In the last article we discussed the basics of Functions. We saw some definitions and examples of Higher-Order Functions. But that might have left you high and dry.

You could be wondering,

How is any of this terminology useful to me?

Sure, now I know what functions are. How do I use them?

All these are very valid responses to that article, I didn't cover any day-to-day uses for Higher-Order Functions (the article was already getting too long).

So, in this article, we will try to use some common higher-order functions. Namely, map, filter, and fold(reduce).

A little refresher

A function is a mapping between some input and some output

A Higher-Order function is a function that maps between function(s) and data (as either input and/or output)

Let's get to it!

map

We'll get right to the definition.

According to wikipedia (and most literature),

map is the name of a higher-order function that applies a given function to each element of a functor

You might be cursing and saying

"What the F@#$ is a functor?"

Let's ignore that for now, and try to define map in a way that sounds (a bit more) human,

map is a function that takes a container data structure and applies a function to each value and creates a new container with the results of said application

Or,

map is a thing that takes a collection of things and gives you a new collection of things by applying a function over each thing

Notice how I'm trying to avoid naming any data structures?

That's partially to not piss of the FP neckbeards and the Lambda gods, but also to make it clear that map can be implemented in any data structure*. Like most FP concepts, it's very abstract and can be applied to a whole grocery list of things.

JavaScript only implements map (natively) in only one Data Structure, Array. It's implemented as a function on the Array prototype. But, it doesn't have to be tied down to Arrays (😉)

NOTE: If you are already familiar with Array.prototype.map, try not to think of it as a 1:1 implementation of the concept of map

Let's look at an example of using JavaScript's map.

let fruits = ["apple", "banana", "carrot"] // The collection

let firstLetter = str => str[0] // Our transformation

let firstLetters = fruits.map(firstLetter) // The new collection.
// => ['a', 'b', 'c']

So, what's happening here?

Let's start from the top, we defined an array named fruits and stored a few strings in it.

Next, we defined a function named firstLetter that takes a string input and return's its first character.

Then, we make a call to Array.prototype.map by invoking fruits.map with the argument firstLetter. What this is doing is, telling the map function to iterate over every element contained by fruits and apply firstLetter to each element, store the results in a new array, and then return the new resulting array. This return value is what we assign to firstLetters.

Illustration adapted from John Ferris' article

Note: Libraries like Ramda(seriously awesome, check it out) allow you to map over additional data structures such as objects. Let's try to implement a map (using mutable code) that works for both containers (object & array).

let map = function (func, ftor) {
  let result
  try {
    result = ftor.constructor()
  } catch (e) {
    result = {} // Some exotic container given, degrade to Object
  }
  for (let k in ftor)
    result[k] = func(ftor[k])
  return result
}

With this map there's a bunch of different things happening, but keep in mind that for an ordinary array, its functionally same.

Lets try to break it down,

Arguments: this function takes two arguments, func and ftor. As the name might imply, func is our function (the transformation). ftor might seem like a weird name for the second argument, this argument is your data structure (array, object, etc.).

So why is it called ftor and not something like data or array? Remember that word we used in the first definition (from Wikipedia)? Yeah Functor, ftor is my way of writing functor. A Functor is basically any data structure that you can map over*. So, in our case Object and Array (and potentially other data structures that store key->value) are both Functors from the perspective of our map function even though natively, only Arrays might be considered Functors.

Congrats! Now you know another FP buzzword/jargon.

Line 8-9: here, we are iterating through the keys of the container (indices in case of arrays) and applying the function func to each value and associating it with the same key in the resulting container.

Result: this function returns a container of the same type as the functor (by calling its constructor), in cases where it fails, I've decided to degrade down to a plain object.

