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dr. M.S. (Matthijs) Berends
2022-08-21 16:37:20 +02:00
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man/first_isolate.Rd
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@ -164,19 +164,6 @@ The default method is phenotype-based (using \code{type = "points"}) and episode
}
}
\section{Stable Lifecycle}{
\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:"5"} \cr}
The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}
\section{Read more on Our Website!}{
On our website \url{https://msberends.github.io/AMR/} you can find \href{https://msberends.github.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR data analysis, the \href{https://msberends.github.io/AMR/reference/}{complete documentation of all functions} and \href{https://msberends.github.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
}
\examples{
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.
@ -184,7 +171,7 @@ On our website \url{https://msberends.github.io/AMR/} you can find \href{https:/
example_isolates[first_isolate(), ]
\donttest{
# get all first Gram-negatives
example_isolates[which(first_isolate() & mo_is_gram_negative()), ]
example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]
if (require("dplyr")) {
# filter on first isolates using dplyr:
@ -193,12 +180,13 @@ if (require("dplyr")) {
# short-hand version:
example_isolates \%>\%
filter_first_isolate()
filter_first_isolate(info = FALSE)
# grouped determination of first isolates (also prints group names):
# flag the first isolates per group:
example_isolates \%>\%
group_by(hospital_id) \%>\%
mutate(first = first_isolate())
mutate(first = first_isolate()) \%>\%
select(hospital_id, date, patient_id, mo, first)
# now let's see if first isolates matter:
A <- example_isolates \%>\%
@ -213,6 +201,9 @@ if (require("dplyr")) {
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.