[pgcudahy]

In this article and others I've read, people complain about the tidyverse's lack of performance, but I think that places too much emphasis on speed of execution versus speed of development. As an academic, most of the R users I know only code as a portion of their scientific projects. Besides data analysis we're doing data collection, manuscript writing and grant writing. The tidyverse's more english-like syntax (eg select() versus `[`) and following a series of pipes rather than unnesting ten sets of brackets makes it so much easier to come back to my code after a few weeks or months and pick up where I left off, or to work with other people's code. My time is more valuable than computer time so the tidyverse is my choice.

[FranzFerdiNaN]

This is my experience as a data engineer/analyst. I work in the healthcare space and my analyses are run on datasets that are at the uppermost half a million rows. Yes, data.table is faster than dplyr/tibbles, but i dont care. Like you said, most of my time is spent on expressing my thoughts into code, not running the code. Tidyverse really simplifies it compard to base R.

Its good to know tools like data.table exist though, people shouldnt think the tidyverse is the only way to do things.