Uploaded April 2025 | Updated September 2026, 2 weeks ago
#datascience #dataanalysis #technology #clinicaltrials #rshiny #shinyforpython #pharmatech
Shiny paved the way for R users to create interactive, production-ready applications without switching stacks. In this episode, Eric Nantz reflects on Shiny’s origins, its "lazy by design" reactivity model, and how the ecosystem matured. We dive into how Shiny for Python expands this power to new audiences, and how Shiny is becoming key to modern clinical trial workflows. Eric shares real-world examples, user reactions, and the future of interactive data science.
In this episode, you will learn:
- How Shiny became a game changer for people working with R
- How the framework evolved from a simple prototyping tool to a critical asset in life sciences
- How open source is surpassing proprietary software
- The future of Shiny in drug development
Links for the episode:
R-Podcast Episode 18 Interview with Joe Cheng r-podcast.org/018-interviews-with-the-rstudio-team
Joe Cheng - The Past and Future of Shiny (rstudio::conf 2022 keynote) youtube.com/watch?v=HpqLXB_TnpI
echarts4r - Interactive visualizations for R via Apache ECharts echarts4r.john-coene.com
reactable -Interactive data tables glin.github.io/reactable
htmlwidgets Gallery gallery.htmlwidgets.org
renv - Project environments for R rstudio.github.io/renv/articles/renv.html
rix - Reproducible data science environments for R with Nix docs.ropensci.org/rix
R Weekly rweekly.org
R Weekly Highlights podcast https://serve.podhome.fm/r-weekly-highlights
Shiny Developer Series shinydevseries.com
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
_________________________________________
00:00 - Introduction to the episode and our guest
03:07 - The evolution of Shiny: From R to Python
06:09 - Community impact: The role of open source in Shiny's growth
08:59 - Barriers to adoption: Challenges in embracing Shiny
12:12 - Shiny for Python: New opportunities and comparisons
14:57 - Clinical trials and Shiny: Enhancing data analysis
18:05 - The future of Shiny: Evolution and industry adoption
21:06 - Alternatives to Shiny: Competition and cultural barriers
24:05 - The unsolved problems in software development
35:02 - Outro and closing remarks
#datascience #dataanalysis #technology #clinicaltrials #rshiny #shinyforpython #pharmatech
Shiny paved the way for R users to create interactive, production-ready applications without switching stacks. In this episode, Eric Nantz reflects on Shiny’s origins, its "lazy by design" reactivity model, and how the ecosystem matured. We dive into how Shiny for Python expands this power to new audiences, and how Shiny is becoming key to modern clinical trial workflows. Eric shares real-world examples, user reactions, and the future of interactive data science.
In this episode, you will learn:
- How Shiny became a game changer for people working with R
- How the framework evolved from a simple prototyping tool to a critical asset in life sciences
- How open source is surpassing proprietary software
- The future of Shiny in drug development
Links for the episode:
R-Podcast Episode 18 Interview with Joe Cheng r-podcast.org/018-interviews-with-the-rstudio-team
Joe Cheng - The Past and Future of Shiny (rstudio::conf 2022 keynote) youtube.com/watch?v=HpqLXB_TnpI
echarts4r - Interactive visualizations for R via Apache ECharts echarts4r.john-coene.com
reactable -Interactive data tables glin.github.io/reactable
htmlwidgets Gallery gallery.htmlwidgets.org
renv - Project environments for R rstudio.github.io/renv/articles/renv.html
rix - Reproducible data science environments for R with Nix docs.ropensci.org/rix
R Weekly rweekly.org
R Weekly Highlights podcast https://serve.podhome.fm/r-weekly-highlights
Shiny Developer Series shinydevseries.com
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
_________________________________________
00:00 - Introduction to the episode and our guest
03:07 - The evolution of Shiny: From R to Python
06:09 - Community impact: The role of open source in Shiny's growth
08:59 - Barriers to adoption: Challenges in embracing Shiny
12:12 - Shiny for Python: New opportunities and comparisons
14:57 - Clinical trials and Shiny: Enhancing data analysis
18:05 - The future of Shiny: Evolution and industry adoption
21:06 - Alternatives to Shiny: Competition and cultural barriers
24:05 - The unsolved problems in software development
35:02 - Outro and closing remarks
