# functional-programming

**Repository Path**: lyb901301/functional-programming

## Basic Information

- **Project Name**: functional-programming
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-01
- **Last Updated**: 2024-05-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

This repo introduces functional programming concepts using TypeScript and possibly libraries in the fp-ts ecosystem.

This fork is an edited translation of [Giulio Canti](https://gcanti.github.io/about.html)'s ["Introduction to Functional Programming (Italian)"](https://github.com/gcanti/functional-programming). The author uses the original as a reference and supporting material for his lectures and workshops on functional programming.

The purpose of the edits is to expand on the material without changing the concepts nor structure, for more information about the edit's goals see the [CONTRIBUTING](/CONTRIBUTING.md) file.


## Translations

- [Simplified Chinese and Japanese](https://github.com/YiCChi/functional-programming)  by @YiCChi


# What is functional programming

> Functional Programming is programming with pure functions. Mathematical functions.

A quick search on the internet may lead you to the following definition:

> A (pure) function is a procedure that given the same input always return the same output without any observable side-effect.

The term "side effect" does not yet have any specific meaning (we'll see in the future how to give a formal definition), what matters is to have some sort of intuition, think about opening a file or writing into a database.

For the time being we can limit ourselves to say that a side effect is _anything_ a function does besides returning a value.

What is the structure of a program that uses exclusively pure functions?

A functional program tends to be written like a **pipeline**:

```ts
const program = pipe(
  input,
  f1, // pure function
  f2, // pure function
  f3, // pure function
  ...
)
```

What happens here is that `input` is passed to the first function `f1`, which returns a value that is passed to the second function `f2`, which returns a value that is passed as an argument to the third function `f3`, and so on.

**Demo**

[`00_pipe_and_flow.ts`](src/00_pipe_and_flow.ts)

We'll see how functional programming provides us with tools to structure our code in that style.

Other than understanding what functional programming _is_, it is also essential to understand what is it's goal.

Functional programming's goal is to **tame a system's complexity** through the use of formal _models_, and to give careful attention to **code's properties** and refactoring ease.

> Functional programming will help teach people the mathematics behind program construction:
>
> - how to write composable code
> - how to reason about side effects
> - how to write consistent, general, less ad-hoc APIs

What does it mean to give careful attention to code's properties? Let's see with an example:

**Example**

Why can we say that the `Array`'s `map` method is "more functional" than a `for` loop?

```ts
// input
const xs: Array<number> = [1, 2, 3]

// transformation
const double = (n: number): number => n * 2

// result: I want an array where each `xs`' element is doubled
const ys: Array<number> = []
for (let i = 0; i <= xs.length; i++) {
  ys.push(double(xs[i]))
}
```

A `for` loop offers a lot of flexibility, I can modify:

- the starting index, `let i = 0`
- the looping condition, `i < xs.length`
- the step change, `i++`.

This also implies that I may introduce **errors** and that I have no guarantees about the returned value.

**Quiz**. Is the `for loop` correct?

> See the [answer here](src/quiz-answers/for-loop.md)

Let's rewrite the same exercise using `map`.

```ts
// input
const xs: Array<number> = [1, 2, 3]

// transformation
const double = (n: number): number => n * 2

// result: I want an array where each `xs`' element is doubled
const ys: Array<number> = xs.map(double)
```

We can note how `map` lacks the same flexibility of a `for loop`, but it offers us some guarantees:

- all the elements of the input array will be processed
- the resulting array will always have the same number of elements as the starting one

In functional programming, where there's an emphasis on code properties rather than implementation details, the `map` operation is interesting **due to its limitations**

Think about how easier it is to review a PR that involves `map` rather than a `for` loop.

# The two pillars of functional programming

Functional programming is based on the following two pillars:

- Referential transparency
- Composition (as universal design pattern)

All of the remaining content derives directly or indirectly from those two points.

## Referential transparency

> **Definition**. An **expression** is said to be _referentially transparent_ if it can be replaced with its corresponding value without changing the program's behavior

**Example** (referential transparency implies the use of pure functions)

```ts
const double = (n: number): number => n * 2

const x = double(2)
const y = double(2)
```

The expression `double(2)` has the referential transparency property because it is replaceable with its value, the number 4.

Thus I can proceed with the following refactor

```ts
const x = 4
const y = x
```

Not every expression is referentially transparent, let's see an example.

**Example** (referential transparency implies not throwing exceptions)

```ts
const inverse = (n: number): number => {
  if (n === 0) throw new Error('cannot divide by zero')
  return 1 / n
}

const x = inverse(0) + 1
```

I can't replace `inverse(0)` with its value, thus it is not referentially transparent.

**Example** (referential transparency requires the use of immutable data structures)

```ts
const xs = [1, 2, 3]

const append = (xs: Array<number>): void => {
  xs.push(4)
}

append(xs)

const ys = xs
```

On the last line I cannot replace `xs` with its initial value `[1, 2, 3]` since it has been changed by calling `append`.

Why is referential transparency so important? Because it allows us to:

- **reason about code locally**, there is no need to know external context in order to understand a fragment of code
- **refactor** without changing our system's behaviour

**Quiz**. Suppose we have the following program:

```ts
// In TypeScript `declare` allows to introduce a definition without requiring an implementation
declare const question: (message: string) => Promise<string>

const x = await question('What is your name?')
const y = await question('What is your name?')
```

Can I refactor in this way? Does the program's behavior change or is it going to change?

```ts
const x = await question('What is your name?')
const y = x
```

The answer is: there's no way to know without reading `question`'s _implementation_.

As you can see, refactoring a program including non-referentially transparent expressions might be challenging.
In functional programming, where every expression is referentially transparent, the cognitive load required to make changes is severely reduced.

## Composition

Functional programming's fundamental pattern is _composition_: we compose small units of code accomplishing very specific tasks into larger and complex units.

An example of a "from the smallest to the largest" composition pattern we can think of:

- composing two or more primitive values (numbers or strings)
- composing two or more functions
- composing entire programs

In the very last example we can speak of _modular programming_:

> By modular programming I mean the process of building large programs by gluing together smaller programs - Simon Peyton Jones

This programming style is achievable through the use of combinators.

The term **combinator** refers to the [combinator pattern](https://wiki.haskell.org/Combinator):

> A style of organizing libraries centered around the idea of combining things. Usually there is some type `T`, some "primitive" values of type `T`, and some "combinators" which can combine values of type `T` in various ways to build up more complex values of type `T`

The general concept of a combinator is rather vague and it can appear in different forms, but the simplest one is this:

```ts
combinator: Thing -> Thing
```

**Example**. The function `double` combines two numbers.

The goal of a combinator is to create new *Thing*s from *Thing*s already defined.

Since the output of a combinator, the new _Thing_, can be passed around as input to other programs and combinators, we obtain a combinatorial explosion of opportunities, which makes this pattern extremely powerful.

**Example**

```ts
import { pipe } from 'fp-ts/function'

const double = (n: number): number => n * 2

console.log(pipe(2, double, double, double)) // => 16
```

Thus the usual design you can find in a functional module is:

- a model for some type `T`
- a small set of "primitives" of type `T`
- a set of combinators to combine the primitives in larger structures

Let's try to implement such a module:

**Demo**

[`01_retry.ts`](src/01_retry.ts)

As you can see from the previous demo, with merely 3 primitives and two combinators we've been able to express a pretty complex policy.

Think at how just adding a single new primitive, or a single combinator to those already defined, adds expressive possibilities exponentially.

Of the two combinators in `01_retry.ts` a special mention goes to `concat` since it refers to a very powerful functional programming abstraction: semigroups.

# Modelling composition with Semigroups

A semigroup is a recipe to combine two, or more, values.

A semigroup is an **algebra**, which is generally defined as a specific combination of:

- one or more sets
- one or more operations on those sets
- zero or more laws on the previous operations

Algebras are how mathematicians try to capture an idea in its purest form, eliminating everything that is superfluous.

> When an algebra is modified the only allowed operations are those defined by the algebra itself according to its own laws

Algebras can be thought of as an abstraction of **interfaces**:

> When an interface is modified the only allowed operations are those defined by the interface itself according to its own laws

Before getting into semigroups, let's see first an example of an algebra, a _magma_.

## Definition of a Magma

A Magma<A> is a very simple algebra:

- a set or type (A)
- a `concat` operation
- no laws to obey

**Note**: in most cases the terms _set_ and _type_ can be used interchangeably.

We can use a TypeScript `interface` to model a Magma.

```ts
interface Magma<A> {
  readonly concat: (first: A, second: A) => A
}
```

Thus, we have have the ingredients for an algebra:

- a set `A`
- an operation on the set `A`, `concat`. This operation is said to be _closed on_ the set `A` which means that whichever elements `A` we apply the operation on the result will still be an element of `A`. Since the result is still an `A`, it can be used again as an input for `concat` and the operation can be repeated how many times we want. In other words `concat` is a `combinator` for the type `A`.

Let's implement a concrete instance of `Magma<A>` with `A` being the `number` type.

```ts
import { Magma } from 'fp-ts/Magma'

const MagmaSub: Magma<number> = {
  concat: (first, second) => first - second
}

// helper
const getPipeableConcat = <A>(M: Magma<A>) => (second: A) => (first: A): A =>
  M.concat(first, second)

const concat = getPipeableConcat(MagmaSub)

// usage example

import { pipe } from 'fp-ts/function'

pipe(10, concat(2), concat(3), concat(1), concat(2), console.log)
// => 2
```

**Quiz**. The fact that `concat` is a _closed_ operation isn't a trivial detail. If `A` is the set of natural numbers (defined as positive integers) instead of the JavaScript number type (a set of positive and negative floats), could we define a `Magma<Natural>` with `concat` implemented like in `MagmaSub`? Can you think of any other `concat` operation on natural numbers for which the `closure` property isn't valid?

> See the [answer here](src/quiz-answers/magma-concat-closed.md)

**Definition**. Given `A` a non empty set and `*` a binary operation _closed on_ (or _internal to_) `A`, then the pair `(A, *)` is called a _magma_.

Magmas do not obey any law, they only have the closure requirement. Let's see an algebra that do requires another law: semigroups.

## Definition of a Semigroup

> Given a `Magma` if the `concat` operation is **associative** then it's a _semigroup_.

The term "associative" means that the equation:

```ts
(x * y) * z = x * (y * z)

// or
concat(concat(a, b), c) = concat(a, concat(b, c))
```

holds for any `x`, `y`, `z` in `A`.

In layman terms _associativity_ tells us that we do not have to worry about parentheses in expressions and that we can simply write `x * y * z` (there's no ambiguity).

**Example**

String concatenation benefits from associativity.

```ts
("a" + "b") + "c" = "a" + ("b" + "c") = "abc"
```

Every semigroup is a magma, but not every magma is a semigroup.

<center>
<img src="images/semigroup.png" width="300" alt="Magma vs Semigroup" />
</center>

**Example**

The previous `MagmaSub` is not a semigroup because its `concat` operation is not associative.

```ts
import { pipe } from 'fp-ts/function'
import { Magma } from 'fp-ts/Magma'

const MagmaSub: Magma<number> = {
  concat: (first, second) => first - second
}

pipe(MagmaSub.concat(MagmaSub.concat(1, 2), 3), console.log) // => -4
pipe(MagmaSub.concat(1, MagmaSub.concat(2, 3)), console.log) // => 2
```

Semigroups capture the essence of parallelizable operations

If we know that there is such an operation that follows the associativity law, we can further split a computation into two sub computations, each of them could be further split into sub computations.

```ts
a * b * c * d * e * f * g * h = ((a * b) * (c * d)) * ((e * f) * (g * h))
```

Sub computations can be run in parallel mode.

As for `Magma`, `Semigroup`s are implemented through a TypeScript `interface`:

```ts
// fp-ts/lib/Semigroup.ts

interface Semigroup<A> extends Magma<A> {}
```

The following law has to hold true:

- **Associativity**: If `S` is a semigroup the following has to hold true:

```ts
S.concat(S.concat(x, y), z) = S.concat(x, S.concat(y, z))
```

for every `x`, `y`, `z` of type `A`

**Note**. Sadly it is not possible to encode this law using TypeScript's type system.

Let's implement a semigroup for some `ReadonlyArray<string>`:

```ts
import * as Se from 'fp-ts/Semigroup'

const Semigroup: Se.Semigroup<ReadonlyArray<string>> = {
  concat: (first, second) => first.concat(second)
}
```

The name `concat` makes sense for arrays (as we'll see later) but, depending on the context and the type `A` on whom we're implementing an instance, the `concat` semigroup operation may have different interpretations and meanings:

- "concatenation"
- "combination"
- "merging"
- "fusion"
- "selection"
- "sum"
- "substitution"

and many others.

**Example**

This is how to implement the semigroup `(number, +)` where `+` is the usual addition of numbers:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

/** number `Semigroup` under addition */
const SemigroupSum: Semigroup<number> = {
  concat: (first, second) => first + second
}
```

**Quiz**. Can the `concat` combinator defined in the demo [`01_retry.ts`](src/01_retry.ts) be used to define a `Semigroup` instance for the `RetryPolicy` type?

> See the [answer here](src/quiz-answers/semigroup-demo-concat.md)

This is the implementation for the semigroup `(number, *)` where `*` is the usual number multiplication:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

/** number `Semigroup` under multiplication */
const SemigroupProduct: Semigroup<number> = {
  concat: (first, second) => first * second
}
```

**Note** It is a common mistake to think about the _semigroup of numbers_, but for the same type `A` it is possible to define more **instances** of `Semigroup<A>`. We've seen how for `number` we can define a semigroup under _addition_ and _multiplication_. It is also possible to have `Semigroup`s that share the same operation but differ in types. `SemigroupSum` could've been implemented on natural numbers instead of unsigned floats like `number`.

Another example, with the `string` type:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

const SemigroupString: Semigroup<string> = {
  concat: (first, second) => first + second
}
```

Another two examples, this time with the `boolean` type:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

const SemigroupAll: Semigroup<boolean> = {
  concat: (first, second) => first && second
}

const SemigroupAny: Semigroup<boolean> = {
  concat: (first, second) => first || second
}
```

## The `concatAll` function

By definition `concat` combines merely two elements of `A` every time. Is it possible to combine any number of them?

The `concatAll` function takes:

- an instance of a semigroup
- an initial value
- an array of elements

```ts
import * as S from 'fp-ts/Semigroup'
import * as N from 'fp-ts/number'

const sum = S.concatAll(N.SemigroupSum)(2)

console.log(sum([1, 2, 3, 4])) // => 12

const product = S.concatAll(N.SemigroupProduct)(3)

console.log(product([1, 2, 3, 4])) // => 72
```

**Quiz**. Why do I need to provide an initial value?

-> See the [answer here](src/quiz-answers/semigroup-concatAll-initial-value.md)

**Example**

Lets provide some applications of `concatAll`, by reimplementing some popular functions from the JavaScript standard library.

```ts
import * as B from 'fp-ts/boolean'
import { concatAll } from 'fp-ts/Semigroup'
import * as S from 'fp-ts/struct'

const every = <A>(predicate: (a: A) => boolean) => (
  as: ReadonlyArray<A>
): boolean => concatAll(B.SemigroupAll)(true)(as.map(predicate))

const some = <A>(predicate: (a: A) => boolean) => (
  as: ReadonlyArray<A>
): boolean => concatAll(B.SemigroupAny)(false)(as.map(predicate))

const assign: (as: ReadonlyArray<object>) => object = concatAll(
  S.getAssignSemigroup<object>()
)({})
```

**Quiz**. Is the following semigroup instance lawful (does it respect semigroup laws)?

> See the [answer here](src/quiz-answers/semigroup-first.md)

```ts
import { Semigroup } from 'fp-ts/Semigroup'

/** Always return the first argument */
const first = <A>(): Semigroup<A> => ({
  concat: (first, _second) => first
})
```

**Quiz**. Is the following semigroup instance lawful?

> See the [answer here](src/quiz-answers/semigroup-second.md)

```ts
import { Semigroup } from 'fp-ts/Semigroup'

/** Always return the second argument */
const last = <A>(): Semigroup<A> => ({
  concat: (_first, second) => second
})
```

## The dual semigroup

Given a semigroup instance, it is possible to obtain a new semigroup instance by simply swapping the order in which the operands are combined:

```ts
import { pipe } from 'fp-ts/function'
import { Semigroup } from 'fp-ts/Semigroup'
import * as S from 'fp-ts/string'

// This is a Semigroup combinator
const reverse = <A>(S: Semigroup<A>): Semigroup<A> => ({
  concat: (first, second) => S.concat(second, first)
})

pipe(S.Semigroup.concat('a', 'b'), console.log) // => 'ab'
pipe(reverse(S.Semigroup).concat('a', 'b'), console.log) // => 'ba'
```

**Quiz**. This combinator makes sense because, generally speaking, the `concat` operation is not [**commutative**](https://en.wikipedia.org/wiki/Commutative_property), can you find an example where `concat` is commutative and one where it isn't?

> See the [answer here](src/quiz-answers/semigroup-commutative.md)

## Semigroup product

Let's try defining a semigroup instance for more complex types:

```ts
import * as N from 'fp-ts/number'
import { Semigroup } from 'fp-ts/Semigroup'

// models a vector starting at the origin
type Vector = {
  readonly x: number
  readonly y: number
}

// models a sum of two vectors
const SemigroupVector: Semigroup<Vector> = {
  concat: (first, second) => ({
    x: N.SemigroupSum.concat(first.x, second.x),
    y: N.SemigroupSum.concat(first.y, second.y)
  })
}
```

**Example**

```ts
const v1: Vector = { x: 1, y: 1 }
const v2: Vector = { x: 1, y: 2 }

console.log(SemigroupVector.concat(v1, v2)) // => { x: 2, y: 3 }
```

<center>
<img src="images/semigroupVector.png" width="300" alt="SemigroupVector" />
</center>

Too much boilerplate? The good news is that the **mathematical theory** behind semigroups tells us we can implement a semigroup instance for a struct like `Vector` if we can implement a semigroup instance for each of its fields.

