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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

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (14) Chapters Close

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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