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212 lines
12 KiB
R
212 lines
12 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/count.R
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\name{count}
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\alias{count}
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\alias{count_resistant}
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\alias{count_susceptible}
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\alias{count_S}
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\alias{count_SI}
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\alias{count_I}
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\alias{count_IR}
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\alias{count_R}
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\alias{count_all}
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\alias{n_sir}
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\alias{count_df}
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\title{Count Available Isolates}
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\usage{
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count_resistant(
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...,
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only_all_tested = FALSE,
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guideline = getOption("AMR_guideline", "EUCAST")
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)
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count_susceptible(
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...,
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only_all_tested = FALSE,
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guideline = getOption("AMR_guideline", "EUCAST")
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)
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count_S(..., only_all_tested = FALSE)
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count_SI(..., only_all_tested = FALSE)
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count_I(..., only_all_tested = FALSE)
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count_IR(..., only_all_tested = FALSE)
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count_R(..., only_all_tested = FALSE)
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count_all(..., only_all_tested = FALSE)
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n_sir(..., only_all_tested = FALSE)
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count_df(
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data,
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translate_ab = "name",
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language = get_AMR_locale(),
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combine_SI = TRUE
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)
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}
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\arguments{
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\item{...}{One or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link[=as.sir]{as.sir()}} if needed.}
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\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a \link{logical} to indicate that isolates must be tested for all antimicrobials, see section \emph{Combination Therapy} below. Use \code{\link[=sir_combine]{sir_combine()}} to create a new variable for a combination therapy.}
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\item{guideline}{Either \code{"EUCAST"} (default) or \code{"CLSI"}. With EUCAST, the 'I' category will be considered as susceptible (see \href{https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/}{EUCAST website}), but with with CLSI, it will be considered resistant. Therefore:
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\itemize{
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\item EUCAST: \code{\link[=count_susceptible]{count_susceptible()}} \eqn{= N_{S} + N_{I}}, \code{\link[=count_resistant]{count_resistant()}} \eqn{= N_{R}}
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\item CLSI: \code{\link[=count_susceptible]{count_susceptible()}} \eqn{= N_{S} + N_{SDD}}, \code{\link[=count_resistant]{count_resistant()}} \eqn{= N_{I} + N_{R}}
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}
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You can also use e.g. \code{\link[=count_R]{count_R()}} or \code{\link[=count_S]{count_S()}} instead, to be explicit.}
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\item{data}{A \link{data.frame} containing columns with class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}}).}
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\item{translate_ab}{A column name of the \link{antimicrobials} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}.}
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\item{language}{Language of the returned text - the default is the current system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}}) and can also be set with the package option \code{\link[=AMR-options]{AMR_locale}}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
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\item{combine_SI}{A \link{logical} to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}.}
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}
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\value{
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An \link{integer}
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}
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\description{
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These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{summarise()} from the \code{dplyr} package and also support grouped variables, see \emph{Examples}.
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\code{\link[=count_resistant]{count_resistant()}} should be used to count resistant isolates, \code{\link[=count_susceptible]{count_susceptible()}} should be used to count susceptible isolates.
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}
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\details{
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These functions are meant to count isolates. Use the \code{\link[=resistance]{resistance()}}/\code{\link[=susceptibility]{susceptibility()}} functions to calculate microbial resistance/susceptibility.
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The function \code{\link[=n_sir]{n_sir()}} is an alias of \code{\link[=count_all]{count_all()}}. They can be used to count all available isolates, i.e. where the input antimicrobial has an available result (S, I or R). For multiple antimicrobials, these are isolates with a result for any of the antimicrobials, or with \code{only_all_tested = TRUE}, for all of the antimicrobials (see \emph{Combination Therapy} below). Their use is equal to \code{dplyr}'s \code{n_distinct()}. For one antimicrobial, their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
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The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) and counts the number of S's, I's and R's. It also supports grouped variables. The function \code{\link[=sir_df]{sir_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
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}
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\section{Interpretation of SIR}{
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In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (\url{https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/}).
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This AMR package follows insight; use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
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}
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\section{Combination Therapy}{
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Results are ordered from susceptible to resistant: S (and WT), SDD, I, R (and NWT, NS). With \code{method = "most_susceptible"} (in \code{\link[=sir_combine]{sir_combine()}}), the most susceptible result of the antimicrobials is returned, and with \code{method = "most_resistant"}, the most resistant result. The value \code{"NI"} (non-interpretable) is considered a missing result.
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A missing result can only be ignored if it cannot change the conclusion: a susceptible result for \code{"most_susceptible"}, and a non-susceptible result for \code{"most_resistant"}. Under EUCAST, the 'I' category is considered susceptible (\emph{susceptible, increased exposure}), and under CLSI, it is not. With \code{only_all_tested = TRUE}, any missing result leads to \code{NA}.