Conveniently the `fp-ts/Semigroup` module exports a `struct` combinator:

```ts
import { struct } from 'fp-ts/Semigroup'

// models the sum of two vectors
const SemigroupVector: Semigroup<Vector> = struct({
  x: N.SemigroupSum,
  y: N.SemigroupSum
})
```

**Note**. There is a combinator similar to `struct` that works with tuples: `tuple`

```ts
import * as N from 'fp-ts/number'
import { Semigroup, tuple } from 'fp-ts/Semigroup'

// models a vector starting from origin
type Vector = readonly [number, number]

// models the sum of two vectors
const SemigroupVector: Semigroup<Vector> = tuple(N.SemigroupSum, N.SemigroupSum)

const v1: Vector = [1, 1]
const v2: Vector = [1, 2]

console.log(SemigroupVector.concat(v1, v2)) // => [2, 3]
```

**Quiz**. Is it true that given any `Semigroup<A>` and having chosen any `middle` of `A`, if I insert it between the two `concat` parameters the result is still a semigroup?

```ts
import { pipe } from 'fp-ts/function'
import { Semigroup } from 'fp-ts/Semigroup'
import * as S from 'fp-ts/string'

export const intercalate = <A>(middle: A) => (
  S: Semigroup<A>
): Semigroup<A> => ({
  concat: (first, second) => S.concat(S.concat(first, middle), second)
})

const SemigroupIntercalate = pipe(S.Semigroup, intercalate('|'))

pipe(
  SemigroupIntercalate.concat('a', SemigroupIntercalate.concat('b', 'c')),
  console.log
) // => 'a|b|c'
```

## Finding a Semigroup instance for any type

The associativity property is a very strong requirement, what happens if, given a specific type `A` we can't find an associative operation on `A`?

Suppose we have a type `User` defined as:

```ts
type User = {
  readonly id: number
  readonly name: string
}
```

and that inside my database we have multiple copies of the same `User` (e.g. they could be historical entries of its modifications).

```ts
// internal APIs
declare const getCurrent: (id: number) => User
declare const getHistory: (id: number) => ReadonlyArray<User>
```

and that we need to implement a public API

```ts
export declare const getUser: (id: number) => User
```

which takes into account all of its copies depending on some criteria. The criteria should be to return the most recent copy, or the oldest one, or the current one, etc..

Naturally we can define a specific API for each of these criterias:

```ts
export declare const getMostRecentUser: (id: number) => User
export declare const getLeastRecentUser: (id: number) => User
export declare const getCurrentUser: (id: number) => User
// etc...
```

Thus, to return a value of type `User` I need to consider all the copies and make a `merge` (or `selection`) of them, meaning I can model the criteria problem with a `Semigroup<User>`.

That being said, it is not really clear right now what it means to "merge two `User`s" nor if this merge operation is associative.

You can **always** define a Semigroup instance for **any** given type `A` by defining a semigroup instance not for `A` itself but for `NonEmptyArray<A>` called the **free semigroup** of `A`:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

// represents a non-empty array, meaning an array that has at least one element A
type ReadonlyNonEmptyArray<A> = ReadonlyArray<A> & {
  readonly 0: A
}

// the concatenation of two NonEmptyArrays is still a NonEmptyArray
const getSemigroup = <A>(): Semigroup<ReadonlyNonEmptyArray<A>> => ({
  concat: (first, second) => [first[0], ...first.slice(1), ...second]
})
```

and then we can map the elements of `A` to "singletons" of `ReadonlyNonEmptyArray<A>`, meaning arrays with only one element.

```ts
// insert an element into a non empty array
const of = <A>(a: A): ReadonlyNonEmptyArray<A> => [a]
```

Let's apply this technique to the `User` type:

```ts
import {
  getSemigroup,
  of,
  ReadonlyNonEmptyArray
} from 'fp-ts/ReadonlyNonEmptyArray'
import { Semigroup } from 'fp-ts/Semigroup'

type User = {
  readonly id: number
  readonly name: string
}

// this semigroup is not for the `User` type but for `ReadonlyNonEmptyArray<User>`
const S: Semigroup<ReadonlyNonEmptyArray<User>> = getSemigroup<User>()

declare const user1: User
declare const user2: User
declare const user3: User

// const merge: ReadonlyNonEmptyArray<User>
const merge = S.concat(S.concat(of(user1), of(user2)), of(user3))

// I can get the same result by "packing" the users manually into an array
const merge2: ReadonlyNonEmptyArray<User> = [user1, user2, user3]
```

Thus, the free semigroup of `A` is merely another semigroup in which the elements are all possible, non empty, finite sequences of `A`.

The free semigroup of `A` can be seen as a _lazy_ way to `concat`enate elements of type `A` while preserving their data content.

The `merge` value, containing `[user1, user2, user3]`, tells us which are the elements to concatenate and in which order they are.

Now I have three possible options to design the `getUser` API:

1. I can define `Semigroup<User>` and I want to get straight into `merge`ing.

```ts
declare const SemigroupUser: Semigroup<User>

export const getUser = (id: number): User => {
  const current = getCurrent(id)
  const history = getHistory(id)
  return concatAll(SemigroupUser)(current)(history)
}
```

2. I can't define `Semigroup<User>` or I want to leave the merging strategy open to implementation, thus I'll ask it to the API consumer:

```ts
export const getUser = (SemigroupUser: Semigroup<User>) => (
  id: number
): User => {
  const current = getCurrent(id)
  const history = getHistory(id)
  // merge immediately
  return concatAll(SemigroupUser)(current)(history)
}
```

3. I can't define `Semigroup<User>` nor I want to require it.

In this case the free semigroup of `User` can come to the rescue:

```ts
export const getUser = (id: number): ReadonlyNonEmptyArray<User> => {
  const current = getCurrent(id)
  const history = getHistory(id)
  // I DO NOT proceed with merging and return the free semigroup of User
  return [current, ...history]
}
```

It should be noted that, even when I do have a `Semigroup<A>` instance, using a free semigroup might be still convenient for the following reasons:

- avoids executing possibly expensive and pointless computations
- avoids passing around the semigroup instance
- allows the API consumer to decide which is the correct merging strategy (by using `concatAll`).

## Order-derivable Semigroups

Given that `number` is **a total order** (meaning that whichever `x` and `y` we choose, one of those two conditions has to hold true: `x <= y` or `y <= x`) we can define another two `Semigroup<number>` instances using the `min` or `max` operations.

```ts
import { Semigroup } from 'fp-ts/Semigroup'

const SemigroupMin: Semigroup<number> = {
  concat: (first, second) => Math.min(first, second)
}

const SemigroupMax: Semigroup<number> = {
  concat: (first, second) => Math.max(first, second)
}
```

**Quiz**. Why is it so important that `number` is a _total_ order?

It would be very useful to define such semigroups (`SemigroupMin` and `SemigroupMax`) for different types than `number`.

Is it possible to capture the notion of being _totally ordered_ for other types?

To speak about _ordering_ we first need to capture the notion of _equality_.

# Modelling equivalence with `Eq`

Yet again, we can model the notion of equality.

_Equivalence relations_ capture the concept of _equality_ of elements of the same type. The concept of an _equivalence relation_ can be implemented in TypeScript with the following interface:

```ts
interface Eq<A> {
  readonly equals: (first: A, second: A) => boolean
}
```

Intuitively:

- if `equals(x, y) = true` then we say `x` and `y` are equal
- if `equals(x, y) = false` then we say `x` and `y` are different

**Example**

This is an instance of `Eq` for the `number` type:

```ts
import { Eq } from 'fp-ts/Eq'
import { pipe } from 'fp-ts/function'

const EqNumber: Eq<number> = {
  equals: (first, second) => first === second
}

pipe(EqNumber.equals(1, 1), console.log) // => true
pipe(EqNumber.equals(1, 2), console.log) // => false
```

The following laws have to hold true:

1. **Reflexivity**: `equals(x, x) === true`, for every `x` in `A`
2. **Symmetry**: `equals(x, y) === equals(y, x)`, for every `x`, `y` in `A`
3. **Transitivity**: if `equals(x, y) === true` and `equals(y, z) === true`, then `equals(x, z) === true`, for every `x`, `y`, `z` in `A`

**Quiz**. Would a combinator `reverse: <A>(E: Eq<A>) => Eq<A>` make sense?

**Quiz**. Would a combinator `not: <A>(E: Eq<A>) => Eq<A>` make sense?

```ts
import { Eq } from 'fp-ts/Eq'

export const not = <A>(E: Eq<A>): Eq<A> => ({
  equals: (first, second) => !E.equals(first, second)
})
```

**Example**

Let's see the first example of the usage of the `Eq` abstraction by defining a function `elem` that checks whether a given value is an element of `ReadonlyArray`.

```ts
import { Eq } from 'fp-ts/Eq'
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'

// returns `true` if the element `a` is included in the list `as`
const elem = <A>(E: Eq<A>) => (a: A) => (as: ReadonlyArray<A>): boolean =>
  as.some((e) => E.equals(a, e))

pipe([1, 2, 3], elem(N.Eq)(2), console.log) // => true
pipe([1, 2, 3], elem(N.Eq)(4), console.log) // => false
```

Why would we not use the native `includes` Array method?

```ts
console.log([1, 2, 3].includes(2)) // => true
console.log([1, 2, 3].includes(4)) // => false
```

Let's define some `Eq` instance for more complex types.

```ts
import { Eq } from 'fp-ts/Eq'

type Point = {
  readonly x: number
  readonly y: number
}

const EqPoint: Eq<Point> = {
  equals: (first, second) => first.x === second.x && first.y === second.y
}

console.log(EqPoint.equals({ x: 1, y: 2 }, { x: 1, y: 2 })) // => true
console.log(EqPoint.equals({ x: 1, y: 2 }, { x: 1, y: -2 })) // => false
```

and check the results of `elem` and `includes`

```ts
const points: ReadonlyArray<Point> = [
  { x: 0, y: 0 },
  { x: 1, y: 1 },
  { x: 2, y: 2 }
]

const search: Point = { x: 1, y: 1 }

console.log(points.includes(search)) // => false :(
console.log(pipe(points, elem(EqPoint)(search))) // => true :)
```

**Quiz** (JavaScript). Why does the `includes` method returns `false`?

-> See the [answer here](src/quiz-answers/javascript-includes.md)

Abstracting the concept of equality is of paramount importance, especially in a language like JavaScript where some data types do not offer handy APIs for checking user-defined equality.

The JavaScript native `Set` datatype suffers by the same issue:

```ts
type Point = {
  readonly x: number
  readonly y: number
}

const points: Set<Point> = new Set([{ x: 0, y: 0 }])

points.add({ x: 0, y: 0 })

console.log(points)
// => Set { { x: 0, y: 0 }, { x: 0, y: 0 } }
```

Given the fact that `Set` uses `===` ("strict equality") for comparing values, `points` now contains **two identical copies** of `{ x: 0, y: 0 }`, a result we definitely did not want. Thus it is convenient to define a new API to add an element to a `Set`, one that leverages the `Eq` abstraction.

**Quiz**. What would be the signature of this API?

Does `EqPoint` require too much boilerplate? The good news is that theory offers us yet again the possibility of implementing an `Eq` instance for a struct like `Point` if we are able to define an `Eq` instance for each of its fields.

Conveniently the `fp-ts/Eq` module exports a `struct` combinator:

```ts
import { Eq, struct } from 'fp-ts/Eq'
import * as N from 'fp-ts/number'

type Point = {
  readonly x: number
  readonly y: number
}

const EqPoint: Eq<Point> = struct({
  x: N.Eq,
  y: N.Eq
})
```

**Note**. Like for Semigroup, we aren't limited to `struct`-like data types, we also have combinators for working with tuples: `tuple`

```ts
import { Eq, tuple } from 'fp-ts/Eq'
import * as N from 'fp-ts/number'

type Point = readonly [number, number]

const EqPoint: Eq<Point> = tuple(N.Eq, N.Eq)

console.log(EqPoint.equals([1, 2], [1, 2])) // => true
console.log(EqPoint.equals([1, 2], [1, -2])) // => false
```

There are other combinators exported by `fp-ts`, here we can see a combinator that allows us to derive an `Eq` instance for `ReadonlyArray`s.

```ts
import { Eq, tuple } from 'fp-ts/Eq'
import * as N from 'fp-ts/number'
import * as RA from 'fp-ts/ReadonlyArray'

type Point = readonly [number, number]

const EqPoint: Eq<Point> = tuple(N.Eq, N.Eq)

const EqPoints: Eq<ReadonlyArray<Point>> = RA.getEq(EqPoint)
```

Similarly to Semigroups, it is possible to define more than one `Eq` instance for the same given type. Suppose we have modeled a `User` with the following type:

```ts
type User = {
  readonly id: number
  readonly name: string
}
```

we can define a "standard" `Eq<User>` instance using the `struct` combinator:

```ts
import { Eq, struct } from 'fp-ts/Eq'
import * as N from 'fp-ts/number'
import * as S from 'fp-ts/string'

type User = {
  readonly id: number
  readonly name: string
}

const EqStandard: Eq<User> = struct({
  id: N.Eq,
  name: S.Eq
})
```

Several languages, even pure functional languages like Haskell, do not allow to have more than one `Eq` instance per data type. But we may have different contexts where the meaning of `User` equality might differ. One common context is where two `User`s are equal if their `id` field is equal.

```ts
/** two users are equal if their `id` fields are equal */
const EqID: Eq<User> = {
  equals: (first, second) => N.Eq.equals(first.id, second.id)
}
```

Now that we made an abstract concept concrete by representing it as a data structure, we can programmatically manipulate `Eq` instances like we do with other data structures. Let's see an example.

**Example**. Rather than manually defining `EqId` we can use the combinator `contramap`: given an instance `Eq<A>` and a function from `B` to `A`, we can derive an `Eq<B>`

```ts
import { Eq, struct, contramap } from 'fp-ts/Eq'
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import * as S from 'fp-ts/string'

type User = {
  readonly id: number
  readonly name: string
}

const EqStandard: Eq<User> = struct({
  id: N.Eq,
  name: S.Eq
})

const EqID: Eq<User> = pipe(
  N.Eq,
  contramap((user: User) => user.id)
)

console.log(
  EqStandard.equals({ id: 1, name: 'Giulio' }, { id: 1, name: 'Giulio Canti' })
) // => false (because the `name` property differs)

console.log(
  EqID.equals({ id: 1, name: 'Giulio' }, { id: 1, name: 'Giulio Canti' })
) // => true (even though the `name` property differs)

console.log(EqID.equals({ id: 1, name: 'Giulio' }, { id: 2, name: 'Giulio' }))
// => false (even though the `name` property is equal)
```

**Quiz**. Given a data type `A`, is it possible to define a `Semigroup<Eq<A>>`? What could it represent?

## Modeling ordering relations with `Ord`

In the previous chapter regarding `Eq` we were dealing with the concept of **equality**. In this one we'll deal with the concept of **ordering**.

The concept of a total order relation can be implemented in TypeScript as following:

```ts
import { Eq } from 'fp-ts/lib/Eq'

type Ordering = -1 | 0 | 1

interface Ord<A> extends Eq<A> {
  readonly compare: (x: A, y: A) => Ordering
}
```

Resulting in:

- `x < y` if and only if `compare(x, y) = -1`
- `x = y` if and only if `compare(x, y) = 0`
- `x > y` if and only if `compare(x, y) = 1`

**Example**

Let's try to define an `Ord` instance for the type `number`:

```ts
import { Ord } from 'fp-ts/Ord'

const OrdNumber: Ord<number> = {
  equals: (first, second) => first === second,
  compare: (first, second) => (first < second ? -1 : first > second ? 1 : 0)
}
```

The following laws have to hold true:

1. **Reflexivity**: `compare(x, x) <= 0`, for every `x` in `A`
2. **Antisymmetry**: if `compare(x, y) <= 0` and `compare(y, x) <= 0` then `x = y`, for every `x`, `y` in `A`
3. **Transitivity**: if `compare(x, y) <= 0` and `compare(y, z) <= 0` then `compare(x, z) <= 0`, for every `x`, `y`, `z` in `A`

`compare` has also to be compatible with the `equals` operation from `Eq`:

`compare(x, y) === 0` if and only if `equals(x, y) === true`, for every `x`, `y` in `A`

**Note**. `equals` can be derived from `compare` in the following way:

```ts
equals: (first, second) => compare(first, second) === 0
```

In fact the `fp-ts/Ord` module exports a handy helper `fromCompare` which allows us to define an `Ord` instance simply by supplying the `compare` function:

```ts
import { Ord, fromCompare } from 'fp-ts/Ord'

const OrdNumber: Ord<number> = fromCompare((first, second) =>
  first < second ? -1 : first > second ? 1 : 0
)
```

**Quiz**. Is it possible to define an `Ord` instance for the game Rock-Paper-Scissor where `move1 <= move2` if `move2` beats `move1`?

Let's see a practical usage of an `Ord` instance by defining a `sort` function which orders the elements of a `ReadonlyArray`.

