mirror of https://github.com/msberends/AMR.git
Becker classification
Lancefield classification Added Lancefield groups to `microorganisms` data set
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Package: AMR
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Version: 0.2.0.9019
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Date: 2018-08-01
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Version: 0.2.0.9020
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Date: 2018-08-02
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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6
NEWS.md
6
NEWS.md
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@ -7,7 +7,11 @@
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* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
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* Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
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* Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
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* Functions `as.bactid` and `is.bactid` to transform/look up microbial ID's
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* Determining bacterial ID:
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* New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
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* The existing function `guess_bactid` is now an alias of `as.bactid`
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* New Becker classification for *Staphylococcus* to categorise them into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS)
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* New Lancefield classification for *Streptococcus* to categorise them into Lancefield groups
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* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
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* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
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* Function `ratio` to transform a vector of values to a preset ratio
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100
R/bactid.R
100
R/bactid.R
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@ -20,10 +20,12 @@
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#'
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#' Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
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#' @param x a character vector or a dataframe with one or two columns
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#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1]. This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".
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#' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, i.e. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. Groups D and E will be ignored, since they are \emph{Enterococci}.
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#' @rdname as.bactid
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#' @details \code{guess_bactid} does exactly the same as \code{as.bactid}.
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#' @details \code{guess_bactid} is an alias of \code{as.bactid}.
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#'
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#' Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. For example:
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#' Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. These are:
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#' \itemize{
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#' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
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#' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
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@ -32,6 +34,11 @@
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#' }
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#' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
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#' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
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#' @source
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#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \cr
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#' \url{https://dx.doi.org/10.1128/CMR.00109-13} \cr
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#' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \cr
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#' \url{https://dx.doi.org/10.1084/jem.57.4.571}
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#' @export
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#' @importFrom dplyr %>% filter pull
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#' @return Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
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@ -48,6 +55,12 @@
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#' as.bactid("VISA") # Vancomycin Intermediate S. aureus
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#' as.bactid("VRSA") # Vancomycin Resistant S. aureus
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#'
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#' guess_bactid("S. epidermidis") # will remain species: STAEPI
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#' guess_bactid("S. epidermidis", Becker = TRUE) # will not remain species: STACNS
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#'
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#' guess_bactid("S. pyogenes") # will remain species: STCAGA
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#' guess_bactid("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA
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#'
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#' \dontrun{
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#' df$bactid <- as.bactid(df$microorganism_name)
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#'
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@ -66,7 +79,7 @@
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#' df <- df %>%
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#' mutate(bactid = guess_bactid(paste(genus, species)))
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#' }
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as.bactid <- function(x) {
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as.bactid <- function(x, Becker = FALSE, Lancefield = FALSE) {
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failures <- character(0)
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@ -96,13 +109,79 @@ as.bactid <- function(x) {
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x <- trimws(x, which = "both")
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x.backup <- x
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# replace space by regex sign
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x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE)
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x <- gsub(" ", ".*", x, fixed = TRUE)
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# add start and stop
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# for species
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x_species <- paste(x, 'species')
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# add start en stop regex
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x <- paste0('^', x, '$')
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x_withspaces <- paste0('^', x_withspaces, '$')
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for (i in 1:length(x)) {
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if (Becker == TRUE | Becker == "all") {
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mo <- suppressWarnings(guess_bactid(x.fullbackup[i]))
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if (mo %like% '^STA') {
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# See Source. It's this figure:
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# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/
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species <- left_join_microorganisms(mo)$species
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if (species %in% c("arlettae", "auricularis", "capitis",
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"caprae", "carnosus", "cohnii", "condimene",
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"devriesei", "epidermidis", "equorum",
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"fleurettii", "gallinarum", "haemolyticus",
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"hominis", "jettensis", "kloosii", "lentus",
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"lugdunensis", "massiliensis", "microti",
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"muscae", "nepalensis", "pasteuri", "perrasii",
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"pettenkoleri", "piscifermentans", "rostri",
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"saccharott", "saprophyticus", "sciuri",
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"siepanovicii", "simulans", "succinus",
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"vitulinus", "warneri", "xylosus")) {
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x[i] <- "STACNS"
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next
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} else if ((Becker == "all" & species == "aureus")
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| species %in% c("simiae", "agnetis", "chromogenes",
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"delphirul", "felis", "futrae",
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"hyicus", "intermedius",
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"pseudointermedius", "schleiferi")) {
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x[i] <- "STACPS"
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next
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}
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}
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}
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if (Lancefield == TRUE) {
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mo <- suppressWarnings(guess_bactid(x.fullbackup[i]))
