Links to slides from rstudio::conf 2019

Links to slides to talks at the 2019 rstudio::conf

Pull requests welcome! Or add an issue or email Karl Broman.

Videos of the talks are at https://resources.rstudio.com/rstudio-conf-2019.

Workshops material

RStudio blog post with links to all workshops' materials: https://blog.rstudio.com/2019/02/06/rstudio-conf-2019-workshops/

• Advanced R Markdown
• Applied Machine Learning
• Big Data with R
• Data Science in the Tidyverse
• Deep learning with R (Tensorflow) day 1 and day 2
• Introduction to Shiny and R Markdown workshop
• Shiny in Production
  • Workshop slides
  • Supplement book
  • GitHub material
• Rstudio Pro Products Admin Training
• Train-the-trainer workshop
• What they forgot to teach you about R

Wednesday 2019-01-16 ePosters

• Colin Fay, @_ColinFay, Building big Shiny Apps, blogpost

• Alex Gold, @alexkgold, Upgrading to R: Tips and mistakes you don't have to make; see also blog post for slide-by-slide extended narrative

• Leonard Kiefer, @lenkiefer, Using R to Analyze Economic and Housing Market Trends

• Ted Laderas, @tladeras, and Jessica Minnier, @datapointier, Democratizing data science using Shiny and LearnR

• Nick Strayer, @nicholasstrayer, Multimorbidity explorer: A shiny app for exploring EHR and biobank data

• Nick Tierney, @njtierney, A Missing Data Carol: Ghosts of Missing Data Past, Present, and Future

• Jeremy Wildfire, @jwildfire, Modernizing the clinical trial analysis pipeline with R and JavaScript

• Hiroaki Yutani, @yutannihilat_en, Introduction to gghighlight

Thursday 2019-01-17

Welcome

• Welcome to rstudio::conf

• Tareef Kawaf, @tareefk, Welcome and RStudio Vision

9:30 Keynote

• Joe Cheng, @jcheng, Shiny in production (related book by Kelly O'Briant)

11:00 Session 1, Track 1: Tidyverse

• Karthik Ram, @_inundata, rOpenSci, A guide to modern reproducible data science with R

• Jeffrey Arnold, @jrnold, Insight, Solving R for data science (solutions to R4DS exercises)

• Kara Woo, @kara_woo, Sage Bionetworks, Box plots: A case study in debugging and perseverance (the PR of interest)

• Amelia McNamara, @AmeliaMN, University of St. Thomas, Working with categorical data in R without losing your mind (related paper from the DSS collection)

11:00 Session 1, Track 2: Interop

• Wes McKinney, RStudio, Ursa Labs and Apache Arrow in 2019

• Jonathan McPherson, @jmcphers, RStudio, New language features in RStudio 1.2, (example code and data)

• Edgar Ruiz, @theotheredgar, RStudio, Databases using R: The latest

• Kelly O’Briant, @kellrstats, RStudio, Configuration management tools for the R admin

11:00 Session 1, Track 3: Production

• Heather & Jacqueline Nolis, @heatherklus and @skyetetra, Nolis, LLC, Push straight to prod: API development with R and Tensorflow at T-Mobile

• Mark Sellors, @sellorm, Mango Solutions, R in production

• Jeff Allen, RStudio, RStudio Connect: Past, present, and future

• Sean Lopp, RStudio, Announcing RStudio Package Manager

2:00 Session 2, Track 1: Teaching

• Jesse Mostipak, @kierisi, Teaching Trust, R4DS online learning community: Improvements to self-taught data science & the critical need for diversity, equity, and inclusion in data science education

• Irene Steves, @i_steves, Teaching data science with puzzles

• Kelly Nicole Bodwin, @kellybodwin, California Polytechnic State University, Introductory statistics with R: Easing the transition to software for beginner students (github repo)

• Tracy Teal, @tracykteal, The Carpentries, Teaching R using inclusive pedagogy: Practices and lessons learned from over 700 Carpentries workshops

2:00 Session 2, Track 2: Distributed

• Darby Hadley, RStudio, RStudio Job Launcher: Changing where we run R stuff

• Javier Luraschi, @javierluraschi, RStudio, Scaling R with Spark

• Kevin Kuo, RStudio, Introducing mlflow

• James Blair, @blair09m, RStudio, Democratizing R with Plumber APIs

2:00 Session 2, Track 3: Industry

• Brooke Watson, @brookLYNevery1, ACLU, R at the ACLU: Joining tables to reunite families

• Emily Robinson, @robinson_es, Data Scientist at DataCamp, Building an A/B testing analytics system with R and Shiny

• Nic Crane, @nic_crane, Elucidata, The future’s Shiny: Pioneering genomic medicine in R

• Joe Rickert, @rstudiojoe, RStudio, R Consortium initiatives in medicine

4:00 Session 3, Track 1: Tidyverse

• Earo Wang, @earowang, Monash University, Melt the clock: Tidy time series analysis

• Tyler Morgan-Wall, @tylermorganwall, Institute for Defense Analyses, 3D mapping, plotting, and printing with rayshader

• Edzer Pebesma, @edzerpebesma & Michael Sumner, Etienne Racine, Institute for Geoinformatics, University of Muenster, Germany, Spatial data science in the Tidyverse

• Thomas Lin Pedersen, @thomasp85, RStudio, gganimate live cookbook

4:00 Session 3, Track 2: Modeling

• Alex Hayes, @alexpghayes, University of Wisconsin, Madison, Solving the model representation problem with broom

• Sigrid Keydana, @zkajdan, RStudio, Why TensorFlow eager execution matters

• Max Kuhn, @topepos, RStudio, parsnip: A tidy model interface

• Claus Wilke, @clauswilke, The University of Texas at Austin, Visualizing uncertainty with hypothetical outcomes plots

