# Folding Things With Functional Programming

### Have some fun folding things with FP!

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## Introduction

In this post, we will discuss the concept of `fold`

in functional programming: `fold`

functions are used to reduce a data structure containing multiple values into a single one. Associated to the idea of `fold`

are the concepts of...

**recursion**: via recursion, fold traverses the different elements of the data structure.**summarisation**: the data structure with all its elements is reduced to a single value

You may also like: 10 Amazing Scala Collection Functions

In the following example, the class Rectangle can be reduced to a single value representing its area. However, it is not *'*`foldable`

*'* in the sense that there is no recursion involved.

```
case class Rectangle(side1: Int, side2: Int)
def area(rectangle: Rectangle): Int = rectangle.side1 * rectangle.side2
println(area(Rectangle(2,3))) //6
```

A typical use case of `fold`

is to calculate the sum of the elements of a list: there are recursion and a summary value. Here are three different implementations to illustrate how `fold`

is similar to a loop or a recursive operation:

```
//iterating over the elements of the list with a loop
def sumLoop(xs: List[Int]): Int = {
var acc = 0
for(x <- xs){
acc += x
}
acc
}
//iterating over the elements of the list using recursion
def sumRecursive(xs: List[Int]): Int = xs match {
case Nil => 0
case x :: tail => x + sumRecursive(tail)
}
//using Scala API
def sumFold(xs: List[Int]): Int = xs.reduce((acc, x) => acc + x)
```

Actually, Scala API implements `foldLeft`

as a loop in TraversableOnce:

```
def foldLeft[B](z: B)(op: (B, A) => B): B = {
var result = z
this foreach (x => result = op(result, x))
result
}
```

## Fold Functions

Intuitively, based on the previous examples, `fold`

is equivalent to a loop that applies a function *'f'* to the elements of a collection (in our example, that function is the addition of numbers).

On the Scala API, there is a multitude of `fold`

functions: `fold`

, `foldRight`

, `foldLeft`

, `reduce`

, `reduceRight`

, `reduceLeft`

, etc.

### Left Vs. Right

In order to understand the purpose of all these variants, let's start by doing a simple experiment. What is the result of this subtraction?

`1 - 2 - 3 - 4`

If your answer was -8, then we have been taught the same kind of Maths. The kind in which, when there are no parentheses, subtraction is done from left to right. This way we save ourselves the need to write

`( ( (1 - 2) - 3) - 4)`

So why is it necessary to specify the order in which subtractions are done? Well, because subtraction is not an associative operation and therefore the order in which it is done matters.

**From left to right:**

`List(1,2,3,4).reduceLeft((acc,x) => acc - x) = ( ( (1 - 2) - 3) - 4) = -8`

**From right to left:**

` List(1,2,3,4).reduceRight((x, acc) => x - acc) = (1 - (2 - (3 - 4) ) ) = -2`

**No specific direction:**

`((1 - 2) - (3 - 4)) = 0`

**Therefore:**

`left`

variants apply functions from left to right`right`

variants apply functions from right to left`non-directional`

variants apply functions without guaranteeing any specific order

Directional variants should be used when the function is not associative.

### Reduce Vs. Fold

The main difference between the `fold`

and `reduce`

families is that `fold`

functions take an extra parameter that is added to the collection. This way, `fold`

functions can handle empty collections; `reduce`

functions, on the other hand, cannot handle empty collections and will throw an exception.

**From left to right:**

`List(1,2,3,4).foldLeft(0)((acc,x) => acc - x) = ( ( ( (0 - 1) - 2) - 3) - 4) = -10`

**From right to left:**

` List(1,2,3,4).foldRight(0)((x, acc) => x - acc) = (1 - (2 - (3 - (4 - 0) ) ) ) = -2`

The previous statement about *"fold functions taking an extra parameter that is added to the collection"* needs to be clarified as that interpretation is only true when the function applied produces an element of the same type as the elements in the collection. Otherwise, the extra parameter is needed to be able to start applying the function.

Now, it is clear that the previously defined functions, *'*`sumLoop`

*'* and *'*`sumRecursive`

*'*, are implemented as '`reduceLeft`

'.

## Climbing the Abstraction Ladder

Once we are familiar with the basics, we can start building more elaborated abstractions. Inspired by Cats, that has a type class called Foldable, we can create our own abstraction of `Fold`

. The following trait represents things that can be folded from left to right.

```
trait FoldLeft[F[_]] {
def foldLeft[A, B](xs: F[A], b: B, f: (B, A) => B): B
}
```

### Folding Lists

Here's the companion object with the implementation of `FoldLeft`

for Lists:

```
object FoldLeft{
def apply[F[_]: FoldLeft]: FoldLeft[F] = implicitly[FoldLeft[F]]
implicit val foldLeftList: FoldLeft[List] = new FoldLeft[List] {
override def foldLeft[A, B](xs: List[A], b: B, f: (B, A) => B): B = xs.foldLeft(b)(f)
}
}
```

Finally, we define an API to sum the elements of a foldable structure:

```
object FoldableAPI {
def sum[F[_]: FoldLeft](xs: F[Int]): Int = {
FoldLeft[F].foldLeft[Int, Int](xs, 0, (acc, x) => acc + x)
}
}
```

