rstudio::conf 2018 talks

rstudio::conf 2018 will be remembered for San Diego sunshine and J.J. Allaire’s keynote Machine Learning with R and Tensorflow. This was the year that RStudio brought deep learning to R with the keras, tensorflow and reticulate R packages. In his talk, J.J. described the underlying technology and presented a balanced overview of deep learning, discussing its promise, successes and challenges.

rstudio::conf 2018 talks

rstudio::conf 2018 will be remembered for San Diego sunshine and J.J. Allaire’s keynote Machine Learning with R and Tensorflow. This was the year that RStudio brought deep learning to R with the keras, tensorflow and reticulate R packages. In his talk, J.J. described the underlying technology and presented a balanced overview of deep learning, discussing its promise, successes and challenges.

rstudio::conf 2018 will be remembered for San Diego sunshine and J.J. Allaire’s keynote Machine Learning with R and Tensorflow. This was the year that RStudio brought deep learning to R with the keras, tensorflow and reticulate R packages. In his talk, J.J. described the underlying technology and presented a balanced overview of deep learning, discussing its promise, successes and challenges.

Over eleven hundred people attended the Friday and Saturday event choosing from talks organized into three sessions that emphasized talks describing case studies. Memorable talks included Google’s Michael Quinn discussion of large-scale machine learning using TensorFlow, BigQuery and Cloud ML, RStudio’s Javier Luraschi talk on Deploying TensorFlow Models with tfdeploy, and Ali Zaid from Microsoft describing Reinforcement learning in Minecraft with CNTK-R.

The conference closed with a “fireside chat” where Jenny Bryan, Mine Çetinkaya-Rundel, Max Kuhn, Hadley Wickham and Mara Avererick left a fairly large crowd feeling that they were part of a special event.

Keynotes and Panels

Fireside Chat: R In Industry Discussion

Fireside Chat: R In Industry Discussion

Eduardo Ariño de la Rubia, Tanya Cashorali, Eric Colson, Elaine McVey, David Robinson, Tareef Kawaf
Fireside Chat: Tidyverse Discussion

Fireside Chat: Tidyverse Discussion

Jenny Bryan, Mine Çetinkaya-Rundel, Max Kuhn, Hadley Wickham, Mara Averick
To the Tidyverse and Beyond

To the Tidyverse and Beyond

Dianne Cook
Machine Learning with R and TensorFlow

Machine Learning with R and TensorFlow

J.J. Allaire

Production

Parameterized R Markdown reports with RStudio Connect

Parameterized R Markdown reports with RStudio Connect

Aron Atkins
Sharing analysis results with RStudio Connect

Sharing analysis results with RStudio Connect

Jeff Allen
The R Admin is rad: A guide to professional R tooling and integration

The R Admin is rad: A guide to professional R tooling and integration

Nathan Stephens
Best practices for working with databases

Best practices for working with databases

Edgar Ruiz
Connecting to open source databases

Connecting to open source databases

Kirill Muller
Plumber: turning your R code into an API

Plumber: turning your R code into an API

Jeff Allen
R for Presidents

R for Presidents

Tareef Kawaf

Shiny

Developing robust Shiny apps with regression testing

Developing robust Shiny apps with regression testing

Winston Chang
Make Shiny fast by doing as little work as possible

Make Shiny fast by doing as little work as possible

Alan Dipert
Scaling Shiny

Scaling Shiny

Sean Lopp
Scaling Shiny apps with async programming

Scaling Shiny apps with async programming

Joe Cheng

R Markdown

Adaptive feedback for learnr tutorials

Adaptive feedback for learnr tutorials

Danny Kaplan
Beyond R: Using R Markdown with python, sql, bash, and more

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

Aaron Berg
Branding and automating your work with R Markdown

Branding and automating your work with R Markdown

Daniel Hadley
Create and maintain websites with R Markdown and blogdown

Create and maintain websites with R Markdown and blogdown

Yihui Xie
R Markdown: Eight ways

R Markdown: Eight ways

Mine Cetinkaya-Rundel

Tidyverse

Modeling in the Tidyverse

Modeling in the Tidyverse

Max Kuhn
Tidy spatial data analysis

Tidy spatial data analysis

Edzer Pebesma
Creating interactive web graphics suitable for exploratory data analysis

