Chapter 5
Higher-Order Functions

Tzu-li and Tzu-ssu were boasting about the size of their latest programs. ‘Two-hundred thousand lines,’ said Tzu-li, ‘not counting comments!’ Tzu-ssu responded, ‘Pssh, mine is almost a million lines already.’ Master Yuan-Ma said, ‘My best program has five hundred lines.’ Hearing this, Tzu-li and Tzu-ssu were enlightened.

Master Yuan-Ma, The Book of Programming

There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies.

C.A.R. Hoare, 1980 ACM Turing Award Lecture

A large program is a costly program, and not just because of the time it takes to build. Size almost always involves complexity, and complexity confuses programmers. Confused programmers, in turn, tend to introduce mistakes (bugs) into programs. A large program also provides a lot of space for these bugs to hide, making them hard to find.

Let’s briefly go back to the final two example programs in the introduction. The first is self-contained and six lines long.

var total = 0, count = 1;
while (count <= 10) {
  total += count;
  count += 1;
}
console.log(total);

The second relies on two external functions and is one line long.

console.log(sum(range(1, 10)));

Which one is more likely to contain a bug?

If we count the size of the definitions of sum and range, the second program is also big—even bigger than the first. But still, I’d argue that it is more likely to be correct.

It is more likely to be correct because the solution is expressed in a vocabulary that corresponds to the problem being solved. Summing a range of numbers isn’t about loops and counters. It is about ranges and sums.

The definitions of this vocabulary (the functions sum and range) will still involve loops, counters, and other incidental details. But because they are expressing simpler concepts than the program as a whole, they are easier to get right.

Abstraction

In the context of programming, these kinds of vocabularies are usually called abstractions. Abstractions hide details and give us the ability to talk about problems at a higher (or more abstract) level.

As an analogy, compare these two recipes for pea soup:

Put 1 cup of dried peas per person into a container. Add water until the peas are well covered. Leave the peas in water for at least 12 hours. Take the peas out of the water and put them in a cooking pan. Add 4 cups of water per person. Cover the pan and keep the peas simmering for two hours. Take half an onion per person. Cut it into pieces with a knife. Add it to the peas. Take a stalk of celery per person. Cut it into pieces with a knife. Add it to the peas. Take a carrot per person. Cut it into pieces. With a knife! Add it to the peas. Cook for 10 more minutes.

And the second recipe:

Per person: 1 cup dried split peas, half a chopped onion, a stalk of celery, and a carrot.

Soak peas for 12 hours. Simmer for 2 hours in 4 cups of water (per person). Chop and add vegetables. Cook for 10 more minutes.

The second is shorter and easier to interpret. But you do need to understand a few more cooking-related words—soak, simmer, chop, and, I guess, vegetable.

When programming, we can’t rely on all the words we need to be waiting for us in the dictionary. Thus, you might fall into the pattern of the first recipe—work out the precise steps the computer has to perform, one by one, blind to the higher-level concepts that they express.

It has to become second nature, for a programmer, to notice when a concept is begging to be abstracted into a new word.

Abstracting array traversal

Plain functions, as we’ve seen them so far, are a good way to build abstractions. But sometimes they fall short.

In the previous chapter, this type of for loop made several appearances:

var array = [1, 2, 3];
for (var i = 0; i < array.length; i++) {
  var current = array[i];
  console.log(current);
}

It’s trying to say, “For each element in the array, log it to the console”. But it uses a roundabout way that involves a counter variable i, a check against the array’s length, and an extra variable declaration to pick out the current element. Apart from being a bit of an eyesore, this provides a lot of space for potential mistakes. We might accidentally reuse the i variable, misspell length as lenght, confuse the i and current variables, and so on.

So let’s try to abstract this into a function. Can you think of a way?

Well, it’s easy to write a function that goes over an array and calls console.log on every element.

function logEach(array) {
  for (var i = 0; i < array.length; i++)
    console.log(array[i]);
}

But what if we want to do something other than logging the elements? Since “doing something” can be represented as a function and functions are just values, we can pass our action as a function value.

function forEach(array, action) {
  for (var i = 0; i < array.length; i++)
    action(array[i]);
}

forEach(["Wampeter", "Foma", "Granfalloon"], console.log);
// → Wampeter
// → Foma
// → Granfalloon

Often, you don’t pass a predefined function to forEach but create a function value on the spot instead.

var numbers = [1, 2, 3, 4, 5], sum = 0;
forEach(numbers, function(number) {
  sum += number;
});
console.log(sum);
// → 15

This looks quite a lot like the classical for loop, with its body written as a block below it. However, now the body is inside the function value, as well as inside the parentheses of the call to forEach. This is why it has to be closed with the closing brace and closing parenthesis.

