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Activity Number: 167 - Statistical Computing and Statistical Graphics: Student Paper Award and Chambers Statistical Software Award
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #329348
Title: Liftr: An R Package for Persistent Reproducible Research
Author(s): Nan Xiao*
Companies: Central South University
Keywords: liftr ; R Markdown ; Docker ; Containerization ; R package ; John M. Chambers Statistical Software Award

The R package liftr aims to solve the problem of persistent reproducible reporting in statistical computing. It is one of the winners of the 2018 John M. Chambers Statistical Software Award. The R Markdown format and its backend compilation engine knitr offer a de facto standard for creating dynamic documents. However, the reproducibility of such computing environments is often limited to individual machines. It is not easy to replicate the system environment (libraries, R versions, R packages) where the document was compiled. By introducing Docker, the open source containerization technology, liftr solves this reproducibility problem. With the help of liftr, R Markdown users can quickly create and manage Docker containers for rendering their documents, thus making the computations utterly reproducible across machines and systems. liftr redefined the meaning of reproducible research by offering system-level reproducibility for data analysis for the first time and made it easier to create large-scale dynamic document building services. We will discuss the design philosophy, implementation, and applications of the liftr package.

Authors who are presenting talks have a * after their name.

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