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https://github.com/msberends/AMR.git
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new functions R, RI, SI, S
This commit is contained in:
20
R/bactid.R
20
R/bactid.R
@ -101,6 +101,18 @@ as.bactid <- function(x) {
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x <- paste0('^', x, '$')
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for (i in 1:length(x)) {
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if (x.fullbackup[i] %in% AMR::microorganisms$bactid) {
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# is already a valid bactid
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x[i] <- x.fullbackup[i]
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next
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}
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if (x.backup[i] %in% AMR::microorganisms$bactid) {
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# is already a valid bactid
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x[i] <- x.backup[i]
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next
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}
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if (tolower(x[i]) == '^e.*coli$') {
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# avoid detection of Entamoeba coli in case of E. coli
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x[i] <- 'Escherichia coli'
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@ -255,3 +267,11 @@ as.data.frame.bactid <- function (x, ...) {
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as.data.frame.vector(x, ...)
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}
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}
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#' @exportMethod pull.bactid
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#' @export
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#' @importFrom dplyr pull
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#' @noRd
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pull.bactid <- function(.data, ...) {
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pull(as.data.frame(.data), ...)
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}
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@ -53,7 +53,7 @@
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#' @export
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#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
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#' @return A vector to add to table, see Examples.
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#' @source Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
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#' @source Methodology of this function is based on: \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/}.
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#' @examples
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#' # septic_patients is a dataset available in the AMR package
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#' ?septic_patients
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215
R/resistance.R
215
R/resistance.R
@ -18,19 +18,22 @@
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#' Calculate resistance of isolates
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#'
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#' 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}.
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#' @param ab,ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}
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#' 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
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#' \code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
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#' \code{n_rsi} counts all cases where antimicrobial interpretations are available.
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#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}
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#' @param 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.
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#' @param include_I logical to indicate whether antimicrobial interpretations of "I" should be included
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#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.
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#' @param 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.
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#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
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#' @param interpretation antimicrobial interpretation
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#' @param info \emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}
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#' @param warning \emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}
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#' @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.
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#'
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#' 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.
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#' 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.
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#' \if{html}{
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#' \cr
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#' \cr\cr
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#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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#' \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
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#' 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
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@ -41,88 +44,143 @@
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#' Theoretically for three antibiotics:
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#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
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#' }
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#' @keywords resistance susceptibility rsi_df antibiotics isolate isolates
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#' @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/}.
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#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
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#' @return Double or, when \code{as_percent = TRUE}, a character.
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#' @rdname resistance
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#' @export
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#' @examples
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#' library(dplyr)
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(p = susceptibility(cipr),
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#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(cipro_p = susceptibility(cipr, as_percent = TRUE),
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#' cipro_n = n_rsi(cipr),
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#' genta_p = susceptibility(gent, as_percent = TRUE),
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#' genta_n = n_rsi(gent),
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#' combination_p = susceptibility(cipr, gent, as_percent = TRUE),
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#' combination_n = n_rsi(cipr, gent))
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#'
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#'
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#' # Calculate resistance
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#' resistance(septic_patients$amox)
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#' rsi(septic_patients$amox, interpretation = "IR") # deprecated
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#' R(septic_patients$amox)
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#' IR(septic_patients$amox)
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#'
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#' # Or susceptibility
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#' susceptibility(septic_patients$amox)
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#' rsi(septic_patients$amox, interpretation = "S") # deprecated
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#' S(septic_patients$amox)
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#' SI(septic_patients$amox)
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#'
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#' # Since n_rsi counts available isolates (and is used as denominator),
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#' # you can calculate back to e.g. count resistant isolates:
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#' IR(septic_patients$amox) * n_rsi(septic_patients$amox)
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#'
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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(p = S(cipr),
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#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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#'
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#' # Calculate 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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#' susceptibility(septic_patients$amcl) # p = 67.8%
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#' n_rsi(septic_patients$amcl) # n = 1641
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#' S(septic_patients$amcl) # p = 67.3%
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#' n_rsi(septic_patients$amcl) # n = 1570
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#'
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#' susceptibility(septic_patients$gent) # p = 69.1%
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#' n_rsi(septic_patients$gent) # n = 1863
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#' S(septic_patients$gent) # p = 74.0%
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#' n_rsi(septic_patients$gent) # n = 1842
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#'
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#' with(septic_patients,
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#' susceptibility(amcl, gent)) # p = 90.6%
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#' S(amcl, gent)) # p = 92.1%
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#' with(septic_patients,
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#' n_rsi(amcl, gent)) # n = 1580
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#' n_rsi(amcl, gent)) # n = 1504
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(cipro_p = S(cipr, as_percent = TRUE),
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#' cipro_n = n_rsi(cipr),
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#' genta_p = S(gent, as_percent = TRUE),
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#' genta_n = n_rsi(gent),
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#' combination_p = S(cipr, gent, as_percent = TRUE),
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#' combination_n = n_rsi(cipr, gent))
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#'
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#' \dontrun{
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#'
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#' # calculate current empiric combination therapy of Helicobacter gastritis:
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#' my_table %>%
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#' filter(first_isolate == TRUE,
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#' genus == "Helicobacter") %>%
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#' summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
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#' summarise(p = S(amox, metr), # amoxicillin with metronidazole
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#' n = n_rsi(amox, metr))
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#'
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#'
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#' # How fast is this hybrid evaluation in C++ compared to R?
