Charts, assemble!

From the past posts, you would have gathered that dashboards are tools to solve specific problems. They are also formed from individual charts and data elements.

Selecting information

That dashboards are so specific is great, because the problem that they are designed to solve will help choosing the information that we need and also prioritizing it – two essential tasks in dashboard creation. Again, we don’t want to shove every data point we have.

Another great tool to help us do those two tasks is user research. As a designer, we may think we chose the right metrics, but they have to make sense to real users and resonate with them. The bias that we may have is that we would favor data which is easy to obtain or that makes sense to us, compared to data which can be more elaborate, more sophisticated or more expensive to collect or compute, even if that makes more sense to the user.

Here’s an illustration of that.

When I was working at Facebook on this product, Audience Insights, we designed this page to help marketers understand how a group of users they could be interested in used Facebook. (The link / screenshot showcases fans of the Golden State Warriors). One of the main ways we classified users at Facebook, for internal purposes, is by counting how many days of the last four weeks they have been on Facebook. It’s a metric called L28 and one of the high-level things Facebook knows about everyone. So, we integrated it in the first version of this page. But, even though it’s not a concept unique to Facebook, it wasn’t that useful to our users, and it was taking space from a more relevant indicator.

Instead, we have included indicators which are more relevant to the task at hand (ie getting a sense of who those users are through the prism of their activity). For instance we can see that very few Warriors fans only use Facebook on their computer, compared to the general population of US Facebook users. They tend to skew more towards Android and mobile web (going to www.facebook.com from their phone, versus using an app.) They tend to be more active in terms of likes, comments and shares.

Information hierarchy

Once information is chosen and you get a sense of what is more important than the rest, it’s time to represent that visually.

Here are some of the choices you can make.

Show some metrics on top or bigger than others

That’s probably the first thing that comes to mind when thinking hierarchy and prioritization. And it needs to be done! Typically, you should get one to three variables that really represent the most important thing you want your users to read or remember. If you come up with more than 3, you should refine your question/task and possibly split it in two.

The rest of the variables will support these very high level metrics. Again, in a typical situation, you could come up with up to three levels of data (with more than three being a good indication to rethink your scope). Some metrics can support the high-level metrics (i.e. show them with a different angle, or explain them) and some metrics could in turn support them.

Present some metrics together

Stephen Few argues that dashboards should fit on one page or one screen because their virtue is to present information together. With the flexibility offered by the modern web, and the size constraints of mobile, this is a requirement that shouldn’t be absolute. But it’s relevant to remember that some variables add value when seen along other variables. With that in mind, you can have part of your dashboard as a fixed element (always visible on screen) while the rest can scroll away, for instance.

Push some metrics to secondary cards (such mouseovers, pop-ups or drill-down views)

Hierarchizing information is not just about promoting important information. It’s also about demoting information which, while is useful in its own right, doesn’t deserve to steal the show from the higher level metric. The great thing about interactive dashboards is that there are many mechanisms for that. Some information can be kept as “details on demand” and only shown when needed.

Figure out what form to give to the data

So you have data. It probably changes over time, too (and you have that history as well!). And a sense of how important it is.

You can represent it as a static number (and, further, to adjust the precision of that number) or as a time series (i.e. line graph, area graph, bar graph etc.), or both.

The key question to answer is whether the history and how the metric moved over time is relevant and important, versus the latest figures.  If you think that the history is always important or that it doesn’t hurt to have it for context anyway, consider that it’s yet another visual element to digest, another thing that can be misinterpreted, and that unless its importance is clearly demonstrated, you’d rather not include it. Yes – even as a tiny sparkline.

Here is another example from my work at Facebook of a page where proper hierarchy has been applied.

Page Insights, to use a parallel with a better known product, is like google analytics, only for Facebook Pages instead of web sites. Unsurprisingly, the metric we put to the top left is the Page Likes, which is the number of people who like a page. The whole point of the system is to let people understand what affects that number and how to grow it. Two other high-level metrics are shown on the same row in the two cards on the right: the Post Reach for the week (number of people who have seen content from this page this week, whether they like the Page or not) and Engagement (number of people who acted on the content – actions could be liking, commenting, sharing, clicking, etc.)

The number of new Page Likes of the past week, which is represented as a both a line chart and a number in the left card, is an example of a level two metric. It supports the top metric – total likes. The number of Page Likes of the past week, which is represented as a line chart only, is a level three metric. It’s here just as a comparison to the number of the current week – here, it helps us figuring out that last week has been a better week.

Connecting the dots

Ultimately, a dashboard is more than a collection of charts. It’s an ensemble: charts and data are meant to be consumed as a whole, with an order and a structure. Charts that belong together should be seen together. The information gained like so will be much more useful than from looking at them in sequence.

Linking, for instance, is the concept of highlighting an element in a given chart with repercussions on other charts, depending on the element highlighted. A common use case is to look at a data for one given time point, and see the value for that time point highlighted in related charts. Here is an example:

In this specific case, the fact that both charts share the same x-axis makes comparing the shape of both charts easier even without linking.

Each variable doesn’t have to be on its own chart. Your variables can have an implicit relation between one another. Bringing them together might make that relation explicit. Here are some interesting relationship between variables or properties of variables that can be made apparent through the right chart choice.

  • One variable could be always greater than another one, because the second is a subset of the first. Here are some examples:
  1. The number of visits on a website last week will always be greater or equal than the number of unique visitors that week, which will always be greater than the number of visitors last day.
  2. The number of visitors will always be greater to the number of first-time visitors.
  3. The cumulative number of orders over a period of time will always be greater than the number of daily orders over that same period.
  4. The time that users spend with a website in an active window of their browser will always be greater than the time they spend actively interacting with the site.

What’s interesting here is that these relations are not just true because of experience, they are true by definition. It’s also metrics that are expressed in the same units, and, in most cases, with the same order of magnitude, so they can be displayed on the same chart. When applicable, showing them together can show how they, indeed, move together or not.

  • One variable could be the sum of two other, less important variables.

In the example below we go even one step further and we show that one variable is the sum of two variables minus a fourth one.
Here, we look at the net likes of a Facebook Page, that is, the difference between the number of people who like a page on a given day and the day before.
Two factors can make more people like a page: paid likes (a user sees an ad, is interested, and from it, likes the page) or organic likes (a user visits a page, or somehow see content from that page, and likes it, without advertisement involved). Finally, people may also decide to stop liking the page (“unlikes”).
Here, net likes = organic likes + paid likes – unlikes. The reason why we have decomposed Likes between organic and paid is because we wanted to show that ads can amplify the effect of good content. So, visually, we chose to represent that as a layer on top of the rest. (important remark: your dashboard doesn’t have to be neutral. If it can show that your product, company, team etc. is delivering, and you have an occasion to demonstrate it, don’t hesitate a moment). By showing the unlikes as a negative number, as opposed to a positive variable, going up, possibly above the likes (which would be unpredictable) we can keep the visual legible and uncluttered. A user can do the visual combination of all these variables. This chart, by the way, shows the typical dynamic of a Page : new content will generate peaks of new users, but also will cause some users to stop liking the page.

  • One variable could be always growing. Or always positive.

When that is the case this can be used to make choices to represent the chart. If a variable is always growing by nature (i.e. cumulative revenue) you may want to consider representing a growth rate rather than the raw numbers. A reason to consider that is that your axis scale will have to change over time (i.e. if you plot a product that sells for around $1m per day, having an axis that goes from 0 to $10m would be enough for a week, but not for a month let alone for a year, whereas with a growth rate you can represent a long period of time consistently). And if a variable is always positive (ie stock price), your y axis can start at 0, or even at an arbitrary positive value, as opposed to allocate space for negative values.

Conversely, if a variable doesn’t change over time, it doesn’t mean that it’s not interesting to plot. That absence of change could be a sign of health of the system (which is the kind of task that dashboards can be useful for). So the absence of change doesn’t mean that there’s an absence of message.

 

Dashboards as products

In the past few articles I’ve exposed what dashboards are not:

  • an exercise in visual design,
  • an exercise in data visualization technique.

Another way to put this is that “let’s do this just because we can” is a poor mantra when it comes to designing dashboards, or visualizations in the broader sense by the way.

Do it for the users

Now saying that dashboards should be products is a bit tautological. Products, in product design, refer to the result of a holistic process that solves problems of users – a process that includes research, conception, exploration, implementation and testing.

Most importantly, it’s about putting the needs of your users first. And your users first. Interestingly, treating your dashboard as a product means that the dashboard – your product – doesn’t come first.

Creating an awesome dashboard is a paradox. Googling for that phrase yields results such as: 20+ Awesome Dashboard Designs That Will Inspire You25 Innovative Dashboard Concepts and Designs24 beautifully-designed web dashboards that data geeks or 25 Visually Stunning App Dashboard Design Concepts. This is NOT dashboard product design (though it’s a good source of inspiration for visual design of individual charts).

