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A **functor data type** is something you can map over. It’s a container which has an interface which can be used to apply a function to the values inside it. When you see a functor, you should think *“mappable”.* Functor types are typically represented as an object with a `.map()`

method that maps from inputs to outputs while preserving structure. In practice, “preserving structure” means that the return value is the same type of functor (though values inside the container may be a different type).

A functor supplies a box with zero or more things inside, and a mapping interface. An array is a good example of a functor, but many other kinds of objects can be mapped over as well, including promises, streams, trees, objects, etc. JavaScript’s built in array and promise objects act like functors. For collections (arrays, streams, etc.), `.map()`

typically iterates over the collection and applies the given function to each value in the collection, but not all functors iterate. Functors are really about applying a function in a specific context.

Promises use the name `.then()`

instead of `.map()`

. You can usually think of `.then()`

as an asynchronous `.map()`

method, except when you have a nested promise, in which case it automatically unwraps the outer promise. Again, for values which are not promises, `.then()`

acts like an asynchronous `.map()`

. For values which are promises themselves, `.then()`

acts like the `.flatMap()`

method from monads (sometimes also called `.chain()`

). So, promises are not quite functors, and not quite monads, but in practice, you can usually treat them as either. Don't worry about what monads are, yet. Monads are a kind of functor, so you need to learn functors first.

Lots of libraries exist that will turn a variety of other things into functors, too.

In Haskell, the functor type is defined as:

`fmap :: (a -> b) -> f a -> f b`

Given a function that takes an `a`

and returns a `b`

and a functor with zero or more `a`

s inside it: `fmap`

returns a box with zero or more `b`

s inside it. The `f a`

and `f b`

bits can be read as “a functor of `a`

” and “a functor of `b`

”, meaning `f a`

has `a`

s inside the box, and `f b`

has `b`

s inside the box.

Using a functor is easy — just call `map()`

:

`const f = [1, 2, 3];`

f.map(double); // [2, 4, 6]

# Functor Laws

Categories have two important properties:

- Identity
- Composition

Since a functor is a mapping between categories, functors must respect identity and composition. Together, they’re known as the functor laws.

# Identity

If you pass the identity function (`x => x`

) into `f.map()`

, where `f`

is any functor, the result should be equivalent to (have the same meaning as) `f`

:

`const f = [1, 2, 3];`

f.map(x => x); // [1, 2, 3]

# Composition

Functors must obey the composition law: `F.map(x => f(g(x)))`

is equivalent to `F.map(g).map(f)`

.

Function Composition is the application of one function to the result of another, e.g., given an `x`

and the functions, `f`

and `g`

, the composition `(f ∘ g)(x)`

(usually shortened to `f ∘ g`

- the `(x)`

is implied) means `f(g(x))`

.

A lot of functional programming terms come from category theory, and the essence of category theory is composition. Category theory is scary at first, but easy. Like jumping off a diving board or riding a roller coaster. Here’s the foundation of category theory in a few bullet points:

- A category is a collection of objects and arrows between objects (where “object” can mean literally anything).
- Arrows are known as morphisms. Morphisms can be thought of and represented in code as functions.
- For any group of connected objects,
`a -> b -> c`

, there must be a composition which goes directly from`a -> c`

. - All arrows can be represented as compositions (even if it’s just a composition with the object’s identity arrow). All objects in a category have identity arrows.

Say you have a function `g`

that takes an `a`

and returns a `b`

, and another function `f`

that takes a `b`

and returns a `c`

; there must also be a function `h`

that represents the composition of `f`

and `g`

. So, the composition from `a -> c`

, is the composition `f ∘ g`

(`f`

*after* `g`

). So, `h(x) = f(g(x))`

. Function composition works right to left, not left to right, which is why `f ∘ g`

is frequently called `f`

*after* `g`

.

Composition is associative. Basically that means that when you’re composing multiple functions (morphisms if you’re feeling fancy), you don’t need parenthesis:

`h∘(g∘f) = (h∘g)∘f = h∘g∘f`

Let’s take another look at the composition law in JavaScript:

Given a functor, `F`

:

`const F = [1, 2, 3];`

The following are equivalent:

F.map(x => f(g(x)));// is equivalent to...F.map(g).map(f);

# Endofunctors

An endofunctor is a functor that maps from a category back to the same category.

A functor can map from category to category: `X -> Y`

An endofunctor maps from a category to the same category: `X -> X`

A monad is an endofunctor. Remember:

“A monad is just a monoid in the category of endofunctors. What’s the problem?”

Hopefully that quote is starting to make a little more sense. We’ll get to monoids and monads later.

