I am looking for a graph of conditional density, as a built-in function cdplot, but using ggplot2.
Here is an example of vanilla cdplot:
with(iris, cdplot(Sepal.Length, Species))
The ggplot2book (p. 188) states that the following calls should be equivalent:
cdplot(x, y)
qplot(x, fill=y, geom="density", position="fill")
However, it seems that this behavior was violated when upgrading to ggplot2(it also gives a warning `position` is deprecated):
with(iris, qplot(Sepal.Length, fill=Species, geom="density", position="fill"))
I found a blog entry about who is trying to do the same , but apparently now is also broken (this is a warning, `position` is deprecated):
cdens <- cdplot(iris$Sepal.Length, iris$Species, plot = F)
x <- seq(min(iris$Sepal.Length), max(iris$Sepal.Length), length.out = 100)
y <- c(cdens[[1]](x), cdens[[2]](x), rep(1, length(x)))
type <- ordered(rep(levels(iris$Species), each = length(x)),
levels=rev(levels(iris$Species)))
x <- rep(x, 3)
qplot(x, y, geom="area", fill = type, position="identity",
xlab="Sepal.Length", ylab="Species") + theme_bw()
How can this be done? What is broken in these examples?
(I want a solution ggplotbecause it has better labeling and axis legends, especially when the independent variable is a date.)
: , @bouncyball ggplot(iris, aes(x = Sepal.Length, fill = Species))+ geom_density(position = 'fill'), - :
with(data, cdplot(time, cat))
abline(v=as.POSIXct(c('2017-04-01', '2017-03-01')), col='red')
ggplot(data, aes(x=time, fill=cat)) + geom_density(position = 'fill')
cdplot - , , , ggplot. cdplot , , 2017 :
> with(subset(data, time>'2017-03-01' & time <'2017-04-01'), table(cat))
cat
<1s <3s <5s <10s <20s <1m <2m <5m <1h <24h >1d
175484 31837 19078 16146 15013 20200 1142 1207 944 17 0