Eventually, no one cares for your dashboard. When designing a dashboard, it’s nice to think that somebody out there will now spend one hour everyday looking at all this information nicely collected and beautifully arranged, but who would want to do that? Who would want to add to their already busy day an extra task, just to look at information the way you decided to organize it? This point of view is a delusion. We must not work accordingly.

Instead, let’s focus on the task at hand. What is something that your users would try to accomplish that could be supported by data and insights?

What is the task at hand?

If you start to think “show something at the weekly meeting” or “make a high-level dashboard” I invite you to go deeper. Show what? a dashboard for what? not for its own sake.

Trickier – how about: “to showcase the data that we have”? That is still not good enough. You shouldn’t start from your data to create your dashboard, and for several reasons. Doing so would limit yourself to the data that you have or which is readily available for you. But maybe that this data, in its raw form, is not going to be relevant or useful to your users. Conversely, you would be tempted to include all the data that you have, but each additional information that you bring to your dashboard would make it harder to digest and eventually detrimental to the process. Most importantly, if you don’t have an idea of what the user would want to accomplish with your data, you cannot prioritize and organize it, which is the whole point of dashboard design.

Finally – “to discover insights” is not a task either. Dashboards are a curated way to present data for a certain purpose. They are not unspecified, multi-purpose analytical exploration tools. In other words: dashboards will answer a specific, already formulated question. And they will answer in the best possible way, if they are designed as such. For exploration, ad-hoc analysis is more efficient, and is probably best left to analysts or data scientists than end users.

Here are some example of tasks:

  • check that things are going ok – that there is no preventable disaster going on somewhere. For instance: website is up – visits follow a predictable pattern.
  • Specifically, check that a process had completed in an expected way. For instance: all payments have been cleared.
  • If something goes wrong, troubleshoot it – find the likely cause. For instance: sales were down for this shop… because we ran out of an important product. Order more to fix the problem, make sure to stock accordingly next time.
  • Support a tactical decision. For instance: here are the sales of the new product, here are the costs. Should we keep on selling it or stop?
  • Decide where to allocate resources. For instance: we launched three variations of a product, one is greatly outperforming the other two, let’s run an ad campaign to promote the winner.
  • Try to better understand a complex system. For instance: user flow between pages can show where users are dropping out or where efficiency gains lie.

This list is by no means limitative. But it’s really useful to start from the problem at hand than just try to create a visual repository for data.

Next, we’ll see how to implement these in the last article: charts assemble!

 

Dashboards versus data visualization

Dashboards are extreme data visualizations

In the recent Information is Beautiful 2014 awards,  I found interesting that there is an infographics and a data visualization categories. My interpretation is that the entries in the infographics section are static and illustrated, while those in the data visualization are generated and data-driven. However, all the featured data visualization projects are about a one-off dataset. So aesthetical choices of the visualization depend on the characteristics of this particular dataset. By contrast, the dashboards I have worked with are about a live, real-time datastream. They have to look good (or at least – to function) whatever the shape and size of the data that they show. The google quote and news chart that we saw earlier must work for super volatile shares, for more stable ones, for indices, currencies, etc. So, if the distinction between infographics and data visualization makes sense to you, imagine that dashboards sit further in that continuum than data visualization. Not only are dashboards generated from data, like data visualizations, but they are also real-time and should function with datasets of many shapes and sizes.

But dashboards problems are not data visualization problems

Data visualization provides superior tools and techniques to present or analyze data. With libraries and languages dedicated to making visualizations, there is little that can’t be done. In many successful visualizations, the author will create an entirely new form, or at least control the form very finely to match their data and their angle. Even without inventing a new form, there are many which have been created for a specific use, and which are relatively easy to make on the web (as opposed to say, in Excel): treemaps, force-directed graphs and other node-link diagrams, chord diagrams, trees, bubble charts and the like. And even good old geographic maps.

In most cases, it is not a good idea to be too clever and have a more advanced form.

Up until mid November 2014, Google Analytics allowed users to view their data using motion charts.

This was really an example of having a hammer and considering all problems as nails. Fortunately, this function disappeared from the latest redesign.

Likewise, on twitter followers dashboard, the treemap might be a bit over the top:

and possibly confusing and not immediately legible to some users. On the other hand, it is economical in terms of space and would probably work in almost every case which are two things that dashboards should be good at. So while I wouldn’t have used it myself I can understand why this decision has been made.

Dashboards are not an exercise in visual design either

A dashboard such as this:

(for which I can’t find the source. I found it on pinterest and was able to trace it to this post but not prior) is well designed visually, it makes proper use of space, colors and type, its charts are simple.

But what good is it? what do I learn, what can I take away from it, what actions can I perform?

Most of the dashboards examples I find on sites like dribbble or beyance (see my Pinterest board) fall into that category: inspiring visual design, probably not real data, no flow, no obvious use.

Dashboards are problems of their own

What makes a dashboard, or any other information-based design successful, is neither the design execution nor the clever infovis technique. Dashboards, eventually, are meant to be useful and to solve a specific problem.

How so? We’ll see in the next article: dashboards as products. 

 

 

Charts in the age of the web

In 2008, when I was working at OECD, my job description was that of an editor. That implied I was mostly working on books. I was designing charts, but they were seen as components of books. And this was typical of the era.

So we would create charts like this one:

And it was awesome! (kind of). I mean, we respected all the rules. Look at that nicely labelled y-axis! and all the categories are on the x-axis! the bars are ordered in size, it’s easy to see which has the biggest or smallest value! And with those awesome gridlines, we can lookup values – at least get an order of magnitude.

What we really did though was apply styling to an excel chart (literally).

Print charts vs interactive charts

Origin of rules for print charts

Rules that govern traditional charts (which are many: ask Tufte, Few) make a certain number of assumptions which are interesting to question today.

One is that charts should be designed so that values can be easily looked up (even approximately) from the chart. This is why having labeled axes and gridlines is so useful. This is also why ordering bar charts in value order is nice. With that in mind, it also makes sense that charts like bar charts or area charts, which compare surfaces, be drawn on axes that start at 0.

The other assumption is that a chart will represent the entirety of a dataset that can be shown at a time. We have to come up with ways to make sure that every data point can be represented and remains legible. The chart author has to decide, once and for all, which is the dataset that will be represented, knowing that there will be “no backsies”.

In the same order of thought, the author must decide the form of his chart. If she wants to compare categories, she may go for a bar chart. If she wants to show an evolution over time, for a line chart. And if she wants the user to have exact values, she will choose a table.

And so, when everything else than a table is chosen, we typically don’t show values with all the data points, because adding data labels would burden the chart and make its overall shape harder to make out.

In this framework, it makes sense to think in term of data-ink (the cornerstone of Tuftean concepts): make sure that out of all the ink needed to print the chart (you can tell it’s a print concept already…), as much should go to encode the data as possible, versus anything else.

How about now

However, there is not a single of these reasons which is valid today in the world of web or mobile charts.

Web charts have many mechanisms to let the user get extra information on a given data point. That can be information that updates on mouseover, callouts and tooltips… This might be less true of mobile in general where the distinction between hovering and clicking is less distinct. But it is definitely possible to obtain more than what is originally displayed. If I want to have an exact value, I shouldn’t have to simply deduce that from the shape of the chart. There can be mechanisms that can deliver that to me on demand.

An example: Google Finance Quote & News

The Google Finance Quote and News chart is a very representative example of a web-native chart. Around since 2006, they provide the price of a given security, along with news for context. While its visual design has probably been topped by other dashboards, what makes it a great example is that it’s publicly available, which is uncommon for business data.

While this chart has gridlines and labelled axes, that is not enough to lookup precise values. However, moving the mouse over the chart allows the user to read a precise value at a given point in time. A blue point appears and the precise value can be read in the top left corner.

One very common data filter in chart is controls that affect the time range: date pickers. By selecting a different time range, we make the chart represent a different slice of the dataset – we effectively filter the dataset so that only the relevant dates are shown. This is in contrast with the traditional printed charts, again, where all of the dataset is shown at once. For instance, we can click on “6m” and we’ll be treated with data from the last 6 months:

Comparing the selected security with others will make the chart show the data in a different mode. This is the same data (plus added series), in the same screen and the same context, but the chart is visually very different:

As to the other two characteristics of web charts I mentioned, data exports and drill downs, they are also featured (but less graphical to show, so I haven’t captured a screenshot for those). There is a link on a left-side column to get the equivalent data (so it is always possible to go beyond what is shown on screen). The little flags with letters in the 3 first screenshots are clickable, and represent relevant news. Clicking them will highlight that article in a right-side column. So it is always possible to get more information.

What does that change?

Everything.

Rules or best practices based on the assumption that data is hard to lookup or to compare are less important. The chart itself has to be legible though. So, for instance, it’s ok to have pie charts or donut charts, as long as the number of categories doesn’t go totally overboard.

