Class in R

Answering the top questions on Object Oriented Programming in R: What is S4? What is a Reference Class? When should I use them? This post provides definitive answers on S4 class features, RC key characteristics, and how generics enable multiple dispatch. Level up your R programming skills today.

What is OOP in R?

OOP stands for Object Oriented Programming in R, and it is a popular programming language. OOP allows us to construct modular pieces of code that are used as building blocks for large systems. R is a functional language. It also supports exists for programming in an object-oriented style. OOP is a superb tool to manage complexity in larger programs. It is particularly suited to GUI development.

Object Oriented Programming in R is a paradigm for structuring your code around objects, which are data structures that have attributes (data) and methods (functions). However, unlike most other languages, R has three distinct object-oriented systems:

1. S3: The simplest and most common system. Informal and flexible.
2. S4: A more formal and rigorous version of S3.
3. R6 (and others): A modern system that supports more familiar OOP features like reference semantics (objects that can be modified in place).

What is S4 Class in R?

S4 Class in R is a formal object-oriented programming (OOP) system in R. It is a more structured and rigorous evolution of the simpler S3 system. While S3 is informal and flexible, S4 introduces formal class definitions, validity checks, and a powerful feature called multiple dispatch.

One can think of it as providing a blueprint for your objects, ensuring they are constructed correctly and used properly.

When to use S4 Class in R?

Use S4 when you are building large, complex systems or packages where the integrity of your objects is critical. It’s heavily used in the Bioconductor project, which manages complex biological data, because its rigor helps prevent bugs and ensures interoperability between packages. For simpler, more interactive tasks, S3 or R6 is often preferable.

What is the Reference Class?

The Reference Class (often abbreviated RC) is another object-oriented system in R, introduced in the methods package around 2010. It was the precursor to the more modern and robust R6 system.

What are the key features of Reference Class?

1. Encapsulation: Methods (functions) and fields (data) are defined together within the class. You use the $ operator to access both.
2. Mutable State: Because of reference semantics, the object’s internal state can be changed by its methods.
3. Inheritance: RC supports single inheritance, allowing a class to inherit fields and methods from a parent class.
4. Built-in: They are part of the base methods package, so no additional installations are needed (unlike R6, which is a separate package, though also very popular).

When to use Reference Class?

• When maintaining legacy code that already uses them.
• When you need mutable state and reference semantics and cannot rely on an external package (though R6 is a lightweight, recommended package).
• For modeling real-world entities that have a changing identity over time (e.g., a game character, a bank account, a connected device).

What is S4 Generic Function?

An S4 generic function is a fundamental concept in R’s S4 object-oriented system. It’s the mechanism that enables polymorphism, allowing the same function name to perform different actions depending on the class of its arguments.

What are the key features of S4 Class in R?

1. Multiple Dispatch: This is the superpower of S4. While S3 generics only dispatch on the first argument, S4 generics can look at the class of multiple arguments to choose the right method.
2. Formal Definition: S4 generics are formally defined, which makes the system more robust and less prone to error than the informal S3 system.
3. Existing Generics: You can define new methods for existing generics (like show, plot) without creating a new generic function. This is very common.

Learn Statistics Software

The post is about S3 Classes in R. Here we will learn how the S3 class system works in R, a simple yet powerful way to implement object-oriented programming in the R Language. This guide covers S3 class creation, methods like print(), and summary(), debugging tools like getS3method(), and getAnywhere(). This guide includes working code examples to better understand the S3 Classes in R!

What is mean by S3 Classes in R Language?

In R, S3 refers to the S3 object-oriented system, a simple and widely used class system in R. The S3 class in R is used to overload any function. The key features of the S3 Class System in R Language are:

• Informal Class System: No formal class definition; objects are assigned a class attribute.
• Generic Functions: Uses functions like print(), summary(), and plot() that behave differently based on the object’s class (method dispatch).
• Method Naming: Methods follow the pattern generic.class() (e.g., print.lm() for linear models).
• Flexible but Simple: Easy to implement but lacks strict structure (unlike S4 or R6).

S3 is commonly used in base R (e.g., lm(), glm(), and data.frame use S3).

