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new functions R, RI, SI, S

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dr. M.S. (Matthijs) Berends 2018-07-29 22:14:51 +02:00
parent 8421e3f005
commit 826694323b
10 changed files with 256 additions and 183 deletions
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6
NAMESPACE
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@ -21,6 +21,7 @@ S3method(print,bactid)
S3method(print,frequency_tbl)
S3method(print,mic)
S3method(print,rsi)
S3method(pull,bactid)
S3method(skewness,data.frame)
S3method(skewness,default)
S3method(skewness,matrix)
@ -30,8 +31,12 @@ export("%like%")
export(BRMO)
export(EUCAST_exceptional_phenotypes)
export(EUCAST_rules)
export(IR)
export(MDRO)
export(MRGN)
export(R)
export(S)
export(SI)
export(abname)
export(anti_join_microorganisms)
export(as.bactid)
@ -94,6 +99,7 @@ exportMethods(print.bactid)
exportMethods(print.frequency_tbl)
exportMethods(print.mic)
exportMethods(print.rsi)
exportMethods(pull.bactid)
exportMethods(skewness)
exportMethods(skewness.data.frame)
exportMethods(skewness.default)

2
NEWS.md
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@ -1,6 +1,6 @@
# 0.2.0.90xx (development version)
#### New
* **BREAKING**: `rsi_df` was removed in favour of new functions `resistance` and `susceptibility`. Now, all functions used to calculate resistance (`resistance` and `susceptibility`) use **hybrid evaluation**. This means calculations are not done in R directly but rather in C++ using the `Rcpp` package, making them 25 to 30 times faster. The function `rsi` still works, but is deprecated.
* **BREAKING**: `rsi_df` was removed in favour of new functions `R`, `IR`, `SI` and `S` to selectively calculate resistance or susceptibility. These functions use **hybrid evaluation**, which means that calculations are not done in R directly but rather in C++ using the `Rcpp` package, making them 20 to 30 times faster. The function `rsi` still works, but is deprecated.
* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
* Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin

20
R/bactid.R
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@ -101,6 +101,18 @@ as.bactid <- function(x) {
x <- paste0('^', x, '$')
for (i in 1:length(x)) {
if (x.fullbackup[i] %in% AMR::microorganisms$bactid) {
# is already a valid bactid
x[i] <- x.fullbackup[i]
next
}
if (x.backup[i] %in% AMR::microorganisms$bactid) {
# is already a valid bactid
x[i] <- x.backup[i]
next
}
if (tolower(x[i]) == '^e.*coli$') {
# avoid detection of Entamoeba coli in case of E. coli
x[i] <- 'Escherichia coli'
@ -255,3 +267,11 @@ as.data.frame.bactid <- function (x, ...) {
as.data.frame.vector(x, ...)
}
}
#' @exportMethod pull.bactid
#' @export
#' @importFrom dplyr pull
#' @noRd
pull.bactid <- function(.data, ...) {
pull(as.data.frame(.data), ...)
}

2
R/first_isolate.R
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@ -53,7 +53,7 @@
#' @export
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
#' @return A vector to add to table, see Examples.
#' @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/}.
#' @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/}.
#' @examples
#' # septic_patients is a dataset available in the AMR package
#' ?septic_patients

