AMR/R/mean_amr_distance.R

168 lines
7.0 KiB
R
Raw Normal View History

2022-08-30 21:48:02 +02:00
# ==================================================================== #
# TITLE #
2022-10-05 09:12:22 +02:00
# AMR: An R Package for Working with Antimicrobial Resistance Data #
2022-08-30 21:48:02 +02:00
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
2022-10-05 09:12:22 +02:00
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
2022-08-30 21:48:02 +02:00
# 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)
}