4:00 Session 3, Track 3: Kaleidoscope

• Matt Dancho, @mdancho84, Business Science, Using R, the Tidyverse, H2O, and Shiny to reduce employee attrition

• Hao Zhu, @haozhu233, Hebrew SeniorLife – Institute for Aging Research, Empowering a data team with RStudio addins (example/demo)

• Karl Broman, University of Wisconsin, R/qtl2: Rewrite of a very old R package

• Amanda Gadrow, @ajmcoqui, RStudio, Getting it right: Writing reliable and maintainable R code

Friday 2019-01-18

9:00 Keynote

• Felienne, @felienne, LIACS - Universiteit Leiden, Explicit direct instruction in programming education

10:30 Session 4, Track 1: org-thinking

• James (JD) Long, @CMastication, Renaissance Re, Putting empathy in action: Building a `community of practice' for analytics in a global corporation

• Tonya Filz, @TonyaFilz, RStudio, The resilient R champion

• Hilary Parker, @hspter, Stitch Fix, Cultivating creativity in data work

• Angela Bassa, @AngeBassa, iRobot, Data science as a team sport

10:30 Session 4, Track 2: programming

• Gabor Csardi, @GaborCsardi, RStudio, pkgman: A fresh approach to package installation

• Jim Hester, @jimhester_, RStudio, It depends: A dialog about dependencies

• Jeroen Ooms, @opencpu, rOpenSci, A preview of Rtools 4.0

• Miles McBain, @MilesMcBain, ACEMS, Queensland University of Technology, Our colour of magic: The open sourcery of fantastic R packages

10:30 Session 4, Track 3: publication

• Garrett Grolemund, @StatGarrett, RStudio, R Markdown: The bigger picture

• Yihui Xie, @xieyihui, RStudio, pagedown: Creating beautiful PDFs with R Markdown and CSS (pagedown github repo)

• Rich Iannone, @riannone, RStudio, Introducing the gt package

• Mike K Smith, @MikeKSmith, Pfizer Ltd, The lazy and easily distracted report writer: Using rmarkdown and parameterized reports

1:00 Session 5, Track 1: teaching

• Caitlin Hudon, @beeonaposy, R-Ladies Austin, Learning from eight years of data science mistakes

• Mary Rudis, @mrshrbrmstr, Penn State Harrisburg, Catching the R wave: How R and RStudio are revolutionizing statistics education in community colleges (and beyond)

• Carl Howe, @cdhowe, RStudio, The next million R users

• Mel Gregory, RStudio, RStudio Cloud for education

1:00 Session 5, Track 2: programming

• Hadley Wickham, @hadleywickham, RStudio, vctrs: Tools for making size and type consistent functions

• Jenny Bryan, @jennybryan, RStudio, Tidy eval in context

• Lionel Henry, @_lionelhenry, RStudio, Working with names and expressions in your tidy eval code

• Jesse Sadler, @vivalosburros, independent researcher, Learning and using the tidyverse for historical research

1:00 Session 5, Track 3: shiny

• Eric Nantz, @thercast, Eli Lilly, Effective use of Shiny modules in application development

• Barret Schloerke, @schloerke, RStudio, Reactlog 2.0: Debugging the state of Shiny

• Alan Dipert, @alandipert, RStudio, Integrating React.js and Shiny

• Ian Fellows, Fellows Statistics, Don't let long running tasks hang your users: introducing ipc for Shiny

3:00 Keynote

• David Robinson, @drob, DataCamp, The unreasonable effectiveness of public work

Panel discussion

• Edgar Ruiz (@theotheredgar), Angela Bassa (@AngeBassa), Karthik Ram (@_inundata), Hilary Parker (@hspter), Tracy Teal (@tracykteal), Growth & change of careers, organizations and responsibility in data science

Followup blog posts

• Jacqueline Nolis, @skyetetera, T-Mobile, rstudio::conf 2019 takeaways: Serious Shiny, R in Production, and Data Science Skill Growth

• Athanasia Monika Mowinckel, @DrMowinckels, Center for Lifespan Changes in Brain and Cognition, Oslo, Norway, Why RStudio::conf is the best conference experience I have had

• Julia Silge, @juliasilge, StackExchange, Feeling the rstudio::conf ❤️

• Sean Lopp, @lopp_sean, RStudio, rstudio::conf 2019 — The theme you may have missed!

• Laura Ellis, @LittleMissData, IBM, R Studio Conf 2019 - Easing your FOMO with R Resources

• Brooke Watson, @brookLYNevery1, ACLU, Rstudio::conf 2019: lessons learned

• Hannah Frick for Mango Solutions, rstudio::conf 2019 roundup

• Hernando Cortina @cortinah, Climate change: Modeling 140+ years of temperature data with tsibble and fable

• Zev Ross, @zevross, 15 new ideas and new tools for R gathered from the RStudio Conference 2019

• Ryan Johnson, @ryjohnson09, RStudio Conference Recap

• Sam Tyner, @sctyner, Iowa State University, Lessons learned at rstudio::conf

• Eric Scott, LeafyEricScott, Tufts University, RStudio::conf reflections

• Eric Nantz, @thercast, R-Podcast episode 26: the podcast trifecta from rstudio::conf

• Pranav Dar, Top 10 Presentations from rstudio::conf 2019 – The Best R Conference of the Year!

• Ryo Nakagawara, @R_by_Ryo, My #TidyverseDevDay and #RStudioConf 2019 Reflections!

• Ted Laderas, @tladeras, Rstudio Conf 2019: Education and Organizations

• (in Portuguese) Bruna Wundervald, @bwundervald, Resumo da rstudio::conf 2019