Usage

This comes in handy when you need to make a collection of things from an existing collection by transforming each value.

filter

Once again, here goes the wikipedia definition

filter is a higher-order function that processes a data structure (usually a list) in some order to produce a new data structure containing exactly those elements of the original data structure for which a given predicate returns the boolean value true

This time, I think the wiki definition is very expressive. Put differently,

filter is a function that goes through a Filterable collection and makes a new collection that contains only the values for which the predicate returns true

That might sound like a mouthful, but the concept is simple (you will see for yourself once we go through an example).

Once again, JS has a native implementation of filter, but only in Arrays. Same as map, its implemented in the Array prototype. But it could be used with any **Filterable* data structure.*

Similar to Functors, a Filterable is a data structure that you can filter. Most Functors tend to be Filterable as well, but there is no guarantee that a Functor is Filterable. If we assume that Array.prototype.filter* is the only possible implementation of filter, then only Arrays can be considered Filterable. But, because we can write a function that can filter objects, we can consider plain JS objects as Filterable too.

Now you know another Category of data structures.

NOTE: It should go without saying, Array.prototype.filter may/may not be 1:1 with the spec of filter

Let's look at an example of JavaScript's filter.

// The collection
let fruits = ["apple", "orange", "banana"]

// The predicate (a function that returns either true or false)
let isCitrus = fruit => /lemon|lime|orange|grapefruit/i.test(fruit)

// The new collection
let citrusFruits = fruits.filter(isCitrus)

Let's start from the top, we defined an array named fruits and stored a few strings in it (same as our map example).

Next, we defined a function named isCitrus that takes a string input and checks it against a regular expression and returns either true or false.

Then, we make a call to Array.prototype.filter by invoking fruits.filter with the argument isCitrus. What this does is, tell the filter function to iterate over every element contained by fruits and call isCitrus with each element as argument, if isCitrus returns true that elements is kept, otherwise the element is skipped over and the next element is checked. This process is repeated for all the elements of the array. An array is constructed containing only the elements for which isCitrus returned true.

Illustration adapted from John Ferris' article

Let's try to implement a filter (using mutable code) that works for different containers (object & array).

let filter = function (predicate, filterable) {
  let result
  try {
    result = filterable.constructor()
  } catch (e) {
    console.warn('Error on trying to call ', filterable.constructor, e)
    result = {}
  }
  let arrKey = 0;
  let isArray = Array.isArray(filterable)
  for (let key in filterable) {
    if (predicate(filterable[key])) {
      let newKey = isArray ? arrKey++ : key;
      result[newKey] = filterable[key]
    }
  }
  return result
}

With this filter there's a bunch of different things happening, but keep in mind that for an ordinary array, its functionally the same as calling Array.protorype.filter.

Lets try to break it down,

Arguments: this function takes two arguments, predicate and filterable. As the name might imply, predicate is our predicate (a function that takes a value and returns either true or false). The argument filterable is your data structure (array, object, etc.).

Line 11-16: here, we are iterating through the keys of the container (indices in case of arrays) and checking if the predicate returns true for a particular value. If a value does return true, we are keeping it in the result container.

Result: this function returns a container of the same type as the filterable (by calling its constructor), in cases where it fails, I've decided to degrade down to a plain object.

Usage

This comes in handy when you need to make a collection of things from an existing collection by keeping values that meet a certain criteria.

fold (reduce)

You know the drill, wiki first

fold (also termed reduce...) are ... functions that analyze a recursive data structure and through use of a given combining operation, recombine the results of recursively processing its constituent parts, building up a return value.

Lots of stuff to unpack there, but lets try to get to the gist of it

fold is a function that goes trough a Foldable collection and accumulates a value using an accumulating function and then finally returns the accumulated value

To a shock to nobody, JavaScript has a native implementation of fold as well, its named Array.prototype.reduce. Once again we have to make the note that JS's reduce/fold can only fold arrays, but it doesn't have to be tied down to just JS arrays. A fold can be implemented for any data structure that can be classified as Foldable.

