17 Big picture | Advanced R
Development
Tidyverse
The tidyverse is an integrated collection of R packages designed to make data science fast, fluid, and fun.
Structuring R projects: Chris von Csefalvay's perspective on the ideal R project
Structuring R projects is not fun. However, especially in a collaborative data science setting, it is indispensable, and the mark of a true team player.
Production
RStudio's webinars offer helpful perspective and advice to data scientists, data science leaders, DevOps engineers and IT Admins. Presenters come from companies around the globe, as well as the RStudio staff.
daattali/addinslist: Discover and install useful RStudio addins
Discover and install useful RStudio addins . Contribute to daattali/addinslist development by creating an account on GitHub.
clientapp/devtstuff_history.R at master · ThinkR-open/clientapp
Showcase of Shiny App for client database and after-sales calls exploration - ThinkR-open/clientapp
Better Specs { rspec guidelines with ruby }
Better Specs is a collection of best practices developers learned while testing apps that you can use to improve your coding skills, or simply for inspiration.
Better Specs. Testing Guidelines for Developers.
Better Specs is a collection of best practices developers learned while testing apps that you can use to improve your coding skills, or simply for inspiration.
qinwf/awesome-R: A curated list of awesome R packages, frameworks and software.
A curated list of awesome R packages, frameworks and software. - qinwf/awesome-R
Cookbook for R
This site is powered by knitr and Jekyll. If you find any errors, please email winston@stdout.org
Building a Corporate R Package | Steven M. Mortimer
Always wanted to write an R package for your team? This article gives tips for what to include in your team's R package. Get started today!
Version Control for Excel Spreadsheets | XLTools – Excel Add-ins You Need Daily
FreedCamp
Milestone
Milestones mark the specific points in project success. Learn what is project management milestone and how to use it for project scheduling.
nanxstats/awesome-shiny-extensions: Awesome R packages that offer extended UI or server components for the R web framework Shiny
🐝 Awesome R packages that offer extended UI or server components for the R web framework Shiny - nanxstats/awesome-shiny-extensions
Episode 1: Shiny Development - Past and Future | Shiny Developer Series
Recording of RStudio webinar with Winston Chang & Curtis Kephart
HTML Reference
Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, PHP, Python, Bootstrap, Java and XML.
06_org_eda_withnotes.pdf
RMarkdown Driven Development (RmdDD)
A workflow for refactoring one-time analyses to sustainable data products
Manage functionality as a package
Building a package that lasts — eRum 2018 workshop - Speaker Deck
CircleCI
Resources
Links to resources on reproducible research and related tools.
Home - Cookiecutter Data Science
A project template and directory structure for Python data science projects.
Data Carpentry
Data Carpentry is a lesson program of The Carpentries that develops and provides data skills training to researchers.
14 Strings | R for Data Science
knsv/mermaid: Generation of diagram and flowchart from text in a similar manner as markdown
Generation of diagram and flowchart from text in a similar manner as markdown - mermaid-js/mermaid
DiagrammeR
DiagrammeR, an R package that allows you to create flowcharts, diagrams, and grhs with Markdown-like text.
Efficient R programming
Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency.
18.1 Deriving from built-in formats | R Markdown: The Definitive Guide
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.