I agree - declarative in memory dataframe manipulation is extremely powerful. And the composability of plotting in the tidyverse is really nice as well.

It looks like there are the beginnings of both of these in Julia:

[0] http://gadflyjl.org/stable/

[1] https://github.com/JuliaStats/DataFramesMeta.jl

I often ask myself the question, how can Julia do things that R cannot do. After all, when something is good at doing something, why replace it?

Part of this is why we did JuliaDB: http://juliadb.org/ and continue to try push the boundaries on parallelism, missing data, OnlineStats.jl and making data manipulation and modeling that much easier.

In some sense, it doesn't really need to compete with R, many times it's better just to use the R, Python, Java, C++, packages via RCall, PyCall, JavaCall, Cxx, or use the built-in ccall to use libraries written in any number of languages that conform to the C ABI (C, Fortran, Rust, ...).

I've joked before, about how there is no such thing as a "One Language To Replace Them All", however, I feel Julia is the best candidate for the "One Language To Rule Them All", since while it solves the "two-language" problem in many cases, you can use it bind code written in many languages together (hopefully in a bit nicer fashion than the "One Ring" bound the other rings and their users!)

Agree completely with this, tidyverse is whats keeping me in R when I'd prefer to mostly use Python.