tidytable VS Tidier.jl

If you need speed, you should consider using dtplyr (or tidytable), or even dbplyr with duckdb.

What do you think of this instead?

Here's an example - I use this in a package I've built called tidytable. Here is the as_tidytable() function I use that uses method dispatch.

If you're having performance issues with dplyr you can also try out tidytable

I’d say tidytable is a better option these days as it supports more functions. Although I think dtplyr has improved on this front recently, but still lags. The author of tidytable contributes to dtplyr as well.

I know you said it 'feels much faster' which isn't exactly a data oriented comparison, but tidymodels performs very well. You can see one of the dplyr functions as step_* in tidymodels, for example mutate vs. step_mutate under recipes library. The author of tidytable, which uses data.table, had some revisions due to this conversation, just as an example.

Tidytable is what you might be looking for: https://markfairbanks.github.io/tidytable/, this will require a bit of refactoring (e.g group-bys happen as arguments in summarise/mutate). You'll get data.table like speed in a very compact & complete package.

People can also use tidytable and keep the same workflow they're already used to 😄

btw, there has been a pretty nice effort of reimplementing the tidyverse in julia with https://github.com/TidierOrg/Tidier.jl and it seems to be quite nice to work with, if you were missing that from R at least

Indeed DataFrames.jl isn't and won't be the fastest way to do many things. It makes a lot of trade offs in performance for flexibility. The columns of the dataframe can be any indexable array, so while most examples use 64-bit floating point numbers, strings, and categorical arrays, the nice thing about DataFrames.jl is that using arbitrary precision floats, pointers to binaries, etc. are all fine inside of a DataFrame without any modification. This is compared to things like the Pandas allowed datatypes (https://pbpython.com/pandas_dtypes.html). I'm quite impressed by the DataFrames.jl developers given how they've kept it dynamic yet seem to have achieved pretty good performance. Most of it is smart use of function barriers to avoid the dynamism in the core algorithms. But from that knowledge it's very clear that systems should be able to exist that outperform it even with the same algorithms, in some cases just by tens of nanoseconds but in theory that bump is always there.

In the Julia world the one which optimizes to be fully non-dynamic is TypedTables (https://github.com/JuliaData/TypedTables.jl) where all column types are known at compile time, removing the dynamic dispatch overhead. But in Julia the minor performance gain of using TypedTables vs the major flexibility loss is the reason why you pretty much never hear about it. Probably not even worth mentioning but it's a fun tidbit.

> For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

I would be interested to hear what about the ergonomics of data.table you find useful. if there are some ideas that would be helpful for DataFrames.jl to learn from data.table directly I'd be happy to share it with the devs. Generally when I hear about R people talk about tidyverse. Tidier (https://github.com/TidierOrg/Tidier.jl) is making some big strides in bringing a tidy syntax to Julia and I hear that it has had some rapid adoption and happy users, so there are some ongoing efforts to use the learnings of R API's but I'm not sure if someone is looking directly at the data.table parts.

“Tidier.jl is a 100% Julia implementation of the R tidyverse mini-language in Julia.”

https://github.com/TidierOrg/Tidier.jl

A recent package, Tidier.jl, is coming from a R package developer: https://github.com/kdpsingh/Tidier.jl