Using this pattern, we can specify a variable name for the current element (number), rather than having to pick it out of the array manually.

In fact, we don’t need to write forEach ourselves. It is available as a standard method on arrays. Since the array is already provided as the thing the method acts on, forEach takes only one required argument: the function to be executed for each element.

To illustrate how helpful this is, let’s look back at a function from the previous chapter. It contains two array-traversing loops.

function gatherCorrelations(journal) {
  var phis = {};
  for (var entry = 0; entry < journal.length; entry++) {
    var events = journal[entry].events;
    for (var i = 0; i < events.length; i++) {
      var event = events[i];
      if (!(event in phis))
        phis[event] = phi(tableFor(event, journal));
    }
  }
  return phis;
}

Working with forEach makes it slightly shorter and quite a bit cleaner.

function gatherCorrelations(journal) {
  var phis = {};
  journal.forEach(function(entry) {
    entry.events.forEach(function(event) {
      if (!(event in phis))
        phis[event] = phi(tableFor(event, journal));
    });
  });
  return phis;
}

Higher-order functions

Functions that operate on other functions, either by taking them as arguments or by returning them, are called higher-order functions. If you have already accepted the fact that functions are regular values, there is nothing particularly remarkable about the fact that such functions exist. The term comes from mathematics, where the distinction between functions and other values is taken more seriously.

Higher-order functions allow us to abstract over actions, not just values. They come in several forms. For example, you can have functions that create new functions.

function greaterThan(n) {
  return function(m) { return m > n; };
}
var greaterThan10 = greaterThan(10);
console.log(greaterThan10(11));
// → true

And you can have functions that change other functions.

function noisy(f) {
  return function(arg) {
    console.log("calling with", arg);
    var val = f(arg);
    console.log("called with", arg, "- got", val);
    return val;
  };
}
noisy(Boolean)(0);
// → calling with 0
// → called with 0 - got false

You can even write functions that provide new types of control flow.

function unless(test, then) {
  if (!test) then();
}
function repeat(times, body) {
  for (var i = 0; i < times; i++) body(i);
}

repeat(3, function(n) {
  unless(n % 2, function() {
    console.log(n, "is even");
  });
});
// → 0 is even
// → 2 is even

The lexical scoping rules that we discussed in Chapter 3 work to our advantage when using functions in this way. In the previous example, the n variable is a parameter to the outer function. Because the inner function lives inside the environment of the outer one, it can use n. The bodies of such inner functions can access the variables around them. They can play a role similar to the {} blocks used in regular loops and conditional statements. An important difference is that variables declared inside inner functions do not end up in the environment of the outer function. And that is usually a good thing.

Passing along arguments

The noisy function defined earlier, which wraps its argument in another function, has a rather serious deficit.

function noisy(f) {
  return function(arg) {
    console.log("calling with", arg);
    var val = f(arg);
    console.log("called with", arg, "- got", val);
    return val;
  };
}

If f takes more than one parameter, it gets only the first one. We could add a bunch of arguments to the inner function (arg1, arg2, and so on) and pass them all to f, but it is not clear how many would be enough. This solution would also deprive f of the information in arguments.length. Since we’d always pass the same number of arguments, it wouldn’t know how many arguments were originally given.

For these kinds of situations, JavaScript functions have an apply method. You pass it an array (or array-like object) of arguments, and it will call the function with those arguments.

function transparentWrapping(f) {
  return function() {
    return f.apply(null, arguments);
  };
}

That’s a useless function, but it shows the pattern we are interested in—the function it returns passes all of the given arguments, and only those arguments, to f. It does this by passing its own arguments object to apply. The first argument to apply, for which we are passing null here, can be used to simulate a method call. We will come back to that in the next chapter.

JSON

Higher-order functions that somehow apply a function to the elements of an array are widely used in JavaScript. The forEach method is the most primitive such function. There are a number of other variants available as methods on arrays. To familiarize ourselves with them, let’s play around with another data set.

