Navigating Functions in R: A Reflection on Code Reading

by jsendak | Apr 25, 2025 | DS Articles

In which I confront the way I read code in different languages, and end up
wishing that R had a feature that it doesn’t.

This is a bit of a thought-dump as I consider some code – please don’t take it
as a criticism of any design choices; the tidyverse team have written magnitudes
more code that I have and have certainly considered their approach more than I
will. I believe it’s useful to challenge our own assumptions and dig in to how
we react to reading code.

The blog post
describing the latest updates to the tidyverse {scales} package neatly
demonstrates the usage of the new functionality, but because the examples are
written outside of actual plotting code, one feature stuck out to me in
particular…

label_glue("The {x} penguin")(c("Gentoo", "Chinstrap", "Adelie"))
# The Gentoo penguin
# The Chinstrap penguin
# The Adelie penguin

Here, label_glue is a function that takes a {glue} string as an argument and
returns a ’labelling” function’. That function is then passed the vector of
penguin species, which is used in the {glue} string to produce the output.

  ## R:
  name <- "Jonathan"
  glue::glue("My name is {name}")
  # My name is Jonathan

  ## Python:
  >>> name = 'Jonathan'
  >>> f"My name is {name}"
  # 'My name is Jonathan'
  

There’s nothing magic going on with the label_glue()() call – functions are
being applied to arguments – but it’s always useful to interrogate surprise when
reading some code.

Spelling out an example might be a bit clearer. A simplified version of
label_glue might look like this

tmp_label_glue <- function(pattern = "{x}") {
  function(x) {
    glue::glue_data(list(x = x), pattern)
  }
}

This returns a function which takes one argument, so if we evaluate it we get

tmp_label_glue("The {x} penguin")
# function(x) {
#   glue::glue_data(list(x = x), pattern)
# }
# <environment: 0x1137a72a8>

This has the benefit that we can store this result as a new named function

penguin_label <- tmp_label_glue("The {x} penguin")
penguin_label
# function(x) {
#    glue::glue_data(list(x = x), pattern)
# }
# <bytecode: 0x113914e48>
# <environment: 0x113ed4000>

penguin_label(c("Gentoo", "Chinstrap", "Adelie"))
# The Gentoo penguin
# The Chinstrap penguin
# The Adelie penguin

This is versatile, because different {glue} strings can produce different
functions – it’s a function generator. That’s neat if you want different
functions, but if you’re only working with that one pattern, it can seem odd to
call it inline without naming it, as the earlier example

label_glue("The {x} penguin")(c("Gentoo", "Chinstrap", "Adelie"))

It looks like we should be able to have all of these arguments in the same
function

label_glue("The {x} penguin", c("Gentoo", "Chinstrap", "Adelie"))

but apart from the fact that label_glue doesn’t take the labels as an
argument, that doesn’t return a function, and the place where this will be used
takes a function as the argument.

So, why do the functions from {scales} take functions as arguments? The reason
would seem to be that this enables them to work lazilly – we don’t necessarily
know the values we want to pass to the generated function at the call site;
maybe those are computed as part of the plotting process.

We also don’t want to have to extract these labels out ourselves and compute on
them; it’s convenient to let the scale_* function do that for us, if we just
provide a function for it to use when the time is right.

But what is passed to that generated function? That depends on where it’s
used… if I used it in scale_y_discrete then it might look like this

library(ggplot2)
library(palmerpenguins)

p <- ggplot(penguins[complete.cases(penguins), ]) +
  aes(bill_length_mm, species) +
  geom_point()

p + scale_y_discrete(labels = penguin_label)

since the labels argument takes a function, and penguin_label is a function
created above.

I could equivalently write that as

p + scale_y_discrete(labels = label_glue("The {x} penguin"))

and not need the “temporary” function variable.

So what gets passed in here? That’s a bit hard to dig out of the source, but one
could reasonably expect that at some point the supplied function will be called
with the available labels as an argument.

I have a suspicion that the “external” use of this function, as

label_glue("The {x} penguin")(c("Gentoo", "Chinstrap", "Adelie"))

is clashing with my (much more recent) understanding of Haskell and the way that
partial application works. In Haskell, all functions take exactly 1 argument,
even if they look like they take more. This function

ghci> do_thing x y z = x + y + z

looks like it takes 3 arguments, and it looks like you can use it that way

ghci> do_thing 2 3 4
9

but really, each “layer” of arguments is a function with 1 argument, i.e. an
honest R equivalent would be

do_thing <- function(x) {
  function(y) {
    function(z) {
      x + y + z
    }
  }
}
do_thing(2)(3)(4)
# [1] 9

What’s important here is that we can “peel off” some of the layers, and we get
back a function that takes the remaining argument(s)

do_thing(2)(3)
# function(z) {
#    x + y + z
# }
# <bytecode: 0x116b72ba0>
# <environment: 0x116ab2778>

partial <- do_thing(2)(3)
partial(4)
# [1] 9

In Haskell, that looks like this

ghci> partial = do_thing 2 3
ghci> partial 4
9

Requesting the type signature of this function shows

ghci> :type do_thing
do_thing :: Num a => a -> a -> a -> a

so it’s a function that takes some value of type a (which needs to be a Num
because we’re using + for addition; this is inferred by the compiler) and then
we have

a -> a -> a -> a

This can be read as “a function that takes 3 values of a type a and returns 1
value of that same type” but equivalently (literally; this is all just syntactic
sugar) we can write it as

a -> (a -> (a -> a))

which is “takes a value of type a and returns a function that takes a value of
type a, which itself returns a function that takes a value of type a and
returns a value of type a”. With a bit of ASCII art…

a -> (a -> (a -> a))
|     |     |    |
|     |     |_z__|
|     |_y________|
|_x______________|

If we ask for the type signature when some of the arguments are provided

ghci> :type do_thing 2 3
do_thing 2 3 :: Num a => a -> a

we see that now it is a function of a single variable (a -> a).

