Good to read. Opinionated takes add value. I wasn't completely clear when libraries were being used. For example, did the authors use base R's `read.csv()` or `

Good to read. Opinionated takes add value. I wasn't completely clear when libraries were being used. For example, did the authors use base R's `read.csv()` or `readr::read_csv()` or something else to load the CSV file? Similarly, though they say R has no facilities for dependency management, that's true in the base language, but the "renv" package does a reasonable job. I'd add that for very many people, computation speed is second order. I work with datasets of about half a million rows, which is large for economics, and I'm basically OK using R on a ten-year-old laptop. Sometimes a little pipeline management is necessary. Actually, pipeline software would be a good addition to the article. In my experience, R's "drake" is pretty overengineeered, and I suspect its successor "targets" is not much better. It would be interesting to compare the solutions in other languages. Another useful addition would be the ease of expressing simple data management tasks. So far my impression is that R with the tidyverse excels. There's not much simpler than dataset |> filter(row == cond) |> mutate(a = b + c) |> group_by(group_variable) |> summarize(x = per_group_computation(a,b,c)) where the code hews very closely to the problem domain.