RStudio Package Manager 1.1.2 introduces beta support for Windows package binaries. These binaries make it easier and faster to install R packages on Windows Desktop. With this release, all the benefits of Package Manager are available to desktop users including versioned repositories, curated subsets of CRAN, centralized access to CRAN, Git, local packages, and usage tracking. Now data scientists on Windows can easily share work, collaborate, and spend more time doing analysis instead of debugging packages. Read more →

This blog post is part of a series on new features in RStudio 1.3, currently available as a preview release. We’re excited to announce that RStudio v1.3 will gain a newly-minted pane: the Tutorial pane, used to host tutorials powered by the learnr package. The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. Here are some example tutorials from the learnr package, hosted on shinyapps. Read more →

This blog post is part of a series on new features in RStudio 1.3, currently available as a preview release. Today, we’re going to talk about a number of improvements we’ve made to RStudio 1.3 around configuration and settings. To set the stage, here’s how you configure RStudio today: This point-and-click dialog makes it easy for users to select the settings they want, but has a couple of limitations: Read more →

Over the years, we have loved interacting with the Shiny community and loved seeing and sharing all the exciting apps, dashboards, and interactive documents Shiny developers have produced. So last year we launched the Shiny contest and we were overwhelmed (in the best way possible!) by the 136 submissions! Reviewing all these submissions was incredibly inspiring and humbling. We really appreciate the time and effort each contestant put into building these apps, as well as submitting them as fully reproducible artifacts via RStudio Cloud. Read more →

As part of the upcoming 1.3 release of the RStudio IDE we are excited to show you a preview of the real time spellchecking feature that we’ve added. Prior to RStudio 1.3, spellchecking was an active process requiring the user to step word by word in a dialog. By integrating spellchecking directly into the editor we can check words as you’re typing them and are able to give suggestions on demand. Read more →

We started the RStudio project because we were excited and inspired by R. The creators of R provided a flexible and powerful foundation for statistical computing; then made it free and open so that it could be improved collaboratively and its benefits could be shared by the widest possible audience. It’s better for everyone if the tools used for research and science are free and open. Reproducibility, widespread sharing of knowledge and techniques, and the leveling of the playing field by eliminating cost barriers are but a few of the shared benefits of free software in science. Read more →

Today we are excited to share that sparklyr 1.1 is now available on CRAN! In a nutshell, you can use sparklyr to scale datasets across computing clusters running Apache Spark. For this particular release, we would like to highlight the following new features: Delta Lake enables database-like properties in Spark. Spark 3.0 preview is now available through sparklyr. Barrier Execution paves the way to use Spark with deep learning frameworks. Read more →

RStudio Connect helps data science teams quickly make an impact by enabling them to publicize reports, models, dashboards, and applications created in R and Python with business stakeholders. The 1.8.0 release makes it even easier for teams to start sharing. For Data Scientists, highlights include: Python Support: Improvements that make it easier to share Jupyter Notebooks and mixed R and Python content. pins: An easy way to share data, models, and other objects. Read more →

There will be no better time or place this year to accelerate your knowledge of all things R and RStudio than at rstudio::conf 2020 in San Francisco. While we’re approaching capacity, there’s still room for you! Whether you’re a dedicated R user or one of the many people who use R and Python, come join us and more than 2,000 fellow data scientists and data science team leaders in San Francisco January 27 - 30. Read more →

We’re excited to announce that reticulate 1.14 is now available on CRAN! You can install it with: install.packages("reticulate") With this release, we are introducing a major new feature: reticulate can now automatically configure a Python environment for the user, in coordination with any loaded R packages that depend on reticulate. This means that: R package authors can declare their Python dependency requirements to reticulate in a standardized way, and reticulate will automatically prepare the Python environment for the user; and Read more →