YakData SmartDesktop with RStudio Desktop Server is the best way to download & install RStudio Desktop for use on Mac or Windows computers in 2022. It includes R 4.1.2 from the Rocker project, the RStudio IDE as a web app, hundreds of top data science packages included and it is all managed easily with Docker Desktop. This is the smart way to develop R programs, Shiny web apps & RMarkdown docs on your desktop in 2022.
+ Save hours of time to get up and running with a complete desktop IDE for R.
+ Includes over 200 of the most commonly used R packages pre-installed. Preloaded with the tidyverse, verse and geospatial-related tools from the R rocker-org project. Includes tex & publishing-related packages from verse.
+ Designed to install packages from RStudio Package Manager on a fixed date. Easily change this in Rprofile.site. Rapid installation of most packages as this image is based on Ubuntu.
+ Easy access to all the RStudio configuration and startup values.
+ The end of complex uninstall/upgrade paths for R and RStudio on your laptop/desktop system.
+ Easily recreate an identical environment on another system.
+ Run multiple versions of R side-by-side with multiple SmartDesktop project directories.
+ Define various environments for a particular R version with multiple docker-compose files.
+ Control resources used by your R sessions with docker-compose. No more system lock because an R session unexpectedly stole all your desktop resources.
+ Pause and restart a long-running R session with Docker Desktop!
🧰 Install and setup
Head over to https://github.com/Stephen-McDaniel/SmartDesktop-RStudio#-install-and-setup to get up and running with YakData SmartDesktop for RStudio.
If you benefit from this project, please click the ⭐ button on this GitHub repository and let your colleagues know about it.
+ RStudio Open Source Server IDE is a free, open-source IDE for R, Shiny apps and RMarkdown content.
+ The RStudio Open Source Server IDE is backed by years of development, feedback and releases.
+ Make it easy to create a reproducible, self-contained R development environment on any system.
RStudio IDE repository: https://github.com/rstudio/rstudio
RStudio Package Manager: https://packagemanager.rstudio.com/client/#/
Docker Desktop: https://docs.docker.com/desktop/
Alternatives include self-install of the R, the RStudio IDE, tidyverse, verse and more directly on your OS.
👨🏼💻 About RStudio
RStudio is the leading development environment for R programming, R Shiny apps and RMaarkdown documents. Features include:
+ Highly customizable with powerful tools for R including a console, source editor, plot viewer, workspace management, integrated help, command history and more.
+ Execute code incrementally by line, by selected text or as a complete program.
+ Syntax highlighting editor with drop-down code completion.
+ Smooth integration of R Shiny app development including a local Shiny server and the ability to debug your app in a local browser.
+ RStudio Desktop in this project runs as a desktop server, ensuring reproducibility and portability.
🐳 Docker Desktop
Once you work with Docker Desktop, you will wonder why you wasted all those hours struggling with R installation directly on your computer!
+ Containerize and share any application locally and on your favorite cloud platform, in multiple languages and frameworks.
+ It is easy to install, setup and use a complete Docker development environment.
+ If you use a Windows computer, gain the ability to toggle between Linux and Windows Server environments ON YOUR LOCAL COMPUTER!
+ Volume mounting for code and data, including file change notifications and easy access to running containers on the localhost network.
🥇 The tidyverse
The tidyverse includes many leading R packages for data science, forecasting, analytics and data management. The developers state, “The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.”
A subset of the included packages: ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats and many other packages with more specialized usage. They are not loaded automatically with library(tidyverse), so you’ll need to load each one with its own call to library().