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What is the strategy behind the dizzying pace of product changes in the R/Python space? I heard that RStudio was a great product, yet they have to redo everyth

• 0cf8612b2e1e 2y ago
  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.
• juujian 2y ago
  As a regular R/bash/python user, I think the Posit philosophy is increasingly to break down barriers between the languages. It's no longer an either/or relationship, you can use both. And some packages or features that have been ported to python are just objectively worse in that language, so the want for R is definitely there. Also, a lot of the Posit team are fully "bilingual", it's not like the old guard of academic R contributors. My impression is they appreciate both languages for what they have to offer. For me, I'm apathetic to the languages, the only thing I care about is the output.
  • ImaCake 2y ago
    >ported to python are just objectively worse in that language This is absolutely the case. Dplyr syntax is much more intuitive for many use cases than Pandas or Polars equivalents. One thing I miss from RStudio is the Rmarkdown documents with inline outputs. Jupyter notebooks, even in VSCode, are so needlessly over-engineered and under-featured compared to the elegance of RMarkdown. So I am excited to see what Posit can do to bring that experience to python. My git repos will be thankfull anyway.
    • cd4plus 2y ago
      I can't stand jupyter notebooks for several reasons. I've been using https://quarto.org/ https://quarto.org/ and writing .qmd docs and really enjoy it.
    • archiewood 2y ago
      You might be interested in what we’re building over at Evidence.dev It’s basically RMarkdown for SQL
      • xmcqdpt2 2y ago
        Neat! I have to deal with splunk at work and every time I do I'm annoyed that they decided to create their own query language.
    • asdff 2y ago
• manilbeat 2y ago
  Rstudio came out a long time ago at this point. I actually think it feels quite dated personally. A data science focused version of VS Code with some kind of notebook sounds rather awesome to me.
  • esalman 2y ago
    I agree, Rstudio is really dated. Been using R and Python at work in parallel, I find Rstudio cannot do obvious things that R extension for VS Code can- like stopping at a breakpoint in the middle or knitting an Rmd file that has embedded code.
  • asdff 2y ago
    There is already Rmarkdown and great tutorials to go along with that for the budding R data scientist
  • lbeltrame 2y ago
    Does it still bundle an entire, old version of libclang?
• aydyn 2y ago
  The issue is R is too small compared to Python and the gap is getting bigger. Theyre trying to grow the company, and realistically the only way is to support python.
• steve1977 2y ago
  > Is it Jupyter envy? The funny thing is that the R in Jupyter actually stands for R (the language). It's Julia, Python and R. No need for envy. Of course, RStudio/Posit != R (at least in theory)
• kkfx 2y ago
  Modern software can't work, that's is and no, I'm serious and have no intention to start a flame. Original desktops was a single OS-environment-framework, witch was "fragile" to a certain extent, but anything have to evolve in a fully integrated environment, this means FAR LESS code FAR LESS deps for anything. This means that's easy for the user to bend the environment to their wishes and for devs to create something "on the shoulder of giants" being with them instead of having something like https://xkcd.com/2347/ https://xkcd.com/2347/ modern projects are typically written in Silicon Valley mode, anchoring the project and some deps without any reasoning about they future, scalability, maintainability and so on. Something change, anything on top collapse. In practice we have nearly ZERO development for desktop apps, simply because modern desktops are still widget based stuff who was sold a "what we need", against the complexity of classic DocUIs, and then we migrate to modern web witch is a bad DocUI, so developing for the desktop is simply a nightmare. To add features we can't much "use the environment" so all apps tend to try doing anything inside evolving toward unmaintainable monsters no one can handle their codebase and at a certain point in time they became a kind of a framework where "features" became "ideas of someone" without a coherent vision or a target "for the application" like Eclipse from an IDE to a platform some use to code, some others to pay taxes (yes, Italian gov. have made Desktop Telematico witch is a custom Eclipse to fill taxes). As the classic Greenspun's tenth rule we witness the same: we damn need an OS as a single user-programmable application like Emacs or doing ANYTHING is a nightmare and there is no long lasting solution.
• dcreater 2y ago
  VS code has been a pillar all along? MATLAB is utter trash on macos, slow and a dated ui. Also why support proprietary languages with predatory marketing tactics