Creating interactive web graphics suitable for exploratory data analysis

Carson Sievert
infer: a package for tidy statistical inference

infer: a package for tidy statistical inference

Andrew Bray
The future of time series and financial analysis in the Tidyverse

The future of time series and financial analysis in the Tidyverse

Davis Vaughan
The lesser known stars of the Tidyverse

The lesser known stars of the Tidyverse

Emily Robinson
tidycf: Turning analysis on its head by turning cashflows on their sides

tidycf: Turning analysis on its head by turning cashflows on their sides

Emily Riederer
Tidying up your network analysis with tidygraph and ggraph

Tidying up your network analysis with tidygraph and ggraph

Thomas Lin Pedersen

Case Study

A SAS-to-R success story

A SAS-to-R success story

Elizabeth J. Atkinson
Achieving impact with advanced analytics: Breaking down the adoption barrier

Achieving impact with advanced analytics: Breaking down the adoption barrier

Aaron Horowitz
Agile data science

Agile data science

Elaine McVey
Differentiating by data science

Differentiating by data science

Eric Colson
How I Learned to Stop Worrying and Love the Firewall

How I Learned to Stop Worrying and Love the Firewall

Ian Lyttle
Imagine Boston 2030: Using R-Shiny to keep ourselves accountable and empower the public

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

Kayla Patel
Storytelling with R

Storytelling with R

Olga Pierce
Teach the Tidyverse to beginners

Teach the Tidyverse to beginners

David Robinson
Accelerating cancer research with R

Accelerating cancer research with R

Sandra Griffith
Developing and deploying large scale shiny applications

Developing and deploying large scale shiny applications

Herman Sontrop
Open-source solutions for medical marijuana

Open-source solutions for medical marijuana

Carl Ganz
Phrasing: Communicating data science through tweets, gifs, and classic misdirection

Phrasing: Communicating data science through tweets, gifs, and classic misdirection

Mara Averick
Rapid prototyping data products using Shiny

Rapid prototyping data products using Shiny

Tanya Cashorali
The unreasonable effectiveness of empathy

The unreasonable effectiveness of empathy

JD Long
Understanding PCA using Shiny and Stack Overflow data

Understanding PCA using Shiny and Stack Overflow data

Julia Silge

Integrated Development Environment

Learning R with rstudio.cloud

Learning R with rstudio.cloud

Mel Gregory
RStudio 1.1 new features

RStudio 1.1 new features

Kevin Ushey
RStudio Server Pro 1.1 new features

RStudio Server Pro 1.1 new features

Jonathan McPherson

Packages

Contributing to Tidyverse packages

Contributing to Tidyverse packages

Mara Averick
Five packages in five weeks – from boredom to contribution via blogging

Five packages in five weeks – from boredom to contribution via blogging

Giora Simchoni
What makes a great R package?

What makes a great R package?

Joseph Rickert
You can make a package in 20 minutes

You can make a package in 20 minutes

Jim Hester

Programming

Tidy eval - programming with dplyr, tidyr, and ggplot2

Tidy eval - programming with dplyr, tidyr, and ggplot2

Hadley Wickham
An assignment operator to unpack vectors and lists

An assignment operator to unpack vectors and lists

Nathan Teetor
Data rectangling

Data rectangling

Jenny Bryan
Debugging techniques in RStudio

Debugging techniques in RStudio

Amanda Gadrow

Teaching

Data-driven product development

Data-driven product development

Ramnath Vaidyanathan
Kaggle in the classroom: using R and GitHub to run predictive modeling competitions

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!)

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

Chester Ismay
Training an army of new data scientists

Training an army of new data scientists

Marco Blume