Links to resources on reproducible research and related tools.

A project template and directory structure for Python data science projects.

Generation of diagram and flowchart from text in a similar manner as markdown - mermaid-js/mermaid

DiagrammeR, an R package that allows you to create flowcharts, diagrams, and grhs with Markdown-like text.

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.

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.

Best practices for staying organized to promote reproducibility

The new bookmarking functionality is great. I'm attempting to use it with rhandsontable and running into a slight issue when attempting to restore the table data. The widget is currently bu...

An RStudio addin providing shortcuts for writing in Markdown. This package provides a series of functions that allow the user to be more efficient when using Markdown. For example, you can select a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea is to map all the functionalities from remedy on keyboard shortcuts, so that it provides an interface close to what you can find in any other text editor.

A Framework for Building Robust Shiny Apps . Contribute to ThinkR-open/golem development by creating an account on GitHub.

Data model diagrams in R. Contribute to bergant/datamodelr development by creating an account on GitHub.

My search for the ultimate user-friendly data filtering system in R Shiny brought me to the dark arts of quosures.

Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub, licenses, Rcpp, RStudio projects, and more.

The R task Force - R experts for all your needs

(Or, how to write a Shiny app.R file that only contains a single line of code) This post is long...

Contribute to jdbarillas/executive_summary development by creating an account on GitHub.

The stuff you need to know about R, besides statistical analysis.

Overview RStudio Desktop stores its internal state in a hidden directory called RStudio-Desktop. If this directory does not exist, RStudio will create it on start up. This directory includes inform...

At RStudio, I have the pleasure of interacting with data science teams around the world. Many of these teams are led by R users stepping into the role of analytic admins. These users are responsible for supporting and growing the R user base in their organization and often lead internal R user groups. One of the most successful strategies to support a corporate R user group is the creation of an internal R package.

My workflow is like below: Plan out the type of app. For a lot of applications, sometimes a htmlwidget in Rmd will do, and covers more cases as time goes on and javascript gets cooler. This is preferable as then you can publish to any server, not needing Shiny. But if it needs more server side interaction, I look at using flexdashboard to keep Rmd elements, but have shiny embedded. If its a big app though, its easier to keep track using a ui.R or a HTML template. I have a HTML template ba...