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11
DESCRIPTION
11
DESCRIPTION
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@ -1,7 +1,7 @@
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Package: AMR
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Version: 0.1.0
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Date: 2018-02-20
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Title: Antimicrobial Resistance (AMR) Analysis
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Version: 0.1.1
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Date: 2018-02-22
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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given = c("Matthijs", "S."),
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@ -19,8 +19,9 @@ Authors@R: c(
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email = "e.hassing@certe.nl",
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role = "ctb"))
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Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
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of microbial isolates, by using new S3 classes and applying EUCAST expert rules
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on antibiograms.
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of microbial isolates, by using new S3 classes and applying EUCAST expert rules
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on antibiograms according to Leclercq (2013)
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<doi:10.1111/j.1469-0691.2011.03703.x>.
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Depends: R (>= 3.0)
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Imports: dplyr (>= 0.7.0), reshape2 (>= 1.4.0), xml2, rvest
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URL: https://github.com/msberends/AMR
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@ -21,7 +21,6 @@ export(interpretive_reading)
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export(is.mic)
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export(is.rsi)
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export(key_antibiotics)
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export(key_antibiotics_equal)
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export(left_join_bactlist)
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export(mo_property)
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export(right_join_bactlist)
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@ -28,6 +28,7 @@
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#' @rdname EUCAST
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#' @export
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#' @importFrom dplyr %>% left_join select
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#' @return table with edited variables of antibiotics.
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#' @source
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#' EUCAST Expert Rules Version 2.0: \cr
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#' Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
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@ -37,7 +38,7 @@
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#' \url{http://www.eucast.org/expert_rules_and_intrinsic_resistance}
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#' @examples
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#' \dontrun{
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#' tbl <- interpretive_reading(tbl)
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#' tbl <- EUCAST_rules(tbl)
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#' }
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EUCAST_rules <- function(tbl,
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col_bactcode = 'bacteriecode',
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10
R/atc.R
10
R/atc.R
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@ -18,7 +18,7 @@
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#' Properties of an ATC code
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#'
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#' Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic.
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#' Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic. \strong{This function requires an internet connection.}
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#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
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#' @param property property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
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#' @param administration type of administration, see \emph{Details}
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@ -54,6 +54,11 @@
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#' @importFrom xml2 read_html
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#' @importFrom rvest html_nodes html_table
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#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
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#' @examples
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#' \donttest{
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#' atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
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#' atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
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#' }
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atc_property <- function(atc_code,
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property,
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administration = 'O',
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@ -128,6 +133,7 @@ atc_property <- function(atc_code,
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#' @param textbetween text to put between multiple returned texts
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#' @param tolower return output as lower case with function \code{\link{tolower}}.
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#' @keywords ab antibiotics
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#' @source \code{\link{ablist}}
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#' @export
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#' @importFrom dplyr %>% filter select slice
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#' @examples
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@ -148,8 +154,6 @@ atc_property <- function(atc_code,
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#'
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#' abname("J01CR02", from = "atc", to = "umcg")
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#' # "AMCL"
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#'
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#' @source \code{\link{ablist}}
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abname <- function(abcode, from = 'umcg', to = 'official', textbetween = ' + ', tolower = FALSE) {
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ablist <- AMR::ablist
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21
R/classes.R
21
R/classes.R
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@ -26,8 +26,16 @@
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#' @importFrom dplyr %>%
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#' @examples
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
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#'
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
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#' is.rsi(rsi_data)
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#' plot(rsi_data)
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#'
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#' \donttest{
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#' library(dplyr)
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#' tbl %>%
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#' mutate_at(vars(ends_with("_rsi")), as.rsi)
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#' sapply(mic_data, is.rsi)
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#' }
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as.rsi <- function(x) {
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if (is.rsi(x)) {
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x
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@ -157,6 +165,17 @@ plot.rsi <- function(x, ...) {
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#' @return New class \code{mic}
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#' @export
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#' @importFrom dplyr %>%
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#' @examples
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#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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#' is.mic(mic_data)
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#' plot(mic_data)
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#'
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#' \donttest{
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#' library(dplyr)
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#' tbl %>%
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#' mutate_at(vars(ends_with("_mic")), as.mic)
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#' sapply(mic_data, is.mic)
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#' }
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as.mic <- function(x, na.rm = FALSE) {
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if (is.mic(x)) {
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x
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#' @examples
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#' \dontrun{
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#'
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#' # set key antibiotics to a new variable
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#' tbl$keyab <- key_antibiotics(tbl)
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#'
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#' tbl$first_isolate <-
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@ -355,7 +356,13 @@ first_isolate <- function(tbl,
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#' @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.
