RStudio Package Manager

2020 at RStudio: A Year in Review

2021-01-19 Lou Bajuk
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In this blog post, I take a look back at some of the many announcements, product releases, etc. that RStudio did in 2020. It was an exciting year, despite the challenges that 2020 presented to everyone, and we were pleased to continue to support and deliver value to the R and Python data science community. Read more →

RStudio Package Manager 1.2.0 - Bioconductor & PyPI

2020-12-07 Sean Lopp
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Packages are the heart of open source data science, but we know they aren’t always easy. Data scientists need access to ever-evolving tools to do their best work, and IT needs to understand the risk of new software while providing a stable environment for reproducible work. RStudio Package Manager helps teams work together to accomplish these goals. Today we are excited to announce a greatly expanded focus, enabling teams to realize these benefits across languages and ecosystems by adding support for Bioconductor, beta support for Python packages from PyPI, and new options for managing historical CRAN snapshots. Read more →

Why RStudio Focuses on Code-Based Data Science

2020-11-17 Lou Bajuk, Carl Howe
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Michael Lippis of The Outlook podcast recently interviewed RStudio's Lou Bajuk to discuss open source data science, support for R and Python, and how leaders are getting value from data science. Read more →

Announcing Public Package Manager and v1.1.6

2020-07-01 Sean Lopp
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Today we are excited to release version 1.1.6 of RStudio Package Manager and announce https://packagemanager.rstudio.com. This service builds on top of the work done by CRAN, to offer the R community: Access to pre-compiled packages on Linux via install.packages resulting in significantly faster package install times on Linux systems including cloud servers, CI/CD systems, and Docker containers. Historical checkpoints for CRAN enabling reproducible work, and even time travel, by freezing package dependencies with a one-line repository option. Read more →

Equipping Your Data Science Team to Work from Home

2020-05-12 Carl Howe
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Photo by Djurdjica Boskovic on Unsplash If your data science team experienced an abrupt transition to working at home, it may be a good time to rethink their development tools. In this post, I’ll talk about why laptop-centric data science gets in the way of strong data science teams and why you should consider deploying development and publishing servers. Working from Home Has Affected Both People and Data Like tigers and koalas, we data scientists are fairly solitary creatures. Read more →