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This is an example for two antimicrobials, Drug A and Drug B, under EUCAST (the order of the antimicrobials does not matter):
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\if{html}{\out{<div class="sourceCode">}}\preformatted{-------------------------------------------------------------------------
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Result of method = Result of method =
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"most_susceptible" "most_resistant"
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----------------------- -----------------------------
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only_all_tested = only_all_tested =
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Drug A Drug B FALSE TRUE FALSE TRUE
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-------- -------- --------- --------- ----------- -------------
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S S S S S S
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S I S S I I
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S R S S R R
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I R I I R R
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R R R R R R
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S <NA> S <NA> <NA> <NA>
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I <NA> I <NA> <NA> <NA>
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R <NA> <NA> <NA> R <NA>
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<NA> <NA> <NA> <NA> <NA> <NA>
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-------------------------------------------------------------------------
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}\if{html}{\out{</div>}}
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Under CLSI, the combination of I with a missing result is \verb{<NA>} for \code{"most_susceptible"} and I for \code{"most_resistant"}.
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The \code{\link[=count]{count_*()}} and \code{\link[=proportion]{proportion_*()}} functions apply these rules when more than one variable is used. With \code{only_all_tested = FALSE} (default), the functions for susceptibility, such as \code{\link[=susceptibility]{susceptibility()}} and \code{\link[=count_susceptible]{count_susceptible()}}, count an isolate as susceptible if any of the antimicrobials is susceptible (\code{"most_susceptible"}), and the functions for resistance, such as \code{\link[=resistance]{resistance()}} and \code{\link[=count_resistant]{count_resistant()}}, count an isolate as resistant if any of the antimicrobials is resistant (\code{"most_resistant"}). An isolate is considered tested if it has such a result, or if it was tested for all antimicrobials. Therefore, for combinations with \code{only_all_tested = FALSE}:
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\if{html}{\out{<div class="sourceCode">}}\preformatted{ count_S() + count_I() + count_R() >= count_all()
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proportion_S() + proportion_I() + proportion_R() >= 1
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}\if{html}{\out{</div>}}
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With \code{only_all_tested = TRUE}, all these functions count the result of the combination therapy (\code{"most_susceptible"}) of isolates tested for all antimicrobials, so that:
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\if{html}{\out{<div class="sourceCode">}}\preformatted{ count_S() + count_I() + count_R() = count_all()
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proportion_S() + proportion_I() + proportion_R() = 1
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}\if{html}{\out{</div>}}
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In \code{\link[=antibiogram]{antibiogram()}} and \code{\link[=wisca]{wisca()}}, the result of the combination therapy (\code{"most_susceptible"}) is used, the 'I' and 'SDD' categories are considered susceptible if \code{combine_SI = TRUE} (default), and the result is then reduced to S or R.
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}
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\examples{
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# example_isolates is a data set available in the AMR package.
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# run ?example_isolates for more info.
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# base R ------------------------------------------------------------
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count_resistant(example_isolates$AMX) # counts "R"
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count_susceptible(example_isolates$AMX) # counts "S" and "I"
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count_all(example_isolates$AMX) # counts "S", "I" and "R"
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# be more specific
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count_S(example_isolates$AMX)
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count_SI(example_isolates$AMX)
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count_I(example_isolates$AMX)
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count_IR(example_isolates$AMX)
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count_R(example_isolates$AMX)
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# Count all available isolates
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count_all(example_isolates$AMX)
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n_sir(example_isolates$AMX)
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# n_sir() is an alias of count_all().
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# Since it counts all available isolates, you can
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# calculate back to count e.g. susceptible isolates.
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# These results are the same:
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count_susceptible(example_isolates$AMX)
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susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX)
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# dplyr -------------------------------------------------------------
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\donttest{
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if (require("dplyr")) {
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example_isolates \%>\%
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group_by(ward) \%>\%
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summarise(
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R = count_R(CIP),
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I = count_I(CIP),
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S = count_S(CIP),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_sir(CIP), # same - analogous to n_distinct
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total = n()
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) # NOT the number of tested isolates!
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# Number of available isolates for a whole antibiotic class
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# (i.e., in this data set columns GEN, TOB, AMK, KAN)
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example_isolates \%>\%
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group_by(ward) \%>\%
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summarise(across(aminoglycosides(), n_sir))
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# Count co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy.
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# Please mind that `susceptibility()` calculates percentages right away instead.
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example_isolates \%>\% count_susceptible(AMC) # 1433
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example_isolates \%>\% count_all(AMC) # 1879
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example_isolates \%>\% count_susceptible(GEN) # 1399
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example_isolates \%>\% count_all(GEN) # 1855
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example_isolates \%>\% count_susceptible(AMC, GEN) # 1764
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example_isolates \%>\% count_all(AMC, GEN) # 1936
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# Get number of S+I vs. R immediately of selected columns
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example_isolates \%>\%
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select(AMX, CIP) \%>\%
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count_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates \%>\%
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select(ward, AMX, CIP) \%>\%
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group_by(ward) \%>\%
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count_df(translate = FALSE)
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}
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}
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}
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\seealso{
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\code{\link[=proportion]{proportion_*}} to calculate microbial resistance and susceptibility.
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}
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