Recursion, Maps, And Folds

Temporary note: If you're working on this chapter, beware that chapters 4 and 5 were swapped in November 2023.

Chapter Goals

In this chapter, we will look at how recursive functions can be used to structure algorithms. Recursion is a basic technique used in functional programming, which we will use throughout this book.

We will also cover some standard functions from PureScript's standard libraries. We will map, fold, and some useful special cases, like filter and concatMap.

The motivating example for this chapter is a library of functions for working with a virtual filesystem. We will apply the techniques learned in this chapter to write functions that compute properties of the files represented by a model of a filesystem.

Project Setup

The source code for this chapter is contained in src/Data/Path.purs and test/Examples.purs. The Data.Path module contains a model of a virtual filesystem. You do not need to modify the contents of this module. Implement your solutions to the exercises in the Test.MySolutions module. Enable accompanying tests in the Test.Main module as you complete each exercise and check your work by running spago test.

The project has the following dependencies:

• maybe, which defines the Maybe type constructor
• arrays, which defines functions for working with arrays
• strings, which defines functions for working with JavaScript strings
• foldable-traversable, which defines functions for folding arrays and other data structures
• console, which defines functions for printing to the console

Introduction

Recursion is an important technique in programming in general, but particularly common in pure functional programming, because, as we will see in this chapter, recursion helps to reduce the mutable state in our programs.

Recursion is closely linked to the divide and conquer strategy: to solve a problem on certain inputs, we can break down the inputs into smaller parts, solve the problem on those parts, and then assemble a solution from the partial solutions.

Let's see some simple examples of recursion in PureScript.

Here is the usual factorial function example:

factorial :: Int -> Int
factorial 0 = 1
factorial n = n * factorial (n - 1)

Here, we can see how the factorial function is computed by reducing the problem to a subproblem – computing the factorial of a smaller integer. When we reach zero, the answer is immediate.

Here is another common example that computes the Fibonacci function:

fib :: Int -> Int
fib 0 = 0
fib 1 = 1
fib n = fib (n - 1) + fib (n - 2)

Again, this problem is solved by considering the solutions to subproblems. In this case, there are two subproblems, corresponding to the expressions fib (n - 1) and fib (n - 2). When these two subproblems are solved, we assemble the result by adding the partial results.

Recursion on Arrays

We are not limited to defining recursive functions over the Int type! We will see recursive functions defined over a wide array of data types when we cover pattern matching later in the book, but for now, we will restrict ourselves to numbers and arrays.

Just as we branch based on whether the input is non-zero, in the array case, we will branch based on whether the input is non-empty. Consider this function, which computes the length of an array using recursion:

import Prelude

import Data.Array (null, tail)
import Data.Maybe (fromMaybe)

length :: forall a. Array a -> Int
length [] = 0
length arr = 1 + (length $ fromMaybe [] $ tail arr)

In this function, we branch based on the emptiness of the array. The null function returns true on an empty array. Empty arrays have a length of zero, and a non-empty array has a length that is one more than the length of its tail.

The tail function returns a Maybe wrapping the given array without its first element. If the array is empty (i.e., it doesn't have a tail), Nothing is returned. The fromMaybe function takes a default value and a Maybe value. If the latter is Nothing it returns the default; in the other case, it returns the value wrapped by Just.

This example is a very impractical way to find the length of an array in JavaScript, but it should provide enough help to allow you to complete the following exercises:

Exercises

1. (Easy) Write a recursive function isEven that returns true if and only if its input is an even integer.
2. (Medium) Write a recursive function countEven that counts the number of even integers in an array. Hint: the function head (also available in Data.Array) can be used to find the first element in a non-empty array.

Maps

The map function is an example of a recursive function on arrays. It is used to transform the elements of an array by applying a function to each element in turn. Therefore, it changes the contents of the array but preserves its shape (i.e., its length).

When we cover type classes later in the book, we will see that the map function is an example of a more general pattern of shape-preserving functions which transform a class of type constructors called functors.

Let's try out the map function in PSCi:

$ spago repl

> import Prelude
> map (\n -> n + 1) [1, 2, 3, 4, 5]
[2, 3, 4, 5, 6]

Notice how map is used – we provide a function that should be "mapped over" the array in the first argument, and the array itself in its second.

