Jargon from the functional programming world in simple terms!

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Functional programming (FP) provides many advantages, and its popularity has been increasing as a result. However, each programming paradigm comes with its own unique jargon and FP is no exception. By providing a glossary, we hope to make learning FP easier.

Examples are presented in JavaScript (ES2015). Why JavaScript?

Where applicable, this document uses terms defined in the Fantasy Land spec

**Translations*** Portuguese* Spanish* Chinese* Bahasa Indonesia* Python World* Scala World* Rust World* Korean* Haskell Turkish

**Table of Contents**<!-- RM(noparent,notop) -->

- Arity
- Higher-Order Functions (HOF)
- Closure
- Partial Application
- Currying
- Auto Currying
- Function Composition
- Continuation
- Purity
- Side effects
- Idempotent
- Point-Free Style
- Predicate
- Contracts
- Category
- Value
- Constant
- Functor
- Pointed Functor
- Lift
- Referential Transparency
- Equational Reasoning
- Lambda
- Lambda Calculus
- Lazy evaluation
- Monoid
- Monad
- Comonad
- Applicative Functor
- Morphism
- Setoid
- Semigroup
- Foldable
- Lens
- Type Signatures
- Algebraic data type
- Option
- Function
- Partial function
- Functional Programming Libraries in JavaScript
## Arity

The number of arguments a function takes. From words like unary, binary, ternary, etc. This word has the distinction of being composed of two suffixes, "-ary" and "-ity." Addition, for example, takes two arguments, and so it is defined as a binary function or a function with an arity of two. Such a function may sometimes be called "dyadic" by people who prefer Greek roots to Latin. Likewise, a function that takes a variable number of arguments is called "variadic," whereas a binary function must be given two and only two arguments, currying and partial application notwithstanding (see below).

`const sum = (a, b) =\> a + b const arity = sum.length console.log(arity) // 2 // The arity of sum is 2`

A function which takes a function as an argument and/or returns a function.

`const filter = (predicate, xs) =\> xs.filter(predicate)`

`const is = (type) =\> (x) =\> Object(x) instanceof type`

`filter(is(Number), [0, '1', 2, null]) // [0, 2]`

A closure is a way of accessing a variable outside its scope. Formally, a closure is a technique for implementing lexically scoped named binding. It is a way of storing a function with an environment.

A closure is a scope which captures local variables of a function for access even after the execution has moved out of the block in which it is defined. ie. they allow referencing a scope after the block in which the variables were declared has finished executing.

`const addTo = x =\> y =\> x + y; var addToFive = addTo(5); addToFive(3); //returns 8`

The function

`addTo()`

returns a function(internally called

`add()`

), lets store it in a variable called

`addToFive`

with a curried call having parameter 5.

Ideally, when the function

`addTo`

finishes execution, its scope, with local variables add, x, y should not be accessible. But, it returns 8 on calling

`addToFive()`

. This means that the state of the function

`addTo`

is saved even after the block of code has finished executing, otherwise there is no way of knowing that

`addTo`

was called as

`addTo(5)`

and the value of x was set to 5.

Lexical scoping is the reason why it is able to find the values of x and add - the private variables of the parent which has finished executing. This value is called a Closure.

The stack along with the lexical scope of the function is stored in form of reference to the parent. This prevents the closure and the underlying variables from being garbage collected(since there is at least one live reference to it).

Lambda Vs Closure: A lambda is essentially a function that is defined inline rather than the standard method of declaring functions. Lambdas can frequently be passed around as objects.

A closure is a function that encloses its surrounding state by referencing fields external to its body. The enclosed state remains across invocations of the closure.

**Further reading/Sources*** Lambda Vs Closure* JavaScript Closures highly voted discussion

Partially applying a function means creating a new function by pre-filling some of the arguments to the original function.