# Build Your Own Functor

Here’s a simple example of a functor:

`const Identity = value => ({`

map: fn => Identity(fn(value))

});

As you can see, it satisfies the functor laws:

// trace() is a utility to let you easily inspect

// the contents.

const trace = x => {

console.log(x);

return x;

};const u = Identity(2);// Identity law

u.map(trace); // 2

u.map(x => x).map(trace); // 2const f = n => n + 1;

const g = n => n * 2;// Composition law

const r1 = u.map(x => f(g(x)));

const r2 = u.map(g).map(f);r1.map(trace); // 5

r2.map(trace); // 5

Now you can map over any data type, just like you can map over an array. Nice!

That’s about as simple as a functor can get in JavaScript, but it’s missing some features we expect from data types in JavaScript. Let’s add them. Wouldn’t it be cool if the `+`

operator could work for number and string values?

To make that work, all we need to do is implement `.valueOf()`

-- which also seems like a convenient way to unwrap the value from the functor:

const Identity = value => ({

map: fn => Identity(fn(value)), valueOf: () => value,

});const ints = (Identity(2) + Identity(4));

trace(ints); // 6const hi = (Identity('h') + Identity('i'));

trace(hi); // "hi"

Nice. But what if we want to inspect an `Identity`

instance in the console? It would be cool if it would say `"Identity(value)"`

, right. Let's add a `.toString()`

method:

`toString: () => `Identity(${value})`,`

Cool. We should probably also enable the standard JS iteration protocol. We can do that by adding a custom iterator:

`[Symbol.iterator]: function* () {`

yield value;

}

Now this will work:

`// [Symbol.iterator] enables standard JS iterations:`

const arr = [6, 7, ...Identity(8)];

trace(arr); // [6, 7, 8]

What if you want to take an `Identity(n)`

and return an array of Identities containing `n + 1`

, `n + 2`

, and so on? Easy, right?

`const fRange = (`

start,

end

) => Array.from(

{ length: end - start + 1 },

(x, i) => Identity(i + start)

);

Ah, but what if you want this to work with any functor? What if we had a spec that said that each instance of a data type must have a reference to its constructor? Then you could do this:

const fRange = (

start,

end

) => Array.from(

{ length: end - start + 1 },

// change `Identity` to `start.constructor`

(x, i) => start.constructor(i + start)

);const range = fRange(Identity(2), 4);

range.map(x => x.map(trace)); // 2, 3, 4

What if you want to test to see if a value is a functor? We could add a static method on `Identity`

to check. We should throw in a static `.toString()`

while we're at it:

`Object.assign(Identity, {`

toString: () => 'Identity',

is: x => typeof x.map === 'function'

});

Let’s put all this together:

const Identity = value => ({

map: fn => Identity(fn(value)),

valueOf: () => value,

toString: () => `Identity(${value})`,

[Symbol.iterator]: function* () {

yield value;

},

constructor: Identity

});Object.assign(Identity, {

toString: () => 'Identity',

is: x => typeof x.map === 'function'

});

Note you don’t need all this extra stuff for something to qualify as a functor or an endofunctor. It’s strictly for convenience. All you *need* for a functor is a `.map()`

interface that satisfies the functor laws.

# Why Functors?

Functors are great for lots of reasons. Most importantly, they’re an abstraction that you can use to implement lots of useful things in a way that works with any data type. For instance, what if you want to kick off a chain of operations, but only if the value inside the functor is not `undefined`

or `null`

?

// Create the predicate

const exists = x => (x.valueOf() !== undefined && x.valueOf() !== null);const ifExists = x => ({

map: fn => exists(x) ? x.map(fn) : x

});const add1 = n => n + 1;

const double = n => n * 2;// Nothing happens...

ifExists(Identity(undefined)).map(trace);

// Still nothing...

ifExists(Identity(null)).map(trace);// 42

ifExists(Identity(20))

.map(add1)

.map(double)

.map(trace)

;

Of course, functional programming is all about composing tiny functions to create higher level abstractions. What if you want a generic map that works with any functor? That way you can partially apply arguments to create new functions.

Easy. Pick your favorite auto-curry, or use this magic spell from before:

`const curry = (`

f, arr = []

) => (...args) => (

a => a.length === f.length ?

f(...a) :

curry(f, a)

)([...arr, ...args]);

Now we can customize map:

const map = curry((fn, F) => F.map(fn));const double = n => n * 2;const mdouble = map(double);

mdouble(Identity(4)).map(trace); // 8

# Conclusion

Functors are things we can map over. More specifically, a functor is a mapping from category to category. A functor can even map from a category back to the same category (i.e., an *endofunctor*).

A category is a collection of objects, with arrows between objects. Arrows represent morphisms (aka functions, aka compositions). Each object in a category has an identity morphism (`x => x`

). For any chain of objects `A -> B -> C`

there must exist a composition `A -> C`

.

Functors are great higher-order abstractions that allow you to create a variety of generic functions that will work for any data type.

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