# Covariance and Correlation in R Programming

• Last Updated : 01 Jun, 2020

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, 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 represents mean of x data vector represents mean of y data vector
N represents total obeservations

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:

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

Output:

 30.66667
 30.66667
 12
 1.666667


#### Correlation

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 represents mean of x data vector represents mean of y data vector

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:

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

Output:

 0.9724702
 0.9724702
 1
 1


#### Conversion of Covariance to Correlation

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:

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