“Big-ish” Data in R: Efficient tools for large in-memory datasets

Between the small datasets of classical statistical analysis and the massive databases of distributed systems lies "big-ish" data: datasets that can be read directly into R on a personal computer, but that are large enough to make common data operations slow. This session highlights recent work in developing and testing R tools designed to speed up analysis of such large in-memory datasets, such as {arrow}, {data.table}, and {vroom}. We will share insights into the design, development, and maintenance of such tools; as well as examples of their use in real-world applications.

Yes

Section on Statistical Computing

Section for Statistical Programmers and Analysts

Speaker

Toby Hocking, Northern Arizona University

Speaker

Nic Crane, Voltron Data

Speaker

Tyson Barrett, Utah State University