mirror of https://github.com/msberends/AMR.git
133 lines
5.3 KiB
R
133 lines
5.3 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/count.R
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\name{count}
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\alias{count}
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\alias{count_R}
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\alias{count_IR}
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\alias{count_I}
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\alias{count_SI}
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\alias{count_S}
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\alias{count_all}
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\alias{n_rsi}
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\alias{count_df}
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\title{Count isolates}
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\source{
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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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count_R(..., also_single_tested = FALSE)
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count_IR(..., also_single_tested = FALSE)
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count_I(..., also_single_tested = FALSE)
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count_SI(..., also_single_tested = FALSE)
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count_S(..., also_single_tested = FALSE)
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count_all(...)
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n_rsi(...)
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count_df(data, translate_ab = getOption("get_antibiotic_names",
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"official"), 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.}
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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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Integer
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}
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\description{
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These functions can be used to count resistant/susceptible microbial isolates. 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{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
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}
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\details{
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These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
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\code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
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\code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of 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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}
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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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# Count resistant isolates
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count_R(septic_patients$amox)
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count_IR(septic_patients$amox)
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# Or susceptible isolates
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count_S(septic_patients$amox)
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count_SI(septic_patients$amox)
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# Count all available isolates
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count_all(septic_patients$amox)
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n_rsi(septic_patients$amox)
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# Since n_rsi counts available isolates, you can
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# calculate back to count e.g. non-susceptible isolates.
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# This results in the same:
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count_IR(septic_patients$amox)
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portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
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library(dplyr)
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = count_R(cipr),
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I = count_I(cipr),
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S = count_S(cipr),
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n1 = count_all(cipr), # the actual total; sum of all three
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n2 = n_rsi(cipr), # same - analogous to n_distinct
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total = n()) # NOT the number of tested isolates!
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# Count co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy.
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# Please mind that `portion_S` calculates percentages right away instead.
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count_S(septic_patients$amcl) # S = 1342 (71.4\%)
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count_all(septic_patients$amcl) # n = 1879
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count_S(septic_patients$gent) # S = 1372 (74.0\%)
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count_all(septic_patients$gent) # n = 1855
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with(septic_patients,
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count_S(amcl, gent)) # S = 1660 (92.3\%)
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with(septic_patients, # n = 1798
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n_rsi(amcl, 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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count_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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count_df(translate = FALSE)
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}
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\seealso{
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\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
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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{susceptibility}
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