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small fixes

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dr. M.S. (Matthijs) Berends 2022-08-29 09:35:36 +02:00
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24 changed files with 590 additions and 62 deletions
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2
.github/workflows/check.yaml vendored
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@ -57,7 +57,7 @@ jobs:
- {os: ubuntu-22.04, r: '4.1', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '4.1', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '4.0', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '4.0', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '3.6', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '3.6', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '3.5', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '3.5', allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '3.4', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '3.4', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '3.3', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '3.3', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
- {os: ubuntu-22.04, r: '3.2', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"} - {os: ubuntu-22.04, r: '3.2', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}

4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR Package: AMR
Version: 1.8.1.9045 Version: 1.8.1.9046
Date: 2022-08-28 Date: 2022-08-29
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

2
NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.8.1.9045 # AMR 1.8.1.9046
### New ### New
* EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations. * EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.

22
R/data.R
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@ -27,7 +27,7 @@
#' #'
#' 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. #' 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.
#' @format #' @format
#' ## For the [antibiotics] data set: a [tibble[tibble::tibble] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables: #' ## For the [antibiotics] data set: a [tibble][tibble::tibble] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
#' - `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 #' - `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
#' - `cid`\cr Compound ID as found in PubChem #' - `cid`\cr Compound ID as found in PubChem
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO #' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO
@ -43,7 +43,7 @@
#' - `iv_units`\cr Units of `iv_ddd` #' - `iv_units`\cr Units of `iv_ddd`
#' - `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()]. #' - `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()].
#' #'
#' ## For the [antivirals] data set: a [tibble[tibble::tibble] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables: #' ## For the [antivirals] data set: a [tibble][tibble::tibble] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
#' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC #' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
#' - `cid`\cr Compound ID as found in PubChem #' - `cid`\cr Compound ID as found in PubChem
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO #' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO
@ -76,7 +76,7 @@
#' #'
#' A data set containing the full microbial taxonomy (**last updated: `r CATALOGUE_OF_LIFE$yearmonth_LPSN`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()]. #' A data set containing the full microbial taxonomy (**last updated: `r CATALOGUE_OF_LIFE$yearmonth_LPSN`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
#' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection catalogue_of_life Catalogue of Life
#' @format A [tibble[tibble::tibble] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
#' - `mo`\cr ID of microorganism as used by this package #' - `mo`\cr ID of microorganism as used by this package
#' - `fullname`\cr Full name, like `"Escherichia coli"` #' - `fullname`\cr Full name, like `"Escherichia coli"`
#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism #' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism
@ -134,7 +134,7 @@
#' #'
#' 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()]. #' 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()].
#' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection catalogue_of_life Catalogue of Life
#' @format A [tibble[tibble::tibble] with `r format(nrow(microorganisms.old), big.mark = ",")` observations and `r ncol(microorganisms.old)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.old), big.mark = ",")` observations and `r ncol(microorganisms.old)` variables:
#' - `fullname`\cr Old full taxonomic name of the microorganism #' - `fullname`\cr Old full taxonomic name of the microorganism
#' - `fullname_new`\cr New full taxonomic name of the microorganism #' - `fullname_new`\cr New full taxonomic name of the microorganism
#' - `ref`\cr Author(s) and year of concerning scientific publication #' - `ref`\cr Author(s) and year of concerning scientific publication
@ -152,7 +152,7 @@
#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes #' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
#' #'
#' 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. #' 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.
#' @format A [tibble[tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables:
#' - `code`\cr Commonly used code of a microorganism #' - `code`\cr Commonly used code of a microorganism
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set #' - `mo`\cr ID of the microorganism in the [microorganisms] data set
#' @details #' @details
@ -166,7 +166,7 @@
#' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates #' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates
#' #'
#' 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). #' 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).
#' @format A [tibble[tibble::tibble] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` variables:
#' - `date`\cr Date of receipt at the laboratory #' - `date`\cr Date of receipt at the laboratory
#' - `patient`\cr ID of the patient #' - `patient`\cr ID of the patient
#' - `age`\cr Age of the patient #' - `age`\cr Age of the patient
@ -183,7 +183,7 @@
#' Data Set with Unclean Data #' Data Set with Unclean Data
#' #'
#' 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. #' 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.
#' @format A [tibble[tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables:
#' - `patient_id`\cr ID of the patient #' - `patient_id`\cr ID of the patient
#' - `date`\cr date of receipt at the laboratory #' - `date`\cr date of receipt at the laboratory
#' - `hospital`\cr ID of the hospital, from A to C #' - `hospital`\cr ID of the hospital, from A to C
@ -198,7 +198,7 @@
#' Data Set with `r format(nrow(WHONET), big.mark = ",")` Isolates - WHONET Example #' Data Set with `r format(nrow(WHONET), big.mark = ",")` Isolates - WHONET Example
#' #'
#' 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. #' 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.
#' @format A [tibble[tibble::tibble] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables:
#' - `Identification number`\cr ID of the sample #' - `Identification number`\cr ID of the sample
#' - `Specimen number`\cr ID of the specimen #' - `Specimen number`\cr ID of the specimen
#' - `Organism`\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using [as.mo()]. #' - `Organism`\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using [as.mo()].
@ -234,7 +234,7 @@
#' Data Set for R/SI Interpretation #' Data Set for R/SI Interpretation
#' #'
#' Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are 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)))`) and 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)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values. #' Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are 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)))`) and 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)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
#' @format A [tibble[tibble::tibble] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables:
#' - `guideline`\cr Name of the guideline #' - `guideline`\cr Name of the guideline
#' - `method`\cr Either `r vector_or(rsi_translation$method)` #' - `method`\cr Either `r vector_or(rsi_translation$method)`
#' - `site`\cr Body site, e.g. "Oral" or "Respiratory" #' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
@ -258,7 +258,7 @@
#' Data Set with Bacterial Intrinsic Resistance #' Data Set with Bacterial Intrinsic Resistance
#' #'
#' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations. #' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
#' @format A [tibble[tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables:
#' - `mo`\cr Microorganism ID #' - `mo`\cr Microorganism ID
#' - `ab`\cr Antibiotic ID #' - `ab`\cr Antibiotic ID
#' @details #' @details
@ -275,7 +275,7 @@
#' Data Set with Treatment Dosages as Defined by EUCAST #' Data Set with Treatment Dosages as Defined by EUCAST
#' #'
#' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()]. #' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()].
#' @format A [tibble[tibble::tibble] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables:
#' - `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 #' - `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
#' - `name`\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO #' - `name`\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO
#' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)` #' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)`

