From the very beginning, two key ideas have driven the work we do at RStudio:
Some data scientists, and even some organizations, believe they have to pick between R or Python. However, this turns out to be a false choice. In talking to our many customers and others in the data science field, as well as in the surveys we’ve done of the data science community, we’ve seen that many data science teams today are bilingual, leveraging both R and Python in their work. And while both languages have unique strengths, these teams frequently struggle to use them together.
We’ve heard three common criticisms from data science teams about using R and Python together:
Contrary to these concerns, in talking with many data science teams, we’ve found that:
As you can see, many of the potential concerns of using two languages are addressed through better tooling. In line with our ongoing mission to support the open source data science ecosystem, we’ve invested heavily in creating the best platform for data science using both R AND Python. This effort includes many features in the products that comprise RStudio Team. We have also made significant investments in our open source offerings to make it easier than ever to combine R and Python in a single data science project.
In our open source products, we improved and invested in a number of different features over the past year, including:
torch, one of the most widely used deep learning frameworks.
In RStudio Server Pro, which provides collaboration, centralized management, and security for data science teams developing in R and Python, we’ve added beta support for the VSCode IDE. This work is in addition to our existing support for Jupyter Notebooks and JupyterLab. These enhancements make RStudio Server Pro a true workbench for open source data science.
RStudio Connect provides a centralized platform where data science teams can operationalize the works they create in R and Python. We’ve solved the same challenges for Python users that have made Connect so popular with R users including:
Finally, in RStudio Package Manager, which helps organize, manage and centralize packages across a team or an entire organization, we recently added beta support for PyPI, giving users access to full documentation, automatic syncs, and historic snapshots of Python packages.
If you’d like to learn more about the many ways that RStudio provides a single home for teams using both R and Python, we encourage you to register for our upcoming webinar on February 3rd and explore the information at R & Python: A Love Story.
We’ve also discussed R & Python in several previous blog posts, including:
Many tools used routinely by software developers can also be useful to data scientists.
In this post, we explore possible challenges to putting Shiny in production and how to overcome them.