A complete explanation on how to build heatmaps with base R: how to use the
heatmap() function, how to custom appearance, how to normalize data and more.
How to do it: below is the most basic heatmap you can build in base R, using the
heatmap() function with no parameters. Note that it takes as input a matrix. If you have a data frame, you can convert it to a matrix with
as.matrix(), but you need numeric variables only.
How to read it: each column is a variable. Each observation is a row. Each square is a value, the closer to yellow the higher. You can transpose the matrix with
t(data) to swap X and Y axis.
Note: as you can see this heatmap is not very insightful: all the variation is absorbed by the
disp variables that have very high values compared to the others. We need to normalize the data, as explained in the next section.
Normalizing the matrix is done using the
scale argument of the
heatmap() function. It can be applied to
row or to
column. Here the
column option is chosen, since we need to absorb the variation between column.
You may have noticed that order of both rows and columns is different compare to the native
mtcar matrix. This is because
heatmap() reorders both variables and observations using a clustering algorithm: it computes the distance between each pair of rows and columns and try to order them by similarity.
Moreover, the corresponding
dendrograms are provided beside the heatmap. We can avoid it and just visualize the raw matrix: use the
Colv arguments as follow.
# No dendrogram nor reordering for neither column or row heatmap(data, Colv = NA, Rowv = NA, scale="column")
There are several ways to custom the color palette:
RColorBrewer. See list of available palettes here.
You can custom title & axis titles with the usual
ylab arguments (left).
You can also change labels with
colRow and their size with
# Add classic arguments like main title and axis title heatmap(data, Colv = NA, Rowv = NA, scale="column", col = coul, xlab="variable", ylab="car", main="heatmap") # Custom x and y labels with cexRow and labRow (col respectively) heatmap(data, scale="column", cexRow=1.5, labRow=paste("new_", rownames(data),sep=""), col= colorRampPalette(brewer.pal(8, "Blues"))(25))
Often, heatmap intends to compare the observed structure with an expected one.
You can add a vector of color beside the heatmap to represents the expected structure using the