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I think the R people see the writing on the wall. Python has sucked all the air out of the room, and it is becoming increasingly difficult for them to target th

I think the R people see the writing on the wall. Python has sucked all the air out of the room, and it is becoming increasingly difficult for them to target the R space exclusively. If you had no legacy or compliance requirements, are you going to start a new data project in SAS, R, or Python? Where are you going to find the most talent?

esalman 2y ago
I have an impression that most SotA algorithms in many fields that are not deep learning are made available first as R (or even Matlab) package. Obviously they do get ported to python once they receive enough traction.
- greenpresident 2y ago
  This has been my impression in social science academia: a lot of ready to run methods are released as R packages.
kyawzazaw 2y ago
the problem is life sciences and statisticans are not going to learn Python though. and that's the talent
- asdff 2y ago
  On the ground however, they learn both. Or I should say if you find someone who is exclusive to one or the other language (which happens), they aren't much of a programmer to begin with. There is a whole food chain essentially in life sciences in academia with computer knowledge. There are those who write the tooling, who are perhaps so abstracted they don't know the underlying biology and vet their tooling based on simulated data and a comparison with existing tooling, toiling in their own castles on tooling that might not ever see a real dataset. There are those who use the tooling to create novel pipelines to analyze data and draw conclusions based on their own or their collaborators literature research or life science perspectives, they might not care if the finding is truly novel or if its merely proving an existing gumption with a tool that hasn't yet been applied to an existing dataset. Then there are also those who run the pipelines created by others within their research group, sometimes others who have long left that given research group, with brittle hardcoded paths and other "DO NOT TOUCH" segments in a massive single 2500 line file that gurgitates some plots from a standard sort of csv file as input.
- _Wintermute 2y ago
  They are though. I work in computational biology, I would say the majority of work I see now is in python.
  - kyawzazaw 2y ago
    that's a much much smaller subset than the vast users of R in academic/research
dongobread 2y ago
I'm not sure what would lead to you believe this. I've worked in the data science/ML space for over a decade now and I see the majority of pure analytics projects started in R, including at big tech companies I've worked at recently. Of course, ML projects and other things that need to result in production-grade models are almost always done in Python. This is currently the most visible form of "data project" due to all the ML/AI hype, but it is far from the only data work going on.
- aydyn 2y ago
  Youre wrong. Python is outpacing R in usage. Every metric you can find proves it. R also has fundamental issues and lacks serious development.
  - stoperaticless 2y ago
    s/serious/hyped
    - aydyn 2y ago
      No. R fundamentally has not really improved in the past ~10 years. Do you know much about how R works? Also try: gsub('serious', 'hyped', x)
      - lottin 2y ago
        Maybe because it already does what it intends to do reasonably well? I mean, what do you think needs to be improved?
        smcin 2y ago
        Here are 14 years of HN discussions/criticisms of R: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&query=R%20language&sort=byPopularity&type=story https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que... "Does what it intends to do reasonably well" is going to be widely subjective, depending on whether the user's use-case is statistical/life-sciences vs more general purpose coding and relying on many packages; prototyping/experimentation vs production code; whether the user uses base-R, or tidyverse/data.table, etc. Here are two of those many posts: * An opinionated view of the Tidyverse “dialect” of the R language (July 5, 2019) https://news.ycombinator.com/item?id=20362626 https://news.ycombinator.com/item?id=20362626 * The R programming language: The good, the bad, and the ugly (epatters.org, 2018) https://news.ycombinator.com/item?id=35571659 https://news.ycombinator.com/item?id=35571659 -> https://www.epatters.org/post/r-lang/ https://www.epatters.org/post/r-lang/
dnawlp 2y ago
Students I spoke to were forced to use SAS. They looked at me as if I was from Mars for suggesting alternatives. For a "big data" project, people will probably use Python (though Google apparently retreats from it except in machine learning). Why could you not use R and C++ or Java though? For example, Arrow has bindings for both, so the argument that Python is needed to shovel/scrape/steal data becomes less and less valid.

esalman 2y ago

I have an impression that most SotA algorithms in many fields that are not deep learning are made available first as R (or even Matlab) package. Obviously they do get ported to python once they receive enough traction.

greenpresident 2y ago
This has been my impression in social science academia: a lot of ready to run methods are released as R packages.

kyawzazaw 2y ago

the problem is life sciences and statisticans are not going to learn Python though. and that's the talent

asdff 2y ago
On the ground however, they learn both. Or I should say if you find someone who is exclusive to one or the other language (which happens), they aren't much of a programmer to begin with. There is a whole food chain essentially in life sciences in academia with computer knowledge. There are those who write the tooling, who are perhaps so abstracted they don't know the underlying biology and vet their tooling based on simulated data and a comparison with existing tooling, toiling in their own castles on tooling that might not ever see a real dataset. There are those who use the tooling to create novel pipelines to analyze data and draw conclusions based on their own or their collaborators literature research or life science perspectives, they might not care if the finding is truly novel or if its merely proving an existing gumption with a tool that hasn't yet been applied to an existing dataset. Then there are also those who run the pipelines created by others within their research group, sometimes others who have long left that given research group, with brittle hardcoded paths and other "DO NOT TOUCH" segments in a massive single 2500 line file that gurgitates some plots from a standard sort of csv file as input.
_Wintermute 2y ago
They are though. I work in computational biology, I would say the majority of work I see now is in python.
- kyawzazaw 2y ago
  that's a much much smaller subset than the vast users of R in academic/research

dongobread 2y ago

I'm not sure what would lead to you believe this. I've worked in the data science/ML space for over a decade now and I see the majority of pure analytics projects started in R, including at big tech companies I've worked at recently. Of course, ML projects and other things that need to result in production-grade models are almost always done in Python. This is currently the most visible form of "data project" due to all the ML/AI hype, but it is far from the only data work going on.

aydyn 2y ago
Youre wrong. Python is outpacing R in usage. Every metric you can find proves it. R also has fundamental issues and lacks serious development.
- stoperaticless 2y ago
  s/serious/hyped
  - aydyn 2y ago
    No. R fundamentally has not really improved in the past ~10 years. Do you know much about how R works? Also try: gsub('serious', 'hyped', x)
    - lottin 2y ago
      Maybe because it already does what it intends to do reasonably well? I mean, what do you think needs to be improved?
      - smcin 2y ago
        Here are 14 years of HN discussions/criticisms of R: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&query=R%20language&sort=byPopularity&type=story https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que... "Does what it intends to do reasonably well" is going to be widely subjective, depending on whether the user's use-case is statistical/life-sciences vs more general purpose coding and relying on many packages; prototyping/experimentation vs production code; whether the user uses base-R, or tidyverse/data.table, etc. Here are two of those many posts: * An opinionated view of the Tidyverse “dialect” of the R language (July 5, 2019) https://news.ycombinator.com/item?id=20362626 https://news.ycombinator.com/item?id=20362626 * The R programming language: The good, the bad, and the ugly (epatters.org, 2018) https://news.ycombinator.com/item?id=35571659 https://news.ycombinator.com/item?id=35571659 -> https://www.epatters.org/post/r-lang/ https://www.epatters.org/post/r-lang/

dnawlp 2y ago

Students I spoke to were forced to use SAS. They looked at me as if I was from Mars for suggesting alternatives. For a "big data" project, people will probably use Python (though Google apparently retreats from it except in machine learning). Why could you not use R and C++ or Java though? For example, Arrow has bindings for both, so the argument that Python is needed to shovel/scrape/steal data becomes less and less valid.