Question

I read in Programming in Scala section 23.5 that map, flatMap and filter operations can always be converted into for-comprehensions and vice-versa.

We’re given the following equivalence:

def map[A, B](xs: List[A], f: A => B): List[B] =
  for (x <- xs) yield f(x)

I have a value calculated from a series of map operations:

val r = (1 to 100).map{ i => (1 to 100).map{i % _ == 0} }
                  .map{ _.foldLeft(false)(_^_) }
                  .map{ case true => "open"; case _ => "closed" }

I’m wondering what this would look like as a for-comprehension. How do I translate it?

(If it’s helpful, in words this is:

• take integers from 1 to 100
• for each, create a list of 100 boolean values
• fold each list with an XOR operator, back into a boolean
• yield a list of 100 Strings “open” or “closed” depending on the boolean

I imagine there is a standard way to translate map operations and the details of the actual functions in them is not important. I could be wrong though.)

Answer 1

Is this the kind of translation you’re looking for?

for (i <- 1 to 100;
     val x = (1 to 100).map(i % _ == 0);
     val y = x.foldLeft(false)(_^_);
     val z = y match { case true => "open"; case _ => "closed" })
  yield z

If desired, the map in the definition of x could also be translated to an “inner” for-comprehension.

In retrospect, a series of chained map calls is sort of trivial, in that you could equivalently call map once with composed functions:

 s.map(f).map(g).map(h) == s.map(f andThen g andThen h)

I find for-comprehensions to be a bigger win when flatMap and filter are involved. Consider

for (i <- 1 to 3;
     j <- 1 to 3 if (i + j) % 2 == 0;
     k <- 1 to 3) yield i ^ j ^ k

versus

(1 to 3).flatMap { i =>
  (1 to 3).filter(j => (i + j) % 2 == 0).flatMap { j =>
    (1 to 3).map { k => i ^ j ^ k }
  }
}