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In michaelquinn32/generators: Generators: Iterable functions

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Generators: A grammar of iteration

Generators are functions that behave like iterators, a type of object that contains a mutable internal state. When used correctly, they offer an efficient solution to many computational problems. This package provides a grammar for working with these tools.

This package was written to accompany a post on The Big Blog of R Adventures. It provides much more detail on how design and implementation of this project.

Design concepts and goals

Constructing generators

Iterators typically contain a next method to create new values of the internal state. Generators accomplish this through successive function calls. In R, we can implement this tool using closures. The simplest of these is the counter. Using an example from Hadley Wickham, we can implement a simple count generator as follows,

counter <- function() {
  i <- 0
  function() {
    i <<- i + 1
    i
  }
}

This is what it looks like in action.

my_counter <- counter()
my_counter()
#> 1
my_counter()
#> 2

This simple function has a very useful structure that can built upon to create a much more general tool:

• At the heart of it all, we have a mutable state: i.
• We modify it with the scoping assignment operator <<-.
• Subsequent calls to the function update the state.
• And each call returns (a version of) the changed state.

Together, these components form the arguments of the function generator.

• .state is a vector. It doesn't necessarily have to be a scalar
• .update controls how that state changes over time
• .yield governs how values from the state are returned to form the series

Working with iterators

A wide variety of iterators can be constructed using these three arguments, but many users might want to use other tools to create new generators. For these purposes, the following are provided:

• copy create a new generator whose state does not affect the original
• keep or discard controls which elements of the series are shown
• wrap changes the series output, while rebase changes the update function
• skip moves the series forward a specific number of steps
• take transforms a set number of values into a list or vector
• reset sends it back to its start
• limit provides an end to the series (when the series is exhausted)
• recycle resets the generator when it is exhausted
• consume generates the series until its limits
• foldn and foldc reduces the series to a single value, using a binary function

Other iteration tools

Generators are a common type of object in many programming languages, and this package draws heavily from concepts impelemented in Javascript and Python. Reginald Braithwaite's book, Javascript Allonge, has an excellent introduction to generators in that language, and I learned about their implementation in Python thanks to Jeff Knupp's blog.

An iterator syntax is provided in Rust. It is described well in Rust's documentation and its accompanying book. These concepts informed the generator grammar described in this package.

Iterators already exist in R, through the iterators and itertools packages, but the implementations and design goals are different. This package is obviously indebted to Rich Calaway, Steve Weston and Hadley Wickham for their work, and I am incredibly grateful to be able to reference it.

Installation

To install the development version of this package:

# install.packages("devtools")
devtools::install_github("michaelquinn32/generators")

michaelquinn32/generators documentation built on May 22, 2019, 9:52 p.m.