Visualize correlation matrix using correlogram in R Programming
A graph of the correlation matrix is known as Correlogram. This is generally used to highlight the variables in a data set or data table that are correlated most. The correlation coefficients in the plot are colored based on the value. Based on the degree of association among the variables, we can reorder the correlation matrix accordingly.
Correlogram 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 shall now see how to implement the correlogram in R programming. We shall see the detailed explanation of the implementation with an example in a step by step manner.
Example:
Step 1: [Data for Correlation Analysis]: The first job is to select a proper dataset to implement the concept. For our example, we will be using the “mtcars” data set which is an inbuilt data set of R. We will see some of the data in this data set.
R
library (corrplot)
head (mtcars)
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Output:
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Step 2: [Computing Correlation Matrix]: We will now compute a correlation matrix for which we want to plot the correlogram. We shall use the cor() function for computing a correlation matrix.
R
library (corrplot)
head (mtcars)
M<- cor (mtcars)
head ( round (M,2))
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Output:
head(round(M,2))
mpg cyl disp hp drat wt qsec vs am gear carb
mpg 1.00 -0.85 -0.85 -0.78 0.68 -0.87 0.42 0.66 0.60 0.48 -0.55
cyl -0.85 1.00 0.90 0.83 -0.70 0.78 -0.59 -0.81 -0.52 -0.49 0.53
disp -0.85 0.90 1.00 0.79 -0.71 0.89 -0.43 -0.71 -0.59 -0.56 0.39
hp -0.78 0.83 0.79 1.00 -0.45 0.66 -0.71 -0.72 -0.24 -0.13 0.75
drat 0.68 -0.70 -0.71 -0.45 1.00 -0.71 0.09 0.44 0.71 0.70 -0.09
wt -0.87 0.78 0.89 0.66 -0.71 1.00 -0.17 -0.55 -0.69 -0.58 0.43
Step 3: [Visualizing using Method argument]: At first, we shall see how to visualize the correlogram in different shapes like circles, pie, ellipse, and so on. We shall 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" )
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Output:
Step 4: [Visualizing using type argument]: We shall see how to visualize the correlogram in different types like upper and lower triangular matrices. We shall use the corrplot() function and mention the type argument.
R
library (corrplot)
head (mtcars)
M<- cor (mtcars)
head ( round (M,2))
corrplot (M, type= "upper" )
corrplot (M, type= "lower" )
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Output:
Step 5: [Reordering the correlogram]: We shall see how to reorder the correlogram. We shall use the corrplot() function and mention the order argument. We are going to use the “hclust” ordering for hierarchical clustering.
R
library (corrplot)
head (mtcars)
M<- cor (mtcars)
head ( round (M, 2))
corrplot (M, type = "upper" , order = "hclust" )
col<- colorRampPalette ( c ( "red" , "white" , "blue" ))(20)
corrplot (M, type= "upper" , order = "hclust" , col = col)
corrplot (M, type= "upper" , order= "hclust" ,
col = c ( "black" , "white" ),
bg = "lightblue" )
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Output:
Step 6: [Changing the color in correlogram]: We shall now see how to change the color in correlogram. For this purpose, we have installed the “RColorBrewer” package and added it to our R script to use its palette colors.
R
library (corrplot)
library (RColorBrewer)
head (mtcars)
M<- cor (mtcars)
head ( round (M, 2))
corrplot (M, type= "upper" , order = "hclust" ,
col= brewer.pal (n = 8, name = "RdBu" ))
corrplot (M, type= "upper" , order = "hclust" ,
col= brewer.pal (n = 8, name = "RdYlBu" ))
corrplot (M, type= "upper" , order = "hclust" ,
col= brewer.pal (n = 8, name = "PuOr" ))
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Output:
Step 7: [Changing the color and rotation of the text labels]: For this purpose, we shall include the tl.col and tl.str arguments in the corrplot() function.
R
library (corrplot)
library (RColorBrewer)
head (mtcars)
M<- cor (mtcars)
head ( round (M, 2))
corrplot (M, type = "upper" , order = "hclust" ,
tl.col = "black" , tl.srt = 45)
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Output:
Step 8: [Computing the p-value of correlations]: Before we can add significance test to the correlogram we shall compute the p-value of the correlations using a custom R function as follows:
R
library (corrplot)
head (mtcars)
M<- cor (mtcars)
head ( round (M,2))
cor.mtest <- function (mat, ...)
{
mat <- as.matrix (mat)
n <- ncol (mat)
p.mat<- matrix ( NA , n, n)
diag (p.mat) <- 0
for (i in 1:(n - 1))
{
for (j in (i + 1):n)
{
tmp <- cor.test (mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames (p.mat) <- rownames (p.mat) <- colnames (mat)
p.mat
}
p.mat <- cor.mtest (mtcars)
head (p.mat[, 1:5])
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Output:
head(p.mat[, 1:5])
mpg cyl disp hp drat
mpg 0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07 1.776240e-05
cyl 6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09 8.244636e-06
disp 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08 5.282022e-06
hp 1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00 9.988772e-03
drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 0.000000e+00
wt 1.293959e-10 1.217567e-07 1.222320e-11 4.145827e-05 4.784260e-06
Step 9: [Add Significance Test]: We need to add the sig.level and insig argument in the corrplot() function. If the p-value is greater than 0.01 then it is an insignificant value for which the cells are either blank or crossed.
R
library (corrplot)
head (mtcars)
M<- cor (mtcars)
head ( round (M, 2))
library (corrplot)
cor.mtest <- function (mat, ...)
{
mat <- as.matrix (mat)
n <- ncol (mat)
p.mat<- matrix ( NA , n, n)
diag (p.mat) <- 0
for (i in 1:(n - 1))
{
for (j in (i + 1):n)
{
tmp <- cor.test (mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames (p.mat) <- rownames (p.mat) <- colnames (mat)
p.mat
}
p.mat <- cor.mtest (mtcars)
head (p.mat[, 1:5])
corrplot (M, type = "upper" , order = "hclust" ,
p.mat = p.mat, sig.level = 0.01)
corrplot (M, type = "upper" , order = "hclust" ,
p.mat = p.mat, sig.level = 0.01,
insig = "blank" )
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Output:
Step 10: [Customizing the Correlogram]: We can customize our correlogram using the required arguments in corrplot() function and adjusting their values.
R
library (corrplot)
library (RColorBrewer)
head (mtcars)
M<- cor (mtcars)
head ( round (M,2))
library (corrplot)
col <- colorRampPalette ( c ( "#BB4444" , "#EE9988" ,
"#FFFFFF" , "#77AADD" ,
"#4477AA" ))
corrplot (M, method = "color" , col = col (200),
type = "upper" , order = "hclust" ,
addCoef.col = "black" ,
tl.col= "black" , tl.srt = 45,
p.mat = p.mat, sig.level = 0.01, insig = "blank" ,
diag = FALSE
)
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Output:
Last Updated :
05 Sep, 2020
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