funcadelic

funcadelic.js

The Fundamentals of Functional Programming are Fun!

funcadelic takes the simple idea that a single function can operate on many different data structures (typeclass oriented programming) and brings it to JavaScript. Sure, there are a lot of FP libraries out there, but this one is geared towards unlocking the magical powers of functional programming while always maintaining a tangible and accessible experience for JavaScript developers. Because if you're a JavaScript developer today, you're already using most of the structures in funcadelic!

Quick example:

import { map } from 'funcadelic';
 
function double(i) { return i * 2; }
 
map(double, [1,2]); //=> [2,4]
map(double, {one: 1, two: 2}) //=> {one: 2, two: 4}

Notice how we used a single function map to operate on both an Array and an Object? That's because they're both instances of the Functor typeclass which has a single operation called map. Look familiar? It should because map is something you probably do with Arrays all the time. Funcadelic just takes this concept and lets you make it available to any data type for which it makes sense, not just Array. And as it turns out there are tons of them out there!

And just remember: Don't be afraid of weird names like Semigroup, Functor, Monad, Monoid and the like! They're just arbitrary names that describe really useful concepts. Kinda like this one.

Taskulamppu

Looks pretty weird right? Exotic, even. It's hard to parse, so it must be a hard concept.

Turns out it's the Finnish word for flashlight; A strange name at first (to non-finnish ears) for a very useful and easily understood tool.

Type Classes

These typeclasses are currently contained within funcadelic.

typeclass	instance operations
Semigroup	append(a, b)
Monoid	reduce(Monoid, list)
Functor	map(fn, functor)
Applicative	apply(Applicative, fn, list) pure(Applicitave, value)
Monad	flatMap(fn, monad)
Foldable	foldl(fn, initial, foldable) foldr(fn, initial, foldable)

Semigroup

A semi group is fancy name isn't it? It's just a data structure that can be "mashed" together with itself. Instead of Semigroup, you could call it MashableTogetherableWithItselfable, but that's a lot to say every time. You mash two items in a Semigroup together with the append function.

append(a, b)

Combine two members of a semigroup into a single value of the same semigroup.

Arrays are classic example of Semigroup. What do you get when you mash two arrays together? Well another array of course! Array + Array => Array

import { append } from 'funcadelic';
 
append([1,2], [3,4]) //=> [1,2,3,4]
 

But arrays aren't the only things that can be mashed together. Objects can too!

append({name: 'Charles'}, {occupation: 'Developer'}) //=> {name: 'Charles', occupation: 'Developer'}

When you smush two members of a semigroup together, you always get the same type back. In that example above: Object + Object => Object

And when you smush multiple members of a semigroup together, it doesn't matter which ones you smush together first, you get the same answer either way:

append([1,2], append([3,4], [5,6])) //=> [1,2,3,4,5,6]
append(append([1,2], [3,4]), [5,6]) //=> [1,2,3,4,5,6]

Monoid

Monoids take the concept of Semigroup and extend it just teeeeeny weeeny bit further so that you can use the append operation to fold any list of monoids really easily into a single value.

reduce(Monoid, list)

Fold a list of monoids into a single value by smushing them together, starting with an empty value.

For example, a thing you see often is merging a list of objects together to produce a single object with all of the keys and values.

let name = {name: 'Charles'}
let occupation = {occupation: 'Developer'}
let height = {height: {unit: 'cm', amount: 200}}

In order to produce this single merged value you might use Array.prototype.reduce

[name, occupation, height].reduce((accumulator, object) => append(accumulator, object), {})

Here we start with an initial value of our accumulator (an empty object) and successively append each object to it to produce the final object.

But if you know for certain what your start state is ({}), and how to combine any two objects (append), then you don't actually need to bother with writing your reduce, you can just use a monoidal reduction where those operations are implicit:

reduce(Object, [name, occupation, height])
//=> {name: 'Charles', occupation: 'Developer', height: {unit: 'cm', amount: 200}}

Of course, you may have recognized this as something akin to using Object.assign, with an empty object:

Object.assign({}, name, occupation, height);

The only difference is that it's not treating it as a unique, snowflake of an interface, but instead acknowledging it for what it is: the same fundamental operation as array concatenation and string concatenation (to name just a few)

Oh, and surprise, suprise! Arrays are Monoids as well....

reduce(Array, [[1,2], [3,4]]) //=> [1,2,3,4]

Some types, like numbers have many ways that they could be reduced. You could add them or multiply them just to name two. For these cases, funcadelic provides you a helper Monoid.create

const Sum = Monoid.create(class {
  empty() { return 0; }
  append(a, b) { return a + b; }
})
 
Sum.reduce([1,2,3,4]) //=> 10
 
const Multiplication = Monoid.create(class {
  empty() { return 1; }
  append(a, b) { return a * b; }
})
 
Multiplication.reduce([1,2,3,4]) //=> 24

Functor

I used to think that Functor was some strange amalgamation of the words "Function" and "Constructor". I don't think that any more because it isn't true, although I still have yet to find a satisfying origin for that word.

