Classes in detail

Classes and Objects form the foundation of Object-Oriented Programming (OOP). They revolve around real-world entities. In R, everything is treated as an object. An object is essentially a data structure that contains attributes and methods. A class acts as a template or blueprint for creating objects, defining a set of properties or behaviors common to all objects of that type.

Unlike many other programming languages, R features a three-class system: S3, S4, and Reference Classes.

Classes and Objects

A class is a template or blueprint from which objects are created by encapsulating data and methods. An object is a data structure containing attributes and methods that act upon those attributes.

S3 Class

The S3 class is the simplest and most flexible OOP system in R. It does not enforce a rigid structure or formal definitions. You can create an object of the S3 class by assigning a class attribute to it.

Example:

# Create a list with relevant attributes
bookDetails <- list(title = "The Alchemist", author = "Paulo Coelho")

# Assign a class name
class(bookDetails) <- "book"

bookDetails

Output:

$title
[1] "The Alchemist"

$author
[1] "Paulo Coelho"

In S3, methods are not tied to specific classes but to generic functions. For example, you can customize how the print function behaves for your objects.

Example: Custom Print Method

# Define a custom print method for class "book"
print.book <- function(obj) {
    cat("Title:", obj$title, "\n")
    cat("Author:", obj$author, "\n")
}

# Print the object
print(bookDetails)

Output:

Title: The Alchemist
Author: Paulo Coelho

S4 Class

The S4 class offers a more structured approach to OOP in R, which programmers familiar with C++ or Java may find more intuitive. It provides formal class definitions, which include specified slots for storing data.

Example:

library(methods)

# Define an S4 class
setClass("novel", slots = list(title = "character", author = "character"))

# Create an object of the S4 class
novelDetails <- new("novel", title = "1984", author = "George Orwell")
novelDetails

Output:

An object of class "novel"
Slot "title":
[1] "1984"

Slot "author":
[1] "George Orwell"

S4 classes also allow you to define methods. Here’s an example:

Example: Custom Method

# Define a custom method for class "novel"
setMethod("show", "novel",
          function(object) {
              cat("Title:", object@title, "\n")
              cat("Author:", object@author, "\n")
          })

# Display the object
novelDetails

Output:

# View attributes
attributes(student)

You can also define methods for Reference Classes:

Example: Adding Methods

libraryDetails <- setRefClass("library", fields = list(
    name = "character",
    location = "character",
    booksAvailable = "numeric"
), methods = list(
    addBooks = function(count) {
        booksAvailable <<- booksAvailable + count
    },
    removeBooks = function(count) {
        booksAvailable <<- booksAvailable - count
    }
))

# Create an object and manipulate its fields
libraryInfo <- libraryDetails(name = "Central Library", location = "Downtown", booksAvailable = 5000)

# Print books available
libraryInfo$booksAvailable

# Add 200 books and print the count
libraryInfo$addBooks(200)
libraryInfo$booksAvailable

# Remove 100 books and print the count
libraryInfo$removeBooks(100)
libraryInfo$booksAvailable

Output:

[1] 5000
[1] 5200
[1] 5100

Introduction

Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around objects, rather than functions and logic alone. Objects represent real-world entities and combine data (attributes) and behavior (methods) into a single unit.

In R, Object-Oriented Programming is especially important for:

• Statistical modeling
• Data analysis
• Package development
• Complex data structures
• Reusable and maintainable code

Unlike many languages (Java, C++), R supports multiple OOP systems, each designed for different use cases.

---

What is Object-Oriented Programming?

Object-Oriented Programming is based on four core principles:

1. Encapsulation – bundling data and methods together
2. Abstraction – exposing only essential details
3. Inheritance – creating new objects from existing ones
4. Polymorphism – same function behaving differently for different objects

---

Why OOP is Important in R

OOP helps:

• Organize large programs
• Reuse code efficiently
• Write cleaner and modular code
• Build extensible statistical models
• Create professional R packages

Many built-in R functions like plot(), print(), and summary() use OOP concepts internally.

---

Object-Oriented Systems in R

R supports three main OOP systems:

1. S3 – Simple and informal (most common)
2. S4 – Formal and strict
3. R6 – Modern, reference-based OOP

---

Objects in R

An object in R is any data structure stored in memory.

Examples:

x <- 10
y <- "R"
z <- c(1, 2, 3)

Everything in R is an object:

• Variables
• Vectors
• Data frames
• Functions
• Models

---

Classes in R

A class defines the type or category of an object.

class(x)

Example:

v <- c(1, 2, 3)
class(v)

Output:

"numeric"

---

Encapsulation in R

Encapsulation means combining data and functions that operate on that data.

