Streamlit vs Shiny
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 meets 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. Here's our take.
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
Streamlit
Nice PickDevelopers 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
Shiny
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
The Verdict
Use Streamlit if: You want 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 and can live with specific tradeoffs depend on your use case.
Use Shiny if: You prioritize 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 over what Streamlit offers.
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
Disagree with our pick? nice@nicepick.dev