Infix Operators

The map function can also be written between the mapping function and the array, by wrapping the function name in backticks:

> (\n -> n + 1) `map` [1, 2, 3, 4, 5]
[2, 3, 4, 5, 6]

This syntax is called infix function application, and any function can be made infix in this way. It is usually most appropriate for functions with two arguments.

There is an operator which is equivalent to the map function when used with arrays, called <$>.

> (\n -> n + 1) <$> [1, 2, 3, 4, 5]
[2, 3, 4, 5, 6]

Let's look at the type of map:

> :type map
forall (f :: Type -> Type) (a :: Type) (b :: Type). Functor f => (a -> b) -> f a -> f b

The type of map is actually more general than we need in this chapter. For our purposes, we can treat map as if it had the following less general type:

forall (a :: Type) (b :: Type). (a -> b) -> Array a -> Array b

This type says that we can choose any two types, a and b, with which to apply the map function. a is the type of elements in the source array, and b is the type of elements in the target array. In particular, there is no reason why map has to preserve the type of the array elements. We can use map or <$> to transform integers to strings, for example:

> show <$> [1, 2, 3, 4, 5]

["1","2","3","4","5"]

Even though the infix operator <$> looks like special syntax, it is in fact just an alias for a regular PureScript function. The function is simply applied using infix syntax. In fact, the function can be used like a regular function by enclosing its name in parentheses. This means that we can use the parenthesized name (<$>) in place of map on arrays:

> (<$>) show [1, 2, 3, 4, 5]
["1","2","3","4","5"]

Infix function names are defined as aliases for existing function names. For example, the Data.Array module defines an infix operator (..) as a synonym for the range function, as follows:

infix 8 range as ..

We can use this operator as follows:

> import Data.Array

> 1 .. 5
[1, 2, 3, 4, 5]

> show <$> (1 .. 5)
["1","2","3","4","5"]

Note: Infix operators can be a great tool for defining domain-specific languages with a natural syntax. However, used excessively, they can render code unreadable to beginners, so it is wise to exercise caution when defining any new operators.

In the example above, we parenthesized the expression 1 .. 5, but this was actually not necessary, because the Data.Array module assigns a higher precedence level to the .. operator than that assigned to the <$> operator. In the example above, the precedence of the .. operator was defined as 8, the number after the infix keyword. This is higher than the precedence level of <$>, meaning that we do not need to add parentheses:

> show <$> 1 .. 5
["1","2","3","4","5"]

If we wanted to assign an associativity (left or right) to an infix operator, we could do so with the infixl and infixr keywords instead. Using infix assigns no associativity, meaning that you must parenthesize any expression using the same operator multiple times or using multiple operators of the same precedence.

Filtering Arrays

The Data.Array module provides another function filter, which is commonly used together with map. It provides the ability to create a new array from an existing array, keeping only those elements which match a predicate function.

For example, suppose we wanted to compute an array of all numbers between 1 and 10 which were even. We could do so as follows:

> import Data.Array

> filter (\n -> n `mod` 2 == 0) (1 .. 10)
[2,4,6,8,10]

Exercises

1. (Easy) Write a function squared which calculates the squares of an array of numbers. Hint: Use the map or <$> function.
2. (Easy) Write a function keepNonNegative which removes the negative numbers from an array of numbers. Hint: Use the filter function.
3. (Medium)
   • Define an infix synonym <$?> for filter. Note: Infix synonyms may not be defined in the REPL, but you can define it in a file.
   • Write a keepNonNegativeRewrite function, which is the same as keepNonNegative, but replaces filter with your new infix operator <$?>.
   • Experiment with the precedence level and associativity of your operator in PSCi. Note: There are no unit tests for this step.

Flattening Arrays

Another standard function on arrays is the concat function, defined in Data.Array. concat flattens an array of arrays into a single array:

> import Data.Array

> :type concat
forall (a :: Type). Array (Array a) -> Array a

> concat [[1, 2, 3], [4, 5], [6]]
[1, 2, 3, 4, 5, 6]

There is a related function called concatMap which is a combination of the concat and map functions. Where map takes a function from values to values (possibly of a different type), concatMap takes a function from values to arrays of values.

