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Scala has other abstractions for common collections operations. reduceLeft allows you to perform an operation on adjacent elements of the collection where the result of the first
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CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
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operation is fed into the next operation. For example, if we want to find the biggest number in a List[Int]:
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scala> List(8, 6, 22, 2).reduceLeft(_ max _)
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res50: Int = 22
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In this case, reduceLeft takes 8 and 6 and feeds them into our function, which returns the maximum value of the two numbers: 8. Next, reduceLeft feeds 8 (the output of the last iteration) and 22 into the function, resulting in 22. Next, reduceLeft feeds 22 and 2 into the function, resulting in 22. Because there are no more elements, reduceLeft returns 22. We can use reduceLeft to find the longest word:
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scala> List("moose", "cow", "A", "Cat"). reduceLeft((a, b) => if (a.length > b.length) a else b)
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res41: java.lang.String = moose
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Because Scala s if expression works like Java s ternary operator, the if in the previous code returns a if it s longer than b. We can also find the shortest word:
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scala> List("moose", "cow", "A", "Cat"). reduceLeft((a, b) => if (a.length < b.length) a else b)
res42: java.lang.String = A
reduceLeft will throw an exception on an Nil (empty) List. This is correct behavior as there is no way to apply the function on the members of the List as a Nil List has no elements.
CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
foldLeft is similar to reduceLeft, but it starts with a seed value. The return type of the function and the return type of foldLeft must be the same type as the seed. The first
example is summing up List[Int]:
scala> List(1,2,3,4).foldLeft(0) (_ + _)
res43: Int = 10
In this case, the seed value is 0. Its type is Int. foldLeft feeds the seed and the first element of the List, 1, into the function, which returns 1. Next, foldLeft feeds 1 (the result of the previous iteration) and 2 (the next element) into the function, resulting in 3. The process continues, and the sum of the List[Int] is generated: 10. We can generate the product of the List the same way:
scala> List(1,2,3,4).foldLeft(1) (_ * _)
res44: Int = 24
But because the return type of foldLeft is the type of the seed, not the type of the List, we can figure out the total length of a List[String]:
scala> List("b", "a", "elwood", "archer").foldLeft(0)(_ + _.length)
res51: Int = 14
I find that sometimes I need to work with more than one collection at a time. For example, if we want to generate the List of products of the numbers from 1 to 3:
scala> val n = (1 to 3).toList
n: List[Int] = List(1, 2, 3)
CHAPTER 3 COLLECTIONS AND THE JOY OF IMMUTABILITY
scala> n.map(i => n.map(j => i * j))
res53: List[List[Int]] = List(List(1, 2, 3), List(2, 4, 6), List(3, 6, 9))
We have nested map invocations, and that results in a List[List[Int]]. In some cases, this may be what we want. In other cases, we want the results in a single List[Int]. In order to nest the map operations but flatten the results of nested operations, we use the flatMap method:
scala> n.flatMap(i => n.map(j => i * j))
res58: List[Int] = List(1, 2, 3, 2, 4, 6, 3, 6, 9)
Look Ma, No Loops
So far, we ve written a bunch of code that manipulates collections without explicit looping. By passing functions, that is, logic, to methods that control the looping, we let the library writers define the looping, and we define the logic in our app. However, syntactically, nested map, flatMap, and filter can get ugly. For example, if we want to find the product of the odd numbers from 1 to 10 times the even numbers from 1 to 10, we could write the following:
scala> scala> scala> scala> def isOdd(in: Int) = in % 2 == 1 def isEven(in: Int) = !isOdd(in) val n = (1 to 10).toList n.filter(isEven).flatMap(i => n.filter(isOdd).map(j => i * j))
res60: List[Int] = List(2, 6, 10, 14, 18, 10, 30, 50, 70, 90)
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