R Package Downloads Using the following snippet of code in R, after installing the ggplot and dlstats libraries, one can plot how often a given set of packages has been downloaded. Install install.packages("ggplot2") install.packages("dlstats") Use library(ggplot2) library(dlstats) # Here I use my packages as an example, you can check any packages on CRAN x <- cran_stats(c("BigQuic", "DCD", "DiffNet", "AssocAFC", "famSKATRC", "RGBM")) ggplot(x, aes(end, downloads, group=package, color=package)) + geom_line() + geom_point(aes(shape=package)) + xlab("Time") + ylab("Downloads") png(width = 600, height = 400) ggsave(filename = "R_Package_Downloads.

Nvidia GPUs Nvidia has unfortunately come to own the HPC (High Performance Computing) market, mainly through the early lead they gained with CUDA and its relative ease of use over alternatives like the more open and less proprietary OpenCL and ATI’s perhaps too late and too little GPU Open. They are certainly trying, but they just don’t seem to have enough resources behind these efforts to keep up with the latest and the install process is both not easy for the average layman (requiring a fairly new kernel) and needs pretty recent GPUs.

Installing R Properly on Ubuntu Introduction There are a lot of ‘wrong’ ways to install R. Usually the result is a system on which R does not update (e.g. installed from source), does not update as fast as upstream (e.g. installed from package manager in Debian or Ubuntu), or requires additional tools (e.g. Bioconda). These aren’t necessarily wrong, and you may need to do these at times (e.g. if you write R packages that are on CRAN, then you may need to test on a dev version, which may need to be built from source), but there is a way that is better in most cases.