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AMR
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new AMR distance function

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dr. M.S. (Matthijs) Berends 2022-08-30 21:48:02 +02:00
parent fbd5d32541
commit 3fca703fbf
10 changed files with 343 additions and 6 deletions
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2
.github/workflows/check.yaml vendored
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@ -156,4 +156,4 @@ jobs:
uses: actions/upload-artifact@v2
with:
name: artifacts-${{ matrix.config.os }}-r${{ matrix.config.r }}
path: ${{ github.workspace }}/AMR.Rcheck
path: /home/runner/work/AMR.Rcheck

6
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.8.1.9046
Date: 2022-08-29
Version: 1.8.1.9047
Date: 2022-08-30
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by
@ -13,7 +13,7 @@ Authors@R: c(
person(family = "Hassing", c("Erwin", "E.", "A."), role = c("aut", "ctb")),
person(family = "Albers", c("Casper", "J."), role = "ths", comment = c(ORCID = "0000-0002-9213-6743")),
person(family = "Dutey-Magni", c("Peter"), role = "ctb", comment = c(ORCID = "0000-0002-8942-9836")),
person(family = "Fonville", c("Judith", "M"), role = "ctb"),
person(family = "Fonville", c("Judith", "M."), role = "ctb"),
person(family = "Friedrich", c("Alex", "W."), role = "ths", comment = c(ORCID = "0000-0003-4881-038X")),
person(family = "Glasner", c("Corinna"), role = "ths", comment = c(ORCID = "0000-0003-1241-1328")),
person(family = "Hazenberg", c("Eric", "H.", "L.", "C.", "M."), role = "ctb"),

7
NAMESPACE
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@ -101,6 +101,11 @@ S3method(log1p,mic)
S3method(log2,mic)
S3method(max,mic)
S3method(mean,mic)
S3method(mean_amr_distance,data.frame)
S3method(mean_amr_distance,default)
S3method(mean_amr_distance,disk)
S3method(mean_amr_distance,mic)
S3method(mean_amr_distance,rsi)
S3method(median,mic)
S3method(min,mic)
S3method(plot,disk)
@ -220,6 +225,7 @@ export(count_resistant)
export(count_susceptible)
export(custom_eucast_rules)
export(custom_mdro_guideline)
export(distance_from_row)
export(eucast_dosage)
export(eucast_exceptional_phenotypes)
export(eucast_rules)
@ -259,6 +265,7 @@ export(macrolides)
export(mdr_cmi2012)
export(mdr_tb)
export(mdro)
export(mean_amr_distance)
export(mo_authors)
export(mo_class)
export(mo_domain)

3
NEWS.md
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@ -1,7 +1,8 @@
# AMR 1.8.1.9046
# AMR 1.8.1.9047
### New
* EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.
* Function to calculate the mean AMR distance: `mean_amr_distance()`. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.
* Support for `data.frame`-enhancing R packages, more specifically: `data.table`, `tibble`, and `tsibble`. AMR package functions that have a data set as output (such as `rsi_df()` and `bug_drug_combinations()`), will now return the same data type as the input. Furthermore, all our data sets are now in `tibble` format.
* Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info [in this article on our website](https://msberends.github.io/AMR/articles/datasets.html).
* Support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. We are very grateful for the valuable input by our colleagues from other countries. The `AMR` package is now available in 16 languages.

7
R/ab_property.R
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@ -374,7 +374,12 @@ set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale
if (is.data.frame(data)) {
if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
df <- pm_select(data, ...)
out <- tryCatch(suppressWarnings(c(...)), error = function(e) NULL)
if (!is.null(out)) {
df <- data[, out, drop = FALSE]
} else {
df <- pm_select(data, ...)
}
} else {
df <- data
}

