Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
14
Epilogue

Summarizing your data with pivot-like tables


When moving to R, one of the common questions that arises is this, how do I produce a pivot table with R? Purists of the language will probably be horrified at this question, but we do not have to be too fussy: pivot tables are an effective and convenient way to summarize and show data, and are therefore relevant to be able to perform the same summarization in our beloved language.

As you might be guessing, yes, it is actually possible to perform the same kind of summarization, even if it is not called a pivot table. But before getting into detail, let's discuss the concept. What is a pivot table? 

We define with this concept a summary of a given detailed dataset, showing descriptive statistics of attributes stored within the dataset, aggregated by keys composed from other attributes of the same dataset.

To be clear, let's imagine having to deal with the following dataset:

It could be useful to know the total amount and the total number of accounts...