P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-08 11:11:54 +02:00
Code Releases Activity

(v0.8.0.9017) keywords update

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2019-11-06 14:43:23 +01:00
parent be9a3e6b12
commit e2d05cb1b0
55 changed files with 755 additions and 108 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
R/ab.R
View File

@ -25,7 +25,6 @@
#' @param x character vector to determine to antibiotic ID
#' @param ... arguments passed on to internal functions
#' @rdname as.ab
#' @keywords atc
#' @inheritSection WHOCC WHOCC
#' @export
#' @importFrom dplyr %>% filter slice pull

1
R/ab_property.R
View File

@ -83,6 +83,7 @@ ab_name <- function(x, language = get_locale(), tolower = FALSE, ...) {
}
#' @rdname ab_property
#' @aliases ATC
#' @export
ab_atc <- function(x, ...) {
ab_validate(x = x, property = "atc", ...)

1
R/age.R
View File

@ -106,7 +106,6 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) {
#' \item{\code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.}
#' }
#' }
#' @keywords age_group age
#' @return Ordered \code{\link{factor}}
#' @seealso To determine ages, based on one or more reference dates, use the \code{\link{age}} function.
#' @export

1
R/count.R
View File

@ -37,7 +37,6 @@
#' @inheritSection portion Combination therapy
#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
#' @keywords resistance susceptibility rsi antibiotics isolate isolates
#' @return Integer
#' @rdname count
#' @name count

2
R/disk.R
View File

@ -27,7 +27,7 @@
#' @param na.rm a logical indicating whether missing values should be removed
#' @details Interpret disk values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
#' @return Ordered integer factor with new class \code{disk}
#' @keywords disk
#' @aliases disk
#' @export
#' @seealso \code{\link{as.rsi}}
#' @inheritSection AMR Read more on our website!

2
R/eucast_rules.R
View File

@ -122,7 +122,7 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
#' \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
#' \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
#' \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
#' @keywords interpretive eucast reading resistance
#' @aliases EUCAST
#' @rdname eucast_rules
#' @export
#' @importFrom dplyr %>% select pull mutate_at vars group_by summarise n

1
R/filter_ab_class.R
View File

@ -29,7 +29,6 @@
#' @param ... parameters passed on to \code{filter_at} from the \code{dplyr} package
#' @details The \code{group} column in \code{\link{antibiotics}} data set will be searched for \code{ab_class} (case-insensitive). If no results are found, the \code{atc_group1} and \code{atc_group2} columns will be searched. Next, \code{x} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set.
#' @rdname filter_ab_class
#' @keywords filter fillter_class
#' @importFrom dplyr filter_at %>% select vars any_vars all_vars
#' @importFrom crayon bold blue
#' @export

1
R/first_isolate.R
View File

@ -70,7 +70,6 @@
#' \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}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
#' @rdname first_isolate
#' @keywords isolate isolates first
#' @seealso \code{\link{key_antibiotics}}
#' @export
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange pull ungroup

1
R/g.test.R
View File

@ -61,7 +61,6 @@
#' where \code{df} are the degrees of freedom.
#'
#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course.
#' @keywords chi
#' @seealso \code{\link{chisq.test}}
#' @references [1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
#' @source This code is almost identical to \code{\link{chisq.test}}, except that:

5
R/like.R
View File

@ -49,9 +49,8 @@
#' # get frequencies of bacteria whose name start with 'Ent' or 'ent'
#' library(dplyr)
#' example_isolates %>%
#' left_join_microorganisms() %>%
#' filter(genus %like% '^ent') %>%
#' freq(genus, species)
#' filter(mo_genus(mo) %like% '^ent') %>%
#' freq(mo_fullname(mo))
like <- function(x, pattern, ignore.case = TRUE) {
if (length(pattern) > 1) {
if (length(x) != length(pattern)) {

