My R Timeline

GA Effect

My CRAN packages

• searchConsoleR
• googleAnalyticsR
• googleAuthR -> gargle
• googleComputeEngineR (cloudyr)
  
• googleCloudStorageR (cloudyr)
• bigQueryR (cloudyr)
• googleLanguageR (rOpenSci)
• googleCloudRunner (New!)

googleAuthRverse

Slack: #googleAuthRverse

Agenda today

• Abstracting R applications into the Cloud using Docker (10min)
• Demo on what that abstraction offers (10 min)
• My evolving mindset for using R/Docker/Cloud (10 min)
• Any questions? (10 min)

What is..?

Docker - a container system for building and sharing applications

Cloud - computing delivered via the internet, not locally

What does serverless offer?

Serverless - cloud services that often use containers to host applications without configuring servers

• Focus on code, not dev-ops
• Scale from 0 to billions
• Reliability and security
• Abstraction

Climbing up the R pyramid

Climbing up the Cloud pyramid

The keystone

• R - abstraction of R environments
• Cloud - run any code on cloud systems

Docker + R = R in Production

• Flexible No need to ask IT to install R places, use docker run; across cloud platforms; ascendent tech

• Version controlled No worries new package releases will break code

• Scalable Run multiple Docker containers at once, fits into event-driven, stateless serverless future

Useful R Docker images

• rocker/r-ver
• rocker/rstudio
• rocker/tidyverse
• rocker/shiny
• rocker/ml-gpu

Thanks to Rocker Team

Dockerfiles

FROM rocker/tidyverse:3.6.0

# install R package dependencies
RUN apt-get update && apt-get install -y \
    libssl-dev 

## Install packages from CRAN
RUN install2.r --error \ 
    -r 'http://cran.rstudio.com' \
    googleAnalyticsR \ 
    searchConsoleR \ 
    googleCloudStorageR \
    bigQueryR \ 
    ## install Github packages
    && installGithub.r MarkEdmondson1234/youtubeAnalyticsR

Schedule an R script in the Cloud

Create an R API that scales from 0 to 1 billion

googleCloudRunner - Use Cases

install.packages("googleCloudRunner")
library(googleCloudRunner)

As easy as possible enabling of R use cases in the Cloud

• Scheduled R scripts (API calls, data updates)
• Long-running R scripts (Batched R scripts)
• Scale to 0 R APIs (R events, R-a-a-Service)
• Continuous development (build R website/packages upon Git commit)

A use anywhere R/RStudio

• Configure RStudio Server just like home…?

library(googleComputeEngineR)
gce_vm(template = "rstudio", name = "my-rstudio-server",
       username = "mark", password = "r2020",
       predefined_type = "n1-highmem-2")

• Doesn’t use the cloud to full potential

Tailored R environment

• Workshops with material pre-loaded
• ML training machine
• package sets (tidyverse, googleauthrverse etc.)
• Shiny

library(googleComputeEngineR)
gce_vm(name = "deeplearning-r", 
       template = "rstudio-gpu", 
       username = "mark", password = "mark123", 
       acceleratorType = "nvidia-tesla-k80")

Scaling up R

• library(future) for cluster parallel work
• Run many of the same R environments at a time

library(future)
library(googleComputeEngineR)

vms <- gce_vm_cluster()
plan(cluster, workers = as.cluster(vms))

f <- function(my_data, args){
   ## ....expensive...computations
   result
}

result %<-% f(my_data) 

Code. Data. Config.

• Good data science principles -> Good Cloud principles
• The right tool for the data - BigQuery, Cloud Storage etc.
• Switch configuration via environment args, yaml etc.

Swap to new platforms

Kubernetes

Docker image originally for GCE, deployed to Kubernetes:

kubectl run shiny1 \
  --image gcr.io/gcer-public/shiny-googleauthrdemo:prod \
  --port 3838

kubectl expose deployment shiny1 \
  --target-port=3838  --type=NodePort

https://code.markedmondson.me/r-on-kubernetes-serverless-shiny-r-apis-and-scheduled-scripts/

Shiny apps on k8s

Build R on a schedule

Cloud Build (like the demo earlier)

Trigger R on events

Cloud Run (like the demo earlier)

My current R / Cloud setup

Same R/Docker container, many options:

• Docker on VM - GPU support, ML dev work
• Kubernetes - Shiny apps
• Cloud Build - Batched and scheduled jobs
• Cloud Run - R APIs, event driven workflows

Ability to share R code with non-R users

Take-aways

• Abstracting R using Docker opens up R horizons
• Cloud offers to make hard things easy for R tasks
• Code & data & config separation means best tool for the job

Gratitude

• Thank you for listening
• Thanks to Anne Petersen for inviting me
• Thanks to R Core team for Rv1.0.0 and beyond
• Thanks to RStudio for all their cool things
• Thanks again to Rocker
• Thanks to Google for Developer Expert programme

Say hello

• Twitter - @HoloMarkeD
• Blog - https://code.markedmondson.me
• Help working with R in the Cloud - mark@iihnordic.com
• Any questions now?