duckdf VS RCall.jl

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

I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.

[1] https://github.com/JuliaInterop/RCall.jl

You can have your cake and eat it with the likes of

* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl

* NodeCall.jl - https://github.com/sunoru/NodeCall.j

* RCall.jl - https://github.com/JuliaInterop/RCall.jl

I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself

To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL

https://github.com/JuliaInterop/RCall.jl may help

If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl

I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl

You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl

I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.

I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.

I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.

[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl

[RCall.jl]: https://github.com/JuliaInterop/RCall.jl

[Julia Interop]: https://github.com/JuliaInterop

[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html