Season 2 of the R-Podcast is up and running! This episode begins a multi-part series on reproducible analysis using R. In this episode I discuss the usage of Sweave and LaTeX for producing reproducible reports, an introduction to the capabilities of the knitr package (more episodes will be coming dedicated to this package), and my motivation for adapting reproducible analysis techniques and tools into my workflow. In our listener feedback segment I discuss a new means of providing feedback to the R-Podcast using our new sub-reddit page and introduce new segments highlighting interesting stories around the R community and useful packages. This promises to be an exciting season of the R-Podcast, and I hope you enjoy this episode!

Episode 11: Episode 11: Reproducible Analysis Part 1

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In this special live recording of the Shiny Developer Series held at the first-ever Appsilon Shiny Conferenbce, host Eric Nantz welcomed back an all-star panel of Dean Attali, Tanya Cashorali, Pedro Silva, and Mike Thomas to share their unique perspectives on the life of a Shiny application developer in the world of consulting. The panel discussed topics such as their favorite techniques used across production apps, advice for becoming a Shiny consultant, and ways Shiny can integrate seamlessly with other tech stacks.Resources mentioned in the episodeShiny Modules: shiny.rstudio.com/articles/modules.htmlOverview of the {sass} package: rstudio.github.io/sass/articles/sass.html{waiter} Loading screens for Shiny: github.com/JohnCoene/waiterTooltips with {shinyBS}: ebailey78.github.io/shinyBS/docs/Tooltips_and_Popovers.htmlInteractive web-based data visualization with R, {plotly}, and {shiny}: plotly-r.com{shinyjs} Easily improve the user experience of your Shiny apps in seconds: deanattali.com/shinyjs/{reactable} Interactive data tables for R: glin.github.io/reactableClean Code: A Handbook of Agile Software CraftsmanshipEpisode Timestamps00:00:00 Panelist introductions  00:03:20 What are key skills to build production-quality Shiny apps  00:10:05 Creating an app that doesn't feel like a standard Shiny app  00:17:15 Getting started with consulting/freelancing Shiny development  00:27:00 Advice for teams to increase Shiny dev skills  00:36:10 Tools/packages used every day  00:45:15 App development workflows  00:49:30 Underrated techniques  00:58:20 Episode wrapup shinydevseries::session_info()

Episode 32: Consulting with Shiny Panel Discussion

Whether in a sports pickup game or a different friendly competition, we often strive for balancing the distributions of skills so everyone can have fun. What does this have to do with Shiny? In episode 29, our coverage of the 2021 Shiny contest continues as I am joined by Douglas Mesquita and Luis Gustavo Silve e Silve who created the novel FairSplit Shiny application! Motivated by regular football pickup games, Douglas and Luis developed a novel algorithm to balance the distribution of individuals incorporating multiple traits that eventually wound up into a snazzy Shiny app that easily has something for everyone. This is yet another example of the many use cases Shiny brings at your fingertips.Resources mentioned in the episodeFairSplit App - voronoys.shinyapps.io/fairsplitFairSplit Code - github.com/voronoys/fairsplitContest submission post - community.rstudio.com/t/fairsplit-shiny-contest-submission/104752Episode Timestamps00:00:00 Episode & Douglas' introduction  00:02:50 Luis' introduction  00:07:30 Fairsplit Shiny app  00:17:10 Application walkthrough  00:27:20 Sidenote: Statistical bias introduced into the system  00:47:47 Code discussion  01:12:25 Episode wrapup 

