FromFile.jl
RCall.jl
FromFile.jl | RCall.jl | |
---|---|---|
6 | 8 | |
131 | 311 | |
- | 0.6% | |
1.5 | 5.5 | |
about 1 year ago | about 1 month ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FromFile.jl
-
A Programming language ideal for Scientific Sustainability and Reproducibility?
On include-- you might like FromFile.jl as an alternative.
- Modules in Julia
-
How to import an own module from the current directory?
For this and other oddities with Julia's include/import system (and especially as you're coming from Python), I'd recommend FromFile as a readable way to approach things.
-
Why not Julia?
You might like FromFile.jl.
-
Problems with nested `include`s and solutions?
However, if you prefer a Python-like experience, checkout FromFile.jl
-
Julia 1.6: what has changed since Julia 1.0?
I'm not using modules. I usually start with one file with a demo or similarly named function that is called if the file is called as an entry point (like if __name__ == '__main__', except Julia makes it even worse).
I tend to refactor code out of there to separate files, and then somehow import it. An ugly way is include, and I've tried Revise.jl with includet.
But I think the least ugly approach is the @from macro from here: https://github.com/Roger-luo/FromFile.jl Judging from some opinion in bug trackers, this is probably gonna get totally shunned by core devs and they'll keep on bikeshedding about the import stuff forever.
With this setup I have about 400 lines of code in three files. It compiles for 15 seconds. After every single change, and actually without any changes too.
I think performance wise this should be equivalent to using modules, but saving some pointless ceremony.
RCall.jl
-
Makie, a modern and fast plotting library for Julia
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
-
Making Python 100x faster with less than 100 lines of Rust
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
-
Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
-
Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
-
I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl
-
translate R code to Julia code
I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl
-
Julia 1.6: what has changed since Julia 1.0?
You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl
-
Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
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
What are some alternatives?
julia - The Julia Programming Language
Makie.jl - Interactive data visualizations and plotting in Julia
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
org-mode - This is a MIRROR only, do not send PR.
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Revise.jl - Automatically update function definitions in a running Julia session
SymbolicRegression.jl - Distributed High-Performance Symbolic Regression in Julia
cmssw - CMS Offline Software
TwoBasedIndexing.jl - Two-based indexing
PyCall.jl - Package to call Python functions from the Julia language