(v1.5.0.9008) Internal data sets to pkg, speed for auto col determination

v1.8.2
parent 27f084d819
commit 1ba44776a1
  1. 4
      DESCRIPTION
  2. 1
      NAMESPACE
  3. 8
      NEWS.md
  4. 3
      R/aa_helper_functions.R
  5. 19
      R/ab_class_selectors.R
  6. 1
      R/eucast_rules.R
  7. 13
      R/guess_ab_col.R
  8. 77
      R/isolate_identifier.R
  9. 23
      R/rsi.R
  10. 4
      R/rsi_calc.R
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      R/sysdata.rda
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  13. 2
      cran-comments.md
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      data-raw/AMR_1.5.0.9007.tar.gz
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  17. 2
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  18. 2
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  19. 8
      docs/articles/SPSS.html
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.5.0.9007
Date: 2021-01-18
Version: 1.5.0.9008
Date: 2021-01-22
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(role = c("aut", "cre"),

@ -22,6 +22,7 @@ S3method("[[<-",isolate_identifier)
S3method("[[<-",mic)
S3method("[[<-",mo)
S3method("[[<-",rsi)
S3method(all.equal,isolate_identifier)
S3method(as.data.frame,ab)
S3method(as.data.frame,mo)
S3method(as.double,mic)

@ -1,5 +1,5 @@
# AMR 1.5.0.9007
## <small>Last updated: 18 January 2021</small>
# AMR 1.5.0.9008
## <small>Last updated: 22 January 2021</small>
### New
* Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.
@ -32,10 +32,12 @@
* Updated the data set `microorganisms.codes` (which contains popular LIS and WHONET codes for microorganisms) for some species of *Mycobacterium* that previously incorrectly returned *M. africanum*
* Added Pretomanid (PMD, J04AK08) to the `antibiotics` data set
* WHONET code `"PNV"` will now correctly be interpreted as `PHN`, the antibiotic code for phenoxymethylpenicillin ('peni V')
* Fix for verbose output of `mdro(..., verbose = TRUE)` for German guideline (3MGRN and 4MGRN) and *P. aeruginosa* in Dutch guideline (BRMO)
* Fix for verbose output of `mdro(..., verbose = TRUE)` for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only *P. aeruginosa*)
* `is.rsi.eligible()` now returns `FALSE` immediately if the input does not contain any of the values "R", "S" or "I". This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.
### Other
* Big documentation updates
* Loading the package (i.e., `library(AMR)`) now is ~50 times faster than before
# AMR 1.5.0

@ -585,7 +585,7 @@ get_current_data <- function(arg_name, call) {
stop_("argument `", arg_name, "` is missing with no default", call = call)
}
}
# try a (base R) method, by going over the complete system call stack with sys.frames()
not_set <- TRUE
frms <- lapply(sys.frames(), function(el) {
@ -615,6 +615,7 @@ get_current_data <- function(arg_name, call) {
NULL
}
})
vars_df <- tryCatch(frms[[which(!vapply(FUN.VALUE = logical(1), frms, is.null))]], error = function(e) NULL)
if (is.data.frame(vars_df)) {
return(vars_df)

@ -173,7 +173,15 @@ ab_selector <- function(ab_class, function_name) {
}
vars_df <- get_current_data(arg_name = NA, call = -3)
ab_in_data <- get_column_abx(vars_df, info = FALSE)
# improve speed here so it will only run once when e.g. in one select call
if (!identical(pkg_env$ab_selector, unique_call_id())) {
ab_in_data <- get_column_abx(vars_df, info = FALSE)
pkg_env$ab_selector <- unique_call_id()
pkg_env$ab_selector_cols <- ab_in_data
} else {
ab_in_data <- pkg_env$ab_selector_cols
}
if (length(ab_in_data) == 0) {
message_("No antimicrobial agents found.")
@ -199,13 +207,14 @@ ab_selector <- function(ab_class, function_name) {
} else {
agents_formatted <- paste0("column '", font_bold(agents, collapse = NULL), "'")
agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
agents_formatted[agents != agents_names] <- paste0(agents_formatted[agents != agents_names],
" (", agents_names[agents != agents_names], ")")
need_name <- tolower(agents) != tolower(agents_names)
agents_formatted[need_name] <- paste0(agents_formatted[need_name],
" (", agents_names[need_name], ")")
message_("Selecting ", ab_group, ": ", paste(agents_formatted, collapse = ", "),
as_note = FALSE,
extra_indent = nchar(paste0("Selecting ", ab_group, ": ")))
}
remember_thrown_message(function_name)
}
remember_thrown_message(function_name)
}
unname(agents)
}

