cormatrixplot.R

################################################
#Correlation matrix plot
raw.data <- raw.data[complete.cases(raw.data),]


c<-cor(raw.data[colstart:colend])

#Correlation p-value tests
cor.pvalues <- function(X){

    nc <- ncol(X) 
    res <- matrix(0, nc, nc) 
    for (i in 2:nc){

        for (j in 1:(i - 1)){
            res[i, j] <- res[j, i] <- cor.test(X[,i], X[,j])$p.value
            }
        }
    res 
    }
	
p <- cor.pvalues(raw.data[colstart:colend])

#convert p-values into stars for significance level
stars <- as.character(symnum(p, cutpoints=c(0,0.001,0.01,0.05,1),
                      symbols=c('***', '**', '*', '' ),
                      legend=FALSE,
                      )) 
					  
#Combine into data.frame for plotting
molten.p<- melt(p)
molten.c<- melt(c)
molten.stars<- melt(stars)
molten.raw.data<-cbind(molten.c, molten.p[3], molten.stars[1])

#rename columns to avoid the same name
names(molten.raw.data) <- c("M1", "M2", "corr", "pvalue", "stars")

#define each triangle of the plot matric and the diagonal (mi.ids)
mi.ids <- subset(molten.raw.data, M1 == M2)
mi.lower <- subset(molten.raw.data[lower.tri(c),], M1 != M2)
mi.upper <- subset(molten.raw.data[upper.tri(c),], M1 != M2)
stars.lower <- subset(molten.raw.data[lower.tri(c),], M1 != M2)
meas <- as.character(unique(molten.raw.data$M2))


plot<-ggplot(molten.raw.data, aes(M1, M2, fill=corr))+
 theme_bw()+
 geom_tile(data=mi.lower) +
 geom_text(data=mi.lower, aes(label=paste(stars)), size=1) +
 geom_text(data=mi.ids, aes(label=M2), hjust=0, colour="grey40", size=1)+
 scale_colour_identity() +
 scale_fill_gradientn(colours= c("darkblue", "lightblue", "white", "pink", "darkred"), limits=c(1,-1)) +
 scale_x_discrete("", limits=meas[length(meas):1]) + #flip the x axis
 scale_y_discrete("", limits=meas)+
 theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))+
 guides(fill=F, colour=F)


 ggsave("cor.matrix.plot.png", width=cor.size, height=cor.size)