Visualization methods

Seven different visualization methods can be used : “circle”, “square”, “ellipse”, “number”, “shade”, “color”, “pie”.

library(corrplot)
corrplot(M, method="circle")

corrplot(M, method="pie")

corrplot(M, method="color")

Positive correlations are displayed in blue and negative correlations in red color. Color intensity and the size of the circle are proportional to the correlation coefficients.

Display the correlation coefficient :

corrplot(M, method="number")

Types of correlogram layout

There are three types of layout :

• “full” (default) : display full correlation matrix
• “upper”: display upper triangular of the correlation matrix
• “lower”: display lower triangular of the correlation matrix

corrplot(M, type="upper")

corrplot(M, type="lower")

Reordering the correlation matrix

The correlation matrix can be reordered according to the correlation coefficient. This is important to identify the hidden structure and pattern in the matrix. “hclust” for hierarchical clustering order is used in the following examples.

# correlogram with hclust reordering
corrplot(M, type="upper", order="hclust")

# Using different color spectrum
col<- colorRampPalette(c("red", "white", "blue"))(20)
corrplot(M, type="upper", order="hclust", col=col)

# Change background color to lightblue
corrplot(M, type="upper", order="hclust", col=c("black", "white"),
         bg="lightblue")

Changing the color of the correlogram

As shown in the above section, the color of the correlogram can be customized. RcolorBrewer palette of colors are used in the R script below :

library(RColorBrewer)
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"))

Changing the color and the rotation of text labels

tl.col (for text label color) and tl.srt (for text label string rotation) are used to change text colors and rotations.

corrplot(M, type="upper", order="hclust", tl.col="black", tl.srt=45)

Combining correlogram with the significance test

# mat : is a matrix of data
# ... : further arguments to pass to the native R cor.test function
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
}
# matrix of the p-value of the correlation
p.mat <- cor.mtest(mtcars)
head(p.mat[, 1:5])

           mpg       cyl      disp        hp      drat
mpg  0.000e+00 6.113e-10 9.380e-10 1.788e-07 1.776e-05
cyl  6.113e-10 0.000e+00 1.803e-12 3.478e-09 8.245e-06
disp 9.380e-10 1.803e-12 0.000e+00 7.143e-08 5.282e-06
hp   1.788e-07 3.478e-09 7.143e-08 0.000e+00 9.989e-03
drat 1.776e-05 8.245e-06 5.282e-06 9.989e-03 0.000e+00
wt   1.294e-10 1.218e-07 1.222e-11 4.146e-05 4.784e-06

# Specialized the insignificant value according to the significant level
corrplot(M, type="upper", order="hclust", 
         p.mat = p.mat, sig.level = 0.01)

# Leave blank on no significant coefficient
corrplot(M, type="upper", order="hclust", 
         p.mat = p.mat, sig.level = 0.01, insig = "blank")

Customize the correlogram

col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(M, method="color", col=col(200),  
         type="upper", order="hclust", 
         addCoef.col = "black", # Add coefficient of correlation
         tl.col="black", tl.srt=45, #Text label color and rotation
         # Combine with significance
         p.mat = p.mat, sig.level = 0.01, insig = "blank", 
         # hide correlation coefficient on the principal diagonal
         diag=FALSE 
         )