51–60 of 61 posts
Unfortunately, it's almost impossible to work with a very large datasets in R, because of the speed limitations. Many researchers I know use Matlab because of this.
For statistical genetics at least, it's common to process much of the data in parallel, so the RAM limitations on one R instance are not the gating factor.
Earlier quoted context omitted.
Surely that would still be the case under any license.
He is probably comparing R to SAS (which are the two most popular statistical programming languages). SAS doesn't really have libraries, instead you buy additional packages from SAS, which are very reliable and well supported, but expensive. My company shuns R (although I personally like it), primarily because of this issue. If we need to run a rare or uncommon statistical procedure, it is a lot easier to trust the S…
I think open-source eventually replaces commercial products, in the same way that proprietary products become commoditized. The response for commercial products is also the same: continual differentiation, adding new features, benefits, support, documentation etc. Exceptions are also the same: natural monopolies (e.g. strong network effects). Open-source is great at hill-climbing, where there are clear directions for…
I don't think it's obvious that open source displaces commercial for scientific computing. For every example like R which has in many places displaced S-Plus, there are counterexamples like matlab, for which the open source clone Octave is a bad joke, at least the last time I tried using it: missing functions, slowness, extreme difficulty installing; or Mathematica, or eviews, or gauss, or Maple. One other potential…
I think open-source eventually replaces commercial products, in the same way that proprietary products become commoditized. The response for commercial products is also the same: continual differentiation, adding new features, benefits, support, documentation etc. Exceptions are also the same: natural monopolies (e.g. strong network effects). Open-source is great at hill-climbing, where there are clear directions for…
Counter-examples abound. Can you name even one open source app that has displaced a mature, user-facing desktop app with a non-trivial UI, other than a web browser? Open source only seems to win in domains in which it makes sense for companies to share work in order to compete at a higher tier of functionality.
I don't think it makes sense to make generalizations about where "open source seems to win". Things are changing too fast; the circumstances that made it possible for Mozilla to beat IE in the mid-2000s no longer exist, for example.
Earlier quoted context omitted.
I don't think it's obvious that open source displaces commercial for scientific computing. For every example like R which has in many places displaced S-Plus, there are counterexamples like matlab, for which the open source clone Octave is a bad joke, at least the last time I tried using it: missing functions, slowness, extreme difficulty installing; or Mathematica, or eviews, or gauss, or Maple. One other potential…
What's replacing Matlab is not octave, but Python/Numpy.
Earlier quoted context omitted.
For statistical genetics at least, it's common to process much of the data in parallel, so the RAM limitations on one R instance are not the gating factor.
Having seen and heard about what Bioconductor had to do to process genetic data, memory is a huge issue. It is even more so with next-generation sequencing data.
I think open-source eventually replaces commercial products, in the same way that proprietary products become commoditized. The response for commercial products is also the same: continual differentiation, adding new features, benefits, support, documentation etc. Exceptions are also the same: natural monopolies (e.g. strong network effects). Open-source is great at hill-climbing, where there are clear directions for…
Unfortunately, it's almost impossible to work with a very large datasets in R, because of the speed limitations. Many researchers I know use Matlab because of this.
http://radar.oreilly.com/2011/10/oracles-big-data-appliance....
It's probably more an issue of easily pre-filtering/aggregating the data before analysing it with R. I like this approach of moving the calculation to the data, but we must be very late on the adoption curve if Oracle are doing it already.
I use R as my primary data-analysis tool for almost all of my work, with occasional recourse to SAS for certain specialized models (e.g., PROC GLIMMIX for generalized mixed models). My only complaint is the awful default IDE, which can be mitigated to a large extent by scripting elsewhere and source()ing the script, and some odd edge behaviors including the mystifying row names of dataframes, the difficulty of droppi…
Default IDE? Do you mean the R interpreter REPL? If you are looking for a nice IDE for R, I would suggest RStudio: http://rstudio.org/