To fly is to be frustrated. If you've been traveling for long, you no doubt have your opinions about what airlines and airports are the biggest sources of suffering. Whether it is weather delays, getting stuck on the tarmac due to air traffic, maintenance problems, or missing a connection, it all feels outside of your control.

But a little knowledge can help. The Bureau of Transportations has maintained a giant database of air traffic information for decades of flights -- point of origin, flight times, flight delays, type of delay, etc. It is 72 gigabytes of data...just the type of data that needs some visualization. JuiceKit to the rescue.

We've put together a pair of visualizations that can make this data accessible to your average non-data-monkey traveler:

• Treemap uses size to represent the number of flights by airline and by point of origin. The color is used to show delay time -- we've got all sorts of delay metrics, each of which tells an interesting story.

• US map uses size to represent the number of flights and the color to display delay time. Filtering by airline yields additional details.

There are some interesting insights that pop-out when you build a visualization this data.

• The different airline strategies are quickly apparent in the treemap. Hub-and-spoke airlines (Delta, Continental) have one or two dominant boxes (origin location), surrounded by lots of small locations. A point-to-point airline like Southwest looks entirely different with lots of similarly sized boxes.

• Flipping between delay types uncovers some unexpected results. For example, you might expect weather delays to be heavily correlated by airport. The data shows something a little different: Comair appears to be abnormally impacted by weather delays -- as if a dark cloud chases around their airplanes. While Comair might be overstating weather delay data to prevent paying for meal vouchers, a more reasonable Wikipedia investigation suggests that Comair flies smaller weather-susceptible Bombardier airplanes.

A few details about this demo for our technical audience:

For those of you following JuiceKit development, this is a demo of some of the newer features available in our open source Juicekit 1.2 distribution, and some of the features that will be coming to the 1.3 version. Treemap styling is now elegant, crisp, and allows for white borders, fixing a couple rendering bugs. There is a new tree-level depth feature that can make it easier to navigate treemaps with lots of layers. The airports map demonstrates a geographic layout built using GeoLayout JuiceKit and Flare components. A major improvement demonstrated by the airline-selector dropdown is the ability to keep nodes consistent between data reloads. This allows us to animate the nodes even though they are generated by our new LiveQuery component.

Are you ready to explore the Google Analytics API?

At Juice, we were very excited about the public release of the Google Analytics Data Export API. Our product Concentrate has been running on a hackish home-brew Google Analytics export tool since its release last November, and we were happy to be able to relaunch as a Customer Example of the Google Analytics Data Export API.

Today, we are releasing a new, free tool called Vasco de GAPI. Vasco is a web-based tool for exploring the API, for downloading complex slices of data using the API, and to even automatically generate code that will allow coders easy replication of the API calls in question. Instead of describing it in more detail, I am just going to demo it.

I am going to start with a relatively rare but curious functionality of Google Analytics. I keep track of who wrote each blog using a Google Analytics user-defined setting that is set to the author's name for each specific blog post. Slicing our blog by author can be cool for me as an employee so that I can brag during my yearly review about how many visitors I bring in or what natural search visits we get for free as a result of my posting. For the demo, I'm going to discover the natural keywords that bring traffic to my blogposts on the website.

Let's get started.

The first step is to authenticate using Google's OAuth system.

I select ga:keyword as a dimension.

ga:pageviews is the metric I am interested in. The results will automatically get sorted by the first metric, so I do not need to explicitly specify a sort value.

I set ga:userDefinedValue as a filter, and filter it to saluryasev, and select this last week as a reference point.

Here is the list of parameters that Vasco de GAPI is passing to google.

What are my results?

It turns out that of all my posts, the Google Trends API that I put out about a year ago drives the most natural traffic to our site. Hopefully, this will change with a few more blog posts, but this is still rather interesting data. I could target that specific audience with something Google-trendy. On an unrelated note, a slap to my face was that Zach's name sent fifteen users to my blogposts. Go figure. Sixteen users searched on my last name, and were probably looking for my more popular father.

To get at the rest of the data, I can click the download link at the bottom of the page or, for developers, another link downloads working code that will replicate this exact pull.

Vasco runs using an open source Python gdata wrapper for the API that can be downloaded here. This wrapper is powerful, and I will write another blogpost about it next week. It is plugged into the Google gdata module, and as such allows all forms of authentication available to gdata users, including OAuth, AuthSub, and clientside.

Hopefully, Vasco de GAPI can help all other potential explorers sail smoothly through the API. When it comes to data, Google is just an great company. They have had powerful APIs for most of their major services for years, and while the Analytics API is a latecomer, it actually is more powerful than the analytics interface itself. This sort of openness is something to be envied by all other analytics and web companies in the market.

By the way, please let me know if the explorer theme works well. It was a lot of fun working on a project with a slightly esoteric approach.

There is new life in the tool that shows change in Google Analytics. A year after releasing our Greasemonkey script, we are pleased to release an updated version of the Enhanced Google Analytics script as a free Firefox Plugin. For those already using the older Greasemonkey script, you can skip ahead to the What's new? and How do I get this plugin? sections of the page. For the rest, you may be wondering: Why does my Google Analytics need change?

