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bring back antibiogram(), without deps
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30
R/data.R
30
R/data.R
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# how to conduct AMR data 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 = ",")` Antimicrobial Drugs
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#' Data Sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = " ")` Antimicrobial Drugs
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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. Note that some drugs have multiple ATC codes.
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#' @format
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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 full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()].
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#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
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#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables:
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#' - `mo`\cr ID of microorganism as used by this package
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#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
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#' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status)`
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@ -150,10 +150,10 @@
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#' microorganisms
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"microorganisms"
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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 [tibble][tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables:
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#' @format A [tibble][tibble::tibble] 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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#' @details
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@ -163,10 +163,10 @@
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#' microorganisms.codes
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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. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
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#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` variables:
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#' A data set containing `r format(nrow(example_isolates), big.mark = " ")` microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
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#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates), big.mark = " ")` observations and `r ncol(example_isolates)` variables:
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#' - `date`\cr Date of receipt at the laboratory
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#' - `patient`\cr ID of the patient
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#' - `age`\cr Age of the patient
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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 data analysis. This data set can be used for practice.
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#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables:
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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 data analysis. This data set can be used for practice.
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#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = " ")` observations and `r ncol(example_isolates_unclean)` variables:
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#' - `patient_id`\cr ID of the patient
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#' - `date`\cr date of receipt at the laboratory
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#' - `hospital`\cr ID of the hospital, from A to C
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@ -195,10 +195,10 @@
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#' example_isolates_unclean
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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 [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables:
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#' @format A [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = " ")` observations and `r ncol(WHONET)` variables:
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#' - `Identification number`\cr ID of the sample
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#' - `Specimen number`\cr ID of the specimen
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#' - `Organism`\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using [as.mo()].
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@ -234,7 +234,7 @@
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#' Data Set with Clinical Breakpoints for SIR Interpretation
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#'
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#' Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. Currently implemented guidelines are EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`). Use [as.sir()] to transform MICs or disks measurements to SIR values.
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#' @format A [tibble][tibble::tibble] with `r format(nrow(clinical_breakpoints), big.mark = ",")` observations and `r ncol(clinical_breakpoints)` variables:
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#' @format A [tibble][tibble::tibble] with `r format(nrow(clinical_breakpoints), big.mark = " ")` observations and `r ncol(clinical_breakpoints)` variables:
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#' - `guideline`\cr Name of the guideline
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#' - `method`\cr Either `r vector_or(clinical_breakpoints$method)`
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#' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
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@ -258,7 +258,7 @@
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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 [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables:
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#' @format A [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = " ")` observations and `r ncol(intrinsic_resistant)` variables:
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#' - `mo`\cr Microorganism ID
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#' - `ab`\cr Antibiotic ID
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#' @details
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@ -275,7 +275,7 @@
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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 [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables:
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#' @format A [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = " ")` observations and `r ncol(dosage)` 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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#' - `name`\cr Official name of the antimicrobial drug as used by WHONET/EARS-Net or the WHO
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#' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)`
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