kotlinx-knit VS vega-lite

That sounds about right. The approach I'm working on won't allow you to embed runnable code in your docs. Instead, it requires you to write your documentation in Kotlin e.g. using a Markdown DSL where source code from the codebase can be included in the output. I'm working on a separate project to programmatically generate file trees and Docusaurus doc sites in code.

I'm not aware of anything exactly like Livebook in the Kotlin ecosystem but you might be interested in kotlinx-knit which takers a more incremental approach to executable documentation:

https://github.com/Kotlin/kotlinx-knit

I like Vega-Lite: https://vega.github.io/vega-lite/

It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/]

My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.

Box zoom would need to be added to Vega-Lite first, and there has been some discussion around it in https://github.com/vega/vega-lite/issues/4742. Bottom line is that there's nothing blocking its implementation, someone just needs to do the work in Vega-Lite. And once released in Vega-Lite, Altair would pick it up automatically with how we generate the Altair API from the Vega-Lite schema.

I also have difficulties with Gnuplot and Matplotlib. I like Vega that allows me to create visualisations in a declarative way. If I really need something special I go with d3.js, which had a really steep learning curve but with ChatGPT it should have become easier for beginners.

[1] https://vega.github.io/vega-lite/

To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).

In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.

Nice, would be nice to have it integrated in GitHub markdown.

Looks similar to Vega or Vega-lite(https://vega.github.io/vega-lite/). Definitely as rich as D3.js but gets the job done for simple visualisations.

Vega-lite can do this https://vega.github.io/vega-lite/

The underlying data is from an online betting site. Data analysis was done in Python and I used Vega/Altair for the visualisation.