Before-class announcements:

This lecture: DOM Manipulation

In the last lecture, we’ve seen all of the JavaScript we need. What we’re going to do now is start using JavaScript to change the DOM. Like we’ve seen in class, the HTML we write is represented as a tree inside a web browser. What we are going to turn to now are the JavaScript APIs that web browsers provide to let you edit the DOM dynamically, so that we can build our visualizations with code instead of text editors.

You should copy and paste the JavaScript snippets below into the console prompt to check that they behave the way you expect to. You’re also encouraged to experiment with the snippets yourself, changing them slightly.

Getting started

First, let’s get some boilerplate out of the way. In what follows, we will be adding a number of different elements to an existing div element, with id “hi”. So first, we create a div with that id1:


Go ahead and check with the inspector that there is, in fact, a div with that id.

The method getElementById is used to return get an element from the DOM (remember that an ‘element’ is simply a tree node):

mainDiv = document.getElementById("hi");

To add nodes to an existing node, use appendChild. To create text content, use document.createTextNode:

var aTextNode = document.createTextNode("This is some text");

With these, we can start to build software that creates more complex trees:

function divWithText(text) {
    var result = document.createElement("div");
    var textNode = document.createTextNode(text);
    return result;

for (i=0; i<10; ++i) {

x = divWithText("X");

Sometimes, the appearance of an element is controlled by its attributes (the things inside the opening tag; in <div id="foo"/>, the attribute id has value foo:

var forecasts = [
    { "city": "DCA", "temperature": 92,  order: 0 },
    { "city": "JFK", "temperature": 96,  order: 1 },
    { "city": "SEA", "temperature": 77,  order: 2 },
    { "city": "TUS", "temperature": 102, order: 3 },
    { "city": "SFO", "temperature": 65,  order: 4 }

function textAt(text, x, y) {
    var node = divWithText(text);
    node.setAttribute("style", "position:absolute; left: " + x + "px; top: " + y + "px;");
    return node;

With this, we can place text at specific positions (in this case, 20 pixels right from the origin of the hi div, and 30 pixels down)2:

mainDiv.appendChild(textAt("hi", 20, 30));

forecasts.forEach(function(forecast) {
        forecast.order * 40 + 10,

Remember that in JavaScript we can attach new fields to existing objects. You can do this to DOM elements returned by the API, and that turns out to be very powerful:

function forecastText(forecast) {
    var node = divWithText(String(forecast.temperature));
    var x = forecast.order * 40 + 10;
    var y = 130 - forecast.temperature;
    node.reset = function() {
        node.textContent = String(forecast.temperature); = "absolute"; = x + "px";  = y + "px";
        // We could have written it like this as well:
        // node.setAttribute("style", "position:absolute; left: " + x + "px; top: " + y + "px;");
    node.update = function() {
        // move 1% of the way to the "bottom"
        var oldY =;
        oldY = Number(oldY.substr(0, oldY.length-2)); // remove "px", convert to number = (oldY * 0.99 + 0.01 * 130) + "px";
    return node;

Note how in the above snippet, we are adding two new methods, reset and update, to the node returned by divWithText. When this method is called, the position of the text node is slightly nudged toward 130px3.

With this function on hand, we can start working towards an animated demo. We begin by creating a list of nodes and storing them in an array.

var nodes =;
nodes.forEach(function(forecastNode) {

Then every time we want to move the nodes, we call the method update:

nodes.forEach(function(node) { node.update(); });

If we wrap this in a function, then all we need to do is call the function over and over again to keep animating:

function tick() {
    nodes.forEach(function(node) { node.update(); });

We’re almost there. The main issue, now, is that we have to be careful not to send the web browser into an endless loop. For example, the following does not work:

// This will crash your browser (well, it'll send it looping
// forever until Chrome decides to kill the JavaScript process)
while (true) {

