Fitting a density curve to a histogram in R

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 countslines(density(X))             # add a density estimate with defaultslines(density(X, adjust=2), lty="dotted")   # add another "smoother" density

Edit a long while later:

Here is a slightly more dressed-up version:

X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4))hist(X, prob=TRUE, col="grey")# prob=TRUE for probabilities not countslines(density(X), col="blue", lwd=2) # add a density estimate with defaultslines(density(X, adjust=2), lty="dotted", col="darkgreen", lwd=2) 

along with the graph it produces:

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()

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 ...