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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
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
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