A data type is Foldable if we can implement some form of fold for it. But due to the nature of the fold operation, Foldables are usually list(array)-like or have a valid list-representation. While not strictly required, most Foldables tend to be Functor and Filterable as well. This is because both map and filter can be implemented using fold.

Another note, there are many variations of fold out there. The essential functionality is the same but some implementation details change the nature and name of the fold. In this article we will look at a left-fold as the native reduce method is a left-fold. This fold is also called foldl or fold_left in some languages and libraries.

Once again, disclaimer: JS reduce may not follow the spec of fold 100%.

Let's try using Array.prototype.reduce to do something.

// The collection
let fruits = ["apple", "banana", "orange"]

// The accumulating function
let makeSalad = (salad, fruit) => `${fruit}-${salad}`

// Inital Salad
let saladStarter = "salad"

// The Result
let salad = fruits.reduce(makeSalad, saladStarter) //=> orange-banana-apple-salad

Let's start from the top again, we defined an array named fruits and stored a few strings in it.

Next, we defined a function named makeSalad that takes two strings and returns a string by concatenating them.

We then define another variables, this time its a string named saladStarter.

Then, we make a call to Array.prototype.reduce by invoking fruits.reduce with the arguments makeSalad and saladStarter. What this does is, it tells the fold function to iterate over every element contained in fruits and call makeSalad with an accumulated value and an element from fruits. For the first iteration, there is no accumulated value, so makeSalad is called with saladStarter as the accumulated value. For every subsequent iteration, makeSalad is called with the return value of the previous iteration as the accumulated value and the next item in the array. This process is continued until makeSalad has been called with the accumulated value from its previous iteration and the last item in fruits. Finally the return value from the final call is passed on as the return value for the reduce call and stored in the variable named salad.

Illustration adapted from John Ferris' article

Let's try to implement a fold of our own. Using mutable and imperative code, of course.

let fold_left = function (folding_fn, inital_value, foldable) {
  let accumulated = inital_value
  for (let key in foldable) {
    accumulated = folding_fn(accumulated, foldable[key])
  }
  return accumulated
}

You might be thinking...

What? Where is all the code?

Folds are notoriously very simple to implement, but they are so useful that you'll find yourself wondering why more people don't use them.

I think its pretty obvious how this function works, so I won't bore you with the explanation. Let's instead come back to our claim that we can usually map and filter using a fold.

map

// le folded map
let map = (fn, ftr) => fold_left((acc, val) => acc.concat(fn(val)), ftr.constructor(), ftr)

Yeah, this code is not very readable, but its not meant to be. This is a one-liner that shows a very simple implementation of map using fold. It works because fold carries the return value from the accumulating function on to the next iteration, allowing us to successively construct a larger list of values resulting from applying fn to val. Try and tinker with it a little bit, and I have faith that you will figure it out.

On to the next one...

filter

// le folded filter
let filter = (pred, flt) => fold_left((acc, val) => pred(val) ? acc.concat(val) : acc, flt.constructor, flt)

Once again, this is a one-liner. This follows the same principle as map, except we are only concatenating to the list if the predicate is satisfied by the value (i.e., pred(val) returns true).

Usage

Folds should come in handy when you need to,

• Iterate over a list and carry over a value to the next iteration
• Fold a list onto itself to arrive at a single value
• Transform a list to a single value (even if the resulting value is of a completely different type, like transforming the items of a list to be items of a Map or a Set)

Appendix / Additional Links

I talk briefly about a few Categories of data types. If you wanna look at more of these categories, take a look at the wonderful fantasy-land specifications that defines Algebraic Data Types in terminology we JS devs can understand.

Also check out the amazing ramda library for more useful functional utilities like performant and curried implementations of map, filter, reduce or even helper functions that help you easily combine these operations into a transducer (more on this in a later article).

If you are even slightly mathematically minded, lookup Category theory as well as Algebraic Data Types. These are wonderful topics to study regardless, but they also help us understand the world of FP even better.

That's all for today, folks.

Peace ✌️