This event has ended. Visit the official site or create your own event on Sched.

user2016

Click here to return to main conference site. For a one page, printable overview of the schedule, see this.

RL

Rebecca Louise Barter

University of California, Berkeley

9:00am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 1) Econ 140

10:30am PDT

Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 2) Econ 140

1:00pm PDT

Introduction to SparkR (Part 1) Econ 140

2:30pm PDT

Introduction to SparkR (Part 2) Econ 140

8:30am PDT

Opening Session McCaw Hall

9:00am PDT

Forty years of S McCaw Hall

10:30am PDT

Using Spark with Shiny and R Markdown SIEPR 130

10:48am PDT

RcppParallel: A Toolkit for Portable, High-Performance Algorithms SIEPR 130

11:06am PDT

Taking R to new heights for scalability and performance SIEPR 130

11:24am PDT

Distributed Computing using parallel, Distributed R, and SparkR SIEPR 130

11:42am PDT

FlashR: Enable Parallel, Scalable Data Analysis in R SIEPR 130

12:00pm PDT

Lunch with Poster Exhibits Sponsor Pavilion

1:00pm PDT

Linking htmlwidgets with crosstalk and mobservable McCaw Hall

1:18pm PDT

Transforming a museum to be data-driven using R McCaw Hall

2:12pm PDT

FiveThirtyEight's data journalism workflow with R McCaw Hall

3:30pm PDT

Literate Programming McCaw Hall

4:45pm PDT

flexdashboard: Easy interactive dashboards for R Lane & Lyons & Lodato

5:03pm PDT

New Paradigms In Shiny App Development: Designer + Data Scientist Pairing Lane & Lyons & Lodato

5:21pm PDT

Data validation infrastructure: the validate package Barnes & McDowell & Cranston

5:39pm PDT

R AnalyticFlow 3: Interactive Data Analysis GUI for R Lane & Lyons & Lodato

5:57pm PDT

Visual Pruner: A Shiny app for cohort selection in observational studies Lane & Lyons & Lodato

6:30pm PDT

Welcome Reception Bing Concert Hall

9:00am PDT

Towards a grammar of interactive graphics McCaw Hall

10:30am PDT

OPERA: Online Prediction by ExpeRts Aggregation Barnes & McDowell & Cranston

10:48am PDT

permuter: An R package for randomization inference Barnes & McDowell & Cranston

11:06am PDT

Calculation and economic evaluation of acceptance sampling plans Barnes & McDowell & Cranston

11:24am PDT

Heatmaps in R: Overview and best practices Barnes & McDowell & Cranston

11:42am PDT

Phylogenetically informed analysis of microbiome data using adaptive gPCA in R Barnes & McDowell & Cranston

1:00pm PDT

R at Google Lane & Lyons & Lodato

1:20pm PDT

How Teradata Aster R Scales Data Science Lane & Lyons & Lodato

1:30pm PDT

Statistical Computing with R Lane & Lyons & Lodato

1:40pm PDT

Changing lives with Data Science at Microsoft Lane & Lyons & Lodato

1:55pm PDT

Bringing the Power of R to Citizen Data Scientists Lane & Lyons & Lodato

2:05pm PDT

Making the R community more open Lane & Lyons & Lodato

3:30pm PDT

Flexible and Interpretable Regression Using Convex Penalties McCaw Hall

9:00am PDT

Statistical Thinking in a Data Science Course McCaw Hall

10:30am PDT

Gradient Boosted Trees Model: deploying R models into production environments* Lane & Lyons & Lodato

10:50am PDT

ranger: A fast implementation of random forests for high dimensional data Lane & Lyons & Lodato

11:08am PDT

Superheat: Supervised heatmaps for visualizing complex data Lane & Lyons & Lodato

1:00pm PDT

Catching up with Rstudio Lane & Lyons & Lodato

1:20pm PDT

R for Big Data and Applications: Using R at Oracle Lane & Lyons & Lodato

1:35pm PDT

Scalable Machine Learning in R with H2O Lane & Lyons & Lodato

2:00pm PDT

RCloud - Collaborative Environment for Visualization and Big Data Analytics McCaw Hall

3:00pm PDT

Closing Remarks McCaw Hall