Fitting a density curve to a histogram in R?

Question

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.

Answer 1

For purposes of data re-usability, we can set your data example as a data frame:

x <- (c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))

df <- as.data.frame(x)

Then we will need to draw the histogram, BUT, we will need to set the y-scale as a function of the probability density with the addition of "prob = TRUE". This is necessary because density functions are the likelihood of a data point being observed, and not the data itself.

hist(df$x, prob = T)

Now, we can add the density of the histogram with the "lines" function which writes on top of any prior base-r graphic.

lines(density(df$x))

In all, your code would look like:

#Create your data and set it to a dataframe for re-usability

x <- (c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))

df <- as.data.frame(x)

#Create a histogram of your data with the probability of each datat point as the y-axis

hist(df$x, prob = T)

#Draw a density funciton over your histogram

lines(density(df$x))