2018-02-27 20:01:02 +01:00
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{septic_patients}
\alias{septic_patients}
\title{Dataset with 2000 blood culture isolates of septic patients}
2018-07-25 14:17:04 +02:00
\format{A data.frame with 2000 observations and 49 variables:
2018-02-27 20:01:02 +01:00
\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}
2018-03-23 14:46:02 +01:00
\item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
2018-07-25 14:17:04 +02:00
\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}}}
2018-02-27 20:01:02 +01:00
}}
\usage{
septic_patients
}
\description{
2018-03-23 14:46:02 +01:00
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. For examples, press F1.
}
\examples{
# ----------- #
# PREPARATION #
# ----------- #
# Save this example dataset 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 dataset:
2018-04-02 11:11:21 +02:00
my_data <- my_data \%>\%
mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
2018-03-23 14:46:02 +01:00
# -------- #
# ANALYSIS #
# -------- #
2018-07-25 14:17:04 +02:00
# 1. Get the amoxicillin resistance percentages (p)
# and numbers (n) of E. coli, divided by hospital:
2018-03-23 14:46:02 +01:00
my_data \%>\%
2018-07-25 14:17:04 +02:00
filter(bactid == guess_bactid("E. coli"),
2018-04-02 11:11:21 +02:00
first_isolates == TRUE) \%>\%
group_by(hospital_id) \%>\%
2018-07-25 14:17:04 +02:00
summarise(n = n_rsi(amox),
p = resistance(amox))
2018-04-02 11:11:21 +02:00
# 2. Get the amoxicillin/clavulanic acid resistance
2018-03-23 14:46:02 +01:00
# percentages of E. coli, trend over the years:
2018-04-02 11:11:21 +02:00
my_data \%>\%
2018-03-23 14:46:02 +01:00
filter(bactid == guess_bactid("E. coli"),
2018-04-02 11:11:21 +02:00
first_isolates == TRUE) \%>\%
group_by(year = format(date, "\%Y")) \%>\%
2018-07-25 14:17:04 +02:00
summarise(n = n_rsi(amcl),
p = resistance(amcl, minimum = 20))
2018-02-27 20:01:02 +01:00
}
\keyword{datasets}