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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.
#r-development#design-patterns#r#software-architecture#object-oriented-programming#r-package#r-package-devt#api#r-api-devt
·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.
#r-development#dev#snapshot-testing#r-testing
·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 .
#r#r-package#json#geojson#data#parser#generator#tool#r-development#r-package-devt
·coolbutuseless.github.io·
Fast JSON, NDJSON and GeoJSON Parser and Generator
Shiny
Shiny
Shiny is a package that makes it easy to create interactive web apps using R and Python.
Shiny was designed with an emphasis on distinct input and output components in the UI. Inputs send values from the client to the server, and when the server has values for the client to display, they are received and rendered by outputs.
You want the server to trigger logic on the client that doesn’t naturally relate to any single output.
You want the server to update a specific (custom) output on the client, but not by totally invalidating the output and replacing the value, just making a targeted modification.
You have some client JavaScript that isn’t related to any particular input, yet wants to trigger some behavior in R. For example, binding keyboard shortcuts on the web page to R functions on the server, or alerting R when the size of the browser window has changed.
R - ShinyR - Shiny#r-development#r-shiny#javascript#article#docs#r-shiny-modules#r-package
·shiny.posit.co·
Shiny
Rectangling
Rectangling
Rectangling is the art and craft of taking a deeply nested list (often sourced from wild caught JSON or XML) and taming it into a tidy data set of rows and columns. This vignette introduces you to the main rectangling tools provided by tidyr: `unnest_longer()`, `unnest_wider()`, and `hoist()`.
#tidy#r-development#data
·tidyr.tidyverse.org·
Rectangling
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr ... Read more
#JSON#r-development#data#tidy
·robotwealth.com·
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
R - JSON Files
R - JSON Files
R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.
RMarkdownRMarkdown#JSON#r-package#r-development
·tutorialspoint.com·
R - JSON Files
hendrikvanb
hendrikvanb
Working with complex, hierarchically nested JSON data in R can be a bit of a pain. In this post, I illustrate how you can convert JSON data into tidy tibbles with particular emphasis on what I’ve found to be a reasonably good, general approach for converting nested JSON into nested tibbles. I use three illustrative examples of increasing complexity to help highlight some pitfalls and build up the logic underlying the approach before applying it in the context of some real-world rock climbing competition data.
#JSON#r-development#data#r-api-devt#r
·hendrikvanb.gitlab.io·
hendrikvanb
JSON files & tidy data | The Byrd Lab
JSON files & tidy data | The Byrd Lab
My lab investigates how blood pressure can be treated more effectively. Much of that work involves the painstaking development of new concepts and research methods to move forward the state of the art. For example, our work on urinary extracellular vesicles’ mRNA as an ex vivo assay of the ligand-activated transcription factor activity of mineralocorticoid receptors is challenging, fun, and rewarding. With a lot of work from Andrea Berrido and Pradeep Gunasekaran in my lab, we have been moving the ball forward on several key projects on that front.
#JSON#r-development#r#r-api-devt#api#tidy#data
·byrdlab.org·
JSON files & tidy data | The Byrd Lab
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
You can read the original post in its original format on Rtask website by ThinkR here: Pimping your shiny app with a JavaScript library : an example using sweetalert2 You think that some of the components of {shiny} are not very functional or downright austere? Are you looking to implement some feature in your app but it is not available in the {shiny} toolbox? Take a look at JavaScript! JavaScript is a very popular programming language that is often used to add features to web pages. With HTML and This post is better presented on its original ThinkR website here: Pimping your shiny app with a JavaScript library : an example using sweetalert2
R - ShinyR - Shiny#r#r-shiny#r-development
·r-craft.org·
Pimping your shiny app with a JavaScript library : an example using sweetalert2 – R-Craft
Pack YouR Code
Pack YouR Code
This book showcases a basic example of how to create an R package based on S3 classes.
#r-development
·gastonsanchez.com·
Pack YouR Code