Productionizing R scripts in the cloud

One of the greatest strength of R is the ease and speed of developing a prototype (let it be a report or dashboard, a statistical model or rule-based automation to solve a business problem etc), but deploying to production is not a broadly discussed topic — despite its importance. This hands-on talk focuses on best practices and actual R packages to help transforming the prototypes developed by eg a business analysts and data scientist into production jobs running in a secure environment that is easy to maintain — discussing the importance of logging, standardizing code style, source-code versioning, unit and integration tests, securing credentials, effective helper functions to connect to databases, open-source and SaaS job schedulers, dockerizing the run environment and scaling infrastructure.

Daróczi Gergely
Senior Director of Data Operations, System1

Gergely Daróczi is an enthusiast R user and package developer, Ph.D. in Sociology, former assistant professor and founder/CTO of an R-based web reporting application at, ex Lead R Developer & Research Data Scientist, then Director of Analytics at, currently working as the Senior Director of Data Operations at System1 with a strong interest in designing a scalable data platform built on the top of R, AWS and various APIs. He maintains CRAN packages mainly dealing with using R in production (automated reports, logging, database connections, API integrations), co-authored a number of journal articles in social and medical sciences, and wrote a book on “Mastering Data Analysis with R”.