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factory

Lifecycle: maturing Travis build status AppVeyor build status Codecov test coverage CRAN status

The goal of factory is to make construction of function factories more straightforward, without requiring the user to learn the rlang package.

Installation

Install the released version of factory from CRAN:

install.packages("factory")

Or install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("jonthegeek/factory")

Motivation

Function factories are functions that make functions. They can be confusing to work with. For example, as we’ll see below, they can produce functions that are fragile, or that are confusing to work with as a user.

WARNING: All code shown below is “wrong” in some way until we get to the example at the end! These examples show the dangers of working with function factories, and why this package exists.

(examples adapted from Advanced R by Hadley Wickham (2nd Edition), 10.2.3: Forcing Evaluation)

The Simplest Factories are Fragile

power1 is a function factory. It returns a function based on the exponent argument.

power1 <- function(exponent) {
  function(x) {
    x ^ exponent
  }
}

For many use cases, power1 works fine. For example, we can define a square function by calling power1 with exponent = 2.

square1 <- power1(2)
square1(2)
#> [1] 4
# 2 ^ 2 = 4
square1(3)
#> [1] 9
# 3 ^ 2 = 9

However, power1 is fragile. Let’s think about what the definition of power1 means. The function returned by power1 raises its argument to whatever the exponent variable is defined as. Let’s see what happens if we use a variable in the global environment to define our square function.

my_exponent <- 2
square1a <- power1(my_exponent)

Due to R’s lazy evaluation, when we call power1, the exponent variable gets a promise to take on the value of the my_exponent variable. But my_exponent doesn’t actually have the value of 2 yet. Until we use my_exponent, it has a promise to get the value of 2. If we call square1a right away, it works as expected.

square1a(2)
#> [1] 4
# 2 ^ 2 = 4
my_exponent <- 3
square1a(3)
#> [1] 9
# 3 ^ 2 = 9

The my_exponent promise (which was passed in during the definition of square1a) resolves to 2 the first time it is needed (when square1a is first called). After that initial call, that is the value used in square1a forever.

But if my_exponent changes between definition of our function and first call of that function, we get a different result.

my_exponent <- 2
square1b <- power1(my_exponent)
my_exponent <- 3
square1b(2)
#> [1] 8
# 2 ^ 3 = 8
square1b(3)
#> [1] 27
# 3 ^ 3 = 27

What happened? When square1b was defined, my_exponent was passed in as a promise. However, before my_exponent was ever actually used, its value changed. The promise isn’t evaluated until it is used, which, in this case, is the first time square1b is called. Once the promise is evaluated, its value is “fixed,” and the function works as expected.

Forcing Arguments Trades Fragility for Complexity

We can make factories that are less fragile, if we remember to force the variables.

power2 <- function(exponent) {
  force(exponent) # Gah, easy to forget!
  function(x) {
    x ^ exponent
  }
}

my_exponent <- 2
square2 <- power2(my_exponent)
my_exponent <- 3
square2(2)
#> [1] 4
# 2 ^ 2 = 4
square2(3)
#> [1] 9
# 3 ^ 2 = 9

Why does this work? The force function forces the evaluation of its argument. We don’t really need to use force, per se. Any function that forces evaluation would work, but force makes it obvious why we’re doing it. For example, we could produce the same result by messageing within the factory.

power2b <- function(exponent) {
  message("The exponent's value is ", exponent)
  function(x) {
    x ^ exponent
  }
}

my_exponent <- 2
square2b <- power2b(my_exponent)
#> The exponent's value is 2
my_exponent <- 3
square2b(2)
#> [1] 4
# 2 ^ 2 = 4
square2b(3)
#> [1] 9
# 3 ^ 2 = 9

Since the value of exponent is needed for the message, the promise is evaluated when the factory is invoked, and the resulting function is stable.

While such factories are more stable, it’s easy to miss a force. And, in both of these cases, the resulting functions are difficult to understand as a user.

square1
#> function(x) {
#>     x ^ exponent
#>   }
#> <environment: 0x000000001499b588>
square2
#> function(x) {
#>     x ^ exponent
#>   }
#> <environment: 0x000000001d0daba8>
cube <- power2(3)
cube
#> function(x) {
#>     x ^ exponent
#>   }
#> <bytecode: 0x000000001db4af70>
#> <environment: 0x000000001da4d950>

It isn’t clear what these functions will do, since the definitions of exponent are hidden inside the function environments.

Using rlang

We can use {rlang} to make functions that are easier to understand, but building the function factory is much more difficult (from Advanced R by Hadley Wickham (2nd Edition), 19.7.4: Creating functions):

power3 <- function(exponent) {
  rlang::new_function(
    rlang::exprs(x = ), 
    rlang::expr({
      x ^ !!exponent
    }), 
    rlang::caller_env()
  )
}

The resulting functions look like a “normal” function, though, and are thus easier for users to understand.

square3 <- power3(2)
square3
#> function (x) 
#> {
#>     x^2
#> }

The {rlang} calls are very difficult to understand, though. It would be nice to get the stability and interpretability of the rlang-produced functions, with the ease-of-programming of the simplest function factories.

Enter {factory}

The goal of factory is to make function factories as straightforward to create as in power1, but to make the resulting functions make as much sense as in power3. Right now, the calls are still a little more complicated than I would like, but they’re definitely easier to understand than the {rlang} calls.

library(factory)
power4 <- build_factory(
  fun = function(x) {
    x ^ exponent
  },
  exponent
)

my_exponent <- 2
square4 <- power4(my_exponent)
my_exponent <- 3
square4(2)
#> [1] 4
# 2 ^ 2 = 4

The resulting function makes sense, as with power3.

square4
#> function (x) 
#> {
#>     x^2
#> }

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factory: Build Function Factories

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