occurrence of an event through a pattern from the historic data.

In order to understand sequence analysis, we will consider a sequential dataset. The zaki dataset contains sequential data that comes along with the arulesSequences package. We use the summary function to get the details of the sequential dataset. For better understanding, we convert the dataset into a data frame:

library(arulesSequences)
data(zaki)
summary(zaki)

The output of the preceding command is as follows:

Let us have a look into the data using the following command:

as(zaki, "data.frame")

The output of the preceding command is as follows:

This data frame conveys lots of information in a very friendly way for easy understanding. The transactionID.size parameter...

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