```ts
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { Ord } from 'fp-ts/Ord'

export const sort = <A>(O: Ord<A>) => (
  as: ReadonlyArray<A>
): ReadonlyArray<A> => as.slice().sort(O.compare)

pipe([3, 1, 2], sort(N.Ord), console.log) // => [1, 2, 3]
```

**Quiz** (JavaScript). Why does the implementation leverages the native Array `slice` method?

Let's see another `Ord` pratical usage by defining a `min` function that returns the smallest of two values:

```ts
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { Ord } from 'fp-ts/Ord'

const min = <A>(O: Ord<A>) => (second: A) => (first: A): A =>
  O.compare(first, second) === 1 ? second : first

pipe(2, min(N.Ord)(1), console.log) // => 1
```

## Dual Ordering

In the same way we could invert the `concat` operation to obtain the `dual semigroup` using the `reverse` combinator, we can invert the `compare` operation to get the dual ordering.

Let's define the `reverse` combinator for `Ord`:

```ts
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { fromCompare, Ord } from 'fp-ts/Ord'

export const reverse = <A>(O: Ord<A>): Ord<A> =>
  fromCompare((first, second) => O.compare(second, first))
```

A usage example for `reverse` is obtaining a `max` function from the `min` one:

```ts
import { flow, pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { Ord, reverse } from 'fp-ts/Ord'

const min = <A>(O: Ord<A>) => (second: A) => (first: A): A =>
  O.compare(first, second) === 1 ? second : first

// const max: <A>(O: Ord<A>) => (second: A) => (first: A) => A
const max = flow(reverse, min)

pipe(2, max(N.Ord)(1), console.log) // => 2
```

The **totality** of ordering (meaning that given any `x` and `y`, one of the two conditions needs to hold true: `x <= y` or `y <= z`) may appear obvious when speaking about numbers, but that's not always the case. Let's see a slightly more complex scenario:

```ts
type User = {
  readonly name: string
  readonly age: number
}
```

It's not really clear when a `User` is "smaller or equal" than another `User`.

How can we define an `Ord<User>` instance?

That depends on the context, but a possible choice might be ordering `User`s by their age:

```ts
import * as N from 'fp-ts/number'
import { fromCompare, Ord } from 'fp-ts/Ord'

type User = {
  readonly name: string
  readonly age: number
}

const byAge: Ord<User> = fromCompare((first, second) =>
  N.Ord.compare(first.age, second.age)
)
```

Again we can get rid of some boilerplate using the `contramap` combinatorL given an `Ord<A>` instance and a function from `B` to `A`, it is possible to derive `Ord<B>`:

```ts
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { contramap, Ord } from 'fp-ts/Ord'

type User = {
  readonly name: string
  readonly age: number
}

const byAge: Ord<User> = pipe(
  N.Ord,
  contramap((_: User) => _.age)
)
```

We can get the youngest of two `User`s using the previously defined `min` function.

```ts
// const getYounger: (second: User) => (first: User) => User
const getYounger = min(byAge)

pipe(
  { name: 'Guido', age: 50 },
  getYounger({ name: 'Giulio', age: 47 }),
  console.log
) // => { name: 'Giulio', age: 47 }
```

**Quiz**. In the `fp-ts/ReadonlyMap` module the following API is exposed:

```ts
/**
 * Get a sorted `ReadonlyArray` of the keys contained in a `ReadonlyMap`.
 */
declare const keys: <K>(
  O: Ord<K>
) => <A>(m: ReadonlyMap<K, A>) => ReadonlyArray<K>
```

why does this API requires an instance for `Ord<K>`?

Let's finally go back to the very first issue: defining two semigroups `SemigroupMin` and `SemigroupMax` for types different than `number`:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

const SemigroupMin: Semigroup<number> = {
  concat: (first, second) => Math.min(first, second)
}

const SemigroupMax: Semigroup<number> = {
  concat: (first, second) => Math.max(first, second)
}
```

Now that we have the `Ord` abstraction we can do it:

```ts
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { Ord, contramap } from 'fp-ts/Ord'
import { Semigroup } from 'fp-ts/Semigroup'

export const min = <A>(O: Ord<A>): Semigroup<A> => ({
  concat: (first, second) => (O.compare(first, second) === 1 ? second : first)
})

export const max = <A>(O: Ord<A>): Semigroup<A> => ({
  concat: (first, second) => (O.compare(first, second) === 1 ? first : second)
})

type User = {
  readonly name: string
  readonly age: number
}

const byAge: Ord<User> = pipe(
  N.Ord,
  contramap((_: User) => _.age)
)

console.log(
  min(byAge).concat({ name: 'Guido', age: 50 }, { name: 'Giulio', age: 47 })
) // => { name: 'Giulio', age: 47 }
console.log(
  max(byAge).concat({ name: 'Guido', age: 50 }, { name: 'Giulio', age: 47 })
) // => { name: 'Guido', age: 50 }
```

**Example**

Let's recap all of this with one final example (adapted from [Fantas, Eel, and Specification 4: Semigroup](http://www.tomharding.me/2017/03/13/fantas-eel-and-specification-4/)).

Suppose we need to build a system where, in a database, there are records of customers implemented in the following way:

```ts
interface Customer {
  readonly name: string
  readonly favouriteThings: ReadonlyArray<string>
  readonly registeredAt: number // since epoch
  readonly lastUpdatedAt: number // since epoch
  readonly hasMadePurchase: boolean
}
```

For some reason, there might be duplicate records for the same person.

We need a merging strategy. Well, that's Semigroup's bread and butter!

```ts
import * as B from 'fp-ts/boolean'
import { pipe } from 'fp-ts/function'
import * as N from 'fp-ts/number'
import { contramap } from 'fp-ts/Ord'
import * as RA from 'fp-ts/ReadonlyArray'
import { max, min, Semigroup, struct } from 'fp-ts/Semigroup'
import * as S from 'fp-ts/string'

interface Customer {
  readonly name: string
  readonly favouriteThings: ReadonlyArray<string>
  readonly registeredAt: number // since epoch
  readonly lastUpdatedAt: number // since epoch
  readonly hasMadePurchase: boolean
}

const SemigroupCustomer: Semigroup<Customer> = struct({
  // keep the longer name
  name: max(pipe(N.Ord, contramap(S.size))),
  // accumulate things
  favouriteThings: RA.getSemigroup<string>(),
  // keep the least recent date
  registeredAt: min(N.Ord),
  // keep the most recent date
  lastUpdatedAt: max(N.Ord),
  // boolean semigroup under disjunction
  hasMadePurchase: B.SemigroupAny
})

console.log(
  SemigroupCustomer.concat(
    {
      name: 'Giulio',
      favouriteThings: ['math', 'climbing'],
      registeredAt: new Date(2018, 1, 20).getTime(),
      lastUpdatedAt: new Date(2018, 2, 18).getTime(),
      hasMadePurchase: false
    },
    {
      name: 'Giulio Canti',
      favouriteThings: ['functional programming'],
      registeredAt: new Date(2018, 1, 22).getTime(),
      lastUpdatedAt: new Date(2018, 2, 9).getTime(),
      hasMadePurchase: true
    }
  )
)
/*
{ name: 'Giulio Canti',
  favouriteThings: [ 'math', 'climbing', 'functional programming' ],
  registeredAt: 1519081200000, // new Date(2018, 1, 20).getTime()
  lastUpdatedAt: 1521327600000, // new Date(2018, 2, 18).getTime()
  hasMadePurchase: true
}
*/
```

**Quiz**. Given a type `A` is it possible to define a `Semigroup<Ord<A>>` instance? What could it possibly represent?

**Demo**

# Modeling composition through Monoids

Let's recap what we have seen till now.

We have seen how an **algebra** is a combination of:

- some type `A`
- some operations involving the type `A`
- some laws and properties for that combination.

The first algebra we have seen has been the magma, an algebra defined on some type A equipped with one operation called `concat`. There were no laws involved in `Magma<A>` the only requirement we had was that the `concat` operation had to be _closed_ on `A` meaning that the result:

```ts
concat(first: A, second: A) => A
```

has still to be an element of the `A` type.

Later on we have seen how adding one simple requirement, _associativity_, allowed some `Magma<A>` to be further refined as a `Semigroup<A>`, and how associativity captures the possibility of computations to be parallelized.

Now we're going to add another condition on Semigroup.

Given a `Semigroup` defined on some set `A` with some `concat` operation, if there is some element in `A` – we'll call this element _empty_ – such as for every element `a` in `A` the two following equations hold true:

- **Right identity**: `concat(a, empty) = a`
- **Left identity**: `concat(empty, a) = a`

then the `Semigroup` is also a `Monoid`.

**Note**: We'll call the `empty` element **unit** for the rest of this section. There's other synonyms in literature, some of the most common ones are _neutral element_ and _identity element_.

We have seen how in TypeScript `Magma`s and `Semigroup`s, can be modeled with `interface`s, so it should not come as a surprise that the very same can be done for `Monoid`s.

```ts
import { Semigroup } from 'fp-ts/Semigroup'

interface Monoid<A> extends Semigroup<A> {
  readonly empty: A
}
```

Many of the semigroups we have seen in the previous sections can be extended to become `Monoid`s. All we need to find is some element of type `A` for which the Right and Left identities hold true.

```ts
import { Monoid } from 'fp-ts/Monoid'

/** number `Monoid` under addition */
const MonoidSum: Monoid<number> = {
  concat: (first, second) => first + second,
  empty: 0
}

/** number `Monoid` under multiplication */
const MonoidProduct: Monoid<number> = {
  concat: (first, second) => first * second,
  empty: 1
}

const MonoidString: Monoid<string> = {
  concat: (first, second) => first + second,
  empty: ''
}

/** boolean monoid under conjunction */
const MonoidAll: Monoid<boolean> = {
  concat: (first, second) => first && second,
  empty: true
}

/** boolean monoid under disjunction */
const MonoidAny: Monoid<boolean> = {
  concat: (first, second) => first || second,
  empty: false
}
```

**Quiz**. In the semigroup section we have seen how the type `ReadonlyArray<string>` admits a `Semigroup` instance:

```ts
import { Semigroup } from 'fp-ts/Semigroup'

const Semigroup: Semigroup<ReadonlyArray<string>> = {
  concat: (first, second) => first.concat(second)
}
```

Can you find the `unit` for this semigroup? If so, can we generalize the result not just for `ReadonlyArray<string>` but `ReadonlyArray<A>` as well?

**Quiz** (more complex). Prove that given a monoid, there can only be one unit.

The consequence of the previous proof is that there can be only one unit per monoid, once we find one we can stop searching.

We have seen how each semigroup was a magma, but not every magma was a semigroup. In the same way, each monoid is a semigroup, but not every semigroup is a monoid.

<center>
<img src="images/monoid.png" width="300" alt="Magma vs Semigroup vs Monoid" />
</center>

**Example**

Let's consider the following example:

```ts
import { pipe } from 'fp-ts/function'
import { intercalate } from 'fp-ts/Semigroup'
import * as S from 'fp-ts/string'

const SemigroupIntercalate = pipe(S.Semigroup, intercalate('|'))

console.log(S.Semigroup.concat('a', 'b')) // => 'ab'
console.log(SemigroupIntercalate.concat('a', 'b')) // => 'a|b'
console.log(SemigroupIntercalate.concat('a', '')) // => 'a|'
```

Note how for this Semigroup there's no such `empty` value of type `string` such as `concat(a, empty) = a`.

And now one final, slightly more "exotic" example, involving functions:

**Example**

An **endomorphism** is a function whose input and output type is the same:

```ts
type Endomorphism<A> = (a: A) => A
```

Given a type `A`, all endomorphisms defined on `A` are a monoid, such as:

- the `concat` operation is the usual function composition
- the unit, our `empty` value is the identity function

```ts
import { Endomorphism, flow, identity } from 'fp-ts/function'
import { Monoid } from 'fp-ts/Monoid'

export const getEndomorphismMonoid = <A>(): Monoid<Endomorphism<A>> => ({
  concat: flow,
  empty: identity
})
```

**Note**: The `identity` function has one, and only one possible implementation:

```ts
const identity = (a: A) => a
```

Whatever value we pass in input, it gives us the same value in output.

<!--
TODO:
We can start having a small taste of the importance of the `identity` function. While apparently useless per se, this function is vital to define a monoid for functions, in this case, endomorphisms. In fact, _doing nothing_, being _empty_ or _neutral_ is a tremendously valuable property to have when it comes to composition and we can think of the `identity` function as the number `0` of functions.
-->

## The `concatAll` function

One great property of monoids, compared to semigrops, is that the concatenation of multiple elements becomes even easier: it is not necessary anymore to provide an initial value.

```ts
import { concatAll } from 'fp-ts/Monoid'
import * as S from 'fp-ts/string'
import * as N from 'fp-ts/number'
import * as B from 'fp-ts/boolean'

console.log(concatAll(N.MonoidSum)([1, 2, 3, 4])) // => 10
console.log(concatAll(N.MonoidProduct)([1, 2, 3, 4])) // => 24
console.log(concatAll(S.Monoid)(['a', 'b', 'c'])) // => 'abc'
console.log(concatAll(B.MonoidAll)([true, false, true])) // => false
console.log(concatAll(B.MonoidAny)([true, false, true])) // => true
```

**Quiz**. Why is the initial value not needed anymore?

## Product monoid

As we have already seen with semigroups, it is possible to define a monoid instance for a `struct` if we are able to define a monoid instance for each of its fields.

**Example**

```ts
import { Monoid, struct } from 'fp-ts/Monoid'
import * as N from 'fp-ts/number'

type Point = {
  readonly x: number
  readonly y: number
}

const Monoid: Monoid<Point> = struct({
  x: N.MonoidSum,
  y: N.MonoidSum
})
```

**Note**. There is a combinator similar to `struct` that works with tuples: `tuple`.

```ts
import { Monoid, tuple } from 'fp-ts/Monoid'
import * as N from 'fp-ts/number'

type Point = readonly [number, number]

const Monoid: Monoid<Point> = tuple(N.MonoidSum, N.MonoidSum)
```

**Quiz**. Is it possible to define a "free monoid" for a generic type `A`?

**Demo** (implementing a system to draw geoetric shapes on canvas)

[`03_shapes.ts`](src/03_shapes.ts)

# Pure and partial functions

In the first chapter we've seen an informal definition of a pure function:

> A pure function is a procedure that given the same input always returns the same output and does not have any observable side effect.

Such an informal statement could leave space for some doubts, such as:

- what is a "side effect"?
- what does it means "observable"?
- what does it mean "same"?

Let's see a formal definition of the concept of a function.

**Note**. If `X` and `Y` are sets, then with `X × Y` we indicate their _cartesian product_, meaning the set

```
X × Y = { (x, y) | x ∈ X, y ∈ Y }
```

The following [definition](https://en.wikipedia.org/wiki/History_of_the_function_concept) was given a century ago:

**Definition**. A \_function: `f: X ⟶ Y` is a subset of `X × Y` such as
for every `x ∈ X` there's exactly one `y ∈ Y` such that `(x, y) ∈ f`.

The set `X` is called the _domain_ of `f`, `Y` is it's _codomain_.

**Example**

The function `double: Nat ⟶ Nat` is the subset of the cartesian product `Nat × Nat` given by `{ (1, 2), (2, 4), (3, 6), ...}`.

In TypeScript we could define `f` as

```ts
const f: Record<number, number> = {
  1: 2,
  2: 4,
  3: 6
  ...
}
```

<!--
TODO:
Please note that the set `f` has to be described _statically_ when defining the function (meaning that the elements of that set cannot change with time for no reason).
In this way we can exclude any form of side effect and the return value is always the same.
-->

The one in the example is called an _extensional_ definition of a function, meaning we enumerate one by one each of the elements of its domain and for each one of them we point the corresponding codomain element.

Naturally, when such a set is infinite this proves to be problematic. We can't list the entire domain and codomain of all functions.

We can get around this issue by introducing the one that is called _intensional_ definition, meaning that we express a condition that has to hold for every couple `(x, y) ∈ f` meaning `y = x * 2`.

This the familiar form in which we write the `double` function and its definition in TypeScript:

```ts
const double = (x: number): number => x * 2
```

The definition of a function as a subset of a cartesian product shows how in mathematics every function is pure: there is no action, no state mutation or elements being modified.
In functional programming the implementation of functions has to follow as much as possible this ideal model.

**Quiz**. Which of the following procedures are pure functions?

```ts
const coefficient1 = 2
export const f1 = (n: number) => n * coefficient1

// ------------------------------------------------------

let coefficient2 = 2
export const f2 = (n: number) => n * coefficient2++

// ------------------------------------------------------

let coefficient3 = 2
export const f3 = (n: number) => n * coefficient3

// ------------------------------------------------------

export const f4 = (n: number) => {
  const out = n * 2
  console.log(out)
  return out
}

// ------------------------------------------------------

interface User {
  readonly id: number
  readonly name: string
}

export declare const f5: (id: number) => Promise<User>

// ------------------------------------------------------

import * as fs from 'fs'

export const f6 = (path: string): string =>
  fs.readFileSync(path, { encoding: 'utf8' })

// ------------------------------------------------------

export const f7 = (
  path: string,
  callback: (err: Error | null, data: string) => void
): void => fs.readFile(path, { encoding: 'utf8' }, callback)
```

The fact that a function is pure does not imply automatically a ban on local mutability as long as it doesn't leak out of its scope.

![mutable / immutable](images/mutable-immutable.jpg)

**Example** (Implementazion details of the `concatAll` function for monoids)

```ts
import { Monoid } from 'fp-ts/Monoid'

const concatAll = <A>(M: Monoid<A>) => (as: ReadonlyArray<A>): A => {
  let out: A = M.empty // <= local mutability
  for (const a of as) {
    out = M.concat(out, a)
  }
  return out
}
```

The ultimate goal is to guarantee: **referential transparency**.