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#' # In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
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#'
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#' library(microbenchmark)
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#' df <- septic_patients %>% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
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#'
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#' microbenchmark(old_IR = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
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#' new_IR = df %>% summarise(p = resistance(amox, minimum = 0)),
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#' old_S = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
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#' new_S = df %>% summarise(p = susceptibility(amox, minimum = 0)),
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#' times = 5,
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#' unit = "s")
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#'
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#' # Unit: seconds
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#' # expr min lq mean median uq max neval
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#' # old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
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#' # new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
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#' # old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
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#' # new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
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#'
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#' # The old function took roughly 2 seconds, the new ones take 0.07 seconds.
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#' }
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resistance <- function(ab,
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#' @rdname resistance
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#' @name resistance
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#' @export
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#' @rdname resistance
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#' @export
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S <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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susceptibility(ab1 = ab1,
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ab2 = ab2,
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname resistance
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#' @export
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SI <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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susceptibility(ab1 = ab1,
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ab2 = ab2,
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname resistance
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#' @export
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IR <- function(ab1,
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minimum = 30,
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as_percent = FALSE) {
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resistance(ab1 = ab1,
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname resistance
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#' @export
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R <- function(ab1,
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minimum = 30,
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as_percent = FALSE) {
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resistance(ab1 = ab1,
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname resistance
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#' @export
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n_rsi <- function(ab1, ab2 = NULL) {
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if (NCOL(ab1) > 1) {
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stop('`ab` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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}
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if (!is.null(ab2)) {
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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}
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sum(!is.na(ab1) & !is.na(ab2))
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} else {
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sum(!is.na(ab1))
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}
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}
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#' @rdname resistance
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#' @export
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resistance <- function(ab1,
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include_I = TRUE,
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minimum = 30,
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as_percent = FALSE) {
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if (NCOL(ab) > 1) {
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stop('`ab` must be a vector of antimicrobial interpretations', call. = FALSE)
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if (NCOL(ab1) > 1) {
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stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.logical(include_I)) {
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stop('`include_I` must be logical', call. = FALSE)
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@ -134,14 +192,17 @@ resistance <- function(ab,
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stop('`as_percent` must be logical', call. = FALSE)
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}
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if (!is.rsi(ab)) {
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x <- as.rsi(ab)
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# ab_name <- deparse(substitute(ab))
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if (!is.rsi(ab1)) {
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x <- as.rsi(ab1)
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warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
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} else {
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x <- ab
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x <- ab1
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}
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total <- length(x) - sum(is.na(x)) # faster than C++
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if (total < minimum) {
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# warning("Too few isolates available for ", ab_name, ": ", total, " < ", minimum, "; returning NA.", call. = FALSE)
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return(NA)
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}
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found <- .Call(`_AMR_rsi_calc_R`, x, include_I)
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@ -180,6 +241,7 @@ susceptibility <- function(ab1,
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print_warning <- TRUE
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}
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if (!is.null(ab2)) {
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# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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@ -193,9 +255,11 @@ susceptibility <- function(ab1,
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FUN = min)
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} else {
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x <- ab1
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# ab_name <- deparse(substitute(ab1))
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}
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total <- length(x) - sum(is.na(x))
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if (total < minimum) {
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# warning("Too few isolates available for ", ab_name, ": ", total, " < ", minimum, "; returning NA.", call. = FALSE)
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return(NA)
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}
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found <- .Call(`_AMR_rsi_calc_S`, x, include_I)
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@ -211,28 +275,6 @@ susceptibility <- function(ab1,
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}
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}
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#' @rdname resistance
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#' @export
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n_rsi <- function(ab1, ab2 = NULL) {
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if (NCOL(ab1) > 1) {
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stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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}
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if (!is.null(ab2)) {
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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}
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sum(!is.na(ab1) & !is.na(ab2))
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} else {
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sum(!is.na(ab1))
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}
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}
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#' @rdname resistance
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#' @export
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rsi <- function(ab1,
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@ -242,6 +284,9 @@ rsi <- function(ab1,
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as_percent = FALSE,
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info = FALSE,
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warning = TRUE) {
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.Deprecated()
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ab1.name <- deparse(substitute(ab1))
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if (ab1.name %like% '.[$].') {
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ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
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