# median

##### Median Value

Compute the sample median.

##### Usage

`median(x, na.rm = FALSE)`

##### Arguments

- x
- an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.
- na.rm
- a logical value indicating whether
`NA`

values should be stripped before the computation proceeds.

##### Details

This is a generic function for which methods can be written. However,
the default method makes use of `is.na`

, `sort`

and
`mean`

from package `"Date"`

) for which a median is a reasonable
concept.

##### Value

- The default method returns a length-one object of the same type as
`x`

, except when`x`

is 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]`

).

##### References

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

##### See Also

`quantile`

for general quantiles.

##### Examples

`library(stats)`

```
median(1:4) # = 2.5 [even number]
median(c(1:3, 100, 1000)) # = 3 [odd, robust]
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*

### Community examples

**richie@datacamp.com**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) ```