Change, and why it is important

When I first started working at Juice Analytics, my boss Zach showed me a part of his daily Google Analytics routine. He would open up the Referring Sites page, glance at all of our 942 referrers. Using his superior intellect and capacity for remembering random urls, Zach would discover interesting deviations in the traffic from sites linking to our blog.

Our top referrers looked more or less similar day to day. Even once you get past the more recognizable top sites such as Twitter and Google, the various somethingblog.com pages, without context, often look a lot like somethingelseblog.com. To top it off, most of the information is not even specifically interesting. Our chartchooser.juiceanalytics.com domain sends us consistent regular referrals, but so what? Day to day, I don't even really care about Google or Twitter unless something changes. With change, I know whether someone has posted something new about me, sending valuable traffic. A good read on the topic is Avinash's rant about "actionable analytics".

Our Firefox plugin is designed to allow analysts to get more action out of what changed in the Referring Sites and Keyword Reports. Here are a couple examples of the plugin in action from our Google Analytics account:

What's new?

Our focus for this release has been to improve functionality, to reduce the barrier to entry for new users, and to allow automatic updates for the plugin. The new version of the script works nearly instantaneously, and the installation involves only two clicks (in contrast to the 7 clicks of the Greasemonkey version). As a Firefox plugin, updates are now automatic and require no reinstall. Keyword sensitivity has been raised to 50% for consistency. As a slight bonus, the design and layout of the form and buttons is now sleeker and the table stands out in a pretty Google blue.

Greasemonkey itself is no longer required for the plugin, but you may want to keep it around for any of the other cool scripts available from the community. If you ever find yourself wishing that something about the web looked different, acted different or had different functionality, there may be a Greasemonkey script to ease your pain.

How do I get this plugin?

First, you need Firefox 2.0+.

If you are a user of the equivalent older Greasemonkey version of this script, you may want to go ahead and uninstall it. Go to Tools=>Greasemonkey=>Manage User Scripts..., select Google Analytics Downloader, and uncheck the Enabled box.

If you never had the script installed, or once you removed it, simply click here to go the mozilla addon site, select the checkbox and click the button. Once installed, navigate to Google Analytics, and go to either the Referring Sites or Keyword pages, and click the blue button.

Happy analyzing!

A couple months ago, we put together a Greasemonkey tool that sucked data out of Google Analytics, and after mining it for trend information, integrated it back into the GA interface. This week's tool combines and extends Google Analytics with data from an outside source.

Here is a quick alpha of our Greasemonkey integration of external data reporting into Google Analytics for Kampyle, a "feedback analytics service." Click on the images to zoom in.

Clicking on the 'Kampylize' tab queries the Kampyle site in real-time to populate the standard GA data table.

Our friends at Kampyle run a service that allows website owners to put a feedback button on individual pages of their website. All information submitted by the user is uploaded to a central Kampyle database that compiles the user feedback with web page url and standard internet statistics such as the name of the browser. Website owners can access a server-end service that consists of a reporting site complete with summary data tables, graphs, and charts.

Since both sites are web-based reporting suites segmented in a similar fashion (individual website, date, web browser, etc.), they integrate together naturally. There is a lot of value in placing related data side by side, allowing users to get a more holistic picture of web site performance. If you have other ideas of data sources that would fit neatly with Google Analytics, let us know and we'll consider building the integration.

If you're interested in technical details, continue to Open Juice to see how this is all accomplished...

Last week, we shared a rendition of a Tufte graphic using just a few lines of Nodebox code. As our commenters pointed out, Python is great, but it may not be every business analyst's carnal desire to learn a programming language just to generate some nifty graphs. I spent some time to push Chris's Nodebox rendition into a PIL-based Windows tool that can generate the same sort of comparison graph from an Excel file on the fly.

The result is The Comparison Chart Generator 1.0. The installation instructions are relatively simple. Unzip the zip file, and run comparisionchartgenerator.exe.

Alternatively, we have a new excel chart that creates the same effect using only excel functionality. Download the Excel Tufte Line Chart here.

If you are using the Chart Generator, start with some data in an Excel (xls) or Comma Delimited (csv) format. The data for this graph has to be contained within the first sheet starting with cell A1, as in the following picture.

Select an input file. There are a couple example files bundled with the download.

After selecting a file, you'll be prompted to modify a few of the basic options available for the chart.

Finally, save the result as a jpeg.

Here is the same image found in Tufte's textbook processed using the Comparison Chart Generator. It is generated using the csv example file bundled with the download.

Those of us who have undergone lasik eye-improvement surgery may still prefer the sharp crisp Nodebox results, but for the rest of us, this image looks pretty good. Let us know if this tool is useful. If there is enough of a positive response, we may consider expanding functionality for other fancy Tufte-esque charts.

If you do prefer Nodebox, I have an updated script here. This pushes the script up to 20 lines of code or so, but the extra 9 lines allow the labels to push themselves apart on their own. If you want to look at the source code for the Windows program, you can get it here. I used py2exe to compile it into an executable. The code, however, has not been thoroughly commented or cleaned as of yet, so edit it at your own risk.