[throwway_278314]

Just imagine how much more efficient you would be if you were using R's DataTable.

Look, I applaud your skill, but at some point even a master craftsman realizes that the swiss army knife may not be the best tool, and a leatherman offers certain advantages.

[data-ottawa]

I really like R’s library and I’ll use them any chance I get (libraries like lmer are still orders of magnitude more efficient than the same model in Statsmodels).

From my experience the biggest impediment to using R in production is many orgs don’t have a blessed way to run it.

R is my favourite language for data processing, the manual section Computing on the Language[1]is why R is such an ergonomic tool. I had hoped Julia would catch up, but Julia’s macros are not comparable in their depth.

I think pandas is probably the data equivalent of editing files using default vim or processing data with awk.

[1] https://rstudio.github.io/r-manuals/r-lang/Computing-on-the-...

[cpcloud]

As a joke, I wrote an Ibis backend (https://github.com/cpcloud/ibish) that processes expressions using shell commands strung together with named pipes. It supports joins using the coreutils join command, projections, filters and some aggregations with awk.

It's faster than pandas in some cases and folks should put it into production immediately!

[cpcloud]

Sometimes I write data analysis code in R to make myself aware of how it is occasionally possible to have nice things.