Using Python with RStudio Team

Using Python with RStudio Team
Led by David Aja, Solutions Engineer at RStudio

00:30 - Overview of RStudio Team - RStudio Workbench, RStudio Connect, RStudio Package Manager
2:38 - Use case of RStudio Package Manager with Python packages
4:29 - RStudio Workbench (ability to use RStudio, Jupyter Notebook, JupyterLab, VS Code)
5:48 - VS Code running on RStudio Workbench
11:50 - Deploying a Streamlit app to RStudio Connect
14:00 - Pause for questions
18:55 - Jupyter on RStudio Workbench
19:41 - Extension in Jupyter that gives you push-button publishing from RStudio Workbench
20:35 - Publishing with source code vs. publishing only the finished document
21:10 - Hiding the code that is generated when publishing Jupyter to RStudio Connect
23:30 - Creating a custom url in RStudio Connect for your content
24:30 - Adding viewers / collaborators to your content in RStudio Connect
25:18 - Scheduling your Notebooks to run repeatedly
25:54 - Stepping back to describe the differences between RStudio open-source IDE and RStudio Workbench

*please note that this meetup will cover our enterprise product, RStudio Team but all who are interested in joining are welcome!

Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages have unique strengths, teams frequently struggle to use them together:

⬢ Data Scientists constantly need to switch contexts among multiple environments.
⬢ Data Science Leaders wrestle with how to share results consistently and deliver value to the larger organization, while providing tools for collaboration between R and Python users on their team.
⬢ DevOps engineers and IT Admins spend time and resources attempting to maintain, manage and scale separate environments for R and Python in a cost-effective way.

Join David Aja in this meetup, which will highlight how other data science teams are able to solve these problems with RStudio Team by:

⬢ Combining R and Python in a single data science project.
⬢ Launching and managing RStudio, Jupyter Notebooks, JupyterLab, and VS Code from the RStudio Workbench environment
⬢ Sharing Jupyter Notebooks, Python APIs via Flask, Dash, Streamlit, Bokeh, FastAPI, Shiny, R Markdown, etc. with the business through RStudio Connect.
⬢ Controlling and distributing Python and R packages with RStudio Package Manager.

A few helpful links shared and mentioned during the call:

Product / Conference Questions: