Generics and methods

This vignette dives into the details of S7 generics and method dispatch, building on the basics discussed in vignette("S7"). We’ll first introduce the concept of generic-method compatibility, then discuss some of the finer details of creating a generic with new_generic(). This vignette first discusses generic-method compatibility, and you might want to customize the body of the generic, and generics that live in suggested packages. We’ll then pivot to talk more details of method dispatch including super() and multiple dispatch.


Generic-method compatibility

When you register a method, S7 checks that your method is compatible with the generic.

The formal arguments of the generic and methods must agree. This means that:

Generic with dots; method without dots

The default generic includes but generally the methods should not. That ensures that misspelled arguments won’t be silently swallowed by the method. This is an important difference from S3. Take a very simple implementation of mean():

mean <- new_generic("mean", "x")
method(mean, class_numeric) <- function(x) sum(x) / length(x)

If we pass an additional argument in, we’ll get an error:

mean(100, na.rm = TRUE)

But we can still add additional arguments if we desired:

method(mean, class_numeric) <- function(x, na.rm = TRUE) {
  if (na.rm) {
    x <- x[!]

  sum(x) / length(x)
#> Overwriting method mean(<integer>)
#> Overwriting method mean(<double>)
mean(c(100, NA), na.rm = TRUE)
#> [1] 100

(We’ll come back to the case of requiring that all methods implement a na.rm = TRUE argument shortly.)

Generic and method with dots

There are cases where you do need to take in a method, which is particularly problematic if you need to re-call the generic recursively. For example, imagine a simple print method like this:

simple_print <- new_generic("simple_print", "x")
method(simple_print, class_double) <- function(x, digits = 3) {}
method(simple_print, class_character) <- function(x, max_length = 100) {}

What if you want to print a list?

method(simple_print, class_list) <- function(x, ...) {
  for (el in x) {
    simple_print(el, ...)

It’s fine as long as all the elements of the list are numbers, but as soon as we add a character vector, we get an error:

simple_print(list(1, 2, 3), digits = 3)
simple_print(list(1, 2, "x"), digits = 3)

To solve this situation, methods generally need to ignore arguments that they haven’t been specifically designed to handle, i.e. they need to use :

method(simple_print, class_double) <- function(x, ..., digits = 3) {}
#> Overwriting method simple_print(<double>)
method(simple_print, class_character) <- function(x, ..., max_length = 100) {}
#> Overwriting method simple_print(<character>)

simple_print(list(1, 2, "x"), digits = 3)

In this case we really do want to silently ignore unknown arguments because they might apply to other methods. There’s unfortunately no easy way to avoid this problem without relying on fairly esoteric technology (as done by rlang::check_dots_used()).

simple_print(list(1, 2, "x"), diggits = 3)

Generic and method without dots

Occasional it’s useful to create a generic without because such functions have a useful property: if a call succeeds for one type of input, it will succeed for any type of input. To create such a generic, you’ll need to use the third argument to new_generic(): an optional function that powers the generic. This function has one key property: it must call call_method() to actually perform dispatch.

In general, this property is only needed for very low-level functions with precisely defined semantics. A good example of such a function is length():

length <- new_generic("length", "x", function(x) {

Omitting from the generic signature is a strong restriction as it prevents methods from adding extra arguments. For this reason, it’s should only be used in special situations.

Customizing generics

In most cases, you’ll supply the first two arguments to new_generic() and allow it to automatically generate the body of the generic:

display <- new_generic("display", "x")
#> function (x, ...) 
#> S7::S7_dispatch()

The most important part of the body is S7_dispatch(); this function finds the method the matches the arguments used for dispatch and calls it with the arguments supplied to the generic.

It can be useful to customize this body. The previous section showed one case when you might want to supply the body yourself: dropping from the formals of the generic. There are three other useful cases:

A custom fun must always include a call to call_method(), which will usually be the last call.

Add required arguments

To add required arguments that aren’t dispatched upon, you just need to add additional arguments that lack default values:

foo <- new_generic("foo", "x", function(x, y, ...) {

Now all methods will need to provide that y argument. If not, you’ll get a warning:

method(foo, class_integer) <- function(x, ...) {
#> Warning: foo(<integer>) doesn't have argument `y`

This is a warning, not an error, because the generic might be defined in a different package and is in the process of changing interfaces. You’ll always want to address this warning when you see it.

