RStudio: A Single Home for R & Python

Develop, collaborate, manage and share your data science work in R and Python--all with RStudio

RStudio: A Single Home for R & Python


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Develop, collaborate, manage and share your data science work in R and Python--all with RStudio

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 scientist using R and Python

Data Scientists
Constantly need to switch contexts among multiple environments. Read More

Data science team leader

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. Read More

DevOps and IT Admin

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. Read More

To help Data Science teams solve these problems, and in line with our ongoing mission to support the open-source data science ecosystem, we’ve made the love story between R and Python a happier one:

  • RStudio IDE makes it easy to combine R and Python in a single data science project.
  • RStudio Workbench launches and manages Jupyter Notebooks, JupyterLab, and VS Code environments.
  • RStudio Connect makes it easy to share Jupyter Notebooks, Python APIs via Flask, and interactive Python applications via Dash, Streamlit, Bokeh, FastAPI, and other popular Python frameworks.
  • RStudio Package Manager makes it easy to control and distribute Python and R packages.

To learn more, schedule a conversation with our team.

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Data scientist using R and Python

Data Scientist

Use R and Python in a single project

As a data scientist, you might want to use R for part of your project (e.g. for interactive web applications via Shiny), and call out to Python scripts for other tasks. You may be worried that mixing R and Python will require overhead, manual translation, and context switching.

With RStudio products you can combine R and Python seamlessly without extra overhead. You can use the RStudio IDE for R, but also for bilingual tasks. With RStudio Workbench, launch Jupyter Notebooks, JupyterLab, or VS Code for Python. Share all of your outputs from R and Python on RStudio Connect, preventing repetitive manual work or ad-hoc copy and paste.

Learn more:

  • The reticulate package provides a comprehensive set of tools for interoperability between Python and R.
  • Watch the R & Python in RStudio with Reticulate Webinar
  • RStudio professional products enable you to develop and publish Jupyter Notebooks, Python scripts, and even Python APIs and applications. To learn more, see Using Python with RStudio.
  • RStudio Connect helps you share and schedule Jupyter Notebooks or deploy and scale interactive Python content via Dash, Streamlit, Bokeh, FastAPI, and other popular Python frameworks. Watch a demo.
Data science team leader

Data Science Leader

Build a happy, collaborative and effective team, regardless of the programming language they use

As a Data Science leader, you’ve seen your bilingual team struggle to collaborate and share work across their disparate open source tools, or waste time translating code in order to place it in production. This wastes precious time and distracts them from their core work, and as a result business stakeholders don’t see results. Most of all, these frustrations lead to an unhappy team, and you may struggle to recruit the best talent.

With RStudio products, your bilingual team can work together, building off of each other’s work. They can publish, schedule, and email updates and reports or share interactive analyses built in R and Python. Your stakeholders have a common home to view your team's valuable insights. You are able to recruit from a diverse talent pool, regardless of language expertise.

Learn more:

  • RStudio Team enables your bilingual Data Science team to develop, collaborate, manage, and share your data science work. Leverage R, Python, Jupyter & VS Code, and frameworks such as R Markdown, Shiny, Plumber, Flask, Dash, Streamlit, FastAPI, and Bokeh.
  • For a deeper view on how RStudio professional products work with Python, see Using Python with RStudio.
DevOps and IT Admin

DevOps / IT Admins

Manage a unified environment for R & Python without incurring twice the cost

As a DevOps engineer or an IT Admin, you often find it time-consuming and difficult to support separate environments for Data Scientists using a variety of tools (R, Python, RStudio, and Jupyter plus supporting packages). You’ve seen your Data Science teams struggle with unfamiliar tools and concepts for deployment, production, and scaling. Instead of using the infrastructure you provide for scaling out computation, such as Kubernetes or Slurm, data scientists continue to ask for help troubleshooting their desktop environments--and your team is forced to acquire expertise in supporting multiple open source platforms.

With RStudio products, you can maintain a single infrastructure for provisioning, scaling, and managing environments for both R and Python users, meaning that you only need to configure, maintain and secure a single system. This makes it easy to leverage your existing automation tools to provide data scientists with access to your servers or Kubernetes/Slurm clusters in a transparent way, directly from the development tools they prefer. Access, monitoring, and environment management are easily configured, and RStudio’s Support, Customer Success, and Solutions Engineering teams are poised to offer advice as you scale.

Learn more:

  • RStudio Team enables the Data Science team you support to develop, collaborate, manage and share their data science work, while providing you the tools you need to administer, maintain and scale.
  • For a deeper view on how RStudio professional products work with Python, Jupyter, and VS Code see Using Python with RStudio.

While data science teams come in different sizes and speak different languages,

at the end of the day they want to get work done - not worry about tools. We’ve focused on helping them tackle key challenges of bilingual environments, by making it easy to combine R and Python in a single data science project, to launch and manage Jupyter Notebooks and JupyterLab in RStudio Workbench, and to share Jupyter Notebooks, Flask APIs, and interactive Python applications with your business stakeholders through RStudio Connect.

To learn more...

Schedule a conversation with our team
Schedule your meeting

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