The first step of any data analysis is importing data, but for tables in a database this can be a surprisingly challenging step that takes analysts out of their workflow. The dbcooper package turns a database connection into a collection of accessor functions, letting users take advantage of autocomplete as they browse a database in the same ways they would engage with local tables. I'll walk through how dbcooper can be used to build a database-specific package, whether for an organization's internal use or to wrap a public data source. The dbcooper package supports both R (built on top of dbplyr) and Python (built on top of siuba), and in either language makes it easy and intuitive to extract insights from a database.
David Robinson is Director of Data Science at Heap Analytics, where he's helping to build the next generation of product analytics technology. He's the co-author with Julia Silge of the tidytext package and the O’Reilly book Text Mining with R. He also created the broom, fuzzyjoin, and widyr packages, and authored the e- book Introduction to Empirical Bayes. David is passionate about R, statistics, education, live-coding, probability, and his two children.
