JavaScript – Functional Programming

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JavaScript is not a functional programming language like Lisp or
Haskell, but the fact that JavaScript can manipulate functions as
objects means that we can use functional programming techniques in
JavaScript. The ECMAScript 5 array methods such as map() and reduce() lend themselves particularly well
to a functional programming style. The sections that follow
demonstrate techniques for functional programming in JavaScript. They
are intended as a mind-expanding exploration of the power of
JavaScript’s functions, not as a prescription for good programming

Processing Arrays with Functions

Suppose we have an array of numbers and we want to compute the
mean and standard deviation of those values. We might do that in
nonfunctional style like this:

var data = [1,1,3,5,5];  // This is our array of numbers

// The mean is the sum of the elements divided by the number of elements
var total = 0;
for(var i = 0; i < data.length; i++) total += data[i];
var mean = total/data.length;                  // The mean of our data is 3

// To compute the standard deviation, we first sum the squares of 
// the deviation of each element from the mean.
total = 0;
for(var i = 0; i < data.length; i++) {
    var deviation = data[i] - mean;
    total += deviation * deviation;
var stddev = Math.sqrt(total/(data.length-1));  // The standard deviation is 2

We can perform these same computations in concise functional
style using the array methods map() and reduce() like this (see ECMAScript 5 Array Methods to review these methods):

// First, define two simple functions
var sum = function(x,y) { return x+y; };
var square = function(x) { return x*x; };

// Then use those functions with Array methods to compute mean and stddev
var data = [1,1,3,5,5];
var mean = data.reduce(sum)/data.length;
var deviations = {return x-mean;});
var stddev = Math.sqrt(;

What if we’re using ECMAScript 3 and don’t have access to
these newer array methods? We can define our own map() and reduce() functions that use the built-in
methods if they exist:

// Call the function f for each element of array a and return
// an array of the results.  Use if it is defined.
var map =
    ? function(a, f) { return; }  // Use map method if it exists
    : function(a,f) {                      // Otherwise, implement our own
        var results = [];
        for(var i = 0, len = a.length; i < len; i++) {
            if (i in a) results[i] =, a[i], i, a);
        return results;

// Reduce the array a to a single value using the function f and
// optional initial value.  Use Array.prototype.reduce if it is defined.
var reduce = Array.prototype.reduce
    ? function(a, f, initial) {   // If the reduce() method exists.
        if (arguments.length > 2)
            return a.reduce(f, initial);   // If an initial value was passed.
        else return a.reduce(f);           // Otherwise, no initial value.
    : function(a, f, initial) {   // This algorithm from the ES5 specification
        var i = 0, len = a.length, accumulator;
        // Start with the specified initial value, or the first value in a
        if (arguments.length > 2) accumulator = initial;
        else { // Find the first defined index in the array
            if (len == 0) throw TypeError();
            while(i < len) {
                if (i in a) {
                    accumulator = a[i++];
                else i++;
            if (i == len) throw TypeError();

        // Now call f for each remaining element in the array
        while(i < len) {
            if (i in a) 
                accumulator =, accumulator, a[i], i, a);

        return accumulator;

With these map() and
reduce() functions defined, our
code to compute the mean and standard deviation now looks like

var data = [1,1,3,5,5];
var sum = function(x,y) { return x+y; };
var square = function(x) { return x*x; };
var mean = reduce(data, sum)/data.length;
var deviations = map(data, function(x) {return x-mean;});
var stddev = Math.sqrt(reduce(map(deviations, square), sum)/(data.length-1));

Higher-Order Functions

A higher-order function is a function
that operates on functions, taking one or more functions as
arguments and returning a new function. Here is an

// This higher-order function returns a new function that passes its 
// arguments to f and returns the logical negation of f's return value;
function not(f) {
    return function() {                        // Return a new function
        var result = f.apply(this, arguments); // that calls f
        return !result;                        // and negates its result.

var even = function(x) { // A function to determine if a number is even
    return x % 2 === 0;
var odd = not(even);     // A new function that does the opposite
[1,1,3,5,5].every(odd);  // => true: every element of the array is odd

The not() function above is
a higher-order function because it takes a function argument and
returns a new function. As another example, consider the mapper() function below. It takes a
function argument and returns a new function that maps one array to
another using that function. This function uses the map() function defined earlier, and it is
important that you understand how the two functions are

// Return a function that expects an array argument and applies f to
// each element, returning the array of return values.
// Contrast this with the map() function from earlier.
function mapper(f) {
    return function(a) { return map(a, f); };

var increment = function(x) { return x+1; };
var incrementer = mapper(increment);
incrementer([1,2,3])  // => [2,3,4]

Here is another, more general, example that takes two
functions f and g and returns a new function that computes

// Return a new function that computes f(g(...)).
// The returned function h passes all of its arguments to g, and then passes
// the return value of g to f, and then returns the return value of f.
// Both f and g are invoked with the same this value as h was invoked with.
function compose(f,g) {
    return function() {
        // We use call for f because we're passing a single value and 
        // apply for g because we're passing an array of values.
        return, g.apply(this, arguments));

var square = function(x) { return x*x; };
var sum = function(x,y) { return x+y; };
var squareofsum = compose(square, sum);  
squareofsum(2,3)                         // => 25

The partial() and memoize() functions defined in the
sections that follow are two more important higher-order

