I’m chipping away at the last chapter of my dissertation, with the plan to wrap it up by the end of the year. Part of my last chapter involved running tens of thousands of simulations of fish movement in an estuary. I completed the simulations a while ago and archived the raw output, but I recently discovered that I needed to reprocess the data for part of my analysis. Unfortunately, I ended up losing access to my computer powerhouse due to Covid-19 and having to switch from a desktop computer sitting in a cubicle to a laptop I could take home with me. The laptop isn’t particularly powerful, and it locks up trying to process the raw data. I needed access to more powerful hardware in order to generate the outputs I want to present in my dissertation.

Conveniently, Google Cloud Platform is currently offering a free trial of the service. Signing up now gives you $300 to spend in three months, and they offer a pretty wide variety of virtual machine (VM) configurations. Even better, there are a number of R packages that have been built specifically to work with the Google Cloud Platform. I used two R packages to complete the work: googleComputeEngineR to create the VM and googleCloudStorageR to move data between the virtual machine and the cloud storage container or “bucket”. ## Setting up a project The first step is to set up a project. You can do this in the Google Cloud Console. If you’re setting up your Cloud account for the first time this will probably be one of the initial steps you’re asked to complete; if you already have an existing project, you can create a new one from the drop-down menu on the blue banner at the top of any console page. Defining a project lets you create and group VMs and storage buckets. In order to use googleComputeEngineR and googleCloudStorageR, we also need to set up a service account, which you can do through the IAM & Admin console. When you create service account you’ll have the option to download a JSON file containing an authorization key which the R packages use to interact with your project. ## Setting up the virtual machine While you can set up a virtual machine from the Compute Engine Console, it’s much easier to create one using GoogleCloudComputeR. The code below creates an RStudio Server VM using the e2 machine type with 8 GB. Before creating a VM, make sure to set the project and the authorization file: library(GoogleCloudComputeR) sys.setenv("GCE_AUTH_FILE" = "path/to/auth.json") gce_global_project(project = "my_project_name") vm2 <- gce_vm(template = "rstudio", zone="us-west2-a", name = "my_vm_name", username = "my_name", password = "my_password", predefined_type = "e2-highmem-8", dynamic_image = "gcr.io/gcer-public/persistent-rstudio", disk_size_gb=200)  The dynamic_image argument lets us specify the RStudio image we want. There are lots of images available, but I struggled to get some of them working; the “persistent-rstudio” image ended up working best for me. ## Setting up the storage bucket The next step is to set up a storage bucket. Creating up a bucket is straightforward in the Storage Console (note that there are lots of ways to handle storage; the default Google Cloud Storage Buckets are in the ambiguously-named “Storage” menu item, which for me was sandwiched betweeen “Memorystore” and “Spanner”). The trick here is to use a storage bucket that lives in the same “zone” as your VM. If your bucket and VM live in different zones, the data transfer will be slower and cost more (or possibly won’t work at all). Easy enough to do, but this is why I had you set up the VM first; since some VM types are only available in certain regions, you don’t want to set up your storage bucket only to find out you can’t set up the VM you want in the same region. The buckets have a drag and drop interface for files so it’s really easy to upload your stuff through the console. But if you’re going to need to transfer files from your VM back to the bucket once your analysis is completed (which you probably will) then you need to need to upload your JSON authorization file to the bucket as well. ## Transferring data to the VM Getting data from Google Cloud Storage onto the VM is easy. After installing the googleCloudStorageR package, just set the storage bucket and try querying objects: # install.packages(googleCloudStorageR) library(googleCloudStorageR) gcs_global_bucket("rre-paper-bucket") obj = gcs_list_objects()  If you get an error when you try to get a list of objects, it means that the VM is not able to automatically authenticate when accessing the bucket. I don’t know why this happens (or rather, I don’t know why it is ever able to automatically authenticate) but the easiest solution is to create a blank JSON file on the VM, copy the contents of your authorization file into the file on the VM, and then set it on the VM using the command gcs_auth("path/to/auth-file.json"). To transfer files over, I usually do some kind of regex to filter the object names, and then use a for loop to copy over each file I need: search.string = "some regexstring" selected.obj = obj$name[grep(search.string, obj\$name)]

for(o in selected.obj){
gcs_get_object(o, saveToDisk = o, overwrite = TRUE)
}


If you have subfolders in your bucket, you’ll probably need to set the argument saveToDisk = basename(o) to drop the folder path, or recreate the directory structure on the VM.

## Transferring data out of the VM

You’ll probably want to transfer data or files back out of the VM once you’re done with your analysis. The googleCloudStorageR packages makes this really easy too: just use the gcs_upload() function.

out.file = "path/to/output.RData"

As with copying data into the VM, you’ll probably need to strip the folder path by specifying the argument name = basename(out.file) or create the equivalent directory structure in your bucket.