https://pudding.cool/process/how-to-make-dope-shit-part-2/
How to Make Dope Shit
This is the second installment of a multi-part series designed to help you familiarize yourself with the tools used to make visual, data-driven essays.
A few months ago, I put together the first part of a guide to help people who wanted to start delving into that hard-to-pin-down brand of storytelling that we do at The Pudding. I was frustrated by the fact that finding sufficiently beginner-friendly resources was so difficult, and decided to collect the most useful links for jumping into data analysis in this blog post.
Today, I bring you the second installment of How To Make Dope Shit on The Internet; this one, aimed at helping ease some anxieties that people may have about designing visualizations.
Approaching the design of a visualization can be daunting: which insights of your analysis will you visualize? Which chart should you use? How will you code it?
The complexity of this chain of decisions can quickly become frustrating. Picking the right way to communicate data may be difficult enough on its own, but pairing it with code, especially when you’re new to programming, can seem exponentially more maddening. I get it — I’ve been there, and that uncertainty sucks. Below, I’ll provide you with a few thoughts on how to make this a little less anxiety-inducing, because at the end of the day, you’re doing cool, exciting work.
Look at visualization
To learn how to create something, you need to see what others who were practicing it before you were doing. Ernest Hemingway read the works of Shakespeare and Dostoyevsky; Picasso deconstructed the paintings of Velázquez and Goya. While we’re not in the same league, or even playing the same game, the core principle remains the same. In order to know what’s possible — both in terms of what to visualize and how to do it — draw inspiration from past pieces of data visualization that you’ve seen and enjoyed.
Internally, we’re always getting excited about work that our peers are putting out. Some of our most consistent sources of inspiration are:
I’d also suggest you take some time exploring the archives of Kantar’s Information Is Beautiful Awards, which view like an encyclopedia of some of the best-designed visualizations in the past several years.
Another terrific resource is Andy Kirk’s Visualizing Data, which contains a massive monthly archive of his favourite visualizations.
Archive the work that you like
I frequently come across data viz work that I admire, either due to the clarity of its message, the aesthetic design, or certain subtle touches that solve visual problems I’ve previously struggled to overcome. Since you’re likely to be learning viz on the fly, either while you’re working on a technical project or in your spare time, it’s imperative to keep notes on the most interesting and impressive pieces you encounter.
To make sure that I remember the lessons to be gleaned from each of these, I save them to an ever-open Google doc. It’s as simple as taking a screenshot and adding it to the doc with an annotation of what I liked about it, and a link to the original.
Another option is to use Sightline, a tool created by Jordan Sechler and Evan Peck, which allows you to automatically save data visualizations you encounter online through a Chrome extension. In addition to automatically compiling your personal archive, you can explore the visualizations that have been saved by other users, which makes trawling for inspiration even easier.