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Learn JS Data

Data cleaning, manipulation, and wrangling in JavaScript

Iterating Over and Reducing Data

Most of the functions we used to summarize our data had to iterate over the entire dataset to generate their results - but the details were hidden behind the function. Now let's look at how we might perform this iteration ourselves for other metrics and manipulations!

Again, we start with a basic data set already loaded:

var data = [
  {"city":"seattle", "state":"WA", "population":652405, "land_area":83.9},
  {"city":"new york", "state":"NY", "population":8405837, "land_area":302.6},
  {"city":"boston", "state":"MA", "population":645966, "land_area":48.3},
  {"city":"kansas city", "state":"MO", "population":467007, "land_area":315}


First some basic iteration. We already saw this in the data loading task, but a common way to process each data object is by using forEach

var count = 0;

data.forEach(function(d) {
  count += 1;


=> 4

Of course, data also has the property length which would be the actual way to get the number of data elements in data - but this is just an example.


=> 4


Let me sidetrack this task just a bit to talk about

forEach provides for a basic way to loop through our data set. We can use this to modify the data in place, generate counts, or perform other manipulations that deal with each piece of data individually.

This works, but can get clunky and confusing fast. Keeping straight what form the data is in at any given time can be confusing, as can side effects of modifying your data that you might not be aware of.

To combat this confusion, it can be useful to think of the data as immutable. Immutable data cannot be modified once created. Immutability seems a bit counterintuitive for a task where we want to coerce our data into the form we want - but it comes together with the concept of transformations.

The idea is simple: each immutable dataset can be transformed into another immutable dataset through the use of a transformation function that works on each component of the data.

This process helps simplify the data flow, but if you have to make a copy of your data object each time, it can make code a bit brittle as you have to keep track of every attribute of your dataset.


To help with this issue of brittle transformations, lodash provides the clone function.

This function takes an object and returns a copy of that object. That copy is now a separate data object that you can edit without effecting the original object.

var dataObject = {"name":"Carl", "age":"48", "salary":"12300"};
var copyOfData = _.clone(dataObject);
copyOfData.age = +copyOfData.age;
copyOfData.salary = +copyOfData.salary;

=> {name: "Carl", age: "48", salary: "12300"}

This code is using lodash

=> {name: "Carl", age: 48, salary: 12300}

By default, the clone function will not copy over nested objects. Instead these nested objects are simply passed by referenced - meaning the original and the copy will still share them.

var dataObject = {"name":"Saul", "stats":{"age":"55"}};
var shallowCopy = _.clone(dataObject);
shallowCopy.stats.age = +shallowCopy.stats.age;

=> {"name":"Saul","stats":{"age":55}}

This code is using lodash

=> {"name":"Saul","stats":{"age":55}}

Note that because stats is a nested object the modification happened in both spots!

To prevent this "feature", we can pass true as the second parameter to clone to indicate that the copy should be deep and copy nested objects as well.

var dataObject = {"name":"Saul", "stats":{"age":"55"}};
var deepCopy = _.clone(dataObject, true);
deepCopy.stats.age = +deepCopy.stats.age;

=> {"name":"Saul","stats":{"age":"55"}}

This code is using lodash

=> {"name":"Saul","stats":{"age":55}}

lodash also has a cloneDeep that can be used to make the deep-ness more explicit.


JavaScript's map can be a very useful tool to implement this concept of a transformation on immutable data.

map takes an array and produces another array which is the result of the callback function being executed on each element in the array.

var smallData =,i) {

  return {
    index: i + 1,
    rounded_area: Math.round(d.land_area)

=> {city: "seattle", state: "WA", population: 652405, land_area: 83.9}
  {name: "SEATTLE", index: 1, rounded_area: 84}

The callback function gets called for each element in the array, and also has access to the index of that element in the array. The result is an array of returned values from the callback.

With plain JavaScript, the immutability of an array is just in the mind of the developer. While map does not modify the array, it is easy for your callback method to do so. That is why we return a new object in the callback. lodash's clone would be another approach to getting a copy of each data element as a starting point for the transformation.


