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Big Book of R

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Re: Big Book of R

#101

Post by goosedragons »

aquafox wrote: post #58
cye131 wrote: post #37

R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.

Why not mix R and Python in interactive analysis workflows: 1) Download positron: https://github.com/posit-dev/positron 2) Set up a quarto (.qmd) notebook 3) Set up R and Python code chunks in tour quarto document 4a) Use reticulate to spawn a Python session inside R and exchange objects beween both languages ( https://github.com/posit-dev/positron/pull/4603 ) 4b) Write a few helper functions that pass objects betwee…

Org mode in Emacs is even better at this IMO. Only downside is that no guarantee other people use Emacs too.

Re: Big Book of R

#102

Post by oscarbaruffa »

marginatum wrote: post #94
oscarbaruffa wrote: post #68

I'm the curator of Big Book of R and am really happy to see it on the front page of HN :). New books are added every 6 weeks or so and I send a notifications of the new adds to my newsletter subs. Link is at the footer of every page

Well done Oscar. I got you a$5 coffee as with the economic crisis I don't think you'll find a good $2 one.

Thank you :)

Re: Big Book of R

#103

Post by wdkrnls »

disgruntledphd2 wrote: post #67
JackeJR wrote: post #39

Earlier quoted context omitted.

browser() ?

trace subsumes browser, it's much more flexible and can be applied to library code without editing it.

Is there a way to trace an attribute to a function? I couldn't find one, but curious if it exists. I seemed blocked by the fact that trace seemed to expect a name as a character string. Some functions in base R have functions in their attributes which modify their behavior (e.g. selfStart). I ended up just copying the whole code locally and then naming it, but for a better interactive experience I really wish there was a way to pass a function object as I can with debug.

Re: Big Book of R

#104

Post by b-rodrigues »

aquafox wrote: post #58
cye131 wrote: post #37

R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.

Why not mix R and Python in interactive analysis workflows: 1) Download positron: https://github.com/posit-dev/positron 2) Set up a quarto (.qmd) notebook 3) Set up R and Python code chunks in tour quarto document 4a) Use reticulate to spawn a Python session inside R and exchange objects beween both languages ( https://github.com/posit-dev/positron/pull/4603 ) 4b) Write a few helper functions that pass objects betwee…

I'm writing a package called rixpress that leverages Nix to build reproducible pipelines with targets in either R or Python

Here's the github to the package https://github.com/b-rodrigues/rixpress/tree/master

and here's an example pipeline https://github.com/b-rodrigues/rixpress_demos/tree/master/py...

Re: Big Book of R

#105

Post by CreRecombinase »

gsf_emergency_2 wrote: post #45

Any Julians comment? Having seen Julia proposed as the nemesis of R (not python, that too political, non-lispy) > the creator of the R programming language, Ross Ihaka, who provided benchmarks demonstrating that Lisp’s optional type declaration and machine-code compiler allow for code that is 380 times faster than R and 150 times faster than Python (Would especially love an overview of the controversies in graphics/r…

The comment you linked is a response to my comment where I tried (and failed) to articulate the world in which R is situated. I finally "RTFA" and the benchmark I think perfectly deomonstrates why conversations about R tend not to be very productive. The benchmark is of a hypothetical "sum" function. In R, if you pass a vector of numbers to the sum function, it will call a C function sum. That's it. In R when you want to do lispy tricky metaprogramming stuff you do that in R, when you want stuff to go fast you write C/C++/Rust extensions. These extensions are easy to write in a really performant way because R objects are often thinly wrapped contiguous arrays. I think in other programming language communitues, the existence of library code written in another language is some kind of sign of failure. R programmers just do not see the world that way.

Re: Big Book of R

#106

Post by kgwgk »

tylermw wrote: post #78
uptownfunk wrote: post #30

I will say, now after 15 years messing with this. With LLM I just do it all in Python. But, I still miss the elegance and simplicity of R for data manipulation and analysis. Especially the dplyr semantics. They really nailed it. I think they got crushed by the namespace / import system. There’s something about R that makes you so fluid and intuitive. But the engineering, the efficiency, I get with Python now, I can’t…

Funny you mention namespacing: R 4.5.0 was just released today with the new `use()` function, which allows you import just what you need instead of clobbering your global namespace, equivalent to python’s `from x import y` syntax. e.g. avoid dplyr overriding base::filter use(“dplyr”, c(“mutate”, “summarize”))

The release notes say:

    (Actually already available since R 4.4.0.)

