collapse VS fastverse

The ask is a little unclear. You might be interested in collapse and more generally in other packages in the fastverse. I guess it's also worth pointing out that data.table already provides alternative methods for certain base R descriptive stats functions (e.g., mean, etc.) that are automatically used when applied to datatables.

If you are looking for performance I would recommend to check the collapse package. The following line "collapse" = collapse::fsum(df_datatable$x, g=df_datatable$g) is around 2x faster than base::rowsum, and the dplyr style syntax doesn't add that much of an overhead "collapse dplyr" = df_datatable |> fgroup_by(g) |> fsum(x)

For writing faster code, the first thing you want to try is making sure it's properly vectorised. See The R Inferno for this. Some problems are more difficult than others to vectorise. When vectorisation is impossible, you probably want to interface with C++. First, check if there's already a fast package that serves your needs. If your problem is too specific, consider writing your own C++ code with Rcpp.

I understand the sentiment and I'm not trying to convince you to start writing optimised code to save ~2ms. There's a ton of optimised tools that I don't use myself because the time benefit is immaterial for what I do.