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if (mo %like% '^STC') {
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# See Source
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species <- left_join_microorganisms(mo)$species
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if (species == "pyogenes") {
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x[i] <- "STCGRA"
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next
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}
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if (species == "agalactiae") {
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x[i] <- "STCGRB"
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next
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}
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if (species %in% c("equisimilis", "equi",
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"zooepidemicus", "dysgalactiae")) {
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x[i] <- "STCGRC"
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next
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}
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if (species == "anginosus") {
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x[i] <- "STCGRF"
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next
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}
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if (species == "sanguis") {
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x[i] <- "STCGRH"
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next
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}
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if (species == "salivarius") {
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x[i] <- "STCGRK"
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next
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}
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}
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}
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if (identical(x.backup[i], "")) {
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# empty values
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x[i] <- NA
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@ -142,7 +221,7 @@ as.bactid <- function(x) {
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x[i] <- 'PSEAER'
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next
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}
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if (tolower(x[i]) %like% 'coagulase'
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if (tolower(x[i]) %like% 'coagulase negative'
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| tolower(x[i]) %like% 'cns'
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| tolower(x[i]) %like% 'cons') {
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# coerce S. coagulase negative, also as CNS and CoNS
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@ -192,7 +271,14 @@ as.bactid <- function(x) {
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next
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}
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# try any match
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# try any match keeping spaces
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found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x_withspaces[i]),]$bactid
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if (length(found) > 0) {
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x[i] <- found[1L]
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next
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}
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# try any match diregarding spaces
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found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x[i]),]$bactid
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if (length(found) > 0) {
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x[i] <- found[1L]
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@ -200,7 +286,7 @@ as.bactid <- function(x) {
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}
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# try exact match of only genus, with 'species' attached
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# (e.g. this prevents Streptococcus for becoming Peptostreptococcus, since "p" < "s")
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# (this prevents Streptococcus from becoming Peptostreptococcus, since "p" < "s")
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found <- AMR::microorganisms[which(AMR::microorganisms$fullname == x_species[i]),]$bactid
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if (length(found) > 0) {
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x[i] <- found[1L]
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4
R/data.R
4
R/data.R
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@ -236,8 +236,8 @@
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#' Dataset with ~2500 microorganisms
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#'
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#' A dataset containing 2453 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
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#' @format A data.frame with 2453 observations and 12 variables:
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#' A dataset containing 2456 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
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#' @format A data.frame with 2456 observations and 12 variables:
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#' \describe{
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#' \item{\code{bactid}}{ID of microorganism}
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#' \item{\code{bactsys}}{Bactsyscode of microorganism}
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@ -33,6 +33,8 @@ With `AMR` you can:
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* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
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* Specific for Gram-positives: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
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* Specific for Gram-negatives: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
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* Categorise *Staphylococci* into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS) according to [Karsten Becker *et al.*](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25278577/)
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* Categorise *Streptococci* into Lancefield groups
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* Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology ([WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/)), to be able to:
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* Translate antibiotic codes (like *AMOX*), official names (like *amoxicillin*) and even trade names (like *Amoxil* or *Trimox*) to an [ATC code](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like *J01CA04*) and vice versa with the `abname` function
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* Get the latest antibiotic properties like hierarchic groups and [defined daily dose](https://en.wikipedia.org/wiki/Defined_daily_dose) (DDD) with units and administration form from the WHOCC website with the `atc_property` function
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Binary file not shown.
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@ -5,15 +5,25 @@
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\alias{guess_bactid}
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\alias{is.bactid}
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\title{Transform to bacteria ID}
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\source{
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[1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \cr
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\url{https://dx.doi.org/10.1128/CMR.00109-13} \cr
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[2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \cr
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\url{https://dx.doi.org/10.1084/jem.57.4.571}
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}
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\usage{
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as.bactid(x)
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as.bactid(x, Becker = FALSE, Lancefield = FALSE)
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guess_bactid(x)
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guess_bactid(x, Becker = FALSE, Lancefield = FALSE)
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is.bactid(x)
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}
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\arguments{
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\item{x}{a character vector or a dataframe with one or two columns}
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\item{Becker}{a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1]. This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".}
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\item{Lancefield}{a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, i.e. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. Groups D and E will be ignored, since they are \emph{Enterococci}.}
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}
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\value{
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Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
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@ -22,9 +32,9 @@ Character (vector) with class \code{"bactid"}. Unknown values will return \code{
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Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
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}
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\details{
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\code{guess_bactid} does exactly the same as \code{as.bactid}.
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\code{guess_bactid} is an alias of \code{as.bactid}.