With that in mind, the labelling functions look like a great candidate for
partially applied functions! If we had

label_glue(pattern, labels)

then

label_glue(pattern)

would be a function “waiting” for a labels argument. Isn’t that the same as
what we have? Almost, but not quite. label_glue doesn’t take a labels
argument, it returns a function which will use them, so the lack of the labels
argument isn’t a signal for this. label_glue(pattern) still returns a
function, but that’s not obvious, especially when used inline as

scale_y_discrete(labels = label_glue("The {x} penguin"))

When I read R code like that I see the parentheses at the end of label_glue
and read it as “this is a function invocation; the return value will be used
here”. That’s correct, but in this case the return value is another function.
There’s nothing here that says “this will return a function”. There’s no
convention in R for signalling this (and being dynamically typed, all one can do
is read the documentation) but one could imagine one, e.g. label_glue_F in a
similar fashion to how Julia uses an exclamation mark to signify an in-place
mutating function; sort! vs sort.

Passing around functions is all the rage in functional programming, and it’s how
you can do things like this

sapply(mtcars[, 1:4], mean)
#      mpg       cyl      disp        hp
# 20.09062   6.18750 230.72188 146.68750

Here I’m passing a list (the first four columns of the mtcars dataset) and a
function (mean, by name) to sapply which essentially does a map(l, f)
and produces the mean of each of these columns, returning a named vector of the
means.

That becomes very powerful where partial application is allowed, enabling things
like

ghci> add_5 = (+5)
ghci> map [1..10] add_5
[6,7,8,9,10,11,12,13,14,15]

In R, we would need to create a new function more explicitly, i.e. referring to
an arbitrary argument

add_5 <- (x) x + 5
sapply(1:10, add_5)
# [1]  6  7  8  9 10 11 12 13 14 15

Maybe my pattern-recognition has become a bit too overfitted on the idea that in
R “no parentheses = function, not result; parentheses = result”.

This reads weirdly to me

calc_mean <- function() {
  function(x) {
    mean(x)
  }
}
sapply(mtcars[, 1:4], calc_mean())

but it’s exactly the same as the earlier example, since calc_mean()
essentially returns a mean function

calc_mean()(1:10)
[1] 5.5

For that reason, I like the idea of naming the labelling function, since I read
this

p + scale_y_discrete(labels = penguin_label)

as passing a function. The parentheses get used in the right place – where the
function has been called.

Now, having to define that variable just to use it in the scale_y_discrete
call is probably a bit much, so yeah, inlining it makes sense, with the caveat
that you have to know it’s a function.

None of this was meant to say that the {scales} approach is wrong in any way – I
just wanted to address my own perceptions of the arg = fun() design. It does
make sense, but it looks different. Am I alone on this?

Let me know on Mastodon and/or the comment
section below.

Continue reading: Function Generators vs Partial Application in R

An Overview of Function Generators and Partial Application in R

The article explores the approach taken by the author to read and comprehend code in different languages. Notably, the author discussed the usage of function templates and partial application in R as a programming language, using examples from tidyverse’s {scales} package, label_glue and {glue} string.

Key Insights

• In Python, {glue} is R’s equivalent to f-strings.
• label_glue(“The {x} penguin”)(c(“Gentoo”, “Chinstrap”, “Adelie”)) demonstrates the use of {glue} strings in R to output a string of results.
• label_glue functions as a function generator. It returns a function that takes one argument. This allows for flexibility as different {glue} strings can generate different functions.
• The {scales} functions take functions as arguments to work lazily, i.e., they don’t need to know the values they want to pass to the generated function at the call site. These values might be calculated as part of the plotting process.
• The process of partial application allows us to “peel off” each layer of function calls.

Long term implications and future developments

Understanding function generators and partial application is crucial to effective R programming. This provided helpful insights into the code reading process by probing into the usage of {scales}, {glue} strings, and label_glue.

The code examples demonstrate how different {glue} strings can generate different functions and how the concept of function generators and partial application can be applied to enhance R’s versatility as a programming language. These concepts have essential long-term implications for code optimization.

Understanding these methods aid in enhancing programming efficiency, enabling cleaner, more concise, and more efficient coding practices. In the future, the dynamic use of function generators and partial applications may be extended to complex programming scenarios, leading to an increase in the usability of R in tackling complicated tasks.

Actionable Advice

• Try to incorporate the use of function generators and partial applications in your regular R programming routine. Begin with simple tasks and gradually extend to more complex scenarios.
• Remember that with R, “no parentheses = function, not result; parentheses = result”. This is important when trying to distinguish between a function and a result.
• Remember that functions like label_glue and {scales} work lazily – they do not necessarily need to know the values they want to pass to the generated function at the time of its call. This is an essential aspect of programming with R.

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