Chain.jl
TidyverseSkeptic
Chain.jl | TidyverseSkeptic | |
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8 | 13 | |
346 | 508 | |
- | - | |
4.2 | 3.3 | |
2 months ago | 4 months ago | |
Julia | TeX | |
MIT License | - |
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Chain.jl
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Pains of Julia compared to python
The [Chain.jl package](https://github.com/jkrumbiegel/Chain.jl) is becoming idiomatic for these kind of pipelines.
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Transition from R Tidyverse to Julia (VS Code)
If you do have tabular data in a dataframe you have a few options for data manipulation, the most popular packages are probably DataFramesMeta and Query, although in my opinion the best way to manipulate dataframes is with the functions built in to DataFrames.jl and using a package like Chain.jl or Pipe.jl to pipe the functions into each other like magrittr in R.
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The (updated) history of the pipe operator in R
The Julia community built a better piping method than any other language has AFAIK: Chain.jl.
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What are some of your favourite macros?
@chain and @match.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
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https://np.reddit.com/r/Julia/comments/nnu6if/julia_object_oriented_programming_with_dot/h0anaru/
You are right. However, sometimes well used is very useful, and readable. One suggestion, in Julia I suggest Chain.jl, because it allows intercalate easily the output for debugging:
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I also like pipe syntax and I've found there is nice support for it in Julia. There are some nice packages to improve it over base [1].
Have you checked queryverse [2]?
[1] https://github.com/jkrumbiegel/Chain.jl
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?
Pipe.jl - An enhancement to julia piping syntax
RCall.jl - Call R from Julia
Genie.jl - 🧞The highly productive Julia web framework
VegaLite.jl - Julia bindings to Vega-Lite
Revise.jl - Automatically update function definitions in a running Julia session
swirl - :cyclone: Learn R, in R.
JLD2.jl - HDF5-compatible file format in pure Julia
Transformers.jl - Julia Implementation of Transformer models
PaddedViews.jl - Add virtual padding to the edges of an array
magrittr - Improve the readability of R code with the pipe
Infiltrator.jl - No-overhead breakpoints in Julia
PackageCompiler.jl - Compile your Julia Package