Introduction to D3 – Part 1: Hello D3!!!

Everyday is a new day and it brings you new surprises. For me, it came in form of D3.js at Data Visualization Toronto Meetup and amazed by it. It gets more interesting with crossfilter and dc.js – which I will leave it for future posts

Data Driven Document (D3).js is a data visualization library for web created by Michael Bostock, Vadim Ogievetsky and Jeffrey Heer.  Without wasting any further time, let’s start with basics. D3.js alllows you to  manipulate documents based on data. D3 helps you bring data to life using HTML, SVG and CSS.

In order to work with D3 you need to understand Javascript Objects, functions and the method-chaining paradigm of JQuery. A basic understanding of SVG, CSS will go a long way.
Let’s start with 

SVG (Scalable Vector Graphics)
SVG images and their behaviors are defined in XML text files. This means that they can be searched, indexed, scripted, and, if need be, compressed. As XML files, SVG images can be created and edited with any text editor, but it is often more convenient to create them with drawing programs (source:wiki)

SVG specification is developed by W3C and here’s the most basic explanation of it

Scalable – means to increase or decrease uniformly. In terms of graphics, scalable means not being limited to a single, fixed, pixel size.
Vector graphics contain geometric objects such as lines and curves.

Graphics Most existing XML grammars represent either textual information, or represent raw data such as financial information. They typically provide only rudimentary graphical capabilities, often less capable than the HTML ‘img’ element. SVG fills a gap in the market by providing a rich, structured description of vector and mixed vector/raster graphics; it can be used stand-alone, or as an XML namespace with other grammars.

SVG provides basics shapes like rect,circle,ellipse,line,polyline,polygon

<svg xmlns=”; version=”1.1″>
<circle cx=”50″ cy=”70″ r=”40″ fill=”red” />
Now, we are done with SVG basics let’s start with an example
We need
  • A browser and a web server (for Mac/Linux users: there is one build in your machine, just start python -m SimpleHTTPServer 8888 in Terminal Window and the current path becomes ‘server’) . For windows user Apache Tomact or nginx [engine x] would also work
  • Download d3.js (comes with samples, some of them will NOT run when you open the html files as file, so use the web server instead!)
  • Text editor

<!DOCTYPE html>
<title>Hello, D3!!!</title>
<script type="text/javascript" src="lib/d3.js"></script>
<div id="dvto"></div>
<script type="text/javascript">

var vizSVG =“#dvto")
.attr("width", 100)
.attr("height", 100);

.style("stroke", "black")
.style("fill", "red")
.attr("r", 40)
.attr("cx", 100)
.attr("cy", 50)


Here's the output


D3 employs a declarative approach, operating on arbitrary sets of nodes called selections. So every DOM element is essentially a selector as show above.
Equivalent SVG Source of above
<svg xmlns=”; version=”1.1″>
<circle cx=”100″ cy=”50″ r=”40″ stroke=”black”
That’s it for now, have fun!!  I will go more deep into D3, Crossfilter concepts  and explain dc.js (Dimensional Charting) in upcoming posts in Part2 and onwards

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