dsparks · August 19, 2022 06:54 · Oct 16, 2014 · Apr 24, 2014 · Sep 14, 2012 · Sep 13, 2012
diff --git a/Heatmap.R b/Heatmap.R
@@ -1,6 +1,3 @@
-# Simple ggplot2 heatmap
-# with colorBrewer "spectral" palette
-
 doInstall <- TRUE  # 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")}
@@ -26,18 +23,18 @@ head(longData, 20)
 
 # Optionally, reorder both the row and column variables in any order
 # Here, they are sorted by mean value
-longData$X1 <- factor(longData$X1, names(sort(with(longData, by(value, X1, mean)))))
-longData$X2 <- factor(longData$X2, names(sort(with(longData, by(value, X2, mean)))))
+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")
 
 zp1 <- ggplot(longData,
-              aes(x = X2, y = X1, fill = value))
+              aes(x = Var2, y = Var1, fill = value))
 zp1 <- zp1 + geom_tile()
 zp1 <- zp1 + scale_fill_gradientn(colours = myPalette(100))
 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
+print(zp1)
diff --git a/Heatmap.R b/Heatmap.R
@@ -30,7 +30,7 @@ longData$X1 <- factor(longData$X1, names(sort(with(longData, by(value, X1, mean)
 longData$X2 <- factor(longData$X2, names(sort(with(longData, by(value, X2, mean)))))
 
 # Define palette
-myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
+myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")
 
 zp1 <- ggplot(longData,
               aes(x = X2, y = X1, fill = value))

diff --git a/Heatmap.R b/Heatmap.R
@@ -2,7 +2,7 @@
 # with colorBrewer "spectral" palette
 
 doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
-toInstall <- c("ggplot2", "reshape", "RColorBrewer")
+toInstall <- c("ggplot2", "reshape2", "RColorBrewer")
 if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
 lapply(toInstall, library, character.only = TRUE)
 

diff --git a/Heatmap.R b/Heatmap.R
@@ -24,6 +24,11 @@ for(ii in 2:nrow(myData)){  myData[ii, ] <- myData[ii-1, ] + rnorm(ncol(myData))
 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$X1 <- factor(longData$X1, names(sort(with(longData, by(value, X1, mean)))))
+longData$X2 <- factor(longData$X2, names(sort(with(longData, by(value, X2, mean)))))
+
 # Define palette
 myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
 

diff --git a/Heatmap.R b/Heatmap.R
@@ -34,4 +34,5 @@ zp1 <- zp1 + scale_fill_gradientn(colours = myPalette(100))
 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
diff --git a/Heatmap.R b/Heatmap.R
@@ -20,6 +20,7 @@ colnames(myData) <- LETTERS[1:nCol]
 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)
 

diff --git a/Heatmap.R b/Heatmap.R
@@ -0,0 +1,36 @@
+# Simple ggplot2 heatmap
+# with colorBrewer "spectral" palette
+
+doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
+toInstall <- c("ggplot2", "reshape", "RColorBrewer")
+if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
+lapply(toInstall, library, character.only = TRUE)
+
+# Generate a random matrix
+# This can be any type of numeric matrix,
+# though we often see heatmaps of square correlation matrices.
+nRow <- 9
+nCol <- 16
+
+myData <- matrix(rnorm(nRow * nCol), ncol = nCol)
+rownames(myData) <- letters[1:nRow]
+colnames(myData) <- LETTERS[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))  }
+
+longData <- melt(myData)
+head(longData, 20)
+
+# Define palette
+myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
+
+zp1 <- ggplot(longData,
+              aes(x = X2, y = X1, fill = value))
+zp1 <- zp1 + geom_tile()
+zp1 <- zp1 + scale_fill_gradientn(colours = myPalette(100))
+zp1 <- zp1 + scale_x_discrete(expand = c(0, 0))
+zp1 <- zp1 + scale_y_discrete(expand = c(0, 0))
+zp1 <- zp1 + coord_equal()
+print(zp1)  # Your plot will look different, depending on the seed