A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by as.mo().
microorganisms.old
Format
A data.frame with 12,708 observations and 4 variables:
fullname
Old full taxonomic name of the microorganismfullname_new
New full taxonomic name of the microorganismref
Author(s) and year of concerning scientific publicationprevalence
Prevalence of the microorganism, seeas.mo()
Source
Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: 10.1099/ijsem.0.002786
Catalogue of Life
This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
Reference data publicly available
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.
Read more on our website!
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!