Module # 7 R Object: S3 vs. S4 assignment

Github Link: https://github.com/rayhankhan-svg/r-programming-assignments


R Code: 

# Step 1: Load a dataset

data(mtcars)

head(mtcars, 6)


# Step 2: Check if generic functions can be used on this dataset

class(mtcars)

typeof(mtcars)

isS4(mtcars)


# Generic functions examples

summary(mtcars)

plot(mtcars$mpg, mtcars$hp)


# What is a generic function?

mean


# Step 3: Create an S3 example


s3 <- list(name = "Myself", age = 29, GPA = 3.5)

class(s3) <- "student"


s3

class(s3)

typeof(s3)

isS4(s3)


# Step 4: Create an S4 example


setClass("student",

         slots = list(

           name = "character",

           age  = "numeric",

           GPA  = "numeric"

         ))


s4 <- new("student", name = "Myself", age = 29, GPA = 3.5)


s4

class(s4)

typeof(s4)

isS4(s4)


Output: 

> # Step 1: Load a dataset

> data(mtcars)

> head(mtcars, 6)

                   mpg cyl disp  hp drat    wt  qsec vs am

Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1

Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1

Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1

Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0

Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0

Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0

                  gear carb

Mazda RX4            4    4

Mazda RX4 Wag        4    4

Datsun 710           4    1

Hornet 4 Drive       3    1

Hornet Sportabout    3    2

Valiant              3    1

> 

> # Step 2: Check if generic functions can be used on this dataset

> class(mtcars)

[1] "data.frame"

> typeof(mtcars)

[1] "list"

> isS4(mtcars)

[1] FALSE

> 

> # Generic functions examples

> summary(mtcars)

      mpg             cyl             disp      

 Min.   :10.40   Min.   :4.000   Min.   : 71.1  

 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8  

 Median :19.20   Median :6.000   Median :196.3  

 Mean   :20.09   Mean   :6.188   Mean   :230.7  

 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0  

 Max.   :33.90   Max.   :8.000   Max.   :472.0  

       hp             drat             wt       

 Min.   : 52.0   Min.   :2.760   Min.   :1.513  

 1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581  

 Median :123.0   Median :3.695   Median :3.325  

 Mean   :146.7   Mean   :3.597   Mean   :3.217  

 3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610  

 Max.   :335.0   Max.   :4.930   Max.   :5.424  

      qsec             vs               am        

 Min.   :14.50   Min.   :0.0000   Min.   :0.0000  

 1st Qu.:16.89   1st Qu.:0.0000   1st Qu.:0.0000  

 Median :17.71   Median :0.0000   Median :0.0000  

 Mean   :17.85   Mean   :0.4375   Mean   :0.4062  

 3rd Qu.:18.90   3rd Qu.:1.0000   3rd Qu.:1.0000  

 Max.   :22.90   Max.   :1.0000   Max.   :1.0000  

      gear            carb      

 Min.   :3.000   Min.   :1.000  

 1st Qu.:3.000   1st Qu.:2.000  

 Median :4.000   Median :2.000  

 Mean   :3.688   Mean   :2.812  

 3rd Qu.:4.000   3rd Qu.:4.000  

 Max.   :5.000   Max.   :8.000  

> plot(mtcars$mpg, mtcars$hp)

> 

> # What is a generic function?

> mean

function (x, ...) 

UseMethod("mean")

<bytecode: 0xa155718f8>

<environment: namespace:base>

> 

> # Step 3: Create an S3 example

> 

> s3 <- list(name = "Myself", age = 29, GPA = 3.5)

> class(s3) <- "student"

> 

> s3

$name

[1] "Myself"


$age

[1] 29


$GPA

[1] 3.5


attr(,"class")

[1] "student"

> class(s3)

[1] "student"

> typeof(s3)

[1] "list"

> isS4(s3)

[1] FALSE

> 

> # Step 4: Create an S4 example

> 

> setClass("student",

+          slots = list(

+            name = "character",

+            age  = "numeric",

+            GPA  = "numeric"

+          ))

> 

> s4 <- new("student", name = "Myself", age = 29, GPA = 3.5)

> 

> s4

An object of class "student"

Slot "name":

[1] "Myself"


Slot "age":

[1] 29


Slot "GPA":

[1] 3.5


> class(s4)

[1] "student"

attr(,"package")

[1] ".GlobalEnv"

> typeof(s4)

[1] "S4"

> isS4(s4)

[1] TRUE

> 


Explanation: 

I utilized R's built-in mtcars dataset for Module #7. Since class(mtcars) returns "data.frame" and isS4(mtcars) returns FALSE, I was able to verify that mtcars is an S3 object. Additionally, I used typeof(mtcars) to verify its base type, and it returned "list". I next evaluated the dataset's suitability for generic functions. Because they employ method dispatch, generic methods like summary() and plot() are compatible with mtcars. For instance, the function summary.data.frame() is called by summary(mtcars). A generic function is one that, depending on the object's class, chooses the appropriate method; in R, many generics display UseMethod() when printed. Lastly, I produced S3 and S4 samples. In S3, I used a list to create a student object, and then I gave it a class attribute (class(s3) <- "student"). I used setClass() with slots to explicitly define a student class for S4, and I then used new() to construct an instance. The primary distinction is that S4 is formal, has predetermined slots, and enforces a more rigid structure, whereas S3 is informal and flexible.

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