R is full of functions, it is after all a functional programming language, but sometimes the precise function you need isn't provided in the Base resources. You could conceivably install a package containing the function, but maybe your requirements are just so specific that no pre-made function fits the bill? Then you're left with the option of making your own.

A function can be very simple, to the point of being being pretty much pointless. It doesn't even need to take an argument:

```
one <- function() { 1 }
one()
[1] 1
two <- function() { 1 + 1 }
two()
[1] 2
```

What's between the curly braces `{ }`

is the function proper. As long as you can fit everything on a single line they aren't strictly needed, but can be useful to keep things organized.

A function can be very simple, yet highly specific. This function takes as input a vector (`vec`

in this example) and outputs the same vector with the vector's length (6 in this case) subtracted from each of the vector's elements.

```
vec <- 4:9
subtract.length <- function(x) { x - length(x) }
subtract.length(vec)
[1] -2 -1 0 1 2 3
```

Notice that `length()`

is in itself a pre-supplied (i.e. *Base*) function. You can of course use a previously self-made function within another self-made function, as well as assign variables and perform other operations while spanning several lines:

```
vec2 <- (4:7)/2
msdf <- function(x, multiplier=4) {
mult <- x * multiplier
subl <- subtract.length(x)
data.frame(mult, subl)
}
msdf(vec2, 5)
mult subl
1 10.0 -2.0
2 12.5 -1.5
3 15.0 -1.0
4 17.5 -0.5
```

`multiplier=4`

makes sure that `4`

is the default value of the argument `multiplier`

, if no value is given when calling the function `4`

is what will be used.

The above are all examples of *named* functions, so called simply because they have been given names (`one`

, `two`

, `subtract.length`

etc.)