Streamlit vs Shiny: Which is Better?

Streamlit and Shiny are both popular frameworks for building web applications in Python and R, respectively. While they serve a similar purpose, there are some differences between them that can influence which one is better suited for a particular project. In this comparison, we’ll explore these differences and analyze their strengths and weaknesses.

Streamlit is a Python library that allows developers to quickly build interactive web applications. It focuses on simplicity and ease of use, enabling developers to create web apps with just a few lines of code. Streamlit excels in data science and machine learning workflows, where rapid prototyping and sharing results are crucial.

Shiny, on the other hand, is an R package that provides a web framework for creating interactive web applications. It’s tightly integrated with the R ecosystem and leverages the vast array of statistical and visualization packages available in R. Shiny is widely used in the data analysis and statistical modeling domains, making it a popular choice among R developers.

Now, let’s delve into the factors that can help determine which framework is better for a given project.

Language and Ecosystem: The choice between Streamlit and Shiny often depends on the programming language you are comfortable with. If you are proficient in Python, Streamlit provides a seamless development experience with its intuitive syntax and extensive Python ecosystem. Conversely, if you primarily work with R, Shiny leverages the power of R packages and statistical capabilities. Thus, familiarity with the language and its ecosystem can influence your decision.

Ease of Use: Streamlit’s simplicity makes it highly accessible to developers of all levels. Its declarative syntax and intuitive API allow for rapid development, even for those new to web development. Shiny, although more complex due to its reactive programming paradigm, provides a rich set of tools and features for interactive web applications. However, it may have a steeper learning curve for beginners compared to Streamlit.

Customization and Flexibility: Streamlit emphasizes simplicity, which means that while it offers an easy way to create basic web applications, it may have limitations when it comes to customizing the user interface or handling complex interactions. Shiny, on the other hand, provides extensive customization options, allowing developers to build highly tailored web applications with intricate user interfaces. Shiny’s reactive programming model offers fine-grained control over the application’s behavior and enables dynamic updates based on user input.

Visualization Capabilities: Shiny has a significant advantage when it comes to data visualization since it taps into the wide range of plotting libraries available in R. With packages like ggplot2, plotly, and lattice, Shiny allows developers to create highly interactive and visually appealing visualizations. Streamlit, while providing some basic plotting functionalities, may not match the depth and breadth of visualization options available in R.

Community and Ecosystem: Both Streamlit and Shiny have vibrant communities and active ecosystems. Streamlit has gained significant traction in the Python community, and its user base continues to grow rapidly. It has a large number of open-source extensions and a supportive community that actively contributes to its development. Shiny benefits from the extensive R community, which provides a vast array of packages for various statistical and data analysis tasks. Depending on your project requirements, the availability of community support and extensions may impact your choice.

Deployment and Hosting: Streamlit offers a straightforward deployment process, allowing developers to quickly deploy their applications on various platforms, such as Heroku, AWS, or Streamlit’s own hosting service. It provides built-in caching mechanisms and efficient handling of updates, which can enhance application performance. Shiny, on the other hand, requires an R server to host the application, which can introduce additional complexities in the deployment process. However, Shiny applications can be hosted on platforms like or deployed platforms using tools like Docker or shiny-server, providing scalability and flexibility in deployment.

Integration with Data Science Ecosystem: Streamlit seamlessly integrates with popular Python libraries such as NumPy, Pandas, and scikit-learn, making it a natural choice for data science workflows. It also has built-in support for interactive widgets, allowing users to interact with the application and manipulate parameters dynamically. Shiny, being part of the R ecosystem, integrates well with packages like dplyr, tidyr, and caret, enabling smooth data manipulation and modeling within the application.

Learning Resources and Documentation: Streamlit has comprehensive documentation, a user-friendly website, and a growing number of online tutorials, blog posts, and community-driven resources. Its straightforward API and examples make it easy for developers to get started quickly. Shiny also has extensive documentation, a dedicated website, and a strong online presence with numerous tutorials and resources available. The R community is known for its support and willingness to help fellow developers.

Final Conclusion on Streamlit vs Shiny: Which is Better

In summary, the choice between Streamlit and Shiny depends on various factors such as programming language familiarity, project requirements, customization needs, data visualization capabilities, and integration with the existing ecosystem

. Streamlit’s simplicity and Python integration make it ideal for quick prototyping and sharing data science applications. Shiny, with its rich statistical capabilities and extensive R package ecosystem, excels in data analysis and modeling domains.

Ultimately, the “better” framework depends on your specific needs and preferences. It’s recommended to consider the factors discussed here, try out both frameworks, and evaluate which one aligns better with your project goals, programming skills, and ecosystem requirements.