Let's see it in action:

> import Data.Array

> :type concatMap
forall (a :: Type) (b :: Type). (a -> Array b) -> Array a -> Array b

> concatMap (\n -> [n, n * n]) (1 .. 5)
[1,1,2,4,3,9,4,16,5,25]

Here, we call concatMap with the function \n -> [n, n * n] which sends an integer to the array of two elements consisting of that integer and its square. The result is an array of ten integers: the integers from 1 to 5 along with their squares.

Note how concatMap concatenates its results. It calls the provided function once for each element of the original array, generating an array for each. Finally, it collapses all of those arrays into a single array, which is its result.

map, filter and concatMap form the basis for a whole range of functions over arrays called "array comprehensions".

Array Comprehensions

Suppose we wanted to find the factors of a number n. One simple way to do this would be by brute force: we could generate all pairs of numbers between 1 and n, and try multiplying them together. If the product was n, we would have found a pair of factors of n.

We can perform this computation using array comprehension. We will do so in steps, using PSCi as our interactive development environment.

The first step is to generate an array of pairs of numbers below n, which we can do using concatMap.

Let's start by mapping each number to the array 1 .. n:

> pairs n = concatMap (\i -> 1 .. n) (1 .. n)

We can test our function

> pairs 3
[1,2,3,1,2,3,1,2,3]

This is not quite what we want. Instead of just returning the second element of each pair, we need to map a function over the inner copy of 1 .. n which will allow us to keep the entire pair:

> :paste
… pairs' n =
…   concatMap (\i ->
…     map (\j -> [i, j]) (1 .. n)
…   ) (1 .. n)
… ^D

> pairs' 3
[[1,1],[1,2],[1,3],[2,1],[2,2],[2,3],[3,1],[3,2],[3,3]]

This is looking better. However, we are generating too many pairs: we keep both [1, 2] and [2, 1] for example. We can exclude the second case by making sure that j only ranges from i to n:

> :paste
… pairs'' n =
…   concatMap (\i ->
…     map (\j -> [i, j]) (i .. n)
…   ) (1 .. n)
… ^D
> pairs'' 3
[[1,1],[1,2],[1,3],[2,2],[2,3],[3,3]]

Great! Now that we have all of the pairs of potential factors, we can use filter to choose the pairs which multiply to give n:

> import Data.Foldable

> factors n = filter (\pair -> product pair == n) (pairs'' n)

> factors 10
[[1,10],[2,5]]

This code uses the product function from the Data.Foldable module in the foldable-traversable library.

Excellent! We've managed to find the correct set of factor pairs without duplicates.

Do Notation

However, we can improve the readability of our code considerably. map and concatMap are so fundamental, that they (or rather, their generalizations map and bind) form the basis of a special syntax called do notation.

Note: Just like map and concatMap allowed us to write array comprehensions, the more general operators map and bind allow us to write so-called monad comprehensions. We'll see plenty more examples of monads later in the book, but in this chapter, we will only consider arrays.

We can rewrite our factors function using do notation as follows:

factors :: Int -> Array (Array Int)
factors n = filter (\xs -> product xs == n) do
  i <- 1 .. n
  j <- i .. n
  pure [ i, j ]

The keyword do introduces a block of code that uses do notation. The block consists of expressions of a few types:

• Expressions that bind elements of an array to a name. These are indicated with the backwards-facing arrow <-, with a name on the left, and an expression on the right whose type is an array.
• Expressions that do not bind elements of the array to names. The do result is an example of this kind of expression and is illustrated in the last line, pure [i, j].
• Expressions that give names to expressions, using the let keyword.

This new notation hopefully makes the structure of the algorithm clearer. If you mentally replace the arrow <- with the word "choose", you might read it as follows: "choose an element i between 1 and n, then choose an element j between i and n, and return [i, j]".

