Recursion, Maps And Folds
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 see the map
and fold
functions, as well as 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 which 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 theMaybe
type constructorarrays
, which defines functions for working with arraysstrings
, which defines functions for working with JavaScript stringsfoldabletraversable
, which defines functions for folding arrays and other data structuresconsole
, 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 n =
if n == 0 then
1
else
n * factorial (n  1)
Here, we can see how the factorial function is computed by reducing the problem to a subproblem  that of computing the factorial of a smaller integer. When we reach zero, the answer is immediate.
Here is another common example, which computes the Fibonacci function:
fib :: Int > Int
fib n =
if n == 0 then
0
else if n == 1 then
1
else
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.
Note that, while the above examples of factorial
and fib
work as intended, a more idiomatic implementation would use pattern matching instead of if
/then
/else
. Pattern matching techniques are discussed in a later chapter.
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 nonzero, in the array case, we will branch based on whether the input is nonempty. 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 arr =
if null arr then
0
else
1 + (length $ fromMaybe [] $ tail arr)
In this function, we use an if .. then .. else
expression to branch based on the emptiness of the array. The null
function returns true
on an empty array. Empty arrays have length zero, and a nonempty 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 obviously a very impractical way to find the length of an array in JavaScript, but should provide enough help to allow you to complete the following exercises:
Exercises
 (Easy) Write a recursive function
isEven
which returnstrue
if and only if its input is an even integer.  (Medium) Write a recursive function
countEven
which counts the number of even integers in an array. Hint: the functionhead
(also available inData.Array
) can be used to find the first element in a nonempty 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 shapepreserving 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 which 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 <$>
. This operator can be used infix like any other binary operator:
> (\n > n + 1) <$> [1, 2, 3, 4, 5]
[2, 3, 4, 5, 6]
Let's look at the type of map
:
> :type map
forall a b f. 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 b. (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 <$>
looks like special syntax, it is in fact just an alias for a regular PureScript function. The function is simply applied using infix syntax. In fact, the function can be used like a regular function by enclosing its name in parentheses. This means that we can used the parenthesized name (<$>)
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 domainspecific 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 <$> 1 .. 5
["1","2","3","4","5"]
If we wanted to assign an associativity (left or right) to an infix operator, we could do so with the infixl
and infixr
keywords instead. Using infix
assigns no associativity, meaning that you must parenthesize any expression using the same operator multiple times or using multiple operators of the same precedence.
Filtering Arrays
The Data.Array
module provides another function filter
, which is commonly used together with map
. It provides the ability to create a new array from an existing array, keeping only those elements which match a predicate function.
For example, suppose we wanted to compute an array of all numbers between 1 and 10 which were even. We could do so as follows:
> import Data.Array
> filter (\n > n `mod` 2 == 0) (1 .. 10)
[2,4,6,8,10]
Exercises
 (Easy) Write a function
squared
which calculates the squares of an array of numbers. Hint: Use themap
or<$>
function.  (Easy) Write a function
keepNonNegative
which removes the negative numbers from an array of numbers. Hint: Use thefilter
function.  (Medium)
 Define an infix synonym
<$?>
forfilter
. Note: Infix synonyms may not be defined in the REPL, but you can define it in a file.  Write a
keepNonNegativeRewrite
function, which is the same askeepNonNegative
, but replacesfilter
with your new infix operator<$?>
.  Experiment with the precedence level and associativity of your operator in PSCi. Note: There are no unit tests for this step.
 Define an infix synonym
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. 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 like 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 b. (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 an 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 foldabletraversable
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 socalled 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 which uses do notation. The block consists of expressions of a few types:
 Expressions which bind elements of an array to a name. These are indicated with the backwardsfacing arrow
<
, with a name on the left, and an expression on the right whose type is an array.  Expressions which 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 which 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. In fact, we could 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. Alternative m => Boolean > m Unit
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
That is, if guard
is passed an expression which 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
 (Easy) Write a function
isPrime
which tests if its integer argument is prime or not. Hint: Use thefactors
function.  (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 elementsa
,b
, wherea
is an element of the first array, andb
is an element of the second.  (Medium) Write a function
triples :: Int > Array (Array Int)
which takes a numbern
and returns all Pythagorean triples whose components (thea
,b
andc
values) are each less than or equal ton
. A Pythagorean triple is an array of numbers[a, b, c]
such thata² + b² = c²
. Hint: Use theguard
function in an array comprehension.  (Difficult) Write a function
primeFactors
which produces the prime factorization ofn
, i.e. the array of prime integers whose product isn
. Hint: for an integer greater than 1, break the problem down into two subproblems: finding the first factor, and finding the remaining factors.
