Making R Go Faster and Bigger


Speaker Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. His writings on statistics can be found at His work with the Minnesota Vikings during the NHL draft was recently featured in the Wall Street Journal.

Abstract: The features of R that make it easy to use--dynamically typed, in-memory analysis, the interpreter enginge and REPL--can also slow it down. Fortunately, the R Core Team has made dramatic improvements in recent years with better memory management and faster interpretation of code. We look at some of the tools that take advantage of the improved memory management and seamless integration of C++ code such as doParallel, dplyr, data.table and Rcpp. Thanks to packages writing performant R code has never been easier.