Getting to “Hello world” with d3

Back when I started learning programming, it was always fairly simple to achieve the canonical first step of accomplishments, that is, to get the system to announce that you are ready to do more by displaying “hello world” on the screen.

In most systems then, there was a command prompt somewhere that would usually do that when you would type, say:

PRINT "hello world"


Things have changed a lot since the early 80s. In some fields like fashion, I would argue it’s a good thing, but we’re definitely not going in the way of less complexity.

Now if you’re interested in web-oriented visualization and want to do it with d3.js, it’s still fairly simple, but it is built upon a number of technologies that you’re supposed to know a little. Front-end developers live and breathe the web and have been exposed to all things javascript, HTML, CSS, you name it, in enormous doses. Many developers probably have, at some point, tried to interface with the web and know enough of that to get started. So for this crowd, the amount of things you need to know to crack d3 code seems negligible, because they know all that and they are very familiar with it, just as well as people knew the first names of Friends characters by the end of the tenth season.

But what about those who didn’t? and the people who don’t see themselves as developers ? do they have to reimmerse themselves in 10 -odd years of web development history to get started? It turns out that this sum of knowledge, while not insurmountable, is certainly not trivial.

So without further ado, let’s get started

We’re cooking an omelette

And when we do, we need a few things: a pan, a recipe, eggs and stuff, a stove and then plates, knives and forks, etc.

The pan: a text editor

The first thing is really the pan. If you don’t have one when cooking eggs, you borrow one or go buy one. In our analogy the pan is the text editor. This is the tool with which you are going to make the files that will constitute your visualization.

There was a time when it was ok to use notepad (textedit if you are of the apple persuasion). And it’s still possible, but you are not making your life easier. What I recommend instead is that you get a hold of a copy of SublimeText2. ( There are windows versions. And Mac versions. And linux versions. For windows users, there is a mobile version so you don’t need administrator access to install it. There is a free, unlimited evaluation version,  but unless you can’t spend $69, I strongly recommend that you buy it. Sublime Text 2 has a nearly infinite amount of niceties built in. And unlike some other powerful text editors, where the best features are only understandable by the tech masters, what’s really nice about Sublime Text 2 is that it would make you gain time even if you are an absolute beginner. One such nice things that it does is detect what language you are working with, automatically color and format the words as you type them depending on the category they fall in, and when possible, suggesting the word you are trying to type, automatically format and indent your code, all in a very unobtrusive and pleasant way. This will really help you troubleshoot problems like strings not closed properly or loose closing bracket which typically consume a lot of time.

Let’s type a fairly common d3 statement to see how SublimeText2 can help. First, it recognized the var keyword as such and writes it in italics and cyan. Second, when I type my opening parenthesis, it adds a closing one, and as long as my cursor touches either it underlines them both.

Let’s carry on. The function keyword is highlighted in italics cyan too – useful. The opening/closing thing works for curly braces too.

The return statement is highlighted in red. With the cursor on the closing parenthesis, we are starting to get a feel that the underlining function is a useful safety net

New line. Joy! the indentation is aligned with the line above.

We now have four consecutive opening or closing curly braces and parentheses. Typically, this is where errors sneak in, and where sublime text 2 really shines.

And we now have 5 consecutive closing curly braces and parentheses. This is fairly common in d3 code. Is the order correct? Thank you Sublime Text 2!

we finish up writing the statement.

When moving the cursor to the left side, where the line numbers are, we notice down-pointing arrows. We know our code is correct, and we don’t want to see it again, so…

we just click on the top one to collapse this section. If we need to edit it again we can expand it.

Finally, we add a comment above. Notice the syntax highlighting, comments are colored with an unobtrusive dark grey.

The recipe: a basic file structure

In d3, you can’t really type a “print” command from a prompt. You need to write some files, which are loaded by a browser (that’s your “plate” in the metaphor, but let’s not get ahead of ourselves).

You are going to need up to 5 types of files.

First, an html file. This will be the file that your browser will read, either locally, or uploaded on a website. We’ll get to cover this in detail in a minute.

Second, believe it or not, you are going to need the d3 library, which is also a file. You may link to the version on the site, and so not worry about having the actual file handy. That has advantages (like the one we just said, also, you’re pretty sure to always have the latest version on hand), and two problems. First, you always need to have a live internet connection, so there’s no working in the park outside of free wifi space (for example), and also, it will probably be slower than having the file locally or on your own web space. And if having your own web server seems kind of scary, I’ll show you in a short while that it’s not.

The three next kind of files are optional, but hey.

The third file is a javascript .js file which would be where you put your code. Some people would rather put all their code in the html file, which is an option, especially for short programs. Personally, I prefer having a separate file. So to make d3 work, you need some script, but it doesn’t have to be in a separate file.

The fourth file is a style sheet, or css file. This can be used to define some formatting options, for instance to make all your circles blue by default, or some circles that meet some pre-defined criterion. Like the javascript file, any style information can be contained within the html file, but unlike the script, it is completely optional. I also like to keep it separate from the html.

Finally, you may want a data file, you know, with data (csv, txt, json, xml…). If you have lots of data to visualize, it’s easier to keep it in separate files than in variables within the script. But it doesn’t have to be that way. And you could also use d3 without data.

The ingredients: contents of the files

The HTML file

So let’s see how this articulates by looking at a typical d3 html file. I am using templates which I try to change as little as possible from project to project.

<!DOCTYPE html>
   <meta http-equiv="Content-Type" content="text/html;charset=utf-8">
   <title>My project</title>
   <script type="text/javascript" src="../d3.v2.js"></script>
   <link href="style.css" rel="stylesheet">
   <div id="chart">
   <script type="text/javascript" src="script.js"></script>

Well. That is certainly longer than the BASIC one-liner (and we haven’t even printed “hello world” yet).

Let’s take this piece by piece.

The first line is a doctype declaration. What this does is that it tells your browser that what follows should be interpreted as standard, HTML5-compliant HTML (standards mode). If you omit the doctype documentation, your browser will read the html in “quirks mode“, i.e. by replicating the non-standard behavior of Nescape 4 or IE5. You can still try to run d3 under quirks mode, but don’t be surprised if your HTML doesn’t behave as expected.

The doctype declaration doesn’t have to be more complicated than <!DOCTYPE html>.

The second line opens the html document proper. Technically, it’s ok to omit <html>, <head> and <body> tags in HTML5. The document will still be considered valid by tools like the W3C validator. But it seems that some browsers, in some complex cases, don’t like that so much, and I as a person find it more convenient to find those tags when reading code.

The next line opens the header section of the document. Again, it’s not absolutely necessary, but I consider it helpful to explicitly differentiate the header from the rest of the document.

The next line, which goes

<meta http-equiv="Content-Type" content="text/html;charset=utf-8">

is not absolutely required either. It specifies the encoding of the page, that is, what kind of characters will be seen in the page. Since I use non-ascii characters often, being French and all, I make sure to use it all the time. After all, this is a template, not something I type from beginning to end each time.

Next, we specify a title. This is what will appear in the title area of your browser, or, more likely, as the name of your tab.

In the next line, we load the d3 library. This is my preferred syntax. This is how my files are set up:

I have a directory where all my d3 projects are, and in this directory, I am also keeping (and maintaining reasonably up-to-date) a version of the d3 library, a file called d3.v2.min.js. (min stands for minified, which means that it’s not meant to be read by persons, but it’s faster to load). All my projects proper are in folders within that directory. So my html files are one level down from where the d3.v2.min.js file is kept. This is why the src attribute reads “../d3.v2.min.js”: the ../ part means, look one level up. If the d3.v2.min.js file were on the same directory where I keep my html, I would write src=”d3.v2.min.js”, if I kept it within a specific directory like “d3″, I could write src=”../d3/d3.v2.min.js”, and finally, there is always the option of getting it from the website, src=””.

