Correlation Matrix in R Programming
Last Updated :
24 Nov, 2023
Correlation refers to the relationship between two variables. It refers to the degree of linear correlation between any two random variables. This Correlation Matrix in R can be expressed as a range of values expressed within the interval [-1, 1]. The value -1 indicates a perfect non-linear (negative) relationship, 1 is a perfect positive linear relationship and 0 is an intermediate between neither positive nor negative linear interdependency. Hoindependent of each other completely. Correlation Matrix in R computes the linear relationship degree between a set of random variables, taking one pair at a time and performing for each set of pairs within the data.
Properties of Correlation Matrix in R
- All the diagonal elements of the Correlation Matrix in R must be 1 because the correlation of a variable with itself is always perfect, cii=1.
- It should be symmetric cij=cji.
Computing Correlation Matrix in R
In R Programming Language, a correlation matrix can be completed using the cor( ) function, which has the following syntax:
Syntax: cor (x, use = , method = )
Parameters:
x: It is a numeric matrix or a data frame.
use: Deals with missing data.
- all.obs: this parameter value assumes that the data frame has no missing values and throws an error in case of violation.
- complete.obs: listwise deletion.
- pairwise.complete.obs: pairwise deletion.
method: Deals with a type of relationship. Either Pearson, Spearman, or Kendall can be used for computation. The default method used is Pearson.
Correlation in R Programming Language
The Correlation Matrix in R is done after loading the data. The following code snippet indicates the usage of the cor() function:
R
header = TRUE, fileEncoding = "latin1")
print ("Original Data")
head(data)
cor_data = cor(data)
print("Correlation matrix")
print(cor_data)
|
Output:
[1] "Original Data"
Year Mileage..thousands. Price
1 1998 27 9991
2 1997 17 9925
3 1998 28 10491
4 1998 5 10990
5 1997 38 9493
6 1997 36 9991
[1] "Correlation matrix"
Year Mileage..thousands. Price
Year 1.0000000 -0.7480982 0.9343679
Mileage..thousands. -0.7480982 1.0000000 -0.8113807
Price 0.9343679 -0.8113807 1.0000000
Computing Correlation Coefficients of Correlation Matrix in R
R contains an in-built function rcorr() which generates the correlation coefficients and a table of p-values for all possible column pairs of a data frame. This function basically computes the significance levels for Pearson and spearman correlations.
Syntax: rcorr (x, type = c(“pearson”, “spearman”))
In order to run this function in R, we need to download and load the “Hmisc” package into the environment. This can be done in the following way:
install.packages(“Hmisc”)
library(“Hmisc”)
The following code snippet indicates the computation of correlation coefficients in R:
R
header = TRUE, fileEncoding = "latin1")
print("Original Data")
head(data)
install.packages("Hmisc")
library("Hmisc")
p_values <- rcorr(as.matrix(data))
print(p_values)
|
Output:
[1] "Original Data"
Year Mileage..thousands. Price
1 1998 27 9991
2 1997 17 9925
3 1998 28 10491
4 1998 5 10990
5 1997 38 9493
6 1997 36 9991
Year Mileage..thousands. Price
Year 1.00 -0.75 0.93
Mileage..thousands. -0.75 1.00 -0.81
Price 0.93 -0.81 1.00
n= 23
P
Year Mileage..thousands. Price
Year 0 0
Mileage..thousands. 0 0
Price 0 0
Visualize a Correlation Matrix in R
In R, we shall use the “corrplot” package to implement a correlogram. Hence, to install the package from the R Console we should execute the following command:
install.packages("corrplot")
Once we have installed the package properly, we shall load the package in our R script using the library() function as follows:
library("corrplot")
We will use the corrplot() function and mention the shape in its method arguments.
R
library(corrplot)
head(mtcars)
M<-cor(mtcars)
head(round(M,2))
corrplot(M, method="circle")
corrplot(M, method="pie")
corrplot(M, method="color")
corrplot(M, method="number")
|
Output:
Visualize Correlogram as a pie chart
R
corrplot(M, method="pie")
|
Output:
Visualize Correlogram as colored rectangles
R
corrplot(M, method="color")
|
Output:
Visualize Correlogram as numbers
R
corrplot(M, method="number")
|
Output:
Visualize Correlogram as 3D Scatter Plot
R
corrplot(correlation_matrix, method="ellipse")
|
Output:
Correlation Matrix in R Programming
Visualize Correlogram as Density Plot
R
corrplot(M, method="shade")
|
Output:
Correlation Matrix in R Programming
We can choose the visualization method that best suits your needs or preferences. The corrplot package provides various customization options for each visualization method.
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