histogramWithNormalDist.R

# > head(df2)
#   id variable  cartvol time timeb
# 1  1 cartvol0 1.987862    0     0
# 2  2 cartvol0 1.694808    0     0
# 3  3 cartvol0 2.342389    0     0
# 4  4 cartvol0 1.727150    0     0
# 5  5 cartvol0 1.546601    0     0
# 6  6 cartvol0 1.425992    0     0

grid <- with(df2, seq(min(cartvol), max(cartvol), length = 100))
normaldens <- ddply(df2, .(time), function(df) {
  data.frame( 
    predicted = grid,
    density = dnorm(grid, mean(df$cartvol), sd(df$cartvol))
  )
})

p <- ggplot(data = df2, aes(x=cartvol))
p <- p + geom_histogram(aes(y=..density..), binwidth=.3, colour="black", fill="white")
#p <- p + geom_density(alpha=.2, fill="#FF6666")
p <- p + geom_line(aes(x = predicted, y = density), data = normaldens, colour = "red") 
p <- p + xlab("Cartilage volume")
p <- p + ylab("Probability") 
p <- p + theme_bw()
p <- p + theme(legend.position="bottom")
p <- p + facet_grid(.~time)
print(p)