Compatibility with S3 and S4

Methods

method() is designed to be the single tool for method registration that you need when working with S7 classes. You can also register a method for an S7 class and S3 generic without using S7, because all S7 objects have S3 classes, and S3 dispatch will operate on them normally.

Foo := new_class(package = "Bar")
class(Foo())
#> [1] "Bar::Foo"  "S7_object"

`mean.Bar::Foo` <- function(x, ...) {
  "mean of foo"
}

mean(Foo())
#> [1] "mean of foo"

Note that when defining classes in a package, the S3 class name will be {package}::{class}, and that means you’ll need to use ` to define S3 methods.

Generics

If you convert an S3 generic to an S7 generic and want existing S3 methods (typically in other packages) to continue to work, add a class_any method that calls UseMethod():

foo := new_generic("x")

# Default method dispatches to S3
method(foo, class_any) <- function(x, ...) {
  UseMethod("foo")
}

And then if you need a genuine default method, use S3:

# Define the "true" default method
foo.default <- function(x) paste0(x, " + 10")

Classes

It’s possible to extend an S7 class with S3. This is primarily useful because in many cases it allows you to change a class hierarchy from the inside out: you can provide a formal definition of an S3 class using S7, and its subclasses don’t need to change.

List classes

Many simple S3 classes are implemented as lists, e.g. rle.

rle <- function(x) {
  if (!is.vector(x) && !is.list(x)) {
    stop("'x' must be a vector of an atomic type")
  }
  n <- length(x)
  if (n == 0L) {
    Rle(integer(), x)
  } else {
    y <- x[-1L] != x[-n]
    i <- c(which(y | is.na(y)), n)
    Rle(diff(c(0L, i)), x[i])
  }
}
Rle <- function(lengths, values) {
  structure(
    list(
      lengths = lengths,
      values = values
    ),
    class = "Rle"
  )
}

There are two ways to convert this to S7. You could keep the structure exactly the same, using a list as the underlying data structure and using a constructor to enforce the structure:

Rle := new_class(
  parent = class_list,
  constructor = function(lengths, values) {
    new_object(list(lengths = lengths, values = values))
  }
)
rle(1:10)
#> <Rle> List of 2
#>  $ lengths: int [1:10] 1 1 1 1 1 1 1 1 1 1
#>  $ values : int [1:10] 1 2 3 4 5 6 7 8 9 10

Alternatively you could convert it to the most natural representation using S7:

Rle := new_class(properties = list(
  lengths = class_integer,
  values = class_atomic
))

To allow existing code that uses $ to keep working, like inverse.rle(), you’ll need to override $ to access properties instead of list elements:

method(`$`, Rle) <- prop
inverse.rle(rle(1:10))
#>  [1]  1  2  3  4  5  6  7  8  9 10

The chief disadvantage of this approach is any subclasses will need to be converted to S7 as well.

S7 classes that extend S4 classes

To extend an S4 class, supply its class definition or class generator as the parent of a new S7 class. The S4 parent slots become S7 properties on the new class, and new_class() automatically registers the S7 class with S4.

methods::setClass("S4Point",
  slots = c(x = "numeric", y = "numeric")
)

ColoredPoint <- new_class("ColoredPoint",
  parent = methods::getClass("S4Point"),
  properties = list(color = class_character)
)

pt <- ColoredPoint(x = 1, y = 2, color = "red")
pt@x
#> [1] 1
pt@color
#> [1] "red"
methods::is(pt, "S4Point")
#> [1] TRUE

S4 dispatch follows the S4 parent hierarchy, so S4 methods defined for the parent can be called on the S7 child.

methods::setGeneric("radius", function(x) standardGeneric("radius"))
#> [1] "radius"
methods::setMethod("radius", "S4Point", function(x) {
  sqrt(x@x^2 + x@y^2)
})

radius(pt)
#> [1] 2.236068

S4 code that updates an existing object should use initialize() rather than constructing a new object from class(x). The S7 initialize() method accepts stored S7 properties and S4 slots, and later arguments override earlier ones.

pt <- methods::initialize(pt, x = 3, color = "blue")
pt@x
#> [1] 3
pt@color
#> [1] "blue"

There are a few important caveats:

• An S7 object that directly extends S4 is represented as an S3 old-class object, so isS4(pt) is FALSE. Use methods::is() to ask about S4 inheritance.
• The class vector is not scalar. S4 code that needs one primary class name should use class(x)[1L], or methods::class1(x) when available.
• methods::slot() and methods::slot<-() can read and write stored properties, but they bypass custom S7 getters, setters, and ordinary S7 validation. Call methods::validObject() after direct slot updates when the object should satisfy the S7 contract.
• Properties with custom getters or setters cannot cross the S4 inheritance boundary as S4 slots.
• NULL properties are stored using the same sentinel that S4 uses for NULL slots. prop(), @, and methods::slot() translate this back to NULL; raw attributes should be treated as an implementation detail.

S4 classes that extend S7 classes

After calling S4_register(), use S4_contains() when an S4 class should extend an S7 class. It returns the virtual S4 class name for methods::setClass(contains = ). This class exposes stored S7 properties as formal S4 slots and carries the _S7_class slot that S7 uses for dispatch and validation.

Sample <- new_class("Sample",
  properties = list(
    name = class_character,
    score = class_numeric
  ),
  validator = function(self) {
    if (length(self@score) != 1) {
      "@score must have length 1"
    } else if (self@score < 0) {
      "@score must be non-negative"
    }
  }
)

S4_register(Sample)
Sample_S4 <- S4_contains(Sample)
methods::setClass("ReplicatedSample",
  contains = Sample_S4,
  slots = c(replicate = "integer")
)

rs <- methods::new("ReplicatedSample",
  name = "A",
  score = 3,
  replicate = 1L
)

isS4(rs)
#> [1] TRUE
rs@name
#> [1] "A"
prop(rs, "score")
#> [1] 3
methods::validObject(rs)
#> [1] TRUE

S7 dispatch still sees the S7 class, even though the concrete object is an S4 object.

sample_label <- new_generic("sample_label", "x")
method(sample_label, Sample) <- function(x) {
  paste0(x@name, ": ", x@score)
}

sample_label(rs)
#> [1] "A: 3"

The class returned by S4_contains() is a compatibility layer, not a user-facing class. Do not instantiate it directly; use it only as a superclass for another S4 class. Since S4 subclasses are real S4 objects, S4 code can access the S7 properties as slots. This is necessary for compatibility, but it also means S4 code can bypass S7 property setters until validation is run.

Unions

S4 unions are automatically converted to S7 unions when they are used as S7 property classes. There’s an important difference in the way that class unions are handled in S4 and S7. In S4, they’re handled at method dispatch time, so when you create setUnion("u1", c("class1", "class2")), class1 and class2 now extend u1. In S7, unions are handled at method registration time so that registering a method for a union is just short-hand for registering a method for each of the classes.

Class1 := new_class()
Class2 := new_class()
Union1 <- new_union(Class1, Class2)

foo := new_generic("x")
method(foo, Union1) <- function(x) ""
foo
#> <S7_generic> foo(x, ...) with 2 methods:
#> 1: method(foo, Class2)
#> 2: method(foo, Class1)

S7 unions allow you to restrict the type of a property in the same way that S4 unions allow you to restrict the type of a slot. S7 unions can also be registered with S4_register() for use in S4 slots and method signatures.