The way I wrote that comes off as more dismissive than I intended. I think it’s quirky in the sense that there is a wide variance in styles of accomplishing things in (base) R, so something that appears perfectly natural to me can look foreign to someone else. I think this is partly the user base and partly the language itself, and of course the two are interdependent. To me, it’s a joy to write R code because of it’s flexibility and power, but I often have dreaded sharing it with others (especially as a beginner). It’s easy to look at someone else’s R scripts and think “this is horrifying”. By the way, this is referring more to scientific/statistical workflows—for more general purpose data science in R, the Tidyverse (or even just the pipe operator %>% around which the Tidyverse is built) goes a long, long ways towards helping people write expressive but readable code.
By contrast, Python feels a bit too rigid/standardized. Everyone’s code looks like it was copy+pasted from a book of truth somewhere. This is good for sharing and engineering, not as good for expressing mathematical ideas.
So whereas R has evolved organically over decades and Python is for everyone (and alternatives like MATLAB or SAS are first and foremost software for industry rather than languages), Julia seems to be thoughtfully purpose-built to be a modern language for numerical/scientific computing. It polishes off the rough edges and blends some of the best features of each language. Again, this is just an impression from someone who already thinks in R but is learning both Python/Julia.
More to your point, maybe Julia is at a stage of development where it’s good for both students (for developing computational and mathematical thinking) and experts (for slinging concise but performant code), but not yet the rank-and-file users looking to just get things done.
