We human eat different kind of foods. Similarly, functions can eat different kind of things. We’ve seen functions that eat a number, a data frame, and a vector of numbers. Today, I’m going to show you how to create functions that eat functions. By that, I mean functions that take functions as input. For example, look at the following function.
arg_func = function(func) {
function(x, f) {
# x: a numeric vector
# f: a function
fvals = vapply(x, f, numeric(1))
x[fvals == func(fvals)] # notice how func is used
}
}
It uses a function as input (func
) and outputs another function (function(x, f)
). Notice the output function has two parameters: one is a numeric vector and the other is yet another function!
We can use arg_func
to implement a function that takes a vector of numbers and a function as inputs, and returns the elements of the vector where the input function returns the biggest value.
arg_max = arg_func(max)
For example, when applying the function \(f(x)=x^2\) to the vector -10:5
, it should obtain the biggest value at x=-10
. And when appling it to -5:5
, the maximum value should happen at x=-5
and x=5
. Let’s see if arg_max()
gives us the same results.
arg_max(-10:5, function(x) x ^ 2)
## [1] -10
arg_max(-5:5, function(x) x ^ 2)
## [1] -5 5
Indeed, arg_max()
gives us the correct results. We can also implement arg_min()
in the same fashion.
arg_min = arg_func(min)
arg_min(-10:5, function(x) x ^ 2)
## [1] 0
arg_min(-5:5, function(x) x ^ 2)
## [1] 0
Similarly, we can implement arg_mean()
and arg_median()
.
# arg_mean()
arg_mean = arg_func(mean)
arg_mean(-10:5, function(x) x ^ 2)
## integer(0)
arg_mean(-5:5, function(x) x ^ 2)
## integer(0)
# arg_median()
arg_median = arg_func(median)
arg_median(-10:5, function(x) x ^ 2)
## [1] -4 4
arg_median(-5:5, function(x) x ^ 2)
## [1] -3 3
This article is inspired by Hadley’s book “Advanced R”, which can be obtained from Amazon.