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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.
Fast JSON, NDJSON and GeoJSON Parser and Generator
Fast JSON, NDJSON and GeoJSON Parser and Generator
A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .
·coolbutuseless.github.io·
Fast JSON, NDJSON and GeoJSON Parser and Generator
Literate Programming for Writing R Packages
Literate Programming for Writing R Packages
Allows one to fully create an R package in a single .Rmd file. Includes functionality and .Rmd templates for a literate programming approach to R package development.
·jacobbien.github.io·
Literate Programming for Writing R Packages
Best Practice: Development of Robust Shiny Dashboards as R Packages
Best Practice: Development of Robust Shiny Dashboards as R Packages
This article describes best practice approaches for developing shiny dashboards. The creation of the dashboard in package form, as well as the use of unit tests should enable the development of robust solutions and guarantee high quality.
·inwt-statistics.com·
Best Practice: Development of Robust Shiny Dashboards as R Packages