See also scheduling via a master/slave cluster

RStudio server + scheduler

This workflow demonstrates how you can take advantage of premade Docker images. googleComputeEngineR has created custom images that have been built using Google Container Registry build triggers and made public, which you can use.


Run this, and you get an RStudio server instance running with cronR, the tidyverseand googleAnalyticsR etc. running on it:

How to customise your own RStudio Server

Using Dockerfiles is recommended if you are making a lot of changes to a template, as its a lot easier to keep track on what is happening.

In summary, these were the steps I took:

  1. Construct a Dockerfile in a folder with any other files or dependencies, such as cron
  2. Use docker_build() or Google Container build triggers to build and save your custom Docker image
  3. Launch a VM using the dynamic_image argument to load from the custom image
  4. Schedule a script to download from Google Analytics, send an email and upload to BigQuery

You can modify this with your own Dockerfile to use your own custom packages, libraries etc. and load up to your own private Container Registry, that comes with every Google Cloud project.

Construct a Dockerfile

The Dockerfile used here is shown below, which you could base your own upon. Read up on Dockerfile’s here.

This one installs cron for scheduling, and nano a simple text editor. It then also installs some libraries needed for my scheduled scripts:

  • googleAuthR - google authentication
  • shinyFiles - for cron jobs
  • googleCloudStorageR - for uploading to Google Cloud Storage
  • bigQueryR - for uploading to BigQuery
  • gmailR - an email R package
  • googleAnalyticsR - for downloading Google Analytics data
  • bnosac/cronR - to help with creating cron jobs within RStudio.

Create a Container Registry build trigger

A build trigger was then created following the guide here.

In this case, the GitHub repository used was googleComputeEngineR’s own, and the inst/dockerfiles/hadleyverse-crontab pointed at to watch for building the images. The image was saved under the name google-auth-r-cron-tidy and (optionally) made public via this procedure.

  • Make sure the build tags are all lowercase-or-hypens
  • Add the latest tag to the name to ensure it can be pulled upon VM launch

Push to the GitHub repository and you should start to see the image being built in the build history section. If there are any problems you can click through to the log and modify your Dockerfile as needed. It also works with cloud-config files if you are looking to set up the VM beyond a Dockerfile.

Launch a VM to run your custom image

Once built, you can now launch instances using the constructed image.

In this case the image project is different from the project the VM is created in, so the project needs specifing in the gce_tag_container call.

You can also use your custom image to create further Dockerfiles that use it as a dependency, using gce_tag_container() to get its correct name.

A demo script

A demo script for scheduling is below.

It is not recommended to put critical data within a Docker contianer, as it can be destroyed if the container crashes. Instead, call dedicated data stores such as BigQuery or Cloud Storage, which as you are using Google Compute Engine you already have access to under the same project.

In summary the script below:

  1. Downloads data from Google Analytics
  2. Uploads the data to BigQuery
  3. Uploads the data to Google Cloud Storage
  4. Sends an email giving the daily total

Log into your RStudio Server instance and create the following script:

Save the script within RStudio as daily-report.R

You can then use cronR to schedule the script for a daily extract.

Use cronR’s RStudio addin, or in the console issue:

The script will then run every day.

Test the script locally and with a test schedule before using in production. Once satisfied, you can run locally the gce_push_registry() again to save the RStudio image with your scehduled script embedded within.

If you want to call the scheduled data from a Shiny app, you can now fetch the data again via bqr_query from bigQueryR or gcs_get_object from googleCloudStorageR within your server.R to pull in the data into your app at runtime.