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# Covariance and Correlation in R Programming

• Last Updated : 14 Jan, 2022

Covariance and Correlation are terms used in statistics to measure relationships between two random variables. Both of these terms measure linear dependency between a pair of random variables or bivariate data.

In this article, we are going to discuss cov(), cor() and cov2cor() functions in R which use covariance and correlation methods of statistics and probability theory.

## Covariance in R Programming Language

In R programming, covariance can be measured using cov() function. Covariance is a statistical term used to measures the direction of the linear relationship between the data vectors. Mathematically,

where,

x represents the x data vector
y represents the y data vector
[Tex]\bar{x}  [/Tex]represents mean of x data vector
[Tex]\bar{y}  [/Tex]represents mean of y data vector
N represents total observations

### Covariance Syntax in R

Syntax: cov(x, y, method)

where,

• x and y represents the data vectors
• method defines the type of method to be used to compute covariance. Default is “pearson”.

Example:

## R

 # Data vectors x <- c(1, 3, 5, 10)   y <- c(2, 4, 6, 20)   # Print covariance using different methods print(cov(x, y)) print(cov(x, y, method = "pearson")) print(cov(x, y, method = "kendall")) print(cov(x, y, method = "spearman"))

Output:

[1] 30.66667
[1] 30.66667
[1] 12
[1] 1.666667

## Correlation in R Programming Language

cor() function in R programming measures the correlation coefficient value. Correlation is a relationship term in statistics that uses the covariance method to measure how strong the vectors are related. Mathematically,

where,

x represents the x data vector
y represents the y data vector
[Tex]\bar{x}  [/Tex]represents mean of x data vector
[Tex]\bar{y}  [/Tex]represents mean of y data vector

### Correlation in R

Syntax: cor(x, y, method)

where,

• x and y represents the data vectors
• method defines the type of method to be used to compute covariance. Default is “pearson”.

Example:

## R

 # Data vectors x <- c(1, 3, 5, 10)   y <- c(2, 4, 6, 20)   # Print correlation using different methods print(cor(x, y))   print(cor(x, y, method = "pearson")) print(cor(x, y, method = "kendall")) print(cor(x, y, method = "spearman"))

Output:

[1] 0.9724702
[1] 0.9724702
[1] 1
[1] 1

## Conversion of Covariance to Correlation in R

cov2cor() function in R programming converts a covariance matrix into corresponding correlation matrix.

Syntax: cov2cor(X)

where,

• X and y represents the covariance square matrix

Example:

## R

 # Data vectors x <- rnorm(2) y <- rnorm(2)   # Binding into square matrix mat <- cbind(x, y)   # Defining X as the covariance matrix X <- cov(mat)   # Print covariance matrix print(X)   # Print correlation matrix of data  # vector print(cor(mat))   # Using function cov2cor() # To convert covariance matrix to  # correlation matrix print(cov2cor(X))

Output:

           x          y
x  0.0742700 -0.1268199
y -0.1268199  0.2165516

x  y
x  1 -1
y -1  1

x  y
x  1 -1
y -1  1

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