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(v0.8.0.9033) antivirals data set, cleanup
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41
R/data.R
41
R/data.R
@ -34,7 +34,7 @@
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#' \item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)}
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#' \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
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#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
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#' \item{\code{oral_units}}{Units of \code{ddd_units}}
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#' \item{\code{oral_units}}{Units of \code{oral_ddd}}
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#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
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#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
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#' }
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@ -48,9 +48,28 @@
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#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
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#' @inheritSection WHOCC WHOCC
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#' @inheritSection AMR Read more on our website!
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#' @seealso \code{\link{microorganisms}}
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#' @seealso \code{\link{antivirals}} \code{\link{microorganisms}}
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"antibiotics"
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#' Data set with ~100 antivirals
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#'
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#' A data set containing all antivirals, according to the ATC code group 'J05' (Antivirals for systemic use).
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#' @format A \code{\link{data.frame}} with 102 observations and 7 variables:
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#' \describe{
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#' \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC}
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#' \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
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#' \item{\code{atc_group}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC}
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#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
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#' \item{\code{oral_units}}{Units of \code{oral_ddd}}
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#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
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#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
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#' }
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#' @source World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
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#' @inheritSection WHOCC WHOCC
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#' @inheritSection AMR Read more on our website!
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#' @seealso \code{\link{antibiotics}} \code{\link{microorganisms}}
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"antivirals"
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#' Data set with ~70,000 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 \code{\link{as.mo}}.
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@ -126,9 +145,9 @@ catalogue_of_life <- list(
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#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}}
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"microorganisms.codes"
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#' Data set with 2,000 blood culture isolates
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#' Data set with 2,000 example isolates
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#'
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#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found 4 different hospitals in the Netherlands, between 2001 and 2017. This \code{data.frame} can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
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#' A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}.
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#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables:
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#' \describe{
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#' \item{\code{date}}{date of receipt at the laboratory}
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@ -138,7 +157,7 @@ catalogue_of_life <- list(
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#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
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#' \item{\code{age}}{age of the patient}
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#' \item{\code{gender}}{gender of the patient}
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#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
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#' \item{\code{patient_id}}{ID of the patient}
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#' \item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
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#' \item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
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#' }
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@ -183,17 +202,17 @@ catalogue_of_life <- list(
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#' Data set for RSI interpretation
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#'
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#' Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link{as.rsi}} to transform MICs or disks measurements to RSI values.
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#' @format A \code{\link{data.frame}} with 11,559 observations and 9 variables:
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#' @format A \code{\link{data.frame}} with 13,975 observations and 9 variables:
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#' \describe{
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#' \item{\code{guideline}}{Name of the guideline}
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#' \item{\code{method}}{Either "MIC" or "DISK"}
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#' \item{\code{site}}{Body site, e.g. "Oral" or "Respiratory"}
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#' \item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}}
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#' \item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}}
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#' \item{\code{ref_tbl}}{Info about where the guideline rule can be found}
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#' \item{\code{S_mic}}{Lowest MIC value that leads to "S"}
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#' \item{\code{R_mic}}{Highest MIC value that leads to "R"}
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#' \item{\code{dose_disk}}{Dose of the used disk diffusion method}
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#' \item{\code{S_disk}}{Lowest number of millimeters that leads to "S"}
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#' \item{\code{R_disk}}{Highest number of millimeters that leads to "R"}
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#' \item{\code{disk_dose}}{Dose of the used disk diffusion method}
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#' \item{\code{breakpoint_S}}{Lowest MIC value or highest number of millimeters that leads to "S"}
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#' \item{\code{breakpoint_R}}{Highest MIC value or lowest number of millimeters that leads to "R"}
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#' }
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#' @inheritSection AMR Read more on our website!
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"rsi_translation"
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@ -163,6 +163,7 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
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#' }
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' \donttest{
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#' a <- data.frame(mo = c("Staphylococcus aureus",
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#' "Enterococcus faecalis",
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#' "Escherichia coli",
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@ -198,7 +199,6 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
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#' # 5 Pseudomonas aeruginosa R R - - R R R
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#'
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#'
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#' \donttest{
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#' # do not apply EUCAST rules, but rather get a data.frame
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#' # with 18 rows, containing all details about the transformations:
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#' c <- eucast_rules(a, verbose = TRUE)
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17
R/mdro.R
17
R/mdro.R
@ -54,29 +54,24 @@
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#' @rdname mdro
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#' @aliases MDR XDR PDR BRMO 3MRGN 4MRGN
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#' @importFrom dplyr %>% filter_at vars all_vars pull mutate_at
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#' @importFrom crayon blue bold italic
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#' @importFrom crayon blue bold italic red
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#' @importFrom cleaner percentage
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#' @export
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#' @inheritSection AMR Read more on our website!
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#' @source
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#' Please see Details for the list of publications used for this function.
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#' @examples
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#' \donttest{
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#' library(dplyr)
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#'
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#' example_isolates %>%
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#' mdro() %>%
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#' freq()
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#'
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#' \donttest{
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#' example_isolates %>%
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#' mutate(EUCAST = eucast_exceptional_phenotypes(.),
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#' BRMO = brmo(.),
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#' MRGN = mrgn(.))
