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R function to rebin a matrix by taking averages, then plots the heatmap
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| # Simple ggplot2 heatmap | |
| # with colorBrewer "spectral" palette | |
| doInstall <- FALSE # Change to FALSE if you don't want packages installed. | |
| toInstall <- c("ggplot2", "reshape2", "RColorBrewer") | |
| if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
| lapply(toInstall, library, character.only = TRUE) | |
| require('rje') | |
| require('reshape2') | |
| # Generate a random matrix | |
| # This can be any type of numeric matrix, | |
| # though we often see heatmaps of square correlation matrices. | |
| rebin <- function(myData, binW, binL) { | |
| # Set up starting matrix | |
| matDims <- dim(myData) | |
| rownames(myData) <- c(1:matDims[1]) | |
| colnames(myData) <- c(1:matDims[2]) | |
| # Begin rebinned matrix | |
| b <- myData | |
| print(dim(b)) | |
| # Check and change matrix length to be a multple of the bin factors | |
| if (matDims[1] %% binL > 0) { | |
| x <- floor(matDims[1] / binL) | |
| lengthDiff <- matDims[1] - x * binL | |
| delRange <- ((matDims[1]+1)-lengthDiff):matDims[1] | |
| b <- b[-(delRange),] | |
| } | |
| print(dim(b)) | |
| #get the new dimenions | |
| matDims <- dim(myData) | |
| # Get the Means of the matrix reshaped to the bin factor | |
| b <- colMeans(matrix(b, nrow=binL),na.rm=TRUE) | |
| # reshape matrix and transpose back | |
| b <- matrix(b, nrow=matDims[1]/binL) | |
| matDims <- dim(myData) | |
| # Check and change matrix width to be a multple of the bin factors | |
| if (matDims[2] %% binW > 0) { | |
| x <- floor(matDims[2] / binW) | |
| lengthDiff <- matDims[2] - x * binW | |
| delRange <- ((matDims[2]+1)-lengthDiff):matDims[1] | |
| b <- b[,-(delRange)] | |
| } | |
| #get the new dimenions | |
| matDims <- dim(myData) | |
| # Get the Means of the matrix reshaped to the bin factor | |
| b <- rowMeans(matrix(b, ncol=binW), na.rm=TRUE) | |
| # reshape matrix and transpose back | |
| b <- matrix(b, ncol=matDims[2]/binW) | |
| return(b) | |
| } | |
| heatmap <- function(myData, binSize){ | |
| nRow <- dim(myData)[1] | |
| nCol <- dim(myData)[2] | |
| #myData <- matrix(mapfile, ncol = nCol) | |
| rownames(myData) <- c(1:nRow) | |
| colnames(myData) <- c(1:nCol) | |
| # # Replace with numbers that actually have a relationship: | |
| # for(ii in 2:ncol(myData)){ myData[, ii] <- myData[, ii-1] + rnorm(nrow(myData)) } | |
| # for(ii in 2:nrow(myData)){ myData[ii, ] <- myData[ii-1, ] + rnorm(ncol(myData)) } | |
| # For melt() to work seamlessly, myData has to be a matrix. | |
| longData <- melt(myData) | |
| head(longData, 20) | |
| # Optionally, reorder both the row and column variables in any order | |
| # Here, they are sorted by mean value | |
| # longData$Var1 <- factor(longData$Var1, names(sort(with(longData, by(value, Var1, mean))))) | |
| # longData$Var2 <- factor(longData$Var2, names(sort(with(longData, by(value, Var2, mean))))) | |
| # Define palette | |
| #myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab") | |
| myPalette <- cubeHelix(100, start=0.5,r=-1.5,hue=2, gamma=1 ) | |
| zp1 <- ggplot(longData, | |
| aes(x = Var2, y = Var1, fill = value)) | |
| zp1 <- zp1 + geom_tile() | |
| zp1 <- zp1 + scale_fill_gradientn(colours = myPalette) | |
| zp1 <- zp1 + scale_x_discrete(expand = c(0, 0)) | |
| zp1 <- zp1 + scale_y_discrete(expand = c(0, 0)) | |
| zp1 <- zp1 + coord_equal() | |
| zp1 <- zp1 + theme_bw() | |
| print(zp1) # Your plot will look different, depending on the seed | |
| } | |
| myData <- as.matrix(read.csv("map data.csv")) | |
| mat <- matrix(c(1:262144),nrow=512,ncol=512) | |
| heatmap(rebin(myData,2,2)) |
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