`// Helper to create partially applied functions // Takes a function and some arguments const partial = (f, ...args) =\> // returns a function that takes the rest of the arguments (...moreArgs) =\> // and calls the original function with all of them f(...args, ...moreArgs) // Something to apply const add3 = (a, b, c) =\> a + b + c // Partially applying `2` and `3` to `add3` gives you a one-argument function const fivePlus = partial(add3, 2, 3) // (c) =\> 2 + 3 + c fivePlus(4) // 9`

You can also use

`Function.prototype.bind`

to partially apply a function in JS:

`const add1More = add3.bind(null, 2, 3) // (c) =\> 2 + 3 + c`

Partial application helps create simpler functions from more complex ones by baking in data when you have it. Curried functions are automatically partially applied.

The process of converting a function that takes multiple arguments into a function that takes them one at a time.

Each time the function is called it only accepts one argument and returns a function that takes one argument until all arguments are passed.

`const sum = (a, b) =\> a + b const curriedSum = (a) =\> (b) =\> a + b curriedSum(40)(2) // 42. const add2 = curriedSum(2) // (b) =\> 2 + b add2(10) // 12`

Transforming a function that takes multiple arguments into one that if given less than its correct number of arguments returns a function that takes the rest. When the function gets the correct number of arguments it is then evaluated.

lodash & Ramda have a

`curry`

function that works this way.

`const add = (x, y) =\> x + y const curriedAdd = \_.curry(add) curriedAdd(1, 2) // 3 curriedAdd(1) // (y) =\> 1 + y curriedAdd(1)(2) // 3`

**Further reading*** Favoring Curry* Hey Underscore, You're Doing It Wrong!

The act of putting two functions together to form a third function where the output of one function is the input of the other.

`const compose = (f, g) =\> (a) =\> f(g(a)) // Definition const floorAndToString = compose((val) =\> val.toString(), Math.floor) // Usage floorAndToString(121.212121) // '121'`

At any given point in a program, the part of the code that's yet to be executed is known as a continuation.

`const printAsString = (num) =\> console.log(`Given ${num}`) const addOneAndContinue = (num, cc) =\> { const result = num + 1 cc(result) } addOneAndContinue(2, printAsString) // 'Given 3'`

Continuations are often seen in asynchronous programming when the program needs to wait to receive data before it can continue. The response is often passed off to the rest of the program, which is the continuation, once it's been received.

`const continueProgramWith = (data) =\> { // Continues program with data } readFileAsync('path/to/file', (err, response) =\> { if (err) { // handle error return } continueProgramWith(response) })`

A function is pure if the return value is only determined by its input values, and does not produce side effects.

`const greet = (name) =\> `Hi, ${name}` greet('Brianne') // 'Hi, Brianne'`

As opposed to each of the following:

`window.name = 'Brianne' const greet = () =\> `Hi, ${window.name}` greet() // "Hi, Brianne"`

The above example's output is based on data stored outside of the function...

`let greeting const greet = (name) =\> { greeting = `Hi, ${name}` } greet('Brianne') greeting // "Hi, Brianne"`

... and this one modifies state outside of the function.

A function or expression is said to have a side effect if apart from returning a value, it interacts with (reads from or writes to) external mutable state.

`const differentEveryTime = new Date()`

`console.log('IO is a side effect!')`

A function is idempotent if reapplying it to its result does not produce a different result.

`f(f(x)) ≍ f(x)`

`Math.abs(Math.abs(10))`

`sort(sort(sort([2, 1])))`

Writing functions where the definition does not explicitly identify the arguments used. This style usually requires currying or other Higher-Order functions. A.K.A Tacit programming.

`// Given const map = (fn) =\> (list) =\> list.map(fn) const add = (a) =\> (b) =\> a + b // Then // Not points-free - `numbers` is an explicit argument const incrementAll = (numbers) =\> map(add(1))(numbers) // Points-free - The list is an implicit argument const incrementAll2 = map(add(1))`

`incrementAll`

identifies and uses the parameter

`numbers`

, so it is not points-free.

`incrementAll2`

is written just by combining functions and values, making no mention of its arguments. It **is** points-free.