163
R/mean_amr_distance.R Normal file
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@ -0,0 +1,163 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Mean AMR Distance
#'
#' This function calculates a normalised mean for antimicrobial resistance between multiple observations.
#' @param x a vector of class [rsi][as.rsi()], [rsi][as.rsi()] or [rsi][as.rsi()], or a [data.frame] containing columns of any of these classes
#' @param ... variables to select (supports tidy selection such as `column1:column4` and [`where(is.mic)`][tidyselect::language]), and can thus also be [antibiotic selectors][ab_selector()]
#' @param combine_SI a [logical] to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant), defaults to `TRUE`
#' @details The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to [Z scores](https://en.wikipedia.org/wiki/Standard_score) (the number of standard deviations from the mean).
#'
#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
#'
#' R/SI values (see [as.rsi()]) are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If `combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
#'
#' For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using [rowMeans()]), see *Examples*.
#'
#' Use [distance_from_row()] to subtract distances from the distance of one row, see *Examples*.
#' @section Interpretation:
#' Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
#' @export
#' @examples
#' x <- random_mic(10)
#' x
#' mean_amr_distance(x)
#'
#' y <- data.frame(
#' id = LETTERS[1:10],
#' amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
#' cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
#' gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
#' tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
#' )
#' y
#' mean_amr_distance(y)
#' y$amr_distance <- mean_amr_distance(y, where(is.mic))
#' y[order(y$amr_distance), ]
#'
#' if (require("dplyr")) {
#' y %>%
#' mutate(
#' amr_distance = mean_amr_distance(., where(is.mic)),
#' check_id_C = distance_from_row(amr_distance, id == "C")
#' ) %>%
#' arrange(check_id_C)
#' }
#' if (require("dplyr")) {
#' # support for groups
#' example_isolates %>%
#' filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
#' select(mo, TCY, carbapenems()) %>%
#' group_by(mo) %>%
#' mutate(d = mean_amr_distance(., where(is.rsi))) %>%
#' arrange(mo, d)
#' }
mean_amr_distance <- function(x, ...) {
UseMethod("mean_amr_distance")
}
#' @rdname mean_amr_distance
#' @export
mean_amr_distance.default <- function(x, ...) {
x <- as.double(x)
(x - mean(x, na.rm = TRUE)) / stats::sd(x, na.rm = TRUE)
}
#' @rdname mean_amr_distance
#' @export
mean_amr_distance.mic <- function(x, ...) {
mean_amr_distance(log2(x))
}
#' @rdname mean_amr_distance
#' @export
mean_amr_distance.disk <- function(x, ...) {
mean_amr_distance(as.double(x))
}
#' @rdname mean_amr_distance
#' @export
mean_amr_distance.rsi <- function(x, combine_SI = TRUE, ...) {
meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
if (isTRUE(combine_SI)) {
x[x == "I"] <- "S"
}
mean_amr_distance(as.double(x))
}
#' @rdname mean_amr_distance
#' @export
mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
df <- x
if (is_null_or_grouped_tbl(df)) {
df <- get_current_data("x", -2)
}
if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
out <- tryCatch(suppressWarnings(c(...)), error = function(e) NULL)
if (!is.null(out)) {
df <- df[, out, drop = FALSE]
} else {
df <- pm_select(df, ...)
}
}
stop_if(ncol(df) < 2,
"data set must contain at least two variables",
call = -2
)
if (message_not_thrown_before("mean_amr_distance", "groups")) {
message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df)))
}
res <- vapply(
FUN.VALUE = double(nrow(df)),
df,
mean_amr_distance,
combine_SI = combine_SI
)
if (is.null(dim(res))) {
if (all(is.na(res))) {
return(NA_real_)
} else {
return(mean(res, na.rm = TRUE))
}
}
res <- rowMeans(res, na.rm = TRUE)
res[is.infinite(res)] <- 0
res
}
#' @rdname mean_amr_distance
#' @param mean_distance the outcome of [mean_amr_distance()]
#' @param row an index, such as a row number
#' @export
distance_from_row <- function(mean_distance, row) {
meet_criteria(mean_distance, allow_class = c("double", "numeric"), is_finite = TRUE)
meet_criteria(row, allow_class = c("logical", "double", "numeric"))
if (is.logical(row)) {
row <- which(row)
}
abs(mean_distance[row] - mean_distance)
}

12
R/vctrs.R
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@ -84,3 +84,15 @@ vec_ptype2.disk.integer <- function(x, y, ...) {
vec_cast.integer.disk <- function(x, to, ...) {
unclass(x)
}
# S3: mic
vec_cast.character.mic <- function(x, to, ...) {
as.character(x)
}
vec_cast.double.mic <- function(x, to, ...) {
# this calls as.double.mic()
as.double(x)
}
vec_math.mic <- function(.fn, x, ...) {
.fn(as.double(x), ...)
}

3
R/zzz.R
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@ -96,6 +96,9 @@ if (utf8_supported && !is_latex) {
s3_register("vctrs::vec_ptype2", "disk.integer")
s3_register("vctrs::vec_ptype2", "integer.disk")
s3_register("vctrs::vec_cast", "integer.disk")
s3_register("vctrs::vec_cast", "character.mic")
s3_register("vctrs::vec_cast", "double.mic")
s3_register("vctrs::vec_math", "mic")
# if mo source exists, fire it up (see mo_source())
try(

