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.

From stackoverflow

• If I understand your question correctly, then you probably want a density estimate along with the histogram:

  X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4))
  hist(X, prob=TRUE)            # prob=TRUE for probabilities not counts
  lines(density(X))             # add a density estimate with defaults
  lines(density(X, adjust=2), lty="dotted")   # add another "smoother" density
  

• 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 ...

  John Johnson : However, if you want something that is skewed, you can either do the density example from above, transform your data (e.g. foo.log <- log(foo) and try the above), or try fitting a skewed distribution, such as the gamma or lognormal (lognormal is equivalent to taking the log and fitting a normal, btw).

  Dirk Eddelbuettel : But that still requires estimating the parameters of your distribution first.

  John Johnson : This gets a bit far afield from simply discussing R, as we are getting more into theoretical statistics, but you might try this link for the Gamma: http://en.wikipedia.org/wiki/Gamma_distribution#Parameter_estimation For lognormal, just take the log (assuming all data is positive) and work with log-transformed data. For anything fancier, I think you would have to work with a statistics textbook.

  Dirk Eddelbuettel : I think you misunderstand how both the original poster as well as all other answers are quite content to use non-parametric estimates -- like an old-school histogram or a somewhat more modern data-driven densisty estimate. Parametric estimates are great if you have good reason to suspect a distribution. But that was not the case here.

• 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()