P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2024-12-26 06:46:11 +01:00
Code Releases Activity

new algorithm key abs

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends 2018-07-17 13:02:05 +02:00
parent 967ee86757
commit 5cb9c541f8
8 changed files with 315 additions and 230 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
DESCRIPTION
View File

@ -1,5 +1,5 @@
Package: AMR
Version: 0.2.0.9012
Version: 0.2.0.9013
Date: 2018-07-17
Title: Antimicrobial Resistance Analysis
Authors@R: c(

1
NAMESPACE
View File

@ -51,6 +51,7 @@ export(interpretive_reading)
export(is.mic)
export(is.rsi)
export(key_antibiotics)
export(key_antibiotics_equal)
export(kurtosis)
export(left_join_microorganisms)
export(like)

3
NEWS.md
View File

@ -1,6 +1,9 @@
# 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`) or count isolates (`n_rsi`) 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**: the methodology for determining first weighted isolates was changed. The antibiotics (call *key antibiotics*) that are compared between isolated to include more first isolates (called first *weighted* isolates) are now as follows:
* Gram-positive: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
* Gram-negative: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole, gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
* Function `g.test` as added to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`

211
R/first_isolates.R
View File

@ -40,13 +40,16 @@
#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms
#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
#' @section Key antibiotics:
#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates: \cr
#'
#' \strong{DETERMINING WEIGHTED ISOLATES} \cr
#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
#' To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
#'
#' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
#' To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
#' @keywords isolate isolates first
#' @seealso \code{\link{keyantibiotics}}
#' @export
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
#' @return A vector to add to table, see Examples.
@ -401,205 +404,3 @@ first_isolate <- function(tbl,
all_first
}
#' Key antibiotics based on bacteria ID
#'
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
#' @inheritParams first_isolate
#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics, case-insensitive
#' @export
#' @importFrom dplyr %>% mutate if_else
#' @return Character of length 1.
#' @seealso \code{\link{mo_property}} \code{\link{antibiotics}}
#' @examples
#' \donttest{
#' #' # set key antibiotics to a new variable
#' tbl$keyab <- key_antibiotics(tbl)
#' }
key_antibiotics <- function(tbl,
col_bactid = 'bactid',
info = TRUE,
amcl = 'amcl',
amox = 'amox',
cfot = 'cfot',
cfta = 'cfta',
cftr = 'cftr',
cfur = 'cfur',
cipr = 'cipr',
clar = 'clar',
clin = 'clin',
clox = 'clox',
doxy = 'doxy',
gent = 'gent',
line = 'line',
mero = 'mero',
peni = 'peni',
pita = 'pita',
rifa = 'rifa',
teic = 'teic',
trsu = 'trsu',
vanc = 'vanc') {
keylist <- character(length = nrow(tbl))
if (!col_bactid %in% colnames(tbl)) {
stop('Column ', col_bactid, ' not found.', call. = FALSE)
}
