CODEWITHSUNDEEP
rggplot2groupingcorrelationfacet
Joke O.
Joke O.

Reputation: 599

facet or grouped correlation and correlogram plots in R

I'm trying to plot a correlogram by group/facet from a data frame. I'm able to do this if I subset the data for each variable. How can I do this for all the variables at once to generate facet plots based on each variable? 

###Load libraries
library(gdata)
library(corrplot)
library(ggplot2)
library(gtable)
library(ggpmisc)
library(grid)
library(reshape2)
library(plotly)
packageVersion('plotly')

##Subset ample data from the "iris" data set in R
B<-iris[iris$Species == "virginica", ]

##calculate correlation for numeric columns only
M<-cor(B[,1:4])
head(round(M,2))

###calculate significance
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(B[,1:4])

###plot
#color ramp
col<- colorRampPalette(c("red","white","blue"))(40)
corrplot(M, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
p.mat = p.mat, insig = "blank", sig.level = 0.01)

This works well because I took out just one variable "virginica" from the data frame. How do I automate this to have a unique correlation calculation and then corrplot for all the individual variables as individual facets?

Upvotes: 4

Views: 5449

Answers (2)

Joke O.
Joke O.

Reputation: 599

@Jimbou, thanks for your code. I have edited it a bit to add the correlation analysis, unique R and plot in one code and also add a unique name to each plot.Plot with titles

library(ggplot2)
library(Hmisc) 
library(corrplot)
# split the data 
B <- split(iris[,1:4], iris$Species)
##extract names
nam<-names(B)
# Plot three pictures
par(mfrow=c(1,3))
col<- colorRampPalette(c("red","white","blue"))(40)
for (i in seq_along(B)){
# Calculate the correlation in all data.frames using lapply 
M<-rcorr(as.matrix(B[[i]]))
corrplot(M$r, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
 addCoef.col = "black", p.mat = M$P, insig = "blank",sig.level = 0.01)
mtext(paste(nam[i]),line=1,side=3)}

Upvotes: 1

Roman
Roman

Reputation: 17648

As I understand you want a corrplot for each Species level. So, you can try:

library(Hmisc) # this package has implemented a cor function calculating both r and p.  
library(corrplot)
# split the data 
B <- split(iris[,1:4], iris$Species)
# Calculate the correlation in all data.frames using lapply 
M <- lapply(B, function(x) rcorr(as.matrix(x)))

# Plot three pictures
par(mfrow=c(1,3))
col<- colorRampPalette(c("red","white","blue"))(40)
lapply(M, function(x){
corrplot(x$r, type="upper",tl.col="black", tl.cex=0.7,tl.srt=45, col=col,
         p.mat = x$P, insig = "blank", sig.level = 0.01)
})

enter image description here

Upvotes: 3

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