The contract we sign with a user of our APIs is defined by the APIs signature:

```ts
declare const concatAll: <A>(M: Monoid<A>) => (as: ReadonlyArray<A>) => A
```

and by the promise of respecting referential transparency. The technical details of how the function is implemented are not relevant, thus there is maximum freedom implementation-wise.

Thus, how do we define a "side effect"? Simply by negating referential transparency:

> An expression contains "side effects" if it doesn't benefit from referential transparency

Not only functions are a perfect example of one of the two pillars of functional programming, referential transparency, but they're also examples of the second pillar: **composition**.

Functions compose:

**Definition**. Given `f: Y ⟶ Z` and `g: X ⟶ Y` two functions, then the function `h: X ⟶ Z` defined by:

```
h(x) = f(g(x))
```

is called _composition_ of `f` and `g` and is written `h = f ∘ g`

Please note that in order for `f` and `g` to combine, the domain of `f` has to be included in the codomain of `g`.

**Definition**. A function is said to be _partial_ if it is not defined for each value of its domain.

Vice versa, a function defined for all values of its domain is said to be _total_

**Example**

```
f(x) = 1 / x
```

The function `f: number ⟶ number` is not defined for `x = 0`.

**Example**

```ts
// Get the first element of a `ReadonlyArray`
declare const head: <A>(as: ReadonlyArray<A>) => A
```

**Quiz**. Why is the `head` function partial?

**Quiz**. Is `JSON.parse` a total function?

```ts
parse: (text: string, reviver?: (this: any, key: string, value: any) => any) =>
  any
```

**Quiz**. Is `JSON.stringify` a total function?

```ts
stringify: (
  value: any,
  replacer?: (this: any, key: string, value: any) => any,
  space?: string | number
) => string
```

In functional programming there is a tendency to only define **pure and total functions**. From now on with the term function we'll be specifically referring to "pure and total function". So what do we do when we have a partial function in our applications?

A partial function `f: X ⟶ Y` can always be "brought back" to a total one by adding a special value, let's call it `None`, to the codomain and by assigning it to the output of `f` for every value of `X` where the function is not defined.

```
f': X ⟶ Y ∪ None
```

Let's call it `Option(Y) = Y ∪ None`.

```
f': X ⟶ Option(Y)
```

In functional programming the tendency is to define only pure and and total functions.

Is it possible to define `Option` in TypeScript? In the following chapters we'll see how to do it.

# Algebraic Data Types

A good first step when writing an application or feature is to define it's domain model. TypeScript offers many tools that help accomplishing this task. **Algebraic Data Types** (in short, ADTs) are one of these tools.

<!--
  What are the other tools?
-->

## What is an ADT?

> In computer programming, especially functional programming and type theory, an algebraic data type is a kind of composite type, i.e., **a type formed by combining other types**.

Two common families of algebraic data types are:

- **product types**
- **sum types**

<center>
<img src="images/adt.png" width="400" alt="ADT" />
</center>

Let's begin with the more familiar ones: product types.

## Product types

A product type is a collection of types T<sub>i</sub> indexed by a set `I`.

Two members of this family are `n`-tuples, where `I` is an interval of natural numbers:

```ts
type Tuple1 = [string] // I = [0]
type Tuple2 = [string, number] // I = [0, 1]
type Tuple3 = [string, number, boolean] // I = [0, 1, 2]

// Accessing by index
type Fst = Tuple2[0] // string
type Snd = Tuple2[1] // number
```

and structs, where `I` is a set of labels:

```ts
// I = {"name", "age"}
interface Person {
  name: string
  age: number
}

// Accessing by label
type Name = Person['name'] // string
type Age = Person['age'] // number
```

Product types can be **polimorphic**.

**Example**

```ts
//                ↓ type parameter
type HttpResponse<A> = {
  readonly code: number
  readonly body: A
}
```

### Why "product" types?

If we label with `C(A)` the number of elements of type `A` (also called in mathematics, **cardinality**), then the following equation hold true:

```ts
C([A, B]) = C(A) * C(B)
```

> the cardinality of a product is the product of the cardinalities

**Example**

The `null` type has cardinality `1` because it has only one member: `null`.

**Quiz**: What is the cardinality of the `boolean` type.

**Example**

```ts
type Hour = 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12
type Period = 'AM' | 'PM'
type Clock = [Hour, Period]
```

Type `Hour` has 12 members.
Type `Period` has 2 members.
Thus type `Clock` has `12 * 2 = 24` elements.

**Quiz**: What is the cardinality of the following `Clock` type?

```ts
// same as before
type Hour = 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12
// same as before
type Period = 'AM' | 'PM'

// different encoding, no longer a Tuple
type Clock = {
  readonly hour: Hour
  readonly period: Period
}
```

### When can I use a product type?

Each time it's components are **independent**.

```ts
type Clock = [Hour, Period]
```

Here `Hour` and `Period` are independent: the value of `Hour` does not change the value of `Period`. Every legal pair of `[Hour, Period]` makes "sense" and is legal.

## Sum types

A sum type is a a data type that can hold a value of different (but limited) types. Only one of these types can be used in a single instance and there is generally a "tag" value differentiating those types.

In TypeScript's official docs they are called [discriminated union](https://www.typescriptlang.org/docs/handbook/unions-and-intersections.html).

It is important to note that the members of the union have to be **disjoint**, there can't be values that belong to more than one member.

**Example**

The type:

```ts
type StringsOrNumbers = ReadonlyArray<string> | ReadonlyArray<number>

declare const sn: StringsOrNumbers

sn.map() // error: This expression is not callable.
```

is not a disjoint union because the value `[]`, the empty array, belongs to both members.

**Quiz**. Is the following union disjoint?

```ts
type Member1 = { readonly a: string }
type Member2 = { readonly b: number }
type MyUnion = Member1 | Member2
```

Disjoint unions are recurring in functional programming.

Fortunately `TypeScript` has a way to guarantee that a union is disjoint: add a specific field that works as a **tag**.

**Note**: Disjoint unions, sum types and tagged unions are used interchangeably to indicate the same thing.

**Example** (redux actions)

The `Action` sum type models a portion of the operation that the user can take i a [todo app](https://todomvc.com/).

```ts
type Action =
  | {
      type: 'ADD_TODO'
      text: string
    }
  | {
      type: 'UPDATE_TODO'
      id: number
      text: string
      completed: boolean
    }
  | {
      type: 'DELETE_TODO'
      id: number
    }
```

The `type` tag makes sure every member of the union is disjointed.

**Note**. The name of the field that acts as a tag is chosen by the developer. It doesn't have to be "type". In `fp-ts` the convention is to use a `_tag` field.

Now that we've seen few examples we can define more explicitly what algebraic data types are:

> In general, an algebraic data type specifies a sum of one or more alternatives, where each alternative is a product of zero or more fields.

Sum types can be **polymorphic** and **recursive**.

**Example** (linked list)

```ts
//               ↓ type parameter
export type List<A> =
  | { readonly _tag: 'Nil' }
  | { readonly _tag: 'Cons'; readonly head: A; readonly tail: List<A> }
//                                                              ↑ recursion
```

**Quiz** (TypeScript). Which of the following data types is a product or a sum type?

- `ReadonlyArray<A>`
- `Record<string, A>`
- `Record<'k1' | 'k2', A>`
- `ReadonlyMap<string, A>`
- `ReadonlyMap<'k1' | 'k2', A>`

### Constructors

A sum type with `n` elements needs at least `n` **constructors**, one for each member:

**Example** (redux action creators)

```ts
export type Action =
  | {
      readonly type: 'ADD_TODO'
      readonly text: string
    }
  | {
      readonly type: 'UPDATE_TODO'
      readonly id: number
      readonly text: string
      readonly completed: boolean
    }
  | {
      readonly type: 'DELETE_TODO'
      readonly id: number
    }

export const add = (text: string): Action => ({
  type: 'ADD_TODO',
  text
})

export const update = (
  id: number,
  text: string,
  completed: boolean
): Action => ({
  type: 'UPDATE_TODO',
  id,
  text,
  completed
})

export const del = (id: number): Action => ({
  type: 'DELETE_TODO',
  id
})
```

**Example** (TypeScript, linked lists)

```ts
export type List<A> =
  | { readonly _tag: 'Nil' }
  | { readonly _tag: 'Cons'; readonly head: A; readonly tail: List<A> }

// a nullary constructor can be implemented as a constant
export const nil: List<never> = { _tag: 'Nil' }

export const cons = <A>(head: A, tail: List<A>): List<A> => ({
  _tag: 'Cons',
  head,
  tail
})

// equivalent to an array containing [1, 2, 3]
const myList = cons(1, cons(2, cons(3, nil)))
```

### Pattern matching

JavaScript doesn't support [pattern matching](https://github.com/tc39/proposal-pattern-matching) (neither does TypeScript) but we can simulate it with a `match` function.

**Example** (TypeScript, linked lists)

```ts
interface Nil {
  readonly _tag: 'Nil'
}

interface Cons<A> {
  readonly _tag: 'Cons'
  readonly head: A
  readonly tail: List<A>
}

export type List<A> = Nil | Cons<A>

export const match = <R, A>(
  onNil: () => R,
  onCons: (head: A, tail: List<A>) => R
) => (fa: List<A>): R => {
  switch (fa._tag) {
    case 'Nil':
      return onNil()
    case 'Cons':
      return onCons(fa.head, fa.tail)
  }
}

// returns `true` if the list is empty
export const isEmpty = match(
  () => true,
  () => false
)

// returns the first element of the list or `undefined`
export const head = match(
  () => undefined,
  (head, _tail) => head
)

// returns the length of the list, recursively
export const length: <A>(fa: List<A>) => number = match(
  () => 0,
  (_, tail) => 1 + length(tail)
)
```

**Quiz**. Why's the `head` API sub optimal?

-> See the [answer here](src/quiz-answers/pattern-matching.md)

**Note**. TypeScript offers a great feature for sum types: **exhaustive check**. The type checker can _check_, no pun intended, whether all the possible cases are handled by the `switch` defined in the body of the function.

### Why "sum" types?

Because the following identity holds true:

```ts
C(A | B) = C(A) + C(B)
```

> The sum of the cardinality is the sum of the cardinalities

**Example** (the `Option` type)

```ts
interface None {
  readonly _tag: 'None'
}

interface Some<A> {
  readonly _tag: 'Some'
  readonly value: A
}

type Option<A> = None | Some<A>
```

From the general formula `C(Option<A>) = 1 + C(A)` we can derive the cardinality of the `Option<boolean>` type: `1 + 2 = 3` members.

### When should I use a sum type?

When the components would be **dependent** if implemented with a product type.

**Example** (`React` props)

```ts
import * as React from 'react'

interface Props {
  readonly editable: boolean
  readonly onChange?: (text: string) => void
}

class Textbox extends React.Component<Props> {
  render() {
    if (this.props.editable) {
      // error: Cannot invoke an object which is possibly 'undefined' :(
      this.props.onChange('a')
    }
    return <div />
  }
}
```

The problem here is that `Props` is modeled like a product, but `onChange` **depends** on `editable`.

A sum type fits the use case better:

```ts
import * as React from 'react'

type Props =
  | {
      readonly type: 'READONLY'
    }
  | {
      readonly type: 'EDITABLE'
      readonly onChange: (text: string) => void
    }

class Textbox extends React.Component<Props> {
  render() {
    switch (this.props.type) {
      case 'EDITABLE':
        this.props.onChange('a') // :)
    }
    return <div />
  }
}
```

**Example** (node callbacks)

```ts
declare function readFile(
  path: string,
  //         ↓ ---------- ↓ CallbackArgs
  callback: (err?: Error, data?: string) => void
): void
```

The result of the `readFile` operation is modeled like a product type (to be more precise, as a tuple) which is later on passed to the `callback` function:

```ts
type CallbackArgs = [Error | undefined, string | undefined]
```

the callback components though are **dependent**: we either get an `Error` **or** a `string`:

| err         | data        | legal? |
| ----------- | ----------- | ------ |
| `Error`     | `undefined` | ✓      |
| `undefined` | `string`    | ✓      |
| `Error`     | `string`    | ✘      |
| `undefined` | `undefined` | ✘      |

This API is clearly not modeled on the following premise:

> Make impossible state unrepresentable

A sum type would've been a better choice, but which sum type?
We'll see how to handle errors in a functional way.

**Quiz**. Recently API's based on callbacks have been largely replaced by their `Promise` equivalents.

```ts
declare function readFile(path: string): Promise<string>
```

Can you find some cons of the Promise solution when using static typing like in TypeScript?

## Functional error handling

Let's see how to handle errors in a functional way.

A function that throws exceptions is an example of a partial function.

In the previous chapters we have seen that every partial function `f` can always be brought back to a total one `f'`.

```
f': X ⟶ Option(Y)
```

Now that we know a bit more about sum types in TypeScript we can define the `Option` without much issues.

### The `Option` type

The type `Option` represents the effect of a computation which may fail (case `None`) or return a type `A` (case `Some<A>`):

```ts
// represents a failure
interface None {
  readonly _tag: 'None'
}

// represents a success
interface Some<A> {
  readonly _tag: 'Some'
  readonly value: A
}

type Option<A> = None | Some<A>
```

Constructors and pattern matching:

```ts
const none: Option<never> = { _tag: 'None' }

const some = <A>(value: A): Option<A> => ({ _tag: 'Some', value })

const match = <R, A>(onNone: () => R, onSome: (a: A) => R) => (
  fa: Option<A>
): R => {
  switch (fa._tag) {
    case 'None':
      return onNone()
    case 'Some':
      return onSome(fa.value)
  }
}
```

The `Option` type can be used to avoid throwing exceptions or representing the optional values, thus we can move from:

```ts
//                        this is a lie ↓
const head = <A>(as: ReadonlyArray<A>): A => {
  if (as.length === 0) {
    throw new Error('Empty array')
  }
  return as[0]
}

let s: string
try {
  s = String(head([]))
} catch (e) {
  s = e.message
}
```

where the type system is ignorant about the possibility of failure, to:

```ts
import { pipe } from 'fp-ts/function'

//                                      ↓ the type system "knows" that this computation may fail
const head = <A>(as: ReadonlyArray<A>): Option<A> =>
  as.length === 0 ? none : some(as[0])

declare const numbers: ReadonlyArray<number>

const result = pipe(
  head(numbers),
  match(
    () => 'Empty array',
    (n) => String(n)
  )
)
```

where **the possibility of an error is encoded in the type system**.

If we attempt to access the `value` property of an `Option` without checking in which case we are, the type system will warn us about the possibility of getting an error:

```ts
declare const numbers: ReadonlyArray<number>

const result = head(numbers)
result.value // type checker error: Property 'value' does not exist on type 'Option<number>'
```

The only way to access the value contained in an `Option` is to handle also the failure case using the `match` function.

```ts
pipe(result, match(
  () => ...handle error...
  (n) => ...go on with my business logic...
))
```

Is it possible to define instances for the abstractions we've seen in the chapters before? Let's begin with `Eq`.

### An `Eq` instance

Suppose we have two values of type `Option<string>` and that we want to compare them to check if their equal:

```ts
import { pipe } from 'fp-ts/function'
import { match, Option } from 'fp-ts/Option'

declare const o1: Option<string>
declare const o2: Option<string>

const result: boolean = pipe(
  o1,
  match(
    // onNone o1
    () =>
      pipe(
        o2,
        match(
          // onNone o2
          () => true,
          // onSome o2
          () => false
        )
      ),
    // onSome o1
    (s1) =>
      pipe(
        o2,
        match(
          // onNone o2
          () => false,
          // onSome o2
          (s2) => s1 === s2 // <= qui uso l'uguaglianza tra stringhe
        )
      )
  )
)
```

What if we had two values of type `Option<number>`? It would be pretty annoying to write the same code we just wrote above, the only difference afterall would be how we compare the two values contained in the `Option`.

Thus we can generalize the necessary code by requiring the user to provide an `Eq` instance for `A` and then derive an `Eq` instance for `Option<A>`.

In other words we can define a **combinator** `getEq`: given an `Eq<A>` this combinator will return an `Eq<Option<A>>`:

```ts
import { Eq } from 'fp-ts/Eq'
import { pipe } from 'fp-ts/function'
import { match, Option, none, some } from 'fp-ts/Option'

export const getEq = <A>(E: Eq<A>): Eq<Option<A>> => ({
  equals: (first, second) =>
    pipe(
      first,
      match(
        () =>
          pipe(
            second,
            match(
              () => true,
              () => false
            )
          ),
        (a1) =>
          pipe(
            second,
            match(
              () => false,
              (a2) => E.equals(a1, a2) // <= here I use the `A` equality
            )
          )
      )
    )
})

import * as S from 'fp-ts/string'

const EqOptionString = getEq(S.Eq)

console.log(EqOptionString.equals(none, none)) // => true
console.log(EqOptionString.equals(none, some('b'))) // => false
console.log(EqOptionString.equals(some('a'), none)) // => false
console.log(EqOptionString.equals(some('a'), some('b'))) // => false
console.log(EqOptionString.equals(some('a'), some('a'))) // => true
```

The best thing about being able to define an `Eq` instance for a type `Option<A>` is being able to leverage all of the combiners we've seen previously for `Eq`.