I don’t have to load the d3 library then. I could have done it at the end of the page. The only requirement is that it should be before the script that will use it. But honestly, the file is so small that it doesn’t make much of a difference (9ms on my machine).

Next, I link to a style sheet. With this syntax, I am assuming that my style is specified in a file called style.css which will be in the same directory as this html page. And if there is no such file, it’s not a problem. It doesn’t prevent the page to load.

Instead of using this syntax, I could have written:


... // my style definitions


in the html file. And frankly, it is sometimes more convenient. But again, for the general case, it’s just as well to leave it like this.

Note that style information should always be in the header part of the file.

And that concludes the header, as noted by the closing tag </head>. Even if we use the <head> tag to mark the beginning of the header section, we may omit the closing tag </head>, and still get away with a valid (and slightly shorter) document, but I keep it for clarity’s sake.

The next part starts with <body>, and is where the content proper, which will get displayed on the screen, is described. <body> and </body>, just like <html> and <head>, are not mandatory, but do help, somewhat, to make the document easier to read.

So what do we find in the body section? Here, I’ve kept it very simple but also close to the conventions I use.

There is one <div> element, which is the basic building block of HTML, and with an id attribute – a document-wide, unique identifier – called “chart”.

Then, there is the <script> element, which is calling the javascript code we are going to use to create our visualization. It’s at the very bottom of the page, actually just before the closing tags (which, again, could be omitted, but let’s not).

Like for the style element, it is possible to leave the script inside the html document. Instead of using a src attribute – which, incidentally, assumes that the script is within the same directory as the html document with this syntax -, we can write:


// all our javascript instructions


And that’s it for the html document! A final word about the contents of the <body> element. In most of my projects, there is an interface such as buttons or controls which is also done in HTML. In that case, the contents of the <body> element get more complex. I would add a button to tweet the page, copyright notices, and other stuff. But I almost always have a <div> element with an identifier named “chart”.

ok, so now that you’re finished with writing your html file, you must save it under any name and use the “.html” extension (or .htm, but why no love for the l? why?)

The javascript file

In this section I will walk you through a very, very basic file, which includes things I do for every project.

var w=960,h=500,"#chart")

var text=svg
.text("hello world")

I like to define variables that describe the width and length of the visualization that I am creating. By putting these in variables, at the beginning of the file, I can easily modify them in case I need to. 960 and 500 work well for visualizations that should appear on their own page, by the way. No scrolling should be necessary.

The next statement use the construct. Here, it indicates that we are going to build something on top  of the element that meets the criterion that is described between the parentheses. The syntax used by that is that of css selectors, but long story short, #chart refers to whatever has an “id” attribute of “chart”. This is our lone <div> element in the html file. Then, we are going to add an svg element, which is what will hold the visualization proper in svg form, and give it a width of w and a height of h.

I always use that syntax, an “svg” variable that holds the top-level svg container, which resides in a <div> element which has an id of “chart”.

The final part of the file writes, finally, hello world proper. Note that I specify a y attribute (vertical position) else the text have its lower-left corner in the top-left corner of the browser window and will be effectively invisible.

Now, the HTML file we just created expect this file to be called “script.js”, so let’s save it under this name.

In this most simple example, we will not need a css file nor a data file. But, for the sake of discussion, let’s create a css file nonetheless.


and let’s save this under style.css (the name that, again, our HTML file expects). What this does is that it changes the size of the font to whatever the default was to a more massive 36 pixels.

The stove: a web server

As far as writing hello world, we’re done. You can load the html file you created in a browser, you should see the encouraging inscription. Congratulations!

Many visualizations can be seen in a browser directly, just by opening a local file. However, this won’t be the case for some, for instance, those who require external data. In that case, you need a web server. If you have web hosting, you may upload the files to your (remote) server, via FTP for instance, and see your visualization by typing the address of your site in the browser url bar. That said, it is a good idea to have a local web server, that is, one that runs on your computer, so you can view your files as if they were served by a web server, but with the added bonus that you can edit them and see the modifications directly without having to upload them each time you change them.

On Macs, you’re pretty much all set. All you have to do is enable web-sharing in your system preferences. Then, http://localhost/~YOURNAME will point to /Users/YOURNAME/Sites where YOURNAME is your user name. Just put your files there and go at it.

For windows, there are a bunch of solutions. The “Professional” versions of windows include the IIS web server, so, there. But beyond that, there is a lot of web server software available. I personally use EasyPHP. EasyPHP comes up with a web server (Apache), a mySql database, a PHP preprocessor and other niceties. And, as an aside, it doesn’t require administrator rights, for you corporate users.

EasyPHP installation is a breeze. When it’s on, by default, http://localhost/ points to the www/ directory in the install directory of EasyPHP, so you may want to install it in a place that suits you. Alternatively, you can create aliases in the admin panel of EasyPHP (http://localhost/home/index.php), in other words to give a name to any part of your hard drive. This is what I do, I put all my projects there and have a shortcut to that name in my browser, so whenever I want to see a project I use that shortcut and I can see the visualization as if it were on the web.

This is how you create aliases in EasyPHP.

The plate: a browser

We’ve talked browsers before, and chances are you have one (or several) on your computer.

Now I wish that by browsers, we could just skip it and mean “the latest version of chrome”, but it turns out that there are slight differences in the way that browsers handle d3 code so you should really test your work in at least chrome and firefox. As of this writing, Chrome + Firefox (version 5 and up) represent just under 50% of the browser market share. If you add all browsers that are d3-capable (Safari, earlier versions of Firefox, Opera, IE9) you reach about 75% of the market. Sadly, IE8 and IE7 which account for slightly over 20% of the market are not d3-compatible, though they can use the Google ChromeFrame free plug-in and do pretty much all that chrome does.

Knives and forks: the console

At the beginning of my dad’s engineering career, code came on a punch card. People then, allegedly, thought it through. You didn’t want to be the kid who didn’t follow your algorithm carefully enough to forecast an avoidable bug and waste a perfectly good card and oh-so precious computing time.

But now? no code is perfect by the time it hits the browser. You may want to launch incomplete code to get a feel for where you’re going. You may not be too sure of whether that should be a plus or a minus in that equation and just try either because it would be quicker to correct an unexpected outcome than to troubleshoot the formula on paper. You may want to iterate, to bring newer, more complex ideas to your visualization with each change to the code. Or just try out different aesthetic options.

Not too long ago, debugging javascript was really a pain. You’d have to fire those annoying alert boxes to understand what was the value of the variables, and dispatch them manually. Fortunately, that time is gone and now is the age of the Console.

There are console functionalities for Chrome, Firefox and Safari, and while the interface slightly varies, the idea is the same. The console allows you to do three main things:

– first, to see if your code executed without errors or warning. Some of those messages can be generated by javascript, and some can be added by you if certain unfortunate conditions are met. You get the position of the error in your code, which helps you to understand what went wrong and fix it.

I have planted an error at the end of the code and it’s been picked up by the console which explains what’s wrong and when. Notice the red cross in the lower-right corner which counts errors. If there were warning, they would be indicated by a yellow triangle.

– second, to inspect elements, that is to find out all the information about the elements displayed on screen, even if (especially if) they have been generated at runtime. So you can see if those elements you really wanted to create have been indeed added, and if the right attributes have been passed.  third, to interact with the code after it’s run (or while it runs, if you manage to pause if with breakpoints). The most common use of this is, IMO, is to check the value of variables, which you can do simply by typing their name at the console command prompt. But you can also type in one-liner javascript statements, even if they are quite complicated. So it’s a way to test your code before you write it in your script file.

What a relief! all those paths elements that were supposed to be created in the code have been added as expected.

