mirror of https://github.com/msberends/AMR.git
64 lines
2.4 KiB
R
64 lines
2.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/disk.R
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\name{as.disk}
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\alias{as.disk}
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\alias{disk}
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\alias{is.disk}
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\title{Transform Input to Disk Diffusion Diameters}
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\usage{
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as.disk(x, na.rm = FALSE)
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is.disk(x)
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}
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\arguments{
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\item{x}{vector}
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\item{na.rm}{a \link{logical} indicating whether missing values should be removed}
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}
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\value{
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An \link{integer} with additional class \code{\link{disk}}
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}
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\description{
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This transforms a vector to a new class \code{\link{disk}}, which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50.
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}
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\details{
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Interpret disk values as RSI values with \code{\link[=as.rsi]{as.rsi()}}. It supports guidelines from EUCAST and CLSI.
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}
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\section{Stable Lifecycle}{
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\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
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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.
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If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an 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.
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}
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\section{Read more on Our Website!}{
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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}.
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}
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\examples{
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\donttest{
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# transform existing disk zones to the `disk` class
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df <- data.frame(microorganism = "E. coli",
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AMP = 20,
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CIP = 14,
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GEN = 18,
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TOB = 16)
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df[, 2:5] <- lapply(df[, 2:5], as.disk)
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# same with dplyr:
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# df \%>\% mutate(across(AMP:TOB, as.disk))
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# interpret disk values, see ?as.rsi
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as.rsi(x = as.disk(18),
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mo = "Strep pneu", # `mo` will be coerced with as.mo()
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ab = "ampicillin", # and `ab` with as.ab()
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guideline = "EUCAST")
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as.rsi(df)
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}
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}
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\seealso{
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\code{\link[=as.rsi]{as.rsi()}}
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}
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