Assignment #7 - Exploring R's Object Oriented System (S3 & S4)

 R code and outputs: 




Questions:

How can you tell whether an object uses S3 or S4? (Which functions inspect its class system?)

To determine if an object uses S3 or S4, the first check can be done with the isS4( ) function. If it returns TRUE, then it is an S4 object; otherwise, it is likely an S3. Another check can be performed with the class(x) function to determine the labeling format. S3 classes are typically simple, lists, and data frames. S4 classes generally are more complex, with slots and data types. 

How do you determine an object's underlying type (e.g., integer vs list)?

To determine an object's underlying type, you can use the function typeof( ). An example output of the object's underlying type would be "list", "double", or "character". To specifically test for an integer or a list, you can use is.integer( ) or is.list( ).

What is a generic function in R?

A generic function in R is one that behaves differently based on the type of object or argument it is given. To determine which type it should run, it uses "method dispatch", inspects it, and proceeds based on the provided input. 

What are the principal differences between S3 and S4 (e.g., method definition, formal class declarations)?

S3 is simple and flexible, only requiring you to create a class by setting the "class" attribute on the object and then defining methods by naming them (e.g., print. myClass). S4 is more structured, requiring you to define the class with setClass( ), including typed slots, and create methods using setMethod( ). S3 only dispatches on the first argument, whereas S4 supports multiple.     



Reading:

  • Matloff, N. M. (2011). The Art of R Programming, Chapters 9–10 (pp. 207–231).
  • Wickham, H. (2015). R Packages: Organize, Test, Document, and Share Your Code, Chapters 2–3 (pp. 27–31).

Comments

Post a Comment

Popular posts from this blog

Assignment #5: Matrix Algebra in R

Image
  R code for creating A and B, and for computing  invA ,   detA ,   invB , and   detB : Outputs for each operation: Why  solve(A)  and  det(A)  behave this way: Since Matrix A is 10x10 and therefore a square, it could technically have an inverse. However, since the determinant of A is 0, it is considered a singular matrix, meaning it does not have full rank and therefore cannot be inverted. Due to this,  solve(A)   returns an error even though the matrix is a square, yet it is still able to run and confirm the singularity. Why operations on B fail (non‑square matrix): Since Matrix B is 10x100, it is not square. Inverses and determinants can only be defined for square matrices, meaning both solve(B) and det(B) return errors. Though it is expected, it highlights how important it is to check matrix dimensions before attempting further operations.  Notes on numeric stability or performance: There are important considerations w...
Read more

Assignment #3: Analyzing 2016 data “Poll” Data in R

Image
Based on the provided poll data, I observed the following patterns by calculating the mean, median, range, and difference between CBS and ABC. Firstly, I observed that on average, CBS had a slightly higher mean of 27.14 compared to ABC's 24.14. For overall discrepancies,  Donald had the highest with +13 percentage points, and Ted had the lowest with -8 percentage points. While there are stark leaders in candidates, Jeb and Hilary also demonstrated moderately notable  percentage points. Using made-up data can present several issues, mainly rooted in the fact that you cannot draw a confident conclusion from it. To really determine the public's opinion from poll data, you would need to increase the sample size over a much longer period of time. Additionally, as in all statistical datasets, it is important to also compute a margin of error. Assuming you implement these methods, another important consideration would be to collect data from highly reputable organizations that approp...
Read more

Assignment #4: Visualizing and Interpreting Hospital Patient Data

Image
Blood Pressure and Assessments Based on the boxplots, the blood pressures seemed to vary quite a bit. In the first MD assessment, the patients marked "Good" had a wide range of values, with an outlier at 205. Those marked "Bad" seemed to cluster towards the lower or moderate values. The second MD assessment showed patients marked "Low" with blood pressures clustered in the lower range, but showed patients marked "High" with a wide range of values. The Final assessment  seemed to properly correlate "High" with higher values and "Low" with lower values, suggesting the first assessment may have been less related to blood pressures and the second assessment moderately related. Histogram Patterns The histogram of visit frequency shows that most patients clustered around visit rates 0.2-0.6 with no extreme outliers. The histogram for blood pressure shows a much larger distribution, with most patients under 120, some outliers above 1...
Read more