– third, it can be used to interact with the code after it’s run (or even during run-time, because you can pause the code with breakpoints using the console, but we won’t go into that). The most common use for that IMO is to check the value of variables, which can be changed during the code execution, but it can also be used to enter one-liner statements, which can be quite complicated. Such a use allows you to test and preview code hypothesis before you write it down in your script file, or to troubleshoot a problem that you could have difficulties seeing outside of the context.

Here, I am using the console to check the value of one variable, and to enter a statement that turns all the shapes orange.

Voilà! the last thing you need when you cook food is people to share it with, same goes for visualizations!


Hollywood + data II: the sequel

a couple of days ago I posted the contribution of relative keywords to the earnings of a given movie.

Well, it occurs that the cast of a movie is much easier to obtain than keywords and much less messy. So, I also had scraped the 4 lead actors for each movie of the Beautiful Information awards in order to determine their contribution.

I’ve done it slightly differently than with the keywords. I’ve also taken the budget of the movie into account. So, in order to predict the earnings of a movie, you take the budget, multiply it by 3, remove 8 millions and then add (or remove) the contribution of each star.

Since the movie budget already (mostly 🙂 ) includes the pay of the lead actors, the way to read this contribution is how much extra these actor should be paid when they appear on film. For instance, each time Emma Stone does her thing, it would be fair to pay her $500k more.

the bang being a modest 500k pay raise.

Likewise, Kirsten Dunst here could be paid an extra 200k per movie, like Rebecca Hall or Rooney Mara. But I mostly felt like posting a picture of Kirsten to stand up against what google autosuggests as keywords when you look her up.

Kirsten, too, should be paid more.

Nicolas Cage, somewhat unsurprisingly,  should refund $2.44m for each movie he stars in.

Actors of movies who’ve done exceedingly well such as Avatar or the Harry Potter or Twilight sagas, movies which, it’s fair to say, owe their success to more than the actors, come out of this over valued. And because of how this is calculated, actors who have costarred with them in less ambitious movies come out undervalued: for instance, Elizabeth Banks or Anton Yelchin who both played with Sam Worthington are “paying” the fact that Man on the Ledge or Terminator: Renaissance haven’t been as successful as Avatar.

Twilight fans take note, though, that Harry Potter actors are considered more valuable.

Now – the data.

-8.72132 base value, million dollars
3.028754  x your budget
(million dollars)
actor occurrences
6.820566 Daniel Radcliffe 4
6.820566 Rupert Grint 4
6.589512 Emma Watson 5
6.469722 Michelle Rodriguez 4
5.919662 Zoe Saldana 3
5.869411 Sam Worthington 4
5.39418 Sigourney Weaver 4
4.66799 Robert Pattinson 6
4.486437 Kristen Stewart 7
4.131046 Taylor Lautner 4
3.511325 Michael Gambon 2
3.328213 Shia LaBeouf 8
3.091938 Josh Duhamel 4
2.771696 Tyrese Gibson 4
2.430676 Justin Bartha 3
2.177533 Anne Hathaway 8
2.145379 Ed Helms 3
1.985782 Helena Bonham Carter 3
1.922826 Ray Romano 1
1.922826 Denis Leary 1
1.922826 Eunice Cho 1
1.860584 Bradley Cooper 7
1.830633 Zach Galifianakis 6
1.809581 John Leguizamo 3
1.770726 Geoffrey Rush 3
1.722315 Christina Jastrzembska 1
1.681332 Rosie Huntington-Whiteley 1
1.654913 Ellen Page 4
1.521321 Xavier Samuel 1
1.495329 Antonio Banderas 3
1.487379 Brendan Gleeson 2
1.427076 Tim Allen 2
1.40105 Mia Wasikowska 2
1.391394 Heath Ledger 1
1.374587 Mike Myers 3
1.366225 Sandra Bullock 4
1.363958 Ned Beatty 2
1.339647 Johnny Depp 8
1.305249 Stellan Skarsgard 2
1.260785 Michael Caine 2
1.217668 Jae Head 2
1.169856 Tom Hanks 4
1.167732 Jason Lee 2
1.167732 David Cross 2
1.167722 Jason Segel 6
1.161184 Amanda Seyfried 6
1.134598 Freida Pinto 2
1.083914 Ken Watanabe 1
1.081577 Katie Featherston 2
1.081577 Micah Sloat 2
1.072914 Paul Walker 2
1.072914 Jordana Brewster 2
1.07084 Jason Bateman 10
1.055046 Aaron Eckhart 5
1.031855 Joseph Gordon-Levitt 5
0.990123 Megan Fox 4
0.9881 Gerard Butler 9
0.973714 Julie Andrews 2
0.95887 Rose Byrne 4
0.953622 Dan Castellaneta 1
0.953622 Julie Kavner 1
0.953622 Nancy Cartwright 1
0.953622 Yeardley Smith 1
0.946414 Gil Birmingham 1
0.921095 Pierce Brosnan 3
0.888618 Vin Diesel 3
0.869224 George Lopez 2
0.865954 Cameron Diaz 8
0.858122 Jackie Chan 4
0.857169 Tobin Bell 4
0.857169 Costas Mandylor 4
0.827224 Lena Headey 1
0.809226 Mila Kunis 6
0.775858 Vincent Cassel 2
0.768506 Russell Brand 4
0.758058 Wenwen Han 1
0.75382 Justin Timberlake 5
0.746798 Kim Cattrall 2
0.746798 Cynthia Nixon 2
0.746798 Kristin Davis 2
0.738043 Taraji P. Henson 3
0.737633 Katy Perry 1
0.737633 Jonathan Winters 1
0.73718 Karen Allen 1
0.719066 Neil Patrick Harris 3
0.706771 Diane Kruger 3
0.697058 Will Smith 3
0.691195 Meryl Streep 6
0.68017 Karen Disher 1
0.677442 Jesse Eisenberg 6
0.676622 Dominic West 3
0.661994 Zac Efron 3
0.661289 Lucas Grabeel 1
0.659597 Quinton Aaron 1
0.646669 Justin Long 5
0.645497 Edward Asner 1
0.645497 Jordan Nagai 1
0.645497 John Ratzenberger 1
0.644743 Eddie Murphy 6
0.644017 Hank Azaria 2
0.633971 Derek Jacobi 1
0.621914 Craig T. Nelson 1
0.599633 Tim McGraw 2
0.59531 Chloe Csengery 1
0.59531 Jessica Tyler Brown 1
0.59531 Christopher Nicholas Smith 1
0.59531 Lauren Bittner 1
0.586817 Scott Patterson 2
0.585204 John Lithgow 2
0.562875 Christopher Plummer 3
0.562458 Dustin Hoffman 4
0.557976 Terry Crews 2
0.557014 Jaden Smith 2
0.55002 Brad Garrett 1
0.55002 Lou Romano 1
0.55002 Ian Holm 1
0.545008 Mark Fredrichs 1
0.545008 Amber Armstrong 1
0.542971 Adam G. Sevani 2
0.542553 Ian McShane 4
0.536569 Molly Ephraim 1
0.536569 David Bierend 1
0.528423 Natalie Portman 8
0.519192 Emma Stone 6
0.517328 Leonardo DiCaprio 4
0.508157 Dwayne Johnson 6
0.506053 Liam Neeson 5
0.504138 Betsy Russell 3
0.498758 Winona Ryder 2
0.498382 Lucy Punch 1
0.489967 Katherine Heigl 5
0.489965 Saurabh Shukla 1
0.489965 Anil Kapoor 1
0.486999 Famke Janssen 1
0.486999 Leland Orser 1
0.482205 Javier Bardem 3
0.481332 Ross Bagdasarian Jr. 1
0.481332 Janice Karman 1
0.460322 Maya Rudolph 2
0.45842 Kristen Wiig 4
0.455105 Bryce Dallas Howard 2
0.449823 Cary Elwes 2
0.443563 Hailee Steinfeld 1
0.442303 Ashley Tisdale 2
0.436495 Ali Larter 2
0.434599 Salli Richardson-Whitfield 1
0.424407 Sarah Clarke 1
0.423429 Penélope Cruz 3
0.413782 Thandie Newton 3
0.412704 Patton Oswalt 3
0.401531 Octavia Spencer 1
0.401112 James Franco 4
0.385048 Terence Stamp 1
0.377733 Wentworth Miller 1
0.377733 Kim Coates 1
0.375668 Jason Flemyng 1
0.374325 Matt Damon 7
0.374325 Christian Bale 5
0.372746 David James 1
0.372746 Jason Cope 1
0.372746 Nathalie Boltt 1
0.368065 Chiwetel Ejiofor 3
0.368005 Édgar Ramirez 1
0.368005 Julia Stiles 1
0.367801 Mélanie Laurent 2
0.362202 Vera Farmiga 3
0.356965 Johnny Knoxville 1
0.356965 Steve-O 1
0.356965 Bam Margera 1
0.356965 Ryan Dunn 1
0.356953 Bill Nighy 6
0.356736 Elle Fanning 1
0.356736 Amanda Michalka 1
0.356736 Kyle Chandler 1
0.356736 Joel Courtney 1
0.356158 Sarah Jessica Parker 4
0.355846 Maggie Grace 2
0.353032 Louis Ferreira 1
0.351145 Ralph Fiennes 3
0.343755 Keir O’Donnell 1
0.343755 Jayma Mays 1
0.343755 Raini Rodriguez 1
0.339988 P.J. Byrne 1
0.33941 Christopher Mintz-Plasse 3
0.338666 Anna Kendrick 2
0.338404 Eli Roth 1
0.338222 Morgan Lily 1
0.338222 Trenton Rogers 1
0.313245 Jessica Lucas 1
0.313245 Lizzy Caplan 1
0.312825 Cassie Ventura 1
0.307954 Viola Davis 4
0.30272 Ty Simpkins 1
0.30272 Lin Shaye 1
0.298112 Bree Turner 1
0.298112 Eric Winter 1
0.296847 Andy Serkis 2
0.294687 Mike Vogel 2
0.294687 T.J. Miller 2
0.292065 Kurt Fuller 1
0.286915 Maggie Smith 1
0.286915 Ashley Jensen 1
0.284681 Seth Meyers 1
0.280837 Tobey Maguire 2
0.280773 Jason Sudeikis 2
0.269573 Kyra Sedgwick 1
0.269573 Madison Pettis 1
0.269573 Roselyn Sanchez 1
0.268595 Lisa Kudrow 2
0.258727 Patrick Dempsey 2
0.257049 David Wenham 3
0.254023 Briana Evigan 2
0.252523 Matthew Perry 1
0.252523 Thomas Lennon 1
0.246058 John Cusack 3
0.240009 Michael Jackson 1
0.240009 Alex Al 1
0.240009 Alexandra Apjarova 1
0.240009 Nick Bass 1
0.238714 Patricia Clarkson 3
0.235798 Brian Kerwin 1
0.230146 Sharni Vinson 1
0.230146 Rick Malambri 1
0.230146 Alyson Stoner 1
0.229488 Topher Grace 3
0.22835 Meagan Good 2
0.224193 Jaime King 2
0.221369 Zachary Gordon 2
0.221369 Robert Capron 2
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Don’t take my word for it


