Classes {methods} | R Documentation

## Class Definitions

### Description

Class definitions are objects that contain the formal definition of a class of R objects, usually referred to as an S4 class, to distinguish them from the informal S3 classes. This document gives an overview of S4 classes; for details of the class representation objects, see help for the class `classRepresentation`.

### Metadata Information

When a class is defined, an object is stored that contains the information about that class. The object, known as the metadata defining the class, is not stored under the name of the class (to allow programmers to write generating functions of that name), but under a specially constructed name. To examine the class definition, call `getClass`. The information in the metadata object includes:

Slots:

  The data contained in an object from an S4 class is defined by the slots in the class definition.

  Each slot in an object is a component of the object; like components (that is, elements) of a list, these may be extracted and set, using the function `slot()` or more often the operator `"@"`. However, they differ from list components in important ways. First, slots can only be referred to by name, not by position, and there is no partial matching of names as with list elements.

  All the objects from a particular class have the same set of slot names; specifically, the slot names that are contained in the class definition. Each slot in each object always is an object of the class specified for this slot in the definition of the current class. The word “is” corresponds to the R function of the same name (`is`), meaning that the class of the object in the slot must be the same as the class specified in the definition, or some class that extends the one in the definition (a subclass).

  One class name is special, `.Data`. This stands for the ‘data part’ of the object. Any class that contains one of the basic object types in R, has implicitly a corresponding `.Data` slot of that type, allowing computations to extract or replace the data part while leaving other slots unchanged. The `.Data` slot also determines the type of the object; if `x` has a `.Data` slot, the type of the slot is the type of the object (that is, the value of `typeof(x)`). Extending a basic type this way allows objects to use old-style code for the corresponding type as well as S4 methods. Any basic type can be used for `.Data`, with the exception of a few that do not behave like ordinary objects; namely, `"NULL"`, environments, and external pointers.

  There is one additional use of the data part, which is also an exception to the correspondence with the object's type. The exception arises from the special treatment of `matrix` and `array` “classes” in R. Matrix and array objects are managed internally and recognized without regard to any class attribute; therefore, they can be used as the data part of a new class. In this case, the object type for the new class depends on the type of the data in the matrix or array.

  If the new class does not have a data part as described above, the type of objects from the new class is `"S4"`.

Superclasses:

  The definition of a class includes the superclasses —the classes that this class extends. A class `Fancy`, say, extends a class `Simple` if an object from the `Fancy` class has all the capabilities of the `Simple` class (and probably some more as well). In particular, and very usefully, any method defined to work for a `Simple` object can be applied to a `Fancy` object as well.

  This relationship is expressed equivalently by saying that `Simple` is a superclass of `Fancy`, or that `Fancy` is a subclass of `Simple`.

  The direct superclasses of a class are those superclasses explicitly defined. Direct superclasses can be defined in three ways. Most commonly, the superclasses are listed in the `contains=` argument in the call to `setClass` that creates the subclass. In this case the subclass will contain all the slots of the superclass, and the relation between the class is called simple, as it in fact is. Superclasses can also be defined explicitly by a call to `setIs`; in this case, the relation requires methods to be specified to go from subclass to superclass. Thirdly, a class union is a superclass of all the members of the union. In this case too the relation is simple, but notice that the relation is defined when the superclass is created, not when the subclass is created as with the `contains=` mechanism.

  The definition of a superclass will also potentially contain its own direct superclasses. These are considered (and shown) as superclasses at distance 2 from the original class; their direct superclasses are at distance 3, and so on. All these are legitimate superclasses for purposes such as method selection.

  When superclasses are defined by including the names of superclasses in the `contains=` argument to `setClass`, an object from the class will have all the slots defined for its own class and all the slots defined for all its superclasses as well.

  The information about the relation between a class and a particular superclass is encoded as an object of class `SClassExtension`. A list of such objects for the superclasses (and sometimes for the subclasses) is included in the metadata object defining the class. If you need to compute with these objects (for example, to compare the distances), call the function `extends` with argument `fullInfo=TRUE`.

Prototype:

  The objects from a class, typically created by a call to `new` or by assigning another object from the class, are defined by the prototype object for the class and by additional arguments in the call to `new`, which are passed to a method for that class for the function `initialize`.

  Each class definition contains a prototype object for the class. This must have values for all the slots defined by the class definition. By default, these are the prototypes of all the slot classes, if those are not virtual classes. However, the definition of the class can specify any valid object for any of the slots.

