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R for Data Science

You're reading from R for Data Science Learn and explore the fundamentals of data science with R

Product type Paperback

Published in Dec 2014

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Dan Toomey

View More author details

Table of Contents (14) Chapters

Preface

1. Data Mining Patterns

2. Data Mining Sequences FREE CHAPTER

3. Text Mining

4. Data Analysis – Regression Analysis

5. Data Analysis – Correlation

6. Data Analysis – Clustering

7. Data Visualization – R Graphics

8. Data Visualization – Plotting

9. Data Visualization – 3D

10. Machine Learning in Action

11. Predicting Events with Machine Learning

12. Supervised and Unsupervised Learning

Index

Chapter 5. Data Analysis – Correlation

Correlation is a good technique to use on your dataset. Correlation helps us to establish basic relationships between data items.

In this chapter, we look at tools available in R for correlation:

A basic correlation
Visualizing correlations
Covariance
Pearson correlation
Polychoric correlation
Tetrachoric correlation
A heterogeneous correlation matrix
Partial correlation

The rest of the chapter is locked

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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