We're excited to host Jared Lander, Chief Data Scientist of Lander Analytics, the organizer of the New York Open Statistical Programming Meetup & the New York R Conference, & author of R for Everyone, to talk about parallel computing in R.
Agenda:6:30-7: Food & networking7-7:10: Kick-off & announcements7:10-7:45: Talk7:45-8:30: Networking
Talk:Everyone wants their code to run faster & there are numerous ways to achieve this goal. We start by looking at popular packages dplyr, data.table & purrr & the corresponding parallel implementations. We then turn our attention to writing simple C++ functions integrated into R, both sequentially & in parallel. We also build a data.frame aggregation function, starting sequentially, ending in parallel. Throughout this talk we see how to speed up code by running in parallel, locally & across nodes, in R & C++, all within the friendly confines of RStudio.
Jared Lander 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 & the New York R Conference & an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics & a bachelors from Muhlenberg College in mathematics, he has experience in both academic research & industry. His work for both large & small organizations ranges from music & fund raising to finance & humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management & statistical computing. He is the author of R for Everyone: Advanced Analytics & Graphics, a book about R Programming geared toward Data Scientists & Non-Statisticians alike.