r/visualization

Some time ago, I went through two data visualization books by Willard C. Brinton, published in 1914 and 1939.

What surprised me most was how familiar much of the advice still feels.

Simple bar charts were often preferred over more elaborate alternatives. Misleading axes were already a thing. 3D visualizations were criticized. Heatmaps were being used effectively. And there was plenty of discussion about how presentation influences whether people actually understand the data.

The big difference was the tooling. Creating a Sankey diagram could involve manually arranging a thousand strips of paper, while a 3D visualization might literally require plywood and a saw.

I collected some of the more interesting examples here:

https://www.bitmetric.nl/blog/old-school-data-visualization/

It made me wonder: which data visualization “best practices” do we think are modern, but are actually much older than we realize?

Hi everyone,

I’m a first-year PhD student currently preparing my first scientific article in maritime field , and I’d like to improve the quality of my figures.

At the moment, I mainly use PowerPoint and draw.io to create diagrams and schematics. They are useful, but I would like to learn how to produce more polished, consistent, and publication-quality figures.

Which tools do you use for scientific illustrations, diagrams, workflows, or graphical abstracts? Are there any online courses, tutorials, books, or other resources that helped you learn figure design and data visualization?

I’d be especially interested in hearing:

• Which software you recommend and why
• Whether you use different tools for data plots and conceptual diagrams
• Which courses or tutorials are genuinely worth the time
• What you wish you had learned about scientific figure design during your first year

Thank you!

I’ve been working on this for a while, and only recently realised I was basically projecting how I think onto a screen.

I tend to think in clusters, one idea connects sideways to three others, those collect into groups, and eventually the overall shape starts to make sense.

It began as a mind map. Then I wanted separate maps that could connect, and paths that could run through the bigger thing. Later I added AI so I could ramble through an idea, get a rough visual structure back, then move, delete or correct whatever I misunderstood.

It’s called Clarimind and it’s now at the point where other people can poke at it. You can open a Canvas without signing up.

Curious whether this feels genuinely different from a normal mind map, or whether I’ve just spent a long time building a very elaborate one.

https://clarimind.io/

https://reddit.com/link/1vsqjil/video/87gaq7c8sckh1/player

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I kept running into ML explainers that were either a pre-rendered animation or a wall of notation, so I built the version I wanted to learn from: 48 concepts where the algorithm genuinely executes in your browser and you can interfere with it while it runs.

Everything computes for real no canned GIFs, no server calls. The math is hand rolled TypeScript with ~500 tests behind it, so you can grab a data point and watch the model resolve immediately.

A few that seem to click for people:

- k-means — drag the starting centroids into one corner and watch it get stranded in a bad local minimum. Step through assign/update one phase at a time.
- ROC & AUC — scrub the threshold from 1 to 0 and the curve draws itself while the confusion matrix updates in lockstep. Set positives to 1% and watch AUC still look great while precision-recall collapses.
- Decision trees — see the greedy *search*: every candidate cut it scored, and the one it kept, with the Gini drop as live math.
- CNN — draw a digit and a real trained MNIST network classifies it (weights trained offline, exported to JSON). Rotate the digit 90° and confidence collapses, because convolution shares weights across space, not orientation.
- Lasso — step through coordinate descent and watch a coefficient snap to exactly zero when it falls inside the threshold.

Every page also has a deliberate "now break it" control. I think you learn more from watching a perceptron oscillate forever on non-separable data than from watching things succeed.

Each concept also has the plain-language intuition, the math with every symbol decoded, and a worked example traced on real numbers.

Free, no signup, nothing gated: https://www.mlvisualization.com

I'd genuinely rather hear which explanations don't land than which ones do — happy to fix them.