No Clocks

In this post, I talk about parallelism in R. This post is likely biased towards the solutions I use. For example, I never use mcapply nor clusterApply; I prefer to always use foreach. In this post, we will focus on how to parallelize R code on your computer with package {foreach}. In this post, I use mainly silly examples just to show one point at a time. Basics of foreach You can install R package {foreach} with install.packages("foreach"). library(foreach)

"R in Production " is a blog that talks about Security, Automation, Scalability in the R environment. Furthermore, Software Engineering teaches us proven methodologies to bring a prototype into production and to facilitate the work process in all its aspects. (Click on the title to read more ...)

This is a guest post by Chris Paciorek, Department of Statistics, University of California at Berkeley. In this post, I’ll demonstrate that you can easily use the future package in R on a cluster of machines running in the cloud, specifically on a Kubernetes cluster. This allows you to easily doing parallel computing in R in the cloud. One advantage of doing this in the cloud is the ability to easily scale the number and type of (virtual) machines across which you run your parallel computation.

plumber

An opinionated example for implementing logging in Plumber APIs - sol-eng/plumber-logging

An opinionated example for implementing logging in Plumber APIs - sol-eng/plumber-logging

plumber

In March of 2021, Epi-Interactive hosted one of the first in-person R events in Wellington, New Zealand. Here are some takeaways from their experience.

This is a guest post by Chris Paciorek, Department of Statistics, University of California at Berkeley. In this post, I’ll demonstrate that you can easily use the future package in R on a cluster of machines running in the cloud, specifically ...

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How to dockerize a Shiny app that calls a Plumber API

Learn how to use the open-source tool Plumber to create RESTful APIs for R programs.

Learn how to create a REST API in R using the plumber package.

Simple functions to lookup items in key-value pairs. See Mehta (2021) .

If you’ve used the Plumber package to make R models or other code accessible to others via an API, sooner or later you will need to decide how to handle and report errors. By default, Plumber will catch R-level errors (like calls to stop()) and report them to users of your API as a JSON-encoded error message with HTTP status code 500 – also known as Internal Server Error. This might look something like the following from the command line: $ curl -v localhost:8000/ > GET /status HTTP/1.1 > Host: localhost:8000 > User-Agent: curl/7.64.0 > Accept: */* > < HTTP/1.1 500 Internal Server Error < Date: Sun, 24 Mar 2019 22:56:27 GMT < Content-Type: application/json < Date: Sun, 24 Mar 2019 10:56:27 PM GMT < Connection: close < Content-Length: 97 < * Closing connection 0 {"error":["500 - Internal server error"],"message":["Error: Missing required 'id' parameter.\n"]} There are two problems with this approach: first, it gives you almost zero control over how errors are reported to real users, and second, it’s badly behaved at the protocol level – HTTP status codes provide for much more granular and semantically meaningful error reporting. In my view, the key to overcoming these problems is treating errors as more than simply a message and adding additional context when they are emitted. This is sometimes called structured error handling, and although it has not been used much historically in R, this may be changing. As you’ll see, we can take advantage of R’s powerful condition system to implement rich error handling and reporting for Plumber APIs with relative ease.