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

Graphical EDA


You can include as Graphical Exploratory Data Analysis (Graphical EDA) any kind of technique that implies visualizing your data following some kind of conventional system. This can involve representing it on a Cartesian plot or following polar coordinates; the main point here is that you do not rely on any kind of summary, but just on your eyes to read the story your data has to tell.

Visualizing a variable distribution

One of the first things you can do when performing graphical EDA is to look at the distribution of your data, one variable at a time. To do that, two main types of plot come in to help:

  • Histogram
  • Boxplot

Histogram

A histogram is a special kind of bar plot, having on the x axis the unique values shown from your variable, or some kind of clusterization of these values, and on the y axis the frequency of these values. Let's plot a histogram for each of our three variables within the cash_flow_report. We are going to use ggplot, which somebody told me you should already...