Add optional arguments

Adding an optional argument is similar, but it should generally come after . This ensures that the user must supply the full name of the argument when calling the function, which makes it easier to extend your function in the future.

mean <- new_generic("mean", "x", function(x, ..., na.rm = TRUE) {
method(mean, class_integer) <- function(x, na.rm = TRUE) {
  if (na.rm) {
    x <- x[!]
  sum(x) / length(x)

Forgetting the argument or using a different default value will again generate a warning.

method(mean, class_double) <- function(x, na.rm = FALSE) {}
#> Warning: In mean(<double>), default value of `na.rm` is not the same as the generic
#> - Generic: TRUE
#> - Method:  FALSE
method(mean, class_logical) <- function(x) {}
#> Warning: mean(<logical>) doesn't have argument `na.rm`

Do some work

If your generic has additional arguments, you might want to do some additional work to verify that they’re of the expected type. For example, our mean() function could verify that na.rm was correctly specified:

mean <- new_generic("mean", "x", function(x, ..., na.rm = TRUE) {
  if (!identical(na.rm, TRUE) && !identical(na.rm = FALSE)) {
    stop("`na.rm` must be either TRUE or FALSE")

The only downside to performing error checking is that you constraint the interface for all methods; if for some reason a method found it useful to allow na.rm to be a number or a string, it would have to provide an alternative argument.


Sometimes it’s useful to define a method for in terms of its superclass. A good example of this is computing the mean of a date — since dates represent the number of days since 1970-01-01, computing the mean is just a matter of computing the mean of the underlying numeric vector and converting it back to a date.

To demonstrate this idea, I’ll first define a mean generic with a method for numbers:

mean <- new_generic("mean", "x")
method(mean, class_numeric) <- function(x) {
  sum(x) / length(x)
#> [1] 5.5

And a Date class:

date <- new_class("date", parent = class_double)
# Cheat by using the existing base .Date class
method(print, date) <- function(x) print(.Date(x))
date(c(1, 10, 100))
#> [1] "1970-01-02" "1970-01-11" "1970-04-11"

Now to compute a mean we write:

method(mean, date) <- function(x) {
  date(mean(super(x, to = class_double)))
mean(date(c(1, 10, 100)))
#> [1] "1970-02-07"

Let’s unpack this method from the inside out:

  1. First we call super(x, to = class_double) — this will make the call to next generic treat x like it’s a double, rather than a date.
  2. Then we call mean() which because of super() will call the mean() method we defined above.
  3. Finally, we take the number returned by mean and convert it back to a date.

If you’re very familiar with S3 or S4 you might recognize that super() fills a similar role to NextMethod() or callNextMethod(). However, it’s much more explicit: you need to supply the name of the parent class, the generic to use, and all the arguments to the generic. This explicitness makes the code easier to understand and will eventually enable certain performance optimizations that would otherwise be very difficult.

Multiple dispatch

So far we have focused primarily on single dispatch, i.e. generics where dispatch_on is a single string. It is also possible to supply a length 2 (or more!) vector dispatch_on to create a generic that performs multiple dispatch, i.e. it uses the classes of more than one object to find the appropriate method.

Multiple dispatch is a feature primarily of S4, although S3 includes some limited special cases for arithmetic operators. Multiple dispatch is heavily used in S4; we don’t expect it to be heavily used in S7, but it is occasionally useful.

A simple example

Let’s take our speak example from vignette("S7") and extend it to teach our pets how to speak multiple languages:

pet <- new_class("pet")
dog <- new_class("dog", pet)
cat <- new_class("cat", pet)

language <- new_class("language")
english <- new_class("english", language)
french <- new_class("french", language)

speak <- new_generic("speak", c("x", "y"))
method(speak, list(dog, english)) <- function(x, y) "Woof"
method(speak, list(cat, english)) <- function(x, y) "Meow"
method(speak, list(dog, french)) <- function(x, y) "Ouaf Ouaf"
method(speak, list(cat, french)) <- function(x, y) "Miaou"

speak(cat(), english())
#> [1] "Meow"
speak(dog(), french())
#> [1] "Ouaf Ouaf"

# This example was originally inspired by
# which has unfortunately since disappeaed.

Special “classes”

There are two special classes that become particularly useful with multiple dispatch: