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Why I Switched to Positron for My Workflow

Over the last year I’ve transitioned from mainly using RStudio and Jupyter Notebook to almost exclusively using Positron. It wasn’t an overnight decision—I’d been using RStudio for years and was comfortable with it. But after giving Positron a serious try, I can’t imagine going back.

If you’re a researcher or academic doing data science work, I think Positron represents where workflows are heading. Here’s why I made the switch and why it’s been worth it¹:

1. One IDE for R, Python, and Everything In Between

Here’s an example workflow these days: pull data using Python scripts, clean it with R’s tidyverse, run machine learning models in Python, then build an interactive R Shiny App for stakeholders. In the past, this meant constantly switching between RStudio and Jupyter, copying data between environments, and dealing with inevitable compatibility headaches.

Positron handles all of this natively. I can open a Jupyter Notebook (.ipynb) or create a Quarto document (.qmd) and seamlessly integrate R and Python—even switching languages between individual cells when needed. For academic work where I’m mixing analysis, visualization, writing, and reproducibility, this unified environment has been invaluable.

2. Multi-Session Consoles and Terminals: Work While You Wait

This feature alone justifies the switch for me. I can run one console to process data (say, a 20-minute job on large spatial datasets) while simultaneously working on visualization or analysis code in another console. I can even run R in one console and Python in another at the same time.

Multiple Consoles. This keeps my workflow modular—data processing in one script, analysis in another—and lets me test experimental code without disrupting my main working session. I can also compare different parameter settings or model versions side-by-side. No more staring at a progress bar for 20 minutes before I can continue working on other parts of my project.

Multiple Terminals. The same goes for terminals. I can have one open just for Git and another in a folder in my terminal. This came in really handy when I was working on a remote server and needed to toggle between folders. Instead of wasting keystrokes navigating via the command line, I would just create a separate terminal for wherever I needed to go. You can even change the colors and icons to keep them clearly separated.

3. Better Performance and Stability

Positron is noticeably faster and more stable than RStudio, especially with large file structures and long scripts. It doesn’t crash when I’m working with large spatial datasets or long-running pipelines—a problem I regularly encountered in RStudio.

The environment management for both R and Python is more streamlined, and I appreciate the intuitive debugger, better autocomplete, and improved file navigation. Plus, I can view the same script side-by-side in split panes, which eliminates constant scrolling when referencing different parts of my code—especially helpful for long scripts.

Side-by-side. For example in this Jupyter Notebook script, I can edit code on the left while looking at code (and borrowing) from elsewhere in the same script. This comes in really handy when you have variable names or code you need to re-use elsewhere. I’ve also found it helpful to compare the same figure or plot side-by-side too.

4. Extensible and Future-Proof (Built on VS Code)

Because Positron is built on VS Code, I have access to thousands of extensions through the Open VSX marketplace: LSP tools, AI assistants, SQL GUI clients, code linters, Docker support, and more.

One practical example: during my postdoc, I needed to use SSH to access secure eviction data on remote servers. The Remote-SSH extension let me connect directly to high-performance computing clusters and work as if everything were local—all my code editing, debugging, and data exploration happened seamlessly within the IDE.

It’s free, open-source, and designed for research. When I find a useful extension or setting, it’s easy to add. (I’m particularly fond of AIR, which automatically formats R code.) And importantly, Posit is clearly investing in Positron as their future IDE, which gives me confidence I’m not learning a tool that will be deprecated.

5. Cleaner Git and GitHub Integration

The diff viewer is significantly cleaner than RStudio’s, and the side-by-side comparison has improved how I write commit messages. While I still use the command line for staging, committing, and pushing, many people prefer the built-in workflow.

This integration is particularly useful for teaching—showing students reproducible workflows is straightforward when it’s directly integrated into the IDE itself.

6. Advanced Data Exploration Tools

The dataframe viewer lets me sort, filter, and inspect missing values through the GUI without writing additional code, similar to RStudio. However, the Summary Panel on the left-hand side makes it easy to view statistics for specific columns, sort by data type, or filter by column name during analysis.

Inline plots, dataframes, and visualizations work seamlessly, and I can smoothly switch between script and notebook modes as needed. Compared to RStudio’s viewer, it’s less clunky and faster.

7. Built-in Support for Reproducible Research

I can render Quarto PDFs, HTML, and slides directly within the IDE—HTML files even preview within Positron itself, though you can still open them in a web browser if needed. Project folder templates let me start new Python or R projects with ready-made setups using tools like uv and renv, with easy environment switching within my workspace. The project-level settings keep research workflows clean and portable—something I especially appreciate when collaborating or teaching. All the functionality from RStudio’s projects is still here.

8. Context-Aware AI Assistant

Positron’s AI Assistant understands your current working context because the model has access to your entire code base—it sees your Variables pane (dataframe names, dimensions, column names), your Plots pane, and your database connections. This means it can better incorporate what you already have in your code base and offer better integrated solutions than copying and pasting detailed messages into a web interface. It works with Anthropic or GitHub Copilot. The downside is that you need a paid account.

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Making the Switch

If you’re still using RStudio or juggling multiple IDEs for different languages, I’d encourage you to give Positron a serious trial. It’s changed how I work by integrating all the programming languages and tools I use into one place.

The learning curve is minimal if you’re coming from RStudio, and Posit provides helpful migration guides for both RStudio and VS Code users. Their documentation is thorough, and there are good video tutorials to get you started.

Positron (and tools like it) represents where data science workflows are heading. The integration, performance, and extensibility advantages are significant enough that staying with older tools means working harder than necessary.

Footnotes

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