mirror of
https://github.com/msberends/AMR.git
synced 2024-12-26 18:06:11 +01:00
dr. M.S. (Matthijs) Berends
53464ff1c8
- Functions `first_isolate`, `EUCAST_rules` and `rsi_predict` supports tidyverse-like evaluation of parameters (no need to quote columns them anymore) - Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS - Renamed dataset `bactlist` to `microorganisms`
70 lines
2.5 KiB
R
70 lines
2.5 KiB
R
% 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}
|
|
\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{microorganisms}}}
|
|
\item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} and can be translated with \code{\link{abname}}}
|
|
}}
|
|
\source{
|
|
MOLIS (LIS of Certe) - \url{https://www.certe.nl}
|
|
}
|
|
\usage{
|
|
septic_patients
|
|
}
|
|
\description{
|
|
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:
|
|
my_data <- my_data \%>\%
|
|
mutate(first_isolates = first_isolate(my_data, date, patient_id, bactid))
|
|
|
|
# -------- #
|
|
# ANALYSIS #
|
|
# -------- #
|
|
|
|
# 1. Get the amoxicillin resistance percentages
|
|
# of E. coli, divided by hospital:
|
|
|
|
my_data \%>\%
|
|
filter(bactid == "ESCCOL",
|
|
first_isolates == TRUE) \%>\%
|
|
group_by(hospital_id) \%>\%
|
|
summarise(n = n(),
|
|
amoxicillin_resistance = rsi(amox))
|
|
|
|
|
|
# 2. Get the amoxicillin/clavulanic acid resistance
|
|
# percentages of E. coli, trend over the years:
|
|
|
|
my_data \%>\%
|
|
filter(bactid == guess_bactid("E. coli"),
|
|
first_isolates == TRUE) \%>\%
|
|
group_by(year = format(date, "\%Y")) \%>\%
|
|
summarise(n = n(),
|
|
amoxclav_resistance = rsi(amcl, minimum = 20))
|
|
}
|
|
\keyword{datasets}
|