Web charts, and dashboards even more so, should focus on only showing relevant data first, then showing it in the most useful and legible way. Again, a noted difference with the print philosophy where as much data as possible should be shown.

How this play out is what we’ll cover in the next articles of the series: Dashboards versus data visualizations.

 

 

Dashboards ahoy!

During my time at Facebook, I worked almost exclusively on one problem: dashboards. More specifically, how to present frequently-updated data in the most efficient way to business users. And so today, I am starting a series of blog posts / tutorials about dashboards.

Why talk about dashboards?

Legit questions. Dashboards are so uncool and boring! I could make more advanced tutorials on d3 or canvas or processing (which is also… in the plans). Or update new cool visualizations.

But the interest of discussing dashboards is precisely because they are not cool. In the first part to his Information Dashboard Design, Stephen Few presents a lengthy gallery of terrible dashboards he collected over years. Most of these dashboards exhibit a serious and obvious production flaws: they are often gaudy, using 3d columns or pie charts, when not taking the dashboard metaphor too literally with replicas of gauges and meters. Here’s a typical dashboard from the early 2000s:

In the past 5 years though, these problems have largely been solved. The overall “Graph Design IQ”, to borrow another Stephen Few concept, has greatly increased. People who make charts are increasingly aware that there are some best practices to build them and that there are a variety of forms beyond the core “Excel” chart types such as bar charts, line charts, pie charts and scatterplots. Besides, anyone who had to code a chart from scratch realized that, as opposed to Excel or similar software where users can rely on defaults, every detail of a chart needs decisions: not only how to encode the data (ie bars, lines etc.) but also whether to have gridlines or not and if so how, how to format axes, how to present legends, and so on and so forth. Oftentimes, having to make these decisions implies taking the time to think about these choices which makes the overall chart quality stronger. Also, in products like Tableau (and to be honest in every version of Excel) the default choices are much more robust than they used to be.

Down with the old, up with the new

While these old problems are as good as solved, dashboards are still not awesome because they are plagued with a set of new problems.

First, the rules and best practices for charts that we keep perpetuating were thought for an old world of printed or otherwise static charts,  not the interactive environments such as web or mobile. As such, some recommendations of the 90s have become myths that need to be busted (I’m looking squarely at you, data-ink ratio).

Second, dashboard design is neither a data visualization problem, nor a design (UX/UI) problem. By this I mean that thinking strictly as a designer or as a data visualization specialist might provide a textbook answer to some well-identified problems that arise with dashboards, but neither of these approaches is optimal.

The not-so-secret secret to dashboards is to apply product thinking. How will people use the dashboard? That should guide what you try to accomplish.

Finally, it’s really critical to realize that dashboards are not collection of individual charts, but an ensemble. Components of a dashboard should not be thought individually but as pieces that fit with one another.

Each of these themes will be the subject of an individual article!

Follow me on Pinterest

On Pinterest, I maintain two dashboard-related boards you may find interesting.

The first is called “Dashboards” (duh) and is examples of complete dashboards, with no judgment on quality, most often found in the wild.

The second, data vis / dashboard UI elements, is centered around lower-level problems such as charts, parts of dashboards and their visual design. Virtually every dashboard example found on a visual design platform like dribbble or beyance is not so much a true dashboard than a collection of individual charts, not that it’s not interesting.

 

Getting beyond hello world with d3

About a year ago I proposed a very simple template to start working with d3. But is that the way I actually work? Of course not. Because, though you and I know that displaying hello world in d3 is not exactly trivial, this is not a highly marketable skill either. That said, I just can’t afford to start each of my projects from scratch.

So in this article I will present you the template I actually use. I won’t go in as much detail as last time because the exact instructions matter less than the general idea.

My template is a set of two files, an html file and a js file. Of course, extra files can be used as needed.

There’s not much to the html file – its role is really to call the javascript code. There is a little twist though. This is also where the interface elements (ie buttons and other controls) may be. Another thing is that I don’t load a css file through the html. The reason is that when I work with svg, I may export the svg proper to a file to have it reworked in Adobe Illustrator etc. and so having style inside the file makes things easier. So I would instead load a style sheet into the svg through javascript.

The javascript file is written with the following assumptions:

  • there could be other scripts called by the same page, so let’s try to avoid conflict as much as possible.
  • some variables need not to be accessed by other scripts.
  • the execution of my visualization is divided into several phases:
    • initialize: assigning initial values to variables, if needed forecasting interaction,
    • loading data: acquiring and processing external data,
    • drawing: this is where the visualization will be actually rendered or updated

    In addition to these three phases which always occur in my visualizations, there are several optional operations which I may or may not use which are included in the template.

    • reshaping data: operations like filtering or sorting the initial dataset after certain choices of the user. Following such an operation, the visualization has to be re-rendered.
    • self-playing animation: when this is required, then the visualization should be able to update itself at given intervals of time. If that is the case, then the html will include controls such as a start and stop button and a slider that can be used to move to an arbitrary time. Then, the javascript includes functions to start and stop the animation, and the drawing function is done so it can be called with a time argument, and not assuming that it will always just show the next step (because the slider can be used to jump ahead or back).
    • helper functions which can make me gain time but which don’t need to be accessed by other scripts.

To address the first concern, I wrap all my code in an anonymous function, like so:

(function() {
// ... my code
})();

within that function, any variable which is declared using the var keyword is not accessible to other scripts. Variables which are declared without the var keyword, however, can be accessed. So in order to minimize the footprint of my code, I create one single object, vis, so I can store the functions and values I will need to call as properties of that object.

(function() {
vis = {}
vis.init = function() {
// code for my init function ...
}
vis.height = 100;
var local = 50;
})();

So outside of that anonymous function, I can call vis.init(), I can access and change the value of vis.height, but I cannot access local.

One step further:

(function() {
vis = {}
vis.init = function(params) {
  // code for my init function ...
  vis.loaddata(params);
}
vis.loaddata = function(params) {
  // code for loading data ...
  vis.draw(params);
}
vis.draw = function(params) {
  // code for drawing stuff ...
}
})();

This gets a bit closer to how the code actually works. From the HTML, I would call vis.init and pass it parameters. vis.init will do its thing (assigning values to variables, creating the svg object, preparing interactions etc.) then call vis.loaddata, passing the same parameters. vis.loaddata will fill its purpose (namely, load the data and perhaps do a little data processing on the side) then call the drawing function.

Any of these functions can be called from the outside (from the HTML, ot from the console for debugging). The nice thing about it is that nothing really happens unless there’s an explicit instruction to start the visualization.

Let’s go a step deeper:

(function() {
vis = {}
var chart, svg, height, width;
vis.init = function(params) {
  if (!params) {params = {};}
  chart = d3.select(params.chart || "#chart");
  height = params.height || 500;
  width = params.width || 960;
  chart.selectAll("svg").data([{height: height, width: width}]).enter().append("svg");
  svg = chart.select("svg");
  svg
   .attr("height", function(d) {return d.height;})
   .attr("width", function(d) {return d.width;})
  vis.loaddata(params);
}
vis.loaddata = function(params) {
  if (!params) {params = {};}
  d3.csv((params.data || "data.csv") + (params.refresh ? ("#" + Math.random()) : ""), function(error, csv) {
    vis.csv = csv;
    vis.draw(params);
  })
}
vis.draw = function(params) {
  // code for drawing stuff ...
}
})();

Now we’re much closer to how it actually works. After we create our publicly accessible object vis, we create a bunch of local variables. Again, these can be used freely by the functions within our anonymous function, but not outside of it (notably in the console). I’m assuming that the code can be called without passing parameters, in which case within the functions I am testing if params actually exists, failing that I give it an empty object value. This is because down the road, if it is undefined and I try to access its properties, that would cause a reference error. If params has a value, even that of an empty object, if a property is not assigned, its value is “undefined”. So let’s take a look at the first 2 lines of vis.init:

if(!params) {params = {};}
chart = d3.select(params.chart || "#chart");

if params is not passed to vis.init, it gets an empty object value (that’s the first line). So, all of its properties have an undefined value. So the value of (params.chart || “#chart”) will be “#chart”. Likewise, if params is passed to vis.init, but without a chart property, params.chart will also be undefined, and (params.chart || “#chart”) will also be “#chart”. However, if params is passed to vis.init and a chart property is defined (i.e. vis.init({chart: “#mychart”}), then params.chart will be worth “#mychart” and (params.chart || “#chart”) will also be “#mychart”.
So that construct of assigning an empty object value to params then using || is like giving default values which can be overridden.

