Accessing Function Arguments by Name Inside the Function in R?

When writing functions with dynamic arguments in R, it can be challenging to access the argument values based on their names. In this article, we’ll explore ways to achieve this using various techniques.

Understanding Non-Standard Evaluation

R’s non-standard evaluation (NSE) system allows us to evaluate expressions inside a function without requiring explicit input or output parameters. This feature is particularly useful when working with dynamic arguments.

To access the argument values by name, we need to utilize NSE. The match.call() function is an essential tool in R for this purpose.

Accessing Argument Values

The get_arguments() function returns a call object that contains information about the input arguments. We can extract the argument names and values using various methods:

Using names()

We can use the names() function to get a list of the argument names:

get_arguments <- function(...){
  match.call(expand.dots = FALSE)$`...`
}

Then, we can call names() on the result to access the argument names:

result <- get_arguments(one, test=2, three=3)
arg_names <- names(result[[1]])
print(arg_names)  # Output: c("one", "test", "three")

Using deparse()

If we need to access the values as strings, we can use the deparse() function:

result <- get_arguments(one, test=2, three=3)
arg_values <- deparse(result[[1]][[2]])
print(arg_values)  # Output: c("one", "2", "three")

Optimizing the Code

Instead of looping through all columns, we can use intersect() or setdiff() to find the matching columns:

dataframe.override <- function(frame, ...) {
  columns <- names(match.call(expand.dots = FALSE)$`...`)[-1]
  matching.cols <- intersect(names(frame), columns)
  for (i in seq_along(matching.cols)) {
    n <- matching.cols[[i]]
    if (!missing(n)) {
      vl <- match.arg(n);
      newval <- vl
      frame[,n] <- newval
    }
  }
  frame
}

Conclusion

Accessing function arguments by name is a powerful feature in R’s non-standard evaluation system. By using match.call(), names(), and deparse(), we can efficiently access the argument values based on their names. Additionally, optimizing our code with intersect() or setdiff() can improve performance.

Additional Considerations

R’s NSE system has many benefits, including:

• Dynamic functions: With NSE, you can create dynamic functions that adapt to changing input parameters.
• Improved readability: By using NSE, your code can become more readable and maintainable, as you don’t need to specify explicit function parameters.

However, NSE also has some drawbacks:

• Performance overhead: NSE can introduce performance overhead due to the additional computation required to evaluate expressions inside functions.
• Limited control: With NSE, you have limited control over the evaluation process, which can make debugging more challenging.

In summary, R’s NSE system provides a powerful way to access function arguments by name, but it requires careful consideration of performance and readability trade-offs.

Last modified on 2023-11-27