Tableau Software, popular for making data more accessible, mainly in the business sector, just opened up with Tableau Public. The application is similar in spirit to other online data applications like Many Eyes and Swivel. It lets you share data and visualizations online. However, Tableau Public doesn't have a central portal or a place to browse data. Rather it's focused on letting you explore data and stitch modules together on your desktop and then embed your findings on a website or blog.

For example, below is an interactive hosted on Tableau Public and made available online. Those who use Tableau Desktop should be familiar with the layout. You've got a map up top linked to the the time series on the bottom. Filters on the right let you focus on specific parts of the data.

Hot it Works

Like I said, Tableau Public works a little differently than you're used to. You do everything on your own computer first and when you're ready to share, the data and visualization is hosted on Tableau's servers. Copy and paste some javascript to your site and there you go. Easy stuff.

Thoughts

The main trouble with this, and you might have noticed this already, is that the interaction is kind of slow, because everything renders on the server and then is sent back to your browser. So it takes a second or two for every click to process (for me, at least).

For comparison, if we embedded something from Many Eyes, the applet and data are loaded locally, so the visualization interaction is much faster.

Speed can be fixed though. Loading times aside, this is a big shift for Tableau, and it'll be fun to see where it goes from here. It's especially good news for the non-programmer crowd that's interested in data. With the price tag of free, there's nothing to lose and a lot to play with.

Your Thoughts

What do you think? Try for yourself and post your thoughts in the comments below. I'd be especially interested in hearing what regular Tableau users think about the product. How does it compare?

Disclosure: Tableau Software is a FlowingData sponsor.

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World Progress Report - 4 days left to order

Statisticians are generally behind the times when it comes to online applications. There are a lot out-dated Java applets and really rough attempts at getting R, a statistical computing environment, in some useful form through a browser. So imagine my surprise when I tried this tool by Jeroen Ooms, a visiting scholar at UCLA Statistics.

It actually works pretty well, and for a prototype, it isn't half bad.

Statistical Graphics as Layers

The engine behind Jeroen's tool is Hadley Wickham's ggplot2, an R implementation of of Leland Wilkinson's Grammar of Graphics. Yes, it's a big chain of events.

Those who use Adobe products, like Illustrator or Photoshop, are familiar with the idea of layers in your graphics.

Without going into all the details, the main idea is that statistical graphics are made up of layers. There's the data, axes, and the plot. You can do this in R with the ggplot2 library, or, with this tool, you can upload data and specify the layers through the clickety interface.

Here's a short demo of ggplot in action:

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World Progress Report - 4 days left to order

Nebul.us is an online application, currently in private beta, that aggregates and visualizes your online activity. Enter your information for Twitter, Facebook, Flickr, etc and install a plugin in Firefox to record your browsing behavior. Get something that looks like the above, sort of a donut-polar area chart hybrid. Nebul.us calls it a cloud.

While your personal cloud can be interesting to a small extent, the strength of Nebul.us will most likely come out of the social aspect. There's a social cloud to see your friends' activity and then there's a public cloud that lets you see what's trending across the network.

This of course depends on how many people use the service as well as what those users select to share. Right now, for example, the public cloud (above) isn't all that interesting. I see people are going to Twitter, Google, and Facebook. Nothing new there.

Finally, as with most of these types of things, we always have to ask, "Does this visualization do any better than a simple list?" As it stands now, no. The cloud isn't really doing anything for me. If I see something interesting, I'll just bookmark it on del.icio.us or tweet it.

But hey, they're still in the very early stages. There's a lot more work to be done.

Watch the clip below for more:

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World Progress Report - 4 days left to order

This is a guest post by Craig Mod, who collaborated with Information Architects, to develop Web Trend Map. The site, which is largely inspired by iA's previous work, lets you curate links with sources you trust. This post describes the multiple iterations and decisions made during the design process.

Design and development of webtrendmap.com v1.0 took three months. During this period the interaction design and interface underwent countless subtle permutations. What we ended up with is almost totally unlike what we started with. There was a lot of painful iteration. A lot of gut wrenching backtracking.

Let's drill down and take a look at how we iterated on one key webtrendmap.com visual element: the Stack.

Stacks

Stacks are simple: each Stack represents the links of one Twitter user. A bigger Stack = more links. Despite the simplicity, the interface for these Stacks changed considerably from initial prototype to launch:

Prototype and final version (Stack v.1 and v.2):

The interaction model for Stack v.1 mouseover was:

1. display most recent link above Stack
2. move mouse up and click link to open in a new window
3. or click Stack to see next link

Here was our theory behind this model:

• you would 'deplete' all the Stacks on the map
• Stack states were saved in a cookie
• on followup visits, the Stack heights would represent only new links from your previous visit

The act of depleting the Stacks would be satisfying! And having a map of empty Stacks was not unlike achieving inbox zero! ... So we thought.