Within vis.init, we use local variables for things like height, width etc. so we can redefine them with parameters, and they can be easily accessed by anything within the anonymous function, but not outside of it.
I’ve also fleshed out the vis.loaddata function.
Likewise, we use the same construct as above: instead of hardcoding a data file name, we allow it to be overridden by a parameter, but if none is specified, then we can use a default name.
The part with params.refresh is a little trick.
When developing/debugging, if your data is stored in a file, you are going to load that file many times. Soon enough your browser will use the cached version and not reload it each time. That’s great for speed, but not so great if you edit your file as part as your debugging process: changes would be ignored! By adding a hash and a random string of character at the end of the file name, you are effectively telling your browser to get your file from a different url, although it is the same file. What this does is that it will force your browser to reload the file each time. Once you’re happy with the shape of your file, you can stop doing that (by omitting the refresh parameter) and the browser may use a cached version of your file.
In the vis.loaddata function, the most important part is that d3.csv method. As you may remember this is what loads your csv file (and btw if your data is not in csv form, d3 provides other methods to load external files – d3.json, d3.text etc.). How this method works is that the associated function (i.e the bit that goes: function(error, csv) {}) is executed [em]once the file is loaded[/em].
So since loading the file, even from cache, always take some time, what’s inside that function will be executed [em]after[/em] whatever could be written after the d3.csv method. This is why in the loaddata function, nothing is written after the d3.csv method, as there is no reliable way of knowing when that would be executed. The code continues inside the function. At the very end of that function, we call vis.draw, passing parameters along.
If I need to load several files, I would nest the d3.csv functions like this:

d3.csv((params.data || "data.csv"), function(error, csv) {
  // .. do things with this first file
  d3.csv((params.otherfile || "otherfile.csv"), function (error, csv) {
    // .. and then things with that other file. repeat if necessary..

    // the end of the innermost function is when all files are loaded, so this is when we pass control to vis.draw
    vis.draw(params);
  })
})

Another way to do this is using queue.js which I would recommend if the nesting becomes too crazy. For just 2 small files it’s a matter of personal preferences.

It’s difficult to write anything inside the code of vis.draw in a template, because this will have to be overwritten for every project. But here is the general idea though.
vis.draw can be called initially to, well, draw the visualization a first time when nothing exists but an empty svg element. But it can also be called further down the road, if the user presses a button that changes how it should be displayed, etc.
So, if the external context doesn’t change, running vis.draw once more should do nothing. As such, I avoid using constructs like “svg.append(“rect”) ” and instead use “svg.selectAll(“rect”).data(vis.data).enter().append(“rect”)” systematically.
The difference between the two is that using append without enter will add elements unconditionally. Using it after enter would only add new elements if there are new data items.
But what if I need to draw just one element? well, instead of writing “svg.append(“rect”)”, I would write something like “svg.selectAll(“rect.main”).data([{}]).enter().append(“rect”).classed(“main”, 1)”.
Let me explain what’s happening there.
What I want is the function to create one single rectangle if it doesn’t exist already. To differentiate that rectangle from any other possible rectangles I am going to give it a class: “main”. Why a class and not an id if it is unique to my visualization? Well, I may want to have several of these visualizations in my page and ids should really be unique. So I never use ids in selections, to the exception of specifying the div where the svg element will sit.
If there is already one rect element with the class “main”, svg.selectAll(“rect.main”).data([{}]).enter() will return an empty selection and so nothing will happen. No new rect element will be appended. This is great because we can run this as often as we want and what’s supposed not to change will not change.
However, if there is no such rect element, since there is one item in the array that I pass via data, svg.selectAll(“rect.main”).data([{}]).enter().append(“rect”) will create one single rect element. The classed(“main”, 1) at the end will give it the “main” class, so that running that statement again will not create new rectangles. Using [{}] as default, one-item array is a convention, but it’s better than using, say [0] or [“”] because when manipulating our newly-created element, we can add properties to the data element (i.e. d3.selectAll(“rect.main”).attr(“width”, function(d) {d.width = 100; return d.width;}) ) which you couldn’t do if the data elements were not objects. (try this for yourself).

That being said, the general outline of the vis.draw function is so:

  • remove all elements that need to be deleted,
  • create all elements that need to be added, including a bunch of one-off elements that will only be created once (ie legend, gridlines…)
  • select all remaining elements and update them (using transitions as needed).

One last thing: how to call vis.init() in the first place? Well, the call would have to happen in the HTML file.

<script>
var params = {data: "data.csv", width:1400,height:800};
var query = window.location.search.substring(1);

var vars = query.split("&");
vars.forEach(function(v) {
	var p = v.split("=");
	params[p[0]] = p[1]
})
vis.init(params);
</script>

What’s going on there?
First, I initiate the params variable with some values I want to pass in most cases.
Then, the next line is going to look at the url of the page, and more specifically at the search part, that is, whatever happens after the ?. (I use .substring(1) as to not include the “?”).
The idea is that when I would like to pass parameters via the browser, like so: …/vis.html?mode=1&height=500&data=”anotherfile.csv”
The two splits (first by &, then by =) allow to get the parameters passed by url, and add them to params, possibly overriding the existing ones.
Then we pass the resulting params variable to vis.init.

Wihtout further ado, here are the two files in their entirety.

<!DOCTYPE html>
<meta charset="utf-8">
<head>
	<title></title>
	<style>

	</style>
</head>
<body>
<script src="http://d3js.org/d3.v3.min.js">
</script>
<div id="chart"></div>
<script src="template.js"></script>
<script>
var params = {data: "data.csv", width:960,height:500};
var query = window.location.search.substring(1);

var vars = query.split("&");
vars.forEach(function(v) {
	p=v.split("=");
	params[p[0]]=p[1]
})
vis.init(params);
</script>
</body>
</html>
(function() {
	vis={};
	var width,height;
	var chart,svg;
	var defs, style;
	var slider, step, maxStep, running;
	var button;

	vis.init=function(params) {
		if (!params) {params = {}}
		chart = d3.select(params.chart||"#chart"); // placeholder div for svg
		width = params.width || 960;
		height = params.height || 500;
		chart.selectAll("svg")
			.data([{width:width,height:height}]).enter()
			.append("svg");
		svg = d3.select("svg").attr({
			width:function(d) {return d.width},
			height:function(d) {return d.height}
		}); 
		// vis.init can be re-ran to pass different height/width values 
		// to the svg. this doesn't create new svg elements. 

		style = svg.selectAll("style").data([{}]).enter() 
			.append("style")
			.attr("type","text/css"); 
		// this is where we can insert style that will affect the svg directly.

		defs = svg.selectAll("defs").data([{}]).enter()
			.append("defs"); 
		// this is used if it's necessary to define gradients, patterns etc.

		// the following will implement interaction around a slider and a 
		// button. repeat/remove as needed. 
		// note that this code won't cause errors if the corresponding elements 
		// do not exist in the HTML.  
		
		slider = d3.select(params.slider || ".slider");
		
		if (slider[0][0]) {
			maxStep = slider.property("max");
			step = slider.property("value");
			slider.on("change", function() {
				vis.stop(); 
				step = this.value; 
				vis.draw(params);})
			running = params.running || 0; // autorunning off or manually set on
		} else {
			running = -1; // never attempt auto-running
		}
		button = d3.select(params.button || ".button");
		if(button[0][0] && running> -1) {
			button.on("click", function() {
				if (running) {
					vis.stop();
				} else {
					vis.start();
				}
			})
		};
		vis.loaddata(params);
	}
		
	vis.loaddata = function(params) {
		if(!params) {params = {}}
		d3.text(params.style||"style.txt", function (error,txt) {
			// note that execution won't be stopped if a style file isn't found
			style.text(txt); // but if found, it can be embedded in the svg. 
			d3.csv(params.data || "data.csv", function(error,csv) {
				vis.data = csv;
				if(running > 0) {vis.start();} else {vis.draw(params);}
			})
		})
	}
	
	vis.play = function() {
		if(i === maxStep && !running){
			step = -1; 
			vis.stop();
		}
		if(i < maxStep) {
			step = step + 1; 
			running = 1;
			d3.select(".stop").html("Pause").on("click", vis.stop(params));
			slider.property("value",i);
		vis.draw(params);} else {vis.stop();}	
	}

	vis.start = function(params) {
		timer = setInterval(function() {vis.play(params)}, 50);
	}

	vis.stop = function (params) {
		clearInterval(timer);
		running = 0;
		d3.select(".stop").html("Play").on("click", vis.start(params));
	}

	vis.draw = function(params) {
		// make stuff here! 
	}
})();
 

Selections in d3 – the long story

This past week, Scott Murray and I presented a tutorial at Strata on d3 (of all things!)
First things first, you probably want to get Scott’s book on the subject when it’s out. I should be translating it into French eventually.
You’re also welcome to the slides and examples of the tutorial which can be found on https://github.com/alignedleft/strata-d3-tutorial. That include my d3 cheat sheet.

We had done a d3 workshop a few months back at Visweek with Jeff Heer. This time around, we changed our approach: we covered less ground, went at a slower pace, but targeted what is in our opinion the most troublesome aspects of learning d3: selecting, creating and removing elements.

