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WHO update, antibiotics update

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dr. M.S. (Matthijs) Berends
2019-01-25 13:18:41 +01:00
parent 3ec44b6e0c
commit f4ff98b470
99 changed files with 1363 additions and 737 deletions
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1
R/abname.R
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@ -29,6 +29,7 @@
#' @details \strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
#' @keywords ab antibiotics
#' @source \code{\link{antibiotics}}
#' @inheritSection WHOCC WHOCC
#' @export
#' @importFrom dplyr %>% pull
#' @inheritSection AMR Read more on our website!

17
R/age.R
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@ -29,6 +29,9 @@
#' @importFrom dplyr if_else
#' @inheritSection AMR Read more on our website!
#' @export
#' @examples
#' df <- data.frame(birth_date = Sys.Date() - runif(100) * 25000)
#' df$age <- age(df$birth_date)
age <- function(x, reference = Sys.Date()) {
if (length(x) != length(reference)) {
if (length(reference) == 1) {
@ -39,17 +42,21 @@ age <- function(x, reference = Sys.Date()) {
}
x <- base::as.POSIXlt(x)
reference <- base::as.POSIXlt(reference)
if (any(reference < x)) {
stop("`reference` cannot be lower (older) than `x`.")
}
years_gap <- reference$year - x$year
# from https://stackoverflow.com/a/25450756/4575331
years_gap <- reference$year - x$year
ages <- if_else(reference$mon < x$mon | (reference$mon == x$mon & reference$mday < x$mday),
as.integer(years_gap - 1),
as.integer(years_gap))
if (any(ages > 120)) {
if (any(ages < 0, na.rm = TRUE)) {
warning("NAs introduced for ages below 0.")
ages[ages < 0] <- NA_integer_
}
if (any(ages > 120, na.rm = TRUE)) {
warning("Some ages are > 120.")
}
ages
}

1
R/atc.R
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@ -26,6 +26,7 @@
#' @rdname as.atc
#' @aliases atc
#' @keywords atc
#' @inheritSection WHOCC WHOCC
#' @export
#' @importFrom dplyr %>% filter slice pull
#' @details Use the \code{\link{ab_property}} functions to get properties based on the returned ATC code, see Examples.

22
R/data.R
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@ -19,10 +19,10 @@
# Visit our website for more info: https://msberends.gitab.io/AMR. #
# ==================================================================== #
#' Data set with 423 antibiotics
#' Data set with ~500 antibiotics
#'
#' A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
#' @format A \code{\link{data.frame}} with 423 observations and 18 variables:
#' @format A \code{\link{data.frame}} with 488 observations and 16 variables:
#' \describe{
#' \item{\code{atc}}{ATC code, like \code{J01CR02}}
#' \item{\code{certe}}{Certe code, like \code{amcl}}
@ -38,12 +38,14 @@
#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
#' \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
#' \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
#' \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
#' \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
#' \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
#' \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
#' }
#' @source - World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
#' @source - World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
#'
#' EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016: \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
#'
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}
#' @inheritSection AMR Read more on our website!
#' @seealso \code{\link{microorganisms}}
# use this later to further fill AMR::antibiotics
@ -124,9 +126,9 @@
#
"antibiotics"
#' Data set with taxonomic data from ITIS
#' Data set with ~20,000 microorganisms
#'
#' A data set containing the complete microbial taxonomy of the kingdoms Bacteria, Fungi and Protozoa. MO codes can be looked up using \code{\link{as.mo}}.
#' A data set containing the complete microbial taxonomy of the kingdoms Bacteria, Fungi and Protozoa from ITIS. MO codes can be looked up using \code{\link{as.mo}}.
#' @inheritSection ITIS ITIS
#' @format A \code{\link{data.frame}} with 18,833 observations and 15 variables:
#' \describe{
@ -146,12 +148,12 @@
#' \item{\code{prevalence}}{An integer based on estimated prevalence of the microorganism in humans. Used internally by \code{\link{as.mo}}, otherwise quite meaningless. It has a value of 25 for manually added items and a value of 1000 for all unprevalent microorganisms whose genus was somewhere in the top 250 (with another species).}
#' \item{\code{ref}}{Author(s) and year of concerning publication as found in ITIS, see Source}
#' }
#' @source [3] Integrated Taxonomic Information System (ITIS) on-line database, \url{https://www.itis.gov}.
#' @source Integrated Taxonomic Information System (ITIS) public online database, \url{https://www.itis.gov}.
#' @inheritSection AMR Read more on our website!
#' @seealso \code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms.codes}}
"microorganisms"
#' Data set with old taxonomic data from ITIS
#' Data set with previously accepted taxonomic names
#'
#' A data set containing old (previously valid or accepted) taxonomic names according to ITIS. This data set is used internally by \code{\link{as.mo}}.
#' @inheritSection as.mo ITIS
@ -179,7 +181,7 @@
#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}}
"microorganisms.codes"
#' Data set with 2000 blood culture isolates of septic patients
#' Data set with 2,000 blood culture isolates from septic patients
#'
#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. 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}.
#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables:

