duckdf VS tidyquery

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

tidyquery

> SQL is a perfect language for analytics.

Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.

It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.

In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.

This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.

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

[2] https://github.com/ianmcook/tidyquery

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