% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resistance.R
\name{resistance}
\alias{resistance}
\alias{susceptibility}
\alias{n_rsi}
\alias{rsi}
\title{Calculate resistance of isolates}
\usage{
resistance(ab, include_I = TRUE, minimum = 30, as_percent = FALSE)

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

n_rsi(ab1, ab2 = NULL)

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

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

\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.}

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

\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, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
}
\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}, \code{susceptibility} and \code{n_rsi} calculate using hybrid evaluation (i.e. using C++), which makes these functions 25-30 times faster than the old \code{rsi} function. This function is still available for backwards compatibility but is deprecated.
\if{html}{
  \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{
library(dplyr)

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

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


# Calculate resistance
resistance(septic_patients$amox)
rsi(septic_patients$amox, interpretation = "IR") # deprecated

# Or susceptibility
susceptibility(septic_patients$amox)
rsi(septic_patients$amox, interpretation = "S") # deprecated


# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
susceptibility(septic_patients$amcl) # p = 67.8\%
n_rsi(septic_patients$amcl)          # n = 1641

susceptibility(septic_patients$gent) # p = 69.1\%
n_rsi(septic_patients$gent)          # n = 1863

with(septic_patients,
     susceptibility(amcl, gent))     # p = 90.6\%
with(septic_patients,
     n_rsi(amcl, gent))              # n = 1580

\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
  filter(first_isolate == TRUE,
         genus == "Helicobacter") \%>\%
  summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
            n = n_rsi(amox, metr))


# How fast is this hybrid evaluation in C++ compared to R?
# In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?

library(microbenchmark)
df <- septic_patients \%>\% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167

microbenchmark(old_IR = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
               new_IR = df \%>\% summarise(p = resistance(amox, minimum = 0)),
               old_S = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
               new_S = df \%>\% summarise(p = susceptibility(amox, minimum = 0)),
               times = 5,
               unit = "s")

# Unit: seconds
#    expr          min         lq       mean     median         uq        max neval
#  old_IR   1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568     5
#  new_IR   0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192     5
#   old_S   1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137     5
#   new_S   0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738     5

# The old function took roughly 2 seconds, the new ones take 0.07 seconds.
}
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi_df}
\keyword{susceptibility}