**Example**:

An `Eq` instance for the type `Option<readonly [string, number]>`:

```ts
import { tuple } from 'fp-ts/Eq'
import * as N from 'fp-ts/number'
import { getEq, Option, some } from 'fp-ts/Option'
import * as S from 'fp-ts/string'

type MyTuple = readonly [string, number]

const EqMyTuple = tuple<MyTuple>(S.Eq, N.Eq)

const EqOptionMyTuple = getEq(EqMyTuple)

const o1: Option<MyTuple> = some(['a', 1])
const o2: Option<MyTuple> = some(['a', 2])
const o3: Option<MyTuple> = some(['b', 1])

console.log(EqOptionMyTuple.equals(o1, o1)) // => true
console.log(EqOptionMyTuple.equals(o1, o2)) // => false
console.log(EqOptionMyTuple.equals(o1, o3)) // => false
```

If we slightly modify the imports in the following snippet we can obtain a similar result for `Ord`:

```ts
import * as N from 'fp-ts/number'
import { getOrd, Option, some } from 'fp-ts/Option'
import { tuple } from 'fp-ts/Ord'
import * as S from 'fp-ts/string'

type MyTuple = readonly [string, number]

const OrdMyTuple = tuple<MyTuple>(S.Ord, N.Ord)

const OrdOptionMyTuple = getOrd(OrdMyTuple)

const o1: Option<MyTuple> = some(['a', 1])
const o2: Option<MyTuple> = some(['a', 2])
const o3: Option<MyTuple> = some(['b', 1])

console.log(OrdOptionMyTuple.compare(o1, o1)) // => 0
console.log(OrdOptionMyTuple.compare(o1, o2)) // => -1
console.log(OrdOptionMyTuple.compare(o1, o3)) // => -1
```

### `Semigroup` and `Monoid` instances

Now, let's suppose we want to "merge" two different `Option<A>`s,: there are four different cases:

| x       | y       | concat(x, y) |
| ------- | ------- | ------------ |
| none    | none    | none         |
| some(a) | none    | none         |
| none    | some(a) | none         |
| some(a) | some(b) | ?            |

There's an issue in the last case, we need a recipe to "merge" two different `A`s.

If only we had such a recipe..Isn't that the job our old good friends `Semigroup`s!?

| x        | y        | concat(x, y)           |
| -------- | -------- | ---------------------- |
| some(a1) | some(a2) | some(S.concat(a1, a2)) |

All we need to do is to require the user to provide a `Semigroup` instance for `A` and then derive a `Semigroup` instance for `Option<A>`.

```ts
// the implementation is left as an exercise for the reader
declare const getApplySemigroup: <A>(S: Semigroup<A>) => Semigroup<Option<A>>
```

**Quiz**. Is it possible to add a neutral element to the previous semigroup to make it a monoid?

```ts
// the implementation is left as an exercise for the reader
declare const getApplicativeMonoid: <A>(M: Monoid<A>) => Monoid<Option<A>>
```

It is possible to define a monoid instance for `Option<A>` that behaves like that:

| x        | y        | concat(x, y)           |
| -------- | -------- | ---------------------- |
| none     | none     | none                   |
| some(a1) | none     | some(a1)               |
| none     | some(a2) | some(a2)               |
| some(a1) | some(a2) | some(S.concat(a1, a2)) |

```ts
// the implementation is left as an exercise for the reader
declare const getMonoid: <A>(S: Semigroup<A>) => Monoid<Option<A>>
```

**Quiz**. What is the `empty` member for the monoid?

-> See the [answer here](src/quiz-answers/option-semigroup-monoid-second.md)

**Example**

Using `getMonoid` we can derive another two useful monoids:

(Monoid returning the left-most non-`None` value)

| x        | y        | concat(x, y) |
| -------- | -------- | ------------ |
| none     | none     | none         |
| some(a1) | none     | some(a1)     |
| none     | some(a2) | some(a2)     |
| some(a1) | some(a2) | some(a1)     |

```ts
import { Monoid } from 'fp-ts/Monoid'
import { getMonoid, Option } from 'fp-ts/Option'
import { first } from 'fp-ts/Semigroup'

export const getFirstMonoid = <A = never>(): Monoid<Option<A>> =>
  getMonoid(first())
```

and its dual:

(Monoid returning the right-most non-`None` value)

| x        | y        | concat(x, y) |
| -------- | -------- | ------------ |
| none     | none     | none         |
| some(a1) | none     | some(a1)     |
| none     | some(a2) | some(a2)     |
| some(a1) | some(a2) | some(a2)     |

```ts
import { Monoid } from 'fp-ts/Monoid'
import { getMonoid, Option } from 'fp-ts/Option'
import { last } from 'fp-ts/Semigroup'

export const getLastMonoid = <A = never>(): Monoid<Option<A>> =>
  getMonoid(last())
```

**Example**

`getLastMonoid` can be useful to manage optional values. Let's seen an example where we want to derive user settings for a text editor, in this case VSCode.

```ts
import { Monoid, struct } from 'fp-ts/Monoid'
import { getMonoid, none, Option, some } from 'fp-ts/Option'
import { last } from 'fp-ts/Semigroup'

/** VSCode settings */
interface Settings {
  /** Controls the font family */
  readonly fontFamily: Option<string>
  /** Controls the font size in pixels */
  readonly fontSize: Option<number>
  /** Limit the width of the minimap to render at most a certain number of columns. */
  readonly maxColumn: Option<number>
}

const monoidSettings: Monoid<Settings> = struct({
  fontFamily: getMonoid(last()),
  fontSize: getMonoid(last()),
  maxColumn: getMonoid(last())
})

const workspaceSettings: Settings = {
  fontFamily: some('Courier'),
  fontSize: none,
  maxColumn: some(80)
}

const userSettings: Settings = {
  fontFamily: some('Fira Code'),
  fontSize: some(12),
  maxColumn: none
}

/** userSettings overrides workspaceSettings */
console.log(monoidSettings.concat(workspaceSettings, userSettings))
/*
{ fontFamily: some("Fira Code"),
  fontSize: some(12),
  maxColumn: some(80) }
*/
```

**Quiz**. Suppose VSCode cannot manage more than `80` columns per row, how could we modify the definition of `monoidSettings` to take that into account?

### The `Either` type

We have seen how the `Option` data type can be used to handle partial functions, which often represent computations than can fail or throw exceptions.

This data type might be limiting in some use cases tho. While in the case of success we get `Some<A>` which contains information of type `A`, the other member, `None` does not carry any data. We know it failed, but we don't know the reason.

In order to fix this we simply need to another data type to represent failure, we'll call it `Left<E>`. We'll also replace the `Some<A>` type with the `Right<A>`.

```ts
// represents a failure
interface Left<E> {
  readonly _tag: 'Left'
  readonly left: E
}

// represents a success
interface Right<A> {
  readonly _tag: 'Right'
  readonly right: A
}

type Either<E, A> = Left<E> | Right<A>
```

Constructors and pattern matching:

```ts
const left = <E, A>(left: E): Either<E, A> => ({ _tag: 'Left', left })

const right = <A, E>(right: A): Either<E, A> => ({ _tag: 'Right', right })

const match = <E, R, A>(onLeft: (left: E) => R, onRight: (right: A) => R) => (
  fa: Either<E, A>
): R => {
  switch (fa._tag) {
    case 'Left':
      return onLeft(fa.left)
    case 'Right':
      return onRight(fa.right)
  }
}
```

Let's get back to the previous callback example:

```ts
declare function readFile(
  path: string,
  callback: (err?: Error, data?: string) => void
): void

readFile('./myfile', (err, data) => {
  let message: string
  if (err !== undefined) {
    message = `Error: ${err.message}`
  } else if (data !== undefined) {
    message = `Data: ${data.trim()}`
  } else {
    // should never happen
    message = 'The impossible happened'
  }
  console.log(message)
})
```

we can change it's signature to:

```ts
declare function readFile(
  path: string,
  callback: (result: Either<Error, string>) => void
): void
```

and consume the API in such way:

```ts
readFile('./myfile', (e) =>
  pipe(
    e,
    match(
      (err) => `Error: ${err.message}`,
      (data) => `Data: ${data.trim()}`
    ),
    console.log
  )
)
```

# Category theory

We have seen how a founding pillar of functional programming is **composition**.

> And how do we solve problems? We decompose bigger problems into smaller problems. If the smaller problems are still too big, we decompose them further, and so on. Finally, we write code that solves all the small problems. And then comes the essence of programming: we compose those pieces of code to create solutions to larger problems. Decomposition wouldn't make sense if we weren't able to put the pieces back together. - Bartosz Milewski

But what does it means exactly? How can we state whether two things _compose_? And how can we say if two things compose _well_?

> Entities are composable if we can easily and generally combine their behaviours in some way without having to modify the entities being combined. I think of composability as being the key ingredient necessary for achieving reuse, and for achieving a combinatorial expansion of what is succinctly expressible in a programming model. - Paul Chiusano

We've briefly mentioned how a program written in functional styles tends to resemble a pipeline:

```ts
const program = pipe(
  input,
  f1, // pure function
  f2, // pure function
  f3, // pure function
  ...
)
```

But how simple it is to code in such a style? Let's try:

```ts
import { pipe } from 'fp-ts/function'
import * as RA from 'fp-ts/ReadonlyArray'

const double = (n: number): number => n * 2

/**
 * Given a ReadonlyArray<number> the program doubles the first element and returns it
 */
const program = (input: ReadonlyArray<number>): number =>
  pipe(
    input,
    RA.head, // compilation error! Type 'Option<number>' is not assignable to type 'number'
    double
  )
```

Why do I get a compilation error? Because `head` and `double` do not compose.

```ts
head: (as: ReadonlyArray<number>) => Option<number>
double: (n: number) => number
```

`head`'s codomain is not included in `double`'s domain.

Looks like our goal to program using pure functions is over..Or is it?

We need to be able to refer to some **rigorous theory**, one able to answer such fundamental questions.

We need to refer to a **formal definition** of composability.

Luckily, for the last 70 years ago, a large number of researchers, members of the oldest and largest humanity's open source project (mathematics) occupied itself with developing a theory dedicated to composability: **category theory**, a branch of mathematics founded by Saunders Mac Lane along Samuel Eilenberg (1945).

> Categories capture the essence of composition.

Saunders Mac Lane

<center>
<img src="images/maclane.jpg" width="300" alt="Saunders Mac Lane" />

(Saunders Mac Lane)

<img src="images/eilenberg.jpg" width="300" alt="Samuel Eilenberg" />

(Samuel Eilenberg)

</center>

We'll see in the following chapters how a category can form the basis for:

- a model for a generic **programming language**
- a model for the concept of **composition**

## Definition

The definition of a category, even though it isn't really complex, is a bit long, thus I'll split it in two parts:

- the first is merely technical (we need to define its constituents)
- the second one will be more relevant to what we care for: a notion of composition

### Part I (Constituents)

A category is a pair of `(Objects, Morphisms)` where:

- `Objects` is a collection of **objects**
- `Morphisms` is a collection of **morphisms** (also called "arrows") between objects

**Note**. The term "object" has nothing to do with the concept of "objects" in programming. Just think about those "objects" as black boxes we can't inspect, or simple placeholders useful to define the various morphisms.

Every morphism `f` owns a source object `A` and a target object `B`.

In every morphism, both `A` and `B` are members of `Objects`. We write `f: A ⟼ B` and we say that "f is a morphism from A to B".

<img src="images/morphism.png" width="300" alt="A morphism" />

**Note**. For simplicity, from now on, I'll use labels only for objects, skipping the circles.

### Part II (Composition)

There is an operation, `∘`, called "composition", such as the following properties hold true:

- (**composition of morphisms**) every time we have two morphisms `f: A ⟼ B` and `g: B ⟼ C` in `Morphisms` then there has to be a third morphism `g ∘ f: A ⟼ C` in `Morphisms` which is the _composition_ of `f` and `g`

<img src="images/composition.png" width="300" alt="composition" />

- (**associativity**) if `f: A ⟼ B`, `g: B ⟼ C` and `h: C ⟼ D` then `h ∘ (g ∘ f) = (h ∘ g) ∘ f`

<img src="images/associativity.png" width="500" alt="associativity" />

- (**identity**) for every object `X`, there is a morphism `identity: X ⟼ X` called _identity morphism_ of `X`, such as for every morphism `f: A ⟼ X` and `g: X ⟼ B`, the following equation holds true `identity ∘ f = f` and `g ∘ identity = g`.

<img src="images/identity.png" width="300" alt="identity" />

**Example**

<img src="images/category.png" width="300" alt="a simple category" />

This category is very simple, there are three objects and six morphisms (1<sub>A</sub>, 1<sub>B</sub>, 1<sub>C</sub> are the identity morphisms for `A`, `B`, `C`).

## Modeling programming languages with categories

A category can be seen as a simplified model for a **typed programming language**, where:

- objects are **types**
- morphisms are **functions**
- `∘` is the usual **function composition**

The following diagram:

<img src="images/category.png" width="300" alt="a simple programming language" />

can be seen as an imaginary (and simple) programming language with just three types and six functions

Example given:

- `A = string`
- `B = number`
- `C = boolean`
- `f = string => number`
- `g = number => boolean`
- `g ∘ f = string => boolean`

The implementation could be something like:

```ts
const idA = (s: string): string => s

const idB = (n: number): number => n

const idC = (b: boolean): boolean => b

const f = (s: string): number => s.length

const g = (n: number): boolean => n > 2

// gf = g ∘ f
const gf = (s: string): boolean => g(f(s))
```

## A category for TypeScript

We can define a category, let's call it _TS_, as a simplified model of the TypeScript language, where:

- **objects** are all the possible TypeScript types: `string`, `number`, `ReadonlyArray<string>`, etc...
- **morphisms** are all TypeScript functions: `(a: A) => B`, `(b: B) => C`, ... where `A`, `B`, `C`, ... are TypeScript types
- the **identity morphisms** are all encoded in a single polymorphic function `const identity = <A>(a: A): A => a`
- **morphism's composition** is the usual function composition (which we know to be associative)

As a model of TypeScript, the _TS_ category may seem a bit limited: no loops, no `if`s, there's _almost_ nothing... that being said that simplified model is rich enough to help us reach our goal: to reason about a well-defined notion of composition.

## Composition's core problem

In the _TS_ category we can compose two generic functions `f: (a: A) => B` and `g: (c: C) => D` as long as `C = B`

```ts
function flow<A, B, C>(f: (a: A) => B, g: (b: B) => C): (a: A) => C {
  return (a) => g(f(a))
}

function pipe<A, B, C>(a: A, f: (a: A) => B, g: (b: B) => C): C {
  return flow(f, g)(a)
}
```

But what happens if `B != C`? How can we compose two such functions? Should we give up?

In the next section we'll see under which conditions such a composition is possible.

**Spoiler**

- to compose `f: (a: A) => B` with `g: (b: B) => C` we use our usual function composition
- to compose `f: (a: A) => F<B>` with `g: (b: B) => C` we need a **functor** instance for `F`
- to compose `f: (a: A) => F<B>` with `g: (b: B, c: C) => D` we need an **applicative functor** instance for `F`
- to compose `f: (a: A) => F<B>` with `g: (b: B) => F<C>` we need a **monad** instance for `F`

<img src="images/spoiler.png" width="900" alt="The four composition recipes" />

The problem we started with at the beginning of this chapter corresponds to the second situation, where `F` is the `Option` type:

```ts
// A = ReadonlyArray<number>, B = number, F = Option
head: (as: ReadonlyArray<number>) => Option<number>
double: (n: number) => number
```

To solve it, the next chapter will talk about functors.

# Functors

In the last section we've spoken about the _TS_ category (the TypeScript category) and about function composition's core problem:

> How can we compose two generic functions `f: (a: A) => B` and `g: (c: C) => D`?

Why is finding solutions to this problem so important?

Because, if it is true that categories can be used to model programming languages, morphisms (functions in the _TS_ category) can be used to model **programs**.

Thus, solving this abstract problem means finding a concrete way of **composing programs in a generic way**. And _that_ is really interesting for us developers, isn't it?

## Functions as programs

If we want to model programs with functions we need to tackle an issue immediately:

> How is it possible to model a program that produces side effects with a pure function?

The answer is to model side effects through **effects**, meaning types that **represent** side effects.

Let's see two possible techniques to do so in JavaScript:

- define a DSL (domain specific language) for effects
- use a _thunk_

The first technique, using a DSL, means modifying a program like:

```ts
function log(message: string): void {
  console.log(message) // side effect
}
```

changing its codomain to make the function return a **description** of the side effect:

```ts
type DSL = ... // sum type of every possible effect handled by the system

function log(message: string): DSL {
  return {
    type: "log",
    message
  }
}
```

**Quiz**. Is the freshly defined `log` function really pure? Actually `log('foo') !== log('foo')`!

This technique requires a way to combine effects and the definition of an interpreter able to execute the side effects when launching the final program.

The second technique, way simpler in TypeScript, is to enclose the computation in a _thunk_:

```ts
// a thunk representing a synchronous side effect
type IO<A> = () => A

const log = (message: string): IO<void> => {
  return () => console.log(message) // returns a thunk
}
```

The `log` program, once executed, won't cause immediately a side effect, but returns **a value representing the computation** (also known as _action_).

```ts
import { IO } from 'fp-ts/IO'

export const log = (message: string): IO<void> => {
  return () => console.log(message) // returns a thunk
}

export const main = log('hello!')
// there's nothing in the output at this point
// because `main` is only an inert value
// representing the computation

main()
// only when launching the program I will see the result
```

In functional programming there's a tendency to shove side effects (under the form of effects) to the border of the system (the `main` function) where they are executed by an interpreter obtaining the following schema:

> system = pure core + imperative shell

In _purely functional_ languages (like Haskell, PureScript or Elm) this division is strict and clear and imposed by the very languages.

