% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resistance.R
\name{resistance}
\alias{resistance}
\alias{S}
\alias{SI}
\alias{IR}
\alias{R}
\alias{n_rsi}
\alias{susceptibility}
\alias{rsi}
\title{Calculate resistance of isolates}
\source{
\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/}.
}
\usage{
S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)

SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)

IR(ab1, minimum = 30, as_percent = FALSE)

R(ab1, minimum = 30, as_percent = FALSE)

n_rsi(ab1, ab2 = NULL)

resistance(ab1, include_I = TRUE, minimum = 30, as_percent = FALSE)

susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
  as_percent = FALSE)

rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
  as_percent = FALSE, info = FALSE, warning = TRUE)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}

\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}

\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}

\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}

\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}

\item{interpretation}{antimicrobial interpretation}

\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}

\item{warning}{\emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
\code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
\code{n_rsi} counts all cases where antimicrobial interpretations are available.
}
\details{
\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.

The functions \code{resistance} and \code{susceptibility} are wrappers around \code{IR} and \code{S}, respectively. All functions except \code{rsi} use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{rsi} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
  \cr\cr
  To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
  \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
  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
  \cr
  For two antibiotics:
  \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
  \cr
  Theoretically for three antibiotics:
  \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
}
}
\examples{
# Calculate resistance
R(septic_patients$amox)
IR(septic_patients$amox)

# Or susceptibility
S(septic_patients$amox)
SI(septic_patients$amox)

# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
IR(septic_patients$amox) * n_rsi(septic_patients$amox)

library(dplyr)
septic_patients \%>\%
  group_by(hospital_id) \%>\%
  summarise(p = S(cipr),
            n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr

# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
S(septic_patients$amcl)     # p = 67.3\%
n_rsi(septic_patients$amcl) # n = 1570

S(septic_patients$gent)     # p = 74.0\%
n_rsi(septic_patients$gent) # n = 1842

with(septic_patients,
     S(amcl, gent))         # p = 92.1\%
with(septic_patients,
     n_rsi(amcl, gent))     # n = 1504

septic_patients \%>\%
  group_by(hospital_id) \%>\%
  summarise(cipro_p = S(cipr, as_percent = TRUE),
            cipro_n = n_rsi(cipr),
            genta_p = S(gent, as_percent = TRUE),
            genta_n = n_rsi(gent),
            combination_p = S(cipr, gent, as_percent = TRUE),
            combination_n = n_rsi(cipr, gent))

\dontrun{

# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
  filter(first_isolate == TRUE,
         genus == "Helicobacter") \%>\%
  summarise(p = S(amox, metr),     # amoxicillin with metronidazole
            n = n_rsi(amox, metr))
}
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{rsi_df}
\keyword{susceptibility}