RStudio will be focusing on interoperability in this blog during the month of July, highlighting how data scientists are using other tools with R to perform their work. Read more →

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 →

Data science today requires allowing employees to work from home. Mango Solutions believes that a centralized cloud-based platform and collaborative communication are key to making data science teams productive. Read more →

Delivering persistent value over the long haul from your data science team requires reusability, reproducibility, and up-to-date insights, built on a sustainable platform. Read more →

Data science teams struggle to deliver results quickly. Agile techniques such as rapid iteration and continuous collaboration with stakeholders can help overcome these challenges. Read more →

For Data Science to be applied, decision makers must trust and accept the insights. Your team needs the tools to find relevant insights, and to communicate these insights in a way that builds trust and understanding. Read more →

Today we’re excited to announce the general release of RStudio 1.3. This release features many major improvements to the IDE, including: Dramatically improved accessibility for sight-impaired users, which also upgrades keyboard navigation, contrast ratios, and visibility for everyone. A real-time spell-checking engine, with suggestions, customizable dictionaries, and a built-in whitelist for common data science terms. Extensible, in-IDE tutorials powered by the learnr package. Settings and preferences are now stored in plain text files you can back up or manage with other tools; they can also be applied globally to all users on an RStudio Server. Read more →

A slew of new vendors believe that no-code analytics and visualization tools can replace the role of the traditional data scientist. This brief describes why we believe organizations will demand pro-code data scientists for years to come. Read more →

Driving lasting value in an organization with data science is critical but difficult. The truth is most projects fail. What’s the answer? Serious Data Science is credible, agile and durable. Read more →

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 →