mirror of
https://github.com/msberends/AMR.git
synced 2024-12-25 19:26:13 +01:00
67 lines
2.5 KiB
R
Executable File
67 lines
2.5 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/data.R
|
|
\docType{data}
|
|
\name{septic_patients}
|
|
\alias{septic_patients}
|
|
\title{Data set with 2000 blood culture isolates of septic patients}
|
|
\format{A \code{\link{tibble}} with 2,000 observations and 49 variables:
|
|
\describe{
|
|
\item{\code{date}}{date of receipt at the laboratory}
|
|
\item{\code{hospital_id}}{ID of the hospital, from A to D}
|
|
\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{mo}}{ID of microorganism, see \code{\link{microorganisms}}}
|
|
\item{\code{peni:rifa}}{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{abname}}}
|
|
}}
|
|
\usage{
|
|
septic_patients
|
|
}
|
|
\description{
|
|
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, press F1.
|
|
}
|
|
\examples{
|
|
# ----------- #
|
|
# PREPARATION #
|
|
# ----------- #
|
|
|
|
# Save this example data set to an object, so we can edit it:
|
|
my_data <- septic_patients
|
|
|
|
# load the dplyr package to make data science A LOT easier
|
|
library(dplyr)
|
|
|
|
# Add first isolates to our data set:
|
|
my_data <- my_data \%>\%
|
|
mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))
|
|
|
|
# -------- #
|
|
# ANALYSIS #
|
|
# -------- #
|
|
|
|
# 1. Get the amoxicillin resistance percentages (p)
|
|
# and numbers (n) of E. coli, divided by hospital:
|
|
|
|
my_data \%>\%
|
|
filter(mo == guess_mo("E. coli"),
|
|
first_isolates == TRUE) \%>\%
|
|
group_by(hospital_id) \%>\%
|
|
summarise(n = n_rsi(amox),
|
|
p = portion_IR(amox))
|
|
|
|
|
|
# 2. Get the amoxicillin/clavulanic acid resistance
|
|
# percentages of E. coli, trend over the years:
|
|
|
|
my_data \%>\%
|
|
filter(mo == guess_mo("E. coli"),
|
|
first_isolates == TRUE) \%>\%
|
|
group_by(year = format(date, "\%Y")) \%>\%
|
|
summarise(n = n_rsi(amcl),
|
|
p = portion_IR(amcl, minimum = 20))
|
|
}
|
|
\keyword{datasets}
|