• # Haskell Programming Tutorial: Recursive Functions on Lists ## Computing with lists

• There are two approaches to working with lists:
• Write functions to do what you want, using recursive definitions that traverse the list structure.
• Write combinations of the standard list processing functions.
• The second approach is preferred, but the standard list processing functions do need to be defined, and those definitions use the first approach (recursive definitions).
• We’ll cover both methods.

## Recursion on lists

• A list is built from the empty list ([]) and the function (cons; :: ; arightarrow [a] rightarrow [a]). In Haskell, the function (cons) is actually written as the operator ((:)) , in other words : is pronounced as cons.

• Every list must be either
• ([]) or

• ((x : xs)) for some (x) (the head of the list) and (xs) (the tail)

where ((x : xs)) is pronounced as (x, mathit{cons}, xs)

• The recursive definition follows the structure of the data:
• Base case of the recursion is ([]).

• Recursion (or induction) case is ((x : xs)).

### Some examples of recursion on lists

#### Recursive definition of length

The length of a list can be computed recursively as follows:

length :: [a] -> Int -- function type
length [] = 0 -- base case
length (x:xs) = 1 + length xs -- recursion case


#### Recursive definition of filter

• filter is given a predicate (a function that gives a Boolean result) and a list, and returns a list of the elements that satisfy the predicate.

filter :: (a->Bool) -> [a] -> [a]


Filtering is useful for the “generate and test” programming paradigm.

filter (<5) [3,9,2,12,6,4] -- > [3,2,4]


The library definition for filter is shown below. This relies on guards.

filter :: (a -> Bool) -> [a] -> [a]
filter pred [] = []
filter pred (x:xs)
| pred x = x : filter pred xs
| otherwise = filter pred xs


## Computations over lists

• Many computations that would be for/while loops in an imperative language are naturally expressed as list computations in a functional language.

• There are some common cases:
• Perform a computation on each element of a list: (map)

• Iterate over a list, from left to right: (foldl)

• Iterate over a list, from right to left: (foldr)

• It’s good practice to use these three functions when applicable

• And there are some related functions that we’ll see later

### Function composition

• We can express a large computation by “chaining together” a sequence of functions that perform smaller computations

1. Apply a function (g :: a to b) to it, getting an intermediate result of type (b)

1. Then apply a function (f :: b to c) to the intermediate result, getting the final result of type (c)

• The entire computation (first (g), then (f)) is written as (f circ g).

• This is traditional mathematical notation; just remember that in (f circ g), the functions are used in right to left order.

• Haskell uses . as the function composition operator
(.) :: (b->c) -> (a->b) -> a -> c
(f . g) x = f (g x)


## Performing an operation on every element of a list: map

• map applies a function to every element of a list
map f [x0,x1,x2] -- > [f x0, f x1, f x2]


### Composition of maps

• map is one of the most commonly used tools in your functional toolkit

• A common style is to define a set of simple computations using map, and to compose them.
map f (map g xs) = map (f . g) xs


This theorem is frequently used, in both directions.

### Recursive definition of map

map :: (a -> b) -> [a] -> [b]
map _ [] = []
map f (x:xs) = f x : map f xs


## Folding a list (reduction)

• An iteration over a list to produce a singleton value is called a fold

• There are several variations: folding from the left, folding from the right, several variations having to do with “initialisation”, and some more advanced variations.

• Folds may look tricky at first, but they are extremely powerful, and they are used a lot! And they aren’t actually very complicated.

### Left fold: foldl

• foldl is fold from the left

• Think of it as an iteration across a list, going left to right.

• A typical application is (foldl, f, z, xs)

• The (z :: b) is an initial value

• The (xs :: [a]) argument is a list of values which we combine systematically using the supplied function (f)

• A useful intuition: think of the (z :: b) argument as an “accumulator”.

• The function (f) takes the current value of the accumulator and a list element, and gives the new value of the accumulator.
foldl :: (b->a->b) -> b -> [a] -> b


### Examples of foldl with function notation

[begin{aligned}
mathtt{foldl,f,z,[]} &rightsquigarrow & z
mathtt{foldl,f,z,[x0]} & rightsquigarrow & f,z,x0
mathtt{foldl,f,z,[x0,x1]} & rightsquigarrow & f,(f,z,x0),x1
mathtt{foldl,f,z,[x0,x1,x2]} & rightsquigarrow & f,(f,(f,z,x0),x1), x2end{aligned}]

### Examples of foldl with infix notation

In this example, + denotes an arbitrary operator for f; it isn’t supposed to mean specifically addition.

foldl (+) z [] -- > z
foldl (+) z [x0] -- > z + x0
foldl (+) z [x0,x1] -- > (z + x0) + x1
foldl (+) z [x0,x1,x2] -- > ((z + x0) + x1) + x2


### Recursive definition of foldl

foldl :: (b -> a -> b) -> b -> [a] -> b
foldl f z0 xs0 = lgo z0 xs0
where
lgo z [] = z
lgo z (x:xs) = lgo (f z x) xs


### Right fold: foldr

• Similar to (foldl), but it works from right to left

foldr :: (a -> b -> b) -> b -> [a] -> b


### Examples of foldr with function notation

[begin{aligned}
mathtt{foldr,f, z, [] } & rightsquigarrow & z
mathtt{foldr, f, z, [x0] } & rightsquigarrow & f, x0, z
mathtt{foldr, f, z, [x0,x1] } & rightsquigarrow & f, x0, (f, x1, z)
mathtt{foldr, f, z, [x0,x1,x2] } & rightsquigarrow & f, x0, (f, x1, (f, x2, z))end{aligned}]

### Examples of foldr with operator notation

foldr (+) z [] -- > z
foldr (+) z [x0] -- > x0 + z
foldr (+) z [x0,x1] -- > x0 + (x1 + z)
foldr (+) z [x0,x1,x2] -- > x0 + (x1 + (x2 + z))


### Recursive definition of foldr

foldr :: (a -> b -> b) -> b -> [a] -> b
foldr k z = go
where
go [] = z
go (y:ys) = y k go ys


### Relationship between foldr and list structure

We have seen that a list [x0,x1,x2] can also be written as

 x0 : x1 : x2 : []


Folding (cons) (:) over a list using the empty list [] as accumulator gives:

foldr (:) [] [x0,x1,x2]
-- >
x0 : x1 : x2 : []


This is identical to constructing the list using (:) and [] ! We can formalise this relationship as follows:
[foldr ; cons ; [] ; xs ; = ; xs]

### Some applications of folds

sum xs = foldr (+) 0 xs
product xs = foldr (*) 1 xs


We can actually “factor out” the (xs) that appears at the right of each side of the equation, and write:

sum = foldr (+) 0
product = foldr (*) 1

(This is sometimes called “point free” style because you’re programming solely with the functions; the data isn’t mentioned directly.)  