How come error-bars are so unusual in these sorts of libraries? I'm a scientist and NEVER make charts without error-bars on it, but they're rarely included in charting libraries and when they are it feels like an afterthought.
Most of the data visualized by these types of libraries don't have any sort of variance or confidence interval behind the data. You're looking at things like sales forecasts, server latency, number of employee hours, or other precisely measurable quantities. The whole context around those numbers never talks about the notion of variability or reproducibility, so the idea of putting error bars on there doesn't help communicate anything.
Why don't scientists use these libraries, making error bars an appealing thing to add? In my experience, D3 isn't a great too for interactive analysis: plot, stare, tweak, re-plot. I get bogged down in the details of the visualization, vs something like Matlab that almost always throws up a reasonable plot with interactive controls. Since D3 doesn't encourage the type of workflow that results in a graph with error bars, you get a self-reinforcing dynamic where nobody who works with D3 is interested in making it easy to create graphs with error bars.
(author here) Here's a quick implementation of error bars for d3.compose [1]. The general goal of d3.compose was to provide the foundation for creating reusable charts and components while keeping the standard library of charts/components relatively small and focused. With that said, there are plenty of charts/components in the pipeline (scatter, area, and callout to name a few) and I'll make sure error bars are added as well.
