ggplotnim VS pytikz

That is certainly true (and why added a general "embed plot data as bitmap into SVG/PDF" option to https://github.com/Vindaar/ggplotnim that works not only for raster heatmaps). But realistically such plots are often not ideal anyway (too many data points in a plot is often a sign that a different type of plot would be better; typically one that aggregates in some way) and it's just another argument to make the data for plots available as well.

Well, personally as I write almost all my code in Nim and am the developer of ggplotnim [0], I simply write a source code snippet with some short Nim code, generate a plot and dump the filename into the Org file.

If I had more time and wanted something more convenient and magical, I would probably write a elisp function that takes X Y (Z) columns and generates a plot from those using a simple Nim program in the back that receives the data, generates the plot and returns it somehow. Haven't given this much thought though.

[0]: https://github.com/Vindaar/ggplotnim

I have been using Nim for all of my recent data munging and analysis. There's https://github.com/Vindaar/ggplotnim for plots (among others) and everything else has just been normal code. There's also https://github.com/SciNim/Datamancer if you need something more like tidyverse.

I recently discovered this python interface for tikz https://github.com/allefeld/pytikz

While it does not directly address the issues you point at, it does alleviate some issues.

* The syntax is somewhat easier to parse.

* It is a lot easier to write functions to redraw the same components over and over again.

* Doing math calculations to systemically place objects in relation to each other is a lot easier because python's arithmetic syntax is a lot more intuitive than TeX's.

Of course, this does mean that you have to fire up python to draw figures.