Makie.jl VS duckdf

Just look at this lotenz attractor https://docs.makie.org/stable/

There does exist some code for reading glyph geometry from fonts - you can find some of it in the Makie source directory: eg https://github.com/MakieOrg/Makie.jl/blob/master/src/bezier.jl - you can see that there's quite a lot though...

You'll want Makie.

Thanks! Looks like the Julia Makie library can do graphics and gui widgets as well -https://github.com/JuliaPlots/Makie.jl

There was also this recent thread where some folks were talking up Makie.jl, for which there is apparently also a Grammar-of-Graphics-style wrapper called AlgebraOfGraphics.jl.

If you're looking for native then Makie.jl is probably your best bet. It's still a little underdocumented and the integrations with the rest of the ecosystem are still incomplete, but it's a pretty amazing plotting library. Fast with easy GPU acceleration, good interactivity and animations (great for making GUIs), and easy to extend.

How tricky or hard would it be to create nice looking plots from that? Something like https://github.com/JuliaPlots/Makie.jl but more low level.

Quite a while ago, when duckdb was just a duckling, I wrote an R package that supported direct manipulation of R dataframes using SQL.[1] duckdb was the engine for this.

The approach was never as fast as data.table but did approach the speed of dplyr for more complex queries.

Life had other things in store for me and I haven’t touched this library for a while now.

At the time there was no Julia connector for duckdb, but now that there is, I’d like to try this approach in that language.

[1] https://github.com/phillc73/duckdf

Yeah, I agree sqldf is quite slow. Fair point.

As you've seen, duckdb registers an "R data frame as a virtual table." I'm not sure what they mean by "yet" either.

Of course it is possible to write an R dataframe to an on-disk duckdb table, if that's what you want to do.

There are some simple benchmarks on the bottom of the duckdf README[1]. Essentially I found for basic SQL SELECT queries, dplyr is quicker, but for much more complex queries, the duckdf/duckdb combination performs better.

If you really want speed of course, just use data.table.

[1] https://github.com/phillc73/duckdf

That's a really good point that I'd not really thought about. I'd never really considered the difference between calling just functions versus macros.

Thinking about Query.jl and DataFramesMeta.jl, and I am for sure not an expert in either, I can't specifically speak to your `head` example, but other base functions can be combined with macros. For example, see the LINQ examples from DataFramesMeta.jl[1] where `mean` is being used. Or again the LINQ style examples in Query.jl[2], where `descending` is used in the first example, or `length` later in the Grouping examples.

Is that the kind of thing you meant?

For whatever reason, with the way my brain is wired, the LINQ style of query just works for me. I have never directly used LINQ, but do have some SQL experience. In fact, I wrote some dinky little wrapper functions[3] around duckdb[4] so I could directly query R dataframes and datatables with SQL using that backend, rather than sqldf[5].

[1] https://juliadata.github.io/DataFramesMeta.jl/stable/#@linq-...

[2] https://www.queryverse.org/Query.jl/stable/linqquerycommands...

[3] https://github.com/phillc73/duckdf

[4] https://duckdb.org/

[5] https://cran.r-project.org/web/packages/sqldf/index.html