Nowadays, it's enough for me to reflect upon the fact that if George Clinton were running an empire, instead of calling the provincial governors "Satraps", he would have probably called them "Functors".

Once you give up hope on trying to understand the etymology of the word "Functor", you can focus on the idea that it represents some kind of "container" or "context" with its own intrinsic structure separate from the things that it contains. Using the map function, you can swap out the values in a container without changing the structure of the container itself.

map(fn, functor)

Create a new Functor by applying the function fn to the content of functor. The structure of the output Functor exactly matches that of the input.

Arrays are once again a classic example of Functors. You can use map to change the values in an array without changing the structure of the array itself:

import { map } from 'funcadelic';
 
map(i => i * 2, [1,2]); //=> [2,4]

The key here is that structure of the Array doesn't change. The input to map is an array of two elements, therefore the output of map must be an array of two elements. The content of that two element array has changed, but the structure has not.

And guess what? Objects, are also Functors! you can map over them too. When you map over an object, you change the value associated with each key, but the keys present remain the same.

map(i => i * 2, {one: 1, two: 2}); //=> {one: 2, two: 4}

Again, notice how map preserves the structure of the object, even though the values change. Both the input and the output are objects of two keys "one" and "two"

Applicative

Functors let you map a function with a single argument across a single Functor. But what do you do if you have a function with multiple arguments? Applicatives to the rescue.

apply(Applicative, fn, list)

Given list of functors each of which holds a value, call fn using the values held in list as the arguments.

Let's say for example that I have a function greet that takes three arguments:

function greet(say, to, excited) {
  return `${say}, ${to}${excited ? '!!' : '.'}`;
}
 
greet('Hello', 'Charles', false); //=> "Hello, Charles."
greet('Hello', 'Friend', true); //=> "Hello, Friend!!"

That's great if we are using literal values, but what if we're fetching them over the network?

let say = $.get('/greeting'); //=> Promise([pending])
let to = $.get('/entity'); //=> Promise([pending])
let isExcited = $.get('/excitement-level'); //=> Promise([pending])

We could do this manually:

Promise.all([say, to, isExcited])
  .then(([sayResult, toResult, isExcitedResult]) => greet(sayResult, toResult, isExcitedResult))

Of course, this obfuscates our original intent to call greet with the values in the promises. Instead, the actual greeting happens as the very last thing in a rightward drift! It's ugly, and I don't appreciate it.

What if we could just apply the greet function almost like using the builtin Function.apply? like:

greet.apply(null, [say, to, isExcited]);

That would be awesome right? Well with instances of Applicative like Promise, you totally can!

import { apply } from 'funcadelic';
 
apply(Promise, greet, [say, to, isExcited]);

This says: take these three promises, use the values that they contain as the three arguments to greet, and then return a promise containing the result. Nifty!

pure(Applicative, value)

Take value and place it into the minimum possible context of applicative Applicative

Are you tired of hearing about Functors? Well too bad! Because we're going to talk about them some more.

So anyway, what is a Functor really? 🤔

One way to think about it is that it represents a context. It could be the context of being in a sequence like an array. Or it could be the context of something that will happen in the future like a Promise. Whatever the context, how do you get a value into it?

Let's look at a couple of contexts that we know like Array and Promise. How would we get the value "Hello My Friend-o" into each of these contexts? Well, what does "Hello My Friend-o" look like as an Array. How about ["Hello My Friend-o"]? That works. We did the smallest thing we could possibly to take the value and put it into the context of an array.

What's the smallest amount of work that we could do and put that value into a Promise? The answer is.............

Promise.resolve("Hello My Friend-o");

And in fact, both of these techniques, though seemingly different really just the same thing: taking a value and putting into the smallest possible or "purest" context that you can. That's what pure is all about:

pure(Array, "Hello My Friend-o") //=> ["Hello My Friend-o"]
pure(Promise, "Hello My Friend-o") //=> Promise {<resolved>: "Hello My Friend-o"}

By contrast, the Array ["Hello My Friend-o", "Hey what's this!!! How the heck did this get in here?"] is an example of something that is not the purest array context for "Hello My Friend-o".

Monad

Monads are a tad controversial, yes? I think that this might well be because we try to come at them without a full appreciation and understanding of Functors first, and it turns out that that's just silly! So if you don't feel completely comfortable with Functors, I recommend you go read up on them first, then work with them a little bit and then come back here when you've got your head fully inside and around them. Because the simple fact is that Monad's just aren't going to make much sense unless you understand Functor first.

So if you need to go back and learn Functor's first, go for it! Monad's will still be waiting for you right here when you get back. It might take 2 hours, two weeks, or two years.

Ok.

The basic Functor method map lets you transform values, but not the enclosing context. In many ways that's the power of mapping. You're freed from thinking of the context because it remains constant. If you map an Array of 10 elements the result will also have 10 elements. If you map a Promise that resolves at 5:15pm and 3 seconds, the result will also resolve at 5:15pm and 3 seconds.

But sometimes that's not enough. Sometimes you want to change the structure of the Array, or change the timing of the Promise. To unlock this power, you need Monad.