Folds
Left and right folds over arrays provide another class of interesting functions which 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 a b f. Foldable f => (b > a > b) > b > f a > b
> :type foldr
forall a b f. Foldable f => (a > b > b) > b > f a > b
These types are actually more general than we are interested in right now. For the purposes of this chapter, we can assume that PSCi had given the following (more specific) answer:
> :type foldl
forall a b. (b > a > b) > b > Array a > b
> :type foldr
forall a b. (a > b > b) > b > Array a > b
In both of these 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 Int
s to add. For the first argument, we can just 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 does matter, in order 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 are going to adopt recursion as a standard technique from functional programming, then we need a way to deal with possibly unbounded recursion.
PureScript provides a partial solution to this problem in the form of tail recursion optimization.
Note: more complete solutions to the problem can be implemented in libraries using socalled 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 which enables tail recursion optimization is the following: 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 the reason 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 n acc =
if n == 0
then acc
else factorialTailRec (n  1) (acc * n)
Notice that the recursive call to factorialTailRec
is the last thing that happens in this function  it is in tail position.
Accumulators
One common way to turn a function which is not tail recursive into a tail recursive function is to use an accumulator parameter. An accumulator parameter is an additional parameter which is added to a function which accumulates a return value, as opposed to using the return value to accumulate the result.
For example, consider again the length
function presented in the beginning of the chapter:
length :: forall a. Array a > Int
length arr =
if null arr
then 0
else 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' arr' acc =
if null arr'
then acc
else 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, and is in tail position. This means that the generated code will be a while loop, and will 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 out 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 "state", there is no direct mutation going on.
Prefer Folds to Explicit Recursion
If we can write our recursive functions using tail recursion, then 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 wellunderstood, 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
 (Easy) Write a function
allTrue
which usesfoldl
to test whether an array of boolean values are all true.  (Medium  No Test) Characterize those arrays
xs
for which the functionfoldl (==) false xs
returnstrue
. In other words, complete the sentence: "The function returnstrue
whenxs
contains ..."  (Medium) Write a function
fibTailRec
which is the same asfib
but in tail recursive form. Hint: Use an accumulator parameter.  (Medium) Write
reverse
in terms offoldl
.
A Virtual Filesystem
In this section, we're going to apply what we've learned, writing functions which 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 aPath
.  The
size
function returns the file size for aPath
which represents a file.  The
isDirectory
function tests whether aPath
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 which 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 which 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 at the same time.
Finally, we use the cons operator :
to include the current file:
allFiles file = file : concatMap allFiles (ls file)
Note: the cons operator :
actually has poor performance on immutable arrays, so it is not recommended in general. 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 simply call the function recursively for that file. Since we are using 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

(Easy) Write a function
onlyFiles
which returns all files (not directories) in all subdirectories of a directory. 
(Medium) Write a function
whereIs
to search for a file by name. The function should return a value of typeMaybe 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.

(Difficult) Write a function
largestSmallest
which takes aPath
and returns an array containing the single largest and single smallest files in thePath
. Note: consider the cases where there are zero or one files in thePath
by returning an empty array or a oneelement array respectively.
Conclusion
In this chapter, we covered the basics of recursion in PureScript, as a means of expressing algorithms concisely. We also introduced userdefined infix operators, standard functions on arrays such as maps, filters and folds, and array comprehensions which combine these ideas. Finally, we showed the importance of using tail recursion in order to avoid stack overflow errors, and how to use accumulator parameters to convert functions to tail recursive form.