In June 2010, I attended a Wolfram|Alpha event called the London Computational Knowledge Summit where speakers mostly focused on how computers can transform the way we teach and transmit knowledge. Several of the presentations made a lasting impression, and mostly the talk by Jon McLoone:

Jon’s point was that academic papers today look an awful lot like those in the 17th century. Granted, they’re not in latin, they can be displayed online and there is color, but as far as maths are concerned it’s still long pages of difficult language and long formulas. The computer, however, can do so much more than transmit information. In the clip above (around 6’20”) Jon shows how a paper on edge detection can be much more effective if instead of using a static example to demonstrate the technique, the paper were able to use a live example, such as input from the camera. In that talk and throughout the day, there were more examples on how interactive displays could be useful for teaching.

Teaching, telling stories and getting a message across use similar functions. Fast forward to VisWeek 2010 and the first “Telling Stories with Data” workshop. Some of the presentations there (I’m thinking of Nick Diakopoulos and Matthias Shapiro mostly) hinted that there could be a process through which readers/users/audience could be taken through so they can make the most of an intended message. Interestingly, this process is not about transmitting as much data as effortlessly as possible but rather to engage the audience, to get them to challenge their assumptions.

Those two events really made me pause and think. Ever since I had started working in visualization, all my efforts had been focused on being as clear as possible, and my focus, on efficient visuals. However, for some tasks, clarity just isn’t optimal. That wasn’t much of an issue in most of my OECD work where such an approach makes a lot of sense but I started seeing that there was a world of possibility when it comes to changing people’s perception on a subject or even persuading them.


French pension reform

Right at the moment of visWeek 2010, France was plagued by strikes against the proposed pension reform. At the peak of contestation up to 3m people demonstrated (that’s as much as one adult out of 14). I was quite irritated by the protests. In theory, left and right had very comparable views on this problem and only disagreed on unsignificant details. They both knew reform was unavoidable, and, again, had similar plans. But when those of the current government were implemented, the opposition capitalized on the discontent and attacked the plan vigorously. Their rethoric were entirely verbal – no numbers were harmed in the making of their discourse! Consequently, protesters and a large part of the population started to develop notions about the state of pensions which were completely disconnected from reality.

I believe that if numbers had been used early enough, it would have been enough to provide a counterpoint to such fallacies and while it may not have prevented demonstrations, it would have greatly helped to dampen their effect. With that in mind and with official data I tried to build a model to show what would happen if one changed this or that parameter of pension policy. Pension mechanics are quite simple: what you give on one side, you take on another; the evolution of population is quite well known, so making such a model is pretty straight forward. But putting that in a visual application really showed how the current situation was unsustainable. In this application I challenge the user to find a solution – any solution – to the pension problem, by using the same levers as the policy makers. It turns out that there is just one viable possibility. Yet, letting people find that by themselves and challenge that idea as hard as they could was very different from paternalizing and telling people that this was just the way it is.

On the course of the year I got involved in several occasions in situations like this where data visualization could be used to influence people’s opinion, and likewise I tried to use that approach. Instead of sending a top-down message (with or without data), instead confront the assumptions of the audience and get them to interact with a model. After this experience, their perception will have changed. This technique doesn’t try to bypass the viewers critical thinking, but instead to leverage their intelligence.

In politics

I am very concerned with the use of data visualization in politics for many reasons. One of them is because I’m a public servant. In my experience, most decisions are not taken by politicians, but by experts or technicians who are commited to the public good. Yet, when poorly  explained, these decisions can be misunderstood and attacked. Visualization, I believe, can help defend such decisions (those who are justifiable at least) and explain them better to a greater number.

Although a lot of data is available out there (or perhaps for that very reason) only few people have a good grasp of the economic situation of their country. This just can’t be helped. It’s not possible to increase the percentage of people who can guestimate the unemployment rate and it’s not really important. Very few people need to know such a number, now what is important is to be able to use that information in context when it is useful. For instance, at election time, a voter should be able to know if the incumbent has created or destroyed jobs. This is something that data visualization can handle brilliantly.

Finally, my issue with political communication is that it is written by activists, for activists. It works well to motivate people with a certain sensitivity but it is not very effective at getting others to change side. This is a bias which is difficult to detect by those in charge of political communications because, well, they’re activits too… and here this flavor of model-based data visualization, with its appearance of objectivity and neutrality, can complement the more verbal aspect of rhetoric quite well.

