#Creating a imaginary data frame
Lets say
>A < c(2, 4, 6, 8, 12, 5, 7,8)
> B < c(4,5,6,2,9,13,2, 6)
> C < c(3,5,7,2,4,8,3,6)
>D < c(4,6,2,5,7,4,6,9)DD < data.frame(A,B,C, D)
#correlation with package corrplot
> install.packages(“corrplot”)
library(“corrplot”)
#doing the correlation test and plotting the matrix
#we have assigned the result of the correlation to the variable CORT
#you can use any of the test from pearson, kendall, spearman, we are using pearson
CORT < cor (DD, method = “pearson”)
cor(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))
#now plotting with corrplot, the default plot will give you a plot with circular points in a matrix with a key scale on the right
> corrplot(CORT)
*here, if you compare the right scale, higher the color intensity towards blue they are more correlated, while towards red are negatively correlated.
#you can easily change your plot , changing the following arguments
corrplot(CORT, method = “color”, order = “AOE”, tl.col = “black”, tl.cex = 0.8, addCoef.col = “black”)
#where, you can choose any of the following methods,
method = c("circle", "square", "ellipse", "number", "shade", "color", "pie") we choosed color
or any of the order from,
order  Character, the ordering method of the correlation matrix.

tl. col = change the color of the label,
tl.cex= you can adjust the size of the font
The above corrplot is full, let say you only want the lower half or upper half, then what you have to do is,
corrplot(CORT, method = “color”, order = “AOE”, type = ‘lower’, tl.col = “black”, tl.cex = 0.8, addCoef.col = “black”)
add one more argument, type = “lower” if you want lower half or type = “upper” if you want upper half.
corrplot(corr, method = c("circle", "square", "ellipse", "number", "shade", "color", "pie"), type = c("full", "lower", "upper"), add = FALSE, col = NULL, bg = "white", title = "", is.corr = TRUE, diag = TRUE, outline = FALSE, mar = c(0, 0, 0, 0), addgrid.col = NULL, addCoef.col = NULL, addCoefasPercent = FALSE, order = c("original", "AOE", "FPC", "hclust", "alphabet"), hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single", "average", "mcquitty", "median", "centroid"), addrect = NULL, rect.col = "black", rect.lwd = 2, tl.pos = NULL, tl.cex = 1, tl.col = "red", tl.offset = 0.4, tl.srt = 90, cl.pos = NULL, cl.lim = NULL, cl.length = NULL, cl.cex = 0.8, cl.ratio = 0.15, cl.align.text = "c", cl.offset = 0.5, number.cex = 1, number.font = 2, number.digits = NULL, addshade = c("negative", "positive", "all"), shade.lwd = 1, shade.col = "white", p.mat = NULL, sig.level = 0.05, insig = c("pch", "pvalue", "blank", "n", "label_sig"), pch = 4, pch.col = "black", pch.cex = 3, plotCI = c("n", "square", "circle", "rect"), lowCI.mat = NULL, uppCI.mat = NULL, na.label = "?", na.label.col = "black", win.asp = 1, ...)
#You can play with the arguments, changing the parameter to change your plots accordingly.
corrplot(CORT, method = “pie”, order = “AOE”, type = ‘upper’, tl.col = “black”, tl.cex = 0.8, addCoef.col = “black”)
I hope this will help be useful. Please comment and share the post. if you have any question or suggestion, please comment.
Next time we will plot corrplot with p value or using significance.
Author : Saurav Das (https://twitter.com/Moutain_Soul or https://www.facebook.com/saurav12das)