In the last line, we use the pure function. This function can be evaluated in PSCi, but we have to provide a type:

> pure [1, 2] :: Array (Array Int)
[[1, 2]]

In the case of arrays, pure simply constructs a singleton array. We can modify our factors function to use this form, instead of using pure:

factorsV2 :: Int -> Array (Array Int)
factorsV2 n = filter (\xs -> product xs == n) do
  i <- 1 .. n
  j <- i .. n
  [ [ i, j ] ]

and the result would be the same.

Guards

One further change we can make to the factors function is to move the filter inside the array comprehension. This is possible using the guard function from the Control.Alternative module (from the control package):

import Control.Alternative (guard)

factorsV3 :: Int -> Array (Array Int)
factorsV3 n = do
  i <- 1 .. n
  j <- i .. n
  guard $ i * j == n
  pure [ i, j ]

Just like pure, we can apply the guard function in PSCi to understand how it works. The type of the guard function is more general than we need here:

> import Control.Alternative

> :type guard
forall (m :: Type -> Type). Alternative m => Boolean -> m Unit

The Unit type represents values with no computational content — the absence of a concrete meaningful value.

We often use Unit "wrapped" in a type constructor as the return type of a computation where we only care about the effects of the computation (or a "shape" of the result) and not some concrete value.

For example, the main function has the type Effect Unit. Main is an entry point to the project — we don't call it directly.

We'll explain what m in the type signature means in Chapter 6.

In our case, we can assume that PSCi reported the following type:

Boolean -> Array Unit

For our purposes, the following calculations tell us everything we need to know about the guard function on arrays:

> import Data.Array

> length $ guard true
1

> length $ guard false
0

If we pass an expression to guard that evaluates to true, then it returns an array with a single element. If the expression evaluates to false, then its result is empty.

This means that if the guard fails, then the current branch of the array comprehension will terminate early with no results. This means that a call to guard is equivalent to using filter on the intermediate array. Depending on the application, you might prefer to use guard instead of a filter. Try the two definitions of factors to verify that they give the same results.

Exercises

1. (Easy) Write a function isPrime, which tests whether its integer argument is prime. Hint: Use the factors function.
2. (Medium) Write a function cartesianProduct which uses do notation to find the cartesian product of two arrays, i.e., the set of all pairs of elements a, b, where a is an element of the first array, and b is an element of the second.
3. (Medium) Write a function triples :: Int -> Array (Array Int), which takes a number \( n \) and returns all Pythagorean triples whose components (the \( a \), \( b \), and \( c \) values) are each less than or equal to \( n \). A Pythagorean triple is an array of numbers \( [ a, b, c ] \) such that \( a ^ 2 + b ^ 2 = c ^ 2 \). Hint: Use the guard function in an array comprehension.
4. (Difficult) Write a function primeFactors which produces the prime factorization of n, i.e., the array of prime integers whose product is n. Hint: for an integer greater than 1, break the problem into two subproblems: finding the first factor and the remaining factors.

Folds

Left and right folds over arrays provide another class of interesting functions that can be implemented using recursion.

Start by importing the Data.Foldable module and inspecting the types of the foldl and foldr functions using PSCi:

> import Data.Foldable

> :type foldl
forall (f :: Type -> Type) (a :: Type) (b :: Type). Foldable f => (b -> a -> b) -> b -> f a -> b

> :type foldr
forall (f :: Type -> Type) (a :: Type) (b :: Type). Foldable f => (a -> b -> b) -> b -> f a -> b

These types are more general than we are interested in right now. For this chapter, we can simplify and assume the following (more specific) type signatures:

-- foldl
forall a b. (b -> a -> b) -> b -> Array a -> b

-- foldr
forall a b. (a -> b -> b) -> b -> Array a -> b

In both cases, the type a corresponds to the type of elements of our array. The type b can be thought of as the type of an "accumulator", which will accumulate a result as we traverse the array.

The difference between the foldl and foldr functions is the direction of the traversal. foldl folds the array "from the left", whereas foldr folds the array "from the right".

