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Scala Functional Programming Patterns

You're reading from   Scala Functional Programming Patterns Grok and perform effective functional programming in Scala

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Product type Paperback
Published in Dec 2015
Publisher
ISBN-13 9781783985845
Length 298 pages
Edition 1st Edition
Languages
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Author (1):
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Atul S. Khot Atul S. Khot
Author Profile Icon Atul S. Khot
Atul S. Khot
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Table of Contents (13) Chapters Close

Preface 1. Grokking the Functional Way FREE CHAPTER 2. Singletons, Factories, and Builders 3. Recursion and Chasing your Own Tail 4. Lazy Sequences – Being Lazy, Being Good 5. Taming Multiple Inheritance with Traits 6. Currying Favors with Your Code 7. Of Visitors and Chains of Responsibilities 8. Traversals – Mapping/Filtering/Folding/Reducing 9. Higher Order Functions 10. Actors and Message Passing 11. It's a Paradigm Shift Index

FlatMap


The flatMap method takes a function as a parameter, applies it to each element in a container, and flattens the overall result as shown in the following code:

scala> val f1 = (n: Int) => (1 to n).toList map { _ % 2 == 0 }
f1: Int => List[Boolean] = <function1>
scala> :t f1
Int => List[Boolean]
scala> List(2,3) flatMap f1
res4: List[Boolean] = List(false, true, false, true, false)

As earlier, f1 is of the type X => List[Y], where X is bound to Int and Y is bound to Boolean. Applying flatMap to List(2,3), which is List[Int], we get List[Boolean]. So, given the function, X => List[Y] we were able to get a transformation, List[Int] => List[Boolean].

Where does this come in useful? To know this, let's look at the following example:

scala> def f(x: Int) = if (x % 2 == 0) Some(x) else None
f: (x: Int)Option[Int]

The result of evaluating the function is Option, as shown in the following code. However, we wish to do something about this. Let's say, we add...

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