R

Some Shiny applications. Contribute to dreamRs/shinyapps development by creating an account on GitHub.

erum::Conf 2020 workshop session. Contribute to DivadNojnarg/Advanced-User-Interfaces-for-Shiny-Developers development by creating an account on GitHub.

User login and admin panel for shiny apps (Semantic UI) - systats/shinyuser

Demonstration of user management and authentication system in R Shiny - yanirmor/shiny-user-management

:calendar: R htmlwidget for tui-calendar. Contribute to dreamRs/tuicalendr development by creating an account on GitHub.

Take screenshots in Shiny apps. Contribute to dreamRs/capture development by creating an account on GitHub.

Shiny modules to import data into an application or addin - dreamRs/datamods

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

Turn your analyses into high quality documents, reports, presentations and dashboards with R Markdown. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports a reproducible workflow for dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.

A framework to create bootstrap styled HTML reports from knitr Rmarkdown. - jimhester/knitrBootstrap

HTML output formats for RMarkdown documents. Contribute to juba/rmdformats development by creating an account on GitHub.

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.

I was my great pleasure yesterday to be a presenter in the "Why R Webinar" series, on the topic R at Microsoft. In the talk (which you can watch below) I recounted the history of Microsoft's acquisition of Revolution Analytics, and the various way the Microsoft supports R: its membership of the R Consortium, integration with many products (including demos of Azure ML Service with GitHub Actions, and Azure Functions), and how Microsoft has driven adoption of R in large organizations by making R "IT approved". Many thanks to the Why R Foundation for hosting the talk, and to everyone...

: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.

tidyr + jsonlite are magical.

With most computers now having many cores, parallelising your R code is a great way to reduce processing time, and there are lots of packages to help. But if you benchmark your new code, you may fi…