[KKPMW]

> tidyverse code, which is essentially the only thing keeping R in the game

From my experience this is not the case. In biomedicine and bioinformatics few people actually use tidyverse because the data is much better represented as a matrix, and not in the "tidy" form.

Outside of that corporations (well at least 2 I contracted with) used `data.table` explicitly. Join 3 ad-click dataframes matching by userID, sessionID and closest possible time-point - that's one line in `data.table`.

Tidyverse is well suited for learning and for managing (relatively) simple datasets. But becomes cumbersome for more complex data. It can be used for those data too of course, just that it will be adding ad-hoc solutions and maybe get in a way more than help.

[anthony_doan]

I agree with your experiences.

I've only use base R for my medical data (subsetting dataframe and such). Very rarely do I need tidy and also I find the pipe operator makes debugging harder. If and when I need it I'll use it that's that.

I think R have much more packages in medical, especially statistical packages, where many fields within medical cares about inferences not just prediction/forecasting. So I disagree with the "essentially the only thing keeping R in the game". The breath of packages in R is one of the many things that keep R in the game.

The tribalism and highly bias comments makes it very toxic and harder to have an honest discord.

They are just tools, use what makes you happy and get the job done.

[zelda_1]

I have a similar feeling. And that is why I spent one whole chapter in data.table (and pandas). Hope more R users would like to learn and use data.table.