Select a subset of the data using the built in filter method. This creates a new array of data (again see transformation talk above) with only the values that the callback function returns true for.

var large_land = data.filter(function(d) { return d.land_area > 200; });

=> [{"city":"new york","state":"NY","population":8405837,"land_area":302.6},
  {"city":"kansas city","state":"MO","population":467007,"land_area":315}]


Similar to filtering, sorting data based on attributes is something you'll want to do frequently.

The built in sort for arrays can do this. A caveat to this function is that, unlike filter, map, and other functions, this modifies the array you are sorting in place, instead of returning a new array with the objects sorted.

To sort an array, you need a comparator function. This is a function that takes two pieces of data and indicates which one you want higher in the list. The comparator-function-way to do this is to return a negative value if the first value should go higher then the second value, and a positive value if the second value should go higher. If they are equal, and you don't care, then return a 0.

Let's see it in action. Here is a way to sort by population in a descending order (larger populations come first).

data.sort(function(a,b) {
  return b.population - a.population;

=> [{"city":"new york","state":"NY","population":8405837,"land_area":302.6},
   {"city":"kansas city","state":"MO","population":467007,"land_area":315}]

This b - a thing is a pretty common way to generate this kind of sort. But you could also do it more explicitly. Thinking through it, if b's population is larger then a's, then the value returned by b.population - a.population will be positive - so b will be sorted toward the top of the array. If the reverse is true, then the result will be negative, and a will be sorted first.

Note again, that the sort happened on the original data, which I'm not a big fan of.

D3 also has a few helper functions to implement ascending and descending comparator functions - but (as far as I can tell) they only accept arrays of raw numbers instead of objects. So to use d3.ascending or d3.descending you would have to do something like this:

var populations = { return d.population; });

=> [652405, 8405837, 645966, 467007]


=> [8405837, 652405, 645966, 467007]

I'm usually looking to keep my data objects together, so I shy away from using these methods, but they might be great for what you are trying to do.

A big gotcha with sorting that you should watch out for is that if you do not pass a comparator function, the default function sorts alphabetically. So, the array:

var nums = [3,1,10,20];

Would be sorted to:


=> [1, 10, 20, 3]

This is never what you want for data sorting. For this reason, you should never use sort without a comparator function.


These functions all take an array and reduce it down to a single number. But what if that number isn't the one you want? Well, you can take this reduction into your own hands with reduce!

The syntax for reduce is always hard for me to remember, so let's go over it with the classic example: summing up a value.

var landSum = data.reduce(function(sum, d) {
  return sum + d.land_area;
}, 0);

=> 749.8

The first parameter to reduce is the callback function that will return the running "total" of the reduction. This function is passed in the previous value returned from the last time the callback was called. Here, that parameter - sum provides the running total as we move through the array. The second parameter to the callback d is the current value of the array we are working on.

reduce can take an initial value, which is the second parameter to the reduce call. For this example, we start the sum at 0. If there is no starting value provided, then for the first execution of the callback (when there is no previous value) the first parameter to the callback will be the value of the first element of the array, and the reduction starts with the second element.

It always makes more sense to me to provide a starting value - unless you know what you are doing. You can also get the current index into the array (and the whole array itself) if that is useful to you.

var weirdString = data.reduce(function(str, d, i) {
  var ending = (i % 2 === 0) ? " is cool." : " sucks." ;
  return str + " " + + ending;
}, "");

=> seattle is cool. new york sucks. boston is cool. kansas city sucks.

_And summing over a variable is only used for example. You can always just use d3.sum for this instead._

Chaining Functions

One of the great things about these more functional functions is that it is possible to chain them together into one big data wrangling pipeline!

var bigCities = data.filter(function(d) { return d.population > 500000; })
  .sort(function(a,b) { return a.population - b.population; })
  .map(function(d) { return; });

=> ["boston", "seattle", "new york"]

Since we are using sort after filter, sort is working on the returned array from filter. The sort function at least is nice enough to also return the array, so chaining is still possible.

Next Task

Grouping Data

See Also