Module 7. Assignment

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This week, I studied Object-Oriented Programming (OOP) in R and discovered that R perceives all entities as objects, including integers, vectors, data frames, and functions.
The talk addressed two main systems used in R: S3 and S4.
S3 is the preliminary, more accessible way that allows for the rapid addition of a class to a list and the creation of custom print methods.

S4 is the more recent and systematically structured framework that has inherent validation and explicit class definitions with slots.
We further examined the use of common methods such as summary(), print(), and plot() to see their distinct functionalities across different object kinds.
Ultimately, I was able to create my own S3 and S4 objects, implement fundamental methods, and understand how R determines which function version to use, a process known as method dispatch.



# Download Data for Mtcar

data("mtcars")

# Show the first few rows

head(mtcars)

# Describe its structure

str(mtcars)

# Test Generic Functions

summary(mtcars)

print(mtcars)

plot(mtcars$mpg, mtcars$hp,

     main = "MPG vs HP",

     xlab = "Miles per Gallon",

     ylab = "Horsepower")

# Create S3 object

s3_obj <- list(name = "Myself", age = 29, GPA = 3.5)

class(s3_obj) <- "student_s3"


print.student_s3 <- function(x, ...) {

  cat("S3 Student\n",

      "Name: ", x$name, "\n",

      "Age:  ", x$age,  "\n",

      "GPA:  ", x$GPA,  "\n", sep = "")

}

print(s3_obj)   

summary(s3_obj) 

# Create an S4 class and object example

library(methods)

setClass("student_s4",

         slots = c(name = "character", age = "numeric", GPA = "numeric"))

s4_obj <- new("student_s4", name = "Myself", age = 29, GPA = 3.5)

setMethod("show", "student_s4",

          function(object) {

            cat("S4 Student\n",

                "Name: ", object@name, "\n",

                "Age:  ", object@age,  "\n",

                "GPA:  ", object@GPA,  "\n", sep = "")

          })

s4_obj   


# output 

> data("mtcars")
> 
> # Show the first few rows
> head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
> 
> # Describe its structure
> str(mtcars)
'data.frame':	32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
> summary(mtcars)
      mpg             cyl             disp             hp             drat      
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0   Min.   :2.760  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0   Median :3.695  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7   Mean   :3.597  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0   Max.   :4.930  
       wt             qsec             vs               am              gear      
 Min.   :1.513   Min.   :14.50   Min.   :0.0000   Min.   :0.0000   Min.   :3.000  
 1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000  
 Median :3.325   Median :17.71   Median :0.0000   Median :0.0000   Median :4.000  
 Mean   :3.217   Mean   :17.85   Mean   :0.4375   Mean   :0.4062   Mean   :3.688  
 3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000  
 Max.   :5.424   Max.   :22.90   Max.   :1.0000   Max.   :1.0000   Max.   :5.000  
      carb      
 Min.   :1.000  
 1st Qu.:2.000  
 Median :2.000  
 Mean   :2.812  
 3rd Qu.:4.000  
 Max.   :8.000  
> print(mtcars)
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
> plot(mtcars$mpg, mtcars$hp,
+      main = "MPG vs HP",
+      xlab = "Miles per Gallon",
+      ylab = "Horsepower")
> s3_obj <- list(name = "Myself", age = 29, GPA = 3.5)
> class(s3_obj) <- "student_s3"
> print.student_s3 <- function(x, ...) {
+   cat("S3 Student\n",
+       "Name: ", x$name, "\n",
+       "Age:  ", x$age,  "\n",
+       "GPA:  ", x$GPA,  "\n", sep = "")
+ }
> print(s3_obj)   
S3 Student
Name: Myself
Age:  29
GPA:  3.5
> summary(s3_obj) 
     Length Class  Mode     
name 1      -none- character
age  1      -none- numeric  
GPA  1      -none- numeric  

> library(methods)
> setClass("student_s4",
+          slots = c(name = "character", age = "numeric", GPA = "numeric"))
> s4_obj <- new("student_s4", name = "Myself", age = 29, GPA = 3.5)
> setMethod("show", "student_s4",
+           function(object) {
+             cat("S4 Student\n",
+                 "Name: ", object@name, "\n",
+                 "Age:  ", object@age,  "\n",
+                 "GPA:  ", object@GPA,  "\n", sep = "")
+           })
> s4_obj   
S4 Student
Name: Myself
Age:  29
GPA:  3.5


1. How can you tell whether an object uses S3 or S4?

In R, there are two main object systems — S3 and S4.

You can check which one your object uses by trying a few functions:

isS4(object) → checks if it’s an S4 object (TRUE or FALSE)

class(object) → shows what kind of class the object belongs to

if the object was created using setClass() and new(), it’s S4.

If you just set a class with class(x) <- "something", it’s S3.


2 .How do you determine an object’s underlying type?

You can check the internal type or structure of an object using these functions:

mode(object) →  storage mode

typeof(object) → internal descriptor, similar to mode but more specific

class(object) →  defines type of object


3. What is a generic function in R?

A generic function is a function which dispatches methods.

The generic function does not actually do any computation

Examples of generic functions in R:

print()

summary()

plot()

mean()

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

In R, both S3 and S4 are systems for Object-Oriented Programming (OOP).They let us create objects and methods, but they work a little differently.

S3 – older, simpler, more dynamic, less structured.

S4 – newer, more structured, more rigorous.


https://github.com/shanzay28/r-programming-assignments/blob/main/Module-7-Object-Oriented-Programming/README.md?plain=1


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