https://pudding.cool/process/how-to-make-dope-shit-part-3/
How to Make Dope Shit
This is the third installment of a multi-part series designed to help you familiarize yourself with the tools used to make visual, data-driven essays.
In 2017, I began compiling a three-part guide, intended for beginners with next to no technical knowledge, on how to create the sorts of data-driven, visual stories that we publish at The Pudding. This was going to consist of introductions and surveys of data analysis, design, and writing, and while I thought that the first two sections would be all that our readers really wanted to see, we've consistently received word that there remains a substantial interest in the writing guide. I'd tried to compile this third section, which had maintained its "On Writing" heading in my mind, with little success, until I realized that the impasse lay in the fact that I was fixated on precisely that: writing. Compiling resources on how to write within the form of a visual essay while omitting the discussion of storytelling, in both visual and written terms, as well as the importance of story structure, is of next to no help. Thus, I decided to retroactively make good and compile a primer on storytelling with data.
In the first two guides, I'd provided a sort of survey of the tools we use for the data analysis and design of our projects. The aim was to give someone with relatively little experience a sense of the most useful tools involved in visual essays, and point them towards accessible tutorials that would explain these tools without the technical mumbo-jumbo. My reasoning was simple: anxiety about insufficient technical skills, and the difficulties in finding beginner-appropriate resources , has always seemed to me to be the biggest hurdle to getting more people involved in writing code and working on digital art and analysis. Pointing readers towards learning content that required no prior knowledge — as opposed to the countless tutorials that presumed anyone reading already had a grasp of the ecosystem they're working in — would alleviate those worries, and help them get over that initial hump of frustration involved in learning to use unfamiliar tools.
Storytelling, however, is much more abstract — it's not merely a technical matter of creating an image of a map, or designing the right chart; rather, it refers to the broader universe of considerations that impact nearly every decision you make in the way you frame and present a project. The focus is much less on the technical "how," like in the first two installments of these guides, but on the "why" of designing the narrative. It certainly doesn't help that technical tools are inherently more concrete: they're ways of solving specific problems (e.g., "how do I show the locations where people are concentrated on a map?" or "how do get this visual element to move through this specific path?"), while storytelling is much more of a nebulous concept. Thus, in this guide, I'll be focusing on the relevant questions and considerations that we, at The Pudding, tend to consider when creating data-driven projects.
Yourself: The passion project
Before you begin, consider whom you aim to reach with your project. If you're building for yourself, passion projects tend to allow for the greatest degree of creativity and experimentation.
I'd initially worked on one of my first data-driven projects, an exploration of Craigslist's Missed Connections, because I was curious about the topic — something about people yearning for intimate connection in the most public of settings resonated with me. Furthermore, I had lots of editorial opinions about how I wanted to present the story, and while I may have reconsidered some of my choices several years after having published it, I wanted to publish it myself because I didn't want to make the sort of piece an editor would usually want. I wanted to build a personal story in the way that I saw fit, and I'm content to own those choices. I can't imagine a single editor that would have been completely aligned with my initial vision.
All The Lonely People, Ilia Blinderman
That I worked on this outside of any sort of journalistic relationship with a larger outlet meant that I also had the opportunity to learn about many tools at my leisure: front-end development (JavaScript, HTML, and CSS, as well as web technologies like Amazon's cloud computing servers), data analysis in R, geospatial analysis using QGIS, typography, etc. (you can scroll down to the bottom of the piece for an in-depth explanation of the tools I used).
Oftentimes, personal projects are more exciting than commissioned published work, in large part because the author puts so much of themselves into their creation. Take, for example, one of my favorite pieces from friend-of-the-show Riley Hoonan, chronicling the Paris-Roubaix cycling race; one would struggle to read it and fail to experience at least some measure of awestruck excitement. Or take Jackie Gu's melancholy please will anyone speak to me, wherein she meditates on the character of digital relationships. I first saw these projects years ago, but they remain top of mind whenever I think of personal, non-traditional visual work.
please will anyone speak to me, Jackie Gu