AWS lambda

A demo of two newly added features in the {r2lambda} R package. How to deploy R projects with multiple files, and how to use the {renv} lockfile to manage dependencies in the AWS Lambda docker image

An introduction to working with AWS S3 from R and a step-by-step workflow to set an AWS Lambda functon to save datasets to an S3 bucket on a weekly schedule

A step-by-step tutorial on how to deploy an R-based AWS Lambda function, how to set it to run on a recurring schedule, how to validate that the execution happens on schedule, and how to clean up. All from the R console.

A detailed walk through the steps to prepare a custom R script for deployment as AWS Lambda with the r2lambda package. How to prepare an R script? What are the roles of several key arguments? How to request longer timeout or more memory for a Lambda function? How to parse the response payload returned by the Lambda function?

Creating AWS Lambda functions from R code can be a powerful way to make our local R code available in the cloud as an on-demand serverless service. In recent weeks I’ve been working on my package r2lambda to build and deploy AWS Lambda functions from the R console.

Overview and motivation AWS Lambda is a serverless compute service that lets us deploy any code as a cloud function without worrying about setting up a compute server (for example like discussed here and here).

In recent weeks, I have been trying to learn more about using the {paws} software development kit to interact with Amazon Web Services from R. So far, my focus was on the basics of DynamoDB.