Median Value

Compute the sample median.

robust, univar
median(x, na.rm = FALSE, …)

an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.


a logical value indicating whether NA values should be stripped before the computation proceeds.

potentially further arguments for methods; not used in the default method.


This is a generic function for which methods can be written. However, the default method makes use of, sort and mean from package base all of which are generic, and so the default method will work for most classes (e.g., "Date") for which a median is a reasonable concept.


The default method returns a length-one object of the same type as x, except when x is logical or integer of even length, when the result will be double.

If there are no values or if na.rm = FALSE and there are NA values the result is NA of the same type as x (or more generally the result of x[FALSE][NA]).


Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

quantile for general quantiles.

  • median
  • median.default
library(stats) # NOT RUN { median(1:4) # = 2.5 [even number] median(c(1:3, 100, 1000)) # = 3 [odd, robust] # }
Documentation reproduced from package stats, version 3.6.2, License: Part of R 3.6.2

Community examples at Jan 17, 2017 stats v3.3.1

The median of a set with an number of values is the middle one, when sorted from smallest to largest. ```{r} x <- c(1, 10, 5, 8, 9) median(x) sort(x)[3] # same ``` If there are an even number of values, the median is half way between the two middle values. ```{r} x <- c(1, 10, 5, 8, 9, 6) median(x) sorted <- sort(x) (sorted[3] + sorted[4]) / 2 # same ``` If there are any missing values, the median is also missing. ```{r} median(c(1, 10, 5, 8, 9, NA)) ``` You can exclude missing values by setting `na.rm = TRUE`. ```{r} median(c(1, 10, 5, 8, 9, NA), na.rm = TRUE) ``` The median is known as a robust estimator of location, since it ignores outliers. The following dataset has 10% taken from a wide distribution that will generate many outliers. The expected mean and median are both zero. Which one is closest? ```{r} x <- c(rnorm(900), rnorm(100, sd = 1000)) mean(x) median(x) ```