silkita - 3 months ago 56
R Question

# Transform color scale to probability-transformed color distribution with scale_fill_gradientn()

I am trying to visualize heavily tailed raster data, and I would like a non-linear mapping of colors to the range of the values. There are a couple of similar questions, but they don't really solve my specific problem (see links below).

``````library(ggplot2)
library(scales)

set.seed(42)
dat <- data.frame(
x = floor(runif(10000, min=1, max=100)),
y = floor(runif(10000, min=2, max=1000)),
z = rlnorm(10000, 1, 1) )

# colors for the colour scale:
col.pal <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
fill.colors <- col.pal(64)
``````

This is how the data look like if not transformed in some way:

``````ggplot(dat, aes(x = x, y = y, fill = z)) +
geom_tile(width=2, height=30) +
``````

My question is sort of a follow-up question related to
this one or this one , and the solution given here actually yields exactly the plot I want, except for the legend:

``````qn <- rescale(quantile(dat\$z, probs=seq(0, 1, length.out=length(fill.colors))))
ggplot(dat, aes(x = x, y = y, fill = z)) +
geom_tile(width=2, height=30) +
``````

Now I want the colour scale in the legend to represent the non-linear distribution of the values (now only the red part of the scale is visible), i.e. the legend should as well be based on quantiles. Is there a way to accomplish this?

I thought the
`trans`
argument within the colour scale might do the trick, as suggested here , but that throws an error, I think because
`qnorm(pnorm(dat\$z))`
results in some infinite values (I don't completely understand the function though..).

``````norm_trans <- function(){
trans_new('norm', function(x) pnorm(x), function(x) qnorm(x))
}
ggplot(dat, aes(x = x, y = y, fill = z)) +
geom_tile(width=2, height=30) +
> Error in seq.default(from = best\$lmin, to = best\$lmax, by = best\$lstep) : 'from' must be of length 1
``````

So, does anybody know how to have a quantile-based colour distribution in the plot and in the legend?

This code will make manual breaks with a pnorm transformation. Is this what you are after?

``````ggplot(dat, aes(x = x, y = y, fill = z)) +
geom_tile(width=2, height=30) +
trans = 'norm',
breaks = quantile(dat\$z, probs = c(0, 0.25, 1))
)
``````