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I think that you’re right, and broadly speaking, there’s an inverse correlation between how invested someone is in programming and how invested someone is in th

I think that you’re right, and broadly speaking, there’s an inverse correlation between how invested someone is in programming and how invested someone is in the problem domain. Could be Berkson’s paradox at play here, but I think that it’s really a question of opportunity cost—the time you spend becoming a better programmer is time you could have spent learning some problem domain that you could solve with programming. So you end up with stuff like the image crate (a straight up disaster of a crate—lots of fancy Rust bells and whistles, doesn’t support a bunch of stuff you want to do with images, reading the GitHub bug reports make me feel no hope that the issues will be addressed, ever) and R (a straight up disaster of a language, just a nightmare to write, but full of useful statistics packages, always has the package you need, but the language was made by Satan).

wiz21c 4y ago
Funny you mention R. I started a several months work on data analysis/science/you-name-it in python (a good language) and slowly, without noticing it, I migrated all my stuff to R (made by Satan). And exactly for the reason you give: R gets the sh*t done faster (in terms of time spent writing code) than Python... (and you can consider I am a lisper (worse: a schemer)/rust-er at heart !!!)
zelphirkalt 4y ago
There is also some correlation between how solid the base is and how long a library/package/project is used, before it is cast aside for a new hip and trendy one.
- klodolph 4y ago
  Maybe. My sense is that there’s a finite amount of developer energy you have to allocate between different concerns. If you think of R, the language, as a base, then it’s definitely NOT a solid base (in my estimation). If you see it as a continuation of S, then it’s nearly 50 years of use for something you wouldn’t want to build on. I’ll say the same thing about R+statistics as I will for Python+machine learning, for C++&game development / OpenCV, or Fortran and scientific computing. Having domain experts actually use your language to solve problems is such a massive advantage that you can almost ignore the benefits and drawbacks of the language itself. Almost.

wiz21c 4y ago

Funny you mention R. I started a several months work on data analysis/science/you-name-it in python (a good language) and slowly, without noticing it, I migrated all my stuff to R (made by Satan). And exactly for the reason you give: R gets the sh*t done faster (in terms of time spent writing code) than Python... (and you can consider I am a lisper (worse: a schemer)/rust-er at heart !!!)

zelphirkalt 4y ago

There is also some correlation between how solid the base is and how long a library/package/project is used, before it is cast aside for a new hip and trendy one.

klodolph 4y ago
Maybe. My sense is that there’s a finite amount of developer energy you have to allocate between different concerns. If you think of R, the language, as a base, then it’s definitely NOT a solid base (in my estimation). If you see it as a continuation of S, then it’s nearly 50 years of use for something you wouldn’t want to build on. I’ll say the same thing about R+statistics as I will for Python+machine learning, for C++&game development / OpenCV, or Fortran and scientific computing. Having domain experts actually use your language to solve problems is such a massive advantage that you can almost ignore the benefits and drawbacks of the language itself. Almost.