Colour segments of density plot by bin - r

Question

Warning, I am brand-new to R!
I have the R bug and having a play with the possibilities but getting very lost. I want to try and colour segments of a density plot with a condition '>' to indicate bins. In my head it look like:
...but not quartile or % change dependant.
My data shows; x = duration (number of days) and y = frequency. I would like the plot to colour split on 3 month intervals up to 12 months and one colour after (using working days i.e. 63 = 3 months).
I have had a go, but really not sure where to start!
ggplot(df3, aes(x=Investigation.Duration))+
geom_density(fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>0],
fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>63], color = "white",
fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>127], color = "light Grey",
fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>190], color = "medium grey",
fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>253], color = "dark grey",
fill = W.S_CleanNA$Investigation.Duration[W.S_CleanNA$Investigation.Duration>506], color = "black")+
ggtitle ("Investigation duration distribution in 'Wales' complexity sample")+
geom_text(aes(x=175, label=paste0("Mean, 136"), y=0.0053))+
geom_vline(xintercept = c(136.5), color = "red")+
geom_text(aes(x=80, label=paste0("Median, 129"), y=0.0053))+
geom_vline(xintercept = c(129.5), color = "blue")
Any really simple help much appreciated.

Answer 1

Unfortunately, you can't do this directly with geom_density, as "under the hood" it is built with a single polygon, and a polygon can only have a single fill. The only way to do this is to have multiple polygons, and you need to build them yourself.
Fortunately, this is easier than it sounds.
There was no sample data in the question, so we will create a plausible distribution with the same median and mean:
#> Simulate data
set.seed(69)
df3 <- data.frame(Investigation.Duration = rgamma(1000, 5, 1/27.7))
round(median(df3$Investigation.Duration))
#> [1] 129
round(mean(df3$Investigation.Duration))
#> [1] 136
# Get the density as a data frame
dens <- density(df3$Investigation.Duration)
dens <- data.frame(x = dens$x, y = dens$y)
# Exclude the artefactual times below zero
dens <- dens[dens$x > 0, ]
# Split into bands of 3 months and group > 12 months together
dens$band <- dens$x %/% 63
dens$band[dens$band > 3] <- 4
# This us the complex bit. For each band we want to add a point on
# the x axis at the upper and lower ltime imits:
dens <- do.call("rbind", lapply(split(dens, dens$band), function(df) {
df <- rbind(df[1,], df, df[nrow(df),])
df$y[c(1, nrow(df))] <- 0
df
}))
Now we have the polygons, it's just a case of drawing and labelling appropriately:
library(ggplot2)
ggplot(dens, aes(x, y)) +
geom_polygon(aes(fill = factor(band), color = factor(band))) +
theme_minimal() +
scale_fill_manual(values = c("#003f5c", "#58508d", "#bc5090",
"#ff6361", "#ffa600"),
name = "Time",
labels = c("Less than 3 months",
"3 to 6 months",
"6 to 9 months",
"9 to 12 months",
"Over 12 months")) +
scale_colour_manual(values = c("#003f5c", "#58508d", "#bc5090",
"#ff6361", "#ffa600"),
guide = guide_none()) +
labs(x = "Days since investigation started", y = "Density") +
ggtitle ("Investigation duration distribution in 'Wales' complexity sample") +
geom_text(aes(x = 175, label = paste0("Mean, 136"), y = 0.0053),
check_overlap = TRUE)+
geom_vline(xintercept = c(136.5), linetype = 2)+
geom_text(aes(x = 80, label = paste0("Median, 129"), y = 0.0053),
check_overlap = TRUE)+
geom_vline(xintercept = c(129.5), linetype = 2)