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.)