Performing lazy evaluation

R functions evaluate arguments lazily; the arguments are evaluated as they are needed. Thus, lazy evaluation reduces the time needed for computation. In the following recipe, we will demonstrate how lazy evaluation works.

Getting ready

Ensure that you completed the previous recipes by installing R on your operating system.

How to do it...

Perform the following steps to see how lazy evaluation works:

1. First, we create a lazyfunc function with x and y as the argument, but only return x:

   >lazyfunc<- function(x, y){
   + x
   + }
   >lazyfunc(3)
   [1] 3
   

2. On the other hand, if the function returns the summation of x and y but we do not pass y into the function, an error occurs:

   >lazyfunc2<- function(x, y){
   + x + y
   + }
   >lazyfunc2(3)
   Error in lazyfunc2(3) : argument "y" is missing, with no default
   

3. We can also specify a default value to the y argument in the function but pass the x argument only to the function:

   >lazyfunc4<- function(x, y=2){
   + x + y
   + }
   >lazyfunc4(3)
   [1] 5
   

4. In addition to this, we can use lazy evaluation to perform Fibonacci computation in a function:

   >fibonacci<- function(n){
   + if (n==0)
   + return(0)
   + if (n==1)
   + return(1)
   + return(fibonacci(n-1) + fibonacci(n-2))
   + }
   >fibonacci(10)
   [1] 55
   

How it works...

R performs a lazy evaluation to evaluate an expression if its value is needed. This type of evaluation strategy has the following three advantages:

• It increases performance due to the avoidance of repeated evaluation

• It recursively constructs an infinite data structure

• It inherently includes iteration in its data structure

In this recipe, we demonstrate some lazy evaluation examples in the R code. In our first example, we create a function with two arguments, x and y, but return only x. Due to the characteristics of lazy evaluation, we can successfully obtain function returns even though we pass the value of x to the function. However, if the function return includes both x and y, as step 2 shows, we will get an error message because we only passed one value to the function. If we set a default value to y, then we do not necessarily need to pass both x and y to the function.

As lazy evaluation has the advantage of creating an infinite data structure without an infinite loop, we use a Fibonacci number generator as the example. Here, this function first creates an infinite list of Fibonacci numbers and then extracts the nth element from the list.

There's more...

Additionally, we can use the force function to check whether y exists:

>lazyfunc3<- function(x, y){
+ force(y)
+ x
+ }
>lazyfunc3(3)
Error in force(y) : argument "y" is missing, with no default
>input_function<- function(x, func){
+ func(x)
+ }
>input_function(1:10, sum)
[1] 55