Points-free function definitions look just like normal assignments without

`function`

or

`=\>`

.

A predicate is a function that returns true or false for a given value. A common use of a predicate is as the callback for array filter.

`const predicate = (a) =\> a \> 2 ;[1, 2, 3, 4].filter(predicate) // [3, 4]`

A contract specifies the obligations and guarantees of the behavior from a function or expression at runtime. This acts as a set of rules that are expected from the input and output of a function or expression, and errors are generally reported whenever a contract is violated.

`// Define our contract : int -\> boolean const contract = (input) =\> { if (typeof input === 'number') return true throw new Error('Contract violated: expected int -\> boolean') } const addOne = (num) =\> contract(num) && num + 1 addOne(2) // 3 addOne('some string') // Contract violated: expected int -\> boolean`

A category in category theory is a collection of objects and morphisms between them. In programming, typically types act as the objects and functions as morphisms.

To be a valid category 3 rules must be met:

- There must be an identity morphism that maps an object to itself. Where

is an object in some category, there must be a function from`a`

.`a -\> a`

- Morphisms must compose. Where

,`a`

, and`b`

are objects in some category, and`c`

is a morphism from`f`

, and`a -\> b`

is a morphism from`g`

;`b -\> c`

must be equivalent to`g(f(x))`

.`(g • f)(x)`

- Composition must be associative

is the same as`f • (g • h)`

`(f • g) • h`

Since these rules govern composition at very abstract level, category theory is great at uncovering new ways of composing things.

**Further reading**

Anything that can be assigned to a variable.

`5 Object.freeze({name: 'John', age: 30}) // The `freeze` function enforces immutability. ;(a) =\> a ;[1] undefined`

A variable that cannot be reassigned once defined.

`const five = 5 const john = Object.freeze({name: 'John', age: 30})`

Constants are referentially transparent. That is, they can be replaced with the values that they represent without affecting the result.

With the above two constants the following expression will always return

`true`

.

`john.age + five === ({name: 'John', age: 30}).age + (5)`

An object that implements a

`map`

function which, while running over each value in the object to produce a new object, adheres to two rules:

**Preserves identity**

`object.map(x =\> x) ≍ object`

**Composable**

`object.map(compose(f, g)) ≍ object.map(g).map(f)`

(

`f`

,

`g`

are arbitrary functions)

A common functor in JavaScript is

`Array`

since it abides to the two functor rules:

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

and

`const f = x =\> x + 1 const g = x =\> x \* 2 ;[1, 2, 3].map(x =\> f(g(x))) // = [3, 5, 7] ;[1, 2, 3].map(g).map(f) // = [3, 5, 7]`

An object with an

`of`

function that puts *any* single value into it.

ES2015 adds

`Array.of`

making arrays a pointed functor.

`Array.of(1) // [1]`

Lifting is when you take a value and put it into an object like a functor. If you lift a function into an Applicative Functor then you can make it work on values that are also in that functor.

Some implementations have a function called

`lift`

, or

`liftA2`

to make it easier to run functions on functors.

`const liftA2 = (f) =\> (a, b) =\> a.map(f).ap(b) // note it's `ap` and not `map`. const mult = a =\> b =\> a \* b const liftedMult = liftA2(mult) // this function now works on functors like array liftedMult([1, 2], [3]) // [3, 6] liftA2(a =\> b =\> a + b)([1, 2], [3, 4]) // [4, 5, 5, 6]`

Lifting a one-argument function and applying it does the same thing as

`map`

.

`const increment = (x) =\> x + 1 lift(increment)([2]) // [3] ;[2].map(increment) // [3]`

An expression that can be replaced with its value without changing the behavior of the program is said to be referentially transparent.

Say we have function greet:

`const greet = () =\> 'Hello World!'`

Any invocation of

`greet()`

can be replaced with

`Hello World!`

hence greet is referentially transparent.

When an application is composed of expressions and devoid of side effects, truths about the system can be derived from the parts.

An anonymous function that can be treated like a value.