In R:

• Objects store data
• Functions operate on objects

Example:

person <- list(name = "Alice", age = 25)

---

Abstraction in R

Abstraction means hiding implementation details and showing only essential functionality.

Example:

summary(lm(mpg ~ wt, data = mtcars))

You don’t need to know how summary() works internally.

---

Polymorphism in R

Polymorphism allows the same function to behave differently depending on object type.

Example:

print(10)
print("R")
print(c(1, 2, 3))

The print() function behaves differently for each object.

---

Method Dispatch in R

Method dispatch is how R decides which function to call for an object.

Example:

plot(1:10)
plot(mtcars$wt, mtcars$mpg)

Different plots, same function name.

---

S3 Object-Oriented System (Introduction)

What is S3?

S3 is the simplest and most widely used OOP system in R.

Characteristics:

• Informal
• No strict class definitions
• Easy to use and flexible

---

Creating an S3 Object

person <- list(name = "Alice", age = 25)
class(person) <- "Person"

---

Creating an S3 Method

print.Person <- function(obj) {
  cat("Name:", obj$name, "\n")
  cat("Age:", obj$age, "\n")
}

print(person)

---

Polymorphism with S3

print(10)
print(person)

Same function name, different behavior.

---

S4 Object-Oriented System (Brief Introduction)

What is S4?

S4 is a formal OOP system with:

• Explicit class definitions
• Defined slots (attributes)
• Strict type checking

Example:

setClass("Person",
         slots = list(
           name = "character",
           age = "numeric"
         ))

---

R6 Object-Oriented System (Introduction)

What is R6?

R6 is a modern OOP system similar to Python and Java.

Features:

• Reference-based objects
• Encapsulation
• Private and public members

---

Creating an R6 Class

library(R6)

Person <- R6Class(
  "Person",
  public = list(
    name = NULL,
    age = NULL,
    initialize = function(name, age) {
      self$name <- name
      self$age <- age
    },
    greet = function() {
      cat("Hello, my name is", self$name, "\n")
    }
  )
)

p <- Person$new("Alice", 25)
p$greet()

---

Inheritance in R6

Student <- R6Class(
  "Student",
  inherit = Person,
  public = list(
    course = NULL,
    initialize = function(name, age, course) {
      super$initialize(name, age)
      self$course <- course
    }
  )
)

---

Comparison of OOP Systems in R

Feature	S3	S4	R6
Formality	Low	High	High
Syntax	Simple	Verbose	Modern
Inheritance	Yes	Yes	Yes
Encapsulation	Weak	Strong	Strong
Reference-based	No	No	Yes

---

When to Use Which OOP System

• S3: Simple modeling, quick prototyping
• S4: Complex statistical packages
• R6: Applications, APIs, large systems

---

Common Mistakes in OOP with R

• Mixing S3 and S4 incorrectly
• Not understanding method dispatch
• Misusing reference-based objects
• Overcomplicating simple tasks

---

Summary

R supports multiple object-oriented programming systems. OOP in R allows you to write reusable, modular, and maintainable code. Understanding S3, S4, and R6 is essential for advanced R programming, package development, and professional data science work.

max() function in detail

max() Function in R Language
The max() function in R is used to identify the largest element in a given object. This object can be a vector, list, matrix, data frame, etc.

Syntax

max(object, na.rm)

Parameters

• object: Any R object such as a vector, matrix, list, or data frame.
• na.rm: A logical value (TRUE or FALSE) that determines whether to ignore NA values.

Example 1: Finding the Maximum Element in Vectors

# Creating vectors
vector1 <- c(12, 25, 8, 14, 19)
vector2 <- c(5, NA, 17, 4, 10)

# Finding the maximum element
max(vector1)               # Without NA
max(vector2, na.rm = FALSE) # Includes NA
max(vector2, na.rm = TRUE)  # Excludes NA

Output:

[1] 25
[1] NA
[1] 17

Example 2: Finding the Maximum Element in a Matrix

# Creating a matrix
matrix_data <- matrix(1:12, nrow = 3, ncol = 4)
print(matrix_data)

# Finding the maximum element
max(matrix_data)

Output:

[,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

[1] 12

sprintf() Function in detail

The sprintf() function in R uses a format specified by the user to return the formatted string, inserting the values provided.

Syntax:

sprintf(format, values)

Parameters:

• format: The format of the string for printing values.
• values: The variables or values to be inserted into the string.