The reason for it is that although the element attributes are being changed, the user of the web browser does not get to see it, because the web browser does not ever get a chance to update the graphical representation of the DOM. The way to solve this problem is by using a special browser API called requestAnimationFrame. This API lets you tell a web browser that you’d like the opportunity to change something in the DOM. The next time the web browser is sitting idly, after having drawn all of its needed graphics, it will call the function passed as a parameter. Then, we just need to make sure that after updating the graphics, we call requestAnimationFrame again. It looks like this:

// this works!
function tickForever() {

This is very much like a recursive version of the endless loop above (function f() { tick(); f(); }). The fundamental difference here is that instead of making the recursive call directly, we ask the browser to make the recursive call, after it has updated the graphics. This way there’s always a step in between every update where the web browser updates the UI and graphics, and you get nice animations as a result.

Now let’s build some visualizations!

Now that we’ve seen how SVG works, how JavaScript works, and how web browsers provide a JavaScript API for manipulating the DOM, we will create a very basic library for visualizations in JavaScript. Although the library is very limited, its basic idea is similar in spirit to an important part of d3, and when you understand how our library works, you will better understand why d3 works the way it does.

We will start with a very straightforward program that draws a visualization in a very hard-coded way, and we’ll systematically make small changes to this program to make it more generic.

The data

We’ll be working with a small, but real-world dataset that records the number of road fatalities in the United Kingdom, from 1969 to 1984.

Since parsing the data from a typical format like CSV to something that JavaScript can easily process is a boring task, and since we haven’t yet learned how to actually load external data, we’ll simply include an additional JavaScript file called data.js that contains a variable storing this dataset. It is an array of objects, where every object has the fields month, year, and count:

var ukDriverFatalities = [
    { month: 0, year: 1969, count: 1687 },
    { month: 1, year: 1969, count: 1508 },
    { month: 2, year: 1969, count: 1507 },
    { month: 9, year: 1984, count: 1575 },
    { month: 10, year: 1984, count: 1737 },
    { month: 11, year: 1984, count: 1763 }

From zero to visualizations in eight steps

We will build a minimal, but reasonably powerful, SVG visualization library by repeatedly improving a piece of source code, little by little. This is a crucial part the first part of the course. Make sure can understand what we are doing by working through it on your own.

This is the list of programs we will go through:

  1. No reuse
  2. Generic element creator
  3. Loop over arbitrary data
  4. getter functions for specific attributes
  5. use getters of iteration 4 to adjust visualizations
  6. getter generators, higher-order functions
  7. generic plotAll for any SVG element, just like generic make in iteration 2
  8. Three different visualizations, each with 10 lines of code

As you can see, until iteration 7, all the visualizations look essentially the same. What we are doing is setting up our program so that it becomes easy to explore different visualizations and datasets.

Iteration 1: No reuse (source)

The first version of our program is completely hard-coded in terms of the dataset and the visual encoding. It is a simple bar-chart that encodes the number of fatalities as the height of the element, and the position along the x axis as a chronological axis.

Iteration 2: Generic element creator (source)

We’ll start improving the program by noticing that, in iteration 1, our functions make, makeSVG and makeRect all look very similar. Let’s replace them with a single function that creates any elements and sets whatever attributes we might want. We do this by passing an object whose keys and values are respectively the attribute keys and values to be set on the created element (we loop over the object keys using the for (x in obj) ... syntax.

As a result, our code now creates rect elements with the same call it uses to create svg elements. This paves the way to create, for example, circle elements without needing to write new makeCircle functions.

The next thing we do is we notice that we are looping over a global variable. That’s a bad idea.

Iteration 3: Loop over arbitrary data (source)

Instead, what we do now is simply pass an extra parameter to plotAll, so that it doesn’t need to know where the data is coming from (that’s the extra data parameter). Still, the body of plotAll remains dependent on the content of the dataset: if the elements of the data array are not objects with count field, plotAll will just not work.

The next change we need to do is to be able to change the values of width, height, x, and y from outside of plotAll.

