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(v1.5.0.9006) major documentation update
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74
R/data.R
74
R/data.R
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# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Data sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = ",")` antimicrobials
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#' Data Sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = ",")` Antimicrobials
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#'
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#' Two data sets containing all antibiotics/antimycotics and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antibiotics] data set. Three identifiers are included in this data set: an antibiotic ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
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#' @format
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#' ### For the [antibiotics] data set: a [data.frame] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
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#' ## For the [antibiotics] data set: a [data.frame] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
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#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
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#' - `atc`\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like `J01CR02`
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#' - `cid`\cr Compound ID as found in PubChem
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@ -43,7 +43,7 @@
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#' - `iv_units`\cr Units of `iv_ddd`
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#' - `loinc`\cr All LOINC codes (Logical Observation Identifiers Names and Codes) associated with the name of the antimicrobial agent. Use [ab_loinc()] to retrieve them quickly, see [ab_property()].
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#'
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#' ### For the [antivirals] data set: a [data.frame] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
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#' ## For the [antivirals] data set: a [data.frame] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
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#' - `atc`\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC
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#' - `cid`\cr Compound ID as found in PubChem
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#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO
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@ -57,7 +57,7 @@
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#'
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#' Synonyms (i.e. trade names) are derived from the Compound ID (`cid`) and consequently only available where a CID is available.
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#'
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#' ### Direct download
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#' ## Direct download
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#' These data sets are available as 'flat files' for use even without \R - you can find the files here:
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#'
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#' * <https://github.com/msberends/AMR/raw/master/data-raw/antibiotics.txt>
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#' WHONET 2019 software: <http://www.whonet.org/software.html>
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#'
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#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <http://ec.europa.eu/health/documents/community-register/html/atc.htm>
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection WHOCC WHOCC
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Read more on Our Website!
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#' @seealso [microorganisms], [intrinsic_resistant]
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"antibiotics"
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#' @rdname antibiotics
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"antivirals"
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#' Data set with `r format(nrow(microorganisms), big.mark = ",")` microorganisms
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#' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
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#'
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#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using [as.mo()].
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#' @inheritSection catalogue_of_life Catalogue of Life
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#' - `rank`\cr Text of the taxonomic rank of the microorganism, like `"species"` or `"genus"`
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#' - `ref`\cr Author(s) and year of concerning scientific publication
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#' - `species_id`\cr ID of the species as used by the Catalogue of Life
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#' - `source`\cr Either "CoL", "DSMZ" (see Source) or "manually added"
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#' - `source`\cr Either "CoL", "DSMZ" (see *Source*) or "manually added"
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#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
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#' - `snomed`\cr SNOMED code of the microorganism. Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
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#' @details
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#'
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#' For example, *Staphylococcus pettenkoferi* was newly named in Diagnostic Microbiology and Infectious Disease in 2002 (PMID 12106949), but it was not before 2007 that a publication in IJSEM followed (PMID 17625191). Consequently, the AMR package returns 2007 for `mo_year("S. pettenkoferi")`.
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#'
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#' ### Manually additions
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#' ## Manually additions
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#' For convenience, some entries were added manually:
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#'
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#' - 11 entries of *Streptococcus* (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
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#' - 6 families under the Enterobacterales order, according to Adeolu *et al.* (2016, PMID 27620848), that are not (yet) in the Catalogue of Life
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#' - `r format(nrow(subset(microorganisms, source == "DSMZ")), big.mark = ",")` species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications
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#'
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#' ### Direct download
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#' ## Direct download
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#' This data set is available as 'flat file' for use even without \R - you can find the file here:
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#'
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#' * <https://github.com/msberends/AMR/raw/master/data-raw/microorganisms.txt>
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#' The file in \R format (with preserved data structure) can be found here:
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#'
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#' * <https://github.com/msberends/AMR/raw/master/data/microorganisms.rda>
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#' @section About the records from DSMZ (see source):
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#' @section About the Records from DSMZ (see *Source*):
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#' Names of prokaryotes are defined as being validly published by the International Code of Nomenclature of Bacteria. Validly published are all names which are included in the Approved Lists of Bacterial Names and the names subsequently published in the International Journal of Systematic Bacteriology (IJSB) and, from January 2000, in the International Journal of Systematic and Evolutionary Microbiology (IJSEM) as original articles or in the validation lists.