Sadly, this theory was far removed from reality.

Some of the problems this model presented:

1. As soon as you clicked the Stack you 'lost' the link.
2. For Stacks with many links, you had to click dozens of times. Combine this with #1 and you have a recipe for nervous clicking. ("I don't want to accidentally miss anything good!")
3. Seeing only one link at a time made it extremely difficult to understand relativity between links posted before or after.

The idea of depleting Stacks and 'zeroing out' a map was frustrating and stressful in practice. In short: it felt like a chore. We knew we had to reconsider the model.

Stacks v1.5

The first major iterative changes were:

• Move links to side of Stack for easier mousing-over
• Show five links at a time

Stack v1.5 (Stack mouseover) and Stack v1.5 (link mouse-over):

Users could still deplete Stacks by clicking them and seeing multiple links greatly increased parsing speed and solved problems #2, and #3.

To keep visual clutter down we hid Tweet text related to links, revealing them only on mouseover.

Still, there was no intuitive way to allow reverse movement through the Stack. And any attempts to graft a system of moving up and down the Stack onto the interface felt like we were making something that should be simple, more and more unnecessarily complex.

We liked the data, but the interaction was broken.

Trending Links Sidebar

The breakthrough came in the form of a sidebar: the 'Trending Links' sidebar is an aggregate (based on frequency) of all the links on a map.

Adding this gave us the insight we needed to confidently state the following:

1. Depleting the Stacks and zeroing out the map was too much work for too little gain.
2. Individual user link postings weren't (for the most part) very interesting but the aggregate of the map was.
3. The Trending Links sidebar replaced the Stacks as our main data interface.

With these points clearly in mind we were able to execute a final Stack design and interaction revision.

Stacks v2.0

The final Stack changes:

1. Expand tweet text by default
2. Remove Stack depletion functionality
3. Clicking a Stack brings you to the links overview page for that user

The Stacks started as a tiring, complicated, awkward interface to a user's links. They became a simplified gateway to historical link data for a user. That's a hell of a shift of functionality, and one we were able to achieve only through progressive, constant, sometimes painful iteration.

Was it worth the pain? The pain is what drives us — let's us intuit we're breaking down non-trivial problems. So the pain was more than worth it. The webtrendmap.com we arrived at is an order of magnitude more usable than what we started with.

As a bonus, these design iterations were responsible for the creation of the 'Trending Links' sidebar and individual user historical link views. Two excellent additions to the site.

We've cataloged more than 200,000 links in less than a month of public operation. Our roadmap for what we want to produce with this data is pretty inspiring. But you know what? It's going to change. What we see and understand now, and what we'll see and understand mid-production will invariably differ. And we're fine with that — we're iterating, and sometimes it hurts, but the end result is almost always satisfying.

Craig Mod is a developer, book designer, publisher, and professional world-wide digital hobo. He collaborates with individuals and companies in Tokyo on social media projects. Find more about Craig on his site, or follow him on Twitter.

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World Progress Report - 4 days left to order

There's a lot of talking in congressional meetings, but what are your state senators and representatives talking about? Design group Periscopic explores what congress men and women said from 2007 to 2008 in this tongue-in-cheek comparison tool with talking heads. The best part about the tool is that behind the humor is actually something useful.

Compare word distributions of senators, of states, of a senator to a state, or representatives, so on and so forth. We get breakdowns by gender, number of words spoken, and by state. All data come from public records.

[via @krees]

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World Progress Report - 4 days left to order

your.flowingdata (YFD), a Twitter application that lets you collect data about yourself, is now LIVE!

I feel like I've been working on this project forever, but it's finally at a place where I think it's ready for human consumption. And unlike the previous version, what you track is completely up to you.

How to Get Started

You can start collecting data about yourself in just a few easy steps:

1. First off, follow @yfd on Twitter.
2. Second, sign in to your.flowingdata with Twitter.
3. Once you're logged in to YFD, you'll see a link to a quick start guide. Follow the step-by-step directions and you'll be tweeting data in no time.

Once you've started tweeting data, it'll take about two minutes (usually less) for your data to appear on YFD.

Patterns Over Time

The real strength of your.flowingdata is exploration of personal trends over time. Like most data, your data is going to get more interesting the longer you collect.

