ok, this is definitely up my alley. color me nerd-sniped and forgive the onslaught of questions.

my questions are less about the syntax, which i'm largely familiar with knowing both SQL and ggplot.

i'm more interested in the backend architecture. Looking at the Cargo.toml [1], I was surprised to not see a visualization dependency like D3 or Vega. Is this intentional?

I'm certainly going to take this for a spin and I think this could be incredible for agentic analytics. I'm mostly curious right now what "deployment" looks like both currently in a utopian future.

utopia is easier -- what if databases supported it directly?!? but even then I think I'd rather have databases spit out an intermediate representation (IR) that could be handed to a viz engine, similar to how vega works. or perhaps the SQL is the IR?!

another question that arises from the question of composability: how distinct would a ggplot IR be from a metrics layer spec? could i use ggsql to create an IR that I then use R's ggplot to render (or vise versa maybe?)

as for the deployment story today, I'll likely learn most by doing (with agents). My experiment will be to kick off an agent to do something like: extract this dataset to S3 using dlt [2], model it using dbt [3], then use ggsql to visualize.

p.s. @thomasp85, I was a big fan of tidygraph back in the day [4]. love how small our data world is.

[1]: https://github.com/posit-dev/ggsql/blob/main/Cargo.toml

[2]: https://github.com/dlt-hub/dlt

[3]: https://github.com/dbt-labs/dbt-fusion

[4]: https://stackoverflow.com/questions/46466351/how-to-hide-unc...

Let me try to not miss any of the questions :-)

ggsql is modular by design. It consists of various reader modules that takes care of connecting with different data backends (currently we have a DuckDB, an SQLite, and an ODBC reader), a central plot module, and various writer modules that take care of the rendering (currently only Vegalite but I plan to write my own renderer from scratch).

As for deployment I can only talk about a utopian future since this alpha-release doesn't provide much tangible in that area. The ggsql Jupyter kernel already allows you to execute ggsql queries in Jupyter and Quarto notebooks, so deployment of reports should kinda work already, though we are still looking at making it as easy as possible to move database credentials along with the deployment. I also envision deployment of single .ggsql files that result in embeddable visualisations you can reference on websites etc. Our focus in this area will be Posit Connect in the short term

I'm afraid I don't know what IR stands for - can you elaborate?