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Too many R packages: CRAN is inundated with submissions

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Re: Too many R packages: CRAN is inundated with submissions

#21

Post by buellerbueller »

dofm wrote: post #14

R slop. Oof. What an awful thing to imagine. It's already the programming language of choice for egregious abuses of good practice.

Conversely, it is the programming language of choice for people who don't assume that their expertise on one domain (data science) translates into expertise in the whole of human knowledge (as we often see among techbros generally and here specifically).

As a working data scientist, I know I am not a computer scientist or a 10x engineer (hell, I am probably a 0.8x engineer), but that's not where my expertise is. My engineer co-workers are 0.01x data scientists, but you won't see me complaining that they don't know the Central Limit Theorem or how to build a causal inference engine.

Re: Too many R packages: CRAN is inundated with submissions

#22

Post by PaulHoule »

nswizzle31 wrote: post #6
Mairoce wrote: post #3

Frankly the bigger problem is an over reliance among R instructors on the tidyverse, an ever-expanding ecosystem of redundant functions and anti-patterns. They’re teaching new R users that everything can be solved with yet another package import and skipping over teaching them how to use the already powerful and intuitive base packages.

I couldn’t disagree more. The base packages are a complete mess. If R was subset to only the tidyverse 5 years ago then it wouldn’t have lost so much ground to Python in nearly all fields. Posit is obviously the only organization with the pull to do that, and I feel like they got pulled in 10 directions during the move to AI and trying to also support Python. R Shiny is dead too which sucks because reflex.dev just co…

Python is just such a good Swiss army knife and it's never a waste to learn: you can do data science and you can do almost anything else. It's the BASIC of the 21st century.

Re: Too many R packages: CRAN is inundated with submissions

#23

Post by RA_Fisher »

davemp wrote: post #8
RA_Fisher wrote: post #5

Earlier quoted context omitted.

Programming is a lot easier than statistics bc it’s deterministic, whereas statistics is stochastic (that extends and encompasses deterministic functions). AI speeds up learning, so I bet that’s what you’re noticing with R. As an aside, the best programmers these days are probabilistic programmers (who write stochastic functions). Our languages are Stan and PyMC. Both can be called by Python or R, and AI writes all o…

Picking up on some dunning kruger effect here. Programming isn’t even a field in the same way as prob&stats. Computer science does in fact have non-deterministic sub fields such as information theory.

There’ll always be boundary tending, true. Only a portion of CS deals with stochastic functions though, whereas all of statistics is stochastic. That makes a big difference, bc the world is complex.

Information theory doesn’t even incorporate utility.

Re: Too many R packages: CRAN is inundated with submissions

#24

Post by ianbooker »

I see "AI and R" in three perspectives:

First, usage: Using R for our undergrads in time of LLMs is brilliant. ChatGPT slops out working code for their needs. Not pretty but works better that in 2022.

Second, development: Mastering R is hard, because its kalkül. Tidyverse mediates some of it, but still. This is the perfect breeding ground for slopification. Lets see.

Third, errata: I would love to know the percentage of science built on R to this day. I mean insights and analysis supported by it and it vast packages. What if somewhere, deep down in the stack there is an ancient bug that dented all of this? I think AI might help us here, or review slop will negate this?

Re: Too many R packages: CRAN is inundated with submissions

#25

Post by [deleted] »

buellerbueller wrote: post #21
dofm wrote: post #14

R slop. Oof. What an awful thing to imagine. It's already the programming language of choice for egregious abuses of good practice.

Conversely, it is the programming language of choice for people who don't assume that their expertise on one domain (data science) translates into expertise in the whole of human knowledge (as we often see among techbros generally and here specifically). As a working data scientist, I know I am not a computer scientist or a 10x engineer (hell, I am probably a 0.8x engineer), but that's not where my expertise is. My e…

[deleted]

Re: Too many R packages: CRAN is inundated with submissions

#26

Post by Mairoce »

nswizzle31 wrote: post #6
Mairoce wrote: post #3

Frankly the bigger problem is an over reliance among R instructors on the tidyverse, an ever-expanding ecosystem of redundant functions and anti-patterns. They’re teaching new R users that everything can be solved with yet another package import and skipping over teaching them how to use the already powerful and intuitive base packages.

I couldn’t disagree more. The base packages are a complete mess. If R was subset to only the tidyverse 5 years ago then it wouldn’t have lost so much ground to Python in nearly all fields. Posit is obviously the only organization with the pull to do that, and I feel like they got pulled in 10 directions during the move to AI and trying to also support Python. R Shiny is dead too which sucks because reflex.dev just co…

The proof is in the pudding. Every single grad student of mine that was brought up on the tidyverse produces gigantic R markdown files with 20 imports to accomplish something that would be shorter and much much easier to understand (and review!) with a base package or with one of a small number of packages (box, data.table) designed by people who understand programming.

Not to mention the ridiculous styling/formatting of most tidyverse users, which Wickham and others seem to promote. One of the reasons R has lost ground to other languages recently is that most R code these days is ugly

Re: Too many R packages: CRAN is inundated with submissions

#27

Post by parsimo2010 »

I feel like CRAN should be used for packages that are expressly made for others to use, and with effort put in to the documentation and vignettes.

If you’re making a package for a small team or aren’t pushing it to a large audience then just keep it on a GitHub repository. It is almost as easy to install from GitHub with devtools as it is to install.packages().

Re: Too many R packages: CRAN is inundated with submissions

#28

Post by colechristensen »

jdw64 wrote: post #2

People would typically choose based on CRAN TaskViews or follow conventional methodologies, but what I notice from this is that R is truly a language used only by those who use it. And the people who use it are usually master's students or professors; it's rarely used at the undergraduate level. So even those with that level of academic background and training must have had their own implementation roadblocks. Could…

A considerable amount of work for grad students is answering the question: "How the f#$% do I get this code to compile and run"

Some other researcher, often with limited skills in your native tongue, even more limited skills in software development best practices, wrote some code for a paper between 5 and 50 years ago and your PI has told you to use that code and some OTHER code together at the same time to validate some experiment he wants you to do.

In the past you would take days/weeks/months to get this to work, but with an LLM?

I'm envious of the grad students of today for the amount of nonsense which is bypassable.

Re: Too many R packages: CRAN is inundated with submissions

#29

Post by colechristensen »

ianbooker wrote: post #24

I see "AI and R" in three perspectives: First, usage: Using R for our undergrads in time of LLMs is brilliant. ChatGPT slops out working code for their needs. Not pretty but works better that in 2022. Second, development: Mastering R is hard, because its kalkül. Tidyverse mediates some of it, but still. This is the perfect breeding ground for slopification. Lets see. Third, errata: I would love to know the percentage…

>What if somewhere, deep down in the stack there is an ancient bug that dented all of this?

Science is built on libraries with experience, that have been validated extensively against reality. Code often written by people who have retired and died because that exact same code has been validated and pinned to reality for decades. It is of course possible that a load bearing bug survives for a long time conspiring with an incorrect model of reality to give validated results, but wide use tends to eliminate these things.

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