At the very beginning of David Allen’s recorded lecture, Getting Things Done Fast, he tells his audience that the most important but single most difficult

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…

coalesce is a one of those functions that might not strike you as being very useful, mainly because it doesn’t sound very exciting. But it IS useful, and might save you some time and complexity

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

Display HTML Elements on Full Screen in 'Shiny' Apps Using 'Screenfull.js' - etiennebacher/shinyfullscreen

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

I was serious, you don't need to be a SQL expert to work with Databases through R

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

Add Tooltips in 'Shiny' Apps With 'Hint.css'. Contribute to etiennebacher/prompter development by creating an account on GitHub.

A parenting project to catalog my childrens' 2020 library history

R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. When you write R packages, you also unlock a whole ecosystem of tools that will make it easier to test, document, and share your code. Despite these benefits, many believe package development is too advanced for them or that they have nothing to offer. A fundamental belief in Zen is that you are already complete, that you already have everything you need. I’ll talk about why your project is already an R package, why you’re already an R package deve...