`;(function (a) { return a + 1 }) ;(a) =\> a + 1`

Lambdas are often passed as arguments to Higher-Order functions.

`;[1, 2].map((a) =\> a + 1) // [2, 3]`

You can assign a lambda to a variable.

`const add1 = (a) =\> a + 1`

A branch of mathematics that uses functions to create a universal model of computation.

Lazy evaluation is a call-by-need evaluation mechanism that delays the evaluation of an expression until its value is needed. In functional languages, this allows for structures like infinite lists, which would not normally be available in an imperative language where the sequencing of commands is significant.

`const rand = function\*() { while (1 \< 2) { yield Math.random() } }`

`const randIter = rand() randIter.next() // Each execution gives a random value, expression is evaluated on need.`

An object with a function that "combines" that object with another of the same type.

One simple monoid is the addition of numbers:

`1 + 1 // 2`

In this case number is the object and

`+`

is the function.

An "identity" value must also exist that when combined with a value doesn't change it.

The identity value for addition is

`0`

.

`js 1 + 0 // 1`

It's also required that the grouping of operations will not affect the result (associativity):

`1 + (2 + 3) === (1 + 2) + 3 // true`

Array concatenation also forms a monoid:

`;[1, 2].concat([3, 4]) // [1, 2, 3, 4]`

The identity value is empty array

`[]`

`;[1, 2].concat([]) // [1, 2]`

If identity and compose functions are provided, functions themselves form a monoid:

`const identity = (a) =\> a const compose = (f, g) =\> (x) =\> f(g(x))`

`foo`

is any function that takes one argument.

`compose(foo, identity) ≍ compose(identity, foo) ≍ foo`

A monad is an object with [

`of`

](https://github.com/hemanth/functional-programming-jargon/blob/master/#pointed-functor) and

`chain`

functions.

`chain`

is like [

`map`

](https://github.com/hemanth/functional-programming-jargon/blob/master/#functor) except it un-nests the resulting nested object.

`// Implementation Array.prototype.chain = function (f) { return this.reduce((acc, it) =\> acc.concat(f(it)), []) } // Usage Array.of('cat,dog', 'fish,bird').chain((a) =\> a.split(',')) // ['cat', 'dog', 'fish', 'bird'] // Contrast to map Array.of('cat,dog', 'fish,bird').map((a) =\> a.split(',')) // [['cat', 'dog'], ['fish', 'bird']]`

`of`

is also known as

`return`

in other functional languages.

`chain`

is also known as

`flatmap`

and

`bind`

in other languages.

An object that has

`extract`

and

`extend`

functions.

`const CoIdentity = (v) =\> ({ val: v, extract () { return this.val }, extend (f) { return CoIdentity(f(this)) } })`

Extract takes a value out of a functor.

`CoIdentity(1).extract() // 1`

Extend runs a function on the comonad. The function should return the same type as the comonad.

`CoIdentity(1).extend((co) =\> co.extract() + 1) // CoIdentity(2)`

An applicative functor is an object with an

`ap`

function.

`ap`

applies a function in the object to a value in another object of the same type.

`// Implementation Array.prototype.ap = function (xs) { return this.reduce((acc, f) =\> acc.concat(xs.map(f)), []) } // Example usage ;[(a) =\> a + 1].ap([1]) // [2]`

This is useful if you have two objects and you want to apply a binary function to their contents.

`// Arrays that you want to combine const arg1 = [1, 3] const arg2 = [4, 5] // combining function - must be curried for this to work const add = (x) =\> (y) =\> x + y const partiallyAppliedAdds = [add].ap(arg1) // [(y) =\> 1 + y, (y) =\> 3 + y]`

This gives you an array of functions that you can call

`ap`

on to get the result:

`partiallyAppliedAdds.ap(arg2) // [5, 6, 7, 8]`

A transformation function.

A function where the input type is the same as the output.

`// uppercase :: String -\> String const uppercase = (str) =\> str.toUpperCase() // decrement :: Number -\> Number const decrement = (x) =\> x - 1`

A pair of transformations between 2 types of objects that is structural in nature and no data is lost.