I have learned d3 from deciphering script examples and in the earliest ones one ubiquitous construct was this sequence : select / selectAll / data / enter / append.
It does the work, so like everyone else I’ve copied it and reused very often. It happens to be the most proper way of adding new elements in most cases, but the point is, while learning d3, I (and many people before and after me) have copy/pasted it without understanding it deeply. Though, copy pasting something you don’t understand thoroughly is the best way to get errors you don’t understand any better, and it would prevent you from accessing the rest of the potential of the library. Conversely, once this is cleared, you can be “thinking in d3″ and easily do many things you might have thought impossible before.

We did the tutorial hands-on, live coding most of the time. To follow through, I invite you to create or open an empty page with d3 loaded (such as this one – the link opens a new tab) and then open the “console” or “web developer tools” which allow you to type javascript statements directly, without having to write and load scripts. Here are the shortcuts to the console:

  • Chrome: Ctrl-J (windows), ⌥ ⌘+j (Mac)
  • Firefox: Ctrl+Shift+k (windows), ⌥ ⌘+k (Mac)
  • Safari: Ctrl+Alt+c (windows), ⌥ ⌘+c (Mac)
  • IE9+: F12

To make the best of this tutorial, please type the examples. Some tutorials show you impressive stuff and show you step by step how to do it. That’s not one of them. I’ve sticked to very, very basic and mundane things. We’ll be only manipulating HTML elements such as paragraphs, which I assume you have seen earlier (plot twist: you are reading one at this very moment)
Some of the code snippets don’t work. That’s the idea! I think you can’t progress by merely copying code that works. It’s important that you try out code that looks reasonable but that doesn’t produce the expected result or that causes an error, but then understand why.

Adding simple stuff

Creating elements

Our empty page is, well, empty, so we are going to add stuff.
to create elements, we need the append method in d3, which takes as an argument the type of element that needs to be created, while the html method at the end allow us to specify a text.

so let’s go ahead and type:

d3.append("h1").html("My beautiful text")

and see what happens.

what do we get? and why is that?
In d3, every element which is created cannot appear out of thin air, and must be added to a container. If we don’t specify a container element, we just can’t create anything.
In HTML, most elements can be containers, that is, it’s usually possible to add elements to almost everything. Then again, our template is fairly empty, so we can select the tag and take it from there.

d3.select("body").append("h1").html("My beautiful text")

we’re in business! as long as there is a sensible place to put them, you can create as much stuff as you like. Since we’re on a roll, why won’t we throw in a few paragraphs (p element in HTML):

d3.select("body").append("p").html("Look at me, I'm a paragraph.")
d3.select("body").append("p").html("And I'm another paragraph!")
d3.select("body").append("p").html("Woohoo! number 3 baby")

and lo and behold, all our paragraphs appear in sequence. Simply beautiful.
But wait! paragraphs are containers, too. Why don’t we try to add a span element to one paragraph? For those of you with no HTML knowledge, span elements are like paragraphs, except there is no line break by default at the end.

So let’s try this:

d3.select("p").append("span").html("and I'm a span!")

Before typing it, take a minute to think where you expect it to go.
Then go ahead and type it.

Surprised?
you may have guessed that our new bit of text could go on a line of its own at the end of the document, or at the end of the last paragraph. But instead, it goes at the end of the first paragraph.
Why is that? well, our select method stops the first instance of whatever it tries to find. In our case, since we asked it to find paragraphs – p, it stopped at the first p element it found, and added the span at the end of it (append).

Beyond creating new things

adding new elements to a page programmatically is kind of useful, but if d3 stopped at that you probably wouldn’t be so interested in this tutorial to begin with. You can also modify and manipulate elements. We’ve done that to some extent with the html method. But we can also modify the style of the elements, their attributes and their properties. For the time being, don’t bother too much about the difference between these three things. Style refers to the appearance of elements, attributes, to their structure, and properties, to what can be changed in realtime, like values in a form. But again, let’s not worry about that for now and let’s just follow along. Look at this code snippet:

d3.select("p").style("color","red")

this will select the first paragraph and change its style, so that the text color is changed to red.
But wait! our first paragraph, isn’t that the one with a span at the end of it? What will happen to that bit of text? Well, type the statement to find out.
All the paragraph, including its children (that is, everything added to it, in our case the span) is turned to red.

d3.select("span").style("color","blue")

That singles out our span and writes it in blue. Can this be overturned?

d3.select("p").style("color","red")

That won’t change a thing. Our first paragraph is, in fact, already red. But its child, the span, has a style which overrides that of its parent. To have it behave like the rest, we can remove its style like so:

d3.select("span").style("color",null)

then

d3.select("p").style("color","green")

it will behave like its parent, the paragraph.
But let’s try something else:

d3.select("span").style("color","blue")

we write our span in blue,

d3.select("span").style("color","green")

and now back in green, like its parent.

d3.select("p").style("color","red")

What will happen?
well, the paragraph turns red, but the span doesn’t. It’s still following its specific instruction to be written in green.

That goes to illustrate that children behave like their parents, unless they are given specific instructions.

For HTML elements, we can play with styles, not so much with attributes or properties. One thing worth noting though is that an element can be given a class or an id.

Classes and ids can be used to style elements using a cascading style sheet (CSS). Knowing how CSS works is entirely facultative in learning d3, since d3 by itself can take care of all styling needs. Though, knowing basic CSS is not the most useless of endeavors, and some sensible CSS statements can save a lot of tedious manipulation in d3.
The other use of classes and ids is that they can be used to select elements.

Let’s reload our page so we start from scratch.

d3.select("body").append("p").html("First paragraph");
d3.select("body").append("p").html("Second paragraph").attr("class","p2");
d3.select("body").append("p").html("Third paragraph").attr("id","p3");

without the use of classes and ids, it’s still possible to select and manipulate the 2nd or 3rd instance of an element, but it’s a chore. You have to use pseudo-classes like d3.select(“p:nth-of-type(2)”) to select the 2nd instance of a paragraph, for instance.
Personally, I’d rather avoid this and prefer using simpler statements. With classes and IDs set, we can write instead:

d3.select(".p2").html("I'm classy");
d3.select("#p3").html("I've got ideas");

To select things of a given class, you must use a period before the name of the class. To select things of a certain id, you must use the hash sign.
Here, we are looking for the first element of the p2 class. This happens to be our 2nd paragraph. When you know you will have to manipulate elements which are not easily accessible, you may as well give them classes which will make this easier down the road.

In theory, there should only be one element of a given ID in one page, so I recommend not using them dynamically unless you can be 100% sure that there will not be duplicates. And, in case you were wandering, one element can have several (even many) classes.

Two birds, one stone

Introducing selectAll

So far, we’ve changed properties of one element at a time. The exception was when we changed the colors of both a paragraph and a span, but even then, we were still technically only changing the characteristics of one paragraph, which its child, the span, just happened to inherit.

For a complex document, that can be super tedious, especially since we’ve seen that it’s not easy to retrieve an element which is not the first of its kind.

so let’s go ahead and type:

d3.selectAll("p").style("font-weight","bold");

(for a little variety. I mean, changing text color is so 1994.)
What was that? Everything turned to bold!

Indeed: while the select method returns the first element that matches the clause, selectAll matches them all.
Let’s do more.
We’re going to add a span to our first paragraph.

d3.select("p").append("span")
.html("I'm a rebel child.")
.style("background-color","firebrick")

we’re adding a gratuitous styling command.
Now, let’s change the background color of all the paragraphs.

d3.selectAll("p").style("background-color","aliceblue")

As could be expected, the span doesn’t change its background color, and so it appears differently from its parent (which could be a desired effect – this gives us flexibility).
but what if we wanted to change the background color of everything? can we do better?

d3.selectAll("*").style("background-color","whitesmoke")

(quite fitting in these times of papal conclave)

Well – everything gets a background color of “white smoke” (which is a fine background color btw.). Including the “body” element – that is, everything on the page!
selectAll(“*”) matches everything. With it, you can grab all the children, their children etc. (“descendants”. I know…) of a selection, or, if used directly like so: d3.selectAll(“*”), everything on the page.
So we’ve seen we can select moaar. But can we be finer? Can we select the paragraphs and the spans only, without touching the rest?

we sure can!

d3.selectAll("p, span").style("background-color","lawngreen")

The outcome of that one statement probably won’t make it to our web design portfolio, but it does the trick: you can select as much as you like, or as little as you like.

