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There is a lot more to scientific computation than statistics, and the only thing R is good at is statistics.

by roel_v 7y ago

kgwgk 7y ago
It's quite popular in bioinformatics as well: https://www.bioconductor.org/ https://www.bioconductor.org/
- lbeltrame 7y ago
  Unfortunately the quality of the Bioconductor packages varies wildly, from pretty good to downright abysmal. Some can't even work: take a look at this one[1], which is part of the current release of BioC: it will never work because the hardcoded host there is no longer functioning and the author wrote that for their master's thesis and have since moved on. In addition, there's no centralized bug reporting, and some packages use GH issues while for others you may need to email the author and hope for the best (sometimes works, sometimes doesn't). I use BioC regularly every day because I have no other alternatives, but its main advantage is the sheer numbers of packages available for almost every bioinformatic niche, rather than the quality of the packages themselves. [1] https://github.com/AllenTiTaiWang/anamiR/blob/7a7a133c553f8c3262cec65d86735fe121049456/R/database_support.R#L80 https://github.com/AllenTiTaiWang/anamiR/blob/7a7a133c553f8c...
tylermw 7y ago
That's not a true statement. R has packages for a wide variety of fields--statistics is just the most well developed. And an important aspect of scientific computation is data visualization, an area in which R is decidedly more advanced than other languages.
- geoalchimista 7y ago
  One thing I can think of is that R is not well suited for computationally expensive work --- by "computationally expensive" I mean something that takes months to run on a cluster. Not only is R 100--1000x slower than Fortran, but it also does not have strong support for parallelism or GPU computing. I have yet to see a computational fluid dynamicist using R. Though Python is also not much better in that aspect, it can be used as a glue language to interact with native C/C++/Fortran code under the hood.
  - tylermw 7y ago
    R has interfaces for Fortran, C, and C++ as well. Quite good ones, in fact.
  - gnufx 7y ago
    R is actually the basis of one of the CORAL2 benchmark sets, and there are published scaling results to 5000 cores as far as I remember. I've not done it, but presumably you can substitute its BLAS with a GPU version, the same as with an OpenMP-parallelized one to get performance for anything linear algebra-ish. [HPC clusters on which you'd run CFD typically don't let you run for more than days, let alone months. There's probably no reason you couldn't use R the same way as Python for such things.]
locoman88 7y ago
You are soo wrong. R for earth scientists is a god send thing. Not even Python has as many packages/libraries for earth science as R

There is a lot more to scientific computation than statistics, and the only thing R is good at is statistics.

by roel_v 7y ago

There is a lot more to scientific computation than statistics, and the only thing R is good at is statistics.

kgwgk 7y ago

It's quite popular in bioinformatics as well: https://www.bioconductor.org/ https://www.bioconductor.org/

lbeltrame 7y ago
Unfortunately the quality of the Bioconductor packages varies wildly, from pretty good to downright abysmal. Some can't even work: take a look at this one[1], which is part of the current release of BioC: it will never work because the hardcoded host there is no longer functioning and the author wrote that for their master's thesis and have since moved on. In addition, there's no centralized bug reporting, and some packages use GH issues while for others you may need to email the author and hope for the best (sometimes works, sometimes doesn't). I use BioC regularly every day because I have no other alternatives, but its main advantage is the sheer numbers of packages available for almost every bioinformatic niche, rather than the quality of the packages themselves. [1] https://github.com/AllenTiTaiWang/anamiR/blob/7a7a133c553f8c3262cec65d86735fe121049456/R/database_support.R#L80 https://github.com/AllenTiTaiWang/anamiR/blob/7a7a133c553f8c...

tylermw 7y ago

That's not a true statement. R has packages for a wide variety of fields--statistics is just the most well developed. And an important aspect of scientific computation is data visualization, an area in which R is decidedly more advanced than other languages.

geoalchimista 7y ago
One thing I can think of is that R is not well suited for computationally expensive work --- by "computationally expensive" I mean something that takes months to run on a cluster. Not only is R 100--1000x slower than Fortran, but it also does not have strong support for parallelism or GPU computing. I have yet to see a computational fluid dynamicist using R. Though Python is also not much better in that aspect, it can be used as a glue language to interact with native C/C++/Fortran code under the hood.
- tylermw 7y ago
  R has interfaces for Fortran, C, and C++ as well. Quite good ones, in fact.
- gnufx 7y ago
  R is actually the basis of one of the CORAL2 benchmark sets, and there are published scaling results to 5000 cores as far as I remember. I've not done it, but presumably you can substitute its BLAS with a GPU version, the same as with an OpenMP-parallelized one to get performance for anything linear algebra-ish. [HPC clusters on which you'd run CFD typically don't let you run for more than days, let alone months. There's probably no reason you couldn't use R the same way as Python for such things.]

locoman88 7y ago

You are soo wrong. R for earth scientists is a god send thing. Not even Python has as many packages/libraries for earth science as R