A few years ago, someone crawled through a lot of archives and put together a book on the history of my family name (Haverbeke—meaning Oatbrook). I opened it hoping to find knights, pirates, and alchemists ... but the book turns out to be mostly full of Flemish farmers. For my amusement, I extracted the information on my direct ancestors and put it into a computer-readable format.

The file I created looks something like this:

[
  {"name": "Emma de Milliano", "sex": "f",
   "born": 1876, "died": 1956,
   "father": "Petrus de Milliano",
   "mother": "Sophia van Damme"},
  {"name": "Carolus Haverbeke", "sex": "m",
   "born": 1832, "died": 1905,
   "father": "Carel Haverbeke",
   "mother": "Maria van Brussel"},
   and so on
]

This format is called JSON (pronounced “Jason”), which stands for JavaScript Object Notation. It is widely used as a data storage and communication format on the Web.

JSON is similar to JavaScript’s way of writing arrays and objects, with a few restrictions. All property names have to be surrounded by double quotes, and only simple data expressions are allowed—no function calls, variables, or anything that involves actual computation. Comments are not allowed in JSON.

JavaScript gives us functions, JSON.stringify and JSON.parse, that convert data from and to this format. The first takes a JavaScript value and returns a JSON-encoded string. The second takes such a string and converts it to the value it encodes.

var string = JSON.stringify({name: "X", born: 1980});
console.log(string);
// → {"name":"X","born":1980}
console.log(JSON.parse(string).born);
// → 1980

The variable ANCESTRY_FILE, available in the sandbox for this chapter as well as in a downloadable file on the website, contains the content of my JSON file as a string. Let’s decode it and see how many people it contains.

var ancestry = JSON.parse(ANCESTRY_FILE);
console.log(ancestry.length);
// → 39

Filtering an array

To find the people in the ancestry data set who were young in 1924, the following function might be helpful. It filters out the elements in an array that don’t pass a test.

function filter(array, test) {
  var passed = [];
  for (var i = 0; i < array.length; i++) {
    if (test(array[i]))
      passed.push(array[i]);
  }
  return passed;
}

console.log(filter(ancestry, function(person) {
  return person.born > 1900 && person.born < 1925;
}));
// → [{name: "Philibert Haverbeke", …}, …]

This uses the argument named test, a function value, to fill in a “gap” in the computation. The test function is called for each element, and its return value determines whether an element is included in the returned array.

Three people in the file were alive and young in 1924: my grandfather, grandmother, and great-aunt.

Note how the filter function, rather than deleting elements from the existing array, builds up a new array with only the elements that pass the test. This function is pure. It does not modify the array it is given.

Like forEach, filter is also a standard method on arrays. The example defined the function only in order to show what it does internally. From now on, we’ll use it like this instead:

console.log(ancestry.filter(function(person) {
  return person.father == "Carel Haverbeke";
}));
// → [{name: "Carolus Haverbeke", …}]

Transforming with map

Say we have an array of objects representing people, produced by filtering the ancestry array somehow. But we want an array of names, which is easier to read.

The map method transforms an array by applying a function to all of its elements and building a new array from the returned values. The new array will have the same length as the input array, but its content will have been “mapped” to a new form by the function.

function map(array, transform) {
  var mapped = [];
  for (var i = 0; i < array.length; i++)
    mapped.push(transform(array[i]));
  return mapped;
}

var overNinety = ancestry.filter(function(person) {
  return person.died - person.born > 90;
});
console.log(map(overNinety, function(person) {
  return person.name;
}));
// → ["Clara Aernoudts", "Emile Haverbeke",
//    "Maria Haverbeke"]

Interestingly, the people who lived to at least 90 years of age are the same three people who we saw before—the people who were young in the 1920s, which happens to be the most recent generation in my data set. I guess medicine has come a long way.

Like forEach and filter, map is also a standard method on arrays.

Summarizing with reduce

Another common pattern of computation on arrays is computing a single value from them. Our recurring example, summing a collection of numbers, is an instance of this. Another example would be finding the person with the earliest year of birth in the data set.

The higher-order operation that represents this pattern is called reduce (or sometimes fold). You can think of it as folding up the array, one element at a time. When summing numbers, you’d start with the number zero and, for each element, combine it with the current sum by adding the two.

