They can recreate it on their laptop if they have Docker installed… or they can run it in the cloud with GitHub Codespaces. The dev container’s specification-the thing that tells it which tools, languages and packages to include-stays with the project, so everyone using the project gets the same environment (no matter how they have VSCode itself set up). When you use a dev container, VSCode brings your usual extensions, themes and keyboard shortcuts along for the ride-even magical extensions like Live Share. The container might be as simple as just a fresh copy of Linux, or it might have R and a bunch of packages you specify installed.ĭev containers build on the strengths of Docker containers by making them the place you do your development work. When you boot a project up in a dev container, your project folder on your laptop (the host) is mounted inside a Docker container-a sandboxed Linux environment with tools that you specify. So we want people to be able to dive into our analysis quickly, rather than floundering installing R packages or GDAL or something.ĭevelopment containers, or dev containers, don’t just control dependencies like R package versions-they make your analysis shareable, too! They’re real easy to set up and share, even if you haven’t messed around with Docker before. I call it a ‘cake and ingredients’ model-we want to deliver both. We want to make visuals quickly and easily accessible to journalists, but we also want the underlying data, and any analysis we’ve done to it, reproducible too. We publish a lot of open data analyses at 360info. A sample dev container running as a Codespace in the browser.
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