The Importance of Class Hierarchy in R Packages

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In R packages, the class hierarchy plays a crucial role in determining how dispatch works. In this article, we will explore the concept of class inheritance and its implications for creating S3 methods.

Introduction to Classes and Methods in R

In R, classes and methods are used to organize and extend the behavior of functions and objects. A class is essentially a blueprint that defines the characteristics of an object, while a method is a function that operates on an object of a specific class.

When creating an S3 method, we need to specify both the generic name and the class name. The generic name serves as the entry point for dispatching the method, while the class name specifies which objects the method can operate on.

Class Hierarchy and Dispatch

In R, when a function is called with an object of a specific class, the S3 dispatch mechanism searches for methods in the following order:

<generic>.<class>: This is the primary method that gets called when the generic name matches the object’s class.
<generic>.
:::: This is used to access non-exported functions or methods from other packages.

The Problem with Using `:::`

The problem arises when we try to create an S3 method that can operate on objects of a specific class without using the ::: syntax. In this case, R tries to call the primary method <generic>.<class> first, and if it fails, it falls back to the generic method <generic>. This is where the issue with using non-exported methods comes in.

Avoiding Triple Colon (`:::`) when Writing S3 Methods

One common solution to this problem is to create an S3 method that checks whether the object’s class matches a specific class. If it does, the primary method gets called; otherwise, the generic method gets called.

However, we can take it a step further by modifying the way we define our classes and methods. This approach allows us to specify that our myclass should come before the data.frame class in the class hierarchy.

Modifying Class Hierarchy

To achieve this, we need to modify the line where we assign the myclass attribute to an object of class data.frame. Instead of using the append() function or prepending the class with a value greater than 0, we can use the c() function to specify that our myclass should be added first.

# Before
class(df) <- append(class(df), "myclass")

# After
class(df) <- c("myclass", class(df))

This approach ensures that our primary method <generic>.myclass> gets called before the generic method <generic>.data.frame.

General Considerations

When working with S3 methods, it’s essential to consider how dispatch will work. By understanding the class hierarchy and the role of :::, we can write more effective and efficient code that meets our needs.

In conclusion, by modifying our class definitions and using the correct approach to dispatch, we can avoid relying on non-exported methods or functions from other packages.

Conclusion

In this article, we explored the concept of class hierarchy in R packages and its implications for creating S3 methods. We discussed how to modify class definitions to achieve the desired behavior and provided examples to illustrate our points.

By following these guidelines and best practices, you can write more effective and efficient code that leverages the power of S3 methods in your R packages.

References

“S4 Methods for R” by Peter J. R. Murphy
“R Programming Language” by The R Development Core Team

Additional Resources

Last modified on 2023-09-28