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(v1.5.0.9008) Internal data sets to pkg, speed for auto col determination

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dr. M.S. (Matthijs) Berends
2021-01-22 10:20:41 +01:00
parent 27f084d819
commit 1ba44776a1
87 changed files with 408 additions and 292 deletions
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12
man/as.rsi.Rd
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@ -87,15 +87,15 @@ The \code{\link[=as.rsi]{as.rsi()}} function works in four ways:
\item For \strong{cleaning raw / untransformed data}. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as \code{"<0.25; S"} will be coerced to \code{"S"}. Combined interpretations for multiple test methods (as seen in laboratory records) such as \code{"S; S"} will be coerced to \code{"S"}, but a value like \code{"S; I"} will return \code{NA} with a warning that the input is unclear.
\item For \strong{interpreting minimum inhibitory concentration (MIC) values} according to EUCAST or CLSI. You must clean your MIC values first using \code{\link[=as.mic]{as.mic()}}, that also gives your columns the new data class \code{\link{mic}}. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the \code{mo} argument.
\itemize{
\item Using \code{dplyr}, R/SI interpretation can be done very easily with either:\preformatted{your_data \%>\% mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
your_data \%>\% mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0
\item Using \code{dplyr}, R/SI interpretation can be done very easily with either:\preformatted{your_data \%>\% mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
your_data \%>\% mutate(across((is.mic), as.rsi)) # since dplyr 1.0.0
}
\item Operators like "<=" will be stripped before interpretation. When using \code{conserve_capped_values = TRUE}, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (\code{conserve_capped_values = FALSE}) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
}
\item For \strong{interpreting disk diffusion diameters} according to EUCAST or CLSI. You must clean your disk zones first using \code{\link[=as.disk]{as.disk()}}, that also gives your columns the new data class \code{\link{disk}}. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the \code{mo} argument.
\itemize{
\item Using \code{dplyr}, R/SI interpretation can be done very easily with either:\preformatted{your_data \%>\% mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
your_data \%>\% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0
\item Using \code{dplyr}, R/SI interpretation can be done very easily with either:\preformatted{your_data \%>\% mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
your_data \%>\% mutate(across((is.disk), as.rsi)) # since dplyr 1.0.0
}
}
\item For \strong{interpreting a complete data set}, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running \code{as.rsi(data)}.
@ -193,7 +193,7 @@ as.rsi(x = as.disk(18),
if (require("dplyr")) {
df \%>\% mutate_if(is.mic, as.rsi)
df \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
df \%>\% mutate(across(where(is.mic), as.rsi))
df \%>\% mutate(across((is.mic), as.rsi))
df \%>\% mutate_at(vars(AMP:TOB), as.rsi)
df \%>\% mutate(across(AMP:TOB, as.rsi))
@ -238,7 +238,7 @@ if (require("dplyr")) {
# note: from dplyr 1.0.0 on, this will be:
# example_isolates \%>\%
# mutate(across(where(is.rsi.eligible), as.rsi))
# mutate(across((is.rsi.eligible), as.rsi))
}
}
}