This is a companion discussion topic for the original entry at https://www.rstudio.com/blog/develop-collaborate-and-scale-across-r-and-python
Data scientists need many skills, not just in handling data, but working with different programming languages, technical environments, computational requirements, and more. They need to do this while meeting stakeholder expectations quickly and accurately. Juggling their work across systems makes it difficult to coordinate work, creating silos of information and gaps in efficiency and security.
RStudio Workbench is the enterprise-level integrated development environment for data scientists who need to develop, collaborate, and scale in R and Python. TTeam members work from a centralized server in the language and development environment of their choice and with the computing power that they need.
Data scientists on RStudio Workbench can focus on creating insights with the multiple tools available to them, a shared space for all data science development, and easy management of a single infrastructure.
Develop Insights in R and Python
With RStudio Workbench, data scientists can get started quickly regardless of their preferred programming languages. Teams have a wide range of editor options: RStudio, JupyterLab, Jupyter Notebooks, and VS Code. Python users can use Python, R users can use R — or team members can use both within a single project within RStudio Workbench.
A development environment should not hinder data scientists’ work. With RStudio Workbench, users can run concurrent sessions and conduct analyses side-by-side. Users can also manage upgrades and test code by running multiple versions of R and Python at the same time. The additional computing power allows a data science team to scale their work and meet their stakeholders’ needs.
Improve Collaboration With a Centralized Environment
RStudio Workbench provides a single place for data science teams to share projects regardless of the environment used. Team members won’t need to spend time trying to find someone’s old analysis or having to install VSCode to pull up a script.
For data scientists using the RStudio IDE, RStudio Workbench can securely grant their teammates access to a project. When multiple users are active in the project at once, they can see each others’ real-time activity and collaboratively edit the file. Team members can support each other to develop high-quality, efficient analyses.
Reduce Start-up Time With Preconfiguration
RStudio Workbench helps data scientists jump into their first line of code quickly through the configuration of the shared environment. A team can use a standardized set of installed software, ensuring that work is reproducible and valuable time isn’t spent on installation and configuration. A single infrastructure means it’s easy to configure and maintain by IT staff, ensuring the team meets all organizational security requirements.
Run into issues? RStudio is here to help. With RStudio Workbench, we provide individualized support to ensure data scientists have what they need.
RStudio Workbench makes it easy for data scientists to focus on creating insights.