This is why you use YFD the same way that you use Twitter. YFD was designed to be unobtrusive. You simply send direct messages about what you're doing, what you have done, or what's going on around you.

Collect regularly, and you're going to see something interesting.

If you just log a few data points about yourself, you're not going to get much out of it; however, if you log a few data points per day, then you're on to something. Imagine if Al Gore only showed one temperature value in his now-famous global warming presentation. It's the cycles that mattered - not the single data point.

Remember - you want a whole story (about you), not just a few random words.

Explore Your Data

YFD helps you tease out the story in your data. It's all about interaction and exploration, largely inspired by IBM's Many Eyes, except it's all about you.

Maybe you're interested in categories, like the range of your daily emotions. YFD provides a searchable treemap that can be browsed by time:

You could be interested in daily, weekly, or monthly patterns. Use the searchable and interactive stacked time series chart, much like the popular Baby Name Wizard:

You can also browse via a calendar interface like the one at the beginning of this post.

How about a word cloud? That's searchable too:

Again, all of these visualizations are browseable by time. Track your life in data long enough, and you're going to spot something interesting, and hopefully, have some fun in the process.

An Intertwined Stream Without a Dashboard

With that said, some of you might be wondering where the dashboard is. The previous version did after all have a drag and drop dashboard-y feel to it. But I decided to get away from that on this go around.

I just feel like when I put my data in widgets and modules and boxes, it seems disjoint, while in reality, all the choices we make are intertwined.

Your blood sugar, for example, is directly affected by what you eat, but is there anything that changes your eating habits? Maybe mood. Maybe exercise. All of it? It's an intertwined chain of events and sort of flows like river. There might be a boulder way upstream that steered the water to where it is now.

Of course I could be completely wrong and maybe we really do need a dashboard for this type of data, but for now, the focus is on exploration.

Your Private Data Stream

In the early beginnings of YFD, all data was public. I changed that. Now all data is private. The only person who can see your data is you. I did this for two reasons.

The first reason is that I didn't want to add more noise to the stream. There's enough as it is and there's only so much you can take. I mean, I have enough of my own data. I don't need to keep track of others'. It's kind of interesting to peep in on others' lives, but in the end, it doesn't do you much good. I think your Twitter public timeline is a good threshold for what to share and what not to.

The second reason is that there are a lot of aspects of your life that you don't want to share with the world but are worth keeping track of. You don't have to look very far for examples.

I saw this immediately after I started tweeting data about myself. Once you log one thing, a lot of other stuff tends to pop up in your head.

So yeah, I take data privacy seriously. I've been toying around with the idea of the choice to make some data public and some not, but that's further down the timeline.

Much More to Come

This project is far from final, but I think YFD is pretty useful as-is. Some of the major plans I have for YFD include customizable pages, integration of more statistical analysis techniques, and alerts and and pattern recognition.

Finding YFD Help

Please post all of your YFD questions, comments, or bug reports to the new your.flowingdata Forum. I'll be checking it regularly, and knowing me, probably multiple times a day.

If you don't know what OAuth is or don't know a lick (or care) about programming or implementation, feel free to skip the next section or get right to tweeting data on your.flowingdata.

For the Nerds

For the rest of you, here are a few quick facts about how I implemented YFD.

The application itself was built with Django, a Web framework in Python. The prototype was first conceived with a few PHP scripts, so I did try CakePHP, but after a few frustrations, I figured it was a good of a time as any to finally learn Python. I'm glad I did.

Several of the YFD visualizations make use of Jeffrey Heer's Flare visualization toolkit. It's in Actionscript 3 and works pretty well. Because Flare has so much built-in functionality though, it did take a little time sifting through all the documentation to figure out what I wanted to do. However it saved me a lot of time in the end.

I also used Garreth Watts' jQuery sparklines plugin. It was a piece of cake to use. By the way, if you're doing javascript without jQuery, you're seriously missing out on some javascript goodness.

Finally - OAuth. A lot of you were pretty vocal about this one. YFD now uses OAuth, so that you don't have to give your Twitter password to a third-party application.

Disclaimer

Please keep in mind that I am only one graduate student with a thesis to finish. YFD is part of my research in self-surveillance. While I'll do my best to make sure YFD runs smoothly, I will almost without a doubt run into some kinks. Please help me out by reporting any bugs or problems in the your.flowingdata forum.