Re: Big Book of R

#107

Post by freehorse »

raffael_de wrote: post #100
freehorse wrote: post #31

Earlier quoted context omitted.

Until you need to plot anything more than a few hundred thousand data points, in which case ggplot is extremely slow, if it even manages.

I would argue that this is too much for any static plot. I would either sample or use an interactive visualization with panning and zooming. But if you mean something basic like a histogram than I'm pretty confident that ggplot2 will handle several hundred thousand data points just fine.

Fair; so my arguments becomes "until you need anything barely interactive such as zooming in".

Re: Big Book of R

#108

Post by uptownfunk »

dkga wrote: post #62
uptownfunk wrote: post #30

I will say, now after 15 years messing with this. With LLM I just do it all in Python. But, I still miss the elegance and simplicity of R for data manipulation and analysis. Especially the dplyr semantics. They really nailed it. I think they got crushed by the namespace / import system. There’s something about R that makes you so fluid and intuitive. But the engineering, the efficiency, I get with Python now, I can’t…

I agree with all your comment… except the very last bit. Do you really find python to be more efficient at engineering stuff than R? And especially speed, which in my experience at least is broadly the same if not faster with R because it interages easier with Rust and C++?

Everything I need can get done in python, so I don’t even need to deal with rust and cpp. Adding language interop between r and cpp is now just another thing on my plate, so just stick to Python and pay the cost of less elegant code for data manipulation which I am okay with because now I just need to read it and not write it.

There’s a ton more python code out there so the LLM reliability in python code just makes my life easier. R was great and still is, but my world is now more than just data eng, model fitting, and viz. I have to deal with operationalizing and working with people who aren’t just data science and most org don’t have the luxury of having an easy production R system so I can get my python code over the line and trust a good engineer will be okay smeshing that into the production stack which is likely heavy Python. (Instead of saying oh we don’t work with R we do Python Java so it will take 3-5x longer).

Another sad truth is the cool ml kids all want to do pytorch deep ML training / post training / rlhf / ppo / gdpr gtfo so you are not real hardcore ml if you only do R. I know it’s stupid but the world is kind of like that.

You want to hire people who want to build their careers on the cool stack. I know it’s not all the cool talk the hackers here play with but for real world application I have a lot of other considerations.

Re: Big Book of R

#109

Post by uptownfunk »

dkga wrote: post #62
uptownfunk wrote: post #30

I will say, now after 15 years messing with this. With LLM I just do it all in Python. But, I still miss the elegance and simplicity of R for data manipulation and analysis. Especially the dplyr semantics. They really nailed it. I think they got crushed by the namespace / import system. There’s something about R that makes you so fluid and intuitive. But the engineering, the efficiency, I get with Python now, I can’t…

I agree with all your comment… except the very last bit. Do you really find python to be more efficient at engineering stuff than R? And especially speed, which in my experience at least is broadly the same if not faster with R because it interages easier with Rust and C++?

On further reflection I think the sweet spot for R for me Has always been prototyping and exploration. Where you don’t exactly know what the logic needs to be, or how the data needs to be cut to get at what you want. So that rapid type of exploration R is really really good at. Closer to math for me than software engineering. And if I had a job where I could just do that all day I’d be pretty happy at this point in my life. and you can’t use a pivot table Google sheets or excel to get at the cut you want or the logic is too complex to do in Google sheets. So for that sweet spot, which is still a broad niche, R is excellent and shines.

Re: Big Book of R

#110

Post by wdkrnls »

account-5 wrote: post #65
DadBase wrote: post #13

Earlier quoted context omitted.

Totally agree. R is pure pirate energy. Half the functions are hidden on purpose, the other half only work if you chant the right incantation while facing the CRAN mirror at dawn.

I've never used R before, why would functions be hidden on purpose? Sounds like a recipe for frustration.

Computer scientists had this idea that some things should be public and some things private. Java takes this to the nth degree with it's public and private typing keywords. R just forces you to know the lib:::priv_fun versus lib::pub_fun trick. At best it's a signal for package end users to tell which functions they can rely on to have stable interfaces and which they can't. Unfortunately, with R's heavy use of generics it gets confusing for unwary users how developers work with the feature as some methods (e.g. different ways to summarize various kinds of standard data sets as you get with the summary generic or even the print generic) get exported and some don't with seemingly no rhyme or reason.
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