RCall.jl VS Revise.jl

I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.

[1] https://github.com/JuliaInterop/RCall.jl

You can have your cake and eat it with the likes of

* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl

* NodeCall.jl - https://github.com/sunoru/NodeCall.j

* RCall.jl - https://github.com/JuliaInterop/RCall.jl

I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself

To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL

https://github.com/JuliaInterop/RCall.jl may help

If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl

I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl

You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl

I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.

I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.

I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.

[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl

[RCall.jl]: https://github.com/JuliaInterop/RCall.jl

[Julia Interop]: https://github.com/JuliaInterop

[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html

If you want to have changes in your module reflected in the REPL look into using Revise.jl.

Do you try using Revise.jl? Reloads function definitions for you. Indispensable.

In Julia, this can be done with even less efforts on the user-side by loading Revise (https://github.com/timholy/Revise.jl) before any other package you load.

Julia has some packages to address this now.

https://github.com/timholy/Revise.jl