How to get median and quartiles/percentiles of an array in JavaScript (or PHP)?

Question

This question is turned into a Q&A, because I had struggle finding the answer, and think it can be useful for others

I have a JavaScript

Answer 1

I updated the JavaScript translation from the first answer to use arrow functions and a bit more concise notation. The functionality remains mostly the same, except for std, which now computes the sample standard deviation (dividing by arr.length - 1 instead of just arr.length)

// sort array ascending
const asc = arr => arr.sort((a, b) => a - b);

const sum = arr => arr.reduce((a, b) => a + b, 0);

const mean = arr => sum(arr) / arr.length;

// sample standard deviation
const std = (arr) => {
    const mu = mean(arr);
    const diffArr = arr.map(a => (a - mu) ** 2);
    return Math.sqrt(sum(diffArr) / (arr.length - 1));
};

const quantile = (arr, q) => {
    const sorted = asc(arr);
    const pos = (sorted.length - 1) * q;
    const base = Math.floor(pos);
    const rest = pos - base;
    if (sorted[base + 1] !== undefined) {
        return sorted[base] + rest * (sorted[base + 1] - sorted[base]);
    } else {
        return sorted[base];
    }
};

const q25 = arr => quantile(arr, .25);

const q50 = arr => quantile(arr, .50);

const q75 = arr => quantile(arr, .75);

const median = arr => q50(arr);

Answer 2

TL;DR

The other answers appear to have solid implementations of the "R-7" version of computing quantiles. Below is some context and another JavaScript implementation borrowed from D3 using the same R-7 method, with the bonus that this one probably covers a few more edge cases.

---

Background

After a little sleuthing on some math and stats StackExchange sites (1, 2), I found that there are "common sensical" ways of calculating each quantile, but that those don't typically mesh up with the results of the nine generally recognized ways to calculate them.

The answer at that second link from stats.stackexchange says of the common-sensical method that...

Your textbook is confused. Very few people or software define quartiles this way. (It tends to make the first quartile too small and the third quartile too large.)

The quantile function in R implements nine different ways to compute quantiles!

I thought that last bit was interesting, and here's what I dug up on those nine methods...

• Wikipedia's description of those nine methods here, nicely grouped in a table
• An article from the Journal of Statistics Education titled "Quartiles in Elementary Statistics"
• A blog post at SAS.com called "Sample quantiles: A comparison of 9 definitions"

That tells me I probably shouldn't try to code something based on my understanding of what quartiles represent and should borrow someone else's solution.

---

Existing solution from D3

One example is from D3. Its d3.array package has a quantile function that's essentially BSD licensed:

https://github.com/d3/d3-array/blob/master/src/quantile.js

I've quickly created a pretty straight port of d3's version that requires the array of elements to have already been sorted into vanilla JavaScript. Here it is. I've tested it a bit against d3's results itself enough to feel it's a valid port, but your experience might differ (let me know in the comments if it does, though!):

  //Credit D3: https://github.com/d3/d3-array/blob/master/LICENSE
  function quantileSorted(values, p, fnValueFrom) {
    var n = values.length;
    if (!n) {
      return;
    }

    fnValueFrom =
      Object.prototype.toString.call(fnValueFrom) == "[object Function]"
        ? fnValueFrom
        : function (x) {
            return x;
          };

    p = +p;

    if (p <= 0 || n < 2) {
      return +fnValueFrom(values[0], 0, values);
    }

    if (p >= 1) {
      return +fnValueFrom(values[n - 1], n - 1, values);
    }

    var i = (n - 1) * p,
      i0 = Math.floor(i),
      value0 = +fnValueFrom(values[i0], i0, values),
      value1 = +fnValueFrom(values[i0 + 1], i0 + 1, values);

    return value0 + (value1 - value0) * (i - i0);
  }

Note that fnValueFrom is a way to process a complex object into a value. You can see how that might work in a list of d3 usage examples here -- search down where .quantile is used.

The quick version is if the values are tortoises and you're sorting tortoise.age in every case, your fnValueFrom might be x => x.age. More complicated versions, including ones that might require accessing the index (parameter 2) and entire collection (parameter 3) during the value calcuation, are left up to the reader.

I've added a quick check here so that if nothing is given for fnValueFrom or if what's given isn't a function the logic assumes the elements in values are the actual sorted values themselves.

---

Logical comparison to existing answers

I'm reasonably sure this reduces to the same version in the other two answers (see below), but if you needed to justify why you're using this to a product manager or whatever maybe the above will help.

