# Quick Correlation Heatmap in R

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I find myself remaking this plot over and over. So here’s a quick function. Also tests correlation significance. I quite like the spectral palette for the purpose of a heat map. It’s not too painful on the eyes and variation is immediately identifiable.

#' Create a Heatmap #' #' Function creates a correlation heatmap using ggplot2 given a data.frame #' #' @param df A data.frame containing only numeric data #' @param data.only Logical, if TRUE returns correlation and pvalue. heatmap <- function(df, data.only = FALSE) { require(ggplot2) # ggplot2 require(reshape2) # melt test <- melt(cor(df,use = "na.or.complete")) listing <- list() for(x in 1:nrow(test)) { listing[[x]] <- round( cor.test( df[[test[x,1]]], df[[test[x,2]]])$p.value,3 ) } test$Test <- unlist(listing) p1 <- ggplot(test, aes(Var1,Var2,fill = value, label = round(value,2))) + geom_tile() + geom_text() + scale_fill_distiller(palette = "Spectral", trans = "reverse") + labs( x = "", y = "", fill = "Correlation") + theme_grey() + theme( axis.text.x = element_text(angle = 25, size = 12), axis.text.y = element_text(size = 12), plot.background = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), panel.background = element_blank() ) if(data.only) { return(test) } print(p1) }

Usage:

heatmap( diamonds[ sapply(diamonds, is.numeric) # only numeric columns ])

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