# Tbl1 Enterobacteriaceae are also intrinsically resistant to benzylpenicillin, glycopeptides, fusidic acid, macrolides (with some exceptions1), lincosamides, streptogramins, rifampicin, daptomycin and linezolid.
# Tbl2 Non-fermentative Gram-negative bacteria are also generally intrinsically resistant to benzylpenicillin, first and second generation cephalosporins, glycopeptides, fusidic acid, macrolides, lincosamides, streptogramins, rifampicin, daptomycin and linezolid
# Tbl3 Gram-negative bacteria other than Enterobacteriaceae and non-fermentative Gram-negative bacteria listed are also intrinsically resistant to glycopeptides, lincosamides, daptomycin and linezolid.
"ablist"
#' Dataset with ~2500 microorganisms
#'
#' A dataset containing all microorganisms of MOLIS. MO codes of the UMCG can be looked up using \code{\link{bactlist.umcg}}.
#' @format A data.frame with 2507 observations and 10 variables:
#' \describe{
#' \item{\code{bactid}}{ID of microorganism}
#' \item{\code{bactsys}}{Bactsyscode of microorganism}
#' \item{\code{family}}{Family name of microorganism}
#' \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
#' \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
#' \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
#' \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
#' Translation table for UMCG with ~1100 microorganisms
#'
#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}.
#' @format A data.frame with 1090 observations and 2 variables:
#' \describe{
#' \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
#' \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}}
#' }
#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
#' Dataset with 2000 blood culture isolates of septic patients
#'
#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis e.g. with \code{\link{rsi}} or \code{\link{rsi_predict}}, or it can be used to practice other statistics.
#' @format A data.frame with 2000 observations and 47 variables:
#' \describe{
#' \item{\code{date}}{date of receipt at the laboratory}
#' \item{\code{hospital_id}}{ID of the hospital}
#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
#' \item{\code{age}}{age of the patient}
#' \item{\code{sex}}{sex of the patient}
#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
#' \item{\code{bactid}}{ID of microorganism, see \code{\link{bactlist}}}
#' \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}), these column names occur in \code{\link{ablist}} and can be translated with \code{\link{abname}}}
#' }
#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}