Adding Arguments to Executed R Functions: A Deep Dive

In this article, we’ll explore how to add arguments to executed R functions. We’ll delve into the nuances of R’s function execution and provide practical solutions for modifying existing code.

Understanding Function Execution in R

When you execute a function in R, several things happen behind the scenes:

Function Definition: The function is defined with its parameters (args) and body.
Local Environment: A local environment is created to store the arguments, variables, and functions within the function’s scope.
Execution: When you call the function, R evaluates the expression in the function’s body, using the values of the arguments passed at execution time.

Now, let’s examine how we can add an argument to a function that was executed earlier.

The Challenge: Adding Arguments to Executed Functions

Suppose you have a function test.fun defined with two parameters, a and b, where b is optional (with a default value of NULL). You execute this function once, passing 2 as the first argument. Later on, when you want to add an additional argument (b = 4) in the second execution, things get tricky.

The Initial Execution: Understanding the Local Environment

When you call test.fun(2), R creates a local environment with:

a: assigned the value 2
b (optional): assigned the default value of NULL

At this point, c is not defined anywhere in the function.

Adding Arguments to Executed Functions: A Solution

To add an argument to the executed function, you need to modify its behavior at execution time. One way to achieve this is by creating a new function that wraps the original one and adds the desired argument.

Consider the following example:

# Define the original function with optional parameter b
test.fun <- function(a, b = NULL) {
  # ...
}

# Execute the function once with no additional arguments
fun <- test.fun(2)

# Create a new function that wraps the original one and adds an argument b=4
new_fun <- function(a) {
  test.fun(a, b = 4)
}

In this example:

test.fun is defined as before.
We execute it once with no additional arguments, assigning its result to fun.
We create a new function new_fun, which wraps the original test.fun. In new_fun, we pass b = 4 when calling test.fun.

By using this approach, you can add an argument to the executed function without modifying its original definition.

Why This Works

The key insight is that R’s function execution happens at runtime. By creating a new function that wraps the original one and adds the desired argument, we effectively modify the behavior of the function when it gets called. The local environment created by test.fun remains intact, so the original arguments remain unchanged.

Alternative Solutions: Using S4 Classes or Macros

If you’re working with more complex functions, you might want to consider using S4 classes or macros to extend the functionality of your functions. These approaches can provide a more robust and maintainable solution for adding arguments to executed functions.

For instance, you could define an S4 class that inherits from test.fun and adds the desired argument:

# Define the original function with optional parameter b as an S4 class
class("test.fun", "function") {
  function(a, b = NULL) {
    # ...
  }
}

# Create a new S4 class that extends test.fun and adds argument b=4
new_class <- R6::SetClass(
  "new_class",
  public = list,
  initialize = function(.Self, a) {
    .Self$a <- a
    self$b <- 4
  },
  method = list(test_fun = method("test.fun")),
  call = ".init(a)"
)

# Execute the new class with no additional arguments
obj <- new_class(2)

In this example:

We define an S4 class new_class that extends test.fun.
The class has a method test_fun that wraps the original function.
When creating an instance of new_class, we pass a = 2 and initialize b = 4.

These approaches provide more flexibility and control over adding arguments to executed functions.

Conclusion

Adding arguments to executed R functions can be achieved through various means, including creating new functions that wrap the original one. While this approach works for many cases, it might not be suitable for all scenarios. By understanding the nuances of R’s function execution and exploring alternative solutions using S4 classes or macros, you can develop more robust and maintainable code.