No Clocks

I like working in the Jamstack. spences10 has 143 repositories available. Follow their code on GitHub.

Everyday commands, config, hints and tips used for modern web development.

Cheat Sheets 🐭🤖👀. Contribute to spences10/cheat-sheets development by creating an account on GitHub.

My Letter Beautiful Mysterious Notebook. Contribute to spences10/scottspence.com development by creating an account on GitHub.

From scratch to Create React App on Windows An opinionated guide on setting up a web development environment on Windows 10 I have been a…

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.

An opinionated framework for using JavaScript with R.

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.

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