Book Image

Learning Predictive Analytics with R

By : Eric Mayor
Book Image

Learning Predictive Analytics with R

By: Eric Mayor

Overview of this book

This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.
Table of Contents (23 chapters)
Learning Predictive Analytics with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Exercises and Solutions
Index

Chapter 2. Visualizing and Manipulating Data Using R

Data visualization is one of the most important processes in data science. Relationships between variables can sometimes more easily be understood visually than relying only on predictive modeling, or statistics, and this most often requires data manipulation. Visualization is the art of examining distributions and relationships between variables using visual representations (graphics), with the aim of discovering patterns in data. As a matter of fact, a number of software companies provide data visualization tools as their sole or primary product (for example, Tableau, Visual.ly). R has built-in capabilities for data visualization. These capabilities can of course (as with almost everything in R) be extended by recourse to external packages. Furthermore, graphics made for a particular dataset can be reused and adapted for another with relatively little effort. Another great advantage of R is of course that it is a fully functional statistical...