The parameters to the reduce function are, apart from the array, a combining function and a start value. This function is a little less straightforward than filter and map, so pay careful attention.

function reduce(array, combine, start) {
  var current = start;
  for (var i = 0; i < array.length; i++)
    current = combine(current, array[i]);
  return current;
}

console.log(reduce([1, 2, 3, 4], function(a, b) {
  return a + b;
}, 0));
// → 10

The standard array method reduce, which of course corresponds to this function, has an added convenience. If your array contains at least one element, you are allowed to leave off the start argument. The method will take the first element of the array as its start value and start reducing at the second element.

To use reduce to find my most ancient known ancestor, we can write something like this:

console.log(ancestry.reduce(function(min, cur) {
  if (cur.born < min.born) return cur;
  else return min;
}));
// → {name: "Pauwels van Haverbeke", born: 1535, …}

Composability

Consider how we would have written the previous example (finding the person with the earliest year of birth) without higher-order functions. The code is not that much worse.

var min = ancestry[0];
for (var i = 1; i < ancestry.length; i++) {
  var cur = ancestry[i];
  if (cur.born < min.born)
    min = cur;
}
console.log(min);
// → {name: "Pauwels van Haverbeke", born: 1535, …}

There are a few more variables, and the program is two lines longer but still quite easy to understand.

Higher-order functions start to shine when you need to compose functions. As an example, let’s write code that finds the average age for men and for women in the data set.

function average(array) {
  function plus(a, b) { return a + b; }
  return array.reduce(plus) / array.length;
}
function age(p) { return p.died - p.born; }
function male(p) { return p.sex == "m"; }
function female(p) { return p.sex == "f"; }

console.log(average(ancestry.filter(male).map(age)));
// → 61.67
console.log(average(ancestry.filter(female).map(age)));
// → 54.56

(It’s a bit silly that we have to define plus as a function, but operators in JavaScript, unlike functions, are not values, so you can’t pass them as arguments.)

Instead of tangling the logic into a big loop, it is neatly composed into the concepts we are interested in—determining sex, computing age, and averaging numbers. We can apply these one by one to get the result we are looking for.

This is fabulous for writing clear code. Unfortunately, this clarity comes at a cost.

The cost

In the happy land of elegant code and pretty rainbows, there lives a spoil-sport monster called inefficiency.

A program that processes an array is most elegantly expressed as a sequence of cleanly separated steps that each do something with the array and produce a new array. But building up all those intermediate arrays is somewhat expensive.

Likewise, passing a function to forEach and letting that method handle the array iteration for us is convenient and easy to read. But function calls in JavaScript are costly compared to simple loop bodies.

And so it goes with a lot of techniques that help improve the clarity of a program. Abstractions add layers between the raw things the computer is doing and the concepts we are working with and thus cause the machine to perform more work. This is not an iron law—there are programming languages that have better support for building abstractions without adding inefficiencies, and even in JavaScript, an experienced programmer can find ways to write abstract code that is still fast. But it is a problem that comes up a lot.

Fortunately, most computers are insanely fast. If you are processing a modest set of data or doing something that has to happen only on a human time scale (say, every time the user clicks a button), then it does not matter whether you wrote a pretty solution that takes half a millisecond or a super-optimized solution that takes a tenth of a millisecond.

It is helpful to roughly keep track of how often a piece of your program is going to run. If you have a loop inside a loop (either directly or through the outer loop calling a function that ends up performing the inner loop), the code inside the inner loop will end up running N×M times, where N is the number of times the outer loop repeats and M is the number of times the inner loop repeats within each iteration of the outer loop. If that inner loop contains another loop that makes P rounds, its body will run M×N×P times, and so on. This can add up to large numbers, and when a program is slow, the problem can often be traced to only a small part of the code, which sits inside an inner loop.

Great-great-great-great-...

My grandfather, Philibert Haverbeke, is included in the data file. By starting with him, I can trace my lineage to find out whether the most ancient person in the data, Pauwels van Haverbeke, is my direct ancestor. And if he is, I would like to know how much DNA I theoretically share with him.

To be able to go from a parent’s name to the actual object that represents this person, we first build up an object that associates names with people.

var byName = {};
ancestry.forEach(function(person) {
  byName[person.name] = person;
});

console.log(byName["Philibert Haverbeke"]);
// → {name: "Philibert Haverbeke", …}

Now, the problem is not entirely as simple as following the father properties and counting how many we need to reach Pauwels. There are several cases in the family tree where people married their second cousins (tiny villages and all that). This causes the branches of the family tree to rejoin in a few places, which means I share more than 1/2G of my genes with this person, where G for the number of generations between Pauwels and me. This formula comes from the idea that each generation splits the gene pool in two.

