I'm not sure I'd have the same take. Yes, R as a language is kind of wonky and people who use R tend to not be good programmers. However, the APIs of some packages are designed well enough that even with all of those barriers it can still be easy to use for many scientists. I wouldn't copy the language, 6 different object systems and non-standard evaluation is weird. But there is a lot to learn from the APIs of the tidyverse and how it has somehow been able to cover for all of those shortcomings. It would be great to see those aspects with the data science libraries of the Julia language.

It might surprise you to learn that Julia is actively relying on code written in/for R to perform computations. You might be surprised to find out that people who can write R can also write C++ C and other languages of their choosing. You also might be surprised to learn that some of the most vetted statistical code exists in the R ecosystem. If I were someone recruiting for a niche language that had a weak ecosystem, personally I'd take all the help I could get. Can learn Julia with a background in any other programming language in a few weeks... The same can't be said about martingales... But you get to choose your strategy here...

And thus we who transitioned to Julia from R and know a bit about martingales and less about programming have long been trying to degrade the core of the language and its principles by making `mean` a Base function.

R users in the form of statisticians should definitely come around to Julia. More high quality packages never hurt. But I agree with fragmentation and 'object systems', yet I don't think this is a huge danger for Julia.