r-oop

name	r-oop
description	R object-oriented programming guide for S7, S3, S4, and vctrs. Use when mentions "orientado a objetos", "orientado a objetos em R", "object-oriented", "object-oriented in R", "OOP", "OOP in R", "OOP em R", "POO", "POO em R", "classes", "classes em R", "classes in R", "métodos", "métodos em R", "methods", "methods in R", "S3 class", "S4 class", "S7 class", "S7", "classe S3", "classe S4", "classe S7", "sistema S3", "sistema S4", "sistema S7", "S3 system", "S4 system", "S7 system", "method dispatch", "despacho de métodos", "dispatch de métodos", "generic functions", "funções genéricas", "generics", "genéricos", "inheritance", "herança", "vctrs", "vctrs package", "methods", "methods package", "setClass", "setGeneric", "setMethod", "new_class", "new_generic", "new_property", "criar classe", "create class", "design class", "desenhar classe", "definir classe", "define class", "sistema de objetos", "object system", "class system", "sistema de classes", "definir métodos", "define methods", "criar genéricos", "create generics", "class definition", "definição de classe", "class design", "design de classes", "validação de classe", "class validation", "property validation", "validação de propriedades", "herança de classes", "class inheritance", or designing R classes and choosing an OOP system. ONLY R - do NOT activate for Python classes, Java OOP, C++ classes, or object-oriented programming in other languages.
version	1.1.0
user-invocable	false
allowed-tools	Read, Grep, Glob

R Object-Oriented Programming

S7, S3, S4, and vctrs: choosing the right OOP system for your needs

S7: Modern OOP for New Projects

• S7 combines S3 simplicity with S4 structure
• Formal class definitions with automatic validation
• Compatible with existing S3 code

# S7 class definition
Range <- new_class("Range",
  properties = list(
    start = class_double,
    end = class_double
  ),
  validator = function(self) {
    if (self@end < self@start) {
      "@end must be >= @start"
    }
  }
)

# Usage - constructor and property access
x <- Range(start = 1, end = 10)
x@start  # 1
x@end <- 20  # automatic validation

# Methods
inside <- new_generic("inside", "x")
method(inside, Range) <- function(x, y) {
  y >= x@start & y <= x@end
}

OOP System Decision Matrix

S7 vs vctrs vs S3/S4 Decision Tree

Start here: What are you building?

1. Vector-like objects (things that behave like atomic vectors)

Use vctrs when:
- Need data frame integration (columns/rows)
- Want type-stable vector operations
- Building factor-like, date-like, or numeric-like classes
- Need consistent coercion/casting behavior
- Working with existing tidyverse infrastructure

Examples: custom date classes, units, categorical data

2. General objects (complex data structures, not vector-like)

Use S7 when:
- NEW projects that need formal classes
- Want property validation and safe property access (@)
- Need multiple dispatch (beyond S3's double dispatch)
- Converting from S3 and want better structure
- Building class hierarchies with inheritance
- Want better error messages and discoverability

Use S3 when:
- Simple classes with minimal structure needs
- Maximum compatibility and minimal dependencies
- Quick prototyping or internal classes
- Contributing to existing S3-based ecosystems
- Performance is absolutely critical (minimal overhead)

Use S4 when:
- Working in Bioconductor ecosystem
- Need complex multiple inheritance (S7 doesn't support this)
- Existing S4 codebase that works well

Detailed S7 vs S3 Comparison

Feature	S3	S7	When S7 wins
Class definition	Informal (convention)	Formal (new_class())	Need guaranteed structure
Property access	$ or attr() (unsafe)	@ (safe, validated)	Property validation matters
Validation	Manual, inconsistent	Built-in validators	Data integrity important
Method discovery	Hard to find methods	Clear method printing	Developer experience matters
Multiple dispatch	Limited (base generics)	Full multiple dispatch	Complex method dispatch needed
Inheritance	Informal, NextMethod()	Explicit super()	Predictable inheritance needed
Migration cost	-	Low (1-2 hours)	Want better structure
Performance	Fastest	~Same as S3	Performance difference negligible
Compatibility	Full S3	Full S3 + S7	Need both old and new patterns

Practical Guidelines

Choose S7 when you have

# Complex validation needs
Range <- new_class("Range",
  properties = list(start = class_double, end = class_double),
  validator = function(self) {
    if (self@end < self@start) "@end must be >= @start"
  }
)

