No Clocks

No Clocks

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Design Patterns in R
Design Patterns in R
Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. R6P provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.
·tidylab.github.io·
Design Patterns in R
The most efficient way to manage snapshot tests in R.
The most efficient way to manage snapshot tests in R.
Use CI and Github API
Snapshot testing gets difficult when there is more than one variant of the same result. The reason why snapshot testing might be discouraging is due to the fact that snapshots will most likely fail due to environment settings. If one person runs the tests on a Mac and another on a Linux machine, the snapshots of rendered images will almost certainly be different. Comparing these snapshots will result in a failed test even though the code is correct. Add CI to the mix, and you have a hot mess.
The easiest solution is to introduce variants. Variants are versions of snapshots which were created on different environments. In {testthat} variants are stored in separate directories. You can pass a name of the variant to the variant argument of testthat::test_snapshot. If you have a Linux, set variant = "linux", if you have a Mac, set variant = "mac".
Use snapshots generated on CI as the source of truth. Don’t check in snapshots generated on your machine. Generate them on CI and download them to your machine instead.
Step 1: Archive snapshots on CI Add this step to you CI testing workflow to allow downloading generated snapshots.
- name: Archive test snapshots if: always() uses: actions/upload-artifact@v3 with: name: test-snapshots path: | tests/testthat/_snaps/**/**/*
Step 2: Detect the environment to create variants We can create a make_variant function to detect the version of the platform, as well as if we are running on CI. This way even if we use the same OS on CI and locally, we can still differentiate between snapshots generated on CI and locally.
#' tests/testthat/setup.R is_ci <- function() { isTRUE(as.logical(Sys.getenv("CI"))) } make_variant <- function(platform = shinytest2::platform_variant()) { ci <- if (is_ci()) "ci" else NULL paste(c(ci, platform), collapse = "-") } # In tests: testthat::expect_snapshot(..., variant = make_variant())
Step 3: Ignore your local snapshots Don’t check in snapshots generated on your machine. Add them to .gitignore instead. Copy tests/testthat/_snaps/linux-4.4 This way we can still generate snapshots locally to get fast feedback, but we’ll only keep a single source of truth checked in the repository. Since you don’t track changes in local snapshots, you need to regenerate them before you start making changes to see if they change. It adds some complexity to the process, but it allows to keep the number of shared snapshots in the version control minimal. Alternatively, you can keep local snapshots, but when doing code review, focus only on the ones generated on CI.
Step 4: Automate downloading snapshots from CI To update snapshots generated on CI in Github, we need to: Go to Actions. Find our workflow run. Download the test-snapshots artifact. Unpack and overwrite the local snapshots. testthat::snapshot_review() to review the changes. Commit and push the changes. This is a lot of steps. We can automate the most laborious ones with Github API.
The .download_ci_snaps function will: Get the list of artifacts in the repository identified by repo and owner. It’ll search workflows generated from the branch we’re currently on. It will download the latest artifact with the provided name (in our case its “test-snapshots”) in the repository Unzip them and overwrite the local copy of snapshots.
·jakubsob.github.io·
The most efficient way to manage snapshot tests in R.
A Database Model for an Online Survey. Part 4
A Database Model for an Online Survey. Part 4
In this final article in a four-part series, I complete the design for an online survey database to provide flexibility for multiple surveys, question re-use, multiple choice answers, ordering of questions, conditional jumps in the survey based on responses, and control over the users' access to surveys via groups of survey owners.
·vertabelo.com·
A Database Model for an Online Survey. Part 4
Learn About
Learn About
Grasp new topics and deepen your understanding with a conversational learning companion that adapts to your unique curiosity and learning goals. Ask big or small questions, upload material or explore curated topics. Navigate complex concepts with interactive guides. Make connections to deepen understanding with learning aids. Enhance learning with images, videos, and articles from relevant sources. Go as far as your curiosity takes you. Explore broadly or dig into the details.
·learning.google.com·
Learn About
Guide to Windows Batch Scripting - /* steve jansen */
Guide to Windows Batch Scripting - /* steve jansen */
I love shell scripting – it’s the duct tape of programming to me. Low cost, high benefit. And it feels like art, where one can learn to …
·steve-jansen.github.io·
Guide to Windows Batch Scripting - /* steve jansen */
Temporal tables - SQL Server
Temporal tables - SQL Server
System-versioned temporal tables bring built-in support for providing information about data stored in the table at any point in time.
·learn.microsoft.com·
Temporal tables - SQL Server
Shiny App Usage Telemetry
Shiny App Usage Telemetry
Enables instrumentation of Shiny apps for tracking user session events such as input changes, browser type, and session duration. These events can be sent to any of the available storage backends and analyzed using the included Shiny app to gain insights about app usage and adoption.
·appsilon.github.io·
Shiny App Usage Telemetry
Rspress
Rspress
Rspack based static site generator
·rspress.dev·
Rspress
Modern.js vs Remix · web-infra-dev/modern.js · Discussion #4872
Modern.js vs Remix · web-infra-dev/modern.js · Discussion #4872
Hi there, I have been experimenting with Modern js and so far the experience has been good. The main selling point for me really is the micro frontend tooling support over remix especially in the u...
·github.com·
Modern.js vs Remix · web-infra-dev/modern.js · Discussion #4872
Coze: Next-Gen AI Chatbot Developing Platform
Coze: Next-Gen AI Chatbot Developing Platform
Coze is a next-generation AI application and chatbot developing platform for everyone. Regardless of your programming experience, Coze enables you to effortlessly create various chatbots and deploy them across different social platforms and messaging apps.
·coze.com·
Coze: Next-Gen AI Chatbot Developing Platform
Prompt Storm - A Powerful Easy to use Artificial Intelligence Prompt Engineering Chrome Software Extension for ChatGPT, Google's Gemini, and Anthropic's Claude.
Prompt Storm - A Powerful Easy to use Artificial Intelligence Prompt Engineering Chrome Software Extension for ChatGPT, Google's Gemini, and Anthropic's Claude.
Prompt Storm - A Powerful Easy to use AI Prompt Engineering Chrome Extension for ChatGPT, Google's Gemini, and Anthropic's Claude. With just a few clicks you can get the answers you're looking for, create amazing writing, marketing and social media strategies, save time and boost your productivity.
·promptstorm.app·
Prompt Storm - A Powerful Easy to use Artificial Intelligence Prompt Engineering Chrome Software Extension for ChatGPT, Google's Gemini, and Anthropic's Claude.
API for rpkg.net
API for rpkg.net
Developing a vibrant API for acessing rpkg.net
·api.rpkg.net·
API for rpkg.net