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Documenting functions
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.
·roxygen2.r-lib.org·
Documenting functions
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
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
Prompt and empower your LLM, the tidy way
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.
·tjarkvandemerwe.github.io·
Prompt and empower your LLM, the tidy way
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.
·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()`.
·tidyr.tidyverse.org·
Rectangling
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.
·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.
·byrdlab.org·
JSON files & tidy data | The Byrd Lab
R Workflow
R Workflow
This work is intended to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting.
·hbiostat.org·
R Workflow