@ -1049,6 +1049,7 @@ eucast_rules <- function(x,
warn_lacking_rsi_class <- unique(warn_lacking_rsi_class)
warning_("Not all columns with antimicrobial results are of class <rsi>. Transform them on beforehand, with e.g.:\n",
" ", x_deparsed, " %>% mutate_if(is.rsi.eligible, as.rsi)\n",
" ", x_deparsed, " %>% mutate(across((is.rsi.eligible), as.rsi))\n",
" ", x_deparsed, " %>% as.rsi(", ifelse(length(warn_lacking_rsi_class) == 1,
warn_lacking_rsi_class,
paste0(warn_lacking_rsi_class[1], ":", warn_lacking_rsi_class[length(warn_lacking_rsi_class)])),

@ -137,25 +137,25 @@ get_column_abx <- function(x,
} else if (info == TRUE) {
message_("...", appendLF = FALSE, as_note = FALSE)
}
x_bak <- x
# only check columns that are a valid AB code, ATC code, name, abbreviation or synonym,
# or already have the <rsi> class (as.rsi)
# and that they have no more than 50% invalid values
vectr_antibiotics <- unique(toupper(unlist(antibiotics[, c("ab", "atc", "name", "abbreviations", "synonyms")])))
vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
x_columns <- vapply(FUN.VALUE = character(1), colnames(x), function(col, df = x_bak) {
x_columns <- vapply(FUN.VALUE = character(1), colnames(x), function(col, df = x) {
if (toupper(col) %in% vectr_antibiotics ||
is.rsi(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE]) ||
is.rsi.eligible(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE],
threshold = 0.5)) {
is.rsi(x[, col, drop = TRUE]) ||
is.rsi.eligible(x[, col, drop = TRUE], threshold = 0.5)
) {
return(col)
} else {
return(NA_character_)
}
})
x_columns <- x_columns[!is.na(x_columns)]
x <- x[, x_columns, drop = FALSE] # without drop = TRUE, x will become a vector when x_columns is length 1
df_trans <- data.frame(colnames = colnames(x),
abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)),
stringsAsFactors = FALSE)
@ -217,7 +217,6 @@ get_column_abx <- function(x,
}
}
if (!is.null(hard_dependencies)) {
hard_dependencies <- unique(hard_dependencies)
if (!all(hard_dependencies %in% names(x))) {

