Making R Go Bigger and Faster by Jared Lander



The features of R that make it easy to use--dynamically typed, in-memory analysis, the interpreter engine 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.

About the speaker:

Jared Lander ( (LinkedIn ( is the Chief Data Scientist of Lander Analytics ( a data science consultancy based in New York City, 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. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.

Jared specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics (, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Additionally, there will be free pizza and drinks.