AMR/man/portion.Rd

159 lines
6.7 KiB
Plaintext
Raw Normal View History

2018-08-10 15:01:05 +02:00
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/portion.R
\name{portion}
\alias{portion}
\alias{portion_R}
\alias{portion_IR}
\alias{portion_I}
\alias{portion_SI}
\alias{portion_S}
2018-08-11 21:30:00 +02:00
\alias{portion_df}
2018-08-10 15:01:05 +02:00
\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/}.
Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
2018-08-10 15:01:05 +02:00
}
\usage{
2018-08-23 00:40:36 +02:00
portion_R(..., minimum = 30, as_percent = FALSE)
2018-08-10 15:01:05 +02:00
2018-08-23 00:40:36 +02:00
portion_IR(..., minimum = 30, as_percent = FALSE)
2018-08-10 15:01:05 +02:00
2018-08-23 00:40:36 +02:00
portion_I(..., minimum = 30, as_percent = FALSE)
2018-08-10 15:01:05 +02:00
2018-08-23 00:40:36 +02:00
portion_SI(..., minimum = 30, as_percent = FALSE)
2018-08-10 15:01:05 +02:00
2018-08-23 00:40:36 +02:00
portion_S(..., minimum = 30, as_percent = FALSE)
2018-08-11 21:30:00 +02:00
2018-08-13 16:42:37 +02:00
portion_df(data, translate_ab = getOption("get_antibiotic_names",
2018-08-22 00:02:26 +02:00
"official"), minimum = 30, as_percent = FALSE)
2018-08-10 15:01:05 +02:00
}
\arguments{
2018-08-23 00:40:36 +02:00
\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.}
2018-08-10 15:01:05 +02:00
\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.}
2018-08-22 00:02:26 +02:00
\item{as_percent}{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\%"}.}
2018-08-11 21:30:00 +02:00
2018-08-22 00:02:26 +02:00
\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
2018-08-11 21:30:00 +02:00
2018-08-13 16:42:37 +02:00
\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")}.}
2018-08-10 15:01:05 +02:00
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
2018-08-23 00:40:36 +02:00
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage 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}.
2018-08-10 15:01:05 +02:00
\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
}
\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.
2018-08-22 00:02:26 +02:00
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
\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"}.
2018-08-11 21:30:00 +02:00
2018-08-10 15:01:05 +02:00
The old \code{\link{rsi}} 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
2018-08-23 00:40:36 +02:00
For three antibiotics:
2018-08-10 15:01:05 +02:00
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
2018-08-23 00:40:36 +02:00
\cr
And so on.
2018-08-10 15:01:05 +02:00
}
}
\examples{
# septic_patients is a data set available in the AMR package. It is true, genuine data.
?septic_patients
2018-08-10 15:01:05 +02:00
# Calculate resistance
portion_R(septic_patients$amox)
portion_IR(septic_patients$amox)
# Or susceptibility
portion_S(septic_patients$amox)
portion_SI(septic_patients$amox)
2018-08-23 00:40:36 +02:00
# Do the above with pipes:
2018-08-10 15:01:05 +02:00
library(dplyr)
2018-08-23 00:40:36 +02:00
septic_patients \%>\% portion_R(amox)
septic_patients \%>\% portion_IR(amox)
septic_patients \%>\% portion_S(amox)
septic_patients \%>\% portion_SI(amox)
2018-08-10 15:01:05 +02:00
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = portion_S(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = portion_R(cipr, as_percent = TRUE),
I = portion_I(cipr, as_percent = TRUE),
S = portion_S(cipr, as_percent = TRUE),
n = n_rsi(cipr), # works like n_distinct in dplyr
total = n()) # NOT the amount of tested isolates!
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
2018-08-23 00:40:36 +02:00
septic_patients \%>\% portion_S(amcl) # S = 67.3\%
septic_patients \%>\% n_rsi(amcl) # n = 1570
septic_patients \%>\% portion_S(gent) # S = 74.0\%
septic_patients \%>\% n_rsi(gent) # n = 1842
2018-08-10 15:01:05 +02:00
2018-08-23 00:40:36 +02:00
septic_patients \%>\% portion_S(amcl, gent) # S = 92.1\%
septic_patients \%>\% n_rsi(amcl, gent) # n = 1504
2018-08-10 15:01:05 +02:00
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = portion_S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = portion_S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
# Get portions S/I/R immediately of all rsi columns
septic_patients \%>\%
select(amox, cipr) \%>\%
portion_df(translate = FALSE)
# It also supports grouping variables
septic_patients \%>\%
select(hospital_id, amox, cipr) \%>\%
group_by(hospital_id) \%>\%
portion_df(translate = FALSE)
2018-08-10 15:01:05 +02:00
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
}
}
\seealso{
2018-08-22 00:02:26 +02:00
\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
2018-08-10 15:01:05 +02:00
}
\keyword{antibiotics}
\keyword{isolate}
\keyword{isolates}
\keyword{resistance}
\keyword{rsi}
\keyword{rsi_df}
\keyword{susceptibility}