And *voila*, here's the result:

```
object Main extends App {
println(sum(List(1,2,3,4))) //10
}
```

### Folding Trees

What if instead of `List`

, we want to use a different structure like a `Tree`

?

```
sealed abstract class Tree[A]
final case class Leaf[A](value: A) extends Tree[A]
final case class Branch[A](value: A, left: Tree[A], right: Tree[A]) extends Tree[A]
object Branch {
//smart constructor: upcast Branch to Tree
def branch[A](value: A, left: Tree[A], right: Tree[A]): Tree[A] = Branch(value, left, right)
}
```

We need to provide an instance of `FoldLeft`

for `Tree`

:

```
object FoldLeft{
def apply[F[_]: FoldLeft]: FoldLeft[F] = implicitly[FoldLeft[F]]
implicit val foldLeftList: FoldLeft[List] = new FoldLeft[List] {
override def foldLeft[A, B](xs: List[A], b: B, f: (B, A) => B): B = xs.foldLeft(b)(f)
}
implicit val foldLefTree: FoldLeft[Tree] = new FoldLeft[Tree] {
override def foldLeft[A, B](xs: Tree[A], b: B, f: (B, A) => B): B = xs match {
case Leaf(v) => f(b,v)
case Branch(v,l,r) => foldLeft(r, foldLeft(l, f(b,v), f), f)
}
}
}
```

```
object Main extends App {
println(sum(branch(1, Branch(1, Leaf(2), Leaf(3)), Leaf(8)))) //15
}
```

Because '`sum`

' is an operation associative and commutative, we are missing a few things here. Let's extend our API to concatenate String elements of the foldable data structures:

```
object FoldableAPI {
def sum[F[_]: FoldLeft](xs: F[Int]): Int = {
FoldLeft[F].foldLeft[Int, Int](xs, 0, (acc, x) => acc + x)
}
def concatenate[F[_]: FoldLeft](xs: F[String]): String = {
FoldLeft[F].foldLeft[String, String](xs, "", (acc, x) => acc + x)
}
}
```

And given that the string concatenation is not commutative, now, we'll notice some interesting results. According to our definition of *"*`foldLeafTree`

*"*, the algorithm to fold a `Tree`

is:

- Take the initial node
- Concatenate the value of the left branch on the right
- Concatenate the value of the right branch on the right of the previous result

```
object Main extends App {
println(concatenate(branch("1", Branch("1", Leaf("2"), Leaf("3")), Leaf("8")))) //"11238"
}
```

Although correct, I would have expected something like:

- Take the initial node
- Concatenate the value of the left branch on the left
- Concatenate the value of the right branch on the right of the previous result.

...resulting in "21318."

To be able to do this implementation, we need to use `Fold`

instead of `FoldLeft`

. The crucial difference here is that with `Fold,`

we are free to re-arrange the logic of our fold function in a *'*`non-directional`

*'* way. (That would not be possible with **FoldLeft.foldLeftTree** as the types would not match)

```
trait Fold[F[_]] {
def fold[A](xs: F[A], b: A, f: (A, A) => A): A
}
object Fold {
def apply[F[_]: Fold]: Fold[F] = implicitly[Fold[F]]
implicit val foldTree: Fold[Tree] = new Fold[Tree] {
override def fold[A](xs: Tree[A], b: A, f: (A, A) => A): A = xs match {
case Leaf(v) => f(b,v)
case Branch(v,l,r) => f(f(fold(l,b,f), v),fold(r,b,f))
}
}
}
object FoldableAPI {
def sum[F[_]: FoldLeft](xs: F[Int]): Int = {
FoldLeft[F].foldLeft[Int, Int](xs, 0, (acc, x) => acc + x)
}
def concatenate[F[_]: FoldLeft](xs: F[String]): String = {
FoldLeft[F].foldLeft[String, String](xs, "", (acc, x) => acc + x)
}
def concatenate2[F[_]: Fold](xs: F[String]): String = {
Fold[F].fold[String](xs, "", (acc, x) => acc + x)
}
}
object Main extends App {
println(concatenate(branch("1", Branch("1", Leaf("2"), Leaf("3")), Leaf("8")))) //"11238"
println(concatenate2(branch("1", Branch("1", Leaf("2"), Leaf("3")), Leaf("8")))) //"21318"
}
```

With Cat's `Foldable`

, the result would be similar to our own `FoldLeft`

as `Foldable`

only allows 'directional' implementations of 'fold':

```
object CatsFoldable extends App {
import cats._
import cats.implicits._
implicit val foldableTree = new Foldable[Tree] {
override def foldLeft[A, B](fa: Tree[A], b: B)(f: (B, A) => B): B = fa match {
case Leaf(v) => f(b,v)
case Branch(v,l,r) => foldLeft(r, foldLeft(l, f(b,v))(f))(f)
}
override def foldRight[A, B](fa: Tree[A], lb: Eval[B])(f: (A, Eval[B]) => Eval[B]): Eval[B] = ???
}
println(Foldable[Tree].fold(branch("1", Branch("1", Leaf("2"), Leaf("3")), Leaf("8")))) //"11238"
}
```

The code used in this article can be found on FoldExamples.sc and Fold.scala.

## Further Reading

10 Amazing Scala Collection Functions

Folding the Universe: Functional Java

[DZone Refcard] Getting Started With Scala

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