[r] Fitting a density curve to a histogram in R

Is there a function in R that fits a curve to a histogram?

Let's say you had the following histogram

hist(c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))

It looks normal, but it's skewed. I want to fit a normal curve that is skewed to wrap around this histogram.

This question is rather basic, but I can't seem to find the answer for R on the internet.

Here's the way I do it:

foo <- rnorm(100, mean=1, sd=2)
hist(foo, prob=TRUE)
curve(dnorm(x, mean=mean(foo), sd=sd(foo)), add=TRUE)

A bonus exercise is to do this with ggplot2 package ...

I had the same problem but Dirk's solution didn't seem to work. I was getting this warning messege every time

"prob" is not a graphical parameter

I read through ?hist and found about freq: a logical vector set TRUE by default.

the code that worked for me is

hist(x,freq=FALSE)
lines(density(x),na.rm=TRUE)

Such thing is easy with ggplot2

library(ggplot2)
dataset <- data.frame(X = c(rep(65, times=5), rep(25, times=5), 
                            rep(35, times=10), rep(45, times=4)))
ggplot(dataset, aes(x = X)) + 
  geom_histogram(aes(y = ..density..)) + 
  geom_density()

or to mimic the result from Dirk's solution

ggplot(dataset, aes(x = X)) + 
  geom_histogram(aes(y = ..density..), binwidth = 5) + 
  geom_density()

Dirk has explained how to plot the density function over the histogram. But sometimes you might want to go with the stronger assumption of a skewed normal distribution and plot that instead of density. You can estimate the parameters of the distribution and plot it using the sn package:

> sn.mle(y=c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))
$call
sn.mle(y = c(rep(65, times = 5), rep(25, times = 5), rep(35, 
    times = 10), rep(45, times = 4)))

$cp
    mean     s.d. skewness 
41.46228 12.47892  0.99527 

This probably works better on data that is more skew-normal: