## Standard Library

### List

The list is a fundamental data structure in functional programming.

A list having x1, …, xn as elements is written List(x1, …, xn):

val fruit = List("apples", "oranges", "pears")
val nums = List(1, 2, 3, 4)
val diag3 = List(List(1, 0, 0), List(0, 1, 0), List(0, 0, 1))
val empty = List()
• Lists are immutable --- the elements of a list cannot be changed,
• Lists are recursive (as you will see in the next subsection),
• Lists are homogeneous: A list is intended to be composed of elements that all have the same type.

That's because when you create a List of elements with different types it will look for a common ancestor. The common ancestor for all types is Any

val heterogeneousList: List[Any] = List(1, "1", '1')

The type of a list with elements of type T is written List[T]:

val fruit: List[String] = List("apples", "oranges", "pears")
val nums: List[Int] = List(1, 2, 3, 4)
val diag3: List[List[Int]] = List(List(1, 0, 0), List(0, 1, 0), List(0, 0, 1))
val empty: List[Nothing] = List()

#### Constructors of Lists

Actually, all lists are constructed from:

• the empty list Nil, and
• the construction operation :: (pronounced cons): x :: xs gives a new list with the first element x, followed by the elements of xs (which is a list itself).

For example:

val fruit = "apples" :: ("oranges" :: ("pears" :: Nil))
val nums = 1 :: (2 :: (3 :: (4 :: Nil)))
val empty = Nil
##### Right Associativity

Convention: Operators ending in “:” associate to the right.

A :: B :: C is interpreted as A :: (B :: C).

We can thus omit the parentheses in the definition above.

val nums = 1 :: 2 :: 3 :: 4 :: Nil

Operators ending in “:” are also different in the fact that they are seen as method calls of the right-hand operand.

So the expression above is equivalent to:

val nums = Nil.::(4).::(3).::(2).::(1)

#### Manipulating Lists

It is possible to decompose lists with pattern matching:

• Nil: the Nil constant,
• p :: ps: A pattern that matches a list with a head matching p and a tail matching ps.
nums match {
// Lists of `Int` that starts with `1` and then `2`
case 1 :: 2 :: xs => …
// Lists of length 1
case x :: Nil => …
// Same as `x :: Nil`
case List(x) => …
// The empty list, same as `Nil`
case List() =>
// A list that contains as only element another list that starts with `2`
case List(2 :: xs) => …
}

#### Exercise: Sorting Lists

Suppose we want to sort a list of numbers in ascending order:

• One way to sort the list List(7, 3, 9, 2) is to sort the tail List(3, 9, 2) to obtain List(2, 3, 9).
• The next step is to insert the head 7 in the right place to obtain the result List(2, 3, 7, 9).

This idea describes Insertion Sort:

def insertionSort(xs: List[Int]): List[Int] = xs match {
case List() => List()
case y :: ys => insert(y, insertionSort(ys))
}

Complete the definition insertion sort by filling in the blanks in the definition below:

val cond: (Int, Int) => Boolean = res0
def insert(x: Int, xs: List[Int]): List[Int] =
xs match {
case List() => x :: res1
case y :: ys =>
if (cond(x, y)) x :: y :: ys
else y :: insert(x, ys)
}
insert(2, 1 :: 3 :: Nil) shouldBe (1 :: 2 :: 3 :: Nil)
insert(1, 2 :: 3 :: Nil) shouldBe (1 :: 2 :: 3 :: Nil)
insert(3, 1 :: 2 :: Nil) shouldBe (1 :: 2 :: 3 :: Nil)

#### Common Operations on Lists

Transform the elements of a list using map:

List(1, 2, 3).map(x => x + 1) == List(2, 3, 4)

Filter elements using filter:

List(1, 2, 3).filter(x => x % 2 == 0) == List(2)

Transform each element of a list into a list and flatten the results into a single list using flatMap:

val xs =
List(1, 2, 3).flatMap { x =>
List(x, 2 * x, 3 * x)
}
xs == List(1, 2, 3, 2, 4, 6, 3, 6, 9)

### Optional Values

We represent an optional value of type A with the type Option[A]. This is useful to implement, for instance, partially defined functions:

// The `sqrt` function is not defined for negative values
def sqrt(x: Double): Option[Double] = …

An Option[A] can either be None (if there is no value) or Some[A] (if there is a value):

def sqrt(x: Double): Option[Double] =
if (x < 0) None else Some(…)

#### Manipulating Options

It is possible to decompose options with pattern matching:

def foo(x: Double): String =
sqrt(x) match {
case None => "no result"
case Some(y) => y.toString
}

#### Common Operations on Options

Transform an optional value with map:

Some(1).map(x => x + 1) shouldBe Some(2)
None.map((x: Int) => x + 1) shouldBe None
res0.map(x => x + 1) shouldBe res1

Filter values with filter:

Some(1).filter(x => x % 2 == 0) shouldBe None
Some(2).filter(x => x % 2 == 0) shouldBe Some(2)
res0.filter(x => x % 2 == 0) shouldBe res1

Use flatMap to transform a successful value into an optional value:

Some(1).flatMap(x => Some(x + 1)) shouldBe Some(2)
None.flatMap((x: Int) => Some(x + 1)) shouldBe None
res0.flatMap(x => Some(x + 1)) shouldBe res1

### Error Handling

This subsection introduces types that are useful to handle failures.

#### Try

Try[A] represents a computation that attempted to return an A. It can either be:

• a Success[A],
• or a Failure.

The key difference between None and Failures is that the latter provide the reason for the failure:

def sqrt(x: Double): Try[Double] =
if (x < 0) Failure(new IllegalArgumentException("x must be positive"))
else Success(…)
##### Manipulating Try[A] values

Like options and lists, Try[A] is an algebraic data type, so it can be decomposed using pattern matching.

Try[A] also have map, filter and flatMap. They behave the same as with Option[A], except that any exception that is thrown during their execution is converted into a Failure.

#### Either

Either can also be useful to handle failures. Basically, the type Either[A, B] represents a value that can either be of type A or of type B. It can be decomposed in two cases: Left or Right.

You can use one case to represent the failure and the other to represent the success. What makes it different from Try is that you can choose a type other than Throwable to represent the exception. Another difference is that exceptions that occur when transforming Either values are not converted into failures.

def sqrt(x: Double): Either[String, Double] =
if (x < 0) Left("x must be positive")
else Right(…)
##### Manipulating Either[A, B] Values

Since Scala 2.12, Either has map and flatMap. These methods transform the Right case only. We say that Either is “right biased”:

Right(1).map((x: Int) => x + 1) shouldBe Right(2)
Left("foo").map((x: Int) => x + 1) shouldBe Left("foo")

Either also has a filterOrElse method that turns a Right value into a Left value if it does not satisfy a given predicate:

Right(1).filterOrElse(x => x % 2 == 0, "Odd value") shouldBe Left("Odd value")

However, prior to Scala 2.12, Either was “unbiased”. You had to explicitly specify which “side” (Left or Right) you wanted to map:

def triple(x: Int): Int = 3 * x

def tripleEither(x: Either[String, Int]): Either[String, Int] =
x.map(triple)

tripleEither(Right(1)) shouldBe res0
tripleEither(Left("not a number")) shouldBe res1