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#' @export
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#' @importFrom dplyr %>% mutate if_else
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#' @return Character of length 1.
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#' @seealso \code{\link{mo_property}} \code{\link{ablist}}
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#' @examples
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#' \donttest{
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#' #' # set key antibiotics to a new variable
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#' tbl$keyab <- key_antibiotics(tbl)
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#' }
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key_antibiotics <- function(tbl,
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col_bactcode = 'bacteriecode',
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info = TRUE,
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@ -439,15 +446,17 @@ key_antibiotics <- function(tbl,
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}
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#' Compare key antibiotics
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#'
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#' Check whether two text values with key antibiotics match. Supports vectors.
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#' @param x,y tekst (or multiple text vectors) with antimicrobial interpretations
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#' @param ignore_I ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)
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#' @param info print progress
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#' @return logical
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#' @export
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#' @seealso \code{\link{key_antibiotics}}
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# Compare key antibiotics
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#
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# Check whether two text values with key antibiotics match. Supports vectors.
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# @param x,y tekst (or multiple text vectors) with antimicrobial interpretations
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# @param ignore_I ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)
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# @param info print progress
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# @return logical
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# @export
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# @seealso \code{\link{key_antibiotics}}
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# only internal use
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key_antibiotics_equal <- function(x, y, ignore_I = TRUE, info = FALSE) {
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if (length(x) != length(y)) {
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stop('Length of `x` and `y` must be equal.')
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13
R/join.R
13
R/join.R
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#' Join van tabel en \code{bactlist}
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#' Join a table with \code{bactlist}
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#'
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#' Join the list of microorganisms \code{\link{bactlist}} easily to an existing table.
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#' @rdname join
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#' @param ... other parameters to pass trhough to \code{dplyr::\link[dplyr]{join}}.
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#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
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#' @export
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#' @examples
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#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
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#' to = as.Date("2018-01-07"),
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#' by = 1),
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#' bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
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#' "ESCCOL", "ESCCOL", "ESCCOL"),
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#' stringsAsFactors = FALSE)
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#'
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#' colnames(df)
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#' df2 <- left_join_bactlist(df, "bacteria_id")
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#' colnames(df2)
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inner_join_bactlist <- function(x, by = 'bactid', ...) {
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# no name set to `by` parameter
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if (is.null(names(by))) {
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@ -167,7 +167,7 @@ rsi_df <- function(tbl,
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#' Resistance of isolates
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#'
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#' This function can be used in \code{\link[dplyr]{summarise}}, see \emph{Examples}. CaBerekent het percentage S, SI, I, IR of R van een lijst isolaten.
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#' This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
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#' @param ab1,ab2 list with interpretations of an antibiotic
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#' @inheritParams rsi_df
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#' @details This function uses the \code{\link{rsi_df}} function internally.
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#' @examples
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#' \dontrun{
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#' tbl %>%
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#' group_by(hospital) %>%
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#' summarise(cipr = rsi(cipr))
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#'
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#' tbl %>%
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#' group_by(year, hospital) %>%
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#' summarise(
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#' isolates = n(),
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#' cipro = rsi(cipr, percent = TRUE),
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#' amoxi = rsi(amox, percent = TRUE)
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#' )
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#' cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
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#' amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
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#'
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#' rsi(as.rsi(isolates$amox))
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#'
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#' tbl %>%
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#' group_by(hospital) %>%
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#' summarise(cipr = rsi(cipr))
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#'
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#' rsi(isolates$amox)
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#'
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#' rsi(isolates$amcl, interpretation = "S")
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#' rsi(as.rsi(isolates$amcl), interpretation = "S")
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#' }
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rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
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functietekst <- as.character(match.call())
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@ -258,6 +257,7 @@ rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FA
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#' rsi_predict(tbl[which(first_isolate == TRUE & genus == "Haemophilus"),], "amcl")
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#'
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#' # or with dplyr so you can actually read it:
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#' library(dplyr)
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#' tbl %>%
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#' filter(first_isolate == TRUE,
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#' genus == "Haemophilus") %>%
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42
README.md
42
README.md
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@ -12,6 +12,26 @@ AMR can also be predicted for the forthcoming years with the `rsi_predict` funct
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It also contains functions to translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) and vice versa.
