In an era where data drives critical decisions, interactive dashboards and data applications have ascended as indispensable tools across sectors like business, scientific research, and more. This is especially true in fields like life sciences, where the need for accessible data analytics and visualization is critical for research and healthcare outcomes. 

With platforms like Streamlit and Shiny at the forefront, the options for building powerful, user-friendly data apps are plentiful. As a new entrant, PyShiny from Posit brings Shiny’s robustness to the Python ecosystem, adding an intriguing layer to the decision-making process. 

This blog post serves as a comprehensive guide to help you navigate the choice between Streamlit and Shiny (both RShiny and PyShiny!), exploring topics such as:

• Rapid Prototyping vs. Robust Customization
• Language Ecosystems: Python vs. R
• Community Support and Library Integration
• Use-cases where each platform excels

As we delve into the nuances, you’ll discover that Streamlit shines in rapid development and ease of use, ideal for quick prototypes and data exploration tasks. Shiny, on the other hand, offers an extensive framework for complex, feature-rich data apps, with PyShiny extending these capabilities to Python users. Both platforms come with their unique strengths and limitations, making your choice highly dependent on specific project requirements. To the table below we add a Django column. 

This is a complex framework with capabilities far beyond dashboards, that may be preferred in certain cases.

Why Streamlit?

When Streamlit burst onto the scene in mid 2019, it came with the promise of democratizing data app creation. Its core philosophy revolves around simplifying the process of turning data scripts into shareable web applications. What sets Streamlit apart is its focus on rapid prototyping. With just a few lines of Python code, you can have a prototype of a data application up and running, something that resonates well with data scientists and engineers who work in agile environments.

Streamlit’s magic lies in its ease of use. With built-in widgets like sliders, buttons, and text inputs, creating an interactive dashboard is almost as easy as writing a Python script. And let’s not forget its compatibility with Python libraries. Whether you’re looking to integrate an experimental diffusion model using diffusers and Pytorch, classic machine learning model using scikit-learn, or render an intricate plot with matplotlib, Streamlit allows seamless integration, thus making it versatile across different stages of the data lifecycle.

Speaking of community support, Streamlit has garnered a large following (over 27k stars on GitHub in Oct 2023) in a relatively short time. This is important because a strong community translates to better documentation, more third-party plugins, and a wealth of shared knowledge, which in turn accelerates development and problem-solving.

But where does Streamlit really shine? The platform excels in situations that demand quick turnarounds. Whether it’s a proof-of-concept for a machine learning model or a temporary dashboard for data exploration, Streamlit offers a quick and efficient solution. It also finds its niche in specialized sectors that require rapid development cycles or quick data experiments.

Streamlit Example

To give you a hands-on sense of Streamlit’s capabilities, we’ve created a simple yet illustrative example. Our dashboard utilizes a machine learning model from the Mbaza project to classify animals in the Gabonian forest. Not only is it quick to develop such an application with Streamlit, but the result is also user-friendly and intuitive. You can explore the code behind this project in our GitHub repository or directly interact with the deployed dashboard on our Posit Connect instance.

Mbaza Streamlit Interface

When to Choose Shiny Over Streamlit

While Streamlit has carved out its space in the data app landscape with a focus on rapid prototyping and ease of use, Shiny by Posit offers a different set of advantages that make it a strong contender, especially for more complex, robust applications. If Streamlit is the agile sprinter, think of Shiny as the marathon runner designed for the long haul.

Shiny has been around for a while, making it a mature and well-supported platform. One of its most distinctive strengths is its ability to produce incredibly robust and interactive web applications. With Shiny, you’re not just getting a tool to create simple dashboards; you’re getting an extensive framework that allows for more advanced customization, right down to the pixel if needed.

Moreover, RShiny is built on R, a language widely used for statistical modeling and data analysis. If your team is already embedded in the R ecosystem, leveraging Shiny for your data applications becomes a natural extension of your existing workflows. And just when you thought Shiny couldn’t get more versatile, RStudio introduced PyShiny, bringing the robustness and customization features of Shiny into the Python world. This addition opens up exciting possibilities for teams that use both R and Python.

So where do RShiny and PyShiny excel? These platforms are ideally suited for building data products that require extensive features and strong reliability. For instance, you may need to develop an application for scientific computing that allows researchers to input parameters and receive complex visualizations and statistical outputs. Or perhaps you’re aiming for an enterprise-grade application that requires role-based authentication and integrates with a suite of other software. In such cases, the depth of customization and robustness offered by Shiny and PyShiny make them excellent choices.

Interested in crafting your R Shiny dashboard? Explore our open-source and effortlessly adaptable templates.

Shiny proves to be invaluable in the life sciences as it simplifies the creation of interactive web applications directly from R (which is already used extensively for biological data processing and analysis). This is essential for life science organisations aiming to streamline processes, reduce logistical hurdles and enhance safety (for example, in clinical trials). Shiny’s ability to enable complex visualizations and statistical outputs with extensive customizations aligns well with the rigorous data analysis and interactive application needs of this sector. 

Conclusion

Choosing between Streamlit and Shiny for your next data application is a nuanced decision that becomes even more critical in specialized fields like life sciences, where data analytics often directly impacts research outcomes and patient care depends on a variety of factors. Streamlit, with its Python-native ecosystem and emphasis on rapid prototyping, is an excellent choice for:

• Quick proof-of-concepts
• Data exploration tasks
• Projects that demand speed and flexibility

On the other hand, Shiny (and its Python counterpart, PyShiny) offer unparalleled robustness and customization options that make them ideal for:

• Data products with extensive features
• Scientific computing tasks
• Enterprise-level applications requiring intricate user interfaces and robustness

Both platforms come with strong communities and extensive libraries, contributing to their adaptability and effectiveness across various use-cases. Given Appsilon’s deep expertise in both technologies, with a specialization in Shiny-based solutions, we’re equipped to guide you through the maze of choices and trade-offs to arrive at the best solution for your specific needs.

Take a look at how we use Shiny to explore machine learning derived data in the life sciences or check out this project on how we used wearables and AI to assess musculoskeletal health with a team of academics.

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