No Clocks

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.

Using Git and GitHub with R, Rstudio, and R Markdown

R Markdown lets you add dynamic parameters to your report using the params YAML field (see the R Markdown book for examples); parameterized reports are also used in RStudio Connect. The values of these variables can be called inside your R Markdown document using params$field_name. There are several ways to change the values of the parameters: manually change the YAML, use the params argument in rmarkdown::render(), or knit with parameters, which launches a Shiny app to select values for each. yml_params() accepts any number of named R objects to set as YAML fields. You can also pass argume...

Authoring Books and Technical Documents with R Markdown - rstudio/bookdown

Rmarkdown for Scientists. Contribute to njtierney/rmd4sci development by creating an account on GitHub.

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.

You can run more kinds of apps on Cloud Run thanks to support for WebSockets, HTTP-2 end-to-end gRPC bidirectional streaming.

Initialize an 'RStudio' Project. Contribute to dreamRs/addinit development by creating an account on GitHub.

RStudio addins. Contribute to jennybc/jadd development by creating an account on GitHub.

RStudio Addin to evaluate comma separated function parameters vio short cut - petermeissner/assignparams

RStudio Addin to convert selected text to lower, upper, snake, and camel cases. - strboul/caseconverter

🎨 A colour picker tool for Shiny and for selecting colours in plots (in R) - daattali/colourpicker

Shiny Gadget to compare areas (square meters, kilometers, feet, miles, and acres) - daranzolin/compareAreas

RStudio Addins for Enhanced Diff and Merge. Contribute to miraisolutions/compareWith development by creating an account on GitHub.

RStudio add-in to copy data to clipboard, reverse slashes, insert and reformat pipes. - sfr/RStudio-Addin-Snippets

R Studio Addins for common data frame operations. Contribute to sarupurisailalith/commonUtilAddins development by creating an account on GitHub.

RStudio addin to search CRAN packages titles and descriptions - RhoInc/CRANsearcher

On top of spaghetti, all covered in cheese.... Contribute to MilesMcBain/datapasta development by creating an account on GitHub.

RStudio addin to create skeleton documentation for functions and data.frames - mdlincoln/docthis

An add in for RStudio which allows the user to filter and select columns interactively - kieranjmartin/viewenhance

RStudio add-in to make plots with ggplot2. Contribute to dreamRs/esquisse development by creating an account on GitHub.

Overview Administering RStudio server products often requires you to test changes to your environment, but testing changes can be problematic in production. For example, upgrading R can disrupt dev...

Simple convenience RStudio add-in inserting assignment operators and pipes - konradzdeb/extraInserts

R Studio add in to quickly grab default function arguments - kieranjmartin/getfunctionargs

No interruptions. That was our team’s goal for RStudio Package Manager 1.1.0 - we set out to make R package installation fast enough that it wouldn’t interrupt your work. More and more data scientists use Linux environments, whether to access extra horsepower during development or to run production code in containers. Unfortunately, the rise in Linux environments has seen a corresponding increase in package installation pain. For Windows and Mac OS, CRAN provides pre-compiled binary packages that install almost instantly, but the same binaries are not available on Linux.