59
inst/tinytest/test-mean_amr_distance.R Normal file
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@ -0,0 +1,59 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
vctr_disk <- as.disk(c(20:25))
vctr_mic <- as.mic(2^c(0:5))
vctr_rsi <- as.rsi(c("S", "S", "I", "I", "R", "R"))
expect_identical(
mean_amr_distance(vctr_disk),
(as.double(vctr_disk) - mean(as.double(vctr_disk))) / sd(as.double(vctr_disk))
)
expect_identical(
mean_amr_distance(vctr_mic),
(log2(vctr_mic) - mean(log2(vctr_mic))) / sd(log2(vctr_mic))
)
expect_identical(
mean_amr_distance(vctr_rsi, combine_SI = FALSE),
(c(1, 1, 2, 2, 3, 3) - mean(c(1, 1, 2, 2, 3, 3))) / sd(c(1, 1, 2, 2, 3, 3))
)
expect_identical(
mean_amr_distance(vctr_rsi, combine_SI = TRUE),
(c(1, 1, 1, 1, 3, 3) - mean(c(1, 1, 1, 1, 3, 3))) / sd(c(1, 1, 1, 1, 3, 3))
)
expect_equal(
mean_amr_distance(data.frame(vctr_mic, vctr_rsi, vctr_disk)),
c(-1.10603655, -0.74968823, -0.39333990, -0.03699158, 0.96485397, 1.32120229),
tolerance = 0.00001
)
expect_equal(
mean_amr_distance(data.frame(vctr_mic, vctr_rsi, vctr_disk), 2:3),
c(-0.9909017, -0.7236405, -0.4563792, -0.1891180, 1.0463891, 1.3136503),
tolerance = 0.00001
)

87
man/mean_amr_distance.Rd Normal file
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@ -0,0 +1,87 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mean_amr_distance.R
\name{mean_amr_distance}
\alias{mean_amr_distance}
\alias{mean_amr_distance.default}
\alias{mean_amr_distance.mic}
\alias{mean_amr_distance.disk}
\alias{mean_amr_distance.rsi}
\alias{mean_amr_distance.data.frame}
\alias{distance_from_row}
\title{Mean AMR Distance}
\usage{
mean_amr_distance(x, ...)
\method{mean_amr_distance}{default}(x, ...)
\method{mean_amr_distance}{mic}(x, ...)
\method{mean_amr_distance}{disk}(x, ...)
\method{mean_amr_distance}{rsi}(x, combine_SI = TRUE, ...)
\method{mean_amr_distance}{data.frame}(x, ..., combine_SI = TRUE)
distance_from_row(mean_distance, row)
}
\arguments{
\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.rsi]{rsi} or \link[=as.rsi]{rsi}, or a \link{data.frame} containing columns of any of these classes}
\item{...}{variables to select (supports tidy selection such as \code{column1:column4} and \code{\link[tidyselect:language]{where(is.mic)}}), and can thus also be \link[=ab_selector]{antibiotic selectors}}
\item{combine_SI}{a \link{logical} to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant), defaults to \code{TRUE}}
\item{mean_distance}{the outcome of \code{\link[=mean_amr_distance]{mean_amr_distance()}}}
\item{row}{an index, such as a row number}
}
\description{
This function calculates a normalised mean for antimicrobial resistance between multiple observations.
}
\details{
The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to \href{https://en.wikipedia.org/wiki/Standard_score}{Z scores} (the number of standard deviations from the mean).
MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using \code{\link[=rowMeans]{rowMeans()}}), see \emph{Examples}.
Use \code{\link[=distance_from_row]{distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
}
\section{Interpretation}{
Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
}
\examples{
x <- random_mic(10)
x
mean_amr_distance(x)
y <- data.frame(id = LETTERS[1:10],
amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli"))
y
mean_amr_distance(y)
y$amr_distance <- mean_amr_distance(y, where(is.mic))
y[order(y$amr_distance), ]
if (require("dplyr")) {
y \%>\%
mutate(amr_distance = mean_amr_distance(., where(is.mic)),
check_id_C = distance_from_row(amr_distance, id == "C")) \%>\%
arrange(check_id_C)
}
if (require("dplyr")) {
# support for groups
example_isolates \%>\%
filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
select(mo, TCY, carbapenems()) \%>\%
group_by(mo) \%>\%
mutate(d = mean_amr_distance(., where(is.rsi))) \%>\%
arrange(mo, d)
}
}