@ -26,9 +26,10 @@
#' Create Identifier of an Isolate
#'
#' This function will paste the microorganism code with all antimicrobial results into one string for each row in a data set. This is useful to compare isolates, e.g. between institutions or regions, when there is no genotyping available.
#' @inheritSection lifecycle Maturing Lifecycle
#' @inheritSection lifecycle Experimental Lifecycle
#' @inheritParams eucast_rules
#' @param cols_ab a character vector of column names of `x`, or (a combination with) an [antibiotic selector function]([ab_class()]), such as [carbapenems()] and [aminoglycosides()]
#' @rdname isolate_identifier
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
@ -43,7 +44,12 @@
isolate_identifier <- function(x, col_mo = NULL, cols_ab = NULL) {
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x, "mo")
if (is.null(col_mo)) {
# no column found, then ignore the argument
col_mo <- FALSE
}
}
if (isFALSE(col_mo)) {
# is FALSE then ignore mo column
x$col_mo <- ""
@ -60,14 +66,77 @@ isolate_identifier <- function(x, col_mo = NULL, cols_ab = NULL) {
# tryCatch adds 4 calls, so total is -5
error = function(e) stop_(e$message, call = -5))
}
if (length(cols_ab) == 0) {
warning_("no columns with antimicrobial agents found", call = TRUE)
# cope with empty values
if (length(cols_ab) == 0 && all(x[, col_mo, drop = TRUE] == "", na.rm = TRUE)) {
warning_("in isolate_identifier(): no column with microorganisms and no columns with antimicrobial agents found", call = FALSE)
} else if (length(cols_ab) == 0) {
warning_("in isolate_identifier(): no columns with antimicrobial agents found", call = FALSE)
}
out <- x[, c(col_mo, cols_ab), drop = FALSE]
out <- do.call(paste, c(out, sep = ""))
out <- gsub("NA", ".", out, fixed = TRUE)
set_clean_class(out, new_class = c("isolate_identifier", "character"))
out <- set_clean_class(out, new_class = c("isolate_identifier", "character"))
attr(out, "ab") <- cols_ab
out
}
#' @method all.equal isolate_identifier
#' @rdname isolate_identifier
#' @export
all.equal.isolate_identifier <- function(target, current, ignore_empty_results = TRUE, ...) {
if (isTRUE(all.equal.character(target, current))) {
return(TRUE)
}
# vectorise over both target and current
if (length(target) > 1 && length(current) == 1) {
current <- rep(current, length(target))
} else if (length(current) > 1 && length(target) == 1) {
target <- rep(target, length(current))
}
stop_if(length(target) != length(current),
"length of `target` and `current` must be the same, or one must be 1")
get_vector <- function(x) {
if (grepl("|", x, fixed = TRUE)) {
mo <- gsub("(.*)\\|.*", "\\1", x)
} else {
mo <- NULL
}
if (grepl("|", x, fixed = TRUE)) {
ab <- gsub(".*\\|(.*)", "\\1", x)
} else {
ab <- x
}
ab <- strsplit(ab, "")[[1L]]
if (is.null(mo)) {
out <- as.character(ab)
names(out) <- attributes(x)$ab
} else {
out <- as.character(c(mo, ab))
names(out) <- c("mo", attributes(x)$ab)
}
out
}
# run it
for (i in seq_len(length(target))) {
if (i == 1) {
df <- data.frame(object = paste0(c("target[", "current["), i, "]"))
}
trgt <- get_vector(target[i])
crnt <- get_vector(current[i])
if (ignore_empty_results == TRUE) {
diff <- names(trgt[trgt != crnt & trgt != "." & crnt != "."])
} else {
diff <- names(trgt[trgt != crnt])
}
}
stop("THIS FUNCTION IS WORK IN PROGRESS AND NOT AVAILABLE IN THIS BETA VERSION")
}
#' @method print isolate_identifier

@ -49,16 +49,16 @@
#' 2. For **interpreting minimum inhibitory concentration (MIC) values** according to EUCAST or CLSI. You must clean your MIC values first using [as.mic()], that also gives your columns the new data class [`mic`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
#' * Using `dplyr`, R/SI interpretation can be done very easily with either:
#' ```
#' your_data %>% mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
#' your_data %>% mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0
#' your_data %>% mutate_if(is.mic, as.rsi) # until dplyr 1.0.0
#' your_data %>% mutate(across((is.mic), as.rsi)) # since dplyr 1.0.0
#' ```
#' * Operators like "<=" will be stripped before interpretation. When using `conserve_capped_values = TRUE`, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (`conserve_capped_values = FALSE`) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
#'
#' 3. For **interpreting disk diffusion diameters** according to EUCAST or CLSI. You must clean your disk zones first using [as.disk()], that also gives your columns the new data class [`disk`]. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the `mo` argument.
#' * Using `dplyr`, R/SI interpretation can be done very easily with either:
#' ```
#' your_data %>% mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
#' your_data %>% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0
#' your_data %>% mutate_if(is.disk, as.rsi) # until dplyr 1.0.0
#' your_data %>% mutate(across((is.disk), as.rsi)) # since dplyr 1.0.0
#' ```
#'
#' 4. For **interpreting a complete data set**, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running `as.rsi(data)`.
@ -133,7 +133,7 @@
#' if (require("dplyr")) {
#' df %>% mutate_if(is.mic, as.rsi)
#' df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)
#' df %>% mutate(across(where(is.mic), as.rsi))
#' df %>% mutate(across((is.mic), as.rsi))
#' df %>% mutate_at(vars(AMP:TOB), as.rsi)
#' df %>% mutate(across(AMP:TOB, as.rsi))
#'
@ -179,7 +179,7 @@
#'
#' # note: from dplyr 1.0.0 on, this will be:
#' # example_isolates %>%
#' # mutate(across(where(is.rsi.eligible), as.rsi))
#' # mutate(across((is.rsi.eligible), as.rsi))
#' }
#' }
as.rsi <- function(x, ...) {
@ -202,14 +202,19 @@ is.rsi.eligible <- function(x, threshold = 0.05) {
"numeric",
"integer",
"mo",
"ab",
"Date",
"POSIXct",
"POSIXt",
"rsi",
"raw",
"hms")
"hms",
"mic",
"disk")
%in% class(x))) {
# no transformation needed
FALSE
return(FALSE)
} else if (!any(c("R", "S", "I") %in% x, na.rm = TRUE)) {
return(FALSE)
} else {
x <- x[!is.na(x) & !is.null(x) & !identical(x, "")]
if (length(x) == 0) {

