Even in the Era AI, GGPlot's API is still the best charting API. The name "Grammar of Graphics" isn't just marketing, they literally sought to write a god damn grammar to was capable of expressing all possible qualitative graphics.

They even wrote a book about how they went about it (not that it speaks to the quality of the API) https://link.springer.com/book/10.1007/0-387-28695-0

I actually stumbled upon this book when I was trying to look up how draftsmen (with pens and pencils on paper) did qualitative graphics as I found they had a lot of charm as opposed to modern charting libraries. It's something I noticed when looking through a bunch of historical RBA (Reserve bank of Australia) annual reports, the 1960-1980 charts had a lot of character, but then you go into the early 2000s and its a stale chart from excel.

Anyways ggplot doesn't really recapture the magic of those older charts, but it seems use quite a few of those as a baseline for how to communicate information. Like in figure 20.1 they talk about efforts to replicate older inforgraphics that showed Napoleon’s March on Russia, this graphic here (I think the example in the book is a bit nicer than the one in this blogpost IMO)

https://www.andrewheiss.com/blog/2017/08/10/exploring-minard...

On top of the charts just look nicer than anything you could produce with pyplot (and any API built on top of it) as pyplot seems to be have some really limited raster based rendering or something and the text handling is incredibly limited, I've never had this issue in ggplot.

I feel like most software engineers aren't exposed to because it exists in the R ecosystem which is more so data scientist, econometricians, statisticians and other quantitative data professions, but it definitely one of the nicer APIs and I wish more people in the node and python ecosystem copied their homework. I see vega's full name is something to do with grammars, but idk it's for the same reason.

> They even wrote a book about how they went about it (not that it speaks to the quality of the API)

Wilkinson’s Grammar of Graphics inspired ggplot. The textbook doesn’t even mention ggplot.

Shameless plug for my own GoG inspired DSL, Algraf:

https://williamcotton.github.io/algraf/demos

There’s a Minard plot in the demos!

How would you quantify the magic of these older charts? What is it that makes them have charm and character?

To be fair, the Vega-lite backend is json mostly based on GG ideas.

In this vein, I think I prefer ggsql that made it to HN recently [0].

[0]: https://ggsql.org/

Note that if you are affiliated with a University you probably can download a pdf of the book in the link for free.