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Shiny and Javascript Wizardry with Garrick Aiden-Buie

Shiny and Javascript Wizardry with Garrick Aiden-Buie

FromThe R-Podcast


Shiny and Javascript Wizardry with Garrick Aiden-Buie

FromThe R-Podcast

ratings:
Length:
59 minutes
Released:
Mar 3, 2020
Format:
Podcast episode

Description

About this Episode
This is the second of multiple episodes covering the recent rstudio::conf 2020! In this episode, Eric shares the backstory behind his Shiny Community e-poster and welcomes data scientist Garrick Aiden-Buie to discuss his spectacular JavaScript for Shiny Users course, the mind-blowing features of the package accompanying the course, and much more. Plus takeaways from Shiny-related presentations at the conference and a fresh batch of listener feedback.
Links
rstudio::conf(2020L) recordings: resources.rstudio.com/rstudio-conf-2020 (https://resources.rstudio.com/rstudio-conf-2020)
Reaping the benfits of the Shiny community e-poster: rpodcast.shinyapps.io/highlights-shiny (https://rpodcast.shinyapps.io/highlights-shiny)
Poster source code: github.com/rpodcast/highlights.shiny (https://github.com/rpodcast/highlights.shiny)
Javascript for Shiny Users: js4shiny.com/ (https://js4shiny.com/)
Garrick's GitHub: github.com/gadenbuie (https://github.com/gadenbuie)
js4shiny package: pkg.js4shiny.com/ (https://pkg.js4shiny.com/)
Styling Shiny apps with Sass and Bootstrap 4 (Joe Cheng): resources.rstudio.com/rstudio-conf-2020/styling-shiny-apps-with-sass-and-bootstrap-4-joe-cheng (https://resources.rstudio.com/rstudio-conf-2020/styling-shiny-apps-with-sass-and-bootstrap-4-joe-cheng)
Reproducible Shiny apps with shinymeta (Carson Sievert): resources.rstudio.com/rstudio-conf-2020/reproducible-shiny-apps-with-shinymeta-dr-carson-sievert (https://resources.rstudio.com/rstudio-conf-2020/reproducible-shiny-apps-with-shinymeta-dr-carson-sievert)
Getting things logged (Gergely Daroczi): resources.rstudio.com/rstudio-conf-2020/getting-things-logged-gergely-daroczi (https://resources.rstudio.com/rstudio-conf-2020/getting-things-logged-gergely-daroczi)
Listener Ricardo's R & tidyverse tutorial: predictcrypto.org/tutorials (https://predictcrypto.org/tutorials)
xaringan package's infinite moon reader: https://bookdown.org/yihui/rmarkdown/xaringan-preview.html (bookdown.org/yihui/rmarkdown/xaringan-preview.html)
My appearance on Michael Dominick Show: https://www.automator.show/8 (https://www.automator.show/8)
NSSD 101 interview with JJ Allaire: nssdeviations.com/101-special-guest-jj-allaire (http://nssdeviations.com/101-special-guest-jj-allaire)
Feedback
Leave a comment on this episode's post (https://r-podcast.org/33)
Email the show: thercast[at]gmail.com
Use the R-Podcast contact page (https://r-podcast.org/contact)
Episode Timestamps

00:00:00.000 Intro
00:01:48.000 Shiny community e-poster
00:20:12.000 Garrick Aiden-Buie
00:38:10.000 Styling Shiny apps
00:40:56.000 Shinymeta
00:44:20.000 logger
00:49:49.000 listener feedback
00:57:25.000 Wrapup

Music Credits
Opening and closing themes: Training Montage by WillRock (http://ocremix.org/artist/5043/willrock) from the Return All Robots Remix Album (http://ocremix.org/events/returnallrobots/) at ocremix.org (http://ocremix.org/)
Released:
Mar 3, 2020
Format:
Podcast episode

Titles in the series (35)

R is a free and open-source statistical computing environment. It has quickly become the leading choice of software used to develop cutting-edge statistical algorithms, innovative visualizations, and data processing, among other key features. R has seen tremendous growth in popularity and functionality over the last decade, largely due to the vibrant and devoted R community of users. Whether you have experience with commercial statistical software such as SAS or SPSS and want to learn R, or getting into statistical computing for the first time, the R-Podcast will provide you with valuable information and advice that will help you to tap into the power of R. Our intent is to start with the basic concepts that can be a struggle for those new to R and statistical computing. We will give practical advice on how to take advantage of R’s capabilities to accomplish innovative and robust data analyses. Along the way we will highlight the additional tools and packages that greatly enhance the experience of using R, and highlight resources that can help people become experts with R. While this podcast is not meant to be a series of lectures on statistics, we will use freely and publicly available data sets to illustrate both basic statistical analyses as well as state-of-the-art algorithms to show how powerful and robust R can be for analyzing today’s explosion of data. In addition to the audio podcast, we will also produce screencasts for hands-on demonstrations for those topics that are best explained via video.