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.

Welcome to the workshop and hello, bslib!

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 .

mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.

httr2

A guide to R development.

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.

Yet another post with a focus on package documentation! This time, we’ll cover vignettes a.k.a “long-form package documentation”, both basics around vignette building and infrastructure, and some tips for more maintainer- and user- friendliness. What is a vignette? Where does it live? In this section we shall go over basics of package vignettes. Vignette 101 In the “R packages” book by Hadley Wickham and Jenny Bryan, the vignettes chapter starts with “A vignette is a long-form guide to your package.

Data table backend for dplyr. Contribute to tidyverse/dtplyr development by creating an account on GitHub.

On the jobs-to-be-done and design principles for internal tools

Provides a shiny-based front end (the 'ExPanD' app) and a set of functions for exploratory data analysis. Run as a web-based app, 'ExPanD' enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of 'ExPanD' and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use 'ExPanD' and/or the functions of this package.

Contribute to jsugarelli/shinyfilter development by creating an account on GitHub.

all-contributions as an R package. Contribute to ropenscilabs/allcontributors development by creating an account on GitHub.

Add a download button to a 'shiny' plot or 'plotly' that appears when the plot is hovered. A tooltip, styled to resemble 'plotly' buttons, is displayed on hover of the download button. The download button can be used to allow users to download the dataset used for a plot.

A Toolbox for Non-Tabular Data Manipulation. Contribute to renkun-ken/rlist development by creating an account on GitHub.

:rocket: R package: doFuture - A Universal Foreach Parallel Adaptor using the Future API of the 'future' Package - HenrikBengtsson/doFuture

R package: parallelly - Enhancing the 'parallel' Package - HenrikBengtsson/parallelly

:rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone - HenrikBengtsson/future

htmlwidget that binds d3js collapsible trees to R and Shiny to make an interactive search tool - yonicd/d3Tree

R's data.table package extends data.frame:. Contribute to Rdatatable/data.table development by creating an account on GitHub.

An R package to create data libraries and data dictionaries. - dbosak01/libr

Install and run external command-line programs in R through use of Docker and online repositories.

BRowse Over Longitudinal Data Graphically and Analytically in R - njtierney/brolgar

Some R-based tools for navigating the online news landscape - jaytimm/quicknews

Automated testing for shiny apps. Contribute to rstudio/shinytest development by creating an account on GitHub.

Tools to easily enable R scripts in the Google Cloud Platform. Utilise cloud services such as Cloud Run for R over HTTP, Cloud Build for Continuous Delivery and Integration services and Cloud Scheduler for scheduled scripts.

A database interface (DBI) definition for communication between R and RDBMSs - r-dbi/DBI

:package: Generate citations for knitr markdown and html files - cboettig/knitcitations