42
R/mdro.R
View File

@ -26,6 +26,7 @@
#' @param info print progress
#' @inheritParams eucast_rules
#' @param pct_required_classes minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for \emph{S. aureus}. Setting this \code{pct_required_classes} argument to \code{0.5} (default) means that for every \emph{S. aureus} isolate at least 8 different classes must be available. Any lower number of available classes will return \code{NA} for that isolate.
#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the \code{mdro()} function, it follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using \code{combine_SI = FALSE}, resistance is considered when isolates are R or I.
#' @param verbose a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.
#' @inheritSection eucast_rules Antibiotics
#' @details
@ -43,7 +44,7 @@
#' Please suggest your own (country-specific) guidelines by letting us know: \url{https://gitlab.com/msberends/AMR/issues/new}.
#'
#' \strong{Note:} Every test that involves the Enterobacteriaceae family, will internally be performed using its newly named order Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeolu \emph{et al.} in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by this \code{mdro()} function makes sure that results from before 2016 and after 2016 are identical.
#'
#' @inheritSection as.rsi Interpretation of S, I and R
#' @return \itemize{
#' \item{CMI 2012 paper - function \code{mdr_cmi2012()} or \code{mdro()}:\cr Ordered factor with levels \code{Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)}}
#' \item{TB guideline - function \code{mdr_tb()} or \code{mdro(..., guideline = "TB")}:\cr Ordered factor with levels \code{Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant}}
@ -51,6 +52,7 @@
#' \item{Everything else:\cr Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}. The value \code{"Positive, unconfirmed"} means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests}
#' }
#' @rdname mdro
#' @aliases MDR XDR PDR BRMO 3MRGN 4MRGN
#' @importFrom dplyr %>% filter_at vars all_vars pull mutate_at
#' @importFrom crayon blue bold italic
#' @importFrom cleaner percentage
@ -80,8 +82,9 @@ mdro <- function(x,
guideline = NULL,
col_mo = NULL,
info = TRUE,
verbose = FALSE,
pct_required_classes = 0.5,
combine_SI = TRUE,
verbose = FALSE,
...) {
if (verbose == TRUE & interactive()) {
@ -109,7 +112,7 @@ mdro <- function(x,
# allow pct_required_classes = 75 -> pct_required_classes = 0.75
pct_required_classes <- pct_required_classes / 100
}
if (!is.null(list(...)$country)) {
warning("Using `country` is deprecated, use `guideline` instead. Please see ?mdro.", call. = FALSE)
guideline <- list(...)$country
@ -410,8 +413,19 @@ mdro <- function(x,
if (guideline$code == "tb" & length(abx_tb) == 0) {
stop("No antimycobacterials found in data set.", call. = FALSE)
}
if (combine_SI == TRUE) {
search_result <- "R"
} else {
search_result <- c("R", "I")
}
if (info == TRUE) {
if (combine_SI == TRUE) {
cat("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n")
} else {
cat("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n")
}
cat("\nDetermining multidrug-resistant organisms (MDRO), according to:\n",
bold("Guideline: "), italic(guideline$name), "\n",
bold("Version: "), guideline$version, "\n",
@ -444,19 +458,21 @@ mdro <- function(x,
x <<- x %>% mutate_at(vars(cols), as.rsi)
x[rows, "columns_nonsusceptible"] <<- sapply(rows,
function(row, group_vct = cols) {
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE], function(y) y == "R")
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE],
function(y) y %in% search_result)
paste(sort(c(unlist(strsplit(x[row, "columns_nonsusceptible", drop = TRUE], ", ")),
names(cols_nonsus)[cols_nonsus])),
collapse = ", ")
})
if (any_all == "any") {
row_filter <- which(x[, cols] == "R")
search_function <- dplyr::any_vars
} else if (any_all == "all") {
row_filter <- x %>%
mutate(index = seq_len(nrow(.))) %>%
filter_at(vars(cols), all_vars(. == "R")) %>%
pull((index))
search_function <- dplyr::all_vars
}
row_filter <- x %>%
filter_at(vars(cols), search_function(. %in% search_result)) %>%
pull("row_number")
rows <- rows[rows %in% row_filter]
x[rows, "MDRO"] <<- to
x[rows, "reason"] <<- paste0(any_all, " of the required antibiotics ", ifelse(any_all == "any", "is", "are"), " R")
@ -479,7 +495,7 @@ mdro <- function(x,
if (verbose == TRUE) {
x[rows, "columns_nonsusceptible"] <<- sapply(rows,
function(row, group_vct = lst_vector) {
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE], function(y) y %in% c("I", "R"))
cols_nonsus <- sapply(x[row, group_vct, drop = FALSE], function(y) y %in% search_result)
paste(sort(names(cols_nonsus)[cols_nonsus]), collapse = ", ")
})
}
@ -487,14 +503,14 @@ mdro <- function(x,
function(row, group_tbl = lst) {
sum(sapply(group_tbl,
function(group) {
any(x[row, group[!is.na(group)]] == "R", na.rm = TRUE) |
any(x[row, group[!is.na(group)]] == "I", na.rm = TRUE)
any(unlist(x[row, group[!is.na(group)], drop = TRUE]) %in% search_result, na.rm = TRUE)
}),
na.rm = TRUE)
})
# for PDR; all agents are R (or I if combine_SI = FALSE)
x[filter_at(x[rows, ],
vars(lst_vector),
all_vars(. %in% c("R", "I")))$row_number, "classes_affected"] <<- 999
all_vars(. %in% search_result))$row_number, "classes_affected"] <<- 999
}
if (info == TRUE) {

2
R/mic.R
View File

@ -27,7 +27,7 @@
#' @param na.rm a logical indicating whether missing values should be removed
#' @details Interpret MIC values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
#' @return Ordered factor with new class \code{mic}
#' @keywords mic
#' @aliases MIC
#' @export
#' @importFrom dplyr %>%
#' @seealso \code{\link{as.rsi}}

1
R/portion.R
View File

@ -79,7 +79,6 @@
#'
#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character.
#' @rdname portion
#' @name portion

4
R/rsi.R
View File

@ -36,7 +36,7 @@
#'
#' The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
#' @section Interpretation of S, I and R:
#' In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
#'
#' \itemize{
#' \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
@ -48,7 +48,7 @@
#'
#' This AMR package honours this new insight. Use \code{\link{portion_SI}} to determine antimicrobial susceptibility and \code{\link{count_SI}} to count susceptible isolates.
#' @return Ordered factor with new class \code{rsi}
#' @keywords rsi
#' @aliases RSI
#' @export
#' @importFrom dplyr %>% desc arrange filter
#' @seealso \code{\link{as.mic}}