Even with this thunk technique (the same technique used in `fp-ts`) we need a way to combine effects, which brings us back to our goal of composing programs in a generic way, let's see how.

We first need a bit of (informal) terminology: we'll call **pure program** a function with the following signature:

```ts
(a: A) => B
```

Such a signature models a program that takes an input of type `A` and returns a result of type `B` without any effect.

**Example**

The `len` program:

```ts
const len = (s: string): number => s.length
```

We'll call an **effectful program** a function with the following signature:

```ts
(a: A) => F<B>
```

Such a signature models a program that takes an input of type `A` and returns a result of type `B` together with an **effect** `F`, where `F` is some sort of type constructor.

Let's recall that a [type constructor](https://en.wikipedia.org/wiki/Type_constructor) is an `n`-ary type operator that takes as argument one or more types and returns another type. We have seen examples of such constructors as `Option`, `ReadonlyArray`, `Either`.

**Example**

The `head` program:

```ts
import { Option, some, none } from 'fp-ts/Option'

const head = <A>(as: ReadonlyArray<A>): Option<A> =>
  as.length === 0 ? none : some(as[0])
```

is a program with an `Option` effect.

When we talk about effects we are interested in `n`-ary type constructors where `n >= 1`, example given:

| Type constructor   | Effect (interpretation)                        |
| ------------------ | ---------------------------------------------- |
| `ReadonlyArray<A>` | a non deterministic computation                |
| `Option<A>`        | a computation that may fail                    |
| `Either<E, A>`     | a computation that may fail                    |
| `IO<A>`            | a synchronous computation that **never fails** |
| `Task<A>`          | an asynchronous computation **never fails**    |
| `Reader<R, A>`     | reading from an environment                    |

where

```ts
// a thunk returning a `Promise`
type Task<A> = () => Promise<A>
```

```ts
// `R` represents an "environment" needed for the computation
// (we can "read" from it) and `A` is the result
type Reader<R, A> = (r: R) => A
```

Let's get back to our core problem:

> How do we compose two generic functions `f: (a: A) => B` e `g: (c: C) => D`?

With our current set of rules this general problem is not solvable. We need to add some _boundaries_ to `B` and `C`.

We already know that if `B = C` then the solution is the usual function composition.

```ts
function flow<A, B, C>(f: (a: A) => B, g: (b: B) => C): (a: A) => C {
  return (a) => g(f(a))
}
```

But what about other cases?

## A boundary that leads to functors

Let's consider the following boundary: `B = F<C>` for some type constructor `F`, we have the following situation:

- `f: (a: A) => F<B>` is an effectful program
- `g: (b: B) => C` is a pure program

In order to compose `f` with `g` we need to find a procedure that allows us to derive a function `g` from a function `(b: B) => C` to a function `(fb: F<B>) => F<C>` in order to use the usual function composition (this way the codomain of `f` would be the same of the new function's domain).

<img src="images/map.png" width="500" alt="map" />

We have mutated the original problem in a new one: can we find a function, let's call it `map`, that operates this way?

Let's see some practical example:

**Example** (`F = ReadonlyArray`)

```ts
import { flow, pipe } from 'fp-ts/function'

// transforms functions `B -> C` to functions `ReadonlyArray<B> -> ReadonlyArray<C>`
const map = <B, C>(g: (b: B) => C) => (
  fb: ReadonlyArray<B>
): ReadonlyArray<C> => fb.map(g)

// -------------------
// usage example
// -------------------

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const getFollowers = (user: User): ReadonlyArray<User> => user.followers
const getName = (user: User): string => user.name

// getFollowersNames: User -> ReadonlyArray<string>
const getFollowersNames = flow(getFollowers, map(getName))

// let's use `pipe` instead of `flow`...
export const getFollowersNames2 = (user: User) =>
  pipe(user, getFollowers, map(getName))

const user: User = {
  id: 1,
  name: 'Ruth R. Gonzalez',
  followers: [
    { id: 2, name: 'Terry R. Emerson', followers: [] },
    { id: 3, name: 'Marsha J. Joslyn', followers: [] }
  ]
}

console.log(getFollowersNames(user)) // => [ 'Terry R. Emerson', 'Marsha J. Joslyn' ]
```

**Example** (`F = Option`)

```ts
import { flow } from 'fp-ts/function'
import { none, Option, match, some } from 'fp-ts/Option'

// transforms functions `B -> C` to functions `Option<B> -> Option<C>`
const map = <B, C>(g: (b: B) => C): ((fb: Option<B>) => Option<C>) =>
  match(
    () => none,
    (b) => {
      const c = g(b)
      return some(c)
    }
  )

// -------------------
// usage example
// -------------------

import * as RA from 'fp-ts/ReadonlyArray'

const head: (input: ReadonlyArray<number>) => Option<number> = RA.head
const double = (n: number): number => n * 2

// getDoubleHead: ReadonlyArray<number> -> Option<number>
const getDoubleHead = flow(head, map(double))

console.log(getDoubleHead([1, 2, 3])) // => some(2)
console.log(getDoubleHead([])) // => none
```

**Example** (`F = IO`)

```ts
import { flow } from 'fp-ts/function'
import { IO } from 'fp-ts/IO'

// transforms functions `B -> C` to functions `IO<B> -> IO<C>`
const map = <B, C>(g: (b: B) => C) => (fb: IO<B>): IO<C> => () => {
  const b = fb()
  return g(b)
}

// -------------------
// usage example
// -------------------

interface User {
  readonly id: number
  readonly name: string
}

// a dummy in-memory database
const database: Record<number, User> = {
  1: { id: 1, name: 'Ruth R. Gonzalez' },
  2: { id: 2, name: 'Terry R. Emerson' },
  3: { id: 3, name: 'Marsha J. Joslyn' }
}

const getUser = (id: number): IO<User> => () => database[id]
const getName = (user: User): string => user.name

// getUserName: number -> IO<string>
const getUserName = flow(getUser, map(getName))

console.log(getUserName(1)()) // => Ruth R. Gonzalez
```

**Example** (`F = Task`)

```ts
import { flow } from 'fp-ts/function'
import { Task } from 'fp-ts/Task'

// transforms functions `B -> C` into functions `Task<B> -> Task<C>`
const map = <B, C>(g: (b: B) => C) => (fb: Task<B>): Task<C> => () => {
  const promise = fb()
  return promise.then(g)
}

// -------------------
// usage example
// -------------------

interface User {
  readonly id: number
  readonly name: string
}

// a dummy remote database
const database: Record<number, User> = {
  1: { id: 1, name: 'Ruth R. Gonzalez' },
  2: { id: 2, name: 'Terry R. Emerson' },
  3: { id: 3, name: 'Marsha J. Joslyn' }
}

const getUser = (id: number): Task<User> => () => Promise.resolve(database[id])
const getName = (user: User): string => user.name

// getUserName: number -> Task<string>
const getUserName = flow(getUser, map(getName))

getUserName(1)().then(console.log) // => Ruth R. Gonzalez
```

**Example** (`F = Reader`)

```ts
import { flow } from 'fp-ts/function'
import { Reader } from 'fp-ts/Reader'

// transforms functions `B -> C` into functions `Reader<R, B> -> Reader<R, C>`
const map = <B, C>(g: (b: B) => C) => <R>(fb: Reader<R, B>): Reader<R, C> => (
  r
) => {
  const b = fb(r)
  return g(b)
}

// -------------------
// usage example
// -------------------

interface User {
  readonly id: number
  readonly name: string
}

interface Env {
  // a dummy in-memory database
  readonly database: Record<string, User>
}

const getUser = (id: number): Reader<Env, User> => (env) => env.database[id]
const getName = (user: User): string => user.name

// getUserName: number -> Reader<Env, string>
const getUserName = flow(getUser, map(getName))

console.log(
  getUserName(1)({
    database: {
      1: { id: 1, name: 'Ruth R. Gonzalez' },
      2: { id: 2, name: 'Terry R. Emerson' },
      3: { id: 3, name: 'Marsha J. Joslyn' }
    }
  })
) // => Ruth R. Gonzalez
```

More generally, when a type constructor `F` admits a `map` function, we say it admits a **functor instance**.

From a mathematical point of view, functors are **maps between categories** that preserve the structure of the category, meaning they preserve the identity morphisms and the composition operation.

Since categories are pairs of objects and morphisms, a functor too is a pair of two things:

- a **map between objects** that binds every object `X` in _C_ to an object in _D_.
- a **map between morphisms** that binds every morphism `f` in _C_ to a morphism `map(f)` in _D_.

where _C_ e _D_ are two categories (aka two programming languages).

<img src="images/functor.png" width="500" alt="functor" />

Even though a map between two different programming languages is a fascinating idea, we're more interested in a map where _C_ and _D_ are the same (the _TS_ category). In that case we're talking about **endofunctors** (from the greek "endo" meaning "inside", "internal").

From now on, unless specified differently, when we write "functor" we mean an endofunctor in the _TS_ category.

Now we know the practical side of functors, let's see the formal definition.

## Definition

A functor is a pair `(F, map)` where:

- `F` is an `n`-ary (`n >= 1`) type constructor mapping every type `X` in a type `F<X>` (**map between objects**)
- `map` is a function with the following signature:

```ts
map: <A, B>(f: (a: A) => B) => ((fa: F<A>) => F<B>)
```

that maps every function `f: (a: A) => B` in a function `map(f): (fa: F<A>) => F<B>` (**map between morphism**)

The following properties have to hold true:

- `map(1`<sub>X</sub>`)` = `1`<sub>F(X)</sub> (**identities go to identities**)
- `map(g ∘ f) = map(g) ∘ map(f)` (**the image of a composition is the composition of its images**)

The second law allows to refactor and optimize the following computation:

```ts
import { flow, increment, pipe } from 'fp-ts/function'
import { map } from 'fp-ts/ReadonlyArray'

const double = (n: number): number => n * 2

// iterates array twice
console.log(pipe([1, 2, 3], map(double), map(increment))) // => [ 3, 5, 7 ]

// single iteration
console.log(pipe([1, 2, 3], map(flow(double, increment)))) // => [ 3, 5, 7 ]
```

## Functors and functional error handling

Functors have a positive impact on functional error handling, let's see a practical example:

```ts
declare const doSomethingWithIndex: (index: number) => string

export const program = (ns: ReadonlyArray<number>): string => {
  // -1 indicates that no element has been found
  const i = ns.findIndex((n) => n > 0)
  if (i !== -1) {
    return doSomethingWithIndex(i)
  }
  throw new Error('cannot find a positive number')
}
```

Using the native `findIndex` API we are forced to use an `if` branch to test whether we have a result different than `-1`. If we forget to do so, the value `-1` could be unintentionally passed as input to `doSomethingWithIndex`.

Let's see how easier it is to obtain the same behavior using `Option` and its functor instance:

```ts
import { pipe } from 'fp-ts/function'
import { map, Option } from 'fp-ts/Option'
import { findIndex } from 'fp-ts/ReadonlyArray'

declare const doSomethingWithIndex: (index: number) => string

export const program = (ns: ReadonlyArray<number>): Option<string> =>
  pipe(
    ns,
    findIndex((n) => n > 0),
    map(doSomethingWithIndex)
  )
```

Practically, using `Option`, we're always in front of the `happy path`, error handing happens behind the scenes thanks to `map`.

**Demo** (optional)

[`04_functor.ts`](src/04_functor.ts)

**Quiz**. `Task<A>` represents an asynchronous call that always succeed, how can we model a computation that can fail instead?

## Functors compose

Functors compose, meaning that given two functors `F` and `G` then the composition `F<G<A>>` is still a functor and the `map` of this composition is the composition of the `map`s.

**Example** (`F = Task`, `G = Option`)

```ts
import { flow } from 'fp-ts/function'
import * as O from 'fp-ts/Option'
import * as T from 'fp-ts/Task'

type TaskOption<A> = T.Task<O.Option<A>>

export const map: <A, B>(
  f: (a: A) => B
) => (fa: TaskOption<A>) => TaskOption<B> = flow(O.map, T.map)

// -------------------
// usage example
// -------------------

interface User {
  readonly id: number
  readonly name: string
}

// a dummy remote database
const database: Record<number, User> = {
  1: { id: 1, name: 'Ruth R. Gonzalez' },
  2: { id: 2, name: 'Terry R. Emerson' },
  3: { id: 3, name: 'Marsha J. Joslyn' }
}

const getUser = (id: number): TaskOption<User> => () =>
  Promise.resolve(O.fromNullable(database[id]))
const getName = (user: User): string => user.name

// getUserName: number -> TaskOption<string>
const getUserName = flow(getUser, map(getName))

getUserName(1)().then(console.log) // => some('Ruth R. Gonzalez')
getUserName(4)().then(console.log) // => none
```

## Contravariant Functors

In the previous section we haven't been completely thorough with our definitions. What we have seen in the previous section and called "functors" should be more properly called **covariant functors**.

In this section we'll see another variant of the functor concept, **contravariant** functors.

The definition of a contravariant functor is pretty much the same of the covariant one, except for the signature of its fundamental operation, which is called `contramap` rather than `map`.

<img src="images/contramap.png" width="300" alt="contramap" />

**Example**

```ts
import { map } from 'fp-ts/Option'
import { contramap } from 'fp-ts/Eq'

type User = {
  readonly id: number
  readonly name: string
}

const getId = (_: User): number => _.id

// the way `map` operates...
// const getIdOption: (fa: Option<User>) => Option<number>
const getIdOption = map(getId)

// the way `contramap` operates...
// const getIdEq: (fa: Eq<number>) => Eq<User>
const getIdEq = contramap(getId)

import * as N from 'fp-ts/number'

const EqID = getIdEq(N.Eq)

/*

In the `Eq` chapter we saw:

const EqID: Eq<User> = pipe(
  N.Eq,
  contramap((_: User) => _.id)
)
*/
```

## Functors in `fp-ts`

How do we define a functor instance in `fp-ts`? Let's see some example.

The following interface represents the model of some result we get by calling some HTTP API:

```ts
interface Response<A> {
  url: string
  status: number
  headers: Record<string, string>
  body: A
}
```

Please note that since `body` is parametric, this makes `Response` a good candidate to find a functor instance given that `Response` is a an `n`-ary type constructor with `n >= 1` (a necessary condition).

To define a functor instance for `Response` we need to define a `map` function along some [technical details](https://gcanti.github.io/fp-ts/recipes/HKT.html) required by `fp-ts`.

```ts
// `Response.ts` module

import { pipe } from 'fp-ts/function'
import { Functor1 } from 'fp-ts/Functor'

declare module 'fp-ts/HKT' {
  interface URItoKind<A> {
    readonly Response: Response<A>
  }
}

export interface Response<A> {
  readonly url: string
  readonly status: number
  readonly headers: Record<string, string>
  readonly body: A
}

export const map = <A, B>(f: (a: A) => B) => (
  fa: Response<A>
): Response<B> => ({
  ...fa,
  body: f(fa.body)
})

// functor instance for `Response<A>`
export const Functor: Functor1<'Response'> = {
  URI: 'Response',
  map: (fa, f) => pipe(fa, map(f))
}
```

## Do functors solve the general problem?

Not yet. Functors allow us to compose an effectful program `f` with a pure program `g`, but `g` has to be a **unary** function, accepting one single argument. What happens if `g` takes two or more arguments?

| Program f | Program g               | Composition  |
| --------- | ----------------------- | ------------ |
| pure      | pure                    | `g ∘ f`      |
| effectful | pure (unary)            | `map(g) ∘ f` |
| effectful | pure (`n`-ary, `n > 1`) | ?            |

To manage this circumstance we need something _more_, in the next chapter we'll see another important abstraction in functional programming: **applicative functors**.

# Applicative functors

In the section regarding functors we've seen that we can compose an effectful program `f: (a: A) => F<B>` with a pure one `g: (b: B) => C` through the transformation of `g` to a function `map(g): (fb: F<B>) => F<C>` (if and only if `F` admits a functor instance).

| Program f | Program g    | Composition  |
| --------- | ------------ | ------------ |
| pure      | pure         | `g ∘ f`      |
| effectful | pure (unary) | `map(g) ∘ f` |

But `g` has to be unary, it can only accept a single argument as input. What happens if `g` accepts two arguments? Can we still transform `g` using only the functor instance?

## Currying

First of all we need to model a function that accepts two arguments of type `B` and `C` (we can use a tuple for this) and returns a value of type `D`:

```ts
g: (b: B, c: C) => D
```

We can rewrite `g` using a technique called **currying**.

> Currying is the technique of translating the evaluation of a function that takes multiple arguments into evaluating a sequence of functions, **each with a single argument**. For example, a function that takes two arguments, one from `B` and one from `C`, and produces outputs in `D`, by currying is translated into a function that takes a single argument from `C` and produces as outputs functions from `B` to `C`.

(source: [currying on wikipedia.org](https://en.wikipedia.org/wiki/Currying))

Thus, through currying, we can rewrite `g` as:

```ts
g: (b: B) => (c: C) => D
```

**Example**

```ts
interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const addFollower = (follower: User, user: User): User => ({
  ...user,
  followers: [...user.followers, follower]
})
```

Let's refactor `addFollower` through currying

```ts
interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const addFollower = (follower: User) => (user: User): User => ({
  ...user,
  followers: [...user.followers, follower]
})

// -------------------
// usage example
// -------------------

const user: User = { id: 1, name: 'Ruth R. Gonzalez', followers: [] }
const follower: User = { id: 3, name: 'Marsha J. Joslyn', followers: [] }

console.log(addFollower(follower)(user))
/*
{
  id: 1,
  name: 'Ruth R. Gonzalez',
  followers: [ { id: 3, name: 'Marsha J. Joslyn', followers: [] } ]
}
*/
```

## The `ap` operation

Suppose that:

- we do not have a `follower` but only his `id`
- we do not have a `user` but only his `id`
- that we have an API `fetchUser` which, given an `id`, queries an endpoint that returns the corresponding `User`

```ts
import * as T from 'fp-ts/Task'

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const addFollower = (follower: User) => (user: User): User => ({
  ...user,
  followers: [...user.followers, follower]
})

declare const fetchUser: (id: number) => T.Task<User>

const userId = 1
const followerId = 3

const result = addFollower(fetchUser(followerId))(fetchUser(userId)) // does not compile
```

I can't use `addFollower` anymore! How can we proceed?

