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We are a team of data visualization and data science experts who build precise, engaging, and beautiful interactive data-driven visualizations that communicate clearly and allow users to derive meaningful insight.

How We Can Help

Data Science: Our data scientists will explore and analyze your data to find answers to questions about your research, business needs and products.

Visualization Design & Prototyping: Our team can help you design the most appropriate and compelling visualizations to match your goals, as well as prototype interfaces to explore your data interactively.

Product Development: Our expert engineers can help you turn your vision for a data product into reality. We build for the web across the full spectrum: front-end implementation, full-stack web applications and ETL data pipelines.

Contact Us Learn more about Data Visualization at Bocoup

Do you know a bit of R and have some data you need to visualize quickly? In this blog post we take a look at Rstudio's Shiny package and the first steps toward creating a working interactive to explore your data with it.

What is Shiny?

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In part one of this series we started learning how to make maps rendered by WebGL, a browser based hardware-accelerated graphics API for 2D and 3D graphics. Our access to this technology was via Tangram, a map rendering library from Mapzen. This post will focus primarily on shaders, those perplexing parallel programs that power our pixels, and how to create visual effects using them. We will be focusing on 2D effects for now and will also look a bit at how to create interactive effects that are powered by shaders. If you haven’t read part one, you might want to start there.

What are shaders anyway?

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Many points on canvas in phyllotaxis layout

Sometimes in life, you've just gotta move thousands of points around on the screen. For hundreds of points, this can be accomplished with D3 through d3-transition on SVG nodes, but this typically becomes too slow when you need to animate more than a thousand points. So how do you do it?

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How do you visualize the “Health of the Internet”? This was the challenge posed to the Data Vis team at Bocoup by our client Measurement Lab, a nonprofit that collects millions of Internet speed tests every month from around the world since 2009. This data is invaluable to policy makers, researchers, and the general public for understanding how Internet speeds are changing over time as well as for highlighting and understanding the impact of service disruptions. However, with petabytes of individual speed test data reports as a data source, it can be difficult to make a visualization tool that is engaging and useful for such a broad audience.

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Maps are both practical and political. They possess undoubtable utility for navigating the physical world and have a long history of being used to shape and reshape the our social and political conceptions of the world. The ability to mark a territory, carve up a continent (or remember one), count a people, or map our desires is a powerful one. Thus despite being one of the oldest visualization types we have, they remain one of the most popular ways of visualizing data today.

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2016 was an exciting year for the Bocoup DataVis team - we wanted to start a new tradition of sharing some of our key highlights, and telling you more about the fun things to come this year. Here goes!

bocoup data vis 2015 recap

2016 In summary (in no particular order):

  • The very excellent Peter Beshai joined our team, bringing with him a host of expertise, experience, and improvements.
  • We helped Dr. Sorger’s lab at Harvard Medical School to design and develop the Breast Cancer Browser: an online tool for exploring and visualizing the results of a multitude of different experiments focused on how various breast cancer cell lines respond to a number of different perturbagens. We also learned what “perturbagens” meant.
  • We partnered with the amazing folks at Measurement Lab to find ways to visualize over 800,000,000 internet health speed test results. We built a whole lot of pipelines, an API server (so that no one has to build a lot of pipelines) and a pretty robust front end, all open source. Peter, our resident SVG lines expert, wrote about and released a host of plugins that make working with lines even more fun in D3.js. Expect to hear more about our work with Measurement Lab very soon, as we prepare to launch it and all of our code alongside it.
  • Much like the rest of the country, we were glued to our many screens during the election season. We were really thrilled to partner with the Coral project to come up with a way to help news outlets and other interested parties capture the sentiment around the election. We’ve been really excited to see our open source interactive used in some interesting contexts.
  • We worked with Jeff Heer and his fantastic team at UW’s Interactive Data Lab to help build the next version of Lyra, an interactive, open-source visualization environment.
  • Last but not least, we ran the 4th OpenVis Conf, bringing together some amazing speakers and penguins at the New England Aquarium. If you haven’t seen videos from last year, you can check them out in this incredible interactive explorer.

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Much like the rest of the country, we’ve been mesmerized by the election and the coverage surrounding it. This election, more than any previous, has spurred conversations and challenges previously unseen, raising questions around political discourse and campaigning nationwide. We’ve been thinking a lot about those very questions, especially in the coming crescendo of election day itself. How will We The People feel when the results are in?

Building an Open Source interactive with the Coral Project

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animating lines with missing data

While working on visualizing the results of internet speed test data for Measurement Lab, it became clear that there wouldn't always be data for every geographic location on every single day. We might go several days without meeting a minimum threshold of tests, meaning there would be gaps in our data. This is a pretty common problem when working with time series data, and one that is easily ignored by connecting the points that have data-- but it feels wrong and it can be misleading at best.

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Smooth path interpolation with d3-interpolate-path

D3 provides us with many of the basic building blocks needed to make charts in browsers while also making it extremely easy to animate them. One of the most common charts created with D3 is a line chart, often consisting of a series of SVG <path> elements to visualize the data. In this post, I dissect how the animation of paths work in D3 and how they can be improved. The final result has been packed and released as a plugin d3-interpolate-path that you can use in your own line charts.

How do paths work?

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Last year we had a successful Knight Foundation Prototype Grant-funded collaboration with the University of Washington Interactive Data Lab (IDL) to improve their Voyager data exploration tool. At the end of our collaboration we knew we wanted to work with the amazing team from the IDL again, so we were thrilled when Jeff Heer & Arvind Satyanarayan approached us to help build the next version of Lyra. Lyra is an interactive, open-source visualization environment built on top of the IDL’s Vega visualization specification language.

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