tidytable VS poorman

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 😄

You might find the poorman package interesting: https://github.com/nathaneastwood/poorman

It's true that their packages are heavy on dependencies, and if that is a concern, you have alternatives: - poorman: no dependencies, same syntax as dplyr, but only includes basic verbs. - datawizard: low dependencies, slightly different syntax, has base-R implementations of most of dplyr / tidyr functions, plus some other goodies likes scaling, mean-centering, rank transforming, ... - And of course, data.table: 0 dependencies, ultra-fast (everything is written in optimized C under the hood), can manipulate much bigger data than the Tidyverse, and can do everything the tidyverse can when it comes to data wrangling (however, sometimes the tidyverse has convenience functions that make some operations shorter than with data.table). The downside is that data.table's syntax requires more efforts to learn / is less intuitive to read for neophytes.