# check columns
col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
clin, clox, doxy, gent, line, mero, peni,
pita, rifa, teic, trsu, vanc)
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
amox <- col.list[amox]
cfot <- col.list[cfot]
cfta <- col.list[cfta]
cftr <- col.list[cftr]
cfur <- col.list[cfur]
cipr <- col.list[cipr]
clar <- col.list[clar]
clin <- col.list[clin]
clox <- col.list[clox]
doxy <- col.list[doxy]
gent <- col.list[gent]
line <- col.list[line]
mero <- col.list[mero]
peni <- col.list[peni]
pita <- col.list[pita]
rifa <- col.list[rifa]
teic <- col.list[teic]
trsu <- col.list[trsu]
vanc <- col.list[vanc]
# join microorganisms
tbl <- tbl %>% left_join_microorganisms(col_bactid)
tbl$key_ab <- NA_character_
# Staphylococcus
list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
list_ab <- list_ab[list_ab %in% colnames(tbl)]
tbl <- tbl %>% mutate(key_ab =
if_else(genus == 'Staphylococcus',
apply(X = tbl[, list_ab],
MARGIN = 1,
FUN = function(x) paste(x, collapse = "")),
key_ab))
# Rest of Gram +
list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
list_ab <- list_ab[list_ab %in% colnames(tbl)]
tbl <- tbl %>% mutate(key_ab =
if_else(gramstain %like% '^Positive ',
apply(X = tbl[, list_ab],
MARGIN = 1,
FUN = function(x) paste(x, collapse = "")),
key_ab))
# Gram -
list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
list_ab <- list_ab[list_ab %in% colnames(tbl)]
tbl <- tbl %>% mutate(key_ab =
if_else(gramstain %like% '^Negative ',
apply(X = tbl[, list_ab],
MARGIN = 1,
FUN = function(x) paste(x, collapse = "")),
key_ab))
# format
tbl <- tbl %>%
mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
tbl$key_ab
}
#' @importFrom dplyr progress_estimated %>%
#' @noRd
key_antibiotics_equal <- function(x,
y,
type = c("keyantibiotics", "points"),
ignore_I = TRUE,
points_threshold = 2,
info = FALSE) {
# x is active row, y is lag
type <- type[1]
if (length(x) != length(y)) {
stop('Length of `x` and `y` must be equal.')
}
result <- logical(length(x))
if (type == "keyantibiotics") {
if (ignore_I == TRUE) {
# evaluation using regular expression will treat '?' as any character
# so I is actually ignored then
x <- gsub('I', '?', x, ignore.case = TRUE)
y <- gsub('I', '?', y, ignore.case = TRUE)
}
for (i in 1:length(x)) {
result[i] <- grepl(x = x[i],
pattern = y[i],
ignore.case = TRUE) |
grepl(x = y[i],
pattern = x[i],
ignore.case = TRUE)
}
return(result)
} else {
if (info == TRUE) {
p <- dplyr::progress_estimated(length(x))
}
for (i in 1:length(x)) {
if (info == TRUE) {
p$tick()$print()
}
if (is.na(x[i])) {
x[i] <- ''
}
if (is.na(y[i])) {
y[i] <- ''
}
if (nchar(x[i]) != nchar(y[i])) {
result[i] <- FALSE
} else if (x[i] == '' & y[i] == '') {
result[i] <- TRUE
} else {
x2 <- strsplit(x[i], "")[[1]]
y2 <- strsplit(y[i], "")[[1]]
if (type == 'points') {
# count points for every single character:
# - no change is 0 points
# - I <-> S|R is 0.5 point
# - S|R <-> R|S is 1 point
# use the levels of as.rsi (S = 1, I = 2, R = 3)
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
result[i] <- ((points / 2) >= points_threshold)
} else {
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
}
}
}
if (info == TRUE) {
cat('\n')
}
result
}
}

