I was excited about Julia too but it now seems to be a relatively niche HPC language. It's about saving CPU time more than user time.

My sniff test for a successor language to R is whether it can replicate the tidyverse API with 100% fidelity. The API is already optimal for tabular data analysis, especially the dplyr core. It can be thought of as a specification for other languages to implement.

There is a great deal about how R works that is negotiable. But if the language can't implement dplyr to spec, or somehow doesn't "want to", it's not the language for the audience served by the tidyverse.

Follow-up: I did a little legwork and found some Julia folks who were also inspired to implement tidyverse as faithfully as possible: https://github.com/TidierOrg

Here's how dplyr-style chains look in their system:

  using TidierData
  using RDatasets

  movies = dataset("ggplot2", "movies");

  @chain movies begin
      @mutate(Budget = Budget / 1_000_000)
      @filter(Budget >= mean(skipmissing(Budget)))
      @select(Title, Budget)
      @slice(1:5)
  end

Not a character-for-character match to dplyr, but gets much closer than most other attempts!

I love this framing :)