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R (specially w/ R Studio) is effectively a better Excel. The problem it creates is that someone, somewhere, will eventually present some projection they put to

R (specially w/ R Studio) is effectively a better Excel. The problem it creates is that someone, somewhere, will eventually present some projection they put together quickly in R, and that will set expectations on stakeholders that know nothing about software to start demanding that solution to scale indefinitely, compute in realtime, or be used inside any kind of production system really. It doesn't mean the same can't happen w/ Python, but it at least offers some migration path to more scalable / hardened solutions if you're careful.

photoJ 10y ago
I don't know any research statisticians that produce code for excel. R is THE place to see their most recent advances. And sadly despite the algo's being ported to python, sometimes R still has the "best" implementations.
- hcarvalhoalves 10y ago
  > I don't know any research statisticians that produce code for excel. Never said that. What I said is that R, nowadays, creates the same kinds of issues / attrition Excel created on companies back in the 90's - you end up w/ someone, in some corner of the company, creating solutions that can't scale on top of it. In comparison, if the original work is in Python, you usually have more alternatives when this prototype lands on the hands of a software eng. That is the main, and only difference that matters, IMO. As a tool for research, I agree it's completely fine. Also, I too feel the pain of some algos, sadly, being available only on R (had to write my own wrappers to estimate some models on top of R script and export the coefficients to be used in Python's sklearn).
  - photoJ 10y ago
    Sure, I can see that coming from your perspective. Matlab can do things that are similar, a researcher creates a model that is totally detached from the production environment, works beautifully and leaves ALOT of engineering required of the production team. I wonder how probabilistic programing will fit over the next 10(?) years. I'm not a fan of "I programed this, now you implement it" dev cycle and I that will decrease as Data Science matures. Sadly you are at the friction point for now.
    - tnecniv 10y ago
      The plus side with MATLAB is that you can auto-generate code if you are willing to shell out for the add-on.
rcarmo 10y ago
This. I work with both R and Python (I'm at Microsoft, doing ML for customers) and even with vastly improved R performance, scaling R is not so much about the performance of the engine but how you design the whole solution.
- collyw 10y ago
  That's the case with most languages.

photoJ 10y ago

I don't know any research statisticians that produce code for excel. R is THE place to see their most recent advances. And sadly despite the algo's being ported to python, sometimes R still has the "best" implementations.

hcarvalhoalves 10y ago
> I don't know any research statisticians that produce code for excel. Never said that. What I said is that R, nowadays, creates the same kinds of issues / attrition Excel created on companies back in the 90's - you end up w/ someone, in some corner of the company, creating solutions that can't scale on top of it. In comparison, if the original work is in Python, you usually have more alternatives when this prototype lands on the hands of a software eng. That is the main, and only difference that matters, IMO. As a tool for research, I agree it's completely fine. Also, I too feel the pain of some algos, sadly, being available only on R (had to write my own wrappers to estimate some models on top of R script and export the coefficients to be used in Python's sklearn).
- photoJ 10y ago
  Sure, I can see that coming from your perspective. Matlab can do things that are similar, a researcher creates a model that is totally detached from the production environment, works beautifully and leaves ALOT of engineering required of the production team. I wonder how probabilistic programing will fit over the next 10(?) years. I'm not a fan of "I programed this, now you implement it" dev cycle and I that will decrease as Data Science matures. Sadly you are at the friction point for now.
  - tnecniv 10y ago
    The plus side with MATLAB is that you can auto-generate code if you are willing to shell out for the add-on.

rcarmo 10y ago

This. I work with both R and Python (I'm at Microsoft, doing ML for customers) and even with vastly improved R performance, scaling R is not so much about the performance of the engine but how you design the whole solution.

collyw 10y ago
That's the case with most languages.