233
R/key_antibiotics.R Normal file
View File

@ -0,0 +1,233 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# LICENCE #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# ==================================================================== #
#' Key antibiotics for first \emph{weighted} isolates
#'
#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
#' @inheritParams first_isolate
#' @param amcl,amox,cfot,cfta,cfur,cipr,coli,eryt,gent,mero,oxac,pita,rifa,teic,tetr,tobr,trsu,vanc column names of antibiotics, case-insensitive
#' @details The function \code{key_antibiotics} returns a character vector with antibiotic results.
#'
#' The antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
#' amox, amcl, cfur, pita, cipr, trsu, vanc, teic, tetr, eryt, oxac, rifa.
#'
#' The antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
#' amox, amcl, cfur, pita, cipr, trsu, gent, tobr, coli, cfot, cfta, mero.
#'
#'
#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical value.
#' @inheritSection first_isolate Key antibiotics
#' @rdname key_antibiotics
#' @export
#' @importFrom dplyr %>% mutate if_else
#' @seealso \code{\link{first_isolate}}
#' @examples
#' \dontrun{
#' # set key antibiotics to a new variable
#' tbl$keyab <- key_antibiotics(tbl)
#'
#' # add regular first isolates
#' tbl$first_isolate <-
#' first_isolate(tbl)
#'
#' # add first WEIGHTED isolates using key antibiotics
#' tbl$first_isolate_weighed <-
#' first_isolate(tbl,
#' col_keyantibiotics = 'keyab')
#' }
key_antibiotics <- function(tbl,
col_bactid = "bactid",
amcl = "amcl",
amox = "amox",
cfot = "cfot",
cfta = "cfta",
cfur = "cfur",
cipr = "cipr",
coli = "coli",
eryt = "eryt",
gent = "gent",
mero = "mero",
oxac = "oxac",
pita = "pita",
rifa = "rifa",
teic = "teic",
tetr = "tetr",
tobr = "tobr",
trsu = "trsu",
vanc = "vanc",
info = TRUE) {
if (!col_bactid %in% colnames(tbl)) {
stop('Column ', col_bactid, ' not found.', call. = FALSE)
}
# check columns
col.list <- c(amcl, amox, cfot, cfta, cfur, cipr,
coli, eryt, gent, mero, oxac, pita,
rifa, teic, tetr, tobr, trsu, vanc)
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
amcl <- col.list[amcl]
amox <- col.list[amox]
cfot <- col.list[cfot]
cfta <- col.list[cfta]
cfur <- col.list[cfur]
cipr <- col.list[cipr]
coli <- col.list[coli]
eryt <- col.list[eryt]
gent <- col.list[gent]
mero <- col.list[mero]
oxac <- col.list[oxac]
pita <- col.list[pita]
rifa <- col.list[rifa]
teic <- col.list[teic]
tetr <- col.list[tetr]
tobr <- col.list[tobr]
trsu <- col.list[trsu]
vanc <- col.list[vanc]
gram_positive = c(amox, amcl, cfur, pita, cipr, trsu,
# specific for G+:
vanc, teic, tetr, eryt, oxac, rifa)
gram_positive <- gram_positive[!is.na(gram_positive)]
gram_negative = c(amox, amcl, cfur, pita, cipr, trsu,
# specific for G-:
gent, tobr, coli, cfot, cfta, mero)
gram_negative <- gram_negative[!is.na(gram_negative)]
# join microorganisms
tbl <- tbl %>% left_join_microorganisms(col_bactid)
tbl$key_ab <- NA_character_
# Gram +
tbl <- tbl %>% mutate(key_ab =
if_else(gramstain %like% '^Positive ',
apply(X = tbl[, gram_positive],
MARGIN = 1,
FUN = function(x) paste(x, collapse = "")),
key_ab))
# Gram -
tbl <- tbl %>% mutate(key_ab =
if_else(gramstain %like% '^Negative ',
apply(X = tbl[, gram_negative],
MARGIN = 1,
FUN = function(x) paste(x, collapse = "")),
key_ab))
# format
key_abs <- tbl %>%
pull(key_ab) %>%
gsub('(NA|NULL)', '-', .)
key_abs
}
#' @importFrom dplyr progress_estimated %>%
#' @rdname key_antibiotics
#' @export
key_antibiotics_equal <- function(x,
y,
type = c("keyantibiotics", "points"),
ignore_I = TRUE,
points_threshold = 2,
info = FALSE) {
# x is active row, y is lag
type <- type[1]
if (length(x) != length(y)) {
stop('Length of `x` and `y` must be equal.')
}
result <- logical(length(x))
if (type == "keyantibiotics") {
if (ignore_I == TRUE) {
# evaluation using regular expression will treat '?' as any character
# so I is actually ignored then
x <- gsub('I', '?', x, ignore.case = TRUE)
y <- gsub('I', '?', y, ignore.case = TRUE)
}
for (i in 1:length(x)) {
result[i] <- grepl(x = x[i],
pattern = y[i],
ignore.case = TRUE) |
grepl(x = y[i],
pattern = x[i],
ignore.case = TRUE)
}
return(result)
} else {
if (info == TRUE) {
p <- dplyr::progress_estimated(length(x))
}
for (i in 1:length(x)) {
if (info == TRUE) {
p$tick()$print()
}
if (is.na(x[i])) {
x[i] <- ''
}
if (is.na(y[i])) {
y[i] <- ''
}
if (nchar(x[i]) != nchar(y[i])) {
result[i] <- FALSE
} else if (x[i] == '' & y[i] == '') {
result[i] <- TRUE
} else {
x2 <- strsplit(x[i], "")[[1]]
y2 <- strsplit(y[i], "")[[1]]
if (type == 'points') {
# count points for every single character:
# - no change is 0 points
# - I <-> S|R is 0.5 point
# - S|R <-> R|S is 1 point
# use the levels of as.rsi (S = 1, I = 2, R = 3)
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
result[i] <- ((points / 2) >= points_threshold)
} else {
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
}
}
}
if (info == TRUE) {
cat('\n')
}
result
}
}

