The generic functions in R Language are objects that determine how the function will treat it. A generic function performs an action (or task) on its arguments specific to the class of the argument itself. A default action will be performed if an argument lacks any class attribute that is if an argument of the function has a class not catered for specifically by the generic function, a default action will be provided.
The class mechanism in R provides the facility of designing and writing generic functions in R for special purposes. For example, the generic functions in R such as
- the
plot()is used for displaying objects graphically, - the
summary()is used for summarizing analyses of various types of objects - the
anova()is used for comparing different statistical models - the
print()is used to display the results of various types of objects
The Generic Functions in R can handle a large number of classes. For example, the function plot() has a default method and variants for different types of objects such as data.frame, density, factor, and many more. A complete list of Generic Functions in R can be obtained by using
methods(plot) methods(summary)
The body of a Generic function in R is concise and short. For example,
print
## Output
function (x, ...)
UseMethod("print")
<bytecode: 0x0000029448a0aa40>
<environment: namespace:base>From the above code, the body of the Generic Function, UseMethod indicates that this is a generic function.
Key Concepts and Characteristics
The following are key concepts and characteristics of generic functions in R.
- Dispatch: When an object is passed to a generic function, R determines the appropriate method to execute based on the class of the object provided. This process is known as dispatch.
- Methods: A method is a specific implementation of a generic function for a particular class of the object. It provides instructions on how the function should behave when applied to certain objects of that class.
- Class Inheritance: R supports class inheritance, allowing methods defined for a parent class to be inherited by its child classes. This enables generic functions to work seamlessly with objects from different classes within a hierarchy.
- Default Methods: If no method is defined for a specific class, R will look for a default method. The default method is typically defined for the generic function’s base class or a more generic class.
Benefits of Generic Functions in R
The following are some benefits of using and creating generic functions in R
- Code Reusability: Generic functions can be used with different types of objects, reducing the need for redundant code.
- Readability: Generic functions can improve code readability by separating the generic interface from the specific implementations.
- Polymorphism: Generic functions allow the user to write code that can work with objects of different classes, promoting flexibility and adaptability.
- Extensibility: New methods can be added for custom classes, making it easy to extend the functionality of generic functions.
Best Practices for Creating Generic Functions in R Language
For creating or writing generic functions, the following are the best practices to follow:
- Give clear and descriptive names to generic functions and their methods.
- Define methods for commonly used classes to ensure compatibility.
- Consider using inheritance to avoid redundant code in methods for related classes.
- Test the generic functions thoroughly to ensure they work as expected with different types of objects.
Example of Creating Generic Functions
To create/write generic functions in R, define a function with the desired name and arguments. One can then define methods for different classes using the UseMethod function within the body of a generic function. Consider the following example
gf <- function(x) {
UseMethod("gf")
}
gf.numeric <- function(x) {
# Method for numeric objects
mean(x)
}
gf.character <- function(x) {
# Method for character objects
nchar(x)
}In the above exemplary code, gf() is defined as a generic function. The UseMethod() function tells R to dispatch the call to the appropriate method based on the class of the argument x. The gf.numeric and gf.character methods provide specific implementations for numeric and character objects, respectively. Let us check the behaviour of the fg() function created as a generic function
x <- 1:5 # Numeric Vector
gf(x)
## Output
[1] 3
gf("statistics")
## Output
[1] 10
Learn about how to get or view the source code of a function or method.
