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R for Data Science

By : Dan Toomey
Book Image

R for Data Science

By: Dan Toomey

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R for Data Science
Dan Toomey
Dec, 2014 | 364 pages
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Table of Contents (19 chapters)
R for Data Science
R for Data Science
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Data Mining Patterns
Data Mining Patterns
Cluster analysis
Anomaly detection
Association rules
Questions
Summary
2
Data Mining Sequences
Data Mining Sequences
Patterns
Questions
Summary
3
Text Mining
Text Mining
Packages
Questions
Summary
4
Data Analysis – Regression Analysis
Data Analysis – Regression Analysis
Packages
Questions
Summary
5
Data Analysis – Correlation
Data Analysis – Correlation
Packages
Questions
Summary
6
Data Analysis – Clustering
Data Analysis – Clustering
Packages
K-means clustering
Questions
Summary
7
Data Visualization – R Graphics
Data Visualization – R Graphics
Packages
Questions
Summary
8
Data Visualization – Plotting
Data Visualization – Plotting
Packages
Scatter plots
Bar charts and plots
Questions
Summary
9
Data Visualization – 3D
Data Visualization – 3D
Packages
Generating 3D graphics
Questions
Summary
10
Machine Learning in Action
Machine Learning in Action
Packages
Dataset
Questions
Summary
11
Predicting Events with Machine Learning
Predicting Events with Machine Learning
Automatic forecasting packages
Questions
Summary
12
Supervised and Unsupervised Learning
Supervised and Unsupervised Learning
Packages
Questions
Summary
Index
Index

Packages

In R, there are several packages that provide the correlation functionality to the programmer. We will be using the following packages in this chapter:

  • corrgram: This is the tool to graphically display correlations

  • Hmisc: This contains a variety of miscellaneous R functions

  • polycor: This contains functions to compute polychoric correlations

  • ggm: This contains functions for analyzing and fitting graphical Markov models

Correlation

Basic correlation is performed in R using the cor function. The cor function is defined as follows:

cor(x, y = NULL, 
  use = "everything", 
  method = c("pearson", "kendall", "spearman"))

The various parameters of this function are described in the following table:

Parameter

Description

x

This is the dataset.

y

This is the dataset that is compatible with x.

use

This is the optional method for computing the covariance of missing values assigned. The choices are:

  • everything

  • all.obs

  • complete.obs

  • na.or.complete

  • pairwise.complete.obs

method...

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