For example, 2D coordinates could be stored as an array

`[2,3]`

or object

`{x: 2, y: 3}`

.

`// Providing functions to convert in both directions makes them isomorphic. const pairToCoords = (pair) =\> ({x: pair[0], y: pair[1]}) const coordsToPair = (coords) =\> [coords.x, coords.y] coordsToPair(pairToCoords([1, 2])) // [1, 2] pairToCoords(coordsToPair({x: 1, y: 2})) // {x: 1, y: 2}`

A homomorphism is just a structure preserving map. In fact, a functor is just a homomorphism between categories as it preserves the original category's structure under the mapping.

`A.of(f).ap(A.of(x)) == A.of(f(x)) Either.of(\_.toUpper).ap(Either.of("oreos")) == Either.of(\_.toUpper("oreos"))`

A

`reduceRight`

function that applies a function against an accumulator and each value of the array (from right-to-left) to reduce it to a single value.

`const sum = xs =\> xs.reduceRight((acc, x) =\> acc + x, 0) sum([1, 2, 3, 4, 5]) // 15`

An

`unfold`

function. An

`unfold`

is the opposite of

`fold`

(

`reduce`

). It generates a list from a single value.

`const unfold = (f, seed) =\> { function go(f, seed, acc) { const res = f(seed); return res ? go(f, res[1], acc.concat([res[0]])) : acc; } return go(f, seed, []) }`

`const countDown = n =\> unfold((n) =\> { return n \<= 0 ? undefined : [n, n - 1] }, n) countDown(5) // [5, 4, 3, 2, 1]`

The combination of anamorphism and catamorphism.

A function just like

`reduceRight`

. However, there's a difference:

In paramorphism, your reducer's arguments are the current value, the reduction of all previous values, and the list of values that formed that reduction.

`// Obviously not safe for lists containing `undefined`, // but good enough to make the point. const para = (reducer, accumulator, elements) =\> { if (elements.length === 0) return accumulator const head = elements[0] const tail = elements.slice(1) return reducer(head, tail, para(reducer, accumulator, tail)) } const suffixes = list =\> para( (x, xs, suffxs) =\> [xs, ... suffxs], [], list ) suffixes([1, 2, 3, 4, 5]) // [[2, 3, 4, 5], [3, 4, 5], [4, 5], [5], []]`

The third parameter in the reducer (in the above example,

`[x, ... xs]`

) is kind of like having a history of what got you to your current acc value.

it's the opposite of paramorphism, just as anamorphism is the opposite of catamorphism. Whereas with paramorphism, you combine with access to the accumulator and what has been accumulated, apomorphism lets you

`unfold`

with the potential to return early.

An object that has an

`equals`

function which can be used to compare other objects of the same type.

Make array a setoid:

`Array.prototype.equals = function (arr) { const len = this.length if (len !== arr.length) { return false } for (let i = 0; i \< len; i++) { if (this[i] !== arr[i]) { return false } } return true } ;[1, 2].equals([1, 2]) // true ;[1, 2].equals([0]) // false`

An object that has a

`concat`

function that combines it with another object of the same type.

`;[1].concat([2]) // [1, 2]`

An object that has a

`reduce`

function that applies a function against an accumulator and each element in the array (from left to right) to reduce it to a single value.

`const sum = (list) =\> list.reduce((acc, val) =\> acc + val, 0) sum([1, 2, 3]) // 6`

A lens is a structure (often an object or function) that pairs a getter and a non-mutating setter for some other data structure.

`// Using [Ramda's lens](http://ramdajs.com/docs/#lens) const nameLens = R.lens( // getter for name property on an object (obj) =\> obj.name, // setter for name property (val, obj) =\> Object.assign({}, obj, {name: val}) )`

Having the pair of get and set for a given data structure enables a few key features.

