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Time Series Visualization foo7.6 bar−6.2 foo + bar1.4 foo - bar14 Cubism.js is a D3 plugin for visualizing time series. Scalable Cubism fetches time series data incrementally: after the initial display, Cubism reduces server load by polling only the most recent values. Effective Cubism also scales in terms of perception: small multiples aligned by time facilitate rapid comparison. Area (120px)7.6 Area (30px)7.6 In contrast, horizon charts reduce vertical space without losing resolution. Horizon, 1-band (120px)7.6 Horizon, 2-band (60px)7.6 Horizon, 3-band (40px)7.6 Horizon, 4-band (30px)7.6 By combining position and color, horizon charts improve perception: position is highly effective at discriminating small changes, while color differentiates large changes. Flexible Cubism is data-source agnostic. Want to learn more?

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Rickshaw: A JavaScript toolkit for creating interactive time-series graphs Graphing Toolkit Rickshaw provides the elements you need to create interactive graphs: renderers, legends, hovers, range selectors, etc. You put the pieces together. See Demo → Visualizing Data with AngularJS The D3 bits but new donut charts will all have the same data... demo Climbing the d3.js Visualisation Stack Over the last few months, the d3.js Javascript visualisation library has seen widespread use as the powerhouse behind a wide variety of highly effective interactive data visualisations. From the Sankey diagram we used to visualise horse meat exports in the EU, to Anna Powell Smith’s funnel plots generator, to the New York Times’ 512 Paths to the Whitehouse, d3.js provides a rich framework for developing an increasingly rich panoply of data driven animated graphics. Despite the growing number of books and tutorials that are springing up around the library, such as Data-Driven Documents, Defined on the Data Driven Journalism site, creating even the simplest charts using d3.js out of the box can prove a major challenge to those of us who aren’t fluent in writing Javascript or manipulating the DOM (whatever that means!;-) Further up the abstraction layer, we have more specialised Javascript libraries that provide support for complex or compound chart types:

Powering big data at Pinterest Mohammad Big data plays a big role at Pinterest. With more than 30 billion Pins in the system, we’re building the most comprehensive collection of interests online. One of the challenges associated with building a personalized discovery engine is scaling our data infrastructure to traverse the interest graph to extract context and intent for each Pin. We currently log 20 terabytes of new data each day, and have around 10 petabytes of data in S3. zynga/scroller @ GitHub Accelerated panning and zooming for DOM and Canvas Dependencies Zynga Scroller has no dependencies to other JavaScript libraries. Demo Install Just copy over the 3 JavaScript files from the "src" folder to your local JavaScript project and include them in the following order "Raf.js", "Animate.js" and "Scroller.js".

colony Colony is a neat little visualisation tool for exploring Node projects and their dependencies using d3.js. Each file is represented as a node in the graph. If one file depends on another, a link is made between the two files. Using D3.js to visualise Hierarchical Classification Why D3.js? I’ve been playing around with a fairly new visualisation library called D3.js for the last couple of weeks. Given my last post about how awesome the python plotting library matplotlib is, why bother? More Data Visualization Libraries Based on D3.JS - Mike McDearmon There are a lot of ways to visualize data on the Web (with more emerging every day), but the flexibility, versatility, and energized development community surrounding D3.js makes it a great option to explore. The following list of D3 plugins, extensions, and applications below is by no means comprehensive, but oughta be enough to keep you busy for a while. If you’re just getting your feet wet with D3.js, here are some great learning resources to get you acclimated:D3 for mere mortals: Great introductory lessons for those starting from scratch.Try D3 Now: Another great resource for learning about core D3 concepts.Data-Driven Documents (paper): An academic article by Mike Bostock with loads of footnotes.Learning D3, Scott Becker: A quick and effective tutorial series to get yourself up and running.Dashing D3: A very thorough tutorial series covering a LOT more than just D3.Interactive Data Visualization for the Web is a fantastic book by Scott Murray.

Broken by Design: MongoDB Fault Tolerance As you're undoubtedly well-aware, there are some very strong geek fashion trends in the valley. I don't mean fashion in the sense of geek haute-couture -- the fashion trends we're talking about here have to do with tech components. You've heard of it before: "here's how we built using X, Y and Z." javascript motion detection This is a motion detection experiment in javascript, and before starting any explanation: Click here to see the demo. To see this demo with your own webcam, you will need to download a browser that enables some new features as explained in this link.

Protovis Protovis composes custom views of data with simple marks such as bars and dots. Unlike low-level graphics libraries that quickly become tedious for visualization, Protovis defines marks through dynamic properties that encode data, allowing inheritance, scales and layouts to simplify construction. Protovis is free and open-source, provided under the BSD License. It uses JavaScript and SVG for web-native visualizations; no plugin required (though you will need a modern web browser)! Although programming experience is helpful, Protovis is mostly declarative and designed to be learned by example.

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