ggplot2 heatmaps: using different gradients for categories

Question

This Learning R blog post shows how to make a heatmap of basketball stats using ggplot2. The finished heatmap looks like this:

My question (inspired by Jake who commented on the Learning R blog post) is: would it be possible to use different gradient colors for different categories of stats (offensive, defensive, other)?

Answer 1

First, recreate the graph from the post, updating it for the newer (0.9.2.1) version of ggplot2 which has a different theme system and attaches fewer packages:

nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv") nba$Name <- with(nba, reorder(Name, PTS))  library("ggplot2") library("plyr") library("reshape2") library("scales")  nba.m <- melt(nba) nba.s <- ddply(nba.m, .(variable), transform,                rescale = scale(value))  ggplot(nba.s, aes(variable, Name)) +    geom_tile(aes(fill = rescale), colour = "white") +    scale_fill_gradient(low = "white", high = "steelblue") +    scale_x_discrete("", expand = c(0, 0)) +    scale_y_discrete("", expand = c(0, 0)) +    theme_grey(base_size = 9) +    theme(legend.position = "none",         axis.ticks = element_blank(),          axis.text.x = element_text(angle = 330, hjust = 0)) 

Using different gradient colors for different categories is not all that straightforward. The conceptual approach, to map the fill to interaction(rescale, Category) (where Category is Offensive/Defensive/Other; see below) doesn't work because interacting a factor and continuous variable gives a discrete variable which fill can not be mapped to.

The way to get around this is to artificially do this interaction, mapping rescale to non-overlapping ranges for different values of Category and then use scale_fill_gradientn to map each of these regions to different color gradients.

First create the categories. I think these map to those in the comment, but I'm not sure; changing which variable is in which category is easy.

nba.s$Category <- nba.s$variable levels(nba.s$Category) <-    list("Offensive" = c("PTS", "FGM", "FGA", "X3PM", "X3PA", "AST"),        "Defensive" = c("DRB", "ORB", "STL"),        "Other" = c("G", "MIN", "FGP", "FTM", "FTA", "FTP", "X3PP",                     "TRB", "BLK", "TO", "PF")) 

Since rescale is within a few (3 or 4) of 0, the different categories can be offset by a hundred to keep them separate. At the same time, determine where the endpoints of each color gradient should be, in terms of both rescaled values and colors.

nba.s$rescaleoffset <- nba.s$rescale + 100*(as.numeric(nba.s$Category)-1) scalerange <- range(nba.s$rescale) gradientends <- scalerange + rep(c(0,100,200), each=2) colorends <- c("white", "red", "white", "green", "white", "blue") 

Now replace the fill variable with rescaleoffset and change the fill scale to use scale_fill_gradientn (remembering to rescale the values):

ggplot(nba.s, aes(variable, Name)) +    geom_tile(aes(fill = rescaleoffset), colour = "white") +    scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) +    scale_x_discrete("", expand = c(0, 0)) +    scale_y_discrete("", expand = c(0, 0)) +    theme_grey(base_size = 9) +    theme(legend.position = "none",         axis.ticks = element_blank(),          axis.text.x = element_text(angle = 330, hjust = 0)) 

Reordering to get related stats together is another application of the reorder function on the various variables:

nba.s$variable2 <- reorder(nba.s$variable, as.numeric(nba.s$Category))  ggplot(nba.s, aes(variable2, Name)) +    geom_tile(aes(fill = rescaleoffset), colour = "white") +    scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) +    scale_x_discrete("", expand = c(0, 0)) +    scale_y_discrete("", expand = c(0, 0)) +    theme_grey(base_size = 9) +    theme(legend.position = "none",         axis.ticks = element_blank(),          axis.text.x = element_text(angle = 330, hjust = 0))