In the talk I used Al Gore’s An Inconvenient Truth as a counter example. This movie is a fine example of story-telling, and operating at an emotional rather that at a rational novel. I trust that people who feel concerned about climate change will be reinforced in their beliefs after seeing the movie. However, those who do not were unconvinced. In fact, the movie also gave a strong boost to climate skeptics. There was a real barrage of blog posts and websites attempting to debunk the assertions of that “truth”, most often with data. There is a missed opportunity: if the really well-made stories of the movie had been complemented with a climate model that people could experiment with, it would have been perceived as less monolithic, less manichean, less dogmatic.

The conclusions

In my practice using an interactive model can help a lot to get a message across (and no, I don’t have a rigorous evaluation for “a lot”, that’s the advantage of not being an academic).

Such models engage the users, they come out as more objective and truthful as static representations, and they can be very useful to address preconceptions. Chances are they’re more fun, too.

Then again, just because a model is interactive and built on transparent data and equations doesn’t mean it’s objective. It is usually possible to control the model or the interface so that one interpretation is more likely than the other, and that’s precisely the point if you are using data visualization to influence.

It can be very cheap and easy to turn a static representation into an interactive display. Every chart with more than 2 dimensions can be turned in a visualization where the user controls one dimension and sees data for the others evolve.

And if you build a model like this, you must be very open and transparent about the data and the equations and sometimes find ways to get people to overcome their doubts.

Besides, having a working interactive model is no guarantee of success. You really have to be careful that your users are not likely to interpret your visualization in ways you never intended.

The presentation

All examples I used in the presentation both good and bad, both mine and others can be found at


More fun with arrays in protovis

In my short tutorial on working with data and protovis I briefly covered some standard javascript and protovis methods to work with arrays. The more I work with Protovis, the more I am convinced that efficient array manipulation is key to achieving just about anything with the library. So, I would like to go into more detail in some javascript methods for building, processing and testing arrays that can really be helpful.

Going through arrays: map and forEach

I said rapidly that the map method was very useful in protovis especially used in combination with pv.range. But that's not very fair to map() to be treated this lightly. Protovis canon examples do not use many traditional loops such as for or while statements. One reason for that is that many constructs in protovis are de facto loops: when we pass an array to protovis as a data file, to create a bar chart for instance (or panels, pie wedges, you name it), it will go through each element of the array to create individul bars (panels, wedges...), to position them, style them, and so on and so forth. This is why it is so important to have your data elements in the best possible shape when you first pass them to protovis. It makes the rest of your code much nicer. Remember our early example:
var categories = ["a","b","c","d","e","f"];
var vis = new pv.Panel()

  .data([1, 1.2, 1.7, 1.5, .7, .3])
  .height(function(d) d * 80)
  .left(function() this.index * 25)
    .text(function() categories[this.index]);

This is not ideal to have values and categories in two separate places, because one could be changed without updating the other. So let's try to use map to create one single variable.
var categories = ["a","b","c","d","e","f"];
var data=[1, 1.2, 1.7, 1.5, .7, .3].map(function(d,i) {return {value:d, key: categories[i]};});
Let's look at our map() method here. First, it's right after an array. It will run against this array, so it will perform an operation on each element of this array, and the result will be another array of the same size with the outcome of each operation in the same order. The next thing here is a function with two arguments: d and i. Again the naming is arbitrary, call them what you want. And they are both optional. But it has to be a function. pv.range(3).map(3) will not return [3,3,3], you need to write pv.range(3).map(function() 3). The first argument refers to the current item of the array map is working on. So 1, then 1.2, etc. If the array is more complex, and is an array of arrays or similar, the current element can be itself an array, an object or anything. It doesn't have to be a number. Here, we want to create an array of associative arrays where the value handle corresponds to the values of the array, and where key corresponds to the category name. So we start our output by "{value: d,". This puts the value of each array element in sequence where we need it to be. The second argument corresponds to the index of the current item in the array, so - 0, 1, 2 etc. This is not unlike using "this.index" in other parts of protovis. This helps us getting the right category name, the one in the same position as the value we are fetching. So we write "key: categories[i]}". The rest of the code can then be changed to :
  .height(function(d) d.value * 80)
  .left(function() this.index * 25)
    .text(function() d.key);
Now how about forEach()? forEach works in a very similar way to map(), the difference is that it doesn't output an array. It's just a function that runs on each element of the array. It can be used to perform an operation a number of times corresponding to the length of that array, for instance.

Testing arrays

There are some times when you need to know whether some or all the elements of your array fulfill a condition. And some other times, you need to be able to extract a subset of your array also on a conditional basis. Now, that would be possible using forEach or map methods as above, but fortunately javascript provides simpler means to achieve that.

Testing a condition on an array at once

There are two methods that do that: every() and some(). every() will return true is the condition is true for, well, every element of the array. some() will return true if the condition is true for at least one element of the array. So, they can also be used to tell if the condition is false for at least one element of the array, or all elements of the array respectively. This is how they work:
[0,1,2].every(function(d) {return d;})
// will return false: 0 is false, 1 is true and 2 is true.
[0,1,2].every(function(d) {return (d<3);})
// will return true. All elements are less than 3.

[0,1,2].some(function(d) {return d;})
// will return true. 1 is true, so at least one element in the array is true.

Creating conditional subsets of an array

It is also possible to get only the elements that fit a condition using the filter() method.
[0,1,2].filter(function(d) {return d;})
// this will return [1,2]. 0 is evaluated as "false".
[1,2,3,4,5].filter(function(d) {return (d>2);})
// this will return [3,4,5].
Of course, the more complex the array, the more interesting these functions get. With the barley array from part 4:
 barley.filter(function(d) {return d.variety=="Manchuria";}
/* this will return: 
  [{"yield":27,"variety":"Manchuria","year":1931,"site":"University Farm"},
   {"yield":32.96667,"variety":"Manchuria","year":1931,"site":"Grand Rapids"},
   {"yield":26.9,"variety":"Manchuria","year":1932,"site":"University Farm"},
   {"yield":22.13333,"variety":"Manchuria","year":1932,"site":"Grand Rapids"},