16
R/guess_ab_col.R
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@ -30,7 +30,7 @@
#' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x` #' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x`
#' @param verbose a [logical] to indicate whether additional info should be printed #' @param verbose a [logical] to indicate whether additional info should be printed
#' @param only_rsi_columns a [logical] to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`) #' @param only_rsi_columns a [logical] to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
#' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.** #' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic.
#' @return A column name of `x`, or `NULL` when no result is found. #' @return A column name of `x`, or `NULL` when no result is found.
#' @export #' @export
#' @examples #' @examples
@ -40,13 +40,10 @@
#' ) #' )
#' #'
#' guess_ab_col(df, "amoxicillin") #' guess_ab_col(df, "amoxicillin")
#' # [1] "amox"
#' guess_ab_col(df, "J01AA07") # ATC code of tetracycline #' guess_ab_col(df, "J01AA07") # ATC code of tetracycline
#' # [1] "tetr"
#' #'
#' guess_ab_col(df, "J01AA07", verbose = TRUE) #' guess_ab_col(df, "J01AA07", verbose = TRUE)
#' # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline). #' # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
#' # [1] "tetr"
#' #'
#' # WHONET codes #' # WHONET codes
#' df <- data.frame( #' df <- data.frame(
@ -54,19 +51,8 @@
#' AMC_ED20 = "S" #' AMC_ED20 = "S"
#' ) #' )
#' guess_ab_col(df, "ampicillin") #' guess_ab_col(df, "ampicillin")
#' # [1] "AMP_ND10"
#' guess_ab_col(df, "J01CR02") #' guess_ab_col(df, "J01CR02")
#' # [1] "AMC_ED20"
#' guess_ab_col(df, as.ab("augmentin")) #' guess_ab_col(df, as.ab("augmentin"))
#' # [1] "AMC_ED20"
#'
#' # Longer names take precendence:
#' df <- data.frame(
#' AMP_ED2 = "S",
#' AMP_ED20 = "S"
#' )
#' guess_ab_col(df, "ampicillin")
#' # [1] "AMP_ED20"
guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_rsi_columns = FALSE) { guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_rsi_columns = FALSE) {
meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE) meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE) meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE)

4
R/plot.R
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@ -65,7 +65,11 @@
#' \donttest{ #' \donttest{
#' if (require("ggplot2")) { #' if (require("ggplot2")) {
#' autoplot(some_mic_values) #' autoplot(some_mic_values)
#' }
#' if (require("ggplot2")) {
#' autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro") #' autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
#' }
#' if (require("ggplot2")) {
#' autoplot(some_rsi_values) #' autoplot(some_rsi_values)
#' } #' }
#' } #' }

2
_pkgdown.yml
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@ -54,7 +54,7 @@ footer:
right: [logo] right: [logo]
components: components:
devtext: '<code>AMR</code> (for R). Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations<br><a target="_blank" href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a>.' devtext: '<code>AMR</code> (for R). Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations<br><a target="_blank" href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a>.'
logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.png" style="max-width: 200px;"></a>' logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.svg" style="max-width: 200px;"></a>'
home: home:
sidebar: sidebar:

