R

A few helpful design patterns for navigation in {shiny} applications using buttons in a {reactable} widget and

A few tips on using bootstrap 5 popovers in Shiny

Every now and then there is a question on the R4DS slack that produces a large number of replies. This case was particularly interesting because the user that had the issue deleted all the original content, the question, the clarifications and so on.

A workflow on how to create visually pleasing and effective static HTML visualizations of small-scale and heterogenous tabular data

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.

Docker-based variant-calling pipelines Encapsulating large and complex software applications or packages in docker images makes sense for many reasons. Not least because of a one-line install protocol (i.e., docker pull) that takes care of the plethora of dependencies that many of these packages have.

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).