Let's see these functions in action. Let's use foldl to sum an array of integers. The type a will be Int, and we can also choose the result type b to be Int. We need to provide three arguments: a function Int -> Int -> Int, which will add the next element to the accumulator, an initial value for the accumulator of type Int, and an array of Ints to add. For the first argument, we can use the addition operator, and the initial value of the accumulator will be zero:

> foldl (+) 0 (1 .. 5)
15

In this case, it didn't matter whether we used foldl or foldr, because the result is the same, no matter what order the additions happen in:

> foldr (+) 0 (1 .. 5)
15

Let's write an example where the choice of folding function matters to illustrate the difference. Instead of the addition function, let's use string concatenation to build a string:

> foldl (\acc n -> acc <> show n) "" [1,2,3,4,5]
"12345"

> foldr (\n acc -> acc <> show n) "" [1,2,3,4,5]
"54321"

This illustrates the difference between the two functions. The left fold expression is equivalent to the following application:

((((("" <> show 1) <> show 2) <> show 3) <> show 4) <> show 5)

Whereas the right fold is equivalent to this:

((((("" <> show 5) <> show 4) <> show 3) <> show 2) <> show 1)

Tail Recursion

Recursion is a powerful technique for specifying algorithms but comes with a problem: evaluating recursive functions in JavaScript can lead to stack overflow errors if our inputs are too large.

It is easy to verify this problem with the following code in PSCi:

> :paste
… f n =
…   if n == 0
…     then 0
…     else 1 + f (n - 1)
… ^D

> f 10
10

> f 100000
RangeError: Maximum call stack size exceeded

This is a problem. If we adopt recursion as a standard technique from functional programming, we need a way to deal with possibly unbounded recursion.

PureScript provides a partial solution to this problem through tail recursion optimization.

Note: more complete solutions to the problem can be implemented in libraries using so-called trampolining, but that is beyond the scope of this chapter. The interested reader can consult the documentation for the free and tailrec packages.

The key observation that enables tail recursion optimization: a recursive call in tail position to a function can be replaced with a jump, which does not allocate a stack frame. A call is in tail position when it is the last call made before a function returns. This is why we observed a stack overflow in the example – the recursive call to f was not in tail position.

In practice, the PureScript compiler does not replace the recursive call with a jump, but rather replaces the entire recursive function with a while loop.

Here is an example of a recursive function with all recursive calls in tail position:

factorialTailRec :: Int -> Int -> Int
factorialTailRec 0 acc = acc
factorialTailRec n acc = factorialTailRec (n - 1) (acc * n)

Notice that the recursive call to factorialTailRec is the last thing in this function – it is in tail position.

Accumulators

One common way to turn a not tail recursive function into a tail recursive is to use an accumulator parameter. An accumulator parameter is an additional parameter added to a function that accumulates a return value, as opposed to using the return value to accumulate the result.

For example, consider again the length function presented at the beginning of the chapter:

length :: forall a. Array a -> Int
length [] = 0
length arr = 1 + (length $ fromMaybe [] $ tail arr)

This implementation is not tail recursive, so the generated JavaScript will cause a stack overflow when executed on a large input array. However, we can make it tail recursive, by introducing a second function argument to accumulate the result instead:

lengthTailRec :: forall a. Array a -> Int
lengthTailRec arr = length' arr 0
  where
  length' :: Array a -> Int -> Int
  length' [] acc = acc
  length' arr' acc = length' (fromMaybe [] $ tail arr') (acc + 1)

In this case, we delegate to the helper function length', which is tail recursive – its only recursive call is in the last case, in tail position. This means that the generated code will be a while loop and not blow the stack for large inputs.

To understand the implementation of lengthTailRec, note that the helper function length' essentially uses the accumulator parameter to maintain an additional piece of state – the partial result. It starts at 0 and grows by adding 1 for every element in the input array.

Note also that while we might think of the accumulator as a "state", there is no direct mutation.

Prefer Folds to Explicit Recursion

If we can write our recursive functions using tail recursion, we can benefit from tail recursion optimization, so it becomes tempting to try to write all of our functions in this form. However, it is often easy to forget that many functions can be written directly as a fold over an array or similar data structure. Writing algorithms directly in terms of combinators such as map and fold has the added advantage of code simplicity – these combinators are well-understood, and as such, communicate the intent of the algorithm much better than explicit recursion.

