Functional Programming in javascript
Tags: javascript, programming, beginners
Published: 2022-05-08 22:09:42

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. Array methods such as map() and reduce() lend themselves particularly well to a functional programming style.

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

```
let 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
let total = 0;
for(let i = 0; i < data.length; i++) total += data[i];
let mean = total/data.length;  // mean == 3; 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(let i = 0; i < data.length; i++) {
    let deviation = data[i] - mean;
    total += deviation * deviation;
}
let stddev = Math.sqrt(total/(data.length-1));  // stddev == 2

```

We can perform these same computations in concise functional style using the array methods map() and reduce() like this

```
// First, define two simple functions
const sum = (x,y) => x+y;
const square = x => x*x;

// Then use those functions with Array methods to compute mean and stddev
let data = [1,1,3,5,5];
let mean = data.reduce(sum)/data.length;  // mean == 3
let deviations = data.map(x => x-mean);
let stddev = Math.sqrt(deviations.map(square).reduce(sum)/(data.length-1));
stddev  // => 2

```

This new version of the code looks quite different than the first one, but it is still invoking methods on objects, so it has some object-oriented conventions remaining. Let’s write functional versions of the map() and reduce() methods

```
const map = function(a, ...args) { return a.map(...args); };
const reduce = function(a, ...args) { return a.reduce(...args); };

```

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

```
const sum = (x,y) => x+y;
const square = x => x*x;

let data = [1,1,3,5,5];
let mean = reduce(data, sum)/data.length;
let deviations = map(data, x => x-mean);
let stddev = Math.sqrt(reduce(map(deviations, square), sum)/(data.length-1));
stddev  // => 2

```
**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.

```
// 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(...args) {             // Return a new function
        let result = f.apply(this, args);  // that calls f
        return !result;                    // and negates its result.
    };
}

const even = x => x % 2 === 0; // A function to determine if a number is even
const 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

```

This not() function is a higher-order function because it takes a function argument and returns a new function. As another example, consider the mapper() function that follows. 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 different.

```
// 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 a => map(a, f);
}

const increment = x => x+1;
const incrementAll = mapper(increment);
incrementAll([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 f(g())


```
// Return a new function that computes f(g(...)).
// The returned function h passes all of its arguments to g, then passes
// the return value of g to f, 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(...args) {
        // 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 f.call(this, g.apply(this, args));
    };
}

const sum = (x,y) => x+y;
const square = x => x*x;
compose(square, sum)(2,3)  // => 25; the square of the sum

```

**Partial Application of Functions**

The bind() method of a function f 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.

```
// The arguments to this function are passed on the left
function partialLeft(f, ...outerArgs) {
    return function(...innerArgs) { // Return this function
        let args = [...outerArgs, ...innerArgs]; // Build the argument list
        return f.apply(this, args);              // Then invoke f with it
    };
}

// The arguments to this function are passed on the right
function partialRight(f, ...outerArgs) {
    return function(...innerArgs) {  // Return this function
        let args = [...innerArgs, ...outerArgs]; // Build the argument list
        return f.apply(this, args);              // Then invoke f with it
    };
}

// 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, ...outerArgs) {
    return function(...innerArgs) {
        let args = [...outerArgs]; // local copy of outer args template
        let innerIndex=0;          // which inner arg is next
        // Loop through the args, filling in undefined values from inner args
        for(let i = 0; i < args.length; i++) {
            if (args[i] === undefined) args[i] = innerArgs[innerIndex++];
        }
        // Now append any remaining inner arguments
        args.push(...innerArgs.slice(innerIndex));
        return f.apply(this, args);
    };
}

// Here is a function with three arguments
const 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.

```
const increment = partialLeft(sum, 1);
const cuberoot = partialRight(Math.pow, 1/3);
cuberoot(increment(26))  // => 3

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

```
const not = partialLeft(compose, x => !x);
const even = x => x % 2 === 0;
const odd = not(even);
const isNumber = not(isNaN);
odd(3) && isNumber(2)  // => true

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

```
// sum() and square() functions are defined above. Here are some more:
const product = (x,y) => x*y;
const neg = partial(product, -1);
const sqrt = partial(Math.pow, undefined, .5);
const reciprocal = partial(Math.pow, undefined, neg(1));

// Now compute the mean and standard deviation.
let data = [1,1,3,5,5];   // Our data
let mean = product(reduce(data, sum), reciprocal(data.length));
let stddev = sqrt(product(reduce(map(data,
                                     compose(square,
                                             partial(sum, neg(mean)))),
                                 sum),
                          reciprocal(sum(data.length,neg(1)))));
[mean, stddev]  // => [3, 2]

```


Notice that this code to compute mean and standard deviation is entirely function invocations; there are no operators involved, and the number of parentheses has grown so large that this JavaScript is beginning to look like Lisp code.


Again, this is not a style that I advocate for JavaScript programming, but it is an interesting exercise to see how deeply functional JavaScript code can be.

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