PaddedViews.jl
RCall.jl
PaddedViews.jl | RCall.jl | |
---|---|---|
2 | 8 | |
45 | 311 | |
- | 0.6% | |
3.8 | 5.5 | |
27 days ago | about 1 month ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | 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.
PaddedViews.jl
-
Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
As sibling posts have pointed out, you can in fact do all of those things:
1. You can write a getproperty method for a tuple. It is considered to be type piracy and thus runs the risk of colliding with someone else's definition, but the language absolutely lets you do it.
2. You can broadcast over the fields of a NamedTuple by defining appropriate methods. Again, it's type piracy, so take that into consideration but the language lets you do this easily.
3. The https://github.com/JuliaArrays/PaddedViews.jl package implements exactly what you're saying Julia won't let you do.
If anything, Julia errs on the side of allowing you to do too many things! There are very few things the language says really won't let you do.
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?
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.
Makie.jl - Interactive data visualizations and plotting in Julia
Dash.jl - Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.
org-mode - This is a MIRROR only, do not send PR.
Genie.jl - 🧞The highly productive Julia web framework
StatsPlots.jl - Statistical plotting recipes for Plots.jl
Revise.jl - Automatically update function definitions in a running Julia session
cmssw - CMS Offline Software
VegaLite.jl - Julia bindings to Vega-Lite
PyCall.jl - Package to call Python functions from the Julia language