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. As a result, data scientists can lose their train of thought, or put off trying out a new method, all because they have to wait for new packages to compile and install. New users often face a tedious hour-long setup process before they can try out environments. IT/DevOps engineers are forced to wait any time they want to build a new image, deploy to production, or restore an environment.
RStudio Package Manager already makes it easy for an organization to control and distribute R packages. Now, packages from CRAN can be immediately available for deployment on Linux systems, through Linux package binaries. These binaries install significantly faster and are available to all Package Manager clients wherever your organization uses R. Binaries are supported for a range of R versions and platforms, for more than 80% of CRAN packages, and they are updated every week! Binaries make it easier for users to get started, simpler for admins to manage environments, and make it dramatically easier to implement automation.
To see the difference for yourself, try installing a package on your Linux server using our demo server:
# First, pick your operating system DISTRO <- 'xenial' # choices: xenial, bionic, centos7, opensuse42, opensuse15 # Next, install from our demo server install.packages('dplyr', repos = sprintf('https://demo.rstudiopm.com/cran/__linux__/%s/latest', DISTRO), lib = tempdir()) # Finally, compare to how long it takes to install from CRAN install.packages('dplyr', repos = 'https://cran.rstudio.com', lib = tempdir())
At RStudio, we use these binaries in production every day. Although this community post contains more information about the previous beta, we are excited to announce that with v1.1.0, the binaries are ready for your production systems. Support is available for offline or air-gapped environments.
In addition to adding support for Linux package binaries, the 1.1.0 release concludes more than a year of updates since the 1.0.0 release, adding:
- System dependency information for R packages
- Package READMEs to help discover documentation
- A calendar to make version management a breeze
- Significant improvements for IT, including performance improvements, security updates, and new storage options
Please review the full release notes.
Upgrading to 1.1.0 from 1.0.10 or earlier is a major update but will take less than five minutes. If you are upgrading from an earlier version, be sure to consult the release notes for the intermediate releases, as well.
Package management is critical for making your data science reproducible, over time, and across your organization. Wondering where you should start? Email us, our product team is happy to help!
New to RStudio Package Manager?
Download the 45-day evaluation today to see how RStudio Package Manager can help you, your team, and your entire organization access and organize R packages. Learn more with our online demo server or latest webinar.