10
index.md
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@ -175,11 +175,11 @@ To find out how to conduct AMR data analysis, please [continue reading here to g
The development of this package is part of, related to, or made possible by: The development of this package is part of, related to, or made possible by:
<div align="center"> <div align="center">
<a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a> <a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.svg" style="max-width: 200px;"></a>
<a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a> <a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" style="max-width: 200px;"></a>
<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" class="partner_logo"></a> <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" style="max-width: 200px;"></a>
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</div> </div>
### Copyright ### Copyright

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\alias{WHONET} \alias{WHONET}
\title{Data Set with 500 Isolates - WHONET Example} \title{Data Set with 500 Isolates - WHONET Example}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 500 observations and 53 variables: A \link[tibble:tibble]{tibble} with 500 observations and 53 variables:
\itemize{ \itemize{
\item \verb{Identification number}\cr ID of the sample \item \verb{Identification number}\cr ID of the sample
\item \verb{Specimen number}\cr ID of the specimen \item \verb{Specimen number}\cr ID of the specimen

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\alias{antivirals} \alias{antivirals}
\title{Data Sets with 566 Antimicrobial Drugs} \title{Data Sets with 566 Antimicrobial Drugs}
\format{ \format{
\subsection{For the \link{antibiotics} data set: a [tibble\link[tibble:tibble]{tibble::tibble} with 464 observations and 14 variables:}{ \subsection{For the \link{antibiotics} data set: a \link[tibble:tibble]{tibble} with 464 observations and 14 variables:}{
\itemize{ \itemize{
\item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available \item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
\item \code{cid}\cr Compound ID as found in PubChem \item \code{cid}\cr Compound ID as found in PubChem
@ -25,7 +25,7 @@
} }
} }
\subsection{For the \link{antivirals} data set: a [tibble\link[tibble:tibble]{tibble::tibble} with 102 observations and 9 variables:}{ \subsection{For the \link{antivirals} data set: a \link[tibble:tibble]{tibble} with 102 observations and 9 variables:}{
\itemize{ \itemize{
\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC \item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
\item \code{cid}\cr Compound ID as found in PubChem \item \code{cid}\cr Compound ID as found in PubChem

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man/dosage.Rd
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\alias{dosage} \alias{dosage}
\title{Data Set with Treatment Dosages as Defined by EUCAST} \title{Data Set with Treatment Dosages as Defined by EUCAST}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 169 observations and 9 variables: A \link[tibble:tibble]{tibble} with 169 observations and 9 variables:
\itemize{ \itemize{
\item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available \item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
\item \code{name}\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO \item \code{name}\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO

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man/example_isolates.Rd
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@ -5,7 +5,7 @@
\alias{example_isolates} \alias{example_isolates}
\title{Data Set with 2,000 Example Isolates} \title{Data Set with 2,000 Example Isolates}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 2,000 observations and 46 variables: A \link[tibble:tibble]{tibble} with 2,000 observations and 46 variables:
\itemize{ \itemize{
\item \code{date}\cr Date of receipt at the laboratory \item \code{date}\cr Date of receipt at the laboratory
\item \code{patient}\cr ID of the patient \item \code{patient}\cr ID of the patient

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man/example_isolates_unclean.Rd
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\alias{example_isolates_unclean} \alias{example_isolates_unclean}
\title{Data Set with Unclean Data} \title{Data Set with Unclean Data}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 3,000 observations and 8 variables: A \link[tibble:tibble]{tibble} with 3,000 observations and 8 variables:
\itemize{ \itemize{
\item \code{patient_id}\cr ID of the patient \item \code{patient_id}\cr ID of the patient
\item \code{date}\cr date of receipt at the laboratory \item \code{date}\cr date of receipt at the laboratory

