I'm faced with the following problem: a few extreme values are dominating the colorscale of my geom_raster
plot. An example is probably more clear (note that this example only works with a recent ggplot2 version, I use 0.9.2.1):
library(ggplot2)
library(reshape)
theme_set(theme_bw())
m_small_sd = melt(matrix(rnorm(10000), 100, 100))
m_big_sd = melt(matrix(rnorm(100, sd = 10), 10, 10))
new_xy = m_small_sd[sample(nrow(m_small_sd), nrow(m_big_sd)), c("X1","X2")]
m_big_sd[c("X1","X2")] = new_xy
m = data.frame(rbind(m_small_sd, m_big_sd))
names(m) = c("x", "y", "fill")
ggplot(m, aes_auto(m)) + geom_raster() + scale_fill_gradient2()
Right now I solve this by setting the values over a certain quantile equal to that quantile:
qn = quantile(m$fill, c(0.01, 0.99), na.rm = TRUE)
m = within(m, { fill = ifelse(fill < qn[1], qn[1], fill)
fill = ifelse(fill > qn[2], qn[2], fill)})
This does not really feel like an optimal solution. What I would like to do is have a non-linear mapping of colors to the range of values, i.e. more colors present in the area with more observations. In spplot
I could use classIntervals
from the classInt
package to calculate the appropriate class boundaries:
library(sp)
library(classInt)
gridded(m) = ~x+y
col = c("#EDF8B1", "#C7E9B4", "#7FCDBB", "#41B6C4",
"#1D91C0", "#225EA8", "#0C2C84", "#5A005A")
at = classIntervals(m$fill, n = length(col) + 1)$brks
spplot(m, at = at, col.regions = col)
To my knowledge it is not possible to hardcode this mapping of colors to class intervals like I can in spplot
. I could transform the fill
axis, but as there are negative values in the fill
variable that will not work.
So my question is: are there any solutions to this problem using ggplot2?
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