The majority of researchers don’t care about the language superiority. They’re concerned with different issues and software tends to suffer from “publish and forget” attitude. Convenience matters, and R ecosystem is quite good.
As a scientist programmer, that has not been my experience. In my experience, science programming is characterized by having to implement a lot of stuff from the ground up yourself, because unlike web dev or containerization, it's unlikely there is any existing library for metagenomic analysis of modified RNA.
And here Julia is a complete Godsend, since it makes it a joy to implement things from the bottom up.
Sure, you also need a language that already has dataframe libraries, plotting, editor support et cetera, and Julia is lacking behind Python and R in these areas. But Julia's getting there, and at the end of the day, it's a relatively low number of packages that are must-haves.
> In my experience, science programming is characterized by having to implement a lot of stuff from the ground up yourself
It depends on the field, there’re hundreds of biological publications each month that just use existing software. And if I’m developing a new tool for single-cell analysis, it’s either going to be interoperable with Seurat or Bioconductor tools.
And here Julia is a complete Godsend, since it makes it a joy to implement things from the bottom up.
Sure, you also need a language that already has dataframe libraries, plotting, editor support et cetera, and Julia is lacking behind Python and R in these areas. But Julia's getting there, and at the end of the day, it's a relatively low number of packages that are must-haves.