Using Introspection to Collect Provenance in R
1
Computer Science Department, Mount Holyoke College, South Hadley, MA 01075, USA
2
Harvard Forest, Harvard University, Petersham, MA 01366, USA
3
Harvard College, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
†
Current address: Google Inc., Mountain View, CA 94043, USA.
Informatics 2018, 5(1), 12; https://doi.org/10.3390/informatics5010012
Received: 1 December 2017 / Revised: 25 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
(This article belongs to the Special Issue Using Computational Provenance)
Data provenance is the history of an item of data from the point of its creation to its present state. It can support science by improving understanding of and confidence in data. RDataTracker is an R package that collects data provenance from R scripts (https://github.com/End-to-end-provenance/RDataTracker). In addition to details on inputs, outputs, and the computing environment collected by most provenance tools, RDataTracker also records a detailed execution trace and intermediate data values. It does this using R’s powerful introspection functions and by parsing R statements prior to sending them to the interpreter so it knows what provenance to collect. The provenance is stored in a specialized graph structure called a Data Derivation Graph, which makes it possible to determine exactly how an output value is computed or how an input value is used. In this paper, we provide details about the provenance RDataTracker collects and the mechanisms used to collect it. We also speculate about how this rich source of information could be used by other tools to help an R programmer gain a deeper understanding of the software used and to support reproducibility.
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MDPI and ACS Style
Lerner, B.; Boose, E.; Perez, L. Using Introspection to Collect Provenance in R. Informatics 2018, 5, 12.
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Barbara Lerner