There are a few different ways to make heat maps in R. The simplest is to use the heatmap() function in the base library:

heatmap(as.matrix(mtcars), 
Rowv=NA, 
Colv=NA, 
col = heat.colors(256), 
scale="column",
margins=c(2,8),
main = "Car characteristics by Model")

The example code has a lot of arguments, so it might look difficult at first sight. However, if we consider each argument in turn, we can understand how it works. The first argument to the heatmap() function is the dataset. We are using the built-in dataset mtcars, which holds data such as fuel efficiency (mpg), number of cylinders (cyl), weight (wt), and so on for different models of cars. The data needs to be in a matrix format, so we use the as.matrix() function. Rowv and Colv specify whether and how dendrograms should be displayed to the left and top of the heat map.

In our example, we suppress them by setting the two arguments to NA, which is a logical indicator of a missing value in R. The scale argument tells R in which direction the color gradient should apply. We have set it to column, which means the scale for the gradient will be calculated on a per-column basis.

Heat maps are very useful to look at correlations between variables in a large dataset. For example, in bioinformatics, heat maps are often used to study the correlations between groups of genes.

Let's look at an example with the genes.csv example data file. Let's first load the file:

genes<-read.csv("genes.csv",header=T)

Let's use the image() function to create a correlation heat map:

rownames(genes)<-colnames(genes)

image(x=1:ncol(genes),
y=1:nrow(genes),
z=t(as.matrix(genes)),
axes=FALSE,
xlab="",
ylab="" ,
main="Gene Correlation Matrix")

axis(1,at=1:ncol(genes),labels=colnames(genes),col="white",
las=2,cex.axis=0.8)
axis(2,at=1:nrow(genes),labels=rownames(genes),col="white",
las=1,cex.axis=0.8)

We used a few new commands and arguments in the previous example, especially to format the axes. We discuss these in detail starting in Chapter 3, Beyond the Basics – Adjusting Key Parameters, and with more examples in later chapters.

Heat maps are explained in a lot more detail with more examples in Chapter 9, Creating Heat Maps and Contour Plots.