[c7b]

R is great, and so are some of the packages that lead to the tidyverse, but I think the latter was a bit too much. Re-inventing what already worked with new packages, always overloading R syntax in weird ways (looking at you, ggplot2). I've actually found myself moving back to base R for many of the more basic manipulation tasks.

[civilized]

Base R is loved only by those who were unlucky enough to spend years using it when there was no alternative.

[c7b]

Base R is far from perfect, but for many basic manipulation tasks it works just as fine as tidyverse. Maybe not with piping, but that doesn't really save anything if you format it readably.

There's something to be said about code that just works out of the box. I don't see the need to maximize dependence on third-party libraries as long as the gains are purely "ergonomic". Especially when the creators have a somewhat mixed record regarding long-term commitment vs re-inventing their own wheel.

The real selling point of R imho aren't the data science tools anyway - for that we already have the amazing Python ecosystem (which also the RStudio guys have tacitly admitted with their rebranding) - but the pure statistics packages. Especially if you need something more niche, to the point that you'd use any language just to get an implementation of a specific model, you'll find yourself coming back to R more than half the time. It's simply the language of choice where most statisticians publish their code.

[civilized]

R has some superior data science tools. For example, the tabular data packages dplyr and data.table have no adequate parallels in the Python world. There are many also-rans but no real rivals.

[_Wintermute]

Or anyone who has tried to re-run R code that was written more 6 months ago.

[civilized]

I write R full-time using the full suite of tidyverse packages and that's just not an issue these days. Maybe a few years ago.

And anyway, you'll hit the same issue using third-party packages in any language.