Datatrack: An R package for managing data in a multi-stage experimental workflow

In experimental research using computation, a workflow is a sequence of steps involving some data processing or analysis where the output of one step may be used as the input of another. The processing steps may involve user-supplied parameters, that when modified, result in a new version of input to the downstream steps, in turn generating new versions of their own output. As more experimentation is done, the results of these various steps can become numerous. It is important to keep track of which data output is dependent on which other generated data, and which parameters were used. In many situations, scientific workflow management systems solve this problem, but these systems are best suited to collaborative, distributed experiments using a variety of services, possibly batch processing parameter sweeps. This paper presents an R package for managing and navigating a network of interdependent data. It is intended as a lightweight tool that provides some visual data provenance information to the experimenter to allow them to manage their generated data as they run experiments within their familiar scripting environment, where it may not be desirable to commit to a fully-blown comprehensive workflow manager. The package consists of wrapper functions for writing and reading output data that can be called from within the R analysis scripts, as well as a visualization of the data-output dependency graph rendered within the R-studio console. Thus, it offers benefit to the experimenter while requiring minimal commitment for integration in their existing working environment.