easiest way to discretize continuous scales for gg

Question

Suppose I have this plot:

ggplot(iris) + geom_point(aes(x=Sepal.Width, y=Sepal.Length, colour=Sepal.Length)) + scale_colour_gradient()

what is the correct way to discretize the color scale, like the plot shown below the accepted answer here (gradient breaks in a ggplot stat_bin2d plot)?

ggplot correctly recognizes discrete values and uses discrete scales for these, but my question is if you have continuous data and you want a discrete colour bar for it (with each square corresponding to a value, and squares colored in a gradient still), what is the best way to do it? Should the discretizing/binning happen outside of ggplot and get put in the dataframe as a separate discrete-valued column, or is there a way to do it within ggplot? an example of what I'm looking for is similar to the scale shown here:

except I'm plotting a scatter plot and not something like geom_tile/heatmap.

thanks.

Answer 1

You could try the following, I have your example code modified appropriately below:

#I am not so great at R, so I'll just make a data frame this way
#I am convinced there are better ways. Oh well.
df<-data.frame()
for(x in 1:10){
  for(y in 1:10){
    newrow<-c(x,y,sample(1:1000,1))
    df<-rbind(df,newrow)
  }
}
colnames(df)<-c('X','Y','Val')


#This is the bit you want
p<- ggplot(df, aes(x=X,y=Y,fill=cut(Val, c(0,100,200,300,400,500,Inf))))
p<- p + geom_tile() + scale_fill_brewer(type="seq",palette = "YlGn")
p<- p + guides(fill=guide_legend(title="Legend!"))

#Tight borders
p<- p + scale_x_continuous(expand=c(0,0)) + scale_y_continuous(expand=c(0,0))
p

Note the strategic use of cut to discretize the data followed by the use of color brewer to make things pretty.

The result looks as follows.

Answer 2

The solution is slightly complicated, because you want a discrete scale. Otherwise you could probably simply use round.

library(ggplot2)

bincol <- function(x,low,medium,high) {
  breaks <- function(x) pretty(range(x), n = nclass.Sturges(x), min.n = 1)

  colfunc <- colorRampPalette(c(low, medium, high))

  binned <- cut(x,breaks(x))

  res <- colfunc(length(unique(binned)))[as.integer(binned)]
  names(res) <- as.character(binned)
  res
}

labels <- unique(names(bincol(iris$Sepal.Length,"blue","yellow","red")))
breaks <- unique(bincol(iris$Sepal.Length,"blue","yellow","red"))
breaks <- breaks[order(labels,decreasing = TRUE)]
labels <- labels[order(labels,decreasing = TRUE)]


ggplot(iris) + 
  geom_point(aes(x=Sepal.Width, y=Sepal.Length,
                 colour=bincol(Sepal.Length,"blue","yellow","red")), size=4) +
  scale_color_identity("Sepal.Length", labels=labels, 
                       breaks=breaks, guide="legend")