`const person = {name: 'Gertrude Blanch'} // invoke the getter R.view(nameLens, person) // 'Gertrude Blanch' // invoke the setter R.set(nameLens, 'Shafi Goldwasser', person) // {name: 'Shafi Goldwasser'} // run a function on the value in the structure R.over(nameLens, uppercase, person) // {name: 'GERTRUDE BLANCH'}`

Lenses are also composable. This allows easy immutable updates to deeply nested data.

`// This lens focuses on the first item in a non-empty array const firstLens = R.lens( // get first item in array xs =\> xs[0], // non-mutating setter for first item in array (val, [\_\_, ...xs]) =\> [val, ...xs] ) const people = [{name: 'Gertrude Blanch'}, {name: 'Shafi Goldwasser'}] // Despite what you may assume, lenses compose left-to-right. R.over(compose(firstLens, nameLens), uppercase, people) // [{'name': 'GERTRUDE BLANCH'}, {'name': 'Shafi Goldwasser'}]`

Other implementations: * partial.lenses - Tasty syntax sugar and a lot of powerful features * nanoscope - Fluent-interface

Often functions in JavaScript will include comments that indicate the types of their arguments and return values.

There's quite a bit of variance across the community but they often follow the following patterns:

`// functionName :: firstArgType -\> secondArgType -\> returnType // add :: Number -\> Number -\> Number const add = (x) =\> (y) =\> x + y // increment :: Number -\> Number const increment = (x) =\> x + 1`

If a function accepts another function as an argument it is wrapped in parentheses.

`// call :: (a -\> b) -\> a -\> b const call = (f) =\> (x) =\> f(x)`

The letters

`a`

,

`b`

,

`c`

,

`d`

are used to signify that the argument can be of any type. The following version of

`map`

takes a function that transforms a value of some type

`a`

into another type

`b`

, an array of values of type

`a`

, and returns an array of values of type

`b`

.

`// map :: (a -\> b) -\> [a] -\> [b] const map = (f) =\> (list) =\> list.map(f)`

**Further reading*** Ramda's type signatures* Mostly Adequate Guide* What is Hindley-Milner? on Stack Overflow

A composite type made from putting other types together. Two common classes of algebraic types are sum and product.

A Sum type is the combination of two types together into another one. It is called sum because the number of possible values in the result type is the sum of the input types.

JavaScript doesn't have types like this but we can use

`Set`

s to pretend: ```js // imagine that rather than sets here we have types that can only have these values const bools = new Set([true, false]) const halfTrue = new Set(['half-true'])

// The weakLogic type contains the sum of the values from bools and halfTrue const weakLogicValues = new Set([...bools, ...halfTrue]) ```

Sum types are sometimes called union types, discriminated unions, or tagged unions.

There's a couple libraries in JS which help with defining and using union types.

Flow includes union types and TypeScript has Enums to serve the same role.

A **product** type combines types together in a way you're probably more familiar with:

`// point :: (Number, Number) -\> {x: Number, y: Number} const point = (x, y) =\> ({ x, y })`

It's called a product because the total possible values of the data structure is the product of the different values. Many languages have a tuple type which is the simplest formulation of a product type.

See also Set theory.

Option is a sum type with two cases often called

`Some`

and

`None`

.

Option is useful for composing functions that might not return a value.

`// Naive definition const Some = (v) =\> ({ val: v, map (f) { return Some(f(this.val)) }, chain (f) { return f(this.val) } }) const None = () =\> ({ map (f) { return this }, chain (f) { return this } }) // maybeProp :: (String, {a}) -\> Option a const maybeProp = (key, obj) =\> typeof obj[key] === 'undefined' ? None() : Some(obj[key])`

Use

`chain`

to sequence functions that return

`Option`

s ```js

// getItem :: Cart -> Option CartItem const getItem = (cart) => maybeProp('item', cart)

// getPrice :: Item -> Option Number const getPrice = (item) => maybeProp('price', item)

// getNestedPrice :: cart -> Option a const getNestedPrice = (cart) => getItem(cart).chain(getPrice)

getNestedPrice({}) // None() getNestedPrice({item: {foo: 1}}) // None() getNestedPrice({item: {price: 9.99}}) // Some(9.99) ```

`Option`

is also known as

`Maybe`

.

