This paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the errors package, which provides a class for associating uncertainty metadata, automated propagation and reporting. Working with errors enables transparent, lightweight, less error-prone handling and convenient representation of measurements with errors. Finally, we discuss the advantages, limitations and future work of computing with errors.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-075.zip
units, errors, car, msm, metRology, propagate, spup, distr, distrEllipse, distrEx, distrMod, distrRmetrics, distrSim, distrTeach, magrittr, ggplot2, tibble
Distributions, ChemPhys, Econometrics, Finance, Graphics, Multivariate, Phylogenetics, Robust, SocialSciences, Survival, WebTechnologies
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For attribution, please cite this work as
Ucar, et al., "The R Journal: Measurement Errors in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-075,
author = {Ucar, Iñaki and Pebesma, Edzer and Azcorra, Arturo},
title = {The R Journal: Measurement Errors in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-075},
doi = {10.32614/RJ-2018-075},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {549-557}
}