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74 lines
3.0 KiB
R
74 lines
3.0 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/guess_ab_col.R
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\name{guess_ab_col}
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\alias{guess_ab_col}
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\title{Guess Antibiotic Column}
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\usage{
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guess_ab_col(
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x = NULL,
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search_string = NULL,
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verbose = FALSE,
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only_rsi_columns = FALSE
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)
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}
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\arguments{
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\item{x}{a \link{data.frame}}
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\item{search_string}{a text to search \code{x} for, will be checked with \code{\link[=as.ab]{as.ab()}} if this value is not a column in \code{x}}
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\item{verbose}{a \link{logical} to indicate whether additional info should be printed}
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\item{only_rsi_columns}{a \link{logical} to indicate whether only antibiotic columns must be detected that were transformed to class \verb{<rsi>} (see \code{\link[=as.rsi]{as.rsi()}}) on beforehand (defaults to \code{FALSE})}
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}
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\value{
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A column name of \code{x}, or \code{NULL} when no result is found.
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}
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\description{
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This tries to find a column name in a data set based on information from the \link{antibiotics} data set. Also supports WHONET abbreviations.
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}
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\details{
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You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \link{antibiotics} data set for any column containing a name or code of that antibiotic. \strong{Longer columns names take precedence over shorter column names.}
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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:"5"} \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, 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.
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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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df <- data.frame(amox = "S",
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tetr = "R")
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guess_ab_col(df, "amoxicillin")
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# [1] "amox"
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guess_ab_col(df, "J01AA07") # ATC code of tetracycline
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# [1] "tetr"
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guess_ab_col(df, "J01AA07", verbose = TRUE)
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# NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
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# [1] "tetr"
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# WHONET codes
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df <- data.frame(AMP_ND10 = "R",
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AMC_ED20 = "S")
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guess_ab_col(df, "ampicillin")
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# [1] "AMP_ND10"
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guess_ab_col(df, "J01CR02")
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# [1] "AMC_ED20"
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guess_ab_col(df, as.ab("augmentin"))
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# [1] "AMC_ED20"
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# Longer names take precendence:
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df <- data.frame(AMP_ED2 = "S",
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AMP_ED20 = "S")
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guess_ab_col(df, "ampicillin")
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# [1] "AMP_ED20"
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}
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