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Object-Oriented Programming in R: Challenges and Insights from the "Murders" Dataset

 Object-Oriented Programming in R: Challenges and Insights from the "Murders" Dataset


As part of my recent assignment on Object-Oriented Programming (OOP) in R, I delved into applying both the S3 and S4 object systems to the "murders" dataset from the dslabs package. Through this experience, I encountered some interesting challenges, particularly with S4 objects, and learned a lot about the flexibility and formal structure that R's object systems offer.

The dataset provided data on murder rates across U.S. states, and my task was to determine how these object-oriented systems can be applied, test the use of generic functions, and explore key concepts like object classes, slots, and methods. Here’s a reflection on what I learned and the hurdles I faced along the way.


Assigning Generic Functions to the Murders Dataset

In R, generic functions like summary() and print() are widely used to extract basic information about objects, especially data frames. Since S3 is the default system for data frames in R, I knew that my "murders" dataset, which is a data frame, would accept such generic functions without any additional setup.

Running a simple summary() function on the dataset worked as expected:

The output provided basic statistics, confirming that generic functions can easily be applied to the murders dataset, as it is an S3 object by default.

However, things became more interesting when I started working with the S4 system, which is far more formal and requires explicitly defined class structures.



Exploring S3 and S4 Object Systems

In R, S3 is a more flexible, informal system, while S4 is stricter and requires detailed class definitions. I began by creating a custom S3 object to summarize the murders dataset. This was straightforward since S3 doesn’t require formal class definitions. I simply created a constructor function to return a list and set the class as "murders_summary":



The flexibility of the S3 system allowed me to assign methods like print.murders_summary for custom behavior when printing the object. For example, I created a method to print the total number of murders and regions covered by the dataset:



When I ran this code, it printed the results as expected. The ease of defining and modifying methods with S3 was clear, but it also reinforced the fact that S3 doesn’t offer strict error-checking or structure, which can sometimes lead to inconsistencies.


The Challenge with S4: Dealing with Data Types

Moving to the S4 system, I faced my first challenge. Unlike S3, S4 requires a formal definition of classes using setClass(), where the slots (attributes) of the class must be explicitly defined with specific data types. Here, I defined an S4 class called "Murders" to store the total number of entries and the unique regions:



When I tried to create an S4 object from the murders dataset using the new() function, I encountered an error:

Error in validObject(.Object) : invalid class “Murders” object: invalid object for slot "regions" in class "Murders": got class "factor", should be or extend class "character"


This issue arose because the regions column in the murders dataset was a factor, while the S4 class definition expected a character vector. S4 is much stricter in terms of type-checking, which is both a strength and a challenge of the system.

To solve this, I needed to explicitly convert the regions factor to a character vector before assigning it to the S4 object:



Once I made this adjustment, the object was successfully created, and I could define a custom show() method to display the contents of the S4 object


Conclusion

Working with both S3 and S4 systems in R gave me a deeper understanding of their strengths and trade-offs. While S3 is quick and flexible, S4's formality provides a more structured approach with better error-checking. The issue with the factor-to-character conversion was a key learning moment, showcasing how S4 ensures data integrity through strict type validation. This experience not only helped me understand the theoretical differences but also gave me practical insight into how these systems can be applied effectively in real-world datasets.

For those new to R's object-oriented systems, I recommend starting with S3 for its simplicity, but exploring S4 will be beneficial for projects that require more rigor and structure.


For a more detailed exploration of the data manipulation techniques and code examples, you can find everything in my GitHub repository.



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