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## How to get it?
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[](http://cran.r-project.org/package=AMR)
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This package is available on CRAN (latest stable version) and also here on GitHub (latest development version).
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#### Latest stable version from CRAN (recommended)
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RStudio:
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- Click on `Tools` and then `Install Packages..`
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- Type in `AMR` and press <kbd>Install</kbd>
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Other:
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```r
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install.packages("AMR")
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```
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#### Latest development version from GitHub
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```r
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devtools::install_github("msberends/AMR")
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```
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## How to use it?
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```r
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# Call it with:
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@ -95,28 +115,6 @@ abname(...)
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abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
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```
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## How to get it?
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This package is only available here on GitHub, but respects the [CRAN Repository Policy](https://cran.r-project.org/web/packages/policies.html).
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*Installation commands:*
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```r
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library(devtools)
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install_github("msberends/AMR")
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```
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*Working behind a proxy? Then use:*
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```r
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library(httr)
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library(devtools)
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set_config(use_proxy("yourproxydomain.com",
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8080,
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"username",
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"password",
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"any")) # change "any" to "basic" or "digest" if needed
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install_github("msberends/AMR")
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reset_config()
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```
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## Authors
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- [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
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@ -44,11 +44,14 @@ interpretive_reading(...)
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\item{...}{parameters that are passed on to \code{EUCAST_rules}}
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}
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\value{
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table with edited variables of antibiotics.
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}
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\description{
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Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
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}
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\examples{
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\dontrun{
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tbl <- interpretive_reading(tbl)
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tbl <- EUCAST_rules(tbl)
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}
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}
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@ -40,7 +40,6 @@ abname("AMCL", to = "atc")
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abname("J01CR02", from = "atc", to = "umcg")
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# "AMCL"
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}
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\keyword{ab}
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\keyword{antibiotics}
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@ -20,3 +20,15 @@ New class \code{mic}
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\description{
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This transforms a vector to a new class\code{mic}, which is an ordered factor valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
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}
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\examples{
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mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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is.mic(mic_data)
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plot(mic_data)
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\donttest{
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library(dplyr)
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tbl \%>\%
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mutate_at(vars(ends_with("_mic")), as.mic)
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sapply(mic_data, is.mic)
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}
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}
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@ -20,6 +20,14 @@ This transforms a vector to a new class \code{rsi}, which is an ordered factor w
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}
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\examples{
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rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
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rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
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is.rsi(rsi_data)
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plot(rsi_data)
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\donttest{
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library(dplyr)
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tbl \%>\%
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mutate_at(vars(ends_with("_rsi")), as.rsi)
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sapply(mic_data, is.rsi)
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}
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}
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@ -20,7 +20,7 @@ atc_property(atc_code, property, administration = "O",
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\item{url}{url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.}
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}
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\description{
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Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic.
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Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic. \strong{This function requires an internet connection.}
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}
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\details{
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Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
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@ -49,3 +49,9 @@ Abbreviations for the property \code{"U"} (unit):
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\item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
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}
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}
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\examples{
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\donttest{
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atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
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atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
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}
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}
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@ -57,6 +57,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
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\examples{
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\dontrun{
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# set key antibiotics to a new variable
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tbl$keyab <- key_antibiotics(tbl)
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tbl$first_isolate <-
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|
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14
man/join.Rd
14
man/join.Rd
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@ -9,7 +9,7 @@
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\alias{full_join_bactlist}
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\alias{semi_join_bactlist}
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\alias{anti_join_bactlist}
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\title{Join van tabel en \code{bactlist}}
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\title{Join a table with \code{bactlist}}
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\usage{
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inner_join_bactlist(x, by = "bactid", ...)
