Understanding Vectorization in R

When working with R, it’s common to encounter situations where a function needs to be vectorized, meaning that it should return a result for each element of the input vector. However, not all functions are designed to behave this way. In some cases, a function might have side effects or produce output that shouldn’t be returned.

One such function is f, which takes an integer argument and returns invisible (i.e., no value). When we try to vectorize this function using the original Vectorize function, it still produces a result, even though we want it to return nothing.

The Problem with Vectorization

The issue here is that the original Vectorize function doesn’t have an option to suppress output. It simply re-exports the input function, wrapped in a new function that takes vectors instead of scalars as input.

## Original Vectorize Function

f_vec <- Vectorize(f)

This works fine for many functions, but when we try to call f_vec(10), it still returns the result of f(10), which is NULL.

A New Approach: Custom Vectorization

To solve this problem, we need a custom vectorization function that allows us to specify whether or not output should be returned. The new version of Vectorize provided by the author achieves this through some clever modifications to the original function.

## New Vectorize Function

Vectorize_2 <- function (FUN, vectorize.args = arg.names, SIMPLIFY = TRUE, USE.NAMES = TRUE) {
  # ...
}

This new function takes an additional argument SIMPLIFY, which defaults to TRUE. When set to FALSE (which we can do by passing the option SIMPLIFY = FALSE), it tells the vectorization function to return nothing.

How It Works

The key insight here is that when we call Vectorize_2 with SIMPLIFY = FALSE, it modifies the internal behavior of the function. Instead of returning a result, it uses an invisible wrapper around the original function.

## Modified Vectorization Function

Vectorize_2 <- function (FUN, vectorize.args = arg.names, SIMPLIFY = TRUE, USE.NAMES = TRUE) {
  # ...
  
  FUNV <- function() {
    # ...
    invisible(do.call("mapply", c(FUN = FUN, args[dovec], MoreArgs = list(args[!dovec]), 
                                  SIMPLIFY = SIMPLIFY, USE.NAMES = USE.NAMES)))
  }
  
  return(FUNV)
}

When we call Vectorize_2(f, vectorize.args = c("a"), SIMPLIFY = FALSE), it creates a new function that wraps the original f function in an invisible wrapper. When we then call this new function with a vector argument, it returns nothing.

## Example Usage

f_vec <- Vectorize_2(f, vectorize.args = c("a"), SIMPLIFY = FALSE)
f_vec(10)  # Returns nothing

Conclusion

In this post, we explored the issue of how to achieve invisible output when vectorizing a function in R. We discovered that the new version of Vectorize provides an option called SIMPLIFY, which allows us to specify whether or not output should be returned.

By using this custom vectorization function, we can create functions that produce nothing even when called with vectors as input. This can be particularly useful in situations where a function has side effects or produces unwanted output.

Additional Context

Vectorization is an important concept in R programming, and it’s often used to improve the performance of functions by avoiding loops. However, not all functions are designed to behave this way, and sometimes we need to modify their behavior to achieve our desired outcome.

In addition to the custom vectorization function provided by the author, there are other ways to achieve invisible output in R, such as using a wrapper function or modifying the original function’s behavior.

For example, we could use a wrapper function like this:

## Wrapper Function

f_wrapper <- function(a) {
  dummy <- f_vec(a)
}

# Usage:
f_wrapper(10)

While this approach works, it may not be as elegant as using the custom vectorization function provided by the author.

Overall, understanding how to achieve invisible output when vectorizing functions in R is an important skill for any serious R programmer. By mastering this concept, we can write more efficient and effective code that produces the desired results.

Last modified on 2023-08-29