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#'
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#' example_isolates %>%
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#' rename(PIP = TZP) %>% # no piperacillin, so take piperacillin/tazobactam
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#' mrgn() %>% # check German guideline
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#' freq() # check frequencies
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#' }
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mdro <- function(x,
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guideline = NULL,
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@ -155,7 +150,7 @@ mdro <- function(x,
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guideline$name <- "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance."
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guideline$author <- "Magiorakos AP, Srinivasan A, Carey RB, ..., Vatopoulos A, Weber JT, Monnet DL"
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guideline$version <- "N/A"
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guideline$source <- "Magiorakos et al. (2012) Clinical Microbiology and Infection 18:3. DOI: 10.1111/j.1469-0691.2011.03570.x"
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guideline$source <- "Clinical Microbiology and Infection 18:3, 2012. DOI: 10.1111/j.1469-0691.2011.03570.x"
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} else if (guideline$code == "eucast") {
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guideline$name <- "EUCAST Expert Rules, \"Intrinsic Resistance and Exceptional Phenotypes Tables\""
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@ -174,7 +169,7 @@ mdro <- function(x,
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guideline$name <- "Cross-border comparison of the Dutch and German guidelines on multidrug-resistant Gram-negative microorganisms"
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guideline$author <- "M\u00fcller J, Voss A, K\u00f6ck R, ..., Kern WV, Wendt C, Friedrich AW"
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guideline$version <- "N/A"
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guideline$source <- "M\u00fcller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6"
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guideline$source <- "Antimicrobial Resistance and Infection Control 4:7, 2015. DOI: 10.1186/s13756-015-0047-6"
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} else if (guideline$code == "brmo") {
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guideline$name <- "WIP-Richtlijn Bijzonder Resistente Micro-organismen (BRMO)"
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@ -422,9 +417,9 @@ mdro <- function(x,
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if (info == TRUE) {
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if (combine_SI == TRUE) {
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cat("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n")
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cat(red("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n"))
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} else {
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cat("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n")
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cat(red("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n"))
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}
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cat("\nDetermining multidrug-resistant organisms (MDRO), according to:\n",
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bold("Guideline: "), italic(guideline$name), "\n",
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22
R/mo.R
22
R/mo.R
@ -572,22 +572,21 @@ exec_as.mo <- function(x,
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# add start en stop regex
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x <- paste0("^", x, "$")
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x_withspaces_start_only <- paste0("^", x_withspaces)
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x_withspaces_end_only <- paste0(x_withspaces, "$")
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x_withspaces_start_end <- paste0("^", x_withspaces, "$")
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if (isTRUE(debug)) {
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cat(paste0(blue('x'), ' "', x, '"\n'))
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cat(paste0(blue('x_species'), ' "', x_species, '"\n'))
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cat(paste0(blue('x_withspaces_start_only'), ' "', x_withspaces_start_only, '"\n'))
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cat(paste0(blue('x_withspaces_end_only'), ' "', x_withspaces_end_only, '"\n'))
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cat(paste0(blue('x_withspaces_start_end'), ' "', x_withspaces_start_end, '"\n'))
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cat(paste0(blue('x_backup'), ' "', x_backup, '"\n'))
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cat(paste0(blue('x_backup_without_spp'), ' "', x_backup_without_spp, '"\n'))
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cat(paste0(blue('x_trimmed'), ' "', x_trimmed, '"\n'))
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cat(paste0(blue('x_trimmed_species'), ' "', x_trimmed_species, '"\n'))
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cat(paste0(blue('x_trimmed_without_group'), ' "', x_trimmed_without_group, '"\n'))
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cat(paste0(blue("x"), ' "', x, '"\n'))
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cat(paste0(blue("x_species"), ' "', x_species, '"\n'))
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cat(paste0(blue("x_withspaces_start_only"), ' "', x_withspaces_start_only, '"\n'))
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cat(paste0(blue("x_withspaces_end_only"), ' "', x_withspaces_end_only, '"\n'))
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cat(paste0(blue("x_withspaces_start_end"), ' "', x_withspaces_start_end, '"\n'))
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cat(paste0(blue("x_backup"), ' "', x_backup, '"\n'))
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cat(paste0(blue("x_backup_without_spp"), ' "', x_backup_without_spp, '"\n'))
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cat(paste0(blue("x_trimmed"), ' "', x_trimmed, '"\n'))
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cat(paste0(blue("x_trimmed_species"), ' "', x_trimmed_species, '"\n'))
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cat(paste0(blue("x_trimmed_without_group"), ' "', x_trimmed_without_group, '"\n'))
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}
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progress <- progress_estimated(n = length(x), min_time = 3)
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@ -1782,6 +1781,7 @@ pillar_shaft.mo <- function(x, ...) {
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out[is.na(x)] <- pillar::style_na(" NA")
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out[x == "UNKNOWN"] <- pillar::style_na(" UNKNOWN")
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# make it always fit exactly
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pillar::new_pillar_shaft_simple(out, align = "left", width = max(nchar(x)))
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}
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@ -24,7 +24,7 @@
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#' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.
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#' @section WHOCC:
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#' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
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#' This package contains \strong{all ~450 antimicrobial drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
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#' This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
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#'
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#' These have become the gold standard for international drug utilisation monitoring and research.
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#'
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