BookdownsChapter 9 Use httptest2 | HTTP testing in R
In this chapter we aim at adding HTTP testing infrastructure to exemplighratia2 using httptest2. For this, we start from the initial state of exemplighratia2 again. Back to square one!...
Intro to GIS and Spatial Analysis
This is a compilation of lecture notes that accompany my Intro to GIS and Spatial Analysis course.
Welcome | Geocomputation with R
Welcome | Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data.
Enterprise UI Design: Professional Bootstrap 5 for Shiny Apps
Master enterprise-grade UI/UX design for Shiny applications using Bootstrap 5, bslib theming, and professional design systems. Learn to create accessible, responsive interfaces that meet corporate standards for biostatistics and clinical research applications.
Shiny App Workflows
This is a book that covers the standard shiny app
workflow.
Advanced Tidyverse
Use piped workflows for efficient data cleaning and visualization.
Technical Guidelines for R
Best practices with R around select topics.
r-lib/producethis: What the Package Does (One Line, Title Case)
Note the use of the /exec folder for different deployable workflows
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.
Shiny
Shiny is a package that makes it easy to create interactive web apps using R and Python.
rquery/extras/JoinController.md at master · WinVector/rquery
Data Wrangling and Query Generating Operators for R. Distributed under choice of GPL-2 or GPL-3 license. - WinVector/rquery
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.
Quarto
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.