Nested selections

To illustrate the next situation, let’s add a span to our document.

d3.select("body").append("span").html("select me if you can")

Well, just like there is a way to select directly the 2nd paragraph using pseudo classes, there’s also a (complicated) way to select directly that last span (namely: selectAll(“span:not(p)”) )
there’s also a simpler way which is what we’re interested in.
let’s suppose we want to turn it to bold:
we can just do

d3.selectAll("span").style("font-weight","bold");

then change the first one:

d3.select("p").select("span").style("font-weight",null);

Admittedly, the complicated way is more compact. But conceptually, the “simple” way is easier to follow: we can do a selection, and within that selection perform a newer selection, and so on and so forth. That way, we can get away with just using super simple selectors, as opposed to master the intricacies of CSS3 syntax. Do it for the people who will read your source code :)

At this point:

  • You know how to dynamically create content. Pretty cool!
  • More! you can dynamically change every property of every element of the page. woot!
  • Bonus! you’re equipped with tactics to easily reach any element you want to change.

You should also have a good grasp of d3.select, d3.selectAll and the difference between the two.
what more could you possibly want? Well, since this is about data visualization, how about a way to tie our elements to data? This is what d3 is really about.

Putting the data in data visualization

Introducing data: passing values to many elements at once

So far, we’ve entered “hard coded” values for all of our variables. That’s fine, but we can’t really set our elements one by one. I mean, we could, but it’s no way to “industrialize” the way elements are created.
Fortunately, d3 provides. Its more interesting characteristic is the ability to “bind” elements with data.

If you’ve followed the instructions step by step, you should have 3 paragraphs in the page. Plus a span afterwards, but whatever.
Let’s introduce the data method. This will match an array of values to a selection of elements in the page. Let’s go:

var fs=["10px","20px","30px"];
d3.selectAll("p").data(fs).style("font-size",function(d) {return d;})

wow wow wow what just happened?
First, we create an array of values which we intelligently call fs (for font size).
Then, right after the selectAll(“p”) which gathers a selection of elements (3 “p” elements to be exact), we specify a dataset using the data method.
It just happens that our dataset has just the same number of items as our selection of elements!

finally, we use style, like we used to, with a twist: instead of providing one fixed value, which would affect our 3 p elements in the same way, we specify a function.
This function will parse the dataset, and for each element, it will return the result of an operation in the corresponding data point: the result of the function on the first item for the first p element, the result on the 2nd item for our 2nd paragraph, and lastly the result on the last item for our last paragraph.
We write the function with an argument: d. What is d? it’s nothing but a convention. We can call it anything. d is standard fare in d3 code because that’s the writing style of Mike Bostock, the author of the framework and of many of its examples.
This function is nothing special, it returns the element itself, so we are passing “10px” for the font-size of our first paragraph, and so on and so forth (20px, 30px).
As an aside, we can use the String function, which converts any element into a string, instead of writing function(d) {return d;}. So:

d3.selectAll("p").data(fs).style("font-size",String)

would also work and is shorter to write.

Let’s recap what just happened here, because this is important.
We want to apply a dynamic transformation to a bunch of existing elements, as opposed to finding a way to select each individual element, and passing it a hard-coded value.
What’s more, we want to apply a transformation of the same nature, but of a different magnitude, on each of these items.

How to proceed?
well, first we create an array of values. That’s our fs boy over there.

var fs=["10px","20px","30px"];

Then, we will first select all of the elements we want to modify, then we’ll tie our dataset to that selection. This is what selectAll, then data does.

var selection=d3.selectAll("p").data(fs);

By the way, I’ve stored the result of the selectAll then data in a variable. In the original example, I just “chained” the methods, that is, I followed each method by a period and another one. The two syntaxes are equivalent. Chaining works, because each of these methods returns a value which is itself a selection on which further operations can be done. This syntax works well through most of d3 with some exceptions which will be duly noted.

Then, we are going to change the style of the selection, using a function on our data.

selection.style("font-size",function(d) {return d;})

(or

selection.style("font-size",String)

That function will run on each value of our dataset, and return one result per value, which will be passed to all elements in sequence.

At this stage you may have two questions:

  • Can we use more sophisticated functions, because this one is kind of meh?
  • What happens if there is not the same number of items in the dataset and of elements?

The second question is actually more complicated than the first, but we’ll answer it in painstaking detail.
So let’s take care of the question on functions first.
Yes, obviously, we can use the function not just to return the element, but to do any kind of calculation that a language such as javascript is capable of, which is nearly everything.
To illustrate that, here are some variations of our initial code which will return the same result, but with a different form.

var fs=[10,20,30]; // no more px
d3.selectAll("p").data(fs).style("font-size",function(d) {return d+"px";})

Here, instead of returning just the element, we append “px” at its end. Sadly, style(“font-size”,10) doesn’t work, but style(“font-size”,10+”px”) – which is the same as style(“font-size”,”10px”) is valid.

Here is yet another way.

d3.selectAll("p").style("font-size",function(d,i) {return 10*(i+1)+"px";})

function(d,i) ? what is this devilry?
Here, i (or anything we want to call it, as long as it’s the 2nd argument of this function) represents the order of the element in the selection, so the first gets a 0, the second a 1, etc. (well, in our example it goes to 3 elements, so the last one gets a 2).
This may be a bit abstract to say here, but even if we haven’t passed data, this would still work – i represent the order of the element, not the data item. so, if no data had been passed, within this function call, d would be undefined, but i would still be equal to 0,1,2, …

The answer to the second question is the last great mystery of d3. Once you get this, you’re golden.

Creating or removing the right number of elements depending on data

Before we get further, let’s quickly introduce append’s reckless cousin, remove(). Writing remove at the end of a selection deletes all the corresponding elements from the document object model.
so,

d3.selectAll("p").remove()

would remove our 3 paragraphs. Let’s do it and get rid of our paragraphs.
Actually, let’s do

d3.select("body").selectAll("*").remove()

and remove everything below the body.

Now, earlier, we were alluding to what could happen if we didn’t have the same number of elements as of items in our dataset.

That means that we should be able to do the following:

  • If there are fewer elements than items in a dataset, create the missing elements
  • If there are fewer elements than items in a dataset, disregard the extra data items
  • If there are more elements than items in a dataset, remove the extra elements
  • If there are more elements than items in a dataset, don’t change the extra elements/li>
  • As data are updated, keep some elements, remove some, add some

Why would we want to do all of this?
The first case is the most common. When we start a data visualization script, chances are that there are no elements yet but there is data, so you’ll want to add elements based on the data.
Then, if you have interaction or animation, your dataset may be updated, and depending on what you intend to do you may just want to update the existing elements, create new ones, remove old ones, etc. That’s when you may want to do 2, 3 or 4.
The last (5th case) is more complicated, but don’t worry, we’ve got you covered.

Right now, we should have 0 p elements on our page (and if for some reason this is not the case, feel free to reload it).

let’s create a variable like so:

var text=["first paragraph","second paragraph","third paragraph"];

somewhat uninspired, I know, but let’s keep typing to a minimum, if you want to go all lyrical please go ahead.

We are smack in case 1: we’d like to create 3 paragraphs, we have 3 items in our dataset, but 0 elements yet.
Here’s what we’ll type:

d3.select("body").selectAll("p").data(text).enter().append("p").html(String)

A-ha! we meet again, select selectAll data enter append.
After all we’ve done, select selectAll should make some sense, even though, at this stage, this selection returns 0 p elements. There are none yet.
Then we pass data as we’ve done before. Note that there are 3 items in our dataset.

Then, we use the enter() statement. What it does is that it prepares one new element for every unmatched data item. We’ll expand a bit later on the true meaning of unmatched, but for the time being, let’s focus on the difference. We have 0 elements, but 3 data items. 3 – 0 = 3, so the enter() selection will prepare 3 new elements.
What does prepare means? the elements are not created yet at this stage, but they will with the next command. Right after enter(), think of what’s created as placeholders for future element (Scott’s vocabulary), or buds that will eventually blossom into full-fledge elements (mine).
After enter(), we specify an append(“p”) command. Previously, when we had used the append method, we created one element at a time. But in this case, we are going to create as many as there are placeholders returned by enter(). So, in our case, 3.
You may legitimately wonder why we needed a select statement to begin with – after all, enter() works on the difference between selectAll and data. But when we are going to append elements, we will need to create them somewhere, to build them upon a container. This is what the first select does. Omit it, and you’ll have an error, because the system will be asked to create something without knowing where.
The final method, html, will populate our paragraphs with text. The String function, which we have already seen, simply returns the content of each item in our dataset.

We’re using select > selectAll > data > enter > append, but hopefully you will see why (and if you don’t, hang on to the end of the article, and feel free to ask questions).