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#' *(from <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date>)*
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#'
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#' 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}
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#'
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#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date> and <https://lpsn.dsmz.de> (check included version with [catalogue_of_life_version()]).
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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#' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
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"microorganisms"
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yearmonth_DSMZ = "May 2020"
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)
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#' Data set with previously accepted taxonomic names
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#' Data Set with Previously Accepted Taxonomic Names
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#'
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#' 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()].
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#' @inheritSection catalogue_of_life Catalogue of Life
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#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
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#'
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#' 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}
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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#' @seealso [as.mo()] [mo_property()] [microorganisms]
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"microorganisms.old"
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#' Data set with `r format(nrow(microorganisms.codes), big.mark = ",")` common microorganism codes
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#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
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#'
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#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with [set_mo_source()]. They will all be searched when using [as.mo()] and consequently all the [`mo_*`][mo_property()] functions.
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#' @format A [data.frame] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables:
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#' - `code`\cr Commonly used code of a microorganism
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#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection catalogue_of_life Catalogue of Life
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Read more on Our Website!
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#' @seealso [as.mo()] [microorganisms]
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"microorganisms.codes"
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#' Data set with `r format(nrow(example_isolates), big.mark = ",")` example isolates
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#' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates
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#'
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#' A data set containing `r format(nrow(example_isolates), big.mark = ",")` microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
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#' @format A [data.frame] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` variables:
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#' - `patient_id`\cr ID of the patient
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#' - `mo`\cr ID of microorganism created with [as.mo()], see also [microorganisms]
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#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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"example_isolates"
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#' Data set with unclean data
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#' Data Set with Unclean Data
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#'
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#' A data set containing `r format(nrow(example_isolates_unclean), big.mark = ",")` microbial isolates that are not cleaned up and consequently not ready for AMR analysis. This data set can be used for practice.
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#' @format A [data.frame] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables:
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#' - `hospital`\cr ID of the hospital, from A to C
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#' - `bacteria`\cr info about microorganism that can be transformed with [as.mo()], see also [microorganisms]
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#' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.rsi()]
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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"example_isolates_unclean"
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#' Data set with `r format(nrow(WHONET), big.mark = ",")` isolates - WHONET example
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#' Data Set with `r format(nrow(WHONET), big.mark = ",")` Isolates - WHONET Example
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#'
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#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antibiotic results are from our [example_isolates] data set. All patient names are created using online surname generators and are only in place for practice purposes.
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#' @format A [data.frame] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables:
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#' - `Comment`\cr Other comments
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#' - `Date of data entry`\cr Date this data was entered in WHONET
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#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()].
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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"WHONET"
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#' Data set for R/SI interpretation
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#' Data Set for R/SI Interpretation
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#'
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#' Data set to interpret MIC and disk diffusion to R/SI values. Included guidelines are CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`) and EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
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#' @format A [data.frame] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables:
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#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
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#' - `uti`\cr A logical value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
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#' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically.
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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#' @seealso [intrinsic_resistant]
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"rsi_translation"
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#' Data set with bacterial intrinsic resistance
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#' Data Set with Bacterial Intrinsic Resistance
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#'
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#' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
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#' @format A [data.frame] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables:
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#' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/intrinsic_resistant.txt>. This file **allows for machine reading EUCAST guidelines about intrinsic resistance**, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.
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#'
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#' This data set is based on `r format_eucast_version_nr(3.2)`.
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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#' @examples
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#' if (require("dplyr")) {
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#' intrinsic_resistant %>%
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#' }
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"intrinsic_resistant"
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#' Data set with treatment dosages as defined by EUCAST
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#' Data Set with Treatment Dosages as Defined by EUCAST
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#'
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#' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()].
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#' @format A [data.frame] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables:
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#' - `original_txt`\cr Original text in the PDF file of EUCAST
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#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply
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#' @details `r format_eucast_version_nr(11.0)` are based on the dosages in this data set.
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#' @inheritSection AMR Reference data publicly available
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#' @inheritSection AMR Read more on our website!
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#' @inheritSection AMR Reference Data Publicly Available
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#' @inheritSection AMR Read more on Our Website!
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"dosage"
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