For example, we can reverse an array using foldr:

> import Data.Foldable

> :paste
… reverse :: forall a. Array a -> Array a
… reverse = foldr (\x xs -> xs <> [x]) []
… ^D

> reverse [1, 2, 3]
[3,2,1]

Writing reverse in terms of foldl will be left as an exercise for the reader.

Exercises

1. (Easy) Write a function allTrue which uses foldl to test whether an array of boolean values are all true.
2. (Medium - No Test) Characterize those arrays xs for which the function foldl (==) false xs returns true. In other words, complete the sentence: "The function returns true when xs contains ..."
3. (Medium) Write a function fibTailRec which is the same as fib but in tail recursive form. Hint: Use an accumulator parameter.
4. (Medium) Write reverse in terms of foldl.

A Virtual Filesystem

In this section, we'll apply what we've learned, writing functions that will work with a model of a filesystem. We will use maps, folds, and filters to work with a predefined API.

The Data.Path module defines an API for a virtual filesystem as follows:

• There is a type Path which represents a path in the filesystem.
• There is a path root which represents the root directory.
• The ls function enumerates the files in a directory.
• The filename function returns the file name for a Path.
• The size function returns the file size for a Path representing a file.
• The isDirectory function tests whether a Path is a file or a directory.

In terms of types, we have the following type definitions:

root :: Path

ls :: Path -> Array Path

filename :: Path -> String

size :: Path -> Maybe Int

isDirectory :: Path -> Boolean

We can try out the API in PSCi:

$ spago repl

> import Data.Path

> root
/

> isDirectory root
true

> ls root
[/bin/,/etc/,/home/]

The Test.Examples module defines functions that use the Data.Path API. You do not need to modify the Data.Path module, or understand its implementation. We will work entirely in the Test.Examples module.

Listing All Files

Let's write a function that performs a deep enumeration of all files inside a directory. This function will have the following type:

allFiles :: Path -> Array Path

We can define this function by recursion. First, we can use ls to enumerate the immediate children of the directory. For each child, we can recursively apply allFiles, which will return an array of paths. concatMap will allow us to apply allFiles and flatten the results simultaneously.

Finally, we use the cons operator : to include the current file:

allFiles file = file : concatMap allFiles (ls file)

Note: the cons operator : has poor performance on immutable arrays, so it is not generally recommended. Performance can be improved by using other data structures, such as linked lists and sequences.

Let's try this function in PSCi:

> import Test.Examples
> import Data.Path

> allFiles root

[/,/bin/,/bin/cp,/bin/ls,/bin/mv,/etc/,/etc/hosts, ...]

Great! Now let's see if we can write this function using an array comprehension using do notation.

Recall that a backwards arrow corresponds to choosing an element from an array. The first step is to choose an element from the immediate children of the argument. Then we call the function recursively for that file. Since we use do notation, there is an implicit call to concatMap, which concatenates all of the recursive results.

Here is the new version:

allFiles' :: Path -> Array Path
allFiles' file = file : do
  child <- ls file
  allFiles' child

Try out the new version in PSCi – you should get the same result. I'll let you decide which version you find clearer.

Exercises

1. (Easy) Write a function onlyFiles which returns all files (not directories) in all subdirectories of a directory.

2. (Medium) Write a function whereIs to search for a file by name. The function should return a value of type Maybe Path, indicating the directory containing the file, if it exists. It should behave as follows:

   > whereIs root "ls"
   Just (/bin/)
   
   > whereIs root "cat"
   Nothing
   

   Hint: Try to write this function as an array comprehension using do notation.

3. (Difficult) Write a function largestSmallest which takes a Path and returns an array containing the single largest and single smallest files in the Path, including (recursively) any subdirectories. Note: consider the cases where there are zero or one files in the Path by returning an empty or one-element array, respectively.

Conclusion

In this chapter, we covered the basics of recursion in PureScript to express algorithms concisely. We also introduced user-defined infix operators, standard functions on arrays such as maps, filters, and folds, and array comprehensions that combine these ideas. Finally, we showed the importance of using tail recursion to avoid stack overflow errors and how to use accumulator parameters to convert functions to tail recursive form.