But let’s recap once more. Actually, let’s see the many ways to get this wrong (or, surprisingly, right)

d3.selectAll("p").data(text).enter().append("p").html(String)

We’ve alluded to that: without a container to put them in, p elements can’t be created. This will result in a DOM error.

d3.select("body").selectAll("p").data(text).append("p").html(String)

No enter statement. After the selectAll, the selection has 0 items. This doesn’t change after the data method. As such, append creates 0 new elements, and nothing changes in the document. (but no error though)

d3.select("body").data(text).selectAll("p").enter().append("p").html(String)

In many cases in d3, it’s ok to switch the order of chained methods, but that’s not true here. selectAll must come before data. We bind data to elements. The other way round would have made sense, but that’s the way it is. First selectAll, then data. Here, we get an error, because enter() can’t be fired directly from selectAll.

d3.select("body").selectAll("wootwoot")
.data(text).enter().append("p").html(String)

This actually works. Why?
There are actually 0 elements of type “wootwoot” in our document, which may or may not surprise you. There are still 3 items in the dataset, so enter() returns space for 3 new elements. the next append subsequently creates 3 p elements, which are populated by the html method.
It usually makes more sense to use the same selector in the selectAll and the append methods, but that’s not always the case. Sometimes, you will be selecting elements of a specific class, but in an append method, you have to specify the name of an element, not any selector. So you’d go

d3.select("body").selectAll(".myClass")
.data(text).enter().append("p").html(String).attr("class","myClass")

Now that we’ve seen a few variations on the subject, here is a really cool use of enter. Check this out:

d3.select("body").selectAll("h1").data([{}]).enter().insert("h1").html("My title")

ok there are 3 things here worth mentioning. 2 are just for show, though it doesn’t hurt to know them, but the 3rd one is really neat and useful.
In data, we’ve passed: [{}]. This is an array of one object which is empty. There are two interesting things with that construct, one is that there’s only one element, the other one is that it’s an object. When you pass objects, the functions you run on them (like in the attr or style methods) can be used to add properties to them or change them. If that doesn’t make sense yet, just accept for now that it gives you more flexibility than using, say, [0].
We’ve used insert instead of append. What this means is that we’re adding things before the first child of our container, not at the end (ie after the last child). In other words, our h1 (a title) will go at the top of the body element – fitting.

But what’s really interesting is what would happen if you were to run that statement again – nothing. try it. See?
Why is that? Well, on your first go, at a point where there are no h1 elements yet, it works the standard way – you do a selectAll that returns nothing, you bind a dataset with more elements, then enter prepares space for the unmatched elements – 1 in our case – and then append creates that element. You may notice that the html part doesn’t use the data.
When you run it again, the selectAll finds one h1 element, there’s still one item in the dataset, so enter won’t find any unmatched element, so the subsequent append is ignored.

So, you can run this kind of thing in a loop safely, it will only do what it’s supposed to do on the first go, it will be ignored afterwards. Don’t be afraid to use this construct for all the unique parts of your visualization, so you won’t have to worry about creating them multiple times.

Other cases of mismatch between data items and elements

All right, so now we have 3 p elements and 3 items in our dataset.
What happens if we do this:

text2=["hello world"]
d3.selectAll("p").data(text2).html(String)

?

There is now one item in the data set, versus 3 p elements. Try to make a guess before you type this in. At the tutorial, the audience made a few reasonable guesses, namely: the last 2 paragraphs will be removed, only “hello world” will remain. Or: all paragraphs will be changed to “hello world”.
Either could happen if d3 was trying to be smart and guess your intent. Fortunately, d3 is no excel here and behaves consistently even if that means extra work for you. When you do that (and please try this now) what happens is that the first paragraph of text is changed and the other two are untouched.

We are in the case, change the matched elements, ignore the others.

By the way, by now you should be able to guess what would have happened if there had been an enter() right after the data. Do I hear… nothing? almost! There would be no unmatched data element, so enter() would not return anything. Besides, enter() would require an append afterwards to make anything. This is why you’ll get an error: html can’t work directly after enter(). you would need an append.

Now what if we want to remove the extra 2 elements? This is where the exit() method comes into play.
exit() is pretty much to enter() what remove() is to append(). Kind of.

let’s see how this work by example.

let’s recreate our 3 p paragraphs just in case:

d3.selectAll("p").remove();
d3.select("body").selectAll("p").data(text).enter().append("p").html(String);

Now we pass the new dataset:

d3.selectAll("p").data(text2).html(String)

– remember that only the first paragraph has changed, the other two are untouched.
Now, while all the items in the dataset are matched with elements, there are elements which are not matched with an item in the dataset: the last two. This is where exit() comes into play. exit() will select those two paragraphs, so they can be manipulated. Typically, what happens then is a remove(), but you could think of other options.

d3.selectAll("p").data(text2).exit().style("color","red");

That will flag them instead of removing them.
But typically, you do:

d3.selectAll("p").data(text2).exit().remove();

.

note that even though you have already matched a one item dataset to that selection, to use exit(), you will need to use data before. selectAll(“p”).exit() won’t work. You’ll have to re-specify the data match.

So that takes care of the case when you want to remove extraneous data items.
This leaves us with only one simple case: where you have more items in your dataset than you have elements and you don’t want to create elements for the extra data items.
That’s the simplest syntax, really.

Here, for instance, we have only one paragraph left, but there are 3 items in the text variable.
so let’s do:

d3.selectAll("p").data(text).html(String)

(no enter, no exit, no append).
The paragraph text will now come from the new dataset (from its first item to be precise), no extra paragraphs will be created, none will be deleted.

Data joins

the last case (pass a new dataset, create new elements as needed, make some elements stay and make some elements go) requires more complexity and actually I won’t cover it in detail here, instead I will explain the principle and refer you to this tutorial on object constancy by Mike Bostock.
In the general case, when you try to match your dataset to your elements, you count them and deal with the difference. So you have 5 data items and 3 elements: you can make 2 extra elements appear by using enter. With the concept of data joins, you can assign precisely each data item to one given element, so the first data item doesn’t have to be that of the first element, etc. Well, the first time it will be, and each element will receive a key, a unique identifier from the dataset. If the dataset is subsequently updated, the element will only be matched if there is an item in the dataset with the same key. Else, it will be found by an exit() method.

And that’s the general gist of it.
At Strata, we went further – we discussed interaction and transition, but that is downward trivial once you have understood – and by that, really understood, with all the implications and nuances – the selections.

 

d3 tutorial at visWeek 2012

Jeff Heer, Scott Murray and myself have done a d3 tutorial at visWeek 2012. You probably gathered that from the title of the post.

Here is a link to all the slides and code examples that we have presented:

d3 tutorial

For the purpose of the tutorial I have compiled a d3 cheat sheet, on 4 pages it groups some of the most common d3 functions. When I was learning d3 my number one problem was figuring out which property should be set using .attr, and which required .style. And also: which svg element support which property? All of this is addressed in the cheat sheet. It’s part of the link above, but if you want it directly without downloading a 13Mb file, here it is:

d3 cheat sheet

 

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. (http://www.sublimetext.com/2). 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 d3js.org 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>
<html>
 <head>
   <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">
 </head>
 <body>
   <div id="chart">
   </div>
   <script type="text/javascript" src="script.js"></script>
 </body>
</html>

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=”http://d3js.org/d3.v2.min.js”.

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:


<style>

... // my style definitions

</style>

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:

<script>

// all our javascript instructions

</script>

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,
svg=d3.select("#chart")
.append("svg")
.attr("width",w)
.attr("height",h);

var text=svg
.append("text")
.text("hello world")
.attr("y",50);

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 d3.select 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.


text:{font-size:36px;}

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!

 

animations and transitions

That post originally appeared on visual.ly, I’m reproducing it here for clarity and ease of retrieval

In interactive visualisation, there is the word reactive. Well, maybe not literally, but close enough.
The fact is that reactivity, or the propension of a visualisation to respond to user actions, can really help engage the user in a visualisation, and help them understand its results. Both of which are usually good things. And how can this reactivity be achieved? Through animations.

So I’ll go ahead and state that animation, if done right, can make any interactive data visualization better..
How is that?

  • When coupled with interaction, it’s a very useful way to give feedback to the user. What has changed since their last command? If what’s on screen animates from one state to another, it’s obvious, it stands out and it makes sense. Or, when showing any form of real-time data, animation is pretty much required.
  • Animation can bring focus on the important things as a chart loads. Our vision is very sensitive to movement, so using these introduction transitions sensibly helps a lot to ease the effort required to get the right information off a chart.
    Compare these two charts:


    Which is better at getting the viewer’s attention on the last bar?
    [side note on examples: they all use the same model. Click on the button to start an animation. If there is nothing on the chart, clicking the button will make something appear.]
  • animation works well with metaphors, like growing, expanding, moving, dwindling, etc. so it can really enhance the expressiveness of a visualization that tries to convey any of these ideas (those and many others)

That’s said, animation can definitely ruin your visualization, too. Here are three general problems.

  • Animation is very prominent. That can be good to call attention to a specific, unambiguous part of your chart. But what happens when there is too much animation? without other cues it gets difficult for a viewer to determine where to focus their attention.
  • Animation across many states (like a video of animated data) make them difficult to compare to one another, as opposed to showing still images of various states side by side. (see for more on this.
  • If the animation is not continuous, if the chart is somehow wiped out during it, this caused change blindness which pretty much negates any benefit you may have hoped to reap from animation.
    Look at this example.

