I was pleased to receive a physical copy of Jack Dougherty and Ilya Ilyankou’s Hands On Data Visualization: Interactive Storytelling from Spreadsheets to Code not long ago. The complete online open access version is available behind this link.
I’ve worked with Jack before, contributing essays to some of the volumes he’s edited on writing digital history or otherwise crafting for the web with students.
The Hands On Data Visualization (henceforth, HODV) book continues Jack’s work making digital history methods accessible to the widest variety of people. That’s one of the key strengths of this book; it addresses those students who are interested in finding and visualizing patterns in the past but who do not, as yet, have the experience or confidence to ‘open the hood’ and get deep into the coding aspects of digital history. I love and frequently use, refer to, and assign, tutorials from The Programming Historian; but there is so much material there, I find my students often get overwhelmed and find it hard to get started. Of course, that says more about my teaching and pedagogical scaffolding than perhaps I am comfortable with sharing. HODV I think will serve as an on-ramp for these students because it builds on the things that they already know, familiar point-and-click GUIs and so on, but much more important is the way it scaffolds why and how a student, from any discipline, might want to get into data visualization. (And of course, once you can visualize some data, you end up wanting to build more complex explorations, or ask deeper questions.)
Let’s talk about the scaffolding then. The book opens with a series of ‘foundational skills’, most important amongst them being ‘sketching out the data story‘. I love this; starting with pen and paper, the authors guide the student through an exercise moving from problem, to question, to eventual visualization; this exercise bookends the entire volume; the final chapter emphasizes that:
The goal of data visualization is not simply to make pictures about numbers, but also to craft a truthful narrative that convinces readers how and why your interpretation matters….tell your audience what you found that’s interesting in the data, show them the visual evidence to support your argument, and remind us why it matters. In three words: tell—show—why. Whatever you do, avoid the bad habit of showing lots of pictures and leaving it up to the audience to guess what it all means. Because we rely on you, the storyteller, to guide us on a journey through the data and what aspects deserve our attention. Describe the forest, not every tree, but point out a few special trees as examples to help us understand how different parts of the forest stand out.
The focus throughout is on truthfulness and transparency and why it matters. We move from part one, the foundational skills (from mockups, to finding, organizing and data wrangling the data) to building a wide variety of visualizations, charts, maps, and tables and getting these things online at no cost in part two. Part three explores some slightly more complicated visualizations relying on templates that sometimes involve a wee bit of gentle coding, but are laid out and illustrated clearly. This section is the one I’ve directed my own students to the most, as many of them are public history students interested in map making, and this section is one of the best resources on the web I think at the moment for building custom map visualizations (and geocoding, &tc.) I think students navigating this material will be reassured and able to adapt when these various platforms/templates etc change slightly (as they always do), given how carefully the various steps are documented and how they interrelate; this enables the student to see how to adapt to the new circumstances I would think. In my own writing-of-tutorials, I rely too much on writing out the steps without providing enough illustrated materials even though my gang like the reassurance of a screen that matches what the person in charge says should happen (my reasoning about not providing too much illustrative materials is that I’m also trying to teach my students how to identify gaps in their knowledge versus gaps in communicating, and how to roll with things – you can judge for yourself how well that works, see eg https://craftingdh.netlify.app . But I digress).
The final section deals with truthfulness, with sections on ‘how to lie with charts’ and ‘how to lie with maps’, a tongue in cheek set of headings dedicated to helping students recognize and reduce the biases that using these various tools can introduce (whether intentionally or accidentally). The final chapter involves storyboarding, to get that truthful narrative out there on the web, tying us back to chapter one and trying to solve the initial problem we identified. I really appreciate the storyboarding materials; that’s something I want to try more of with my gang.
I’ve spent a lot of years trying to build up the digital skills of my history students, writing many tutorials, spending many hours one-on-one talking students through their projects, goals, and what they need to learn to achieve them. HODV fills an important gap between the dedicated tutorials for academics who know what it is they are after and have a fair degree of digital literacy, and folks who are just starting out, who might be overwhelmed by the wide variety of materials/tutorials/walk-throughs they can find online. HODV helps draw the student into the cultures of data visualization, equipping them with the lingo and the baseline knowledge that will empower them to push their visualizations and analyses further. Make sure also to check out the appendix on ‘common problems’, which gives a series of strategies to deal with the kinds of bugs we encounter most often.
My teaching schedule for the next little while is set, but I could image using HODV as a core text for a class on ‘visualizing history’ at say the second year level. Then, I would rejig my third year ‘crafting digital history’ course to explicitly build on the skills HODV teaches, focussing more on more complex coding challenges (machine vision for historians, or NLP, topic models, text analysis). Then, my fourth year seminar on digital humanities in museums would function as a kind of capstone (the course works with undigitized collections, eventually publishing on the web with APIs, or doing reproducible research on already exposed collections).
Anyway, check it out for yourself at https://handsondataviz.org/ (the website is built with bookdown and R Studio; that’s something I’d like to teach my students too! Happily, there’s an appendix that sketches the process out, so a good place to start.) The physical book can be had at all the usual places. I don’t know what kinds of plans Jack and Ilya have for updating the book, but I expect the online version will probably be kept fresh, and will become a frequent stop for many of us.