Episode 29: Playing fair with the FairSplit Shiny App

In episode 24 of the Shiny Developer Series, we kick off a series of episodes that spotlight amazing Shiny applications submitted in the 2021 RStudio Shiny Contest! David Barkemeyer joins Eric to uncover the technical achievements and design philosophy of his myPaintings application, complete with many innovative capabilities that greatly enhance the user experience and backend infrastructure. Throughout the episode, you will hear David's perspectives on effective techniques to manage application state, integration of custom javascript, and much more!Resources mentioned in the episodeDavid on GitHub: https://github.com/DavidBarkemyPaintings - Painting Trading PlatformShiny app: https://mypaintings.davidbarke.comGitHub Repo: https://github.com/DavidBarke/mypaintings{shinyjs}{bs4Dash}Episode Timestamps00:00:00 Episode Introduction  00:03:24 How David got started with R & Shiny  00:05:40 Introduction to myPaintings  00:24:15 Infinite scroll feature  00:30:22 Design UI & UX of myPaintings  00:34:15 User management & helper functions  00:46:51 Overall app & file organization  00:49:28 Using .values to store & update user state and environment  00:53:40 R6 classes as another option  01:00:20 Recap and conclusion 

Episode 24: Illustrating the MyPaintings Masterpiece

The Shiny Developer Series spotlight on the 2021 Shiny Contest continues with episode 28! Eric is joined by statistics lecturer Dr. Kate Saunders to share her unique coronaSweeper Shiny application which merges a well-known mini-game in the Windows world with modelling the spread of COVID19. On the surface it might seem straight-forward, but Kate walks us through her extensive design ideas of the algorithms and user experience of the application frontend. This was a very inspiring conversation that demonstrates the ways Shiny can be transformative to teaching real principles in mathematics, statistics, and beyond!Resources mentioned in the episodecoronaSweeper App - katerobsau.shinyapps.io/Corona_SweepercoronaSweeper Code - github.com/katerobsau/coronaSweeperKate's contest submission post: community.rstudio.com/t/coronasweeper-shiny-contest-submission/104767Follow Kate on Twitter - @katerobsauEpisode Timestamps00:00:00 Episode Introduction  00:04:45 Inspiration behind coronaSweeper  00:08:15 Demo of coronaSweeper  00:14:35 Code walkthrough  00:43:10 Shiny for teaching?  00:50:20 Episode wrapup 

Episode 28: The transformative effects of Shiny with the coronaSweeper app

The Shiny Developer Series continues our spotlight on the COVID-19 Test Spotter application created by renowned data scientist and entrepreneur Mike Thomas! In this episode we learn how Mike implemented robust database techniques and logic to manage user-supplied data, navigating the use of multiple mapping APIs, and achieving a practical development-to-production strategy for releasing the application to the public. Plus we have a great discussion on what production means in the space of Shiny development, and much more. Resources mentioned in the episodeCOVID-19 At-Home Test Spotter (App) - ketchbrookanalytics.shinyapps.io/covid_test_spotterCOVID-19 At-Home Test Spotter (Code) - github.com/ketchbrookanalytics/covid_test_spotterApp blog post - www.ketchbrookanalytics.com/post/ketchbrook-analytics-launches-website-to-help-connecticut-residents-find-covid-19-test-kits{googleWay} Shiny vignette - symbolixau.github.io/googleway/articles/googleway-vignette.html#shiny{mongolite} user manual - jeroen.github.io/mongolite{reactable} Interactive data tables for R - glin.github.io/reactableEpisode Timestamps00:00:00 Episode Introduction  00:00:46 CRUD workflow and flowchart, with MongoDB database updates  00:09:41 Diving into the Google Maps and Places API  00:15:55 Geographic fencing, and dealing with unexpected user behavior.  00:22:35 Deploying with a dev and production branch.  00:28:15 Future plans for the app  00:30:50 Advice for building Shiny apps in production  00:31:20 Production apps is all about reproducibility: OS, dependencies, and the code.  00:33:15 Pay attention to warning messages in your console.  00:34:22 Modularization and Golem  00:38:08 You can have too much reactivity, such as overextending uiOutput() / renderUI()  00:40:10 Episode wrapup shinydevseries::session_info()

Episode 11: Episode 11: Reproducible Analysis Part 1

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