### Virtual classes; Basic classes

Classes exist for which there are no actual objects, the virtual classes, in fact a very important programming tool. They are used to group together ordinary classes that want to share some programming behavior, without necessarily restricting how the behavior is implemented. Virtual class definitions may if you want include slots (to provide some common behavior without fully defining the object—see the class `traceable` for an example).

A simple and useful form of virtual class is the class union, a virtual class that is defined in a call to `setClassUnion` by listing one or more of subclasses (classes that extend the class union). Class unions can include as subclasses basic data types (whose definition is otherwise sealed).

There are a number of ‘basic’ classes, corresponding to the ordinary kinds of data occurring in R. For example, `"numeric"` is a class corresponding to numeric vectors. The other vector basic classes are `"logical"`, `"integer"`, `"complex"`, `"character"`, `"raw"`, `"list"` and `"expression"`. The prototypes for the vector classes are vectors of length 0 of the corresponding type. Notice that basic classes are unusual in that the prototype object is from the class itself.

In addition to the vector classes there are also basic classes corresponding to objects in the language, such as `"function"` and `"call"`. These classes are subclasses of the virtual class `"language"`. Finally, there are basic classes for specialized objects, such as `"environment"` and `"externalptr"`. The vector and language basic classes can be used as slots or as superclasses for any other class definitions. The classes corresponding to other object types can be used as slots but not always as superclasses, since many of them do not follow the functional behavior of the language; in particular, they are not copied and so cannot have attributes or slots defined locally.

### S3 Classes

Earlier, informal classes of objects (usually referred to as ‘S3’ classes) are used by many R functions. It is natural to consider including them as the class for a slot in a formal class, or even as a class to be extended by the new class. This isn't prohibited but there are some disadvantages, and if you do want to include S3 classes, they should be declared by including them in a call to `setOldClass`. Here are some considerations:

- Using S3 classes somewhat defeats the purpose of defining a formal class: An important advantage to your users is that a formal class provides guarantees of what the object contains (minimally, the classes of the slots and therfore what data they contain; optionally, any other requirements imposed by a validity method).

  But there is no guarantee whatever about the data in an object from an S3 class. It's entirely up to the functions that create or modify such objects. If you want to provide guarantees to your users, you will need a valdity method that explicitly checks the contents of S3-class objects.

- To get the minimal guarantee (that the object in a slot has, or extends, the class for the slot) you should ensure that the S3 classes are known to be S3 classes, possibly with S inheritance. To do this, include a call to `setOldClass` for the S3 classes used.

  Otherwise, the S3 class is undefined (and the code used by `setClass` will issue a warning). Slot assignments, for example, will not then check for possible errors.

- Current versions of R (beginning with 2.8.0) try to accommodate S4 classes that extend S3 classes, that is, those including an S3 class in the `contains=` argument to `setClass` . Specifically, objects from such classes will contain the S3 class as a slot, and some S3 computations will recognize the S3 class, including method dispatch and the function `inherits`. See `S3Class` for details. The S3 classes must have been registered by a call to `setOldClass`. The basic caution remains true however: There is no guarantee that all S3 computations will be compatible, and some are known not to be.
- These caveats apply to S3 classes; that is, objects with a class assigned by some R function but without a formal class definition. In contrast, the built-in vector types (`numeric`, `list`, etc.) are generally fine as slots or for `contains=` classes. These object types don't have formal slots, but the base code in the system essentially forces them to contain the type of data they claim to have.

  Objects with a “class” of `matrix` or `array` are somewhat in between. They do not have an explicit S3 class, but do have one or two attributes. There is no general problem in having these as slots, but because there is no guarantee of a dimnames slot, they don't work as formal classes. The `ts` class, although also ancient in the S language, is implemented in R essentially as an S3 class, with the implications noted above—not suitable for a `contains=` argument—but with a few S4 methods defined. See the documentation for class `structure` for more details.

### References

Chambers, John M. (2008) Software for Data Analysis: Programming with R Springer. (For the R version.)

Chambers, John M. (1998) Programming with Data Springer (For the original S4 version.)

Chambers, John M. and Hastie, Trevor J. eds (1992) Statistical Models in S. Wadsworth & Brooks/Cole (Appendix A for S3 classes.)

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (Out of print.) (The description of vectors, matrix, array and time-series objects.)

### See Also

`Methods` for analogous discussion of methods, `setClass` for details of specifying class definitions, `is`, `as`, `new`, `slot`

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