If only we had a function with the following signature:

```ts
declare const addFollowerAsync: (
  follower: T.Task<User>
) => (user: T.Task<User>) => T.Task<User>
```

we could proceed with ease:

```ts
import * as T from 'fp-ts/Task'

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

declare const fetchUser: (id: number) => T.Task<User>

declare const addFollowerAsync: (
  follower: T.Task<User>
) => (user: T.Task<User>) => T.Task<User>

const userId = 1
const followerId = 3

// const result: T.Task<User>
const result = addFollowerAsync(fetchUser(followerId))(fetchUser(userId)) // now compiles
```

We can obviously implement `addFollowerAsync` manually, but is it possible instead to find a transformation which starting with a function like `addFollower: (follower: User) => (user: User): User` returns a function like `addFollowerAsync: (follower: Task<User>) => (user: Task<User>) => Task<User>`?

More generally what we would like to have is a transformation, call it `liftA2`, which beginning with a function `g: (b: B) => (c: C) => D` returns a function with the following signature:

```ts
liftA2(g): (fb: F<B>) => (fc: F<C>) => F<D>
```

<img src="images/liftA2.png" width="500" alt="liftA2" />

How can we obtain it? Given that `g` is now a unary function, we can leverage the functor instance and the good old `map`:

```ts
map(g): (fb: F<B>) => F<(c: C) => D>
```

<img src="images/liftA2-first-step.png" width="500" alt="liftA2 (first step)" />

Now we are blocked: there's no legal operation the functor instance provides us to "unpack" the type `F<(c: C) => D>` into `(fc: F<C>) => F<D>`.

We need to introduce a new operation `ap` which realizes this unpacking:

```ts
declare const ap: <A>(fa: Task<A>) => <B>(fab: Task<(a: A) => B>) => Task<B>
```

**Note**. Why is it names "ap"? Because it can be seen like some sort of function application.

```ts
// `apply` applies a function to a value
declare const apply: <A>(a: A) => <B>(f: (a: A) => B) => B

declare const ap: <A>(a: Task<A>) => <B>(f: Task<(a: A) => B>) => Task<B>
// `ap` applies a function wrapped into an effect to a value wrapped into an effect
```

Now that we have `ap` we can define `liftA2`:

```ts
import { pipe } from 'fp-ts/function'
import * as T from 'fp-ts/Task'

const liftA2 = <B, C, D>(g: (b: B) => (c: C) => D) => (fb: T.Task<B>) => (
  fc: T.Task<C>
): T.Task<D> => pipe(fb, T.map(g), T.ap(fc))

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const addFollower = (follower: User) => (user: User): User => ({
  ...user,
  followers: [...user.followers, follower]
})

// const addFollowerAsync: (fb: T.Task<User>) => (fc: T.Task<User>) => T.Task<User>
const addFollowerAsync = liftA2(addFollower)
```

and finally, we can compose `fetchUser` with the previous result:

```ts
import { flow, pipe } from 'fp-ts/function'
import * as T from 'fp-ts/Task'

const liftA2 = <B, C, D>(g: (b: B) => (c: C) => D) => (fb: T.Task<B>) => (
  fc: T.Task<C>
): T.Task<D> => pipe(fb, T.map(g), T.ap(fc))

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const addFollower = (follower: User) => (user: User): User => ({
  ...user,
  followers: [...user.followers, follower]
})

declare const fetchUser: (id: number) => T.Task<User>

// const program: (id: number) => (fc: T.Task<User>) => T.Task<User>
const program = flow(fetchUser, liftA2(addFollower))

const userId = 1
const followerId = 3

// const result: T.Task<User>
const result = program(followerId)(fetchUser(userId))
```

We have found a standard procedure to compose two functions `f: (a: A) => F<B>`, `g: (b: B, c: C) => D`:

1. we transform `g` through currying in a function `g: (b: B) => (c: C) => D`
2. we define the `ap` function for the effect `F` (library function)
3. we define the utility function `liftA2` for the effect `F` (library function)
4. we obtain the composition `flow(f, liftA2(g))`

Let's see how's the `ap` operation implemented for some of the type constructors we've already seen:

**Example** (`F = ReadonlyArray`)

```ts
import { increment, pipe } from 'fp-ts/function'

const ap = <A>(fa: ReadonlyArray<A>) => <B>(
  fab: ReadonlyArray<(a: A) => B>
): ReadonlyArray<B> => {
  const out: Array<B> = []
  for (const f of fab) {
    for (const a of fa) {
      out.push(f(a))
    }
  }
  return out
}

const double = (n: number): number => n * 2

pipe([double, increment], ap([1, 2, 3]), console.log) // => [ 2, 4, 6, 2, 3, 4 ]
```

**Example** (`F = Option`)

```ts
import { pipe } from 'fp-ts/function'
import * as O from 'fp-ts/Option'

const ap = <A>(fa: O.Option<A>) => <B>(
  fab: O.Option<(a: A) => B>
): O.Option<B> =>
  pipe(
    fab,
    O.match(
      () => O.none,
      (f) =>
        pipe(
          fa,
          O.match(
            () => O.none,
            (a) => O.some(f(a))
          )
        )
    )
  )

const double = (n: number): number => n * 2

pipe(O.some(double), ap(O.some(1)), console.log) // => some(2)
pipe(O.some(double), ap(O.none), console.log) // => none
pipe(O.none, ap(O.some(1)), console.log) // => none
pipe(O.none, ap(O.none), console.log) // => none
```

**Example** (`F = IO`)

```ts
import { IO } from 'fp-ts/IO'

const ap = <A>(fa: IO<A>) => <B>(fab: IO<(a: A) => B>): IO<B> => () => {
  const f = fab()
  const a = fa()
  return f(a)
}
```

**Example** (`F = Task`)

```ts
import { Task } from 'fp-ts/Task'

const ap = <A>(fa: Task<A>) => <B>(fab: Task<(a: A) => B>): Task<B> => () =>
  Promise.all([fab(), fa()]).then(([f, a]) => f(a))
```

**Example** (`F = Reader`)

```ts
import { Reader } from 'fp-ts/Reader'

const ap = <R, A>(fa: Reader<R, A>) => <B>(
  fab: Reader<R, (a: A) => B>
): Reader<R, B> => (r) => {
  const f = fab(r)
  const a = fa(r)
  return f(a)
}
```

We've seen how with `ap` we can manage functions with two parameters, but what happens with functions that take **three** parameters? Do we need _yet another abstraction_?

Good news is no, `map` and `ap` are sufficient:

```ts
import { pipe } from 'fp-ts/function'
import * as T from 'fp-ts/Task'

const liftA3 = <B, C, D, E>(f: (b: B) => (c: C) => (d: D) => E) => (
  fb: T.Task<B>
) => (fc: T.Task<C>) => (fd: T.Task<D>): T.Task<E> =>
  pipe(fb, T.map(f), T.ap(fc), T.ap(fd))

const liftA4 = <B, C, D, E, F>(
  f: (b: B) => (c: C) => (d: D) => (e: E) => F
) => (fb: T.Task<B>) => (fc: T.Task<C>) => (fd: T.Task<D>) => (
  fe: T.Task<E>
): T.Task<F> => pipe(fb, T.map(f), T.ap(fc), T.ap(fd), T.ap(fe))

// etc...
```

Now we cam update ore "composition table":

| Program f | Program g     | Composition     |
| --------- | ------------- | --------------- |
| pure      | pure          | `g ∘ f`         |
| effectful | pure (unary)  | `map(g) ∘ f`    |
| effectful | pure, `n`-ary | `liftAn(g) ∘ f` |

## The `of` operation

Now we know that given two function `f: (a: A) => F<B>`, `g: (b: B, c: C) => D` we can obtain the composition `h`:

```ts
h: (a: A) => (fc: F<C>) => F<D>
```

To execute `h` we need a new value of type `A` and a value of type `F<C>`.

But what happens if, instead of having a value of type `F<C>`, for the second parameter `fc` we only have a value of type `C`?

It would be helpful to have an operation which can transform a value of type `C` in a value of type `F<C>` in order to use `h`.

Let's introduce such operation, called `of` (other synonyms: **pure**, **return**):

```ts
declare const of: <C>(c: C) => F<C>
```

In literature the term **applicative functors** is used for the type constructors which admith _both_ the `ap` and `of` operations.

Let's see how `of` is defined for some type constructors we've already seen:

**Example** (`F = ReadonlyArray`)

```ts
const of = <A>(a: A): ReadonlyArray<A> => [a]
```

**Example** (`F = Option`)

```ts
import * as O from 'fp-ts/Option'

const of = <A>(a: A): O.Option<A> => O.some(a)
```

**Example** (`F = IO`)

```ts
import { IO } from 'fp-ts/IO'

const of = <A>(a: A): IO<A> => () => a
```

**Example** (`F = Task`)

```ts
import { Task } from 'fp-ts/Task'

const of = <A>(a: A): Task<A> => () => Promise.resolve(a)
```

**Example** (`F = Reader`)

```ts
import { Reader } from 'fp-ts/Reader'

const of = <R, A>(a: A): Reader<R, A> => () => a
```

**Demo**

[`05_applicative.ts`](src/05_applicative.ts)

## Applicative functors compose

Applicative functors compose, meaning that given two applicative functors `F` and `G`, their composition `F<G<A>>` is still an applicative functor.

**Example** (`F = Task`, `G = Option`)

The `of` of the composition is the composition of the `of`s:

```ts
import { flow } from 'fp-ts/function'
import * as O from 'fp-ts/Option'
import * as T from 'fp-ts/Task'

type TaskOption<A> = T.Task<O.Option<A>>

const of: <A>(a: A) => TaskOption<A> = flow(O.of, T.of)
```

the `ap` of the composition is obtained by the following pattern:

```ts
const ap = <A>(
  fa: TaskOption<A>
): (<B>(fab: TaskOption<(a: A) => B>) => TaskOption<B>) =>
  flow(
    T.map((gab) => (ga: O.Option<A>) => O.ap(ga)(gab)),
    T.ap(fa)
  )
```

## Do applicative functors solve the general problem?

Not yet. There's one last very important case to consider: when **both** programs are effectful.

Yet again we need something more, in the following chapter we'll talk about one of the most important abstractions in functional programming: **monads**.

# Monads

<center>
<img src="images/moggi.jpg" width="300" alt="Eugenio Moggi" />

(Eugenio Moggi is a professor of computer science at the University of Genoa, Italy. He first described the general use of monads to structure programs)

<img src="images/wadler.jpg" width="300" alt="Philip Lee Wadler" />

(Philip Lee Wadler is an American computer scientist known for his contributions to programming language design and type theory)

</center>

In the last chapter we have seen how we can compose an effectful program `f: (a: A) => F<B>` with an `n`-ary pure program `g`, if and only if the type constructor `F` admits an applicative functor instance:

| Program f | Program g     | Composition     |
| --------- | ------------- | --------------- |
| pure      | pure          | `g ∘ f`         |
| effectful | pure (unary)  | `map(g) ∘ f`    |
| effectful | pure, `n`-ary | `liftAn(g) ∘ f` |

But we need to solve one last, quite common, case: when **both** programs are effectful:

```ts
f: (a: A) => F<B>
g: (b: B) => F<C>
```

What is the composition of `f` and `g`?

## The problem with nested contexts

Let's see few examples on why we need something more.

**Example** (`F = Array`)

Suppose we want to get followers' followers.

```ts
import { pipe } from 'fp-ts/function'
import * as A from 'fp-ts/ReadonlyArray'

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const getFollowers = (user: User): ReadonlyArray<User> => user.followers

declare const user: User

// followersOfFollowers: ReadonlyArray<ReadonlyArray<User>>
const followersOfFollowers = pipe(user, getFollowers, A.map(getFollowers))
```

There's something wrong here, `followersOfFollowers` has a type `ReadonlyArray<ReadonlyArray<User>>` but we want `ReadonlyArray<User>`.

We need to **flatten** nested arrays.

The function `flatten: <A>(mma: ReadonlyArray<ReadonlyArray<A>>) => ReadonlyArray<A>` exported by the `fp-ts/ReadonlyArray` is exactly what we need:

```ts
// followersOfFollowers: ReadonlyArray<User>
const followersOfFollowers = pipe(
  user,
  getFollowers,
  A.map(getFollowers),
  A.flatten
)
```

Cool! Let's see some other data type.

**Example** (`F = Option`)
Suppose you want to calculate the reciprocal of the first element of a numerical array:

```ts
import { pipe } from 'fp-ts/function'
import * as O from 'fp-ts/Option'
import * as A from 'fp-ts/ReadonlyArray'

const inverse = (n: number): O.Option<number> =>
  n === 0 ? O.none : O.some(1 / n)

// inverseHead: O.Option<O.Option<number>>
const inverseHead = pipe([1, 2, 3], A.head, O.map(inverse))
```

Oops, it happened again, `inverseHead` has type `Option<Option<number>>` but we want `Option<number>`.

We need to flatten again the nested `Option`s.

The `flatten: <A>(mma: Option<Option<A>>) => Option<A>` function exported by the `fp-ts/Option` module is what we need:

```ts
// inverseHead: O.Option<number>
const inverseHead = pipe([1, 2, 3], A.head, O.map(inverse), O.flatten)
```

All of those `flatten` funcitons...They aren't a coincidence, there is a functional pattern behind the scenes: both the type constructors
`ReadonlyArray` and `Option` (and many others) admit a **monad instance** and

> `flatten` is the most peculiar operation of monads

**Note**. A common synonym of `flatten` is **join**.

So, what is a monad?

Here is how they are often presented...

## Monad Definition

**Definition**. A monad is defined by three things:

(1) a type constructor `M` admitting a functor instance

(2) a function `of` (also called **pure** or **return**) with the following signature:

```ts
of: <A>(a: A) => M<A>
```

(3) a `chain` function (also called **flatMap** or **bind**) with the following signature:

```ts
chain: <A, B>(f: (a: A) => M<B>) => (ma: M<A>) => M<B>
```

The `of` and `chain` functions need to obey three laws:

- `chain(of) ∘ f = f` (**Left identity**)
- `chain(f) ∘ of = f` (**Right identity**)
- `chain(h) ∘ (chain(g) ∘ f) = chain((chain(h) ∘ g)) ∘ f` (**Associativity**)

where `f`, `g`, `h` are all effectful functions and `∘` is the usual function composition.

When I saw this definition for the first time I had many questions:

- why exactly those two operation `of` and `chain`? and why to they have those signatures?
- why do they have those synonyms like "pure" or "flatMap"?
- why does laws need to hold true? What do they mean?
- if `flatten` is so important for monads, why it doesn't compare in its definition?

This chapter will try to answer all of these questions.

Let's get back to the core problem: what is the composition of two effectful functions `f` and `g`?

<img src="images/kleisli_arrows.png" alt="two Kleisli arrows, what's their composition?" width="450px" />

<center>(two Kleisli Arrows)</center>

**Note**. An effectful function is also called **Kleisli arrow**.

For the time being I don't even know the **type** of such composition.

But we've already seen some abstractions that talks specifically about composition. Do you remember what we said about categories?

> Categories capture the essence of composition

We can transform our problem into a category problem, meaning: can we find a category that models the composition of Kleisli arrows?

## The Kleisli category

<center>
<img src="images/kleisli.jpg" width="300" alt="Heinrich Kleisli" />

(Heinrich Kleisli, Swiss mathematician)

</center>

Let's try building a category _K_ (called **Kleisli category**) which contains _only_ Kleisli arrows:

- **objects** will be the same objects of the _TS_ category, so all TypeScript types.
- **morphisms** are built like this: every time there is a Kleisli arrow `f: A ⟼ M<B>` in _TS_ we draw an arrow `f': A ⟼ B` in _K_

<center>
<img src="images/kleisli_category.png" alt="above the TS category, below the K construction" width="400px" />

(above the composition in the _TS_ category, below the composition in the _K_ construction)

</center>

So what would be the composition of `f` and `g` in _K_?
It's th red arrow called `h'` in the image below:

<center>
<img src="images/kleisli_composition.png" alt="above the composition in the TS category, below the composition in the K construction" width="400px" />

(above the composition in the _TS_ category, below the composition in the _K_ construction)

</center>

Given that `h'` is an arrow from `A` to `C` in `K`, we can find a corresponding function `h` from `A` to `M<C>` in `TS`.

Thus, a good candidate for the following composition of `f` and `g` in _TS_ is still a Kleisli arrow with the following signature: `(a: A) => M<C>`.

Let's try implementing such a function.

## Defining `chain` step by step

The first point (1) of the monad definition tells us that `M` admits a functor instance, thus we can use the `map` function to transform the function `g: (b: B) => M<C>` into a function `map(g): (mb: M<B>) => M<M<C>>`

<center>
<img src="images/flatMap.png" alt="where chain comes from" width="450px" />

(how to obtain the `h` function)

</center>

We're stuck now though: there is no legal operation for the functor instance that allows us to flatten a value of type `M<M<C>>` into a value of type `M<C>`, we need an additional operation, let's call it `flatten`.