Iteration 4: getter functions for specific attributes (source)

We do this by passing extra parameters to plotAll. These parameters will be functions that pluck the right things from each entry of the data array. Then, what plotAll does is iterate over data, and at each step, it calls widthGetter, heightGetter, and so on. This way, if the dataset changes, we can pass different getters into plotAll, so it will know how to interpret the dataset correctly.

plotAll is now starting to be generally useful, so we will spend the next iteration changing the calls to plotAll for charts 2 and 3.

Iteration 5: use getters of iteration 4 to adjust visualizations (source)

The only changes we will make here are that instead of calling a single function for all charts, we will call different functions for each chart. We do this because the charts have different dimensions, and so we need to set the x, y, width and height attributes differently for each.

The result is that now the charts “scale” correctly. Regardless of the size of the SVG element, all the bars for all the data points in the visualization fit the visualization. That’s nice.

At the same time, you can see how our source code is starting to get a bit cluttered. Lines 27–50 of iteration_5.js, specifically, are quite dense and similar to one another. It’s hard to tell the difference between them. So let’s fix that.

Iteration 6: getter generators, higher-order functions (source)

In the previous iteration we created getters “directly”, and their only difference was how they used the dimensions of the different SVG elements (300 vs. 400 vs. 500 for the height, and similarly for the other elements). What we do in iteration_6.js is that we will create a higher-order function for each of these getters, whose only job is to return a getter specifically configured for the correct width, height, and so on. These are called rectWidth, rectHeight, rectX, rectY as before, but now, notice that these functions, instead of being getters, they return getters. This way, when you write rectWidth(800), the result you get is a getter that is exactly configured to return the correct rectangle width for an SVG element of width 800. The same thing happens with the other “getter generators”.

We are now ready for the final big change in our visualization library. The main problem left is that plotAll still only knows how to draw rectangles, and how to set some attributes of each rectangle. We want a plotAll function that works for any element and attribute. How would you solve it? Before you read on, take some time to think about this.

Iteration 7: generic plotAll for any SVG element, just like generic make in iteration 2 (source)

(Got it?) We could create a plotRects function, and a plotCircle function, and so on. That would work, but it would have the same problem that we saw with makeRect and makeSVG on our first iteration. Every time we wanted to create a new visualization, we’d have to create a new function. That’s not great.

The solution we will use is exactly the same as the one we did in iteration_2.js. Instead of passing parameters specific to rect, we will pass a value that says which element we want plotAll to create, and we will pass an object that contains all the getters that will be called to set specific attributes of the object.

The way plotAll works, then, is that it creates an intermediate object with the same keys as the keys of the attributeGetters parameter it was passed. But plotAll calls each getters with all elements of the array. The getters then configure the attributes, which are passed to the (generic!) make function.

As a result, plotAll now knows nothing about the dataset or specific attributes. The important thing it does, though, is that for each row in the data array, it creates one SVG element, and calls the same getters, consistently, for all elements in the array.

With this, we are ready to create quite different visualizations by just passing the right parameters to plotAll.

Iteration 8: Three different visualizations, each with 10 lines of code (source)

Our last code iteration is a first look into design iterations: we can generate quite different visualizations by just changing the elements we create, and what we map to each of the element attributes.

Note that in the space of 32 lines of code, we create 3 different visualizations. That is really good expressivity for a very simple function like plotAll, which is itself only 12 lines of code.


If you understand how the visualizations in iteration_8.js work, you already understood the most important concept of d3, which is to match data elements to visual elements by implicitly looping over the dataset with the specified attribute getters.

Starting with the next lecture, we will study how d3 works, and what it will enable you to create.

  1. As you type the JavaScript snippets, the elements you create will be added inside the hi div. Scroll back here to see the results. 

  2. forEach is a builtin method in JavaScript. The functions map and filter that you defined in class last time are also builtin methods. In what follows, you should keep an eye out for how we are using these methods and make sure you understand it. 

  3. How would you change the code so that the update() method instead slowly made the text transparent? Hint: use the CSS attribute “opacity” (which varies between a fully-transparent 0 and a fully-opaque 1) for this.