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Episode 25: Interview with Ian Lyttle

Episode 25: Interview with Ian Lyttle

FromThe R-Podcast


Episode 25: Interview with Ian Lyttle

FromThe R-Podcast

ratings:
Length:
55 minutes
Released:
Mar 22, 2018
Format:
Podcast episode

Description

Conversation with Ian Lyttle
Twitter: @ijlyttle (https://twitter.com/ijlyttle) | Github: github.com/ijlyttle (https://github.com/ijlyttle/)
Ian's presentation at rstudio::conf 2018 recording: www.rstudio.com/resources/videos/how-i-learned-to-stop-worrying-and-love-the-firewall/ (https://www.rstudio.com/resources/videos/how-i-learned-to-stop-worrying-and-love-the-firewall/)
bsplus package: ijlyttle.github.io/bsplus/ (https://ijlyttle.github.io/bsplus)
shinychord package: github.com/ijlyttle/shinychord (https://github.com/ijlyttle/shinychord)
ghenter package: github.com/ijlyttle/ghentr (https://github.com/ijlyttle/ghentr)
Rstudio::conf 2018 takeaways and insights
Advanced R 2nd Edition new chapters:
Expressions: adv-r.hadley.nz/expressions.html (https://adv-r.hadley.nz/expressions.html)
Quasiquotation: adv-r.hadley.nz/expressions.html (https://adv-r.hadley.nz/expressions.html)
Evaluation: adv-r.hadley.nz/evaluation.html (https://adv-r.hadley.nz/evaluation.html)
Translating R Code: adv-r.hadley.nz/translation.html (https://adv-r.hadley.nz/translation.html)
lobstr package: github.com/r-lib/lobstr (https://github.com/r-lib/lobstr)
Recordings from all sessions on RStudio site: rstudio.com/resources/webinars/#rstudioconf2018 (https://www.rstudio.com/resources/webinars/#rstudioconf2018)
Collection of presentation links: github.com/simecek/RStudioConf2018Slides/ (https://github.com/simecek/RStudioConf2018Slides/)
Presentations by RStudio staff: github.com/rstudio/rstudio-conf/tree/master/2018/ (https://github.com/rstudio/rstudio-conf/tree/master/2018/)
rstudio::conf 2018 summary (Paul van der Laken): paulvanderlaken.com/2018/02/08/rstudioconf-2018-summary (https://paulvanderlaken.com/2018/02/08/rstudioconf-2018-summary/)
Rewriting the R organism at rstudio::conf 2018 (John David Smith): goo.gl/rGfqip (https://goo.gl/rGfqip)
David Miller's slides on rstudio::conf 2018: github.com/dill/rstudioconf2018-summary/blob/master/talk.Rmd (https://github.com/dill/rstudioconf2018-summary/blob/master/talk.Rmd)
Recap of rstudio::conf 2018 (Colby Ford): www.blue-granite.com/blog/rstudio-conference-2018-recap (https://www.blue-granite.com/blog/rstudio-conference-2018-recap)
Highlights from rstudio::conf 2018 (Methods Consultants blog): blog.methodsconsultants.com/posts/highlights-from-rstudio-conf-2018/ (https://blog.methodsconsultants.com/posts/highlights-from-rstudio-conf-2018/)
lobstr package: github.com/r-lib/lobstr (https://github.com/r-lib/lobstr)
Listener feedback
A completely subjective ranking of data science podcasts: tonyelhabr.rbind.io/posts/data-science-podcasts (https://tonyelhabr.rbind.io/posts/data-science-podcasts/)
Feedback
Leave a comment on this episode's post (https://r-podcast.org/25)
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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 22, 2018
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.