I did some comparisons of the generic BLAS with Intel's MKL (both sequential and parallel) on a Dell PowerEdge 610 server with dual hyperthreading 6-core 3.06GHz Xeon X5675 processors. Here are the results from an R benchmarking script (Normal R indicates the generic BLAS, sMKL is the sequential (single core Intel MKL, and pMKL is the parallel Intel MKL using all 24 threads available on this system).
I recently ran into trouble trying to install the R maps package on Ubuntu 10.04.
I do most of my R work on a server via SSH. Viewing graphics (i.e. plot()) can be excruciatingly slow through the SSH pipe, but it's easy to make it fast. Simply run: X11.options(type="Xlib")once in the R session.
Someone asked me how to move a directory full of files from one place to another using R. The easiest way I've found is as follows (where "oldpath" is the existing directory and "newpath" is the new directory):file.copy(list.files(oldpath),newpath)
R is great for graphics, but I've found that the PDF's R produces when drawing large plots can be extremely large. This is especially common when using spplot() to plot a large raster. I've made a 15 page PDF full of rasters that was hundreds of MB in size. Obviously I don't need all the detail (every pixel of the raster) represented in the pdf and would rather have it reduced in size somehow. So I wrote an R function to automate the following:
I use MODIS data for analysis of vegetation dynamics around the world. The native HDF file format provided by NASA is great for archiving the data (it's amazing how much information they include in each file), but unfortunately there aren't many tools for directly (and easily) extracting data stored across files like there are for the NetCDF format. I wanted to be able to use the NCO tools to extract timeserie