mirror of https://github.com/msberends/AMR.git
173 lines
8.1 KiB
R
173 lines
8.1 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/portion.R
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\name{portion}
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\alias{portion}
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\alias{portion_R}
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\alias{portion_IR}
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\alias{portion_I}
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\alias{portion_SI}
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\alias{portion_S}
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\alias{portion_df}
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\title{Calculate resistance of isolates}
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\source{
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\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
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Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
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}
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\usage{
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portion_R(..., minimum = 30, as_percent = FALSE,
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also_single_tested = FALSE)
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portion_IR(..., minimum = 30, as_percent = FALSE,
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also_single_tested = FALSE)
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portion_I(..., minimum = 30, as_percent = FALSE,
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also_single_tested = FALSE)
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portion_SI(..., minimum = 30, as_percent = FALSE,
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also_single_tested = FALSE)
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portion_S(..., minimum = 30, as_percent = FALSE,
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also_single_tested = FALSE)
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portion_df(data, translate_ab = getOption("get_antibiotic_names",
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"official"), minimum = 30, as_percent = FALSE, combine_IR = FALSE)
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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.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
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\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.}
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\item{as_percent}{a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
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\item{also_single_tested}{a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of \code{portion_S} and R in case of \code{portion_R}). \strong{This would lead to selection bias in almost all cases.}}
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\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
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\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
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\item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)}
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}
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\value{
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Double or, when \code{as_percent = TRUE}, a character.
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}
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\description{
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These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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\code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
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}
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\details{
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\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. \emph{Low counts can infuence the outcome - these \code{portion} functions may camouflage this, since they only return the portion albeit being dependent on the \code{minimum} parameter.}
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\code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
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The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
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\if{html}{
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\cr\cr
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To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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\out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
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To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
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\cr
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For two antibiotics:
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\out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
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\cr
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For three antibiotics:
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\out{<div style="text-align: center;">}\figure{combi_therapy_2.png}\out{</div>}
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\cr
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And so on.
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}
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}
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\section{Read more on our website!}{
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On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
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}
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\examples{
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# septic_patients is a data set available in the AMR package. It is true, genuine data.
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?septic_patients
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# Calculate resistance
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portion_R(septic_patients$amox)
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portion_IR(septic_patients$amox)
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# Or susceptibility
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portion_S(septic_patients$amox)
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portion_SI(septic_patients$amox)
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# Do the above with pipes:
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library(dplyr)
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septic_patients \%>\% portion_R(amox)
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septic_patients \%>\% portion_IR(amox)
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septic_patients \%>\% portion_S(amox)
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septic_patients \%>\% portion_SI(amox)
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(p = portion_S(cipr),
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n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = portion_R(cipr, as_percent = TRUE),
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I = portion_I(cipr, as_percent = TRUE),
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S = portion_S(cipr, as_percent = TRUE),
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n1 = count_all(cipr), # the actual total; sum of all three
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n2 = n_rsi(cipr), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Calculate 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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septic_patients \%>\% portion_S(amcl) # S = 71.4\%
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septic_patients \%>\% count_all(amcl) # n = 1879
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septic_patients \%>\% portion_S(gent) # S = 74.0\%
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septic_patients \%>\% count_all(gent) # n = 1855
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septic_patients \%>\% portion_S(amcl, gent) # S = 92.3\%
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septic_patients \%>\% count_all(amcl, gent) # n = 1798
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
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cipro_n = count_all(cipr),
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genta_p = portion_S(gent, as_percent = TRUE),
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genta_n = count_all(gent),
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combination_p = portion_S(cipr, gent, as_percent = TRUE),
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combination_n = count_all(cipr, gent))
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# Get portions S/I/R immediately of all rsi columns
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septic_patients \%>\%
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select(amox, cipr) \%>\%
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portion_df(translate = FALSE)
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# It also supports grouping variables
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septic_patients \%>\%
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select(hospital_id, amox, cipr) \%>\%
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group_by(hospital_id) \%>\%
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portion_df(translate = FALSE)
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\dontrun{
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# calculate current empiric combination therapy of Helicobacter gastritis:
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my_table \%>\%
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filter(first_isolate == TRUE,
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genus == "Helicobacter") \%>\%
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summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
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n = count_all(amox, metr))
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}
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}
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\seealso{
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\code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
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}
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\keyword{antibiotics}
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\keyword{isolate}
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\keyword{isolates}
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\keyword{resistance}
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\keyword{rsi}
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\keyword{rsi_df}
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\keyword{susceptibility}
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