A reasonable way to think about this problem is to look at it as being analogous to reduce, which condenses an array to a single value by repeatedly combining values, left to right. In this case, we also want to condense our data structure to a single value but in a way that follows family lines. The shape of the data is that of a family tree, rather than a flat list.

The way we want to reduce this shape is by computing a value for a given person by combining values from their ancestors. This can be done recursively: if we are interested in person A, we have to compute the values for A’s parents, which in turn requires us to compute the value for A’s grandparents, and so on. In principle, that’d require us to look at an infinite number of people, but since our data set is finite, we have to stop somewhere. We’ll allow a default value to be given to our reduction function, which will be used for people who are not in the data. In our case, that value is simply zero, on the assumption that people not in the list don’t share DNA with the ancestor we are looking at.

Given a person, a function to combine values from the two parents of a given person, and a default value, reduceAncestors condenses a value from a family tree.

function reduceAncestors(person, f, defaultValue) {
  function valueFor(person) {
    if (person == null)
      return defaultValue;
    else
      return f(person, valueFor(byName[person.mother]),
                       valueFor(byName[person.father]));
  }
  return valueFor(person);
}

The inner function (valueFor) handles a single person. Through the magic of recursion, it can simply call itself to handle the father and the mother of this person. The results, along with the person object itself, are passed to f, which returns the actual value for this person.

We can then use this to compute the amount of DNA my grandfather shared with Pauwels van Haverbeke and divide that by four.

function sharedDNA(person, fromMother, fromFather) {
  if (person.name == "Pauwels van Haverbeke")
    return 1;
  else
    return (fromMother + fromFather) / 2;
}
var ph = byName["Philibert Haverbeke"];
console.log(reduceAncestors(ph, sharedDNA, 0) / 4);
// → 0.00049

The person with the name Pauwels van Haverbeke obviously shared 100 percent of his DNA with Pauwels van Haverbeke (there are no people who share names in the data set), so the function returns 1 for him. All other people share the average of the amounts that their parents share.

So, statistically speaking, I share about 0.05 percent of my DNA with this 16th-century person. It should be noted that this is only a statistical approximation, not an exact amount. It is a rather small number, but given how much genetic material we carry (about 3 billion base pairs), there’s still probably some aspect in the biological machine that is me that originates with Pauwels.

We could also have computed this number without relying on reduceAncestors. But separating the general approach (condensing a family tree) from the specific case (computing shared DNA) can improve the clarity of the code and allows us to reuse the abstract part of the program for other cases. For example, the following code finds the percentage of known ancestors, for a given person, who lived past 70:

function countAncestors(person, test) {
  function combine(person, fromMother, fromFather) {
    var thisOneCounts = test(person);
    return fromMother + fromFather + (thisOneCounts ? 1 : 0);
  }
  return reduceAncestors(person, combine, 0);
}
function longLivingPercentage(person) {
  var all = countAncestors(person, function(person) {
    return true;
  });
  var longLiving = countAncestors(person, function(person) {
    return (person.died - person.born) >= 70;
  });
  return longLiving / all;
}
console.log(longLivingPercentage(byName["Emile Haverbeke"]));
// → 0.145

Such numbers are not to be taken too seriously, given that our data set contains a rather arbitrary collection of people. But the code illustrates the fact that reduceAncestors gives us a useful piece of vocabulary for working with the family tree data structure.

Binding

The bind method, which all functions have, creates a new function that will call the original function but with some of the arguments already fixed.

The following code shows an example of bind in use. It defines a function isInSet that tells us whether a person is in a given set of strings. To call filter in order to collect those person objects whose names are in a specific set, we can either write a function expression that makes a call to isInSet with our set as its first argument or partially apply the isInSet function.

var theSet = ["Carel Haverbeke", "Maria van Brussel",
              "Donald Duck"];
function isInSet(set, person) {
  return set.indexOf(person.name) > -1;
}

console.log(ancestry.filter(function(person) {
  return isInSet(theSet, person);
}));
// → [{name: "Maria van Brussel", …},
//    {name: "Carel Haverbeke", …}]
console.log(ancestry.filter(isInSet.bind(null, theSet)));
// → … same result

The call to bind returns a function that will call isInSet with theSet as first argument, followed by any remaining arguments given to the bound function.

