You can use Python with RStudio professional products to develop and publish interactive applications with Shiny, Dash, Streamlit, or Bokeh; reports with R Markdown or Jupyter Notebooks; and REST APIs with Plumber or Flask.
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RStudio Server Pro is now RStudio Workbench! With growing support for a wide range of development environments, we believe this new release is the best single platform to support open source, code-first data science, whether your team is using R or Python. Please see our official Announcement an. Azure Machine Learning Studio (classic) supports both R and Python. Customers currently using or evaluating Azure Machine Learning Studio (classic) are encouraged to try the designer in Azure Machine Learning, which provides drag-n-drop ML modules plus scalability, version control, and enterprise security.
For an overview of how RStudio helps support Data Science teams using R & Python together, see R & Python: A Love Story.
For more information on administrator workflows for configuring RStudio with Python and Jupyter, refer to the resources on configuring Python with RStudio.
Developing with Python#
Data scientists and analysts can:
- Work with the RStudio IDE, Jupyter Notebook, JupyterLab, or VS Code editors from RStudio Workbench
Want to learn more about RStudio Workbench and Python?#
For more information on integrating RStudio Workbench with Python, refer to the resources on configuring Python with RStudio.
R Studio Summary Statistics
Publishing Python Content#
Data scientists and analysts can publish Python content to RStudio Connect by:
- Publishing Jupyter Notebooks that can be scheduled and emailed as reports
- Publishing Flask applications and APIs
- Publishing Dash applications
- Publishing Streamlit applications
- Publishing Bokeh applications
Ready to publish Jupyter Notebooks to RStudio Connect?#
View the user documentation for publishing Jupyter Notebooks to RStudio Connect
Ready to share interactive Python content on RStudio Connect?#
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Learn more about publishing dash or flask applications and APIs.
View example code as well as samples in the user guide.
Publishing Python and R Content#
Data scientists and analysts can publish mixed Python and R content to RStudio Connect by publishing:
- Shiny applications that call Python scripts
- R Markdown reports that call Python scripts
- Plumber APIs that call Python scripts
Mixed content relies on the reticulate package, which you can read more about on the project's website.
View the user documentation for publishing content that uses Python and R to RStudio Connect
Cheat sheet for using Python with R and reticulate
Managing Python Packages#
RStudio Package Manager supports both R and Python packages. Visit this guide to learn more about how you can securely mirror PyPI.
Additional Resources#
Want to learn more about RStudio Connect and Python?#
Frequently asked questions for using Python with RStudio Connect
Learn about best practices for using Python with RStudio Connect
Want to see examples of using Python with RStudio?#
View code examples on GitHub of Using Python with RStudio
View examples of Flask APIs published to RStudio Connect
R is a free software programming language and a software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
What's the Difference?
R the application is installed on your computer and uses your personal computer resources to process R programming language. RStudio integrates with R as an IDE (Integrated Development Environment) to provide further functionality. RStudio combines a source code editor, build automation tools and a debugger.
We recommend you install both R and RStudio on your personal computer.
Installing R
To install R to your local computer follow these steps:
- Download the R installer
- For Windows at https://cran.r-project.org/bin/windows/base/
- For Mac at https://cran.r-project.org/bin/macosx/
- Install R by opening the installer and following the steps
- That's it! You can now run R from your local computer
Installing RStudio
To install RStudio to your local computer follow these steps:
- Verify you have already installed R and that you can launch the R application
- Download the RStudio Desktop installer athttps://rstudio.com/products/rstudio/download/
- Install RStudio Desktop by opening the installer and following the steps
- That's it! You can now run RStudio from your local computer
Please contact a Statistics CSR if you have any difficulty installing R or using RStudio