    When animated, the line goes through a blank state which makes is close to impossible to track changes between the original and final state. The only way to detect change is to focus on one given point and memorize its original position, but this is very ineffective.

Now how to do it?

So we’ve seen how animation is helpful in data visualization. Now, let’s do it!
For this purpose, let’s use d3. d3 has many, many possibilities when it comes to data animation which are relatively painless to implement.

The principle

If you know how to draw in d3, you almost know how to animate. (and if you don’t know yet, Alignedleft has a splendid collection of tutorials to get you started, and the d3 site lists more including some by yours truly.)
Animations are called transitions in d3 for a reason. A technical definition of animation can be that over a certain lapse of time, one or more characteristics of an object would transition from one value to another.

And what do we mean by characteristics? Well, just about anything that can be expressed numerically.

A few examples of transitions


Unsuprisingly, when you update the position of an item smoothly over time, it moves. In svg, position is determined for most shapes, such as our blue rectangle here, by the x and y attributes, which correspond to the top-left corner of the shape. For circles, you use cx and cy, or the coordinates of the center. For paths, such as our red triangle, you actually specify the position of all of the points in the “d” attribute.


Likewise, when you change size, your object grows (or shrinks!). You can use width and height for shapes like rectangles, or r for circles.


Color is really a numerical attribute too, and it’s indeed possible (and very useful) to transition from one color to another. In svg, color is a style attribute that is defined by fill or stroke.


Not unlike color, it’s very useful to be able to vary opacity. When opacity is set to 0, the corresponding object is completely transparent. So transitioning on opacity is very useful to make objects fade in or out.

How this is done

Now that we’ve seen what transitions can do, let’s see how to code this in d3.
Let’s go back to our first example. In fact, let’s make it even simpler.


To create a square like this in d3, we would write something like:

var mySquare=svg.append("rect")
  .attr("x",60)
  .attr("y",60)
  .attr("width",60)
  .attr("height",60);

4 attributes. Simple enough.
so if we want to make it move to the right, we are going to update the x attribute. That’s how we do it:

mySquare
  .transition()
  .attr("x",320);

It’s that simple: use the transition method, then specify all you want to see changed just as if you were creating a new item. And using that one principle, we can easily reproduce any of the above examples.

mySquare
  .transition()
  .attr("width",120); // will make it bigger

mySquare
  .style("fill","white") // if the fill is originally left blank and comes
                         //  from a style sheet, it will start as black 
  .transition()
  .style("fill","blue");

mySquare
  .transition()
  .style("opacity",0);

Now, in our simple examples, this is not exactly what happens. The transitions occur after an event, namely, when the user clicks on the button. And indeed, transitions are most useful when linked to events and interaction. But this doesn’t add a whole new layer of complexity.
We can just write:

button.on("click", function() {
  mySquare.transition().attr("x",320);
})

And now, our animation only starts when the button is clicked. Obviously, since the transition is within a function, we could even determine where the square should go programmatically, but let’s keep it simple for the examples.

Animation 102

So far, we’ve seen how we can do simple animations in d3 and even throw in a little interaction. We’ve seen that it’s really as simple as creating elements in the same place. But here are some good news. Transitions in d3 are extremely versatile and can be customized with a lot of finesse without getting overly complex to write. It’s more a matter of knowing what to do.


After using the transition() method, it’s possible to specify a value for duration and delay. Duration is the number of milliseconds the transition will last, while delay is the number of milliseconds the system will wait before launching it.
The syntax is:

mySquare.transition()
  .attr("x",320)
  .duration(1000) // this is 1s
  .delay(100)     // this is 0.1s

The default is a 250ms duration, and no delay.
I find 250ms to be a bit harsh. In most cases, transitions should be noticeable, so I oftentimes find myself increasing the duration to 500 or 1000. But unless there is a very good reason for that, durations should not be too long. If you use them to support your data, you don’t want the transition to take center stage by having them take several seconds.
Consider the following two examples (which you’ll have to start with the button)


Isn’t the second one simply atrocious? You may find hard to believe that it only wasted 25 seconds of your time.


Easing is the technical name of the actual function that turns time into attribute changes. From the previous examples, you may have noticed that the values change slowly first, and then faster, then slowly at the end? Well, it turns out that you can use different functions to get different results. In my practice, I’ve only seen the use for the 3 displayed here although there are many others. And yes, you can write your own, although we are not going to cover this here.
The syntax is similar to the above:

mysquare.transition()
  .attr("x",320)
  .ease("elastic")

(and by the way, the order in which you change attributes or specify animation parameters has no effect, so feel free to use .ease first then .attr).


For path objects, through transitions you can update the position of each point. This allows you to effectively turn one shape into another.
This can be especially interesting for line charts (or any chart which is a path)

Like this, if the values that you are plotting change, you can spot these changes very efficiently. If, instead, you just erase your chart redraw your data if would be very difficult to spot where the data has changed.
For both of these examples, the “d” attribute of the path is updated (so they are not intrinsically different from the simplest example).


Sometimes (and actually: often), you want to fire a transition right after another transition.
But in case you were wondering, the following doesn’t work:

mysquare.transition()
  .attr("x",320);
mysquare.transition()
  .attr("y",200);

You may think that this will move the square right, then down. But no: it will start to move the square right, then fire the second transition which will move it down. Since they have the same duration and no delay, what will happen is that only the second will have a visible effect.
If the second transition had a delay, smaller than the first transition’s duration, the first one will be in effect for a while until the delay expires. Then, the second transition will take over. However, chances are you don’t want to do that, because how much of the first transition will have been accomplished depends on the users machine, browser etc. and is therefore unpredictable.
So how about giving the second transition a delay which corresponds exactly to the duration of the first one? This will usually work, however, the delays and durations are not extremely accurate. Firing the transition proper takes a certain time (which is roughly 15ms on my machine and which may vary) so it is difficult to chain two transitions very precisely this way.
In more complex programs than our simplistic examples, sometimes, several events try to trigger transitions on the same object. When this happens, the first transaction is fired, and runs its course unless another transition starts. That second transition would interrupt, then replace the first one. What this means is that the attributes that were in the process of being changed by the first transition will remain as they were when the second transition starts, somewhere between their start and target value.
If you want to make sure that all your transitions update their attribute up to the value they are supposed to reach, you may want to re-specify the attributes of the first transition in subsequent ones, like so:

mysquare.transition()
  .attr("x",320);
mysquare.transition()
  .delay(250)
  .attr("y",200)
  .attr("x",320); // even if the first transition doesn't complete, 
                  // this one will and will update x to 320.

There is a more certain way to chain two transitions. With the following syntax, another event will start exactly at the end of a transition. That other event can be another transition (which is the case in the above example).

mysquare
 .transition() 

 ...

 .each("end", function() { ... });

here, what’s in the callback function on the last line, introduced by .each(“end”, will be fired exactly as the transition ends.

What can be done then? Here are 3 common scenarios.


(btw, if you’re wondering what’s the difference between this and the previous example, there is none – it’s just to save you some scrolling).
One possibility is to launch another transition on the same item. Here, the square moves right, then down.
Here’s how it’s done:

mysquare
  .transition()
  .attr("x",320)
  .each("end",function() { // as seen above
    d3.select(this).       // this is the object 
      transition()         // a new transition!
        .attr("y",180);    // we could have had another
                           // .each("end" construct here.
   });

Another possibility is to delete the object after the transition has run its course. This is super useful, especially when you are creating a lot of temporary objects. An interesting combo is when you decrease opacity all the way to 0, making it invisible, then using remove() if you don’t need it anymore.

mysquare
  .transition()
  .attr("x",320)
  .each("end",function() { 
    d3.select(this).       // so far, as above
      remove();            // we delete the object instead 
   });

Finally, we can create a new object. That can be a nice way to add a special effect. Here’s an example:


Here, at the end of the transition, a circle is created, a transition is started on that circle, which decreases opacity to 0, then the circle is removed.


And here is a last example with several effects combined.

Going further

Believe it or not, we barely scratched the surface of what can be achieved with animations in d3.
There are two other uses of transition that we haven’t seen because they are slightly more complex, so I’ll just mention them here.
Up to now, we have always seen transitions based on the properties of one specific object. We make the x property of that one square vary from what it was to 200.
Sometimes, though, you want many parts of your visualization to be updated according to the changes in one variable.
That is possible, too, by using the .tween and .interpolate methods. All of this is explained in the d3 documentation.
Another possibility is the use of the d3 timer method, which allows to call a function repeatedly, which can also be used to create animation.

The point I was hoping to make was that it’s possible to do a lot with relatively simple code and technique if you know what you are trying to do. Especially, chaining transitions, particularly when adding and removing objects when appropriate, goes a long way in creating powerful effects.