Add another piece to the ever-growing list of Twitter visualizations. What makes Moritz Stefaner’s Revisit different is that it focuses on the conversational threads between Twitter users over time. Tweets (symbolized by authors’ avatars) are stacked vertically and organized by time horizontally. Tweets that have more attention via @mentions are closer to the middle.
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Matt McInerney of pixelspread describes the trustworthiness of the people behind their facial hair. You better be careful when I’m around. I’m questionable, border-line unsavory. Don’t worry though. I’m not a werewolf – and I don’t have the ability to grow a Hitler. How trustworthy are you?

See the full version here. [Thanks, @b_beesley]

Statisticians are mad and out for blood. Someone called R an epic fail and said it wasn’t the next big thing.

I know that R is free and I am actually a Unix fan and think Open Source software is a great idea. However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On that, R is an epic fail. It does NOT fit with the way the vast majority of people in the world use computers. The vast majority of people are NOT programmers. They are used to looking at things and clicking on things.

How dare she, right? Here’s the thing. She’s right. Wait, wait, hear me out. For the general audience – the people who use Excel as their analysis tool – R is not for them. In this case, the one that appeals to non-statistician analysts, R, as they say, is an epic fail (and that is the last time I will say that stupid phrase).

However, R wasn’t designed to enable everyday users to dig into data. It was designed to enable statisticians with computing power. It’s a statistical computing language largely based on S, which was developed in the 1970s by the super smart John Chambers of Bell Labs. The 1970s. Weren’t people using slide rules still? Or maybe it was the abacus. Can’t remember. Oh wait, I wasn’t born yet. In any case, there’s really no need to get into the whole R-for-general-audience conversation — just like we don’t need to talk about why The SpongeBob SquarePants Movie lacked emotional depth.
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Mapping with R and other free and open-source programs feels clunky and hacked-together at times. The plus-side is that it’s all for free, and once you find the time to wrap your head around it, you can get quite a bit done. Tomislav Hengl provides a free e-book, A Practical Guide to Geostatistical Mapping, that can hopefully help you with such tools (namely R, SAGA GIS, and Google Earth). You can also buy the paperback version on Lulu.

[Thanks, Ryan]

Good Mag put on an infographic contest not too long ago that asked people to design around the idea of neighborhood. Any neighborhood would do, just as long the focus was on local. As you might expect, most of the entries were more design than data, but hey, that doesn’t mean they’re not worth looking at.
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A typeface can make or break your graphic. Use Comic Sans, and no one will take you seriously ever again. Luckily, graphic designer Julian Hansen put together this flowchart for a school project to help you figure out what typeface to use. Most of you will probably be interested in the infographic branch.
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In another step towards open data and all that jazz, the World Bank released World Development Indicators 2010 today, which is meant to serve as a progress report of the world.

The WDI provides a valuable statistical picture of the world and how far we’ve come in advancing development,” said Justin Yifu Lin, the World Bank’s Chief Economist and the Senior Vice President for Development Economics. “Making this comprehensive data free for all is a dream come true.

More importantly though, this comes with the launch of the freely available online database and public API to 1,000+ indicators. There used to be a big fee for this data. I can’t speak for the API, but the website is well-designed. It has profile pages for each country, links to download the indicators in Excel and XML, and hey, are those graphs implemented in HTML5? I spy <canvas> tags.
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Jacobo Zanella makes a color palette every day, based on the clothes he’s wearing.

I observe the colors of the shoes and clothes I wear that day, how much skin is exposed, etc., and reproduce that observation digitally, through RGB combinations. No software or programming is involved in the making of the graphs.

String them all together, and you’ve got multi-colors on top (shirts), a lot of black and blue towards the bottom (pants), and whites and grays all the way on the bottom (shoes).

Too bad he’s not logging it programmatically. That could be an interesting view (well, for him).

I don’t often give in to impulse buys, but I just ordered Cartographies of Time, and I’m pretty sure it’s going to be well worth the thirty bucks.
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The Los Angeles Times reports on the chronology of gay rights in the United States. States are colored from fewest to most rights for same-sex couples. “Fewest” means the state bans marriage and legal rights. “Most” means gay marriage is legal in the state. Press play, and watch the changes from 2000 to present. Just disregard the clashing red and green color choices.

In case you’re following the craziness going on over Europe due to that pesky volcano in Iceland, The New York Times provides a tracker for airport closures. The train stations must be packed more than ever before right now.
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A big thank you to FlowingData sponsors who help keep the lights on here. As you might have noticed, I’ve been trying something new in the sidebar, and I was lucky enough to have these great groups come along for the ride.

Check out what they have to offer. They make it easier for you to understand your data.

InstantAtlas – Enables information analysts and researchers to create highly-interactive online reporting solutions that combine statistics and map data to improve data visualization, enhance communication, and engage people in more informed decision making.

Tableau Software – Combines data exploration and visual analytics in an easy-to-use data analysis tool you can quickly master. It makes data analysis easy and fun. Customers are working 5 to 20 times faster using Tableau.

Shirt available on Threadless. I seriously need some infographic tees. Too bad the shirt project isn’t still going on. Suggestions, anyone? [via @alternatekev]

Traffic visualizations, mostly in the form of geographic maps, have been popular lately. Governments and organizations have been releasing lots of GPS data, and as a result, we get to see some impressive animations and explore some slick interactives.

We don’t often get to see how cars, trains, subways, airplanes, etc move in physical space, because, well, we’re usually in them, so it’s always interesting to see the big picture. The activity feels very organic as traffic peaks during rush hours and slows down during the night, taxis provide service to and from the airport, and air traffic continues into the late hours. The maps pulsate with energy.

Let’s take a look at some of these great traffic visualizations, some new and some old.
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Anyone who’s looked at campaign finance data knows it can get messy really quick (especially if you’re getting it directly from the FEC). Sunlight Labs’ newly launched TransparencyData aims to make the process a lot easier.

They’ve merged state data from FollowTheMoney and federal data from OpenSecrets and made it easy to search with a clickable interface. Select from a number of filters such as amount, recipient, or contributor, and then download data in bulk or make use of the API.
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Aaron Koblin (along with Chris Milk) is up to his crowdsourcing mischief again. It started with sheep, then the dollar bill, to a bicycle built for two, and now the Johnny Cash Project. Along the same lines of Aaron’s other projects, viewers are invited to draw an individual frame to the tune of Ain’t No Grave. In the end, drawings are put together to create a whole new music video for the song. Select any illustrated frame to watch a person’s drawing session.
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A recent study [pdf] by Sitaram Asur and Bernardo A. Huberman at HP Labs found that it’s possible to use Twitter chatter to predict first-weekend box office revenues simply based on volume of tweets. The predictions were even more accurate when they introduced sentiment analysis (i.e. classified tweets as positive or negative).
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If you absolutely refuse to touch any code, I suggest Many Eyes or one of the fine FD sponsors, but if you’re looking to get your hands dirty, Processing is a great place to start.

Jer Thorp, whose work we saw not too long ago, posts this introduction tutorial for data visualization with Processing.

I’m going to start from scratch, work through some examples, and (hopefully) make some interesting stuff. One of the nice things, I think, about this process, is that we’re going to start with fresh, new data – I’m not sure what kind of things we’re going to find once we start to get our hands dirty. This is what is really exciting about data visualization; the chance to find answers to your own, possibly novel questions.

The examples are straightforward, the results are interesting, and most importantly, it gives you a lot to work off of with your own data and geometry. Hopefully it’s the first post of many.