The dplyr package within R provides a flexible and powerful syntax for
data wrangling operations. However, data objects within R are typically
stored in memory and performance issues may arise as data become large.
Database management systems implementing SQL (structured query language)
provide a ubiquitous architecture for storing and querying data that is
relational in nature. While there has been support for data retrieval in
R from relational databases such as MySQL, SQLite, and PostgreSQL,
recent advances that have added interfaces between R and SQL enable
users to seamlessly leverage storage and retrieval mechanisms while
remaining within R. In this webinar, we will review key idioms for data
wrangling within dplyr, introduce the backend interfaces for common
database systems, provide examples of ways that the dplyr engine
translates a data pipeline, and discuss common misconceptions and
performance issues.
Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and Modern Data Science with R, and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research.
Nicholas Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College, with methodologic research interests in longitudinal regression models, missing data methods, and statistical computing. He graduated from the Harvard TH Chan School of Public Health in 1999. Nick has received the ASA’s Founders Award, the Waller Education Award, the William Warde Mu Sigma Rho Education Award, and the MAA Hogg Award for Excellence in Teaching. He has published more than 170 papers, co-authored a series of four books on statistical computing and data science, and was co-PI on the NSF funded MOSAIC project. Nick is a Fellow of the ASA and the AAAS, served as a member of the ASA Board, chaired the Committee of Presidents of Statistical Societies and is the ASA Section on Statistical Education. He is a member of the National Academies of Sciences Committee on Applied and Theoretical Statistics and two Academy groups focused on data science education.
This work was partially supported by NIH grant 1R15DC014129-03 (https://projectreporter.nih.gov/project_info_description.cfm?aid=8769352&icde=30039221&ddparam). More info can be found at: http://www.science.smith.edu/wai-database/
Last updated November 03, 2018