Too many R packages: CRAN is inundated with submissions

Join the discussion

• Hacker News
• Many packages could be just articles where they share code. There is no need to package it.
  by fithisux
• 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.
  by greenavocado
• 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.
  by jochapjo
• Bioconductor (another R package repo) has stricter rules for submissions, such as minimum requirements for documentation.

  CRAN should adopt the same set approach. Even widely used historical packages can be light on docs.

  by greazy
• 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...
  by piokoch
• I think a language having a central repository of libraries that anyone can publish stuff in is a stupid idea in the first place. It's not a question of if, it's a question of when it goes to shit.
  by vrighter
• Better search could solve this I think. If packages could be automatically, semantically grouped and made searchable, then there would be a lot less packages. a lot of times is simply faster to remake that to search for something appropriate. I don't think RAG is the right answer, it needs to be more capable than that. i don't know quite what that would look like.

  I would love to be able to filter out low effort, bad docs, no tests, no recent contributions and and more after typing a semantic query for a library to use.

  by frogperson
• It's the same on any package index now.
  by f311a
• 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.
  by Mairoce
• 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 copied them and ate their lunch in 3 months.

  by nswizzle31
• I agree. There has never been anything in tidyverse I couldn't do in base R. Usually a lot cleaner in base R. People complain about plotting in base R, really? It is just a function call with supplied arguments. It is super straight forward. Follow the vignette.

  People also saying python is better don't realize that R is basically like having pandas in the standard library. I don't think there is a better language for wrangling tabular data to be honest.

  by asdff
• I’m not saying it doesn’t have flaws, but the tidyverse is still the most coherent and functional ML/stat computing ecosystem I’ve ever used. R packages outside of the tidyverse can get pretty gnarly. Even the R stdlib is usually considered to be inconsistent and riddled with legacy cruft.
  by mjhay
• 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.
  by alastairr
• spamming refresh on npmjs's latest packages list is a crazy experience.
  by polski-g
• CRAN has very different social structures and culture than other languages' package repos. CRAN _will_ pull your package from the repository if you fail to play nice with all packages, for example. This is evidently controversial, but it's accepted practice and one possible explanation for why CRAN feels more cohesive than NPM or PyPI.
  by cscheid
• 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.
  by jdw64
• 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 of them extremely well. So it seems to me that the underlying language matters less than ever.

  by RA_Fisher
• There is some great stuff in R but from a software engineering level I'd much rather data scientists work in Python.

  At risk of sounding like ChatGPT, it's not an R thing, it's a general thing. Turn [showdead] on in your profile and see how Show HN is flooded with AI slop projects and we all know GitHub is drowning in it.

  by PaulHoule
• > Seeing this, can we really say that AI is always bad?

  Is anyone arguing “AI is always bad”? I think the argument is clearly “the negatives outweigh the positives”.

  by latexr
• 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.

  by colechristensen
• 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().

  by parsimo2010
• Exactly. I made a small package for my lab and just put it on GitHub. My guess is that many academics don't know that you can install packages from other sources than cran
  by MostlyStable