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Since I started learning D3.js as part of the 100 Days of Code project the number one question I’ve gotten is: how are you learning D3? I’ve had lots of people that want to know what learning resources I’m using, what my process is, and what I suggest for how to start on D3. I don’t think I’m any sort of expert, and I certainly don’t claim to have the ultimate learning process, but now that I’m halfway through the project I decided to share what I’ve learned.


I love R and I love ggplot, but there’s always been one thing that’s really irked me: the real lack of support for complex fills, filters, or other graphics effects goodness. In R, there’s basically only support for perfectly rendered shapes and solid fills. If you want something like a gradient fill, blur, or texture, you’re left to your lonesome. I really felt the pain when I discovered the magic of SVG filters and then sadly realized I didn’t have all this awesomeness in R.


How it all started Take me to the project! I started using Mapbox earlier this year and I was hooked from the start. I’ve dabbled in geospatial analysis and visualization before, but I was always put off by the huge barrier of entry. Most spatial analysis requires massive amounts of domain expertise, knowledge of specialized data sources, and a huge time investment to gather the data, clean it, and harmonize it to all work well together.


This month marks the halfway point of my 12 Months of aRt project, and I want to take the opportunity to reflect on the experience so far and share what I’ve learned with you. This past week I was preparing my lightning talk for useR2019, where I’ll be talking about artistic coding in R, and it gave me a chance to realize how much I’ve learned from this project in such a short time.


This month, I’m picking up where I left off last time. If you haven’t seen my previous work on glyphs, go read part 1 of this story. When we left off, I had written an algorithm that drew two types of glyphs with a lot of randomness such that no two glyphs were the same (ok, technically they could be the same, but the probability is very small). I had some grand plans for those algorithms, some of which I’ve achieved, and others are, shall we say, sidelined.



My CV is available in HTML form or as a PDF.

Recent Publications

. CAZypedia: Carbohydrate Binding Module Family 63. CAZypedia, 2018.

Source Document