# Multiple dispatch needs
method(generic, list(ClassA, ClassB)) <- function(x, y) ...

# Class hierarchies with clear inheritance
Child <- new_class("Child", parent = Parent)

Choose vctrs when you need

# Vector-like behavior in data frames
percent <- new_vctr(0.5, class = "percentage")
data.frame(x = 1:3, pct = percent(c(0.1, 0.2, 0.3)))  # works seamlessly

# Type-stable operations
vec_c(percent(0.1), percent(0.2))  # predictable behavior
vec_cast(0.5, percent())          # explicit, safe casting

Choose S3 when you have

# Simple classes without complex needs
new_simple <- function(x) structure(x, class = "simple")
print.simple <- function(x, ...) cat("Simple:", x)

# Maximum performance needs (rare)
# Existing S3 ecosystem contributions

S3 Patterns

Basic S3 Class

# Constructor
new_person <- function(name, age) {
  stopifnot(is.character(name), length(name) == 1)
  stopifnot(is.numeric(age), length(age) == 1)

  structure(
    list(name = name, age = age),
    class = "person"
  )
}

# Print method
print.person <- function(x, ...) {
  cat("Person:", x$name, "(age", x$age, ")\n")
  invisible(x)
}

# Generic + method
greet <- function(x) UseMethod("greet")
greet.person <- function(x) {
  cat("Hello, my name is", x$name, "\n")
}
greet.default <- function(x) {
  cat("Hello!\n")
}

S3 Inheritance

# Child class
new_employee <- function(name, age, company) {
  obj <- new_person(name, age)
  obj$company <- company
  class(obj) <- c("employee", class(obj))
  obj
}

# Method with inheritance
print.employee <- function(x, ...) {
  NextMethod()  # Call parent print method
  cat("Works at:", x$company, "\n")
  invisible(x)
}

S7 Patterns

Basic S7 Class

library(S7)

# Define class
Person <- new_class("Person",
  properties = list(
    name = class_character,
    age = class_numeric
  ),
  validator = function(self) {
    if (self@age < 0) {
      "@age must be non-negative"
    }
  }
)

# Create instance
bob <- Person(name = "Bob", age = 30)
bob@name  # "Bob"
bob@age <- 31  # Validated assignment

S7 Methods

# Define generic
greet <- new_generic("greet", "x")

# Add method
method(greet, Person) <- function(x) {
  cat("Hello, my name is", x@name, "\n")
}

# Default method
method(greet, class_any) <- function(x) {
  cat("Hello!\n")
}

S7 Inheritance

Employee <- new_class("Employee",
  parent = Person,
  properties = list(
    company = class_character
  )
)

# Override method
method(greet, Employee) <- function(x) {
  super(x, Person)@greet()  # Call parent method
  cat("I work at", x@company, "\n")
}

S7 Multiple Dispatch

# Generic with multiple dispatch
combine <- new_generic("combine", c("x", "y"))

# Method for specific combination
method(combine, list(Person, Person)) <- function(x, y) {
  cat(x@name, "meets", y@name, "\n")
}

method(combine, list(Person, class_character)) <- function(x, y) {
  cat(x@name, "receives message:", y, "\n")
}

Migration Strategy

1. S3 -> S7: Usually 1-2 hours work, keeps full compatibility
2. S4 -> S7: More complex, evaluate if S4 features are actually needed
3. Base R -> vctrs: For vector-like classes, significant benefits
4. Combining approaches: S7 classes can use vctrs principles internally

Migration Example: S3 to S7

# Original S3
new_person_s3 <- function(name, age) {
  structure(list(name = name, age = age), class = "person")
}

# Migrated S7
Person <- new_class("Person",
  properties = list(
    name = class_character,
    age = class_numeric
  )
)

# S7 is backwards compatible with S3 generics
# Existing S3 methods still work

When NOT to Use OOP

Sometimes simpler approaches are better:

# Don't create a class for simple data
# BAD
Point <- new_class("Point", properties = list(x = class_double, y = class_double))

# GOOD - just use a named list or vector
point <- c(x = 1.5, y = 2.3)

# Don't create classes for one-off operations
# Use functions instead
distance <- function(p1, p2) {
  sqrt((p1["x"] - p2["x"])^2 + (p1["y"] - p2["y"])^2)
}