2
R/eucast_rules.R
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@ -222,7 +222,7 @@ eucast_rules <- function(tbl,
trsu = guess_ab_col(),
vanc = guess_ab_col()) {
EUCAST_VERSION_BREAKPOINTS <- "8.1, 2018"
EUCAST_VERSION_BREAKPOINTS <- "9.0, 2019"
EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
if (!is.data.frame(tbl)) {

4
R/itis.R
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@ -23,10 +23,10 @@
#'
#' All taxonomic names of all microorganisms are included in this package, using the authoritative Integrated Taxonomic Information System (ITIS).
#' @section ITIS:
#' \if{html}{\figure{itis_logo.jpg}{options: height=60px style=margin-bottom:5px} \cr}
#' \if{html}{\figure{logo_itis.jpg}{options: height=60px style=margin-bottom:5px} \cr}
#' This package contains the \strong{complete microbial taxonomic data} (with all nine taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, \url{https://www.itis.gov}).
#'
#' All ~20,000 (sub)species from \strong{the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package}, as well as all ~2,500 previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
#' All ~20,000 (sub)species from \strong{the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package}, as well as all their ~2,500 previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
#'
#' ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3].
#' @inheritSection AMR Read more on our website!

6
R/mo.R
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@ -707,7 +707,11 @@ exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE,
if (n_distinct(failures) > 1) {
plural <- "s"
}
msg <- paste0("\n", n_distinct(failures), " unique value", plural, " could not be coerced to a valid MO code")
total_failures <- length(x_input[x_input %in% failures & !x_input %in% c(NA, NULL, NaN)])
total_n <- length(x_input[!x_input %in% c(NA, NULL, NaN)])
msg <- paste0("\n", n_distinct(failures), " unique value", plural,
" (^= ", percent(total_failures / total_n, round = 1, force_zero = TRUE),
") could not be coerced to a valid MO code")
if (n_distinct(failures) <= 10) {
msg <- paste0(msg, ": ", paste('"', unique(failures), '"', sep = "", collapse = ', '))
}

1
R/read.4d.R
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@ -124,7 +124,6 @@ read.4D <- function(file,
}
if ("date_birth" %in% colnames(data_4D)) {
data_4D$date_birth <- to_date_4D(data_4D$date_birth)
}
if ("date_received" %in% colnames(data_4D)) {
data_4D$date_received <- to_date_4D(data_4D$date_received)

40
R/whocc.R Executable file
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@ -0,0 +1,40 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# This R package was created for academic research and was publicly #
# released in the hope that it will be useful, but it comes WITHOUT #
# ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitab.io/AMR. #
# ==================================================================== #
#' WHO Collaborating Centre for Drug Statistics Methodology
#'
#' 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.
#' @section WHOCC:
#' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
#' This package contains \strong{all ~500 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}).
#'
#' These have become the gold standard for international drug utilisation monitoring and research.
#'
#' The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
#' @inheritSection AMR Read more on our website!
#' @name WHOCC
#' @rdname WHOCC
#' @examples
#' as.atc("meropenem")
#' ab_name("J01DH02")
#'
#' ab_tradenames("flucloxacillin")
NULL