Functional Programming (FP) is a programming paradigm with some particular techniques.

In programming languages, you’ll find purely functional programming languages as well as programming languages that support functional programming techniques.

Haskell, Clojure and Scala are some of the most popular purely functional programming languages.

Popular programming languages that support functional programming techniques are JavaScript, Python, Ruby and many others.

First class functions

In a functional programming language, functions are first class citizens.

They can be assigned to variables:

const f = (m) => console.log(m)
f('Test')

Since a function is assignable to a variable, they can be added to objects:

const obj = {
  f(m) {
    console.log(m)
  }
}
obj.f('Test')

They can be used as an argument to other functions:

const f = (m) => () => console.log(m)
const f2 = (f3) => f3()
f2(f('Test'))

They can be returned by functions:

const createF = () => {
  return (m) => console.log(m)
}
const f = createF()
f('Test')

Higher Order Functions

Functions that accept functions as arguments or return functions are called Higher Order Functions.

Examples in the JavaScript standard library include Array.map(), Array.filter() and Array.reduce().

Declarative programming

The opposite of declarative is imperative.

An imperative approach is when you tell the machine (in general terms), the steps it needs to take to get a job done.

A declarative approach is when you tell the machine what you need to do, and you let it figure out the details.

For example a declarative programming approach is to avoid using loops and instead use functional programming constructs like map, reduce and filter, because your programs are more abstract and less focused on telling the machine each step of processing.

Immutability

In functional programming data never changes. Data is immutable.

A variable can never be changed. To update its value, you create a new variable.

Instead of changing an array, to add a new item you create a new array by concatenating the old array, plus the new item.

An object is never updated, but copied before changing it.

This is why the ES2015 const is so widely used in modern JavaScript, which embraces functional programming concepts: to enforce immutability on variables.

ES2015 also gave us Object.assign(), which is key to creating objects:

const redObj = { color: 'red' }
const yellowObj = Object.assign({}, redObj, {color: 'yellow'})

To append an item to an array in JavaScript we generally use the push() method on an array, but that method mutates the original array, so it’s not FP-ready.

We instead use the concat() method:

const a = [1, 2]
const b = [1, 2].concat(3)
// b = [1, 2, 3]

or we use the spread operator:

const c = [...a, 3]
// c = [1, 2, 3]

Purity

A pure function:

• never changes any of the parameters that get passed to it by reference (in JS, objects and arrays): they should be considered immutable. It can of course change any parameter copied by value
• the return value of a pure function is not influenced by anything else than its input parameters: passing the same parameters always result in the same output
• during its execution, a pure function does not change anything outside of it

Data Transformations

Since immutability is such an important concept and a foundation of functional programming, you might ask how can data change.

Simple: data is changed by creating copies.

Functions, in particular, change the data by returning new copies of data.

Core functions that do this are map and reduce.

Array.map() on an array will create a new array with the result of a function executed on every item of the original array:

const a = [1, 2, 3]
const b = a.map((v, k) => v * k)
// b = [0, 2, 6]

Array.reduce() on an array allows us to transform that array on anything else, including a scalar, a function, a boolean, an object:

const a = [1, 2, 3]
const sum = a.reduce((partial, v) => partial + v, 0)
// sum = 6

Recursion

Recursion is a key topic in functional programming. When a function calls itself, it’s called a recursive function.

The classic example of recursion is the Fibonacci sequence (N = (N-1 + N-2)) calculation:

var f = (n) => n <= 1 ? 1 : f(n-1) + f(n-2)

Composition

Composition is another key topic of Functional Programming.

Composition is how we generate a higher order function, by combining simpler functions.

A very common way to compose functions in plain JavaScript is to chain them:

obj.doSomething()
   .doSomethingElse()

or, also very widely used, by passing a function execution into a function:

obj.doSomething(doThis())

More generally, composing is the act of putting together a list of many functions to perform a more complicated operation.

lodash/fp comes with an implementation of compose: we execute a list of functions, starting with an argument, each function inherits the argument from the preceding function return value.

import { compose } from 'lodash/fp'

const slugify = compose(
  encodeURIComponent,
  join('-'),
  map(toLowerCase),
  split(' ')
)

slugify('Hello World') // hello-world

Lessons in this unit:

0:	Introduction
1:	Generators
2:	Symbols
3:	Proxy objects
4:	Memoization
5:	▶︎ Functional programming
6:	Dynamic imports