Partial Application of Functions

The bind() method of a
function f (The bind() Method) returns a new function that invokes f in a specified context and with a
specified set of arguments. We say that it binds the function to an
object and partially applies the arguments. The bind() method partially applies arguments
on the left—that is, the arguments you pass to bind() are placed at the start of the
argument list that is passed to the original function. But it is
also possible to partially apply arguments on the right:

// A utility function to convert an array-like object (or suffix of it)
// to a true array.  Used below to convert arguments objects to real arrays.
function array(a, n) { return, n || 0); }

// The arguments to this function are passed on the left
function partialLeft(f /*, ...*/) {
    var args = arguments;  // Save the outer arguments array
    return function() {    // And return this function
        var a = array(args, 1);         // Start with the outer args from 1 on.
        a = a.concat(array(arguments)); // Then add all the inner arguments.
        return f.apply(this, a);        // Then invoke f on that argument list.

// The arguments to this function are passed on the right
function partialRight(f /*, ...*/) {
    var args = arguments;  // Save the outer arguments array
    return function() {    // And return this function
        var a = array(arguments);    // Start with the inner arguments.
        a = a.concat(array(args,1)); // Then add the outer args from 1 on.
        return f.apply(this, a);     // Then invoke f on that argument list.

// The arguments to this function serve as a template.  Undefined values
// in the argument list are filled in with values from the inner set.
function partial(f /*, ... */) {
    var args = arguments;  // Save the outer arguments array
    return function() {
        var a = array(args, 1);   // Start with an array of outer args
        var i=0, j=0;
        // Loop through those args, filling in undefined values from inner
        for(; i < a.length; i++) 
            if (a[i] === undefined) a[i] = arguments[j++];
        // Now append any remaining inner arguments
        a = a.concat(array(arguments, j))
        return f.apply(this, a);

// Here is a function with three arguments
var f = function(x,y,z) { return x * (y - z); };
// Notice how these three partial applications differ
partialLeft(f, 2)(3,4)         // => -2: Bind first argument: 2 * (3 - 4)
partialRight(f, 2)(3,4)        // =>  6: Bind last argument: 3 * (4 - 2)
partial(f, undefined, 2)(3,4)  // => -6: Bind middle argument: 3 * (2 - 4)

These partial application functions allow us to easily define
interesting functions out of functions we already have defined. Here
are some examples:

var increment = partialLeft(sum, 1);
var cuberoot = partialRight(Math.pow, 1/3);
String.prototype.first = partial(String.prototype.charAt, 0);
String.prototype.last = partial(String.prototype.substr, -1, 1);

Partial application becomes even more interesting when we
combine it with other higher-order functions. Here, for example, is
a way to define the not()
function shown above using composition and partial

var not = partialLeft(compose, function(x) { return !x; });
var even = function(x) { return x % 2 === 0; };
var odd = not(even);
var isNumber = not(isNaN)

We can also use composition and partial application to redo
our mean and standard deviation calculations in extreme functional

var data = [1,1,3,5,5];                        // Our data
var sum = function(x,y) { return x+y; };       // Two elementary functions
var product = function(x,y) { return x*y; };
var neg = partial(product, -1);                // Define some others
var square = partial(Math.pow, undefined, 2);        
var sqrt = partial(Math.pow, undefined, .5);
var reciprocal = partial(Math.pow, undefined, -1);

// Now compute the mean and standard deviation. This is all function
// invocations with no operators, and it starts to look like Lisp code!
var mean = product(reduce(data, sum), reciprocal(data.length));
var stddev = sqrt(product(reduce(map(data,
                                             partial(sum, neg(mean)))),


In Defining Your Own Function Properties we defined a
factorial function that cached its previously computed results. In
functional programming, this kind of caching is called
memoization. The code below shows a
higher-order function, memoize()
that accepts a function as its argument and returns a memoized
version of the function:

// Return a memoized version of f.
// It only works if arguments to f all have distinct string representations.
function memoize(f) {
    var cache = {};  // Value cache stored in the closure.

    return function() {
        // Create a string version of the arguments to use as a cache key.
        var key = arguments.length +,",");
        if (key in cache) return cache[key];
        else return cache[key] = f.apply(this, arguments);

memoize() function creates a new
object to use as the cache and assigns this object to a local
variable, so that it is private to (in the closure of) the returned
function. The returned function converts its arguments array to a
string, and uses that string as a property name for the cache
object. If a value exists in the cache, it returns it directly.
Otherwise, it calls the specified function to compute the value for
these arguments, caches that value, and returns it. Here is how we
might use memoize():

// Return the Greatest Common Divisor of two integers, using the Euclidian
// algorithm:
function gcd(a,b) {  // Type checking for a and b has been omitted
    var t;                            // Temporary variable for swapping values
    if (a < b) t=b, b=a, a=t;         // Ensure that a >= b
    while(b != 0) t=b, b = a%b, a=t;  // This is Euclid's algorithm for GCD
    return a;

var gcdmemo = memoize(gcd);
gcdmemo(85, 187)  // => 17

// Note that when we write a recursive function that we will be memoizing,
// we typically want to recurse to the memoized version, not the original.
var factorial = memoize(function(n) {
                            return (n <= 1) ? 1 : n * factorial(n-1);
factorial(5)      // => 120.  Also caches values for 4, 3, 2 and 1.

[14] If this piques your interest, you may be interested in using
(or at least reading about) Oliver Steele’s Functional JavaScript
library. See

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