Visualizing arrays

(without plotting them, of course) When you are manipulating arrays, turning them into maps, performing roll-ups and sorts, you may want to get a glimpse of the array. But, unless it's a single, one-dimensional array, firebug or chrome debugger will represent it as a cryptic [ > Object, > Object, > Object ]. Not being able to follow step by step what's happening to an array makes understanding the data functions much more difficult. Fortunately, you can use the JSON.stringify() method.
{"yield":27,"variety":"Manchuria","year":1931,"site":"University Farm"},
{"yield":32.96667,"variety":"Manchuria","year":1931,"site":"Grand Rapids"},
{"yield":43.06666,"variety":"Glabron","year":1931,"site":"University Farm"},
{"yield":29.13333,"variety":"Glabron","year":1931,"site":"Grand Rapids"},
{"yield":35.13333,"variety":"Svansota","year":1931,"site":"University Farm"},
{"yield":29.66667,"variety":"Svansota","year":1931,"site":"Grand Rapids"},
{"yield":39.9,"variety":"Velvet","year":1931,"site":"University Farm"},
{"yield":23.03333,"variety":"Velvet","year":1931,"site":"Grand Rapids"},
{"yield":36.56666,"variety":"Trebi","year":1931,"site":"University Farm"},
{"yield":29.76667,"variety":"Trebi","year":1931,"site":"Grand Rapids"},
{"yield":43.26667,"variety":"No. 457","year":1931,"site":"University Farm"},
{"yield":58.1,"variety":"No. 457","year":1931,"site":"Waseca"},
{"yield":28.7,"variety":"No. 457","year":1931,"site":"Morris"},
{"yield":45.66667,"variety":"No. 457","year":1931,"site":"Crookston"},
{"yield":32.16667,"variety":"No. 457","year":1931,"site":"Grand Rapids"},
{"yield":33.6,"variety":"No. 457","year":1931,"site":"Duluth"},
{"yield":36.6,"variety":"No. 462","year":1931,"site":"University Farm"},
{"yield":65.7667,"variety":"No. 462","year":1931,"site":"Waseca"},
{"yield":30.36667,"variety":"No. 462","year":1931,"site":"Morris"},
{"yield":48.56666,"variety":"No. 462","year":1931,"site":"Crookston"},
{"yield":24.93334,"variety":"No. 462","year":1931,"site":"Grand Rapids"},
{"yield":28.1,"variety":"No. 462","year":1931,"site":"Duluth"},
{"yield":32.76667,"variety":"Peatland","year":1931,"site":"University Farm"},
{"yield":34.7,"variety":"Peatland","year":1931,"site":"Grand Rapids"},
{"yield":24.66667,"variety":"No. 475","year":1931,"site":"University Farm"},
{"yield":46.76667,"variety":"No. 475","year":1931,"site":"Waseca"},
{"yield":22.6,"variety":"No. 475","year":1931,"site":"Morris"},
{"yield":44.1,"variety":"No. 475","year":1931,"site":"Crookston"},
{"yield":19.7,"variety":"No. 475","year":1931,"site":"Grand Rapids"},
{"yield":33.06666,"variety":"No. 475","year":1931,"site":"Duluth"},
{"yield":39.3,"variety":"Wisconsin No. 38","year":1931,"site":"University Farm"},
{"yield":58.8,"variety":"Wisconsin No. 38","year":1931,"site":"Waseca"},
{"yield":29.46667,"variety":"Wisconsin No. 38","year":1931,"site":"Morris"},
{"yield":49.86667,"variety":"Wisconsin No. 38","year":1931,"site":"Crookston"},
{"yield":34.46667,"variety":"Wisconsin No. 38","year":1931,"site":"Grand Rapids"},
{"yield":31.6,"variety":"Wisconsin No. 38","year":1931,"site":"Duluth"},
{"yield":26.9,"variety":"Manchuria","year":1932,"site":"University Farm"},
{"yield":22.13333,"variety":"Manchuria","year":1932,"site":"Grand Rapids"},
{"yield":36.8,"variety":"Glabron","year":1932,"site":"University Farm"},
{"yield":14.43333,"variety":"Glabron","year":1932,"site":"Grand Rapids"},
{"yield":27.43334,"variety":"Svansota","year":1932,"site":"University Farm"},
{"yield":16.63333,"variety":"Svansota","year":1932,"site":"Grand Rapids"},
{"yield":26.8,"variety":"Velvet","year":1932,"site":"University Farm"},
{"yield":32.23333,"variety":"Velvet","year":1932,"site":"Grand Rapids"},
{"yield":29.06667,"variety":"Trebi","year":1932,"site":"University Farm"},
{"yield":20.63333,"variety":"Trebi","year":1932,"site":"Grand Rapids"},
{"yield":26.43334,"variety":"No. 457","year":1932,"site":"University Farm"},
{"yield":42.2,"variety":"No. 457","year":1932,"site":"Waseca"},
{"yield":43.53334,"variety":"No. 457","year":1932,"site":"Morris"},
{"yield":34.33333,"variety":"No. 457","year":1932,"site":"Crookston"},
{"yield":19.46667,"variety":"No. 457","year":1932,"site":"Grand Rapids"},
{"yield":22.7,"variety":"No. 457","year":1932,"site":"Duluth"},
{"yield":25.56667,"variety":"No. 462","year":1932,"site":"University Farm"},
{"yield":44.7,"variety":"No. 462","year":1932,"site":"Waseca"},
{"yield":47,"variety":"No. 462","year":1932,"site":"Morris"},
{"yield":30.53333,"variety":"No. 462","year":1932,"site":"Crookston"},
{"yield":19.9,"variety":"No. 462","year":1932,"site":"Grand Rapids"},
{"yield":22.5,"variety":"No. 462","year":1932,"site":"Duluth"},
{"yield":28.06667,"variety":"Peatland","year":1932,"site":"University Farm"},
{"yield":26.76667,"variety":"Peatland","year":1932,"site":"Grand Rapids"},
{"yield":30,"variety":"No. 475","year":1932,"site":"University Farm"},
{"yield":41.26667,"variety":"No. 475","year":1932,"site":"Waseca"},
{"yield":44.23333,"variety":"No. 475","year":1932,"site":"Morris"},
{"yield":32.13333,"variety":"No. 475","year":1932,"site":"Crookston"},
{"yield":15.23333,"variety":"No. 475","year":1932,"site":"Grand Rapids"},
{"yield":27.36667,"variety":"No. 475","year":1932,"site":"Duluth"},
{"yield":38,"variety":"Wisconsin No. 38","year":1932,"site":"University Farm"},
{"yield":58.16667,"variety":"Wisconsin No. 38","year":1932,"site":"Waseca"},
{"yield":47.16667,"variety":"Wisconsin No. 38","year":1932,"site":"Morris"},
{"yield":35.9,"variety":"Wisconsin No. 38","year":1932,"site":"Crookston"},
{"yield":20.66667,"variety":"Wisconsin No. 38","year":1932,"site":"Grand Rapids"},
{"yield":29.33333,"variety":"Wisconsin No. 38","year":1932,"site":"Duluth"}
No matter the manipulations you inflict to an array you will always be able to make it reveal its innards by using this.

Protovis: analysis of the Map projections example

What is a map?

before we start looking at the code it may be a good idea to think of the best way to represent a country.
Countries are areas of land surrounded by borders, which are imaginary (or sometimes physical) lines going through a set of points.

Some countries are made of one of such surfaces, but many countries are not one contiguous territory (they may include islands for instance) so they could be made out of several disjointed polygons.

Now let’s put on our protovis hat. Let’s suppose we want to draw a map where each country could be colored differently (choropleth). What kind of data structure should be use to represent that?
First there should be a sort of array of countries. Each country should be an item in that array, so they can be indexed and assigned an individual color and various data points.
Then, at the lowest level, we would be drawing polygons, which are treated as pv.Line in protovis. For each polygon, we would require an array of coordinate pairs. To draw a country, we would need a list (array) of those polygons.

So the data structure we are looking at is:

var world=[  // an array of countries
    [ // an array of polygons
        [ // an array of pairs of coordinates
            [x0, y0], // coordinates of the first point
            [x1, y1], // coordinates of the next one
            [xn, yn],
            [x0, y0]  // coordinates of the first point to close the polygon
       ...              // another polygon, but maybe not.
   [                    // next country

the map projections example

Can be found here:

 * A diverging color scale, using previously-computed quantiles of population
 * densities; in the future, we might use a quantile scale here to do this
 * automatically. Map colors based on, by Cynthia A. Brewer,
 * Penn State.
var fill = pv.Scale.linear()
    .domain(140, 650, 1900)
    .range("#91bfdb", "#ffffbf", "#fc8d59");

/* Precompute the country's population density and color. */
countries.forEach(function(c) {
  c.color = stats[c.code].area
      ? fill(stats[c.code].pop / stats[c.code].area)
      : "#ccc"; // unknown

var w = 860,
    h = 3 / 5 * w,
    geo = pv.Geo.scale("hammer").range(w, h);

var vis = new pv.Panel()

/* Countries. */
    .data(function(c) c.borders)
    .data(function(b) b)
    .title(function(d, b, c)
    .fillStyle(function(d, b, c) c.color)
    .strokeStyle(function() this.fillStyle().darker())

/* Latitude ticks. */
    .data(function(b) b)

/* Longitude ticks. */
    .data(function(b) b)


In addition there are two arrays of the following shape:
First, stats which is an associative arrays of associative arrays, and which associate each 2-letter country code with values of population and area:

var stats = {
'AG': {pop:83039, area:44},
'DZ': {pop:32854159, area:238174},
'US': {pop:299846449, area:915896},

Then, countries, which is an array of associative arrays.

var countries = [
{code:'AG', name:"Antigua and Barbuda", 
borders:[ // an array of one or several areas, 
  [ // an array of coordinates, 
    [ // a pair of the form longitude, lattitude

Now this second data structure looks a lot like the one we’ve drafted in the prologue. All the geographic information is tucked in a property called “borders”. The array has other properties for comfort.
Because the data is put in the right shape and order, this script can produce a very good map with a remarkable economy of code.
This example has been put together to showcase the various map projections of protovis (identity, mercator, and so on.). These projections have zero impact on the way data should be assembled for making maps, so we’ll just treat them as “magic”.