Example 1: Formatted String using sprintf()

# R program to illustrate
# the use of sprintf() function

# Initializing values
name <- "Alice"
greeting <- "Good Morning"

# Calling sprintf() function
sprintf("%s, %s!", greeting, name)

Output:

[1] "Good Morning, Alice!"

In this example, sprintf() is used to create a formatted string where the placeholders %s are replaced by the values of the variables greeting and name.

Example 2: Formatted String with Numbers using sprintf()

# R program to illustrate
# the use of sprintf() function

# Initializing values
item <- "Apples"
quantity <- 12
price_per_item <- 2.5

# Calling sprintf() function
sprintf("The price for %d %s is $%.2f", quantity, item, quantity * price_per_item)

Output:

[1] "The price for 12 Apples is $30.00"

This example demonstrates using sprintf() to format a string that includes both text and numeric values, where the %d placeholder is used for integers, and %.2f is used for floating-point numbers.

Using paste(): The paste() function in R is used for concatenating elements into a single string, with optional separators between them.

Example 3: Formatted String using paste()

# Generate some example statistical results
mean_value <- 35.68
standard_deviation <- 7.42

# Create a formatted string to display the results
formatted_string <- paste("The mean is", round(mean_value, 2),
                          "and the standard deviation is", round(standard_deviation, 2))

# Print the formatted string
cat(formatted_string)

Output:

The mean is 35.68 and the standard deviation is 7.42

Here, paste() is used to concatenate the components of the string, and round() ensures the values are formatted to two decimal places.

strsplit() method in detail

The strsplit() function in R is used to divide a string into smaller parts based on a specified delimiter.

Syntax of strsplit()

strsplit(string, split, fixed)

Parameters:

• string: The input text or vector of strings.
• split: The character or pattern used to split the string.
• fixed: A logical value indicating whether the split should be treated as a literal match (TRUE) or as a regular expression (FALSE).

Return Value:

It returns a list containing the substrings obtained after the split.

Examples of Splitting Strings in R

Example 1: Using strsplit() with a Space as a Delimiter

In this example, we use the strsplit() function to split a given text using space (" ") as a delimiter.

# R program to split a string

# Given String
text <- "Data Science with R"

# Using strsplit() method
result <- strsplit(text, " ")

print(result)

Output:

[[1]]
[1] "Data"    "Science" "with"    "R"

Example 2: Using Regular Expression to Split a String

Here, we use a regular expression to split the string wherever one or more numeric characters ([0-9]+) appear.

# R program to split a string using regex

# Given String
text <- "Learn7R5Programming"

# Using strsplit() method
result <- strsplit(text, split = "[0-9]+")

print(result)

Output:

[[1]]
[1] "Learn"      "R"          "Programming"

Example 3: Splitting Date Strings Using strsplit()

We can also split date strings into separate components using a specific delimiter, such as "-".

# R program to split date strings

# Given Date Strings
date_strings <- c("10-05-2023", "15-06-2023", "20-07-2023", "25-08-2023", "30-09-2023")

# Using strsplit() function
result <- strsplit(date_strings, split = "-")

print(result)

Output:

[[1]]
[1] "10"  "05"  "2023"

[[2]]
[1] "15"  "06"  "2023"

[[3]]
[1] "20"  "07"  "2023"

[[4]]
[1] "25"  "08"  "2023"

[[5]]
[1] "30"  "09"  "2023"

tolower() Method in detail

The tolower() function in R is used to convert uppercase letters in a string to lowercase.

Syntax:

tolower(s)

Return: Returns the string in lowercase.

Example 1:

# R program to convert string
# from uppercase to lowercase

text <- "Hello World"

# Using tolower() method
result <- tolower(text)

print(result)

Output:

[1] "hello world"

Example 2: 

# R program to convert string
# from uppercase to lowercase

# Given String
sentence <- "ProGRamMing in R is FuN!"

# Using tolower() method
result <- tolower(sentence)

print(result)

Output:

[1] "programming in r is fun!"

toupper() function in detail

The toupper() function in R is used to convert a lowercase string to an uppercase string.

Syntax:

Return: Returns the uppercase version of the given string.

Example 1:

# R program to convert a string
# from lowercase to uppercase

# Given String
text <- "Welcome to the world of R programming"

# Using toupper() method
result <- toupper(text)

print(result)

Output:

[1] "WELCOME TO THE WORLD OF R PROGRAMMING"

Example 2:

# R program to convert a string
# from lowercase to uppercase

# Given String
sentence <- "Practice makes a person perfect"

# Using toupper() method
converted <- toupper(sentence)

print(converted)

Output:

[1] "PRACTICE MAKES A PERSON PERFECT"

append() method in detail

The append() function in R is used to insert values into a vector at a specified position. If no position is mentioned, the values are added at the end of the vector.

