The reduce() method applies a function against an accumulator and each value of the array (from left-to-right) to reduce it to a single value. MDN

  • The reduce() method reduces the array to a single value.
  • The reduce() method executes a provided function for each value of the array (from left-to-right).
  • The return value of the function is stored in an accumulator (result/total).
  • Note: reduce() does not execute the function for array elements without values.

use the reduce function on javascript arrays to transform a list of values into something else.

You need two things for .reduce()

  • a reducing function (reducer)
  • an accumulating value (accumulator)
  • an initial value (optional)

A reducer function takes an accumulator and return a new accumulator, by running every item in the array against the accumulator.

.reduce() always returns the final value of the accumulator. Remember, reducer function returns the accumulator. (i.e. Use the return in your functions)

In other words, the reducer function fires once for every item in the array and keeps accumulating the value to give a new value.


sum all the numbers

Here’s an example:

const data = [3, 98, 16, 36, 13, 22, 46]

let reducer = (accumulator, item) => accumulator + item

let initialValue = 0

let total = data.reduce(reducer, initialValue)'total:', total) // total: 234

In the example above, we reduced an array to a final value (final accumulator), by adding the numbers in the data array, one by one to the accumulating value.

We started with an initial value (0), added it to the accumulator (3, first item of the data array) to get a new accumulator (0+3 = 3), then got to the next value (98), added that to the previous accumulator (3) to get a new accumulator (98+3 = 101), then got to the next value (16), added that to the previous accumulator (now 101) to get a new accumulator (16+101 = 117) and so on… till all items in the array were done and we ended with a final accumulator value of 234.

convert an array into an object

let votes = [

let initialValue = {}

let reducer = (tally, vote) => { // tally is initialValue, vote is first item in votes array
  if (!tally[vote]) { // if a key value doesn't exist
    tally[vote] = 1
  } else {
    tally[vote] = tally[vote] + 1 // if a key value exists, increment it
  return tally

let result = votes.reduce(reducer, initialValue) // { angular: 2, react: 3, ember: 1, vanilla: 1 }

reduce() vs. map() and filter()

.map() is a reducer function. So is .filter(). And they can be easily chained to create complex functions. But using reduce() can be faster than mapping and filtering when you have a lot of data. [^footnote] Here’s an example of the time difference between using map+filter vs. reduce

We’re going to get an array containing a million items, get all the even numbers in it, multiply them by two and get the resulting array.

let bigData = []
for (let i = 0; i < 1000000; i++) { 
  bigData[i] = i // get an array containing a million numbers

let filterMappedData = bigData.filter(val => {
   val % 2 === 0 
 }).map(val => val*2)
console.timeEnd('bigData') // bigData: 44.423ms

let reducedData = bigData.reduce((acc, val) => {
  if (val % 2 === 0) {
    acc.push(val *2)
  return acc
}, [])

console.timeEnd('bigDataReduced') // bigDataReduced: 68.954ms

i ran the example which accompanied the claim in the video 8 times, and reduce always took longer than map+filter

~/Sandbox $ node example.js
bigData: 44.423ms
bigDataReduced: 68.954ms
~/Sandbox $ node example.js
bigData: 44.178ms
bigDataReduced: 77.308ms
~/Sandbox $ node example.js
bigData: 34.717ms
bigDataReduced: 48.441ms
~/Sandbox $ node example.js
bigData: 43.447ms
bigDataReduced: 61.104ms
~/Sandbox $ node example.js
bigData: 37.742ms
bigDataReduced: 51.461ms
~/Sandbox $ node example.js
bigData: 34.192ms
bigDataReduced: 46.665ms
~/Sandbox $ node example.js
bigData: 42.159ms
bigDataReduced: 46.572ms
~/Sandbox $ node example.js
bigData: 31.489ms
bigDataReduced: 51.182ms