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 hp
and 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 Rowv
and 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:
terrain.color()
, rainbow()
, heat.colors()
, topo.colors()
or cm.colors()
RColorBrewer
. See list of available palettes here.You can custom title & axis titles with the usual main
and xlab
/ylab
arguments (left).
You can also change labels with labRow
/colRow
and their size with cexRow
/cexCol
.
# 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 RowSideColors
argument.