blog – Albert Rapp
Documenting functions
The basics of roxygen2 tags and how to use them for documenting functions.
Examples
@examples provides executable R code showing how to use the function in practice. This is a very important part of the documentation because many people look at the examples before reading anything. Example code must work without errors as it is run automatically as part of R CMD check.
For the purpose of illustration, it’s often useful to include code that causes an error. You can do this by wrapping the code in try() or using \dontrun{} to exclude from the executed example code.
For finer control, you can use @examplesIf:
#' @examplesIf interactive()
#' browseURL("https://roxygen2.r-lib.org")
Instead of including examples directly in the documentation, you can put them in separate files and use @example path/relative/to/package/root to insert them into the documentation.
All functions must have examples for initial CRAN submission.
dm cheat sheet
UNCHARTED DATA: Interactive Tooltip Tables
How to include tables in your {ggiraph} tooltips.
UNCHARTED DATA: Introducing the {reactablefmtr} Package
An R package created to make the styling and customization of {reactable} tables easier.
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.
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 .
Prompt and empower your LLM, the tidy way
The tidyprompt package allows users to prompt and empower their large language models (LLMs) in a tidy way. It provides a framework to construct LLM prompts using tidyverse-inspired piping syntax, with a library of pre-built prompt wrappers and the option to build custom ones. Additionally, it supports structured LLM output extraction and validation, with automatic feedback and retries if necessary. Moreover, it enables specific LLM reasoning modes, autonomous R function calling for LLMs, and compatibility with any LLM provider.
How to enhance your R shiny application with httpOnly Cookies
httpOnly Cookies are crucial for security, protecting against cross-site scripting attacks in R Shiny apps. Read more about them here.
R - Shiny#shiny#webdev#r-development#dev#r-shiny#shiny-server#security#http-only#cookie#cross-site scripting#xssRoxygen R6 Guide
mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.
RMarkdown
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.
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 data
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.
Working with JSON Data
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.
Shiny App-Packages
Getting your app into an R package
Foreword | Outstanding User Interfaces with Shiny
A book about deeply customizing Shiny app for production.
ixpantia/faucet: Fast and scalable R application deployment and orchestration
Fast and scalable R application deployment and orchestration - ixpantia/faucet
Introduction
Shiny Server @CNR-IBBA. Contribute to cnr-ibba/shiny-server development by creating an account on GitHub.
Bookdowns
R Development Guide
A guide to R development.
Simple, Consistent Package Options
Simple mechanisms for defining and interpreting package options. Provides
helpers for interpreting environment variables, global options, defining
default values and more.
Script progress bar in R that works with purrr and in batch mode
Here is how to create a progress bar in R that is versatile when working with loops, for example, by using package purrr.
rstudio::conf 2022 Keynotes - The Past and Future of Shiny
All things R and RStudio
Have we got NEWS.md for you
When developing a package it is essential to track the changes you make to your code. This is especially vital if they are breaking changes which have implications for any code written that depends on your package, i.e. a major version bump. Although you can always look back at your version control history in git, it is also convenient to have documentation which summarises the changes. This is where the NEWS file comes in.
Codebook generator
Create a CLI for R with npm
How to build a CLI for R, with npm.BackgroundThis blog post was triggered by a discussion on Twitter with MartinSkarzynski,who was looking for a way to build a CLI that launches an RScript.Here’s a way to do this using npm.Please note that this blog post won’t teach you how to build the commandline tool, it will quickly go over the way to create a system-widecommand line interface, using npm.If you want to learn more about building the utility, see thisfantastic series of blogpostsby Mark Sellor.Now, the idea is to have a CLI, i.e. a way to launch your utility with:$ mytoolAnd that, system-wide.What you’ll need An R script (script.R) with in it, for example:#!/usr/bin/env Rscript --vanillacli::cat_rule("yeay")cli::cat_bullet(Sys.time()) npm, which you can get fromthere.Let’s goCreate a new folder, and go inside it.mkdir cli && cd cliCreate the R Script there.echo '#!/usr/bin/env Rscript --vanilla' script.Recho 'cli::cat_rule("yeay")' script.Recho 'cli::cat_bullet(Sys.time())' script.RTry your script to see if it works:Rscript script.RNow launch an npm project:npm init -y(You can also run it without the -y to interactively add informationto the package.json.)Now the important part: add a "bin" value in the package.json, withthe name of the bin you want to create, and the path to the script,relatively to the package file. Here is an example of a package.json(I removed some elements).{ "name": "cli", "version": "1.0.0", "description": "CLI example with npm", "bin" : { "clir" : "./script.R" }, "author": "Colin Fay", "license": "MIT"}Install it globally (need sudo rights):sudo npm linkAnd, voilà! Open your terminal, and you’re done!clirOther way to go See the {littler}implementation
R Workflow
This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting.