geom_density error: object "y" not found
Stavros Macrakis
I have a workaround, but I'd like to understand if I'm doing something
wrong or if it's a bug.
-s
> zz <- data.frame(T = (1:10)^2, E = runif(10))
# This works
> ggplot(zz,aes(x=T))+geom_density()
# This works, though the density plot's y scale is not very useful
> ggplot(zz,aes(x=T))+geom_density(aes(x=T))+geom_line(aes(x=T,y=E)) # Density + line
# Ditto
> ggplot(zz,aes(x=T))+geom_density()+geom_line(aes(x=T,y=E))
# But this doesn't:
> qplot(T,E,data=zz) + geom_line() + geom_density(aes(x=T)) # Line + density
Error in eval(expr, envir, enclos) : object "y" not found
> qplot(T,E,data=zz) + geom_density(aes(x=T)) # Try simpler case
Error in eval(expr, envir, enclos) : object "y" not found
> traceback()
24: eval(expr, envir, enclos)
23: eval(substitute(list(...)), `_data`, parent.frame())
22: transform.data.frame(df, ymin = 0, ymax = y)
21: transform(df, ymin = 0, ymax = y)
20: get("reparameterise", env = .$geom, inherits = TRUE)(.$geom,
...)
19: .$geom$reparameterise(df, .$geom_params)
18: f(data[[1]], ...)
17: FUN(newX[, i], ...)
16: apply(gg, c(1, 2), function(data) {
f(data[[1]], ...)
})
15: gg_apply(data, function(df) {
if (!is.null(df)) {
.$geom$reparameterise(df, .$geom_params)
}
else {
data.frame()
}
})
14: get("reparameterise", env = p, inherits = TRUE)(p, ...)
13: p$reparameterise(d)
12: function (d, p)
p$reparameterise(d)(d = list(list(x = c(1, 1.19373776908023,
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1, 1, 1, 1))), p = <environment>)
11: do.call(flat, c(args, list(...)))
10: .fun(piece, ...)
9: llply(.data = pieces, .fun = .fun, ..., .progress = .progress)
8: alply(.data = .data, .margins = 1, .fun = f, ..., .progress = .progress)
7: mlply(cbind(d = data, p = layers), f)
6: dlapply(function(d, p) p$reparameterise(d))
5: ggplot_build(plot)
4: ggplotGrob(x, ...)
3: grid.draw(ggplotGrob(x, ...))
2: print.ggplot(list(data = list(T = c(1, 4, 9, 16, 25, 36, 49,
64, 81, 100), E = c(0.497166644548997, 0.810376721434295, 0.646959381410852,
0.709339203080162, 0.47301630419679, 0.395266520092264, 0.916957991663367,
0.619665221078321, 0.164813995128497, 0.204367158003151)), layers = list(
<environment>, <environment>, <environment>), scales = <environment>,
mapping = list(x = T, y = E), options = list(), coordinates =
<environment>,
facet = <environment>, plot_env = <environment>))
1: print(list(data = list(T = c(1, 4, 9, 16, 25, 36, 49, 64, 81,
100), E = c(0.497166644548997, 0.810376721434295, 0.646959381410852,
0.709339203080162, 0.47301630419679, 0.395266520092264, 0.916957991663367,
0.619665221078321, 0.164813995128497, 0.204367158003151)), layers = list(
<environment>, <environment>, <environment>), scales = <environment>,
mapping = list(x = T, y = E), options = list(), coordinates =
<environment>,
facet = <environment>, plot_env = <environment>))
>
hadley wickham
>
> I ran into the following problem in ggplot2 0.8.2, R 2.8.1, Windows.
>
> I have a workaround, but I'd like to understand if I'm doing something
> wrong or if it's a bug.
Could you explain what you're trying to do? Why are you trying to
overlay an arbitrary line on a density plot?
Hadley
Stavros Macrakis
Here's an example. Suppose I'm measuring traffic passing some
particular point. T is the time I detect a car and E is its speed. I
want to know the amount of traffic intensity (geom_density) and I want
a scatterplot of speed, both plotted against time, something like:
ggplot(zz,aes(x=T))+geom_density()+geom_point(aes(x=T,y=E))
Of course, the vertical scales for density and for speed need to be
different. I can't find secondary axis scales
in the doc right now, but they must be there somewhere....
By the way, I might also want a scatterplot of speed vs. traffic
intensity, but that's no longer a job for geom_stat, but for
preprocessing....
-s
hadley wickham
> Well, actually it should be a geom_point.
>
> Here's an example. Suppose I'm measuring traffic passing some
> particular point. T is the time I detect a car and E is its speed. I
> want to know the amount of traffic intensity (geom_density) and I want
> a scatterplot of speed, both plotted against time, something like:
>
> ggplot(zz,aes(x=T))+geom_density()+geom_point(aes(x=T,y=E))
>
> Of course, the vertical scales for density and for speed need to be
> different. I can't find secondary axis scales
> in the doc right now, but they must be there somewhere....
There are no secondary scales in ggplot2, and I don't currently have
any intention of adding them, because of the way they distort the
data.
I don't see any particularly easy way to work around this. Your best
bet would be to calculate the density yourself with density() and then
scale it to match your other x variable. I'm not sure how useful a
plot this will create, however.
Hadley
hadley wickham
> to overlay two time series using different vertical scales? The
Yes. The theoretical reason that I don't like multiple y scales is
because there is no longer a unique mapping between position and data
value. The practical implication of this is that it's essentially
impossible to compare two series because you can arbitrarily change
their relationship by differences in scaling.
> alternative is presumably to stack the two graphs, one over the other,
> and let the user try to see correlations by lining them up by eye?
Well, I'd recommend a scatterplot. But if you could provide a bit
more data I could offer more concrete suggestions.
Hadley