215
R/resistance.R
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@ -18,19 +18,22 @@
#' Calculate resistance of isolates
#'
#' 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}.
#' @param ab,ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}
#' 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
#' \code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
#' \code{n_rsi} counts all cases where antimicrobial interpretations are available.
#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}
#' @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.
#' @param include_I logical to indicate whether antimicrobial interpretations of "I" should be included
#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.
#' @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.
#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
#' @param interpretation antimicrobial interpretation
#' @param info \emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}
#' @param warning \emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}
#' @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.
#' 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.
#' \if{html}{
#' \cr
#' \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
@ -41,88 +44,143 @@
#' Theoretically for three antibiotics:
#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
#' }
#' @keywords resistance susceptibility rsi_df antibiotics isolate 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/}.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character.
#' @rdname resistance
#' @export
#' @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
#' R(septic_patients$amox)
#' IR(septic_patients$amox)
#'
#' # Or susceptibility
#' susceptibility(septic_patients$amox)
#' rsi(septic_patients$amox, interpretation = "S") # deprecated
#' S(septic_patients$amox)
#' SI(septic_patients$amox)
#'
#' # Since n_rsi counts available isolates (and is used as denominator),
#' # you can calculate back to e.g. count resistant isolates:
#' IR(septic_patients$amox) * n_rsi(septic_patients$amox)
#'
#' library(dplyr)
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(p = S(cipr),
#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
#'
#' # 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
#' S(septic_patients$amcl) # p = 67.3%
#' n_rsi(septic_patients$amcl) # n = 1570
#'
#' susceptibility(septic_patients$gent) # p = 69.1%
#' n_rsi(septic_patients$gent) # n = 1863
#' S(septic_patients$gent) # p = 74.0%
#' n_rsi(septic_patients$gent) # n = 1842
#'
#' with(septic_patients,
#' susceptibility(amcl, gent)) # p = 90.6%
#' S(amcl, gent)) # p = 92.1%
#' with(septic_patients,
#' n_rsi(amcl, gent)) # n = 1580
#' n_rsi(amcl, gent)) # n = 1504
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(cipro_p = S(cipr, as_percent = TRUE),
#' cipro_n = n_rsi(cipr),
#' genta_p = S(gent, as_percent = TRUE),
#' genta_n = n_rsi(gent),
#' combination_p = S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent))
#'
#' \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
#' summarise(p = S(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.
#' }
resistance <- function(ab,
#' @rdname resistance
#' @name resistance
#' @export
#' @rdname resistance
#' @export
S <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
susceptibility(ab1 = ab1,
ab2 = ab2,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname resistance
#' @export
SI <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
susceptibility(ab1 = ab1,
ab2 = ab2,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname resistance
#' @export
IR <- function(ab1,
minimum = 30,
as_percent = FALSE) {
resistance(ab1 = ab1,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname resistance
#' @export
R <- function(ab1,
minimum = 30,
as_percent = FALSE) {
resistance(ab1 = ab1,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname resistance
#' @export
n_rsi <- function(ab1, ab2 = NULL) {
if (NCOL(ab1) > 1) {
stop('`ab` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab1)) {
ab1 <- as.rsi(ab1)
}
if (!is.null(ab2)) {
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab2)) {
ab2 <- as.rsi(ab2)
}
sum(!is.na(ab1) & !is.na(ab2))
} else {
sum(!is.na(ab1))
}
}
#' @rdname resistance
#' @export
resistance <- function(ab1,
include_I = TRUE,
minimum = 30,
as_percent = FALSE) {
if (NCOL(ab) > 1) {
stop('`ab` must be a vector of antimicrobial interpretations', call. = FALSE)
if (NCOL(ab1) > 1) {
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.logical(include_I)) {
stop('`include_I` must be logical', call. = FALSE)
@ -134,14 +192,17 @@ resistance <- function(ab,
stop('`as_percent` must be logical', call. = FALSE)
}
if (!is.rsi(ab)) {
x <- as.rsi(ab)
# ab_name <- deparse(substitute(ab))
if (!is.rsi(ab1)) {
x <- as.rsi(ab1)
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
} else {
x <- ab
x <- ab1
}
total <- length(x) - sum(is.na(x)) # faster than C++
if (total < minimum) {
# warning("Too few isolates available for ", ab_name, ": ", total, " < ", minimum, "; returning NA.", call. = FALSE)
return(NA)
}
found <- .Call(`_AMR_rsi_calc_R`, x, include_I)
@ -180,6 +241,7 @@ susceptibility <- function(ab1,
print_warning <- TRUE
}
if (!is.null(ab2)) {
# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
@ -193,9 +255,11 @@ susceptibility <- function(ab1,
FUN = min)
} else {
x <- ab1
# ab_name <- deparse(substitute(ab1))
}
total <- length(x) - sum(is.na(x))
if (total < minimum) {
# warning("Too few isolates available for ", ab_name, ": ", total, " < ", minimum, "; returning NA.", call. = FALSE)
return(NA)
}
found <- .Call(`_AMR_rsi_calc_S`, x, include_I)
@ -211,28 +275,6 @@ susceptibility <- function(ab1,
}
}
#' @rdname resistance
#' @export
n_rsi <- function(ab1, ab2 = NULL) {
if (NCOL(ab1) > 1) {
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab1)) {
ab1 <- as.rsi(ab1)
}
if (!is.null(ab2)) {
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab2)) {
ab2 <- as.rsi(ab2)
}
sum(!is.na(ab1) & !is.na(ab2))
} else {
sum(!is.na(ab1))
}
}
#' @rdname resistance
#' @export
rsi <- function(ab1,
@ -242,6 +284,9 @@ rsi <- function(ab1,
as_percent = FALSE,
info = FALSE,
warning = TRUE) {
.Deprecated()
ab1.name <- deparse(substitute(ab1))
if (ab1.name %like% '.[$].') {
ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))

