jnumpy
RCall.jl
jnumpy | RCall.jl | |
---|---|---|
9 | 8 | |
227 | 311 | |
0.0% | 0.6% | |
3.9 | 6.1 | |
27 days ago | 7 days ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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jnumpy
- Making Python 100x faster with less than 100 lines of Rust
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This Week in Python
jnumpy – Writing Python C extensions in Julia within 5 minutes
- GitHub - Suzhou-Tongyuan/jnumpy: Writing Python C extensions in Julia within 5 minutes.
- JNumPy: Writing high-performance C extensions for Python in minutes
RCall.jl
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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
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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
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Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
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Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
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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
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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
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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
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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?
makepackage - Package for easy packaging of Python code
Makie.jl - Interactive data visualizations and plotting in Julia
ideas
org-mode - This is a MIRROR only, do not send PR.
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
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.
PythonCall.jl - Python and Julia in harmony.
Revise.jl - Automatically update function definitions in a running Julia session
log-booster - An VS code extension to quickly add frequently used log statements
cmssw - CMS Offline Software
Schemathesis - Supercharge your API testing, catch bugs, and ensure compliance
PyCall.jl - Package to call Python functions from the Julia language