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You can install another Linux distro alongside ChromeOS easily and relatively seamlessly with crouton, giving you a traditional desktop environment to work with locally, but given Chromebooks’ underwhelming hardware, developing even simple models on them is a non-starter.Īfter a while using a university server, this year I moved all my lab work to Google’s Compute Engine. It’s an interesting, ongoing challenge (a blog post for another day), but one thing I realised very early on was that I’d have to do any serious “lab work” (which for me is sitting at my desk playing with computational models) on a remote machine. When I started my PhD I decided to make my trusty fieldwork laptop – a Google Chromebook – my main work computer and try to do all my research in the cloud. In this example we will create a function to convert fahrenheit to celcius with large readings, 10 millions elements.RStudio Server on a Google Compute Engine instance

Let’s have an example of a single core calculation and a multicore calculation (parallelism) using lapply and mclapply # "getDoSeqWorkers" "getErrorIndex" "getErrorValue" # "getDoSeqName" "getDoSeqRegistered" "getDoSeqVersion" # "getDoParRegistered" "getDoParVersion" "getDoParWorkers" # "setDefaultCluster" "splitIndices" "stopCluster" ls("package:doParallel") # "registerDoParallel" "stopImplicitCluster" ls("package:foreach") # "%:%" "%do%" "%dopar%" # "nextRNGSubStream" "parApply" "parCapply" # "mcmapply" "mcparallel" "nextRNGStream" # "makePSOCKcluster" "mc.reset.stream" "mcaffinity" # "getDefaultCluster" "makeCluster" "makeForkCluster" # "clusterSetRNGStream" "clusterSplit" "detectCores"

# "clusterEvalQ" "clusterExport" "clusterMap" ls("package:parallel") # "clusterApply" "clusterApplyLB" "clusterCall" Let’s list all the function for each package. These packages do not have an extensive amount of functions compared to tidyverse. Let’s load these packages in our environment for (package in c("parallel","doParallel","foreach")) # Loading required package: foreach # Loading required package: iterators There are three packages you have to know to do parallel computing in R. If we could utilise four cores to calculate a subset of the dataset, a quarter each, and add the four subtotals in the end, we could have a much faster outcome.

For example the function sum() runs will process the whole dataset in a single core. As a default, R runs serially, it runs only one one core / thread.