Shiny vs Streamlit

Developers should learn Shiny when they need to create interactive data applications or dashboards for sharing R analyses with non-technical stakeholders, such as in business intelligence, research, or educational contexts meets developers should learn streamlit when they need to build and deploy data-driven web applications rapidly, such as for data visualization dashboards, machine learning model demos, or internal tools for data analysis. Here's our take.

Shiny

Nice Pick

Developers should learn Shiny when they need to create interactive data applications or dashboards for sharing R analyses with non-technical stakeholders, such as in business intelligence, research, or educational contexts

Pros

• It is particularly useful for prototyping data tools quickly, embedding statistical models into user-friendly interfaces, or deploying internal reporting systems where R is the primary analysis language
• Related to: r-programming, ggplot2

Cons

• Specific tradeoffs depend on your use case

Streamlit

Developers should learn Streamlit when they need to build and deploy data-driven web applications rapidly, such as for data visualization dashboards, machine learning model demos, or internal tools for data analysis

Pros

• It's particularly useful for data scientists and engineers who want to share their Python-based work with non-technical stakeholders through an interactive interface, as it eliminates the complexity of traditional web development and focuses on Python logic
• Related to: python, data-visualization

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Shiny if: You want it is particularly useful for prototyping data tools quickly, embedding statistical models into user-friendly interfaces, or deploying internal reporting systems where r is the primary analysis language and can live with specific tradeoffs depend on your use case.

Use Streamlit if: You prioritize it's particularly useful for data scientists and engineers who want to share their python-based work with non-technical stakeholders through an interactive interface, as it eliminates the complexity of traditional web development and focuses on python logic over what Shiny offers.

The Bottom Line

Shiny wins

Developers should learn Shiny when they need to create interactive data applications or dashboards for sharing R analyses with non-technical stakeholders, such as in business intelligence, research, or educational contexts