 * A diverging color scale, using previously-computed quantiles of population
 * densities; in the future, we might use a quantile scale here to do this
 * automatically. Map colors based on, by Cynthia A. Brewer,
 * Penn State.
var fill = pv.Scale.linear()
    .domain(140, 650, 1900)
    .range("#91bfdb", "#ffffbf", "#fc8d59");

This part creates a color scale which will return a color according to the value passed to it. The color returned will be somewhere between the ones specified in the range, depending on where the value is relatively to the values specified in the domain. So a value of 140 will result in a color of #91bfdb (bluish), it will go towards the grey as the value moves up to 650, and towards #fc8d59 (redish) as the value goes up to 1900.

/* Precompute the country's population density and color. */
countries.forEach(function(c) {
  c.color = stats[c.code].area
      ? fill(stats[c.code].pop / stats[c.code].area)
      : "#ccc"; // unknown

As the remark says, this will precompute the country’s color once and for all.
The forEach() method goes to every element of the countries array.
the c.color = statement will add a color key to each element of that array (which, as you may recall, already has values for the code, name and borders keys.
What it does is that is retrieves the country code of that element of countries, c.code, and uses that to find out whether we have an area value for that country code (this is stats[/c].area?).
If this is the case, we are going to compute the color that should be attributed to the country, by passing the population divided by the area to the color scale we just made. Else, we just use light grey.

The next few lines are standard constants that will shape the vis.
Note however

geo = pv.Geo.scale("hammer").range(w, h)

This is a geographic scale, which will be used to convert longitudes and latitudes to X and Y coordinates on the screen.

/* Countries. */
    .data(function(c) c.borders)
    .data(function(b) b)
    .title(function(d, b, c)
    .fillStyle(function(d, b, c) c.color)
    .strokeStyle(function() this.fillStyle().darker())

This is where it all happens.
First, we create a series of panels, one for each country. So, we pass the countries array as data.
Then, we are going to create another series of panels for every country, that is, with as many panels as there are independent areas in the country. For instance, if there are islands, we are going to need extra panels to represent them. If the country is one contiguous mass of land, there will be just one panel here.
This time, we use function(c) c.borders as data. That is, we go into the borders array.

Finally, we are going to create a filled polygon for each of these independent areas. This is achieved by adding a pv.Line to the previous panels. Likewise, we use (function(b) b) as data, meaning that we go yet another level into the borders array. Now, we are accessing the pairs of longitude + latitude numbers.

geo.x and geo.y convert this pair of numbers to X and Y coordinates on the screen.
For the next two lines, title and fillStyle, we need to go back to the country level.
so, we use a function of the form function(d,b,c). d is the current item (pair of longitude, latitude), b its parent (individual area) and c, its grand-parent (the country).
so, function(d,b,c) retrieves the country name, and function(d,b,c) c.color retrieves the color we had computed for that country to begin with.

For the color of the border, we wish to use a darker version of the fill color. This is what the this.fillStyle().darker() does.

The rest of the vis is longitude and latitude ticks, using the built-in properties of the scale.


Working with data in protovis – part 1 of 5

When I started using protovis I had only a very basic knowledge of javascript, which in theory isn’t a problem as protovis is meant to be learned by example, and as it has its own logic and structure which is different from typical javascript code. So I started by looking and modifying examples which was enough to do basic stuff.
But I soon felt limited by what hid behind a single property: data. I knew that protovis had lots of features to manipulate and process data but they were not obvious from the examples.

I mean,

var vis = new pv.Panel()

.data([1, 1.2, 1.7, 1.5, .7, .3])
.height(function(d) d * 80)


Here, it’s pretty obvious that the bars represent the values 1, 1.2, 1.7, 1.5, 0.7 and 0.3 respectively. One can infer that the sizes of bars are 25 pixels wide and 80 times their value long.

But protovis doesn’t usually look like this “hello world” kind of example, but rather like this:

/* Compute yield medians by site and by variety. */
function median(data) pv.median(data, function(d) d.yield);
var site = pv.nest(barley).key(function(d);
var variety = pv.nest(barley).key(function(d) d.variety).rollup(median);
/* Nest yields data by site then year. */
barley = pv.nest(barley)
    .sortKeys(function(a, b) site[b] - site[a])
    .key(function(d) d.year)
    .sortValues(function(a, b) variety[b.variety] - variety[a.variety])
[. . .]
/* A panel per site-year. */
var cell = vis.add(pv.Panel)
    .top(function() this.index * h)

What just happened? pv.nest, key, rollup, sortKeys, entries – what could that do?

To go beyond merely touching up examples, and do your own visualizations from scratch, it is important to get a good grip on how to feed protovis with data. In order to do so, you need a few javascript notions.

Arrays, arrays, how do they work?

In javascript, an array is an ordered list of stuff.

In our initial example, we had one such list:

[1, 1.2, 1.7, 1.5, .7, .3]

Anything can be put in an array: numbers, strings, Booleans (true/false values), objects … including other arrays. All elements of an array don’t have to be of the same type. Arrays can be assigned to a variable.

var a = [1, 1.2, 1.7, 1.5, .7, .3];

Elements of the array can be accessed using the [] notation. In javascript, indices start at 0, so the first element of an array can be obtained so:


This returns 1. Javascript has many functions to create and manipulate arrays, which we will talk about later. For the time being, let’s look at arrays of arrays. If we wrote instead:

var a = [[1, 1.2], [1.7, 1.5], [.7, .3]];

a is now an array of arrays, or “multi-dimensional array”.

a[0] is now worth [1, 1.2]. To access the first number of the array, one has to write a[0][0], which will return the first element (1) of the first element ([1, 1.2]) of a.

Javascript also has another type of array called associative arrays, where values are assigned to keys instead of an index. For instance,

var a = {yield: 27.00000, variety: "Manchuria", year: 1931, site: "University Farm"};

is an associative array. To access a value, one can use a . operator:


will retun 27.


also works.

Like other variable types, it is possible to have an array of associative arrays. In fact, this is used quite often in protovis.

Protovis and arrays – deconstructing the first example

The reason why I introduced javascript arrays is that the data property requires an array. Protovis then loops through that array, performing operations on each of its elements. To that end, it uses things such as accessor functions and properties of an object called this.

To explain all of this let’s go back to the first example and analyse it line by line.

var vis = new pv.Panel()
  .data([1, 1.2, 1.7, 1.5, .7, .3])
  .height(function(d) d * 80)
  .left(function() this.index * 25);

The first 3 lines create a panel, which is like the sheet of paper on which protovis will draw the chart. Its width and height properties must be filled, as they are 0 by default which would make the whole visualization invisible.

The next line adds a bar chart to this panel we’ve just created.

The line after specifies the data on which to work: here comes our array. Here, we have written the array literally in the data property, but nothing prevents us to assign it to a variable first and to pass the variable instead.

The next line, and the line with the bottom property, assign constant numbers to these properties. It means that all the bars will have a width of 20 pixels, and they will all be aligned with the bottom of the panel – that’s what



Now let’s look at the two remaining lines:

.height(function(d) d * 80)
.left(function() this.index * 25);

The first line uses an accessor function. What this does is that it looks at the current element, and perform an operation on it, the result of which will be the height of that element.