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Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. For example:
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Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. These are:
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\itemize{
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\item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
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\item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
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@ -46,6 +56,12 @@ as.bactid("MRSA") # Methicillin Resistant S. aureus
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as.bactid("VISA") # Vancomycin Intermediate S. aureus
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as.bactid("VRSA") # Vancomycin Resistant S. aureus
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guess_bactid("S. epidermidis") # will remain species: STAEPI
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guess_bactid("S. epidermidis", Becker = TRUE) # will not remain species: STACNS
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guess_bactid("S. pyogenes") # will remain species: STCAGA
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guess_bactid("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA
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\dontrun{
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df$bactid <- as.bactid(df$microorganism_name)
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\name{microorganisms}
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\alias{microorganisms}
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\title{Dataset with ~2500 microorganisms}
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\format{A data.frame with 2453 observations and 12 variables:
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\format{A data.frame with 2456 observations and 12 variables:
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\describe{
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\item{\code{bactid}}{ID of microorganism}
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\item{\code{bactsys}}{Bactsyscode of microorganism}
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@ -23,7 +23,7 @@
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microorganisms
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}
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\description{
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A dataset containing 2453 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
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A dataset containing 2456 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
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}
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\seealso{
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\code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
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@ -30,6 +30,33 @@ test_that("as.bactid works", {
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"VISA"))),
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rep("STAAUR", 8))
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# check for Becker classification
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expect_identical(as.character(guess_bactid("S. epidermidis", Becker = FALSE)), "STAEPI")
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expect_identical(as.character(guess_bactid("S. epidermidis", Becker = TRUE)), "STACNS")
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expect_identical(as.character(guess_bactid("STAEPI", Becker = TRUE)), "STACNS")
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expect_identical(as.character(guess_bactid("S. intermedius", Becker = FALSE)), "STAINT")
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expect_identical(as.character(guess_bactid("S. intermedius", Becker = TRUE)), "STACPS")
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expect_identical(as.character(guess_bactid("STAINT", Becker = TRUE)), "STACPS")
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# aureus must only be influenced if Becker = "all"
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expect_identical(as.character(guess_bactid("STAAUR", Becker = FALSE)), "STAAUR")
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expect_identical(as.character(guess_bactid("STAAUR", Becker = TRUE)), "STAAUR")
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expect_identical(as.character(guess_bactid("STAAUR", Becker = "all")), "STACPS")
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# check for Lancefield classification
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expect_identical(as.character(guess_bactid("S. pyogenes", Lancefield = FALSE)), "STCPYO")
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expect_identical(as.character(guess_bactid("S. pyogenes", Lancefield = TRUE)), "STCGRA")
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expect_identical(as.character(guess_bactid("STCPYO", Lancefield = TRUE)), "STCGRA")
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expect_identical(as.character(guess_bactid("S. agalactiae", Lancefield = FALSE)), "STCAGA")
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expect_identical(as.character(guess_bactid("S. agalactiae", Lancefield = TRUE)), "STCGRB") # group B
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expect_identical(as.character(guess_bactid("S. equisimilis", Lancefield = FALSE)), "STCEQS")
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expect_identical(as.character(guess_bactid("S. equisimilis", Lancefield = TRUE)), "STCGRC") # group C
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expect_identical(as.character(guess_bactid("S. anginosus", Lancefield = FALSE)), "STCANG")
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expect_identical(as.character(guess_bactid("S. anginosus", Lancefield = TRUE)), "STCGRF") # group F
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expect_identical(as.character(guess_bactid("S. sanguis", Lancefield = FALSE)), "STCSAN")
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expect_identical(as.character(guess_bactid("S. sanguis", Lancefield = TRUE)), "STCGRH") # group H
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expect_identical(as.character(guess_bactid("S. salivarius", Lancefield = FALSE)), "STCSAL")
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expect_identical(as.character(guess_bactid("S. salivarius", Lancefield = TRUE)), "STCGRK") # group K
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# select with one column
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||||
expect_identical(
|
||||
septic_patients[1:10,] %>%
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
context("first_isolate.R")
|
||||
|
||||
test_that("first isolates work", {
|
||||
# septic_patients contains 1959 out of 2000 first isolates
|
||||
# septic_patients contains 1331 out of 2000 first isolates
|
||||
expect_equal(
|
||||
sum(
|
||||
first_isolate(tbl = septic_patients,
|
||||
|
|
@ -10,9 +10,9 @@ test_that("first isolates work", {
|
|||
col_bactid = "bactid",
|
||||
info = TRUE),
|
||||
na.rm = TRUE),
|
||||
1326)
|
||||
1331)
|
||||
|
||||
# septic_patients contains 1962 out of 2000 first *weighted* isolates
|
||||
# septic_patients contains 1426 out of 2000 first *weighted* isolates
|
||||
expect_equal(
|
||||
suppressWarnings(
|
||||
sum(
|
||||
|
|
@ -24,8 +24,8 @@ test_that("first isolates work", {
|
|||
type = "keyantibiotics",
|
||||
info = TRUE),
|
||||
na.rm = TRUE)),
|
||||
1421)
|
||||
# and 1961 when using points
|
||||
1426)
|
||||
# and 1430 when using points
|
||||
expect_equal(
|
||||
suppressWarnings(
|
||||
sum(
|
||||
|
|
@ -37,9 +37,9 @@ test_that("first isolates work", {
|
|||
type = "points",
|
||||
info = TRUE),
|
||||
na.rm = TRUE)),
|
||||
1425)
|
||||
1430)
|
||||
|
||||
# septic_patients contains 1732 out of 2000 first non-ICU isolates
|
||||
# septic_patients contains 1176 out of 2000 first non-ICU isolates
|
||||
expect_equal(
|
||||
sum(
|
||||
first_isolate(septic_patients,
|
||||
|
|
@ -50,7 +50,7 @@ test_that("first isolates work", {
|
|||
info = TRUE,
|
||||
icu_exclude = TRUE),
|
||||
na.rm = TRUE),
|
||||
1171)
|
||||
1176)
|
||||
|
||||
# set 1500 random observations to be of specimen type 'Urine'
|
||||
random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
|
||||
|
|
|
|||
Loading…
Reference in New Issue