R Weekly 2019-33 {golem}, {RinteRface}, and Giraffes
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Hello and welcome to this new issue!
This week’s release was curated by Colin Fay, with help from the RWeekly team members and contributors.
Release Date: 2019-08-19
Highlight
Insights
Can we use a neural network to generate R Shiny code? Let’s find out!
In search of the perfect partial dependence plot for Random Forests
R in the Real World
R in Academia
Resources
New Packages
???? Go Live for More New Pkgs ????
CRAN
GitHub or Bitbucket
Videos and Podcasts
Episode 4: The RinteRface packages for production-ready Shiny UI
How to make a reproducible version of your R analysis that can be run in a web browser
Gist & Cookbook
R Internationally
Tutorials
How to generate meaningful fake data for learning, experimentation and teaching
First part of a series on how I setup this RStudio distill themed blog with Netlify
Quick exploration of dagitty and ggdag packages for using DAGs in causal analyses.
Synthesizing population time-series data from the USA Long Term Ecological Research Network
Big Data: Wrangling 4.6M Rows with dtplyr (the NEW data.table backend for dplyr)
R Project Updates
Updates from R Core:
Upcoming Events in 3 Months
Events in 3 Months:
Mexico CDSB Workshop 2019, July 29 - August 2 - How to Build and Create Tidy Tools
LatinR 2019, Santiago de Chile, September 25 - 27 - Latinamerican Conference About the Use of R in R&D
rOpenSci OzUnconf (Sydney, Australia), December 11 - 13 2019
More past events at R conferences & meetups.
Call for Participation
Quotes of the Week
i'm now convinced that every project should be a standalone #rstats package and that every single cleaning and analysis step should be a function with tests to ensure they work exactly as expected how do i reign in my too much gene
— Sharla Gelfand (@sharlagelfand) August 14, 2019
Excited to announce my next book project: mastering shiny, https://t.co/0mhQs57TDp. I didn’t write shiny, but I’m having fun writing about it! #rstats
— Hadley Wickham (@hadleywickham) August 13, 2019
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