In proper javascript, we would have written:

function(d) {return d*80;}

but protovis uses a shorthand notation that allows us to omit curly braces and the return statement. By the way, d in the function is completely arbitrary, and could be any variable name –

function(a) a*80

also works. It’s just that the name of the variable between parentheses will represent the value of the current element.

The second line uses the this object. this represents what protovis is working on at the moment, and it has properties that can be used. The most commonly used is index: this.index returns the position of the current element in its array, so it is going to be: 0 for the first bar, 1 for the next one, etc.

So this line specifies that each new bar should start every 25 pixels from the left border of the panel.

You may wonder, why not write

.left(this.index * 25);

and omit the function()? Well, function() means that the content of the property gets re-evaluated. If we had omitted it, this.index * 25 would have been computed once (for a result of 0) and that value would have been used for all the bars.

By the way, instead of writing the height property as it is, we could have written:

.height(function()[1, 1.2, 1.7, 1.5, .7, .3][this.index] * 80)

Using an accessor function is shorter and clearer.

Next: Multi-dimensional arrays, inheritance and hierarchy


Misleading with road statistics

Changing driving behaviors with campaigning alone is a tall order, but is literally a life-or-death matter. Road fatalities range from about 40/million  in Japan, to about 6 times as much in Russia. Fortunately, the numbers tend to decrease in most places, due to better equipment, better roads, harsher punishment and safer behaviors.

Of all of these factors, drivers behavior is the only thing which isn’t directly controlled by governments, so it’s no surprise that it’s what the agencies try to target. Almost every angle has been tried: blaming alcohol, speed, showing the consequences of seemingly inocuous oversights, and, obviously, gore and shocking images.

This year, in France, they’ve tried a different approach with a campaign called the 12000: thanking the drivers for their better behavior, which has saved, well, 12000 lives since 2003.

I really appreciate the upbeat tone of campaign and its much welcome positive spin. Unfortunately, it’s based on such fallacy that it’s difficult to accept as such.


Here’s one view of what has happened. The number of fatalities has dropped since 2003. (By the way, the unit for this and the following chart are fatalities per million population, indexed so that the value for France in 2003 is 100). It can be argued that lives have been saved, because if the number of fatalities had remained constant since 2003, the area in green would represent extra fatalities (around 6,000).

But that’s what the agency wants us to believe.


Says the website,

12000 lives have been saved between 2003 and 2008. Fatalities have dropped from 6126 in 2003 to 4275 in 2008.

To actually come up with that number of 12000 person saved, they’ve simply multiplied the difference between the 2008 and 2003 figures by 6. As if there had been a sudden and drastic drop in 2003.

I wonder why they do that. Behaviors have changed on the road. 75% of French drivers have a perfect driving record, another 15% have only committed minor offences. Those are facts. So why inflate the numbers? and why, for instance, start at 2003 and not 2002, where mortality dropped by over 20% ? 12000, as an absolute figure, is not more striking than 1000 or 100000.

The visuals all repeat this figure. On all the posters of the campaign, we find the following footnote: “* If behaviors had not changed since 2002 in France, 12000 more people would have died on the road between 2003 and 2008. Source: ONISR. “. The ONISR says no such things in their report, so that number must have been invented for the campaign.

Speaking of the ONISR reports, they estimate that if people observed speed, alcohol and seat belt legislations, the numbers would drop by over 2000. So are we doing that well?


That’s a comparison with the UK. Again, the units are ratio per population, not absolute figures. If France had the same road fatalities that the UK, over 10,000 persons would not have been killed over the 2003-2008 period…

Anyway. There’s no good reason why all western countries couldn’t go under 50 killed / million population within a reasonably short time frame.


Using data visualization to disinform

Two weeks ago I have been at DD4D conference, conveniently located at my workplace. I will write some more on DD4D, meanwhile you can see this post on infosthetics by Petra and Marian. One of the things that struck me at DD4D was that several talks were about either data visualization for advocacy, or for education purposes. One speaker said that data visualization could be used to protect people against those who use numbers to mislead and disinform. Yesterday, I saw this typical example of such a manipulation, reminding of the famous Disraeli quote.

This is a poster for restaurants to display. Yesterday, VAT for restaurants in France was cut from 19.6% to 5.5%. This is the result over 10 years of lobbying. Initially, restaurants asked for a VAT drop and committed to cut their listed prices accordingly. That cut in price would have attracted more consumers, eventually generating more profit and possibly more tax money. That would have been a win-win-win situation for the restaurant industry, the consumer and the state.

But eventually, the changes that restaurants have agreed to their price structure are as follow. They would cut the listed price of up to 10 menu items by 11.8% to “reflect the tax drop”. In exchange, they are allowed to display this poster, on which the chart ominously promises a massive price drop.

In reality, 11.8% is not enough to offset the VAT drop.

That should have been approximately 13.4%  or 100*(1.196/1.055 – 1) . Fast-food chains only have to drop some of their prices by 5% to get the poster.

The poster claims: “a cut in VAT is a cut in prices!”. But what happens really? For most items, listed price (incl tax) is unchanged, which means their actual prices raise by 13.4%. And for the discounted items, the sales price excluding tax still raises by 1.4% (or 7.7% for fast-food chains).

Is this what was implied by the chart?

In the past two weeks, I have collected more examples of shameless lies backed by seemingly official numbers and charts, and will continue to collect them.


New data services 2: Wolfram|alpha

In March this year, überscientist Stephen Wolfram, of Mathematica fame, revealed the world he was working on something new, something big, something different. The first time I heard of this was through semantic web prophet Nova Spivack, who is not known to get excited by less-than-revolutionary projects. That, plus the fact that the project was announced so short before its release, contributed to build anticipation to huge levels.


Wolfram|alpha describes itself as a “computational knowledge engine” or, simply put, as an “answer engine”. Like google and search engines, it tries to provide information based on a query. But while search engines simply try to retrieve the keywords of the query in their indexed pages, the answer engine tries to understand the query as a question and forms an educated answer. In a sense, this is similar to the freebase project, which is to put all the knowledge of a world in a database where links could be established across items.

It attempts to detect the nature of each of the word of the query. Is that a city? a mathematic formula? foodstuff? an economic variable? Once it understands the terms of the query, it gives the user all the data it can to answer.

Here for instance:


Using the same find access process present share diagram as before,

Wolfram|alpha’s got “find” covered. More about that below.

It lets you access the data. If data have been used to produce a chart, then there is a query that will retrieve those bare numbers in a table format.

Process is perhaps Wolfram|Alpha’s forte. It will internally reformulate and cook your query to produce all meaningful outputs in its capacity.

The presentation is excellent. It is very legible, consistent across the site, efficient and unpretentious. When charts are provided which is often, the charts are small but both relevant and informative, only the necessary data are plotted. This is unusual enough to be worth mentioning.

Wolfram|alpha doesn’t allow people to share its outputs per se, but since a given query will produce consistent results, users can simply exchange queries or communicate links to a successful query result.

Now back to finding data.

When a user submits a query, the engine does not query external sources of data in real time. Rather, it used its internal, freebase-like database. This, in turn, is updated by external sources when possible.

For each query, sources are available. Unfortunately, the data sources provided are for the general categories. For instance, for all the country-related informations, the listed sources are the same, and some are accurate and dependable (national or international statistical offices), some are less reliable or verifiable (such as the CIA world factbook or what’s cited as Wolfram|Alpha curated data, 2009.). And to me that’s the big flaw of this otherwise impressive system.

Granted, coverage is not perfect. That can only improve. Syntax is not always intuitive – to make some results appear in a particular way can be very elusive. But this, as well, will get gradually better over time. But to be able to verify the data presented, or not, is a huge difference – either it is possible or not. I’m really looking forward to this.