R in a Nutshell, 2nd Edition

Old-School OOP in R: S3

If you want to implement a complex project in R, you should use S4 objects and classes. As we saw above, S4 classes implement many features of modern object-oriented programming languages: formal class definitions, simple and multiple inheritance, parameteric polymorphism, and encapsulation. Unfortunately, S3 classes are implemented and used differently from S4 objects and don’t implement many features that enable good software engineering practices.

Unfortunately, it’s very hard to avoid S3 objects in R because many important and commonly used R functions were written before S4 objects were implemented. For example, most of the modeling tools in the statistics package were written with S3 objects. In order to understand, modify, or extend this software, you have to know how S3 classes are implemented.

S3 Classes

S3 classes are implemented through object attributes. An S3 object is simply a primitive R object with additional attributes, including a class name. There is no formal definition for an S3 object; you can manually change the attributes, including the class. (S3 objects are very similar to objects in prototype-based languages such as JavaScript.)

Above, we used time series as an example of an S4 class. There is an existing S3 class for representing time series, called “ts” objects. Let’s create a sample time series object and look at how it is implemented. Specifically, we’ll look at the attributes of the object and then use typeof and unclass to examine ...