Quick comparison:

function Quartile(data, q) {
  data=Array_Sort_Numbers(data);        // we're assuming it's already sorted, above, vs. the function use here. same difference.
  var pos = ((data.length) - 1) * q;    // i = (n - 1) * p
  var base = Math.floor(pos);           // i0 = Math.floor(i)
  var rest = pos - base;                // (i - i0);
  if( (data[base+1]!==undefined) ) {
    //      value0    + (i - i0)   * (value1 which is values[i0+1] - value0 which is values[i0])
    return data[base] + rest       * (data[base+1]                 - data[base]);
  } else {
    // I think this is covered by if (p <= 0 || n < 2)
    return data[base];
  }
}

So that's logically close/appears to be exactly the same. I think d3's version that I ported covers a few more edge/invalid conditions, which could be useful.

---

By the way, the answers here, according to d3-array's readme, all use the "R-7 method":

This particular implementation uses the R-7 method, which is the default for the R programming language and Excel.

For a little further reading, the differences between d3's use of R-7 to determine quantiles and the common sensical approach is demonstrated nicely in this question and described a bit in a post that's linked to from philippe's original source for the php version over here (in German). Here's a bit from Google Translate:

In our example, this value is at the (n + 1) / 4 digit = 5.25, i.e. between the 5th value (= 5) and the 6th value (= 7). The fraction (0.25) indicates that in addition to the value of 5, ¼ of the distance between 5 and 6 is added. Q1 is therefore 5 + 0.25 * 2 = 5.5.

Answer 3

After searching for a long time, finding different versions that give different results, I found this nice snippet on Bastian Pöttner's web blog, but for PHP. For the same price, we get the average and standard deviation of the data (for normal distributions)...

PHP Version

//from https://blog.poettner.de/2011/06/09/simple-statistics-with-php/

function Median($Array) {
  return Quartile_50($Array);
}

function Quartile_25($Array) {
  return Quartile($Array, 0.25);
}

function Quartile_50($Array) {
  return Quartile($Array, 0.5);
}

function Quartile_75($Array) {
  return Quartile($Array, 0.75);
}

function Quartile($Array, $Quartile) {
  sort($Array);
  $pos = (count($Array) - 1) * $Quartile;

  $base = floor($pos);
  $rest = $pos - $base;

  if( isset($Array[$base+1]) ) {
    return $Array[$base] + $rest * ($Array[$base+1] - $Array[$base]);
  } else {
    return $Array[$base];
  }
}

function Average($Array) {
  return array_sum($Array) / count($Array);
}

function StdDev($Array) {
  if( count($Array) < 2 ) {
    return;
  }

  $avg = Average($Array);

  $sum = 0;
  foreach($Array as $value) {
    $sum += pow($value - $avg, 2);
  }

  return sqrt((1 / (count($Array) - 1)) * $sum);
}

Based on the author's comments, I simply wrote a JavaScript translation that will certainly be useful, because surprisingly, it is nearly impossible to find a JavaScript equivalent on the web, and otherwise requires additional libraries like Math.js

JavaScript Version

//adapted from https://blog.poettner.de/2011/06/09/simple-statistics-with-php/
function Median(data) {
  return Quartile_50(data);
}

function Quartile_25(data) {
  return Quartile(data, 0.25);
}

function Quartile_50(data) {
  return Quartile(data, 0.5);
}

function Quartile_75(data) {
  return Quartile(data, 0.75);
}

function Quartile(data, q) {
  data=Array_Sort_Numbers(data);
  var pos = ((data.length) - 1) * q;
  var base = Math.floor(pos);
  var rest = pos - base;
  if( (data[base+1]!==undefined) ) {
    return data[base] + rest * (data[base+1] - data[base]);
  } else {
    return data[base];
  }
}

function Array_Sort_Numbers(inputarray){
  return inputarray.sort(function(a, b) {
    return a - b;
  });
}

function Array_Sum(t){
   return t.reduce(function(a, b) { return a + b; }, 0); 
}

function Array_Average(data) {
  return Array_Sum(data) / data.length;
}

function Array_Stdev(tab){
   var i,j,total = 0, mean = 0, diffSqredArr = [];
   for(i=0;i<tab.length;i+=1){
       total+=tab[i];
   }
   mean = total/tab.length;
   for(j=0;j<tab.length;j+=1){
       diffSqredArr.push(Math.pow((tab[j]-mean),2));
   }
   return (Math.sqrt(diffSqredArr.reduce(function(firstEl, nextEl){
            return firstEl + nextEl;
          })/tab.length));  
}