`Some`

is sometimes called

`Just`

.

`None`

is sometimes called

`Nothing`

.

A **function**

`f :: A =\> B`

is an expression - often called arrow or lambda expression - with **exactly one (immutable)** parameter of type

`A`

and **exactly one** return value of type

`B`

. That value depends entirely on the argument, making functions context-independant, or referentially transparent. What is implied here is that a function must not produce any hidden side effects - a function is always pure, by definition. These properties make functions pleasant to work with: they are entirely deterministic and therefore predictable. Functions enable working with code as data, abstracting over behaviour:

`// times2 :: Number -\> Number const times2 = n =\> n \* 2 [1, 2, 3].map(times2) // [2, 4, 6]`

A partial function is a function which is not defined for all arguments - it might return an unexpected result or may never terminate. Partial functions add cognitive overhead, they are harder to reason about and can lead to runtime errors. Some examples: ```js // example 1: sum of the list // sum :: [Number] -> Number const sum = arr => arr.reduce((a, b) => a + b) sum([1, 2, 3]) // 6 sum([]) // TypeError: Reduce of empty array with no initial value

// example 2: get the first item in list // first :: [A] -> A const first = a => a[0] first([42]) // 42 first([]) // undefined //or even worse: first([[42]])[0] // 42 first([])[0] // Uncaught TypeError: Cannot read property '0' of undefined

// example 3: repeat function N times // times :: Number -> (Number -> Number) -> Number const times = n => fn => n && (fn(n), times(n - 1)(fn)) times(3)(console.log) // 3 // 2 // 1 times(-1)(console.log) // RangeError: Maximum call stack size exceeded ```

Partial functions are dangerous as they need to be treated with great caution. You might get an unexpected (wrong) result or run into runtime errors. Sometimes a partial function might not return at all. Being aware of and treating all these edge cases accordingly can become very tedious. Fortunately a partial function can be converted to a regular (or total) one. We can provide default values or use guards to deal with inputs for which the (previously) partial function is undefined. Utilizing the [

`Option`

](https://github.com/hemanth/functional-programming-jargon/blob/master/#Option) type, we can yield either

`Some(value)`

or

`None`

where we would otherwise have behaved unexpectedly: ```js // example 1: sum of the list // we can provide default value so it will always return result // sum :: [Number] -> Number const sum = arr => arr.reduce((a, b) => a + b, 0) sum([1, 2, 3]) // 6 sum([]) // 0

// example 2: get the first item in list // change result to Option // first :: [A] -> Option A const first = a => a.length ? Some(a[0]) : None() first([42]).map(a => console.log(a)) // 42 first([]).map(a => console.log(a)) // console.log won't execute at all //our previous worst case first([[42]]).map(a => console.log(a[0])) // 42 first([]).map(a => console.log(a[0])) // won't execte, so we won't have error here // more of that, you will know by function return type (Option) // that you should use

`.map`

method to access the data and you will never forget // to check your input because such check become built-in into the function

// example 3: repeat function N times // we should make function always terminate by changing conditions: // times :: Number -> (Number -> Number) -> Number const times = n => fn => n > 0 && (fn(n), times(n - 1)(fn)) times(3)(console.log) // 3 // 2 // 1 times(-1)(console.log) // won't execute anything ``` Making your partial functions total ones, these kinds of runtime errors can be prevented. Always returning a value will also make for code that is both easier to maintain as well as to reason about.

- mori
- Immutable
- Immer
- Ramda
- ramda-adjunct
- Folktale
- monet.js
- lodash
- Underscore.js
- Lazy.js
- maryamyriameliamurphies.js
- Haskell in ES6
- Sanctuary
- Crocks
- Fluture
- fp-ts

**P.S:** This repo is successful due to the wonderful contributions!