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67 lines
2.5 KiB
R
Executable File
67 lines
2.5 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/data.R
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\docType{data}
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\name{septic_patients}
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\alias{septic_patients}
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\title{Dataset with 2000 blood culture isolates of septic patients}
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\format{A data.frame with 2000 observations and 49 variables:
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\describe{
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\item{\code{date}}{date of receipt at the laboratory}
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\item{\code{hospital_id}}{ID of the hospital, from A to D}
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\item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
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\item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
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\item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
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\item{\code{age}}{age of the patient}
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\item{\code{sex}}{sex of the patient}
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\item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
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\item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
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\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}}}
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}}
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\usage{
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septic_patients
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}
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\description{
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An anonymised dataset containing 2000 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.
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}
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\examples{
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# ----------- #
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# PREPARATION #
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# ----------- #
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# Save this example dataset to an object, so we can edit it:
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my_data <- septic_patients
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# load the dplyr package to make data science A LOT easier
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library(dplyr)
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# Add first isolates to our dataset:
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my_data <- my_data \%>\%
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mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
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# -------- #
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# ANALYSIS #
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# -------- #
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# 1. Get the amoxicillin resistance percentages (p)
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# and numbers (n) of E. coli, divided by hospital:
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my_data \%>\%
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filter(bactid == guess_bactid("E. coli"),
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first_isolates == TRUE) \%>\%
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group_by(hospital_id) \%>\%
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summarise(n = n_rsi(amox),
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p = portion_IR(amox))
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# 2. Get the amoxicillin/clavulanic acid resistance
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# percentages of E. coli, trend over the years:
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my_data \%>\%
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filter(bactid == guess_bactid("E. coli"),
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first_isolates == TRUE) \%>\%
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group_by(year = format(date, "\%Y")) \%>\%
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summarise(n = n_rsi(amcl),
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p = portion_IR(amcl, minimum = 20))
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}
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\keyword{datasets}
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