"Official" May 2019 BARUG Meetup

Dear Attendees:
Building security requires your full name for check in prior to the event. Upon check in, you will also be asked to sign an NDA.

Agenda:
6:30: Food, drink and Networking
7:00: Welcome to Instacart
7:05: Announcements
7:10: Robert Horton: (Lightning talk): Finding the acronym definitions in unstructured text
7:25: Jingjie Xiao: A/B testing for logistics with R
7:50: Peter Li (Lightning Talk) keeping score (:-/) with 'packageRank
8:05: Steve Dahlke: Applied Optimization in R - Energy Markets and Policy

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Robert Horton
Finding the acronym definitions in unstructured text

This will be an introductory presentation focusing on how to use parentheses and backreferences in regular expressions. I programmatically construct a set of regular expressions matching acronym definitions of different lengths, and collect all the results into a table.

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Jingjie Xiao, Senior Data Scientist, Instacart

A/B testing for logistics with R

Instacart's fulfillment dispatching engine is constantly changing—storms, unexpected traffic, shift cancellations, and more can all affect a system that is dynamic, interdependent, and noisy. In this talk, Instacart Senior Data Scientist, Jingjie Xiao will walk you through how controlled experiments and multivariate regression are used to continuously improve the grocery delivery engine @ Instacart.

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Peter Li
keeping score (:-/) with 'packageRank

Building on the efforts of the 'cranlogs' package, 'packageRank' puts the raw counts of package downloads into greater perspective. Numerically, it computes a package's rank percentile among all downloads. Visually, it locates a package' position in the distribution of downloads. Currently, these snapshot are available for individual days (cross-sectionally), and in more limited fashion over time (longitudinally). Along the way, I'll show how I use the 'memoise' package to cache the downloading of log files.

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Steve Dahlke is PhD candidate in applied economics enrolled at the Colorado School of Mines

Applied Optimization in R - Energy Markets and Policy

Steve is building an electricity market model to study the impact
of carbon policy on the U.S. electricity sector. In this talk he will explain the method for setting up an optimization model in R to study a particular energy policy and show how to use the LPSolve package: to solve it.