How to find the statistical mode?

One more solution, which works for both numeric & character/factor data:

Mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}

On my dinky little machine, that can generate & find the mode of a 10M-integer vector in about half a second.

If your data set might have multiple modes, the above solution takes the same approach as which.max, and returns the first-appearing value of the set of modes. To return all modes, use this variant (from @digEmAll in the comments):

Modes <- function(x) {
  ux <- unique(x)
  tab <- tabulate(match(x, ux))
  ux[tab == max(tab)]
}

There is package modeest which provide estimators of the mode of univariate unimodal (and sometimes multimodal) data and values of the modes of usual probability distributions.

mySamples <- c(19, 4, 5, 7, 29, 19, 29, 13, 25, 19)

library(modeest)
mlv(mySamples, method = "mfv")

Mode (most likely value): 19 
Bickel's modal skewness: -0.1 
Call: mlv.default(x = mySamples, method = "mfv")

For more information see this page

found this on the r mailing list, hope it's helpful. It is also what I was thinking anyways. You'll want to table() the data, sort and then pick the first name. It's hackish but should work.

names(sort(-table(x)))[1]

I found Ken Williams post above to be great, I added a few lines to account for NA values and made it a function for ease.

Mode <- function(x, na.rm = FALSE) {
  if(na.rm){
    x = x[!is.na(x)]
  }

  ux <- unique(x)
  return(ux[which.max(tabulate(match(x, ux)))])
}