Tidy Data Neatly Resolves Mass-Spectrometry’s Ragged Arrays

Mass spectrometry (MS) is a powerful tool for measuring biomolecules, but the data produced is often difficult to handle computationally because it is stored as a ragged array. In R, this format is typically encoded in complex S4 objects built around environments, requiring an extensive background in R to perform even simple tasks. However, the adoption of tidy data (Wickham 2014) provides an alternate data structure that is highly intuitive and works neatly with base R functions and common packages, as well as other programming languages. Here, we discuss the current state of R-based MS data processing, the convenience and challenges of integrating tidy data techniques into MS data processing, and present RaMS, a package that produces tidy representations of MS data.

William Kumler (University of Washington School of Oceanography) , Anitra E. Ingalls (University of Washington School of Oceanography)

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-050.zip

H. Wickham. Tidy data. Journal of Statistical Software, 59(10): 1–23, 2014. URL https://doi.org/10.18637/jss.v059.i10.



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For attribution, please cite this work as

Kumler & Ingalls, "The R Journal: Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays", The R Journal, 2022

BibTeX citation

  author = {Kumler, William and Ingalls, Anitra E.},
  title = {The R Journal: Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays},
  journal = {The R Journal},
  year = {2022},
  note = {https://doi.org/10.32614/RJ-2022-050},
  doi = {10.32614/RJ-2022-050},
  volume = {14},
  issue = {3},
  issn = {2073-4859},
  pages = {193-202}