30
README.md
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@ -25,7 +25,7 @@ This R package was created for academic research by PhD students of the Faculty
This R package contains functions to make **microbiological, epidemiological data analysis easier**. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
With `AMR` you can:
* Calculate the resistance (and even co-resistance) of microbial isolates with the `resistance` and `susceptibility` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
* Calculate the resistance (and even co-resistance) of microbial isolates with the `R`, `IR`, `SI` and `S` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
* Predict antimicrobial resistance for the nextcoming years with the `resistance_predict` function
* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
@ -50,35 +50,21 @@ The functions to calculate microbial resistance use expressions that are not eva
#### Read all changes and new functions in [NEWS.md](NEWS.md).
## How to get it?
This package is available on CRAN and also here on GitHub.
This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network.
### From CRAN (recommended)
Latest released version on CRAN:
### Install from CRAN (recommended)
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
(Note: downloads measured only by [cran.rstudio.com](https://cran.rstudio.com/package=AMR), i.e. this excludes the official [cran.r-project.org](https://cran.r-project.org/package=AMR))
Downloads via RStudio CRAN server (downloads by all other CRAN mirrors **not** measured, including the official https://cran.r-project.org):
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](https://cranlogs.r-pkg.org/downloads/daily/last-month/AMR)
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended):
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> Install using [RStudio](http://www.rstudio.com) (recommended):
- Click on `Tools` and then `Install Packages...`
- Type in `AMR` and press <kbd>Install</kbd>
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> In R directly:
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly:
- `install.packages("AMR")`
- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
- (Exploratory.io costs $40/month but the somewhat limited Community Plan is free for students and teachers, [click here to enroll](https://exploratory.io/plan?plan=Community))
- Start the software and log in
- Click on your username at the right hand side top
- Click on `R Packages`
- Click on the `Install` tab
- Type in `AMR` and press <kbd>Install</kbd>
- Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.
### From GitHub (latest development version)
### Install from GitHub (latest development version)
[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)

2
man/first_isolate.Rd
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@ -4,7 +4,7 @@
\alias{first_isolate}
\title{Determine first (weighted) isolates}
\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/}.
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/}.
}
\usage{
first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,

121
man/resistance.Rd
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@ -2,30 +2,47 @@
% Please edit documentation in R/resistance.R
\name{resistance}
\alias{resistance}
\alias{susceptibility}
\alias{S}
\alias{SI}
\alias{IR}
\alias{R}
\alias{n_rsi}
\alias{susceptibility}
\alias{rsi}
\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/}.
}
\usage{
resistance(ab, include_I = TRUE, minimum = 30, as_percent = FALSE)
S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
IR(ab1, minimum = 30, as_percent = FALSE)
R(ab1, minimum = 30, as_percent = FALSE)
n_rsi(ab1, ab2 = NULL)
resistance(ab1, 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{ab1}{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{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.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.}
\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.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
\item{interpretation}{antimicrobial interpretation}
\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}
@ -36,14 +53,16 @@ rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
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}.
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
\code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
\code{n_rsi} counts all cases where antimicrobial interpretations are available.
}
\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.
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.
\if{html}{
\cr
\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
@ -56,80 +75,60 @@ The functions \code{resistance}, \code{susceptibility} and \code{n_rsi} calculat
}
}
\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
R(septic_patients$amox)
IR(septic_patients$amox)
# Or susceptibility
susceptibility(septic_patients$amox)
rsi(septic_patients$amox, interpretation = "S") # deprecated
S(septic_patients$amox)
SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = S(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
# 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
S(septic_patients$amcl) # p = 67.3\%
n_rsi(septic_patients$amcl) # n = 1570
susceptibility(septic_patients$gent) # p = 69.1\%
n_rsi(septic_patients$gent) # n = 1863
S(septic_patients$gent) # p = 74.0\%
n_rsi(septic_patients$gent) # n = 1842
with(septic_patients,
susceptibility(amcl, gent)) # p = 90.6\%
S(amcl, gent)) # p = 92.1\%
with(septic_patients,
n_rsi(amcl, gent)) # n = 1580
n_rsi(amcl, gent)) # n = 1504
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
\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
summarise(p = S(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}
\keyword{rsi_df}
\keyword{susceptibility}