Give an Example of Creating an S3 Class in the R Language

S3 is R’s simplest object-oriented system. You create an S3 class by:

• Assigning an class attribute to an object (it is usually a list of objects)
• Defining methods (functions) for that class (for example, print.classname)

Example of Creating an S3 Class in R

Let us create an S3 class in R

# Define a record object (a list with a class attribute)
record <- list(name = "Imdad", age = 40, site = "https://rfaqs.com")
class(record) <- "record"  # Assign class

After creating an S3 Class, let us create a method for the class

# Custom print method for "record" class
print.record <- function(x) {
  cat("Site Author Name:", x$name, "\n")
  cat("Age:", x$age, "\n")
  cat("Site:", x$site, "\n")
}

One can test an S3 object easily

print(record)

Note that the method/class has the “dot” naming convention of method.class.

What are getS3method() and getAnywhere() in R?

Both getAnywhere() and getAnywhere() methods are useful for exploring R’s object-oriented systems.

• getS3method(): getS3method() retrieves the implementation of an S3 method for a specific class. The general syntax is
  getS3method("print", "data.frame") # shows how ‘print.data.frame‘ works.
  
• getAnywhere(): getAnywhere() finds functions/ methods anywhere (loaded packages, namespaces, or S3/S4 registries). The syntax is getanywhere("print.data.frame") # finds ‘print.data.frame‘ even if not exported

The key difference between getAnywhere() and getAnywhere() is

Write about Useful S3 Generic Methods with examples

The useful S3 generic methods are summary(), plot(), predict(). These S3 generic methods can be customized too.

For example,

summary.record <- function(x){
  paste(x$name, "is author of", x$site)
}

summary(record)

What is the importance of the S3 Class System in R?

The S3 class system is a foundational feature of R’s object-oriented programming (OOP) approach. Despite its simplicity, it plays a crucial role in R’s functionality and ecosystem.

• Simplicity and Flexibility: Unlike S4 or R6, the S3 class system does not require a strict structure. It is easy to implement as one just needs to assign a class attribute to an object. The S3 objects are dynamic dispatch, as methods like print(), summary() and plot() adapt based on class.
• Widely used in Base R and Popular Packages: The R core functions (such as lm(), glm(), data.frame) rely on S3. Similarly, packages such as ggplot2, dply4, and stats use the S3 class system for extensibility. The custom methods, such as print.ggplot() allows seamless integration. For example
• Enables Polymorphism (Generic Functions): Using the S3 class system, one can enable polymorphism, that is, the same function, different behaviour. For example,
  • print() behaves differently for data.frame, lm, and custom objects.
  • plot() adapts to histogram, scatterplot, or custom visualizations.
• Easy Debugging and Inspection: getS3method() and getAnywhere() can be used for easy debugging and inspection.
• Fast for prototyping and Lightweight: The S3 class system requires no complex setup, and it is ideal for quick data analysis and experimental code.

In a nutshell, the S3 system is R’s most widely used OOP framework because of its simplicity and deep integration with R’s ecosystem. While it lacks the rigor of S4 or R6, its flexibility makes it indispensable for statistical computing and interactive data analysis.

FAQs about the S3 Classes in R

1. What is the concept of S3 Classes in R?
2. How can one check the class of an object?
3. For different data types (modes), what are the common classes used in R?
4. How can one change the class of an object?
5. Give examples to determine the class of different objects.
6. Write about getS3method() and getAnywhere().
7. Give an example that explains how S3 Classes are created in R?

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Introduction to Class of an Object in R

In R language, all objects have a class, which can be reported using the class() function. For simple vectors, this is just the mode, such as numeric, character, list, or logical. The other possible modes are array, matrix, factor, and data frame.

A special attribute known as the class of the object is used to allow for an object-oriented style of programming in R language. For example, an object having class as “data.frame” will be printed in a certain way, the plot() function will display it graphically in a certain way, and other generic functions such as summary() will react to it as an argument in a way sensitive to its class.

How to Determine the Class of an Object in R

The class() function is used to determine the class of an object. For example,

class(mtcars)
x <- c(1, 2, 3)
class(x)

y <- c("a", "b", "c")
class(y)

z <- c(TRUE, FALSE)
class(z)

Common Object Classes in R

Here are some of the most common object classes in R:

1. Integer: Represents integer values.
2. Numeric: Represents numerical data.
3. Character: Represents text strings.
4. Factor: Represents categorical data.
5. Logical: Represents logical values (TRUE or FALSE).
6. Date: Represents dates.
7. List: Represents a collection of objects of different types.
8. Matrix: Represents a two-dimensional array of numbers.
9. Data Frame: Represents a tabular data structure with rows and columns.
10. POSIXct: Represents date and time.

