Links to materials from tutorials (and hopefully talks) from useR 2019
Please feel free to make a pull request, add an issue, or tweet @s_owla :)
You might also like materials from rstudio::conf 2019 that @kwbroman and others collected!
Tuesday: Morning Tutorials, Afternoon Tutorials
Morning Tutorials
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AM1: Get up to speed with Bayesian data analysis in R: slides and exercises
Rasmus Bååth (@rabaath) -
AM2: Automatic and Explainable Machine Learning with H2O in R
Jo-Fai Chow (@matlabulous) -
AM3: Visualising High-Dimensional Data
Di Cook (@visnut) -
AM4: Extending R with C++
Dirk Eddelbuettel (@eddelbuettel) -
AM5: Hacking RStudio: Advanced Use of your Favorite IDE Colin Fay (@_ColinFay)
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AM6: CVXR: An R Package for Disciplined Convex Optimization
Anqi Fu (@anqi_fu) and Balasubramanian Narasimhan (@b_naras); joint work with S. Boyd -
AM7: Getting the most out of Git
Colin Gillespie (@csgillespie) and Rhian Davies (@trianglegirl) -
AM8: Package Development
Jim Hester (@jimhester_), Hadley Wickham (@hadleywickham) and Jenny Bryan (@JennyBryan) -
AM9: Generalized Nonlinear Models
Heather Turner (@HeathrTurnr)
Afternoon Tutorials
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PM1: Spatial and Spatiotemporal Data Analysis in R
Edzer Pebesma (@edzerpebesma) and Roger Bivand (@RogerBivand) -
PM2: Watch me: introduction to social media analytics
Maria Prokofieva (@m45haP) -
PM3: Keeping an exotic pet in your home! Taming Python to live in RStudio because sometimes the best language is both!
Emma Rand (@er13_r) -
PM4: bnlearn: Practical Bayesian Networks in R
Marco Scutari -
PM5: Transformation Models
Torsten Hothorn -
PM6: Statistical Data Cleaning using R
Mark Van Der Loo (@markvdloo) and Edwin De Jonge (@edwindjonge) -
PM7: Design For Humans! A Toolkit For Creating Intuitive Modeling Packages
Davis Vaughan (@dvaughan32); joint work with M. Kuhn (@topepos) -
PM8: Docker for Data Science: R, ShinyProxy and more
Tobias Verbeke (@OpenAnalytics) -
PM9: R/exams: A One-For-All Exams Generator
Achim Zeileis (@AchimZeileis)