collapse VS priceR

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)

I was an economist doing econometrics in excel when in 2014 the datasets went being a few 10,000's rows to a few 1,000,000's rows. I found R easiest to learn simply as a CS outsider because it was less strict about package versions and installation requirements, which made it easier for a beginner. I learned it by googling every little step ('how read in csv', 'how create new column in data.frame' etc) until I had a ~40 line R script that did what I was previously doing by hand in excel. It ran in a few seconds and did what took excel about 10 minutes.

A few years later I wrote an open source economics library in R: https://github.com/stevecondylios/priceR#pricer- It converts between nominal and real prices, converts between 171 currencies, and has a few regex's for pulling numeric data out of text (e.g. salaries out of job descriptions).

Some specific observations regarding the article:

- Comparing computation speed seems a bizarre metric to care about. 6x faster matters on things that take minutes, hours or days, but less so for operations that already run in under 1000ms. Developer experience is usually more important IME.