20
man/first_isolate.Rd
View File

@ -62,13 +62,18 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
\details{
\strong{WHY THIS IS SO IMPORTANT} \cr
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
\strong{DETERMINING WEIGHTED ISOLATES} \cr
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
}
\section{Key antibiotics}{
There are two ways to determine whether isolates can be included as first \emph{weighted} isolates: \cr
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
}
\examples{
# septic_patients is a dataset available in the AMR package
?septic_patients
@ -120,6 +125,9 @@ tbl$first_resp_isolate_weighed <-
col_keyantibiotics = 'keyab')
}
}
\seealso{
\code{\link{keyantibiotics}}
}
\keyword{first}
\keyword{isolate}
\keyword{isolates}

71
man/key_antibiotics.Rd
View File

@ -1,37 +1,76 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/first_isolates.R
% Please edit documentation in R/key_antibiotics.R
\name{key_antibiotics}
\alias{key_antibiotics}
\title{Key antibiotics based on bacteria ID}
\alias{key_antibiotics_equal}
\title{Key antibiotics for first \emph{weighted} isolates}
\usage{
key_antibiotics(tbl, col_bactid = "bactid", info = TRUE, amcl = "amcl",
amox = "amox", cfot = "cfot", cfta = "cfta", cftr = "cftr",
cfur = "cfur", cipr = "cipr", clar = "clar", clin = "clin",
clox = "clox", doxy = "doxy", gent = "gent", line = "line",
mero = "mero", peni = "peni", pita = "pita", rifa = "rifa",
teic = "teic", trsu = "trsu", vanc = "vanc")
key_antibiotics(tbl, col_bactid = "bactid", amcl = "amcl", amox = "amox",
cfot = "cfot", cfta = "cfta", cfur = "cfur", cipr = "cipr",
coli = "coli", eryt = "eryt", gent = "gent", mero = "mero",
oxac = "oxac", pita = "pita", rifa = "rifa", teic = "teic",
tetr = "tetr", tobr = "tobr", trsu = "trsu", vanc = "vanc",
info = TRUE)
key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"),
ignore_I = TRUE, points_threshold = 2, info = FALSE)
}
\arguments{
\item{tbl}{table with antibiotics coloms, like \code{amox} and \code{amcl}.}
\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset). Get your bactid's with the function \code{\link{guess_bactid}}, that takes microorganism names as input.}
\item{amcl, amox, cfot, cfta, cfur, cipr, coli, eryt, gent, mero, oxac, pita, rifa, teic, tetr, tobr, trsu, vanc}{column names of antibiotics, case-insensitive}
\item{info}{print progress}
\item{amcl, amox, cfot, cfta, cftr, cfur, cipr, clar, clin, clox, doxy, gent, line, mero, peni, pita, rifa, teic, trsu, vanc}{column names of antibiotics, case-insensitive}
}
\value{
Character of length 1.
\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}
}
\description{
Key antibiotics based on bacteria ID
These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
}
\details{
The function \code{key_antibiotics} returns a character vector with antibiotic results.
The antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
amox, amcl, cfur, pita, cipr, trsu, vanc, teic, tetr, eryt, oxac, rifa.
The antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
amox, amcl, cfur, pita, cipr, trsu, gent, tobr, coli, cfot, cfta, mero.
The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical value.
}
\section{Key antibiotics}{
There are two ways to determine whether isolates can be included as first \emph{weighted} isolates: \cr
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
}
\examples{
\donttest{
#' # set key antibiotics to a new variable
\dontrun{
# set key antibiotics to a new variable
tbl$keyab <- key_antibiotics(tbl)
# add regular first isolates
tbl$first_isolate <-
first_isolate(tbl)
# add first WEIGHTED isolates using key antibiotics
tbl$first_isolate_weighed <-
first_isolate(tbl,
col_keyantibiotics = 'keyab')
}
}
\seealso{
\code{\link{mo_property}} \code{\link{antibiotics}}
\code{\link{first_isolate}}
}

4
tests/testthat/test-first_isolates.R
View File

@ -32,7 +32,7 @@ test_that("first isolates work", {
info = TRUE),
na.rm = TRUE)),
1963)
# and 1998 when using points
# and 1997 when using points
expect_equal(
suppressWarnings(
sum(
@ -44,7 +44,7 @@ test_that("first isolates work", {
type = "points",
info = TRUE),
na.rm = TRUE)),
1998)
1997)
# septic_patients contains 1732 out of 2000 first non-ICU isolates
expect_equal(