sol-eng/db-dashboard

No Clocks
Best Practices for Organizing Data on Your Computer | Alexander's Blog
One of the most common challenges that computer users often face is data organization. I see users with cluttered desktops full of individual files, folders that are duplicated (desktop and default home directory), and users struggling to find their own data which makes content management very difficult. In this article, I will share some guidelines […]
Serverless R functions with Cloud Run
Using serverless containers to deploy scalable R functions.
Effectively Deploying and Scaling Shiny Apps with ShinyProxy, Traefik and Docker Swarm | databentobox
This post provides a guide to effectively and securely scale your ShinyProxy deployment of shiny apps in production with Docker Swarm and Traefik
Breakdown a Monolithic Application to a Suite of Services • Use Microservices in R
Microservice architectural style is an approach to developing a
single application as a suite of small services, each running in its own
process and communicating with lightweight mechanisms, often an HTTP
resource API. These services are built around business capabilities and
independently deployable by fully automated deployment machinery. There
is a bare minimum of centralized management of these services, which may
be written in different programming languages and use different data storage
technologies.
R in Prod: rip, a command line R package installer | sellorm
Supplement to Shiny in Production
This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf 2019.
Field Guide to the R Ecosystem
This guide aims to introduce the reader to the main elements of the R ecosystem.
Executing asynchronous tasks | Cloud Run Documentation | Google Cloud
Authenticating service-to-service | Cloud Run Documentation
dreamRs/shinypop: Collection of notifications, confirm dialogs and alerts for 'Shiny' applications
Collection of notifications, confirm dialogs and alerts for 'Shiny' applications - dreamRs/shinypop
Google API Design Guide
Collections of resources for API Designers
API design guidance - Best practices for cloud applications | Microsoft Docs
Web applications can expose APIs so clients to interact with the application. Well-designed web APIs should support platform independence and service evolution.
Using Cookie Based Authentication with Shiny · calligross.de
dotenv - npm
about_Environment_Variables - PowerShell | Microsoft Docs
PowerShell and Environment Variables: A Deep Dive
Microservices Guide
Test Plan for Software: What, Why and How to Write It? - Bit Blog
Software Development Process: Steps To Follow - Bit Blog
Stop struggling with Python on Windows – Python For Engineers
Structured Errors in Plumber APIs
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.
Functions Similar to VLOOKUP in Excel • lookup
Simple functions to lookup items in key-value pairs. See Mehta (2021) .
Plumber and the Slack API - RStudio :: Solutions
Using Plumber to create APIs for R programs
Learn how to use the open-source tool Plumber to create RESTful APIs for R programs.
REST API with R - Ger Inberg
Learn how to create a REST API in R using the plumber package.
Isochrone - OpenStreetMap Wiki
Database Testing Complete Guide (Why, What, and How to Test Data)
What is CORS (Cross-Origin Resource Sharing)?
CORS allows your web apps to use assets from other domains. Today, we explore CORS and learn to implement it in our own front-end projects.
What are Materialized Views? A 5 Minute Introduction
A materialized view is a type of database object that contains any results derived from a query. Read on for a quick introduction.