sort categorical Cartesian axes by value

src/lib/index.js

@@ -75,6 +75,7 @@ var statsModule = require('./stats');
 lib.aggNums = statsModule.aggNums;
 lib.len = statsModule.len;
 lib.mean = statsModule.mean;
+lib.median = statsModule.median;
 lib.midRange = statsModule.midRange;
 lib.variance = statsModule.variance;
 lib.stdev = statsModule.stdev;

src/lib/stats.js

@@ -74,6 +74,22 @@ exports.stdev = function(data, len, mean) {
     return Math.sqrt(exports.variance(data, len, mean));
 };
 
+/**
+ * median of a finite set of numbers
+ * reference page: https://en.wikipedia.org/wiki/Median#Finite_set_of_numbers
+**/
+exports.median = function(data, len) {
+    if(!len) len = exports.len(data);
+    var b = data.slice().sort();
+    if(len % 2 === 0) {
+        // If even
+        return (b[len / 2 - 1] + b[len / 2]) / 2;
+    } else {
+        // If odd
+        return b[(len - 1) / 2];
+    }
+};
+
 /**
  * interp() computes a percentile (quantile) for a given distribution.
  * We interpolate the distribution (to compute quantiles, we follow method #10 here:

test/jasmine/tests/lib_test.js

@@ -176,6 +176,20 @@ describe('Test lib.js:', function() {
         });
     });
 
+    describe('median() should', function() {
+        it('return the middle value exactly for odd number of observations:', function() {
+            var input = [1, 3, 3, 6, 7, 8, 9];
+            var res = Lib.median(input);
+            expect(res).toEqual(6);
+        });
+
+        it('return the mean of the two middle values for even number of observations', function() {
+            var input = [1, 2, 3, 4, 5, 6, 8, 9];
+            var res = Lib.median(input);
+            expect(res).toEqual(4.5);
+        });
+    });
+
     describe('stdev() should', function() {
         it('return 0 on input [2, 2, 2, 2, 2]:', function() {
             var input = [2, 2, 2, 2];