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Learning Predictive Analytics with R

You're reading from  Learning Predictive Analytics with R

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781782169352
Pages 332 pages
Edition 1st Edition
Languages
Author (1):
Eric Mayor Eric Mayor
Profile icon Eric Mayor

Table of Contents (23) Chapters

Learning Predictive Analytics with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Setting GNU R for Predictive Analytics 2. Visualizing and Manipulating Data Using R 3. Data Visualization with Lattice 4. Cluster Analysis 5. Agglomerative Clustering Using hclust() 6. Dimensionality Reduction with Principal Component Analysis 7. Exploring Association Rules with Apriori 8. Probability Distributions, Covariance, and Correlation 9. Linear Regression 10. Classification with k-Nearest Neighbors and Naïve Bayes 11. Classification Trees 12. Multilevel Analyses 13. Text Analytics with R 14. Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML Exercises and Solutions Further Reading and References Index

Chapter 3. Data Visualization with Lattice

In the previous chapter, we discovered how to easily visualize our data using standard functions of R. You might remember that at some point, when discussing bar plots, we visualized the frequency of an attribute based on the case's membership a group. This required that we generated several plots, each displaying the data in one of the groups. Dealing with this kind of issue more easily is mostly what trellis graphics are about.

Trellis graphics allow the visualization of data based on group membership effortlessly. With features such as multipanel conditioning (Becker & Cleveland, 1996, p. 6), understanding the structure of your data becomes a seamless visualizing experience.

Multipanel conditioning means that data is displayed on multiple panels which are defined as a function of group membership. It is particularly interesting when membership to several characteristics are involved (for instance age group and gender). In these cases we are...

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