The first argument, where the example passes null, is used for method calls, similar to the first argument to apply. I’ll describe this in more detail in the next chapter.

Summary

Being able to pass function values to other functions is not just a gimmick but a deeply useful aspect of JavaScript. It allows us to write computations with “gaps” in them as functions and have the code that calls these functions fill in those gaps by providing function values that describe the missing computations.

Arrays provide a number of useful higher-order methods—forEach to do something with each element in an array, filter to build a new array with some elements filtered out, map to build a new array where each element has been put through a function, and reduce to combine all an array’s elements into a single value.

Functions have an apply method that can be used to call them with an array specifying their arguments. They also have a bind method, which is used to create a partially applied version of the function.

Exercises

Flattening

Use the reduce method in combination with the concat method to “flatten” an array of arrays into a single array that has all the elements of the input arrays.

var arrays = [[1, 2, 3], [4, 5], [6]];
// Your code here.
// → [1, 2, 3, 4, 5, 6]

Mother-child age difference

Using the example data set from this chapter, compute the average age difference between mothers and children (the age of the mother when the child is born). You can use the average function defined earlier in this chapter.

Note that not all the mothers mentioned in the data are themselves present in the array. The byName object, which makes it easy to find a person’s object from their name, might be useful here.

function average(array) {
  function plus(a, b) { return a + b; }
  return array.reduce(plus) / array.length;
}

var byName = {};
ancestry.forEach(function(person) {
  byName[person.name] = person;
});

// Your code here.

// → 31.2

Because not all elements in the ancestry array produce useful data (we can’t compute the age difference unless we know the birth date of the mother), we will have to apply filter in some manner before calling average. You could do it as a first pass, by defining a hasKnownMother function and filtering on that first. Alternatively, you could start by calling map and in your mapping function return either the age difference or null if no mother is known. Then, you can call filter to remove the null elements before passing the array to average.

Historical life expectancy

When we looked up all the people in our data set that lived more than 90 years, only the latest generation in the data came out. Let’s take a closer look at that phenomenon.

Compute and output the average age of the people in the ancestry data set per century. A person is assigned to a century by taking their year of death, dividing it by 100, and rounding it up, as in Math.ceil(person.died / 100).

function average(array) {
  function plus(a, b) { return a + b; }
  return array.reduce(plus) / array.length;
}

// Your code here.

// → 16: 43.5
//   17: 51.2
//   18: 52.8
//   19: 54.8
//   20: 84.7
//   21: 94

The essence of this example lies in grouping the elements of a collection by some aspect of theirs—splitting the array of ancestors into smaller arrays with the ancestors for each century.

During the grouping process, keep an object that associates century names (numbers) with arrays of either person objects or ages. Since we do not know in advance what categories we will find, we’ll have to create them on the fly. For each person, after computing their century, we test whether that century was already known. If not, add an array for it. Then add the person (or age) to the array for the proper century.

Finally, a for/in loop can be used to print the average ages for the individual centuries.

For bonus points, write a function groupBy that abstracts the grouping operation. It should accept as arguments an array and a function that computes the group for an element in the array and returns an object that maps group names to arrays of group members.

Every and then some

Arrays also come with the standard methods every and some. Both take a predicate function that, when called with an array element as argument, returns true or false. Just like && returns a true value only when the expressions on both sides are true, every returns true only when the predicate returns true for all elements of the array. Similarly, some returns true as soon as the predicate returns true for any of the elements. They do not process more elements than necessary—for example, if some finds that the predicate holds for the first element of the array, it will not look at the values after that.

Write two functions, every and some, that behave like these methods, except that they take the array as their first argument rather than being a method.

// Your code here.

console.log(every([NaN, NaN, NaN], isNaN));
// → true
console.log(every([NaN, NaN, 4], isNaN));
// → false
console.log(some([NaN, 3, 4], isNaN));
// → true
console.log(some([2, 3, 4], isNaN));
// → false

The functions can follow a similar pattern to the definition of forEach at the start of the chapter, except that they must return immediately (with the right value) when the predicate function returns false—or true. Don’t forget to put another return statement after the loop so that the function also returns the correct value when it reaches the end of the array.