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.
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")