% 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} \alias{portion_df} \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} } \usage{ portion_R(..., minimum = 30, as_percent = FALSE, also_single_tested = FALSE) portion_IR(..., minimum = 30, as_percent = FALSE, also_single_tested = FALSE) portion_I(..., minimum = 30, as_percent = FALSE, also_single_tested = FALSE) portion_SI(..., minimum = 30, as_percent = FALSE, also_single_tested = FALSE) portion_S(..., minimum = 30, as_percent = FALSE, also_single_tested = FALSE) portion_df(data, translate_ab = getOption("get_antibiotic_names", "official"), minimum = 30, as_percent = FALSE, combine_IR = FALSE) } \arguments{ \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.} \item{minimum}{the minimal amount of available isolates. Any number 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.} \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\%"}.} \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.}} \item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})} \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")}.} \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)} } \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 support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \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. 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.} \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"}. 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{
}\figure{mono_therapy.png}\out{
} 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{
}\figure{combi_therapy_2.png}\out{
} \cr For three antibiotics: \out{
}\figure{combi_therapy_3.png}\out{
} \cr And so on. } } \examples{ # septic_patients is a data set available in the AMR package. It is true, genuine data. ?septic_patients # Calculate resistance portion_R(septic_patients$amox) portion_IR(septic_patients$amox) # Or susceptibility portion_S(septic_patients$amox) portion_SI(septic_patients$amox) # Do the above with pipes: library(dplyr) septic_patients \%>\% portion_R(amox) septic_patients \%>\% portion_IR(amox) septic_patients \%>\% portion_S(amox) septic_patients \%>\% portion_SI(amox) 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: septic_patients \%>\% portion_S(amcl) # S = 67.1\% septic_patients \%>\% count_all(amcl) # n = 1576 septic_patients \%>\% portion_S(gent) # S = 74.0\% septic_patients \%>\% count_all(gent) # n = 1855 septic_patients \%>\% portion_S(amcl, gent) # S = 92.0\% septic_patients \%>\% count_all(amcl, gent) # n = 1517 septic_patients \%>\% group_by(hospital_id) \%>\% summarise(cipro_p = portion_S(cipr, as_percent = TRUE), cipro_n = count_all(cipr), genta_p = portion_S(gent, as_percent = TRUE), genta_n = count_all(gent), combination_p = portion_S(cipr, gent, as_percent = TRUE), combination_n = count_all(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) \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 = count_all(amox, metr)) } } \seealso{ \code{\link[AMR]{count}_*} to count resistant and susceptible isolates. } \keyword{antibiotics} \keyword{isolate} \keyword{isolates} \keyword{resistance} \keyword{rsi} \keyword{rsi_df} \keyword{susceptibility}