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The Podcast Trifecta (rstudio::conf 2019)

The Podcast Trifecta (rstudio::conf 2019)

FromThe R-Podcast


The Podcast Trifecta (rstudio::conf 2019)

FromThe R-Podcast

ratings:
Length:
41 minutes
Released:
Jan 23, 2019
Format:
Podcast episode

Description

Another spectacular rstudio::conf is in the books and the R-Podcast has tons of insights to share! We kick off our coverage with a three-podcast crossover as I am joined by Credibly Curious co-host Nick Tierny and Not So Standard Deviations co-host Hilary Parker! We discuss our impressions of the conference and where we'd like to see R go in 2019. Plus I share how my journey to the Advanced R-Markdown workshop is a testament to the welcoming and openness that the R community offers. This is just the beginning of our coverage and I hope you enjoy this episode!
Conversation with Hilary Parker and Nick Tierney
Credibly Curious podcast: soundcloud.com/crediblycurious (https://soundcloud.com/crediblycurious)
Not So Standard Deviations podcast: nssdeviations.com (http://nssdeviations.com/)
Apache Arrow: arrow.apache.org (https://arrow.apache.org/)
Tidy Evaluation online book: tidyeval.tidyverse.org (https://tidyeval.tidyverse.org/)
Tidy models family of packages: github.com/tidymodels (https://github.com/tidymodels)
The magick package by Jeroen Ooms: github.com/ropensci/magick (https://github.com/ropensci/magick)
pagedown package (paginate HTML output of R Markdown) by Yihui Xie: github.com/rstudio/pagedown (https://github.com/rstudio/pagedown)
Advanced R Markdown workshop highlights
Course website: arm.rbind.io (https://arm.rbind.io/) (powered by blogdown (https://bookdown.org/yihui/blogdown/)!)
Course GitHub repository: github.com/rstudio-education/arm-workshop-rsc2019 (https://github.com/rstudio-education/arm-workshop-rsc2019)
My slides on using the officer package to create PowerPoint slides: rpodcast.github.io/officer-advrmarkdown (https://rpodcast.github.io/officer-advrmarkdown)
The officer package documenation: davidgohel.github.io/officer (https://davidgohel.github.io/officer/)
MegaMan slide generator Shiny app: rpodcast.shinyapps.io/megaman (https://rpodcast.shinyapps.io/megaman/)
GitHub repo for slides and app: github.com/rpodcast/officer-advrmarkdown (https://github.com/rpodcast/officer-advrmarkdown)
Feedback
Leave a comment on this episode's post: r-podcast.org/26 (https://r-podcast.org/26)
Email the show: thercast[at]gmail.com
Use the R-Podcast contact page: r-podcast.org/contact (https://r-podcast.org/contact)
Leave a voicemail at +1-269-849-9780
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:
Jan 23, 2019
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.