This post ends with an infographic, detailing a timeline of TV watching spanning six months:

It begins in mid-September 2009, where I found myself armed with a nagging curiosity about the show Lost – a show I had never once watched. For various reasons, the idea of the show intrigued me greatly. Somehow, in all the many Lost conversations I had overheard in years prior, I strangely managed to remember practically nothing in the realm of spoilers. Time travel, something called the smoke monster, and that someone died in season 3: these were the only items of baggage I had accumulated. Not bad.

The final season was only months away from airing. I knew that I had better take action soon if I ever wanted to take part in the experience of watching it live. It was the discovery that the whole show was available on Netflix’s Watch Instantly which spurred me to action. One rainy Saturday later, I was five episodes in to Season 1 and not looking back. In the end, it took me quite a big longer than expected to get fully caught up – but I was able to do so less than two weeks after the premiere episode of Season 6. And tonight, February 17, 2009 – I shall tune in live to watch LOST, fully caught up, for the first time.

On top of Roger Federer winning his 15th major title at 2009 Wimbledon, I wanted to explore the front-running champions who have walked before him. As a result, I’ve put together an visualization of these champions over time.

I should note that in doing my preparation for this infographic, I decided to extend far beyond the start of the “open era” (1968) entirely for the amount of data. A post-1968 graph would give me much less to work with.

Otherwise I feel I should pay respect to the accomplishments of Rod Laver and Bjorn Borg: both of these men had utterly remarkable runs (both earning 11 titles in less than a decade), but neither becoming the true front-runner within the constraints of this graph.

I hope you enjoy! If you find this interesting, also be sure to check out this comparison of Federer and Sampras in their race to 14 titles. Meanwhile, here’s to Federer’s hard work… let’s see how far he can take it.

In May 2009 I ran a 5.8 mile leg as part of a relay team in the Frederick Marathon. This graph shows the training progress in the weeks leading up to the race (including the unexpected slowdown during weeks 6-8).

I plan on using a similar technique to track my training in preparation for the Army 10 Miler in October, 2009. In that case, I plan on recording the time of each run as well, which will surely bump up the possibilities in how to present the graph.

With Roger Federer’s recent win of the 2009 French Open, he is now tied with Pete Sampras for holding the most Grand Slam titles — fourteen.  Although the two athletes have arrived at the same destination, how do their respective journeys compare with one another?  With this question fueling my curiosity, I set out to create a rich visualization of the data to add some depth to this story.

The final product is available as follows.  For additional notes about the techniques used to create these graphs, keep on reading.

Here are some tips & techniques I employed to put this together.

Have Your Data Ready

Before diving into Illustrator (or whatever your tool of choice may be), spend the necessary time finding all of the data you will need for your graph.  Go the extra mile to arrange and label everything properly — you may return to the data at a much later time and will be glad you did yourself the favor.  Aside from reaping the benefits of good organization, this step is additionally helpful in keeping the grunt work of data-fetching separate from the creative requirements of the task.

Start with the Simplest Graph Possible

An elegant, attractive graph is seldom created from scratch.  There are usually a number of tried & tested variations that must be wrestled with before arriving at the final product.  With this in mind, a good first step is creating some bare-bones, stripped down graphs to get bird’s eye view of the data.  This phase is all about finding the approach that will best server your original vision.  Sketches work great in this stage.

What is the story you want to tell with your data?  This is an important question to keep in mind, as different visualization approaches will yield different results.  Play around with things.  See what looks good as well as which data comparisons are intuitive and interesting.  Seek feedback from friends or colleagues who might offer a valuable opinion.

Using the Grid

Before long, it will be time to create your final, finished product.  At this stage, the very most important thing you can do to keep things looking straight and orderly is use Illustrator’s grid feature.  You’ll want to make grids visible (CTRL/CMD + “), as well as enable “Snap to Grid” (CTRL/CMD + SHIFT + “).  Additionally, you may want to go into Illustrator’s preferences to customize the grid spacing and subdivision width (which you can modify at any point).

Another useful tip to keep in mind when using grids extensively is enabling Overprint Preview (CTRL/CMD + ALT + SHIFT + Y).  This will have the gridlines appear on top of all objects & paths, allowing you to eliminate any guessing that might otherwise be required in keeping things properly arranged.

Using Layers Wisely

Keeping your objects arranged in layers is a huge time-saver when dealing with moderately complex projects in Illustrator.  This was especially true in my case of creating four separate graphs, each of which contained separate groups of objects.  For example: if I wanted to modify the color of the Roger Federer graph plots, I’d only need to target the layer “Federer” and all plots (on each graph) would become active.

Layers can also be locked, combined, or temporarily hidden to make document management easier.

Go Forth and Visualize

And that’s it!  Combined with a simple bit of color and typography, you can transform any crude visualization into an attractive graph.  Keep in mind that data in itself can be rather inert; though when arranged in a conscientious manner it can tell an interesting story.  Hopefully the techniques above can be of use in recreating your own graphs of a similar nature.