@ -148,7 +148,9 @@ rsi_calc <- function(...,
if (print_warning == TRUE) {
if (message_not_thrown_before("rsi_calc")) {
warning_("Increase speed by transforming to class <rsi> on beforehand: your_data %>% mutate_if(is.rsi.eligible, as.rsi)",
warning_("Increase speed by transforming to class <rsi> on beforehand:\n",
" your_data %>% mutate_if(is.rsi.eligible, as.rsi)\n",
" your_data %>% mutate(across((is.rsi.eligible), as.rsi))",
call = FALSE)
remember_thrown_message("rsi_calc")
}

Binary file not shown.

@ -28,35 +28,6 @@ pkg_env <- new.env(hash = FALSE)
pkg_env$mo_failed <- character(0)
.onLoad <- function(libname, pkgname) {
assign(x = "AB_lookup",
value = create_AB_lookup(),
envir = asNamespace("AMR"))
assign(x = "MO_lookup",
value = create_MO_lookup(),
envir = asNamespace("AMR"))
assign(x = "MO.old_lookup",
value = create_MO.old_lookup(),
envir = asNamespace("AMR"))
assign(x = "INTRINSIC_R",
value = create_intr_resistance(),
envir = asNamespace("AMR"))
assign(x = "LANGUAGES_SUPPORTED",
value = sort(c("en", unique(translations_file$lang))),
envir = asNamespace("AMR"))
assign(x = "MO_CONS",
value = create_species_cons_cops("CoNS"),
envir = asNamespace("AMR"))
assign(x = "MO_COPS",
value = create_species_cons_cops("CoPS"),
envir = asNamespace("AMR"))
# Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
# without the need to depend on other packages. This was suggested by the
# developers of the vctrs package:
@ -102,89 +73,5 @@ pkg_env$mo_failed <- character(0)
font_bold("options(AMR_silentstart = TRUE)"), "]"))
}
create_intr_resistance <- function() {
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
paste(AMR::microorganisms[match(AMR::intrinsic_resistant$microorganism, AMR::microorganisms$fullname), "mo", drop = TRUE],
AMR::antibiotics[match(AMR::intrinsic_resistant$antibiotic, AMR::antibiotics$name), "ab", drop = TRUE])
}
create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
# Determination of which staphylococcal species are CoNS/CoPS according to:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
# this function returns class <mo>
MO_staph <- AMR::microorganisms
MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
if (type == "CoNS") {
MO_staph[which(MO_staph$species %in% c("coagulase-negative", "argensis", "arlettae",
"auricularis", "caeli", "capitis", "caprae",
"carnosus", "chromogenes", "cohnii", "condimenti",
"debuckii", "devriesei", "edaphicus", "epidermidis",
"equorum", "felis", "fleurettii", "gallinarum",
"haemolyticus", "hominis", "jettensis", "kloosii",
"lentus", "lugdunensis", "massiliensis", "microti",
"muscae", "nepalensis", "pasteuri", "petrasii",
"pettenkoferi", "piscifermentans", "pseudoxylosus",
"rostri", "saccharolyticus", "saprophyticus",
"sciuri", "simulans", "stepanovicii", "succinus",
"vitulinus", "warneri", "xylosus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
"mo", drop = TRUE]
} else if (type == "CoPS") {
MO_staph[which(MO_staph$species %in% c("coagulase-positive",
"simiae", "agnetis",
"delphini", "lutrae",
"hyicus", "intermedius",
"pseudintermedius", "pseudointermedius",
"schweitzeri", "argenteus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
"mo", drop = TRUE]
}
}
create_AB_lookup <- function() {
AB_lookup <- AMR::antibiotics
AB_lookup$generalised_name <- generalise_antibiotic_name(AB_lookup$name)
AB_lookup$generalised_synonyms <- lapply(AB_lookup$synonyms, generalise_antibiotic_name)
AB_lookup$generalised_abbreviations <- lapply(AB_lookup$abbreviations, generalise_antibiotic_name)
AB_lookup$generalised_loinc <- lapply(AB_lookup$loinc, generalise_antibiotic_name)
AB_lookup
}
create_MO_lookup <- function() {
MO_lookup <- AMR::microorganisms
MO_lookup$kingdom_index <- NA_real_
MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
MO_lookup$fullname_lower <- tolower(trimws(paste(MO_lookup$genus,
MO_lookup$species,
MO_lookup$subspecies)))
ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname)
MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname"])
MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
# add a column with only "e coli" like combinations
MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
# so arrange data on prevalence first, then kingdom, then full name
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), ]
}
create_MO.old_lookup <- function() {
MO.old_lookup <- AMR::microorganisms.old
MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
# add a column with only "e coli"-like combinations
MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
# so arrange data on prevalence first, then full name
MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
}

