S7 VS dtplyr

https://adv-r.hadley.nz/oo.html

"There are multiple OOP systems to choose from. In this book, I’ll focus on the three that I believe are most important: S3, R6, and S4. S3 and S4 are provided by base R. R6 is provided by the R6 package, and is similar to the Reference Classes, or RC for short, from base R.

"There is disagreement about the relative importance of the OOP systems. I think S3 is most important, followed by R6, then S4. Others believe that S4 is most important, followed by RC, and that S3 should be avoided. This means that different R communities use different systems."

https://rconsortium.github.io/OOP-WG/

"The S7 package is a new OOP system designed to be a successor to S3 and S4."

Even if RStudio & the Tidyverse have mostly been promoting a functional programming style in R, it has full support for OOP (see R6 or R7 for more modern implementations of it). Let's not even mention the excellent Stan ecosystem for Probabilistic programming / Bayesian modeling, or Bioconductor, the biggest repository of bioinformatics packages & tools of any language.

Not sure if you or others have missed it, as the link from the readme is dead, but the proposal section of that repo is informative of the current state of things: https://github.com/RConsortium/OOP-WG/blob/master/proposal/proposal.org

Can’t say I’ve used it, but isn’t that what dtplyr is supposed to provide?

https://dtplyr.tidyverse.org/

# I am trying to follow the example at https://dtplyr.tidyverse.org/

FYI the developer of tidytable has been developing dtplyr for the Tidyverse. You might like that too!

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

https://dtplyr.tidyverse.org/ ?

If you want data.table speed, but using dplyr/tidy then dtplyr is a good package to have handy. Personally I love R, and choose R + NodeJS as my gotos for everything I do, and use Python only when I have to.

Also, that dataset is quite big, and the "base" Tidyverse will be excessively slow. You should supplement the "base" Tidyverse packages (i.e. dplyr and tidyr) with either dtplyr or dbplyr (+ duckDB). I'd suggest starting with dtplyr, which should handle 10M+ rows fine.

If you want, there's a "substitute" for dplyr called dtplyr (also part of the Tidyverse), which "translates" your dplyr/tidyr code into data.table behind the scenes, and allows you to make your modifications apply directly to the original dataset by default:

Install the dtplyr package and change your code to:

Get the best of both worlds (tidyverse + data.tables) with dtplyr, a data.table backend for dplyr.