Pharmpy & Pharmr: Ditching Altair For Better Performance

Hey everyone! Today, we're diving into an interesting discussion about optimizing pharmpy and pharmr by potentially removing the Altair dependency. This is a crucial topic for anyone working with these tools, as it can significantly impact performance and future development. Let's break down the details and explore the proposed solutions.

The Challenge with Altair

Currently, pharmpy and pharmr rely on Altair for visualization. However, Altair hasn't seen an update on CRAN (Comprehensive R Archive Network) in quite some time. This is a bit of a sticky situation because the latest Python version of Altair has moved forward, and we need our tools to keep up with the times. An outdated dependency can lead to compatibility issues, limit access to new features, and even pose security risks down the line. So, what's the plan to tackle this? The core idea revolves around reducing our reliance on Altair directly.

The main problem lies in the fact that Altair, while powerful, introduces a dependency that can become a bottleneck. When a package on CRAN isn't updated regularly, it creates a ripple effect. Users might face installation problems, encounter bugs that have already been fixed in newer versions, or miss out on performance improvements. For a critical tool like pharmpy and pharmr, which are used in important pharmaceutical research and development, staying current is not just a nice-to-have—it's a necessity. Moreover, an outdated Altair dependency might clash with other packages in a user's environment, leading to frustrating conflicts and hindering productivity. Think of it like trying to fit a square peg in a round hole; eventually, something's got to give. By addressing this Altair issue head-on, we're essentially future-proofing pharmpy and pharmr, ensuring they remain robust and reliable tools for the community.

To illustrate the importance of this, consider the broader ecosystem of scientific computing in R and Python. Both languages are rapidly evolving, with new packages and updates emerging constantly. If pharmpy and pharmr are tied to an older version of Altair, they risk falling behind. Imagine new visualization techniques or interactive features becoming available in the latest Altair, but we can't use them because of the dependency. It's like having a cutting-edge lab with outdated equipment—the potential is there, but the tools are holding us back. By proactively seeking alternatives or workarounds, we're ensuring that pharmpy and pharmr can leverage the best that the scientific computing world has to offer. This proactive approach also signals to the community that these tools are actively maintained and responsive to the needs of their users. In the long run, this fosters trust and encourages wider adoption.

The Proposed Solution: Overloading the Print Function

The heart of the suggestion is to overload the print function in pharmr. What does this mean in simple terms? It means we're tweaking the way pharmr displays output. Instead of directly relying on Altair for creating plots and visualizations, we can make pharmr smart enough to handle different plotting libraries. This is where vegawidget or even htmlwidgets come into play. These packages offer alternative ways to render visualizations in R, and they might provide a more flexible and maintainable solution.

Overloading the print function is a clever move because it doesn't require a major overhaul of the entire codebase. Think of the print function as the messenger that delivers the visualization to the user. By intercepting this messenger and giving it new instructions, we can seamlessly switch the underlying plotting mechanism without disrupting the rest of the system. This approach is particularly appealing because it minimizes the risk of introducing new bugs or breaking existing functionality. It's like performing a heart transplant without disturbing the rest of the body—a delicate operation, but one that can significantly improve the patient's health. Moreover, overloading the print function allows us to maintain a consistent user interface. Users can continue to use the familiar print command, but under the hood, pharmr will be using a different engine to generate the visualizations. This means a smoother transition and less learning curve for existing users.

But why vegawidget or htmlwidgets specifically? These packages are designed to work well within the R ecosystem and offer a good balance between functionality and maintainability. vegawidget is particularly interesting because it's built on top of Vega and Vega-Lite, which are powerful visualization grammars similar to Altair. This means we can potentially reuse a lot of the existing visualization logic with minimal changes. htmlwidgets, on the other hand, provides a more general framework for creating interactive web visualizations in R. It supports a wide range of JavaScript charting libraries, giving us even more flexibility in the long run. The key is to choose a solution that not only addresses the Altair dependency issue but also aligns with the overall architecture and goals of pharmpy and pharmr. It's about making a strategic decision that benefits the project in the long term, not just applying a quick fix.

Benefits of This Approach

So, what are the upsides of making this change? There are several compelling reasons why this approach makes sense:

• Reduced Dependency on Altair: This is the big one. By decoupling from Altair, we gain more control over our dependencies and reduce the risk of being held back by outdated packages. It's like cutting the cord and gaining independence. We're no longer at the mercy of Altair's release schedule, giving us more flexibility to update and improve pharmpy and pharmr on our own terms.
• Leveraging vegawidget or htmlwidgets: These packages are actively maintained and offer robust solutions for creating visualizations in R. This ensures that pharmpy and pharmr can continue to produce high-quality graphics without relying on a potentially stagnant dependency. It's like upgrading from an old car to a new model with all the latest features and safety enhancements.
• Improved Maintainability: A cleaner dependency tree makes the codebase easier to maintain and update. This translates to fewer headaches for developers and a more stable experience for users. Think of it as decluttering your workspace—when everything is organized and in its place, it's much easier to find what you need and get the job done efficiently.

In addition to these core benefits, there's also the potential for performance improvements. While Altair is a powerful library, it can sometimes be a bit heavy, especially for complex visualizations. By exploring alternative rendering engines, we might be able to optimize the plotting process and make pharmpy and pharmr even faster. This is particularly important for users who work with large datasets or need to generate a lot of plots. A faster tool means more time spent on analysis and less time waiting for results. Furthermore, diversifying our visualization options opens the door to new possibilities. htmlwidgets, for example, supports interactive plots that can be embedded in web applications or dashboards. This could lead to exciting new ways of presenting and exploring pharmpy and pharmr results.

Next Steps and Community Input

This is a significant change, and it's essential that we consider all aspects before moving forward. The next step is to thoroughly evaluate the proposed solution, test it rigorously, and gather feedback from the community. Your thoughts and insights are invaluable in making this decision. So, what do you guys think about this approach? Are there any potential drawbacks we should consider? Are there other alternative solutions we should explore? Let's get the conversation going and work together to make pharmpy and pharmr even better!

We need to dive into the technical details of implementing this change. This involves experimenting with vegawidget and htmlwidgets, comparing their performance and features, and identifying any potential compatibility issues. We also need to develop a clear migration strategy so that existing users can seamlessly transition to the new system. This might involve providing helper functions or tutorials to guide users through the process. The goal is to make the change as smooth and painless as possible. Moreover, it's crucial to document the changes thoroughly so that future developers can understand the rationale behind the decision and maintain the new system effectively. Good documentation is the cornerstone of any successful software project.

Finally, it's important to remember that this is an iterative process. We might not get everything right on the first try. There might be unexpected challenges or unforeseen consequences. But by working collaboratively and staying adaptable, we can overcome these obstacles and build a more robust and sustainable tool. The key is to embrace a spirit of experimentation and continuous improvement. Let's treat this as an opportunity to learn, grow, and make pharmpy and pharmr the best they can be. So, please share your thoughts, your concerns, and your ideas. Together, we can shape the future of these valuable tools.