New in RSTUDIO CONNECT:Now you can share interactive Python content with your stakeholders, using Dash and Flask.

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 Server Pro launches and manages Jupyter Notebooks and JupyterLab environments.
  • RStudio Connect makes it easy to share Jupyter Notebooks, Python APIs via Flask, and interactive Python applications via Dash with your stakeholders, alongside your work in R and your mixed R and Python projects.

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. But mixing R and Python within a single project can require manual translation, duplicating code, and tedious data saving, loading, and type conversions. Even when using these tools for different projects, you are often forced to switch between entry points for your tools, and different ways to share results. This slows down your productivity and distracts you from your core work: doing data science to solve hard problems and deliver value to your organization.

With RStudio products, you can combine R and Python seamlessly. You can use the RStudio IDE for R, but also for bilingual tasks. Or from the same homepage, launch Jupyter Notebooks or JupyterLab for Python. You can also publish and schedule regular updates and custom reports in a central location, leveraging R and Python to give your business users self-serve access to your data products--avoiding the need for repetitive manual work or ad-hoc copy and paste.

Learn more:

Data science team leader

Data Science Leader

Enable collaboration across your bilingual team, and share interactive analyses and custom reports with your business stakeholders

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. Critically, these challenges hinder their ability to provide value to your organization, and leads to too much time spent manually building reports or presentations and running repetitive analyses.

With RStudio products, your bilingual team can work together, building off of each other’s work. Best of all, they can publish, schedule, and email regular updates for interactive analyses and custom reports built in R and Python, so you and your stakeholders will always know where to look for their valuable insights. Your team can also deploy APIs directly into production via Plumber, TensorFlow, or Flask, without the delay of recoding in another language.

Learn more:

  • RStudio Team enables your bilingual Data Science team to develop, collaborate, manage, and share your data science work. Leverage R, Python, Jupyter Notebooks & JupyterLab, and frameworks such as RMarkdown, Shiny, Plumber, Flask and Dash.
  • 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, Jupyter Notebooks and JupyterLab development and deployment

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, Jupyter Notebooks and JupyterLab, plus supporting packages), and 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 and Jupyter Notebooks, and details on how to install and configure this integration, 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 Server Pro, and to share Jupyter Notebooks, Flask APIs, and Dash applications with your business stakeholders through RStudio Connect.

To learn more...

Schedule a conversation with our team
Schedule your meeting