Light-Weight 'python'-Like Object-Oriented System.

bandicoot

The goal of bandicoot is to provide a set of tools for building light-weight object oriented system, which has Python-like syntax and duner methods for simplicity, but uses static dispatch for less overhead. This system also allows multiple inheritances and provides Python-like method resolution order for the possibility of implementing dynamic dispatch by users.

This system is inspired by the OOP systems implemented in R6 and Python.

Installation

Install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("TengMCing/bandicoot")

1. bandicoot OOP system

library(bandicoot)

1.1. Define a new class

A class can be defined with the new_class function. All positional arguments are for specifying parent classes, BASE is the base object class provided by the package, you don’t need to manually specify it. But if you would like to have advanced behaviour, you can try to implement your own object class.

Class name is mandatory and should be unique.

# You don't actually need to specify BASE here. This is only for demonstration.
DEMO <- new_class(BASE, class_name = "DEMO")
DEMO
#> 
#> ── <DEMO class>

The object is an environment containing some useful attributes and methods.

• OBJECT$..type.. gives the current class name.
• OBJECT$..class.. gives the current class name and parent class names.

DEMO$..type..
#> [1] "DEMO"
DEMO$..class..
#> [1] "DEMO" "BASE"

• OBJECT$..dir..() returns all names of attribute and method of the object.
• OBJECT$..methods..() returns all names of method of the object

DEMO$..dir..()
#>  [1] "..mro.."          "..bases.."        "..str.."          "..len.."         
#>  [5] "..class.."        "..new.."          "has_attr"         "del_attr"        
#>  [9] "..repr.."         "set_attr"         "..type.."         "get_attr"        
#> [13] "..dir.."          "..methods.."      "..method_env.."   "..instantiated.."
#> [17] "..init.."         "..class_tree.."   "instantiate"
DEMO$..methods..()
#>  [1] "..str.."     "..len.."     "..new.."     "has_attr"    "del_attr"   
#>  [6] "..repr.."    "set_attr"    "get_attr"    "..dir.."     "..methods.."
#> [11] "..init.."    "instantiate"

• OBJECT$..str..() returns a string representation of the object, which will be used by the S3 print() method. This method usually needs to be overridden in subclass to give short summary of the object.

DEMO$..str..()
#> [1] "<DEMO class>"

1.2. Register a method for the class

Methods can be registered by using register_method(). The first argument is the object you want to bind the function to, the rest of the positional arguments are for specifying method names and functions. The syntax is method_name = function.

You can choose to write inline function or pass pre-defined function. The associative environment of the function doesn’t matter, it will be modified by the register_method() function.

pre_defined_fn <- function() 1 + 2

register_method(DEMO, inline_fn = function() 1 + 1, pre_defined_fn = pre_defined_fn)

DEMO$inline_fn()
#> [1] 2
DEMO$pre_defined_fn()
#> [1] 3

For method that needs to access the object itself, just simply use self in your method. It is an reference to the object.

DEMO$val <- 5

register_method(DEMO, get_val = function() self$val)

DEMO$get_val()
#> [1] 5

1.3. Override the ..init..() method

..init..() method is for instance initialization. To override the ..init..() method, you need to use the register_method() to register it again.

init <- function(first_name, employee_id) {
  self$first_name <- first_name
  self$employee_id <- employee_id
}

register_method(DEMO, ..init.. = init)

Now the class requires two two arguments first_name and employee_id to initialize the instance.

1.4. Build an instance

To new and initialize an instance, you need to use the instantiate() method. The output will show it is an object.

mike <- DEMO$instantiate("Mike", 25)
mike
#> 
#> ── <DEMO object>

first_name and employee_id are stored in the object because of the ..init..() method.

mike$first_name
#> [1] "Mike"
mike$employee_id
#> [1] 25

1.5. A complete workflow

It is recommend to write your class definition in a function to make debugging easier. The following example new a class DEMO_2, defines its own ..init..() method, defines a get_email() function for retrieving the email address, defines its own ..str..() method such that when we print the object, it will provide us with a nicely formatted summary.

super() returns the next class of the method resolution order, which will always be the parent class in single inheritance, but not necessary in multiple inheritance.

use_method() is used to run methods from other classes, which in this case, the ..str..() method from the parent class (BASE).

class_DEMO_2 <- function(env = new.env(parent = parent.frame())) {
  
  new_class(env = env, class_name = "DEMO_2")
  
  init_ <- function(first_name, employee_id) {
    self$first_name <- first_name
    self$employee_id <- employee_id
  }
  
  get_email_ <- function() {
    paste0(self$first_name, "_", self$employee_id, "@company.com")
  }
  
  str_ <- function() {
    paste(use_method(self, super()$..str..)(), 
          paste("Name:", self$first_name,
                "\nEmployee ID:", self$employee_id,
                "\nEmail:", self$get_email()), 
          sep = "\n")
  }
  
  register_method(env,
                  ..init.. = init_,
                  get_email = get_email_,
                  ..str.. = str_)
  
  return(env)
}

DEMO_2 <- class_DEMO_2()
mike <- DEMO_2$instantiate("Mike", 25)
mike$get_email()
#> [1] "[email protected]"

mike$..str..()
#> [1] "<DEMO_2 object>\nName: Mike \nEmployee ID: 25 \nEmail: [email protected]"
mike
#> 
#> ── <DEMO_2 object>
#> Name: Mike 
#> Employee ID: 25 
#> Email: [email protected]