Syntax:

append(x, value, index(optional))

Return Value: It returns a new vector with the specified values appended.

Example 1: Appending a Value at the End of a Vector

vec <- c(2, 4, 6, 8)

# Appending value 12 to the vector
result <- append(vec, 12)

print(result)

Output:

[1]  2  4  6  8 12

Example 2: Inserting a Value at a Specific Position

vec <- c(5, 10, 15, 20)

# Inserting 7 at the second position
result <- append(vec, 7, after = 1)

print(result)

Output:

[1]  5  7 10 15 20

nchar() method in detail

The nchar() function in R is used to determine the number of characters in a string object.

Syntax:

nchar(string)

Return Value:

The function returns the length (number of characters) present in the given string.

Example 1: Finding the Length of a String

In this example, we will calculate the length of a string using the nchar() function.

# R program to determine the length of a string

# Define a string
text <- "Hello R Programming"

# Use nchar() function
length_result <- nchar(text)

print(length_result)

Output:

[1] 20

Example 2: Using nchar() with Character Vectors

This example demonstrates how to apply nchar() to a vector containing different types of elements.

# R program to get the length of character vectors

# Defining a character vector
vec <- c('code', '7', 'world', 99)

# Displaying the type of vector
typeof(vec)

# Applying nchar() function
nchar(vec)

Output:

'character'
4 1 5 2

Example 3: Handling NA Values in nchar()

The nchar() function provides an optional argument keepNA, which helps when dealing with NA values.

# R program to handle NA values using nchar()

# Defining a vector with NULL and NA values
vec <- c(NULL, '3', 'data', NA)

# Applying nchar() with keepNA = FALSE
nchar(vec, keepNA = FALSE)

Output:

1 4 2

Here, NULL returns nothing, and NA is counted as 2 when keepNA = FALSE.

If we set keepNA = TRUE, the output will be:

# Applying nchar() with keepNA = TRUE
vec <- c('', NULL, 'data', NA)

nchar(vec, keepNA = TRUE)

Output:

0 4 <NA>

This means that an empty string returns 0, and NA is explicitly shown as <NA> when keepNA = TRUE.

String Manipulation in detail

In this article, we will explore different ways to find substrings in the R programming language.

R provides multiple methods for substring operations, including:

• Using substr() function
• Using str_detect() function
• Using grep() function

Method 1: Using substr() function

The substr() function in R is used to extract a substring from a given string based on specified start and end positions.

Syntax:

substr(string_name, start, end)

Return Value: This function returns the substring from the given string according to the specified start and end indexes.

Example 1:

Operations on a Single Matrix

R allows element-wise operations on matrices using overloaded arithmetic operators. Let’s look at some examples.

# Given String
text <- "Learning R Programming"

# Using substr() function
result <- substr(text, 1, 8)

print(result)

Example:

# Given String
sentence <- "Data science and machine learning"

# Using substr() function
result <- substr(sentence, 13, 27)

print(result)

Output:

[1] "and machine le"

Method 2: Using str_detect() function

The str_detect() function from the stringr package checks whether a specified substring exists within a string. It returns TRUE if a match is found and FALSE otherwise.

Syntax:

str <- paste(c(2:4), "5", sep = "-")
print(str)

Parameters:

• string: The target string or vector of strings.
• pattern: The substring pattern to be searched.

Example:

# Load library
library(stringr)

# Creating a vector
words <- c("Apple", "Banana", "Cherry", "Mango")

# Pattern to search
pattern <- "Banana"

# Using str_detect() function
str_detect(words, pattern)

Output:

[1] FALSE  TRUE FALSE FALSE

Method 3: Using grep() function

The grep() function returns the indices of elements in a character vector that match a specified pattern. If multiple matches are found, it returns a list of their respective indices.

Syntax:

grep(pattern, string, ignore.case=FALSE)

Parameters:

• pattern: A regular expression pattern to match.
• string: The character vector to search.
• ignore.case: Whether to perform case-insensitive searching (default is FALSE).

Example:

# Define vector
text_values <- c("Orange", "orchestra", "blue", "ocean")

# Using grep() to find pattern "or"
matching_indices <- grep("or", text_values, ignore.case=TRUE)

print(matching_indices)

Output:

[1] 1 2 4