7
tests/testthat/test-bactid.R
View File

@ -60,5 +60,12 @@ test_that("as.bactid works", {
expect_identical(as.bactid("ESCCOL"),
guess_bactid("ESCCOL"))
# test pull
expect_equal(nrow(septic_patients %>% mutate(bactid = as.bactid(bactid))),
2000)
# test data.frame
expect_equal(nrow(data.frame(test = as.bactid("ESCCOL"))),
1)
})

34
tests/testthat/test-resistance.R
View File

@ -1,6 +1,16 @@
context("resistance.R")
test_that("resistance works", {
# check shortcuts
expect_equal(resistance(septic_patients$amox, include_I = TRUE),
IR(septic_patients$amox))
expect_equal(resistance(septic_patients$amox, include_I = FALSE),
R(septic_patients$amox))
expect_equal(susceptibility(septic_patients$amox, include_I = TRUE),
SI(septic_patients$amox))
expect_equal(susceptibility(septic_patients$amox, include_I = FALSE),
S(septic_patients$amox))
# amox resistance in `septic_patients` should be around 66.33%
expect_equal(resistance(septic_patients$amox, include_I = TRUE), 0.6633, tolerance = 0.0001)
expect_equal(susceptibility(septic_patients$amox, include_I = FALSE), 1 - 0.6633, tolerance = 0.0001)
@ -37,18 +47,18 @@ test_that("resistance works", {
test_that("old rsi works", {
# amox resistance in `septic_patients` should be around 66.33%
expect_equal(rsi(septic_patients$amox), 0.6633, tolerance = 0.0001)
expect_equal(rsi(septic_patients$amox, interpretation = "S"), 1 - 0.6633, tolerance = 0.0001)
expect_equal(suppressWarnings(rsi(septic_patients$amox)), 0.6633, tolerance = 0.0001)
expect_equal(suppressWarnings(rsi(septic_patients$amox, interpretation = "S")), 1 - 0.6633, tolerance = 0.0001)
expect_equal(rsi_df(septic_patients,
ab = "amox",
info = TRUE),
0.6633,
tolerance = 0.0001)
# pita+genta susceptibility around 98.09%
expect_equal(rsi(septic_patients$pita,
septic_patients$gent,
interpretation = "S",
info = TRUE),
expect_equal(suppressWarnings(rsi(septic_patients$pita,
septic_patients$gent,
interpretation = "S",
info = TRUE)),
0.9535,
tolerance = 0.0001)
expect_equal(rsi_df(septic_patients,
@ -66,14 +76,14 @@ test_that("old rsi works", {
# count of cases
expect_equal(septic_patients %>%
group_by(hospital_id) %>%
summarise(cipro_S = rsi(cipr, interpretation = "S",
as_percent = TRUE, warning = FALSE),
summarise(cipro_S = suppressWarnings(rsi(cipr, interpretation = "S",
as_percent = TRUE, warning = FALSE)),
cipro_n = n_rsi(cipr),
genta_S = rsi(gent, interpretation = "S",
as_percent = TRUE, warning = FALSE),
genta_S = suppressWarnings(rsi(gent, interpretation = "S",
as_percent = TRUE, warning = FALSE)),
genta_n = n_rsi(gent),
combination_S = rsi(cipr, gent, interpretation = "S",
as_percent = TRUE, warning = FALSE),
combination_S = suppressWarnings(rsi(cipr, gent, interpretation = "S",
as_percent = TRUE, warning = FALSE)),
combination_n = n_rsi(cipr, gent)) %>%
pull(combination_n),
c(202, 482, 201, 499))