I fit a count model to a vector of actual data and would now like to plot the actual and the predicted as a grouped (dodged) bar chart. Since this is a count model, the data are discrete (X=x from 0 to 317). Since I am fitting a model, I only have already-tabulated data for the predicted values.
Here is how my original data frame looks:
actual predicted
1 3236 3570.4995
2 1968 1137.1202
3 707 641.8186
4 302 414.8763
5 185 285.1854
6 104 203.0502
I transformed the data to be plotted with ggplot2:
melted.data <- melt(plot.data)
melted.data$realization <- c(rep(0:317, times=2))
colnames(melted.data)=c('origin','count','realization')
So that my data frame now looks like this:
head(melted.data)
origin count realization
1 actual 3236 0
2 actual 1968 1
3 actual 707 2
4 actual 302 3
5 actual 185 4
6 actual 104 5
> tail(melted.data)
origin count realization
631 predicted 1.564673e-27 312
632 predicted 1.265509e-27 313
633 predicted 1.023552e-27 314
634 predicted 8.278601e-28 315
635 predicted 6.695866e-28 316
636 predicted 5.415757e-28 317
When I try to graph it (again, I'd like to have the actual and predicted count --which is already tabulated in the data-- by discrete realization), I give this command:
ggplot(melted.data, stat="identity", aes(x=realization, fill=origin)) + geom_bar(position="dodge")
Yet it seems like the stat parameter is not liked by ggplot2, as I don't get the correct bar height (which would be those of the variable "count").
Any ideas?
Thanks,
Roberto.
You need y-values in the aes
mapping if you use stat_identity
(column count
). Try the following:
ggplot(melted.data, aes(x=realization, y=count, fill=origin)) +
stat_identity(position="dodge", geom="bar")
or
ggplot(melted.data, aes(x=realization, y=count, fill=origin)) +
geom_bar(position="dodge", stat="identity")
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