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styler dep
This commit is contained in:
parent
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.github/prehooks/pre-commit
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.github/prehooks/pre-commit
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@ -33,7 +33,7 @@ echo "Running pre-commit hook..."
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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if command -v Rscript > /dev/null; then
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if command -v Rscript > /dev/null; then
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if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"', '"'styler'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
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if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
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Rscript -e "source('data-raw/_pre_commit_hook.R')"
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Rscript -e "source('data-raw/_pre_commit_hook.R')"
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currentpkg=`Rscript -e "cat(pkgload::pkg_name())"`
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currentpkg=`Rscript -e "cat(pkgload::pkg_name())"`
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echo "-> Adding all files in 'data-raw' to this commit"
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echo "-> Adding all files in 'data-raw' to this commit"
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@ -1,5 +1,5 @@
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Package: AMR
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Package: AMR
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Version: 1.8.2.9077
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Version: 1.8.2.9078
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Date: 2023-01-05
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Date: 2023-01-05
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Title: Antimicrobial Resistance Data Analysis
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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NEWS.md
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NEWS.md
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# 1.8.2.9077
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# AMR 1.8.2.9078
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*(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
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*(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
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R/sysdata.rda
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R/sysdata.rda
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@ -486,14 +486,16 @@ suppressMessages(devtools::document(quiet = TRUE))
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# Style pkg ---------------------------------------------------------------
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# Style pkg ---------------------------------------------------------------
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# if (interactive()) {
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if (!"styler" %in% rownames(utils::installed.packages())) {
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message("Package 'styler' not installed!")
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} else if (interactive()) {
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# # only when sourcing this file ourselves
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# # only when sourcing this file ourselves
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# usethis::ui_info("Styling package")
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# usethis::ui_info("Styling package")
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# styler::style_pkg(
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# styler::style_pkg(
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# style = styler::tidyverse_style,
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# style = styler::tidyverse_style,
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# filetype = c("R", "Rmd")
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# filetype = c("R", "Rmd")
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# )
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# )
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# }
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}
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# Finished ----------------------------------------------------------------
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# Finished ----------------------------------------------------------------
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@ -62,7 +62,7 @@ set_ab_names(
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\item{tolower}{a \link{logical} to indicate whether the first \link{character} of every output should be transformed to a lower case \link{character}. This will lead to e.g. "polymyxin B" and not "polymyxin b".}
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\item{tolower}{a \link{logical} to indicate whether the first \link{character} of every output should be transformed to a lower case \link{character}. This will lead to e.g. "polymyxin B" and not "polymyxin b".}
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\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: variables to select (supports tidy selection such as \code{column1:column4}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}}
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\item{...}{in case of \code{\link[=set_ab_names]{set_ab_names()}} and \code{data} is a \link{data.frame}: columns to select (supports tidy selection such as \code{column1:column4}), otherwise other arguments passed on to \code{\link[=as.ab]{as.ab()}}}
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\item{only_first}{a \link{logical} to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)}
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\item{only_first}{a \link{logical} to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)}
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@ -60,7 +60,7 @@ eucast_rules(df, rules = "custom", custom_rules = x, info = FALSE)
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\subsection{Using taxonomic properties in rules}{
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\subsection{Using taxonomic properties in rules}{
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There is one exception in variables used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" and "snomed". Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
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There is one exception in columns used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" and "snomed". Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
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\if{html}{\out{<div class="sourceCode r">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
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TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")
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TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")
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@ -16,7 +16,7 @@ mean_amr_distance(x, ...)
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amr_distance_from_row(amr_distance, row)
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amr_distance_from_row(amr_distance, row)
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}
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}
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\arguments{
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\arguments{
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\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.rsi]{rsi} or \link[=as.rsi]{rsi}, or a \link{data.frame} containing columns of any of these classes}
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\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes}
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\item{...}{variables to select (supports \link[tidyselect:language]{tidyselect language} such as \code{column1:column4} and \code{where(is.mic)}, and can thus also be \link[=ab_selector]{antibiotic selectors}}
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\item{...}{variables to select (supports \link[tidyselect:language]{tidyselect language} such as \code{column1:column4} and \code{where(is.mic)}, and can thus also be \link[=ab_selector]{antibiotic selectors}}
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@ -30,13 +30,13 @@ amr_distance_from_row(amr_distance, row)
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Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
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Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
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}
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}
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\details{
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\details{
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The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to \href{https://en.wikipedia.org/wiki/Standard_score}{Z scores} (the number of standard deviations from the mean).
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The mean AMR distance is effectively \href{https://en.wikipedia.org/wiki/Standard_score}{the Z-score}; a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
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MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
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MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is thus calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
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R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
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R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
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For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using \code{\link[=rowMeans]{rowMeans()}}), see \emph{Examples}.
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For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see \emph{Examples}.
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Use \code{\link[=amr_distance_from_row]{amr_distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
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Use \code{\link[=amr_distance_from_row]{amr_distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
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}
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}
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@ -46,14 +46,24 @@ Isolates with distances less than 0.01 difference from each other should be cons
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}
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}
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\examples{
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\examples{
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x <- random_mic(10)
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rsi <- random_rsi(10)
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x
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rsi
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mean_amr_distance(x)
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mean_amr_distance(rsi)
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mic <- random_mic(10)
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mic
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mean_amr_distance(mic)
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# equal to the Z-score of their log2:
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(log2(mic) - mean(log2(mic))) / sd(log2(mic))
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disk <- random_disk(10)
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disk
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mean_amr_distance(disk)
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y <- data.frame(
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y <- data.frame(
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id = LETTERS[1:10],
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id = LETTERS[1:10],
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amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
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amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
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cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
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cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
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gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
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gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
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tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
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tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
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)
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)
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@ -65,7 +75,7 @@ y[order(y$amr_distance), ]
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if (require("dplyr")) {
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if (require("dplyr")) {
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y \%>\%
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y \%>\%
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mutate(
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mutate(
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amr_distance = mean_amr_distance(., where(is.mic)),
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amr_distance = mean_amr_distance(y),
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check_id_C = amr_distance_from_row(amr_distance, id == "C")
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check_id_C = amr_distance_from_row(amr_distance, id == "C")
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) \%>\%
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) \%>\%
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arrange(check_id_C)
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arrange(check_id_C)
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filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
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filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
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select(mo, TCY, carbapenems()) \%>\%
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select(mo, TCY, carbapenems()) \%>\%
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group_by(mo) \%>\%
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group_by(mo) \%>\%
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mutate(d = mean_amr_distance(., where(is.rsi))) \%>\%
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mutate(dist = mean_amr_distance(.)) \%>\%
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arrange(mo, d)
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arrange(mo, dist)
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}
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}
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}
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}
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