jz1584/RStudioConf2018Slides

Links to slides for talks at the rstudio::conf 2018

Links to slides from rstudio::conf 2018

Links to slides for talks at the rstudio::conf 2018

Schedule: https://www.rstudio.com/conference/#conference

Official rstudio-conf repo for all the materials

Pull requests welcome! Or add an issue, or tweet @simecek or email Petr Simecek.

Based on Karl Broman's template from the rstudio::conf 2017.

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Saturday 2018-02-03

Live Stream: Day 2

Keynote

Machine Learning with R and TensorFlow, JJ Allaire, @fly_upside_down

Packages

• Five packages in five weeks - from boredom to contribution via blogging, Giora Simchoni, @GioraSimchoni

• You can make a package in 20 minutes, Jim Hester, @jimhester_

• What makes a great R package?, Joseph Rickert, @rstudiojoe

• Contributing to the tidyverse, Mara Averick, @dataandme

Case-study-3

• Teach the Tidyverse to beginners, David Robinson, @drob

• A SAS-to-R success story, Elizabeth J. Atkinson

• How I Learned to Stop Worrying and Love the Firewall, Ian Lyttle, @ijlyttle

• Achieving impact with advanced analytics: Breaking down the adoption barrier , Aaron Horowitz

Interop

• Large scale machine learning using TensorFlow, BigQuery and CloudML Engine within RStudio, Michael Quinn

• Deploying TensorFlow models with tfdeploy, Javier Luraschi, @javierluraschi

• Building Spark ML pipelines with sparklyr, Kevin Kuo, @kevinykuo

• Reinforcement learning in Minecraft with CNTK-R, Ali Zaidi

Teaching

• Data-driven curriculum development, Nick Carchedi

• Kaggle in the classroom: using R and GitHub to run predictive modeling competitions, Colin Rundel

• Something old, something new, something borrowed, something blue: Ways to teach data science (and learn it too!), Chester Ismay, @old_man_chester

• Training an army of new data scientists, Marco Blume, @PinnacleSports

Case-study-4

• Differentiating by data science, Eric Colson

• Imagine Boston 2030: Using R-Shiny to keep ourselves accountable and empower the public, Kayla Patel

• Agile data science, Elaine McVey, @eamcvey

• Storytelling with R, Olga Pierce, @olgapierce

Production-2

• Sharing analysis results with RStudio Connect, Jeff Allen

• Parameterized R Markdown reports with RStudio Connect (Fruit for Thought), Aron Atkins, @aronatkins

• Drill-down reporting with Shiny, Barbara Borges Ribeiro

• The R Admin is rad: A guide to professional R tooling and integration, Nathan Stephens, @nwstephens

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Friday 2018-02-02

Live Stream: Day 1

Keynote

• To the tidyverse and beyond: Challenges for the Future in Data Science, Diane Cook, @visnut

Tidy-1

• Tidy spatial data analysis, Edzer Pebesma

• The future of time series and financial analysis in the tidyverse, Davis Vaughan, @dvaughan32

• infer: a package for tidy statistical inference, Andrew Bray

• Tidying up your network analysis with tidygraph and ggraph, Thomas Lin Pedersen, @thomasp85

Shiny

• Scaling Shiny apps with async programming, Joe Cheng, @jcheng

• Developing robust shiny apps with regression testing, Winston Chang, @winston_chang

• Make shiny fast by doing as little work as possible, Alan Dipert, @alandipert

• Scaling Shiny, Sean Lopp

IDE/rmarkdown

• RStudio 1.1 new features, Kevin Ushey, @kevin_ushey

• RStudio Server Pro 1.1 new features, Jonathan McPherson

• Learning R with rstudio.cloud, Mel Gregory

• Blogdown, Yihui Xie, @xieyihui

Tidy-2

• The lesser known stars of the tidyverse, Emily Robinson, @robinson_es

• Augmenting data exploration with interactive graphics, Carson Sievert, @cpsievert

• tidycf: Turning analysis on its head by turning cashflows on their sides, Emily Riederer, @EmilyRiederer

• Modeling in the tidyverse, Max Kuhn, @topepos

Case-study-1

• The unreasonable effectiveness of empathy, JD Long, @CMastication

• Open-source solutions for medical marijuana, Carl Ganz

• TBD, Logan Meltabarger

• Phrasing: communicating data science through tweets, gifs, and classic misdirection, Mara Averick, @dataandme

Rmarkdown

• R Markdown: Eight ways, Mine Cetinkaya-Rundel, @minebocek

• Adaptive feedback for learnr tutorials, Daniel Kaplan

• Beyond R: Using R Markdown with python, sql, bash, and more, Aaron Berg

• Branding and automating your work with R Markdown, Daniel Hadley, @danielphadley

Programming

• Tidy eval: programming with dplyr, tidyr, and ggplot2, Hadley Wickham

• Data rectangling, Jenny Bryan, @JennyBryan

• An assignment operator to unpack vectors and lists, Nathan Teetor, @ntweetor

• Debugging techniques in RStudio, Amanda Gadrow, @ajmcoqui

Case-study-2

• Rapid prototyping data products using Shiny, Tanya Cashorali

• Understanding PCA using Shiny and Stack Overflow data, Julia Silge, @juliasilge

• Accelerating cancer research with R, Sandra Griffith

• Developing and deploying large scale shiny applications, Herman Sontrop

Production-1

• R for Presidents, Tareef Kawaf

• Connecting to open source databases, Kirill Müller, @krlmlr

• Best practices for working with databases, Edgar Ruiz, @theotheredgar

• Plumber: turning your R code into an API, Jeff Allen

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Training Days

• Intro to R & RStudio, Amelia McNamara, @AmeliaMN

• Data Science in the Tidyverse, Charlotte Wickham, @cvwickham

• Intro to Shiny & R Markdown, Mine Çetinkaya-Rundel, @minebocek

• Applied Machine Learning, Max Kuhn

• Intermediate Shiny, Joe Cheng, @jcheng

• Extending the Tidyverse, Hadley Wickham, @hadleywickham

• What They Forgot to Teach You About R (aka Getting S*%! Done in R), Jenny Bryan, @JennyBryan

• Big Data with R, Edgar Ruiz, @theotheredgar

• Tidyverse Train-the-Trainer, Garrett Grolemund, @statgarrett