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I think R's biggest strength of R is the package ecosystem. I would not trade the large community of active package developers for slightly friendlier syntax.

by jimmar 14y ago

carterschonwald 14y ago
Indeed, the ecosystem of actively deved numerical libs is the only upside to R over other tools. It's a pretty large cliff for any other tool chain to climb over. --someone who's writing numeric/ data analysis tools in Haskell.
- pilgrim689 14y ago
  Off-topic but: Can you comment on the significance of this cliff for a team considering moving from R to Haskell for data analysis? Is the availability for statistics packages really sparse in Haskell?
  - carterschonwald 14y ago
    the answer is: it depends! Shoot me an email at Wellposed and I can try to better answer your question. I am quite literally building a full data analysis stack (as a product) in haskell, some parts of which will be available as a sort of proprietary augmented version of the haskell platform, and some parts are / will be open source. I do think that there are compelling reasons to consider Haskell / GHC for analytical workloads, but depending on the details it really depends. The principal cliff is just the HUGE number of (mostly poorly designed) libraries for many standard analyses written in R. Theres some nice engineering approaches to circumvent this, and theres some really exciting libs that a uniquely awesome and handy in haskell land. A notable example is AD, a really easy to use auto differentiation lib by Edward Kmett, which has a really exciting refactor thats nearly done that will make it useable by mortal Haskellers :) http://hackage.haskell.org/package/ad http://hackage.haskell.org/package/ad and https://github.com/ekmett/ad https://github.com/ekmett/ad (I've some neat bits i'll be hopefully adding to AD myself in the next month)
    - pilgrim689 14y ago
      Thanks! I'll keep an eye out for these releases. :)
  - carterschonwald 14y ago
    I'll also be pushing out a whole bunch of nice open source ones that (if correct) everyone should use very soon.
th0ma5 14y ago
The package management is weird. It makes sense, but it is weird they go into more detail with dependencies like "enhances" or "enhanced by" and other stuff. It is a more complex graph, and lends more to discovery, which is cool, but it through me for a loop for a second. You can still (after a bit of perusing the model) figure out what concrete dependencies are, but there are some packages that do some circular weirdness. There is also still somewhat of a binaries problem for the various flavors of Mac, Windows, Linux. You can get source for most things, but bubbling up of system dependencies you're on your own like other platforms, although Java & Maven seem to have a solution for distributing some binaries a little better possibly...

I think R's biggest strength of R is the package ecosystem. I would not trade the large community of active package developers for slightly friendlier syntax.

by jimmar 14y ago

I think R's biggest strength of R is the package ecosystem. I would not trade the large community of active package developers for slightly friendlier syntax.

carterschonwald 14y ago

Indeed, the ecosystem of actively deved numerical libs is the only upside to R over other tools. It's a pretty large cliff for any other tool chain to climb over. --someone who's writing numeric/ data analysis tools in Haskell.

pilgrim689 14y ago
Off-topic but: Can you comment on the significance of this cliff for a team considering moving from R to Haskell for data analysis? Is the availability for statistics packages really sparse in Haskell?
- carterschonwald 14y ago
  the answer is: it depends! Shoot me an email at Wellposed and I can try to better answer your question. I am quite literally building a full data analysis stack (as a product) in haskell, some parts of which will be available as a sort of proprietary augmented version of the haskell platform, and some parts are / will be open source. I do think that there are compelling reasons to consider Haskell / GHC for analytical workloads, but depending on the details it really depends. The principal cliff is just the HUGE number of (mostly poorly designed) libraries for many standard analyses written in R. Theres some nice engineering approaches to circumvent this, and theres some really exciting libs that a uniquely awesome and handy in haskell land. A notable example is AD, a really easy to use auto differentiation lib by Edward Kmett, which has a really exciting refactor thats nearly done that will make it useable by mortal Haskellers :) http://hackage.haskell.org/package/ad http://hackage.haskell.org/package/ad and https://github.com/ekmett/ad https://github.com/ekmett/ad (I've some neat bits i'll be hopefully adding to AD myself in the next month)
  - pilgrim689 14y ago
    Thanks! I'll keep an eye out for these releases. :)
- carterschonwald 14y ago
  I'll also be pushing out a whole bunch of nice open source ones that (if correct) everyone should use very soon.

th0ma5 14y ago

The package management is weird. It makes sense, but it is weird they go into more detail with dependencies like "enhances" or "enhanced by" and other stuff. It is a more complex graph, and lends more to discovery, which is cool, but it through me for a loop for a second. You can still (after a bit of perusing the model) figure out what concrete dependencies are, but there are some packages that do some circular weirdness. There is also still somewhat of a binaries problem for the various flavors of Mac, Windows, Linux. You can get source for most things, but bubbling up of system dependencies you're on your own like other platforms, although Java & Maven seem to have a solution for distributing some binaries a little better possibly...