Unlike map, the flatMap method of the Monad type class lets you actually modify the context. So you can do things like generate an Array of a different length than the original, or generate a Promise that resolves at a different time than the original.

Whereas map takes a function that converts a value into another value, flatMap takes a function that converts a value into a new value _in a new context. It then takes that new context and combines it in some way with the existing context. Of course what "combine" means is up to the specific Monad.

flatMap(fn, monad)

Create a new Monad by applying the function fn to the content of monad. fn is expected to return a new monadic context which is then "flattened" into the original context.

What does this look like? Well, here's a monad that you use all the time, but might not know it: Promise.

Have you ever returned a new Promise from within a Promise#then? Well if you have, then you're already well versed in monadic operations because that's exactly the kind of thing you would do with a flatMap.

let user = $.get('/');
 
flatMap(user => $.get(`/wingdings/${user.wingdingId}`))
  .then(wingding => console.log(wingding));

Notice how the flat mapping function takes the value contained within the Promise, but then returns a completely new promise. That's how flatMap allows you to actually change the context.

Foldable

Functors are all about changing the values contained within a structure while at the same time preserving the shape of the structure itself. When we map an array of 10 items, you get an array of 10 items.

Foldable structures are ones where you can consider all of the values it contains and use them to compute a single value. You may have heard of this before if you're familiar with OO design patterns as "visiting". You have a visitor that is passed each value in the collection, and then updates an accumulated value it maintains. Once the visitor has visited every value, the final accumulated value is your result.

Foldable is the same way, Starting with an initial value, you "visit" each piece of data inside of a structure, and then incorporate it into the final value. For many Foldables there is a difference between folding from "left" and folding from the "right", although much of the time you don't need to consider this.

foldl(fn, initial, foldable)

Compute a single value, starting with initial from all of the values contained within foldable by applying fn to each element it contains starting with the "left most" value. For a discussion of "left most" see Right vs Left

For each element in foldable, fn is called with an accumulator value (memo) and the element. The result of this call becomes the new value for the memo. After all elements have been been folded in this way, the final value of the memo is the return value of the fold.

Let's turn to our workhorse friend the Array for a great example of Foldable that's worth a thousand words.

import { foldl } from 'funcadelic';
 
foldl((sum, i) => sum + i, 0, [1,2,3,4]) //=> 10
foldl((sum, i) => sum - i, 0, [1,2,3,4]) //=> -10

If this looks familiar, it is. Folding is exactly what happens when you use Array.prototype.reduce

foldr(fn, initial, foldable)

Compute a single value, starting with initial from all of the values contained within foldable by applying fn to each element it contains starting with the "right most" value. For a discussion of "right most" see Right vs Left

For each element in foldable, fn is called with an accumulator value (memo) and the element. The result of this call becomes the new value for the memo. After all elements have been been folded in this way, the final value of the memo is the return value of the fold.

Like our friend foldl, Array gives a great demonstration of foldr.

import { foldr } from 'funcadelic';
 
foldr((sum, i) => sum + i, 0, [1,2,3,4]) //=> 10
foldr((sum, i) => sum - i, 0, [1,2,3,4]) //=> -10

In this case, foldr is roughly equivalent to Array.prototype.reduceRight

Right vs Left

What "right" and "left" means is a bit mushy and varies from Foldable to Foldable. In an array it means starting from the first value and ending with the last and vice-versa.

In a tree structure, it might be the difference between starting with the leaf nodes instead of starting with the root and moving downward.

For the most part, you can ignore the difference in a language like JavaScript with eager evaluation. It's a pretty advanced topic, and if you find yourself coming up against problems and wondering which to use and this documentation doesn't help. Shoot me a line and we can talk it over about how to improve it. When in doubt, foldl.

Chaining API

Funcadelic is great for composition, but because of JavaScripts syntax composing a sequence of operations can be awkward. For example, suppose you have some variable start that you want to map over, then filter, then append to, and then map again. You'd have to write something like:

let result = map(f2, append(filter(predicate, map(f1, start))))

Picking apart those expressions and divining the intent is a difficult task even for the most experienced set of eyes.

With the chain api, you can rewrite the above as:

import { chain as $ } from 'funcadelic';
 
$(start)
  .map(f1)
  .filter(predicate)
  .append(something)
  .map(f2)
  .valueOf();

Which very clearly describes the sequence of operations.

Each method invocation takes the current value of the chain, applies the transformation using the current value as the last argument, and then returns a new chain contain the result of that operation.

Notice how we had to call valueOf() as the very last step in our chain? This takes the current value of the chain and returns it.

Pro tip: Chaining is lazy, so not only do we need to call valueOf() to get the final result from the chain, but also nothing actually happens at all until we do!

Compatibility

Funcadelic uses Object.getOwnPropertyDescriptors which is not supported by all environments, namely <IE11 and <Node 6. You may consider using a polyfill if you require support for these environments.

Funcadelic uses Function.name to determine the name of the constructor when creating typeclasses. Unfortunately, IE11 does not support retrieving the name of a function. You may use Function.name polyfill to make this feature available in IE11.

Development

$ yarn
$ yarn test