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man/guess_ab_col.Rd
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@ -27,7 +27,7 @@ A column name of \code{x}, or \code{NULL} when no result is found.
This tries to find a column name in a data set based on information from the \link{antibiotics} data set. Also supports WHONET abbreviations. This tries to find a column name in a data set based on information from the \link{antibiotics} data set. Also supports WHONET abbreviations.
} }
\details{ \details{
You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \link{antibiotics} data set for any column containing a name or code of that antibiotic. \strong{Longer columns names take precedence over shorter column names.} You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \link{antibiotics} data set for any column containing a name or code of that antibiotic.
} }
\examples{ \examples{
df <- data.frame( df <- data.frame(
@ -36,13 +36,10 @@ df <- data.frame(
) )
guess_ab_col(df, "amoxicillin") guess_ab_col(df, "amoxicillin")
# [1] "amox"
guess_ab_col(df, "J01AA07") # ATC code of tetracycline guess_ab_col(df, "J01AA07") # ATC code of tetracycline
# [1] "tetr"
guess_ab_col(df, "J01AA07", verbose = TRUE) guess_ab_col(df, "J01AA07", verbose = TRUE)
# NOTE: Using column 'tetr' as input for J01AA07 (tetracycline). # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
# [1] "tetr"
# WHONET codes # WHONET codes
df <- data.frame( df <- data.frame(
@ -50,17 +47,6 @@ df <- data.frame(
AMC_ED20 = "S" AMC_ED20 = "S"
) )
guess_ab_col(df, "ampicillin") guess_ab_col(df, "ampicillin")
# [1] "AMP_ND10"
guess_ab_col(df, "J01CR02") guess_ab_col(df, "J01CR02")
# [1] "AMC_ED20"
guess_ab_col(df, as.ab("augmentin")) guess_ab_col(df, as.ab("augmentin"))
# [1] "AMC_ED20"
# Longer names take precendence:
df <- data.frame(
AMP_ED2 = "S",
AMP_ED20 = "S"
)
guess_ab_col(df, "ampicillin")
# [1] "AMP_ED20"
} }

2
man/intrinsic_resistant.Rd
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@ -5,7 +5,7 @@
\alias{intrinsic_resistant} \alias{intrinsic_resistant}
\title{Data Set with Bacterial Intrinsic Resistance} \title{Data Set with Bacterial Intrinsic Resistance}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 134,956 observations and 2 variables: A \link[tibble:tibble]{tibble} with 134,956 observations and 2 variables:
\itemize{ \itemize{
\item \code{mo}\cr Microorganism ID \item \code{mo}\cr Microorganism ID
\item \code{ab}\cr Antibiotic ID \item \code{ab}\cr Antibiotic ID

2
man/microorganisms.Rd
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@ -5,7 +5,7 @@
\alias{microorganisms} \alias{microorganisms}
\title{Data Set with 70,764 Microorganisms} \title{Data Set with 70,764 Microorganisms}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 70,764 observations and 16 variables: A \link[tibble:tibble]{tibble} with 70,764 observations and 16 variables:
\itemize{ \itemize{
\item \code{mo}\cr ID of microorganism as used by this package \item \code{mo}\cr ID of microorganism as used by this package
\item \code{fullname}\cr Full name, like \code{"Escherichia coli"} \item \code{fullname}\cr Full name, like \code{"Escherichia coli"}

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man/microorganisms.codes.Rd
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@ -5,7 +5,7 @@
\alias{microorganisms.codes} \alias{microorganisms.codes}
\title{Data Set with 5,604 Common Microorganism Codes} \title{Data Set with 5,604 Common Microorganism Codes}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 5,604 observations and 2 variables: A \link[tibble:tibble]{tibble} with 5,604 observations and 2 variables:
\itemize{ \itemize{
\item \code{code}\cr Commonly used code of a microorganism \item \code{code}\cr Commonly used code of a microorganism
\item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set \item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set

2
man/microorganisms.old.Rd
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@ -5,7 +5,7 @@
\alias{microorganisms.old} \alias{microorganisms.old}
\title{Data Set with Previously Accepted Taxonomic Names} \title{Data Set with Previously Accepted Taxonomic Names}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 14,338 observations and 4 variables: A \link[tibble:tibble]{tibble} with 14,338 observations and 4 variables:
\itemize{ \itemize{
\item \code{fullname}\cr Old full taxonomic name of the microorganism \item \code{fullname}\cr Old full taxonomic name of the microorganism
\item \code{fullname_new}\cr New full taxonomic name of the microorganism \item \code{fullname_new}\cr New full taxonomic name of the microorganism

4
man/plot.Rd
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@ -147,7 +147,11 @@ plot(some_disk_values, mo = "Escherichia coli", ab = "cipro", language = "uk")
\donttest{ \donttest{
if (require("ggplot2")) { if (require("ggplot2")) {
autoplot(some_mic_values) autoplot(some_mic_values)
}
if (require("ggplot2")) {
autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro") autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
}
if (require("ggplot2")) {
autoplot(some_rsi_values) autoplot(some_rsi_values)
} }
} }

2
man/rsi_translation.Rd
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@ -5,7 +5,7 @@
\alias{rsi_translation} \alias{rsi_translation}
\title{Data Set for R/SI Interpretation} \title{Data Set for R/SI Interpretation}
\format{ \format{
A [tibble\link[tibble:tibble]{tibble::tibble} with 20,369 observations and 11 variables: A \link[tibble:tibble]{tibble} with 20,369 observations and 11 variables:
\itemize{ \itemize{
\item \code{guideline}\cr Name of the guideline \item \code{guideline}\cr Name of the guideline
\item \code{method}\cr Either "DISK" or "MIC" \item \code{method}\cr Either "DISK" or "MIC"

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