R - Defining Your Own Color schemes for HeatMaps

This post is intended at those who are beginners at R, and is inspired by a small post in Martin's bioblog.

First, we plot a "correlation heatmap" using the same logic that Martin uses. In our example, let's use the Movies dataset that comes with ggplot2.

We take the 6 genre columns, and we can compute the correlation matrix for those 6 columns.

	library(ggplot2)
	library(reshape2)
	data(movies)
	movieGenres <- movies[c(18:23)] #subset to 6 genres
	cor(movieGenres) # 6x6 cor matrix
	#ggplot likes the data 'melted' one value per row
	m <-melt(cor(movieGenres))
	p <- ggplot(data=m, aes(x=Var1, y=Var2, fill=value)) + geom_tile()

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Here's what the matrix looks like:

> cor(movieGenres) # 6x6 cor matrix
                  Action   Animation      Comedy        Drama
Action       1.000000000 -0.05443315 -0.08288728  0.007760094
Animation   -0.054433153  1.00000000  0.17967294 -0.179155441
Comedy      -0.082887284  0.17967294  1.00000000 -0.255784957
Drama        0.007760094 -0.17915544 -0.25578496  1.000000000
Documentary -0.069487718 -0.05204238 -0.14083580 -0.173443622
Romance     -0.023355368 -0.06637362  0.10986485  0.103545195
            Documentary     Romance
Action      -0.06948772 -0.02335537
Animation   -0.05204238 -0.06637362
Comedy      -0.14083580  0.10986485
Drama       -0.17344362  0.10354520
Documentary  1.00000000 -0.07157792
Romance     -0.07157792  1.00000000

When we plot with the default colors we get:

It is difficult to see the details in the tiles. Now, if you want to better control the colors, you can use the handy colorRampPalette() function and combine that with scale_fill_gradient2.
Let's say that we want "red" colors for negative correlations and "green" for positives.
(We can gray out the 1 along the diagonal.)

	#set up a coloring scheme using colorRampPalette
	red=rgb(1,0,0); green=rgb(0,1,0); blue=rgb(0,0,1); white=rgb(1,1,1)
	RtoWrange<-colorRampPalette(c(red, white ) )
	WtoGrange<-colorRampPalette(c(white, green) )
	p <- p + scale_fill_gradient2(low=RtoWrange(100), mid=WtoGrange(100), high="gray")

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Doing this produces:

If there are values close to 1 or to -1, those will pop out visually. Values close to 0 are a lot more muted.

Hope that helps someone.

References: Using R: Correlation Heatmap with ggplot2