Too many R packages: CRAN is inundated with submissions
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Re: Too many R packages: CRAN is inundated with submissions
#2People 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.
Re: Too many R packages: CRAN is inundated with submissions
#3Frankly 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.
Re: Too many R packages: CRAN is inundated with submissions
#4Frankly 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 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.
Re: Too many R packages: CRAN is inundated with submissions
#5People 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…
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.
Re: Too many R packages: CRAN is inundated with submissions
#6Frankly 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 copied them and ate their lunch in 3 months.
Re: Too many R packages: CRAN is inundated with submissions
#7Frankly 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 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.
It's certainly quite pleasant to work with...but I would rather use sql for etl, the backend be whatever it needs to be...
The real world data transformations can get gnarly very quickly and sql is the perfect common debiminator compared to dplyr which is still niche...
How do you feel about polars?
Re: Too many R packages: CRAN is inundated with submissions
#8People 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…
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.
Re: Too many R packages: CRAN is inundated with submissions
#9Earlier quoted context omitted.
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.
It's certainly quite pleasant to work with...but I would rather use sql for etl, the backend be whatever it needs to be... The real world data transformations can get gnarly very quickly and sql is the perfect common debiminator compared to dplyr which is still niche... How do you feel about polars?
I’m a big fan of Polars. It’s really fast and memory efficient. With the lazy streaming functionality, I’ve been able to easily process 1 Tb+ data on a single machine (you do have to be careful to not do any operation that would cause the whole DF to materialize in that case).
It’s certainly miles better than Pandas, which has a terrible API in addition to being comically inefficient. In my group, we generally use it for any new work, and have also swapped out pandas for polars in critical spots of our existing code - the latter giving a huge benefit relative to the amount of work it took.
I largely agree with you on SQL being the common denominator, but there are some things that are just awkward in SQL, and much easier to do in Python or other general purpose language.
Re: Too many R packages: CRAN is inundated with submissions
#10People 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…
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…
I partially agree, but I also differ on some points. The part I agree with is that probabilistic programming is difficult and that advanced programmers tend to enjoy it. Where I differ is on the claim that programming is deterministic. At the script level, programming is deterministic and sequential, but once it crosses a certain threshold, it becomes absolutely probabilistic. That's because latency, locks, and asynchronous communication start to intervene. If programming were Non deterministic , C's undefined behavior wouldn't exist; everyone would have prevented it.
R these days mostly uses the tidyverse, which feels like a variant of DOP (Data-Oriented Programming). It's a kind of data flow, so it's different from typical OOP. I also occasionally work with statisticians (being a freelancer, ETL work is more common than you'd think), and I know what you mean by Stan and PyMC. I know they're powerful tools for Bayesian statistics and multilevel modeling. I know the basic syntax and examples, but I wouldn't say I know them well. My level is mainly focused on the scientists who hire me, and those tools still don't come up often in my country.
That said, I think we differ on the bigger picture because academic code isn't everything. Academic code is typically algorithm‑centric, like LeetCode problems, but most production work revolves around code hygiene and responsibility (algorithms are usually already established ones). Anyway, that's not the main point. What you said is mostly correct, but my focus was on something else: even people who studied at that level can be surprisingly clumsy at expressing themselves through programming. Regardless, thanks for your input, and I agree that AI is good at programming. But using a programming language generally means understanding its tradeoffs, and R is tricky in that regard since it feels like a mix of OOP and DOP variants