RCall.jl
TidyverseSkeptic
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RCall.jl | TidyverseSkeptic | |
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8 | 13 | |
310 | 506 | |
1.0% | - | |
5.5 | 3.3 | |
15 days ago | 4 months ago | |
Julia | TeX | |
GNU General Public License v3.0 or later | - |
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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.
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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
TidyverseSkeptic
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Why Pandas feels clunky when coming from R
I just don't get these to be honest -- besides the fact that author missed simple things like `df.groupby('var',as_index=False)`, isn't this obviously arbitrary "this is easier my way" complaints? (I did R before all the chaining stuff was popular, and I wouldn't stuff everything into a single command like that even now. It isn't like you get lazy evaluation or any special data processing magic.)
So I get people love chaining and tidyverse, good for you, I don't. But at least I can acknowledge that my way (or this way) people have different preferences and one is not intrinsically easier.
Norm Matloff has a blog where he essentially just argues the opposite of all the tidyverse stuff, https://github.com/matloff/TidyverseSkeptic, but it is the same idea in reverse to me (one is not obviously easier to learn than the other IMO).
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Where to learn R?
On the other hand, there is also a more traditional universe outside of the of the newer tidyverse approach. See the criticism of the tidyverse ecosystem by Prof Norm Matloff (of UC Davis). He provides a freely available introductory course in base R.
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I will take that odds
Whenever I hear tidyverse, I just feel the need to leave this: TidyverseSceptic
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Base-R Is Alive and Well
Yeah, I had never heard of him before, but I followed the link in the article above to his GitHub page and think he made some really great points about conciseness and clarity in base R code, and, I admittedly had no idea tapply() was so useful and easy to use, because I almost never see it used in any examples online. Although I agree with others here that he's misrepresenting why package developers use base R (which is to avoid dependences in their packages, which is very important), I also find myself agreeing with him that future R programmers not being taught base R is worrisome (I'm thinking of dependencies in future package development).
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Your thoughts on base R? I never considered it and, after reading seemingly know little about it.
I was in an R group meeting. One of the members mentioned Prof. Norm Matloff and said he has comments about tidyverse. I searched and found Matloff's explanation here. What are your thoughts on tidyverse and Matloff's comments about it? As I read it, I found myself agreeing with certain points. I do not have a computer science background; I'm someone trying to learn coding because I see uses for it in my work. I started my learning, about a year ago, with tidyverse tutorials. My patchwork jumping around, maybe in addition to some of the gaps Matloff indicates, show me that I know very little about base R.
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In charge of making the transition from Excel to R at the office
There are good arguments against tidyverse, especially for beginners. It doesn't lead to a growth in understanding the language fundamentals and requires to learn many functions, paradigms, and syntaxes not shared by base R, which can easily be overwhelming and lead to a learn-by-heart approach more than to a learn-by-understanding. There are many good articles on the topic, such as this one or a more in-depth one, suggesting to consider tidyverse a more advanced application for specific use cases, if you like the dialect. I don't, so I might be biased.
- Teaching R in a Kinder, Gentler, More Effective Manner
- An opinionated view of the Tidyverse “dialect” of the R language
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Thoughts on book?
I would discourage you to get into the tidyverse, at least in the first stages of your R training. It's like trying to learn english AND scottish together as a foreigner. You can read some better worded discussions here https://github.com/matloff/TidyverseSkeptic and here https://towardsdatascience.com/a-thousand-gadgets-my-thoughts-on-the-r-tidyverse-2441d8504433?gi=1b0a3648b6e6
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Ho everyone I am R beginer. I need to to change the data type of these two columns, I tried as many ways I could find on the internet but it just won't work for me. This is really frustrating especially when you are a beginer, can you pleae provide a solution ? Thanks a lot in advance !
My opinions are largely in agreement with Norm Matloff on the subject actually.
What are some alternatives?
Makie.jl - Interactive data visualizations and plotting in Julia
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.
org-mode - This is a MIRROR only, do not send PR.
VegaLite.jl - Julia bindings to Vega-Lite
PackageCompiler.jl - Compile your Julia Package
PyCall.jl - Package to call Python functions from the Julia language
Transformers.jl - Julia Implementation of Transformer models
Revise.jl - Automatically update function definitions in a running Julia session
swirl - :cyclone: Learn R, in R.
cmssw - CMS Offline Software
magrittr - Improve the readability of R code with the pipe