Also, in the case that @yfd doesn't follow you back very promptly, usually within a few minutes, it's likely that @yfd has hit a daily limit for the number of follows in a day. Check back the next day. If it's been more than 24 hours, please let me know in the forums. I've already talked to Twitter about this, and they're working on a solution, but it won't be for a while.

Have fun capturing your life in data!

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World Progress Report - 4 days left to order

As the newest release from Google Labs, Fusion Tables is a tool that aims to make your data more accessible.

Today we're introducing Google Fusion Tables on Labs, an experimental system for data management in the cloud. It draws on the expertise of folks within Google Research who have been studying collaboration, data integration, and user requirements from a variety of domains. Fusion Tables is not a traditional database system focusing on complicated SQL queries and transaction processing. Instead, the focus is on fusing data management and collaboration: merging multiple data sources, discussion of the data, querying, visualization, and Web publishing.

Google Spreadsheets + phpMyAdmin

Fusion Tables will feel familiar to those of you who use Google Spreadsheets, but the use is somewhat different.

Where Spreadsheets is meant to mimic much of the feel of MIcrosoft Excel, Fusion Tables is somewhere in the middle between Excel and database (or at least it hopes to be eventually). You can filter data as well as merge your datasets with others, for example, by country.

Maybe the best way to describe Fusion Tables is a cross between Google Docs and phpMyAdmin, which is a user interface into a MySQL database.

Visualization Options

Probably of most interest are the visualization options. They're what you're used to seeing with line, pie, and bars, all looking very Google-y. The new ones to check out: motion chart and intensity map (above). There's also a regular point mapping option. Again, we've seen these visualizations before, but Fusion Tables is trying to make it easier to use them.

What do you think of Google's new offering? GIve it a whirl with their sample tables, and come back here and let us know what you think in the comments below.

[Thanks Andrew, NoodleGei, Oleks, and everyone else...]

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World Progress Report - 4 days left to order

Axis Maps recently released indieprojector, a new component to indiemapper, their in-development mapping project to "bring traditional cartography into the 21st century." Indieprojector lets you import KML and shapefiles and easily reproject your data into a selection of popular map projections. No longer do you have to live within the bounds of a map that makes Greenland look the same size as Africa.

Indieprojector was built by Axis Maps as the smarter, easier, more elegant way to reproject geographical data. It's platform independent, location independent and huge-software-budget independent. Indiemapper closes the gap between data and map by taking a visual approach to projections. See your data. Make your map. For the first time ever, it's just that simple.

Not only can you map your data; more importantly, you can also export your map in SVG format, which you can in turn edit in Adobe Illustrator or some other tool.

For those who frequently deal with geographical data and want something simpler than the big GIS packages, Axis Maps' indiemapper is a project to keep an eye on.

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World Progress Report - 4 days left to order

Google announced today that they have made a small subset of public datasets searchable. Search for unemployment rate and you'll see a thumbnail at the top of the results. Click on it, and you get a the very Google-y chart like the one above, so instead of searching for unemployment rates for multiple years, you can get it all at once.

This is an obvious move for Google as it continues in its efforts to make the world and all the data in your life searchable. It is still a very limited number of datasets at this point from the U.S. Bureau of Labor Statistics and U.S. Census Bureau's Population Division, but I'm sure you can imagine the growth in the coming months. Maybe Google will make some real use out of Gapminder's Trendalyzer that they purchased a couple years back. Whatever happened to the Palimpsest Project?

Check it out for yourself, or if you're lazy, watch the video:

Data, Data, Data

The most exciting part about this isn't the graphs or even the new searchability. It's this growing availability of data. I think most of the data that Google will index is stuff that's already available. You just have to know where to look. The main point here is that there's so much data out there on the Web that Google (and Wolfram?) has found that indexing is now worth their while, and with data.gov on the way, we, as data scientists are in for some exciting times.

[via ReadWriteWeb]

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World Progress Report - 4 days left to order

The ever popular newsmap (above), a tree map view of Google News, got a facelift a few days weeks ago. Markos Wekamp, the creator, has changed to a rectangularized tree map layout to display headlines more completely, search as you type, and deep linking. Markos also brings the brightness down a notch from that of the original, which I like. It's easier on the eyes.

Earlier last week, Google released its own alternative news view with News Timeline. The interface lets you search the news, blogs, etc and results are displayed in a timeline format. Show by day, month, year, and decade.

The jury is still out on whether the timeline is an improvement over regular search listings. What do you think? How about versus the New York Times article skimmer?

[via infosthetics & Google News Blog & Newsmap Blog]

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World Progress Report - 4 days left to order