(v2.1.1.9267) update ATCs

man/antimicrobials.Rd

@@ -29,7 +29,7 @@
 \itemize{
 \item \code{av}\cr Antiviral ID as used in this package (such as \code{ACI}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. \emph{\strong{This is a unique identifier.}} Combinations are codes that contain a \code{+} to indicate this, such as \code{ATA+COBI} for atazanavir/cobicistat.
 \item \code{name}\cr Official name as used by WHONET/EARS-Net or the WHO. \emph{\strong{This is a unique identifier.}}
-\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
+\item \code{atc}\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, see \emph{Details}
 \item \code{cid}\cr Compound ID as found in PubChem. \emph{\strong{This is a unique identifier.}}
 \item \code{atc_group}\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
 \item \code{synonyms}\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
@@ -59,9 +59,13 @@ antivirals
 Two data sets containing all antimicrobials and antivirals. Use \code{\link[=as.ab]{as.ab()}} or one of the \code{\link[=ab_property]{ab_*}} functions to retrieve values from the \link{antimicrobials} data set. Three identifiers are included in this data set: an antimicrobial ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
 }
 \details{
-Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}.
+Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}. Do note that ATC codes are not unique. For example, J01CR02 is officially the ATC code for "amoxicillin and beta-lactamase inhibitor". Consequently, these two items from the \link{antimicrobials} data set both return \code{"J01CR02"}:
 
-Synonyms (i.e. trade names) were derived from the PubChem Compound ID (column \code{cid}) and consequently only available where a CID is available.
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{ab_atc("amoxicillin/clavulanic acid")
+ab_atc("amoxicillin/sulbactam")
+}\if{html}{\out{</div>}}
+
+Synonyms (i.e. trade names) were derived from the PubChem Compound ID (column \code{cid}) and are consequently only available where a CID is available.
 }
 \section{Download Our Reference Data}{
 

man/as.sir.Rd

@@ -122,7 +122,7 @@ The default \code{"standard"} setting ensures cautious handling of uncertain val
 
 \item{reference_data}{A \link{data.frame} to be used for interpretation, which defaults to the \link{clinical_breakpoints} data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the \link{clinical_breakpoints} data set (same column names and column types). Please note that the \code{guideline} argument will be ignored when \code{reference_data} is manually set.}
 
-\item{substitute_missing_r_breakpoint}{A \link{logical} to indicate that a missing clinical breakpoints for R (resistant) must be substituted with R - the default is \code{FALSE}. Some (especially CLSI) breakpoints only have a breakpoint for S, meaning the outcome can only be \code{"S"} or \code{NA}. Setting this to \code{TRUE} will convert the \code{NA}s to \code{"R"} only if the R breakpoint is missing. Can also be set with the package option \code{\link[=AMR-options]{AMR_substitute_missing_r_breakpoint}}.}
+\item{substitute_missing_r_breakpoint}{A \link{logical} to indicate that a missing clinical breakpoints for R (resistant) must be substituted with R - the default is \code{FALSE}. Some (especially CLSI) breakpoints only have a breakpoint for S, meaning that the outcome can only be \code{"S"} or \code{NA}. Setting this to \code{TRUE} will convert the \code{NA}s in these cases to \code{"R"}. Can also be set with the package option \code{\link[=AMR-options]{AMR_substitute_missing_r_breakpoint}}.}
 
 \item{include_screening}{A \link{logical} to indicate that clinical breakpoints for screening are allowed - the default is \code{FALSE}. Can also be set with the package option \code{\link[=AMR-options]{AMR_include_screening}}.}
 
@@ -138,7 +138,7 @@ The default \code{"standard"} setting ensures cautious handling of uncertain val
 
 \item{col_mo}{Column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
-\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}. This requires no additional packages, as the used \code{parallel} package is part of base \R. On Windows and on \R < 4.0.0 \code{\link[parallel:clusterApply]{parallel::parLapply()}} will be used, in all other cases the most efficient \code{\link[parallel:mclapply]{parallel::mclapply()}} will be used.}
+\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}. This requires no additional packages, as the used \code{parallel} package is part of base \R. On Windows and on \R < 4.0.0 \code{\link[parallel:clusterApply]{parallel::parLapply()}} will be used, in all other cases the more efficient \code{\link[parallel:mclapply]{parallel::mclapply()}} will be used.}
 
 \item{max_cores}{Maximum number of cores to use if \code{parallel = TRUE}. Use a negative value to subtract that number from the available number of cores, e.g. a value of \code{-2} on an 8-core machine means that at most 6 cores will be used. Defaults to \code{-1}. There will never be used more cores than variables to analyse. The available number of cores are detected using \code{\link[parallelly:availableCores]{parallelly::availableCores()}} if that package is installed, and base \R's \code{\link[parallel:detectCores]{parallel::detectCores()}} otherwise.}
 
@@ -269,11 +269,10 @@ Visit \href{https://amr-for-r.org/articles/datasets.html}{our website for direct
 
 \examples{
 example_isolates
-summary(example_isolates) # see all SIR results at a glance
 
-# For INTERPRETING disk diffusion and MIC values -----------------------
+summary(example_isolates[, 1:10]) # see all SIR results at a glance
 
-# example data sets, with combined MIC values and disk zones
+# create some example data sets, with combined MIC values and disk zones
 df_wide <- data.frame(
   microorganism = "Escherichia coli",
   amoxicillin = as.mic(8),
@@ -289,6 +288,11 @@ df_long <- data.frame(
   disks = as.disk(c(6, 10, 14, 18)),
   guideline = c("EUCAST 2021", "EUCAST 2022", "EUCAST 2023", "EUCAST 2024")
 )
+# and clean previous SIR interpretation logs
+x <- sir_interpretation_history(clean = TRUE)
+
+
+# For INTERPRETING disk diffusion and MIC values -----------------------
 
 # most basic application:
 as.sir(df_wide)
@@ -409,13 +413,6 @@ if (require("dplyr")) {
 
 ## Using base R ------------------------------------------------
 
-as.sir(df_wide)
-
-# return a 'logbook' about the results:
-sir_interpretation_history()
-
-# using parallel computing, which is available in base R
-as.sir(df_wide, parallel = TRUE)
 
 # for single values
 as.sir(
@@ -444,6 +441,7 @@ barplot(sir_data) # for frequencies
 
 # as common in R, you can use as.integer() to return factor indices:
 as.integer(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
+
 # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R:
 as.double(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
 
@@ -459,7 +457,7 @@ if (require("dplyr")) {
   example_isolates \%>\%
     mutate_if(is_sir_eligible, as.sir)
 
-  # since dplyr 1.0.0, this can also be:
+  # since dplyr 1.0.0, this can also be the more impractical:
   # example_isolates \%>\%
   #   mutate(across(where(is_sir_eligible), as.sir))
 }