Ongoing dramas with epicurves date scales - r

Question

I'm attempting to use ggplot and R for analysing some epidemiologic data, and I'm continuing to struggle with getting an epidemic curve to appear properly.
Data is here
attach(epicurve)
head(epicurve)
onset age
1 21/12/2012 18
2 14/06/2013 8
3 10/06/2013 64
4 28/05/2013 79
5 14/04/2013 56
6 9/04/2013 66
epicurve$onset <- as.Date(epicurve$onset, format="%d/%m/%Y")
ggplot(epicurve, aes(onset)) + geom_histogram() + scale_x_date(breaks=date_breaks("1 year"), minor_breaks=date_breaks("1 month"), labels = date_format("%b-%Y"))
gives this graph. This is fine, but the binwidths are not related to any time period of note, and adjusting them is a bit trial and error.
For this particular dataset, I'd like to display the cases by month of onset.
One way I worked out how to do this is:
epicurve$monyr <- format(epicurve$onset, "%b-%Y")
epicurve$monyr <- as.factor(epicurve$monyr)
ggplot(epicurve, aes(monyr)) + geom_histogram()
Outputs a graph I can't post because of the reputation system. The bars represent something meaningful, but the axis labels are a bomb-site. I can't format the axes using scale_x_date because they aren't dates and I can't work out what arguments to pass to scale_x_discrete to give useful labels.
I have a feeling there should be an easier way to do this by doing an operation on the onset column. Can anyone give me any pointers, please?

Answer 1

One option is to aggregate the data outside ggplot and then use geom_bar. This will produce counts by month.
edited Sept. 21 2013. Altered plot to show months with no counts.
epicurve <- read.csv("epicurve.csv", sep=",", header=T)
# initial formatting
epicurve$onset <- as.Date(epicurve$onset, format="%d/%m/%Y") # convert to Date class
epicurve$onset <- strftime(epicurve$onset, format="%Y/%m") # convert to Year-month
epicurve$onset <- paste(epicurve$onset, "/01", sep = "") # add arbitrary day on to end to make compatible w/ ggplot2
# aggregate by month
onset_counts <- aggregate(epicurve$onset, by = list(date = epicurve$onset), length) # aggregate by month
onset_counts$date = as.Date(onset_counts$date, format = "%Y/%m/%d") # covert to Date class
# plot
library(ggplot2)
library(scales)
ggplot(onset_counts, aes(x=date, y=x)) + geom_bar(stat="identity") + theme_bw() + theme(axis.text.x = element_text(angle=90, hjust = 1, vjust = 1)) +
ylab("Frequency") + xlab(NULL) + scale_x_date(breaks="month", labels=date_format("%Y-%m"))

Answer 2

I've also just happened across another way of making it look pretty, although it feels like a bit of a kludge.
#read data
epicurve <- read.csv("epicurve.csv", sep=",", header=T)
epicurve$onset <- as.Date(epicurve$onset, format="%d/%m/%Y")
#load libraries
library(ggplot2)
library(scales)
#plot
ggplot(epicurve, aes(onset)) + geom_histogram(colour="white", binwidth=30.4375) +
scale_x_date(breaks=date_breaks("1 year"), minor_breaks=("1 month"), labels=date_format("%b-%Y")) +
scale_y_continuous(breaks=0:10, minor_breaks=NULL) +
theme(axis.text.x = element_text(angle=45, vjust=0.5))
# binwidth = (365.25/12) = 30.4375 - which nicely makes the bins fit the scale nicely
Which gives this (notice the beautiful alignment of the bins!):
Many thanks to Nate for the help, and hopefully this will be useful!