It is important to note that one can define one’s classes using S3 or S4 object-oriented systems. This allows the user to define specific methods and behavior for different objects.

Why Classes Matter in R Language

The class of an object determines how R behaves when a user applies functions to it. In simple words, a class defines the object’s type and determines the operations that can be performed on it. For instance:

• Arithmetic operations: These are typically performed on numeric objects.
• String manipulation: These are performed on character objects.
• Statistical analysis: These are often performed on numeric or factor objects.

The importance of classes can be described as:

• Method Dispatch: The class of an object in R language determines which function to call when you apply a generic function to it. For example, the summary() function behaves differently for numeric vectors, data frames, and linear models.
• Object-Oriented Programming: R supports object-oriented programming, and classes are fundamental to this paradigm. One can create custom classes to represent complex data structures and define methods to operate on these objects.
• Data Manipulation: Understanding the class of an object helps one to choose the appropriate functions for data manipulation. For instance, one might use different functions for subsetting, sorting, and summarizing numeric vectors, character vectors, and data frames.

Remove the Class of an Object in R

To remove temporarily the effect of a class from an object, one can use the unclass() function. For example, if mtcars has the class “data.frame” then typing the just mtcars on the command prompt will print it in data frame form, which is rather like a matrix.

mtcars

Whereas, typing unclass(mtcars) will print/display it as an ordinary list.

unclass(mtcars)

## Output
$mpg
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

$cyl
 [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

$disp
 [1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8 275.8 275.8 472.0 460.0 440.0  78.7  75.7  71.1 120.1 318.0 304.0 350.0 400.0  79.0 120.3  95.1 351.0 145.0 301.0 121.0

$hp
 [1] 110 110  93 110 175 105 245  62  95 123 123 180 180 180 205 215 230  66  52  65  97 150 150 245 175  66  91 113 264 175 335 109

$drat
 [1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62 3.54 4.11

$wt
 [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520 3.435 3.840 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780

$qsec
 [1] 16.46 17.02 18.61 19.44 17.02 20.22 15.84 20.00 22.90 18.30 18.90 17.40 17.60 18.00 17.98 17.82 17.42 19.47 18.52 19.90 20.01 16.87 17.30 15.41 17.05 18.90 16.70 16.90 14.50 15.50 14.60 18.60

$vs
 [1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1

$am
 [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1

$gear
 [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4

$carb
 [1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2

attr(,"row.names")
 [1] "Mazda RX4"       "Mazda RX4 Wag"    "Datsun 710"       "Hornet 4 Drive"   "Hornet Sportabout"
 [6] "Valiant"         "Duster 360"       "Merc 240D"        "Merc 230"         "Merc 280"
[11] "Merc 280C"       "Merc 450SE"       "Merc 450SL"       "Merc 450SLC"      "Cadillac Fleetwood" 
[16] "Lincoln Continental" "Chrysler Imperial"   "Fiat 128"      "Honda Civic"     "Toyota Corolla" 
[21] "Toyota Corona"    "Dodge Challenger"  "AMC Javelin"     "Camaro Z28"      "Pontiac Firebird"   
[26] "Fiat X1-9"       "Porsche 914-2"     "Lotus Europa"     "Ford Pantera L"  "Ferrari Dino"       
[31] "Maserati Bora"   "Volvo 142E"

Changing the Class of an Object in R

While it’s generally not recommended to manually change an object’s class, there are functions like as.numeric(), as.character(), as.factor(), etc., that can coerce objects into different classes. However, be cautious, as inappropriate coercion can lead to unexpected results.

Understanding object classes is fundamental to effective R programming. By recognizing the class of an object, you can choose the appropriate functions and operations to work with it. By understanding the class of an object, you can effectively work with R’s diverse data structures and leverage its powerful data analysis capabilities.

FAQs about Class of an Object

1. What is the concept of class in R language?
2. How can one check the class of an object?
3. For different data types (modes) what are the common classes used in R?
4. How can one change the class of an object?
5. Give examples to determine the class of different objects.

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