|
||||
|
||||
|
|
@ -36,3 +36,15 @@ Join the list of microorganisms \code{\link{bactlist}} easily to an existing tab
|
|||
\details{
|
||||
As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
|
||||
}
|
||||
\examples{
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
|
||||
"ESCCOL", "ESCCOL", "ESCCOL"),
|
||||
stringsAsFactors = FALSE)
|
||||
|
||||
colnames(df)
|
||||
df2 <- left_join_bactlist(df, "bacteria_id")
|
||||
colnames(df2)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -20,9 +20,18 @@ key_antibiotics(tbl, col_bactcode = "bacteriecode", info = TRUE,
|
|||
|
||||
\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.}
|
||||
}
|
||||
\value{
|
||||
Character of length 1.
|
||||
}
|
||||
\description{
|
||||
Key antibiotics based on bacteria ID
|
||||
}
|
||||
\examples{
|
||||
\donttest{
|
||||
#' # set key antibiotics to a new variable
|
||||
tbl$keyab <- key_antibiotics(tbl)
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
\code{\link{mo_property}} \code{\link{ablist}}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,24 +0,0 @@
|
|||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/first_isolates.R
|
||||
\name{key_antibiotics_equal}
|
||||
\alias{key_antibiotics_equal}
|
||||
\title{Compare key antibiotics}
|
||||
\usage{
|
||||
key_antibiotics_equal(x, y, ignore_I = TRUE, info = FALSE)
|
||||
}
|
||||
\arguments{
|
||||
\item{x, y}{tekst (or multiple text vectors) with antimicrobial interpretations}
|
||||
|
||||
\item{ignore_I}{ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)}
|
||||
|
||||
\item{info}{print progress}
|
||||
}
|
||||
\value{
|
||||
logical
|
||||
}
|
||||
\description{
|
||||
Check whether two text values with key antibiotics match. Supports vectors.
|
||||
}
|
||||
\seealso{
|
||||
\code{\link{key_antibiotics}}
|
||||
}
|
||||
21
man/rsi.Rd
21
man/rsi.Rd
|
|
@ -24,28 +24,27 @@ rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30, percent = FALSE,
|
|||
Double or, when \code{percent = TRUE}, a character.
|
||||
}
|
||||
\description{
|
||||
This function can be used in \code{\link[dplyr]{summarise}}, see \emph{Examples}. CaBerekent het percentage S, SI, I, IR of R van een lijst isolaten.
|
||||
This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
|
||||
}
|
||||
\details{
|
||||
This function uses the \code{\link{rsi_df}} function internally.
|
||||
}
|
||||
\examples{
|
||||
\dontrun{
|
||||
tbl \%>\%
|
||||
group_by(hospital) \%>\%
|
||||
summarise(cipr = rsi(cipr))
|
||||
|
||||
tbl \%>\%
|
||||
group_by(year, hospital) \%>\%
|
||||
summarise(
|
||||
isolates = n(),
|
||||
cipro = rsi(cipr, percent = TRUE),
|
||||
amoxi = rsi(amox, percent = TRUE)
|
||||
)
|
||||
cipro = rsi(cipr \%>\% as.rsi(), percent = TRUE),
|
||||
amoxi = rsi(amox \%>\% as.rsi(), percent = TRUE))
|
||||
|
||||
rsi(as.rsi(isolates$amox))
|
||||
|
||||
tbl \%>\%
|
||||
group_by(hospital) \%>\%
|
||||
summarise(cipr = rsi(cipr))
|
||||
|
||||
rsi(isolates$amox)
|
||||
|
||||
rsi(isolates$amcl, interpretation = "S")
|
||||
rsi(as.rsi(isolates$amcl), interpretation = "S")
|
||||
}
|
||||
}
|
||||
\keyword{antibiotics}
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ Create a prediction model to predict antimicrobial resistance for the next years
|
|||
rsi_predict(tbl[which(first_isolate == TRUE & genus == "Haemophilus"),], "amcl")
|
||||
|
||||
# or with dplyr so you can actually read it:
|
||||
library(dplyr)
|
||||
tbl \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
genus == "Haemophilus") \%>\%
|
||||
|
|
|
|||
Loading…
Reference in New Issue