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().
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jdw64
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 that be why the use of R has exploded with the help of AI? Looking at this, I think it's fair to understand that even domain experts found programming difficult. Seeing this, can we really say that AI is always bad? For some people, it has become both the hands and a voice for their words.
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alastairr
The surprising thing to me is that it's taken as long as it has for CRAN to have this problem. As others have said, this is happening everywhere.
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f311a
It's the same on any package index now.
Mairoce
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.
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jochapjo
I'm a recent first-time CRAN submitter. I believe my package went through 2 rounds of human review. I doubt R has a severe "too much AI slop" problem relative to other languages, but I can see how human reviewers would get inundated.
dizhn
CRAN is not a conventional package repo. Its audience is not really people who care about programming or software. It is a means to an end for them and slop is perfectly fine. The language itself is also very simple and has defaults that people don't even bother changing. For example the default output file name. It doesn't ask for an output file name when you save output.
As a result of the above, it is full of packages that come with associated datasets right in the package itself. Packages with a tiny script and gigabytes of data. Or perhaps just the data without any actual code.
Very weird universe.
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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?
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piokoch
We have too many videos (since creating one is so easy), too many music (since recording it is so easy), too many books (since publishing an e-book is so easy). Now the same story happens again, for software. But this time it causes more troubles...
nickcageinacage
vibe coding hell is the reason
greenavocado
The solution to this problem will be a web of trust featuring a vouching system that auto-closes PRs by default. I already see this being implemented in projects.
dofm
R slop. Oof.
What an awful thing to imagine. It's already the programming language of choice for egregious abuses of good practice.
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().
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 that be why the use of R has exploded with the help of AI? Looking at this, I think it's fair to understand that even domain experts found programming difficult. Seeing this, can we really say that AI is always bad? For some people, it has become both the hands and a voice for their words.
The surprising thing to me is that it's taken as long as it has for CRAN to have this problem. As others have said, this is happening everywhere.
It's the same on any package index now.
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'm a recent first-time CRAN submitter. I believe my package went through 2 rounds of human review. I doubt R has a severe "too much AI slop" problem relative to other languages, but I can see how human reviewers would get inundated.
CRAN is not a conventional package repo. Its audience is not really people who care about programming or software. It is a means to an end for them and slop is perfectly fine. The language itself is also very simple and has defaults that people don't even bother changing. For example the default output file name. It doesn't ask for an output file name when you save output.
As a result of the above, it is full of packages that come with associated datasets right in the package itself. Packages with a tiny script and gigabytes of data. Or perhaps just the data without any actual code.
Very weird universe.
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?
We have too many videos (since creating one is so easy), too many music (since recording it is so easy), too many books (since publishing an e-book is so easy). Now the same story happens again, for software. But this time it causes more troubles...
vibe coding hell is the reason
The solution to this problem will be a web of trust featuring a vouching system that auto-closes PRs by default. I already see this being implemented in projects.
R slop. Oof.
What an awful thing to imagine. It's already the programming language of choice for egregious abuses of good practice.