If we can define such operation then we can find the composition we were looking for:

```
h = flatten ∘ map(g) ∘ f
```

By joining the `flatten ∘ map(g)` names we get "flatMap", hence the name!

Thus we can get `chain` in this way

```
chain = flatten ∘ map(g)
```

<center>
<img src="images/chain.png" alt="how chain operates on the function g" width="400px" />

(how `chain` operates on the function `g`)

</center>

Now we can update our composition table

| Program f | Program g     | Composition     |
| --------- | ------------- | --------------- |
| pure      | pure          | `g ∘ f`         |
| effectful | pure (unary)  | `map(g) ∘ f`    |
| effectful | pure, `n`-ary | `liftAn(g) ∘ f` |
| effectful | effectful     | `chain(g) ∘ f`  |

What about `of`? Well, `of` comes from the identity morphisms in _K_: for every identity morphism 1<sub>A</sub> in _K_ there has to be a corresponding function from `A` to `M<A>` (that is, `of: <A>(a: A) => M<A>`).

<center>
<img src="images/of.png" alt="where of comes from" width="300px" />

(where `of` comes from)

</center>

The fact that `of` is the neutral element for `chain` allows this kind of flux control (pretty common):

```ts
pipe(
  mb,
  M.chain((b) => (predicate(b) ? M.of(b) : g(b)))
)
```

where `predicate: (b: B) => boolean`, `mb: M<B>` and `g: (b: B) => M<B>`.

Last question: where do the laws come from? They are nothing else but the categorical laws in _K_ translated to _TS_:

| Law            | _K_                               | _TS_                                                    |
| -------------- | --------------------------------- | ------------------------------------------------------- |
| Left identity  | 1<sub>B</sub> ∘ `f'` = `f'`       | `chain(of) ∘ f = f`                                     |
| Right identity | `f'` ∘ 1<sub>A</sub> = `f'`       | `chain(f) ∘ of = f`                                     |
| Associativity  | `h' ∘ (g' ∘ f') = (h' ∘ g') ∘ f'` | `chain(h) ∘ (chain(g) ∘ f) = chain((chain(h) ∘ g)) ∘ f` |

If we now go back to the examples that showed the problem with nested contexts we can solve them using `chain`:

```ts
import { pipe } from 'fp-ts/function'
import * as O from 'fp-ts/Option'
import * as A from 'fp-ts/ReadonlyArray'

interface User {
  readonly id: number
  readonly name: string
  readonly followers: ReadonlyArray<User>
}

const getFollowers = (user: User): ReadonlyArray<User> => user.followers

declare const user: User

const followersOfFollowers: ReadonlyArray<User> = pipe(
  user,
  getFollowers,
  A.chain(getFollowers)
)

const inverse = (n: number): O.Option<number> =>
  n === 0 ? O.none : O.some(1 / n)

const inverseHead: O.Option<number> = pipe([1, 2, 3], A.head, O.chain(inverse))
```

Let's see how `chain` is implemented for the usual type constructors we've already seen:

**Example** (`F = ReadonlyArray`)

```ts
// transforms functions `B -> ReadonlyArray<C>` into functions `ReadonlyArray<B> -> ReadonlyArray<C>`
const chain = <B, C>(g: (b: B) => ReadonlyArray<C>) => (
  mb: ReadonlyArray<B>
): ReadonlyArray<C> => {
  const out: Array<C> = []
  for (const b of mb) {
    out.push(...g(b))
  }
  return out
}
```

**Example** (`F = Option`)

```ts
import { match, none, Option } from 'fp-ts/Option'

// transforms functions `B -> Option<C>` into functions `Option<B> -> Option<C>`
const chain = <B, C>(g: (b: B) => Option<C>): ((mb: Option<B>) => Option<C>) =>
  match(() => none, g)
```

**Example** (`F = IO`)

```ts
import { IO } from 'fp-ts/IO'

// transforms functions `B -> IO<C>` into functions `IO<B> -> IO<C>`
const chain = <B, C>(g: (b: B) => IO<C>) => (mb: IO<B>): IO<C> => () =>
  g(mb())()
```

**Example** (`F = Task`)

```ts
import { Task } from 'fp-ts/Task'

// transforms functions `B -> Task<C>` into functions `Task<B> -> Task<C>`
const chain = <B, C>(g: (b: B) => Task<C>) => (mb: Task<B>): Task<C> => () =>
  mb().then((b) => g(b)())
```

**Example** (`F = Reader`)

```ts
import { Reader } from 'fp-ts/Reader'

// transforms functions `B -> Reader<R, C>` into functions `Reader<R, B> -> Reader<R, C>`
const chain = <B, R, C>(g: (b: B) => Reader<R, C>) => (
  mb: Reader<R, B>
): Reader<R, C> => (r) => g(mb(r))(r)
```

## Manipulating programs

Let's see now, how thanks to referential transparency and the monad concept we can programmaticaly manipulate programs.

Here's a small program that reads / writes a file:

```ts
import { log } from 'fp-ts/Console'
import { IO, chain } from 'fp-ts/IO'
import { pipe } from 'fp-ts/function'
import * as fs from 'fs'

// -----------------------------------------
// library functions
// -----------------------------------------

const readFile = (filename: string): IO<string> => () =>
  fs.readFileSync(filename, 'utf-8')

const writeFile = (filename: string, data: string): IO<void> => () =>
  fs.writeFileSync(filename, data, { encoding: 'utf-8' })

// API derived from the previous functions
const modifyFile = (filename: string, f: (s: string) => string): IO<void> =>
  pipe(
    readFile(filename),
    chain((s) => writeFile(filename, f(s)))
  )

// -----------------------------------------
// program
// -----------------------------------------

const program1 = pipe(
  readFile('file.txt'),
  chain(log),
  chain(() => modifyFile('file.txt', (s) => s + '\n// eof')),
  chain(() => readFile('file.txt')),
  chain(log)
)
```

The actions:

```ts
pipe(readFile('file.txt'), chain(log))
```

is repeated more than once in the program, but given that referential transparency holds we can factor it and assign it to a constant:

```ts
const read = pipe(readFile('file.txt'), chain(log))
const modify = modifyFile('file.txt', (s) => s + '\n// eof')

const program2 = pipe(
  read,
  chain(() => modify),
  chain(() => read)
)
```

We can even define a combinator and leverage it to make the code more compact:

```ts
const interleave = <A, B>(action: IO<A>, middle: IO<B>): IO<A> =>
  pipe(
    action,
    chain(() => middle),
    chain(() => action)
  )

const program3 = interleave(read, modify)
```

Another example: implementing a function similar to Unix' `time` (the part related to the execution time) for `IO`.

```ts
import * as IO from 'fp-ts/IO'
import { now } from 'fp-ts/Date'
import { log } from 'fp-ts/Console'
import { pipe } from 'fp-ts/function'

// logs the computation lenght in milliseconds
export const time = <A>(ma: IO.IO<A>): IO.IO<A> =>
  pipe(
    now,
    IO.chain((startMillis) =>
      pipe(
        ma,
        IO.chain((a) =>
          pipe(
            now,
            IO.chain((endMillis) =>
              pipe(
                log(`Elapsed: ${endMillis - startMillis}`),
                IO.map(() => a)
              )
            )
          )
        )
      )
    )
  )
```

**Digression**. As you can notice, using `chain` when it is required to maintain a scope leads to verbose code.
In languages that support monadic style natively there is often syntax support that goes by the name of "do notation" which eases this kind of situations.

Let's see a Haskell example

```haskell
now :: IO Int
now = undefined -- `undefined` in Haskell is equivalent to TypeScript's declare

log :: String -> IO ()
log = undefined

time :: IO a -> IO a
time ma = do
  startMillis <- now
  a <- ma
  endMillis <- now
  log ("Elapsed:" ++ show (endMillis - startMillis))
  return a
```

TypeScript does not support such syntax, but it can be emulated with something similar:

```ts
import { log } from 'fp-ts/Console'
import { now } from 'fp-ts/Date'
import { pipe } from 'fp-ts/function'
import * as IO from 'fp-ts/IO'

// logs the computation lenght in milliseconds
export const time = <A>(ma: IO.IO<A>): IO.IO<A> =>
  pipe(
    IO.Do,
    IO.bind('startMillis', () => now),
    IO.bind('a', () => ma),
    IO.bind('endMillis', () => now),
    IO.chainFirst(({ endMillis, startMillis }) =>
      log(`Elapsed: ${endMillis - startMillis}`)
    ),
    IO.map(({ a }) => a)
  )
```

Let's see a usage example of the `time` combinator:

```ts
import { randomInt } from 'fp-ts/Random'
import { Monoid, concatAll } from 'fp-ts/Monoid'
import { replicate } from 'fp-ts/ReadonlyArray'

const fib = (n: number): number => (n <= 1 ? 1 : fib(n - 1) + fib(n - 2))

// launches `fib` with a random integer between 30 and 35
// logging both the input and output
const randomFib: IO.IO<void> = pipe(
  randomInt(30, 35),
  IO.chain((n) => log([n, fib(n)]))
)

// a monoid instance for `IO<void>`
const MonoidIO: Monoid<IO.IO<void>> = {
  concat: (first, second) => () => {
    first()
    second()
  },
  empty: IO.of(undefined)
}

// executes `n` times the `mv` computation
const replicateIO = (n: number, mv: IO.IO<void>): IO.IO<void> =>
  concatAll(MonoidIO)(replicate(n, mv))

// -------------------
// usage example
// -------------------

time(replicateIO(3, randomFib))()
/*
[ 31, 2178309 ]
[ 33, 5702887 ]
[ 30, 1346269 ]
Elapsed: 89
*/
```

Logs also the partial:

```ts
time(replicateIO(3, time(randomFib)))()
/*
[ 33, 5702887 ]
Elapsed: 54
[ 30, 1346269 ]
Elapsed: 13
[ 32, 3524578 ]
Elapsed: 39
Elapsed: 106
*/
```

One of the most interesting aspects of working with the monadic interface (`map`, `of`, `chain`) is the possibility to inject dependencies which the program needs, including the **way of concatenating different computations**.

To see that, let's refactor the small program that reads and writes a file:

```ts
import { IO } from 'fp-ts/IO'
import { pipe } from 'fp-ts/function'

// -----------------------------------------
// Deps interface, what we would call a "port" in the Hexagonal Architecture
// -----------------------------------------

interface Deps {
  readonly readFile: (filename: string) => IO<string>
  readonly writeFile: (filename: string, data: string) => IO<void>
  readonly log: <A>(a: A) => IO<void>
  readonly chain: <A, B>(f: (a: A) => IO<B>) => (ma: IO<A>) => IO<B>
}

// -----------------------------------------
// program
// -----------------------------------------

const program4 = (D: Deps) => {
  const modifyFile = (filename: string, f: (s: string) => string) =>
    pipe(
      D.readFile(filename),
      D.chain((s) => D.writeFile(filename, f(s)))
    )

  return pipe(
    D.readFile('file.txt'),
    D.chain(D.log),
    D.chain(() => modifyFile('file.txt', (s) => s + '\n// eof')),
    D.chain(() => D.readFile('file.txt')),
    D.chain(D.log)
  )
}

// -----------------------------------------
// a `Deps` instance, what we would call an "adapter" in the Hexagonal Architecture
// -----------------------------------------

import * as fs from 'fs'
import { log } from 'fp-ts/Console'
import { chain } from 'fp-ts/IO'

const DepsSync: Deps = {
  readFile: (filename) => () => fs.readFileSync(filename, 'utf-8'),
  writeFile: (filename: string, data: string) => () =>
    fs.writeFileSync(filename, data, { encoding: 'utf-8' }),
  log,
  chain
}

// dependency injection
program4(DepsSync)()
```

There's more, we can even abstract the effect in which the program runs. We can define our own `FileSystem` effect (the effect representing read-write operations over the file system):

```ts
import { IO } from 'fp-ts/IO'
import { pipe } from 'fp-ts/function'

// -----------------------------------------
// our program's effect
// -----------------------------------------

interface FileSystem<A> extends IO<A> {}

// -----------------------------------------
// dependencies
// -----------------------------------------

interface Deps {
  readonly readFile: (filename: string) => FileSystem<string>
  readonly writeFile: (filename: string, data: string) => FileSystem<void>
  readonly log: <A>(a: A) => FileSystem<void>
  readonly chain: <A, B>(
    f: (a: A) => FileSystem<B>
  ) => (ma: FileSystem<A>) => FileSystem<B>
}

// -----------------------------------------
// program
// -----------------------------------------

const program4 = (D: Deps) => {
  const modifyFile = (filename: string, f: (s: string) => string) =>
    pipe(
      D.readFile(filename),
      D.chain((s) => D.writeFile(filename, f(s)))
    )

  return pipe(
    D.readFile('file.txt'),
    D.chain(D.log),
    D.chain(() => modifyFile('file.txt', (s) => s + '\n// eof')),
    D.chain(() => D.readFile('file.txt')),
    D.chain(D.log)
  )
}
```

With a simple change in the definition of the `FileSystem` effect. we can modify the program to make it run asynchronously

```diff
// -----------------------------------------
// our program's effect
// -----------------------------------------

-interface FileSystem<A> extends IO<A> {}
+interface FileSystem<A> extends Task<A> {}
```

now all there's left is to modify the `Deps` instance to adapt to the new definition.

```ts
import { Task } from 'fp-ts/Task'
import { pipe } from 'fp-ts/function'

// -----------------------------------------
// our program's effect (modified)
// -----------------------------------------

interface FileSystem<A> extends Task<A> {}

// -----------------------------------------
// dependencies (NOT modified)
// -----------------------------------------

interface Deps {
  readonly readFile: (filename: string) => FileSystem<string>
  readonly writeFile: (filename: string, data: string) => FileSystem<void>
  readonly log: <A>(a: A) => FileSystem<void>
  readonly chain: <A, B>(
    f: (a: A) => FileSystem<B>
  ) => (ma: FileSystem<A>) => FileSystem<B>
}

// -----------------------------------------
// program (NOT modified)
// -----------------------------------------

const program5 = (D: Deps) => {
  const modifyFile = (filename: string, f: (s: string) => string) =>
    pipe(
      D.readFile(filename),
      D.chain((s) => D.writeFile(filename, f(s)))
    )

  return pipe(
    D.readFile('file.txt'),
    D.chain(D.log),
    D.chain(() => modifyFile('file.txt', (s) => s + '\n// eof')),
    D.chain(() => D.readFile('file.txt')),
    D.chain(D.log)
  )
}

// -----------------------------------------
// a `Deps` instance (modified)
// -----------------------------------------

import * as fs from 'fs'
import { log } from 'fp-ts/Console'
import { chain, fromIO } from 'fp-ts/Task'

const DepsAsync: Deps = {
  readFile: (filename) => () =>
    new Promise((resolve) =>
      fs.readFile(filename, { encoding: 'utf-8' }, (_, s) => resolve(s))
    ),
  writeFile: (filename: string, data: string) => () =>
    new Promise((resolve) => fs.writeFile(filename, data, () => resolve())),
  log: (a) => fromIO(log(a)),
  chain
}

// dependency injection
program5(DepsAsync)()
```

**Quiz**. The previous examples overlook, on purpose, possible errors. Example give: the file we're operating on may not exist at all. How could we modify the `FileSystem` effect to take this into account?

```ts
import { Task } from 'fp-ts/Task'
import { pipe } from 'fp-ts/function'
import * as E from 'fp-ts/Either'

// -----------------------------------------
// our program's effect (modified)
// -----------------------------------------

interface FileSystem<A> extends Task<E.Either<Error, A>> {}

// -----------------------------------------
// dependencies (NOT modified)
// -----------------------------------------

interface Deps {
  readonly readFile: (filename: string) => FileSystem<string>
  readonly writeFile: (filename: string, data: string) => FileSystem<void>
  readonly log: <A>(a: A) => FileSystem<void>
  readonly chain: <A, B>(
    f: (a: A) => FileSystem<B>
  ) => (ma: FileSystem<A>) => FileSystem<B>
}

// -----------------------------------------
// program (NOT modified)
// -----------------------------------------

const program5 = (D: Deps) => {
  const modifyFile = (filename: string, f: (s: string) => string) =>
    pipe(
      D.readFile(filename),
      D.chain((s) => D.writeFile(filename, f(s)))
    )

  return pipe(
    D.readFile('-.txt'),
    D.chain(D.log),
    D.chain(() => modifyFile('file.txt', (s) => s + '\n// eof')),
    D.chain(() => D.readFile('file.txt')),
    D.chain(D.log)
  )
}

// -----------------------------------------
// `Deps` instance (modified)
// -----------------------------------------

import * as fs from 'fs'
import { log } from 'fp-ts/Console'
import { chain, fromIO } from 'fp-ts/TaskEither'

const DepsAsync: Deps = {
  readFile: (filename) => () =>
    new Promise((resolve) =>
      fs.readFile(filename, { encoding: 'utf-8' }, (err, s) => {
        if (err !== null) {
          resolve(E.left(err))
        } else {
          resolve(E.right(s))
        }
      })
    ),
  writeFile: (filename: string, data: string) => () =>
    new Promise((resolve) =>
      fs.writeFile(filename, data, (err) => {
        if (err !== null) {
          resolve(E.left(err))
        } else {
          resolve(E.right(undefined))
        }
      })
    ),
  log: (a) => fromIO(log(a)),
  chain
}

// dependency injection
program5(DepsAsync)().then(console.log)
```

**Demo**

[`06_game.ts`](src/06_game.ts)