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new tibble export

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dr. M.S. (Matthijs) Berends
2022-08-27 20:49:37 +02:00
parent 164886f50b
commit 303d61b473
115 changed files with 836 additions and 996 deletions
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34
man/first_isolate.Rd
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@ -58,7 +58,7 @@ filter_first_isolate(
\item{col_specimen}{column name of the specimen type or group}
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU). This can also be a \link{logical} vector with the same length as rows in \code{x}.}
\item{col_keyantimicrobials}{(only useful when \code{method = "phenotype-based"}) column name of the key antimicrobials to determine first isolates, see \code{\link[=key_antimicrobials]{key_antimicrobials()}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' or 'antimicrobials' (case insensitive). Use \code{col_keyantimicrobials = FALSE} to prevent this. Can also be the output of \code{\link[=key_antimicrobials]{key_antimicrobials()}}.}
@ -87,7 +87,7 @@ filter_first_isolate(
\item{...}{arguments passed on to \code{\link[=first_isolate]{first_isolate()}} when using \code{\link[=filter_first_isolate]{filter_first_isolate()}}, otherwise arguments passed on to \code{\link[=key_antimicrobials]{key_antimicrobials()}} (such as \code{universal}, \code{gram_negative}, \code{gram_positive})}
}
\value{
A \code{\link{logical}} vector
A \link{logical} vector
}
\description{
Determine first isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindler \emph{et al.} in 2007 (\doi{10.1086/511864}). To determine patient episodes not necessarily based on microorganisms, use \code{\link[=is_new_episode]{is_new_episode()}} that also supports grouping with the \code{dplyr} package.
@ -177,36 +177,22 @@ if (require("dplyr")) {
# filter on first isolates using dplyr:
example_isolates \%>\%
filter(first_isolate())
}
if (require("dplyr")) {
# short-hand version:
example_isolates \%>\%
filter_first_isolate(info = FALSE)
}
if (require("dplyr")) {
# flag the first isolates per group:
example_isolates \%>\%
group_by(hospital_id) \%>\%
group_by(ward) \%>\%
mutate(first = first_isolate()) \%>\%
select(hospital_id, date, patient_id, mo, first)
# now let's see if first isolates matter:
A <- example_isolates \%>\%
group_by(hospital_id) \%>\%
summarise(count = n_rsi(GEN), # gentamicin availability
resistance = resistance(GEN)) # gentamicin resistance
B <- example_isolates \%>\%
filter_first_isolate() \%>\% # the 1st isolate filter
group_by(hospital_id) \%>\%
summarise(count = n_rsi(GEN), # gentamicin availability
resistance = resistance(GEN)) # gentamicin resistance
# Have a look at A and B.
A
B
# B is more reliable because every isolate is counted only once.
# Gentamicin resistance in hospital D appears to be 4.2\% higher than
# when you (erroneously) would have used all isolates for analysis.
select(ward, date, patient, mo, first)
}
}
}