@ -1 +1 @@
* Since version 0.3.0 (2018-08-14), CHECK returns a NOTE for having a data directory over 3 MB. This is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package.
* Ever since one of the first CRAN releases, CHECK returns a NOTE for having a data and R directory over 3 MB. This is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package.

Binary file not shown.

Binary file not shown.

@ -23,12 +23,108 @@
# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
# Run this file to update the package using: -------------------------------
# Run this file to update the package using:
# source("data-raw/internals.R")
# --------------------------------------------------------------------------
# See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
library(dplyr, warn.conflicts = FALSE)
devtools::load_all(quiet = TRUE)
old_globalenv <- ls(envir = globalenv())
# Helper functions --------------------------------------------------------
create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
# Determination of which staphylococcal species are CoNS/CoPS according to:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
# this function returns class <mo>
MO_staph <- AMR::microorganisms
MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
if (type == "CoNS") {
MO_staph[which(MO_staph$species %in% c("coagulase-negative", "argensis", "arlettae",
"auricularis", "caeli", "capitis", "caprae",
"carnosus", "chromogenes", "cohnii", "condimenti",
"debuckii", "devriesei", "edaphicus", "epidermidis",
"equorum", "felis", "fleurettii", "gallinarum",
"haemolyticus", "hominis", "jettensis", "kloosii",
"lentus", "lugdunensis", "massiliensis", "microti",
"muscae", "nepalensis", "pasteuri", "petrasii",
"pettenkoferi", "piscifermentans", "pseudoxylosus",
"rostri", "saccharolyticus", "saprophyticus",
"sciuri", "simulans", "stepanovicii", "succinus",
"vitulinus", "warneri", "xylosus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
"mo", drop = TRUE]
} else if (type == "CoPS") {
MO_staph[which(MO_staph$species %in% c("coagulase-positive",
"simiae", "agnetis",
"delphini", "lutrae",
"hyicus", "intermedius",
"pseudintermedius", "pseudointermedius",
"schweitzeri", "argenteus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
"mo", drop = TRUE]
}
}
create_AB_lookup <- function() {
AB_lookup <- AMR::antibiotics
AB_lookup$generalised_name <- generalise_antibiotic_name(AB_lookup$name)
AB_lookup$generalised_synonyms <- lapply(AB_lookup$synonyms, generalise_antibiotic_name)
AB_lookup$generalised_abbreviations <- lapply(AB_lookup$abbreviations, generalise_antibiotic_name)
AB_lookup$generalised_loinc <- lapply(AB_lookup$loinc, generalise_antibiotic_name)
AB_lookup
}
create_MO_lookup <- function() {
MO_lookup <- AMR::microorganisms
MO_lookup$kingdom_index <- NA_real_
MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
MO_lookup$fullname_lower <- tolower(trimws(paste(MO_lookup$genus,
MO_lookup$species,
MO_lookup$subspecies)))
ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname)
MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname"])
MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
# add a column with only "e coli" like combinations
MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
# so arrange data on prevalence first, then kingdom, then full name
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), ]
}
create_MO.old_lookup <- function() {
MO.old_lookup <- AMR::microorganisms.old
MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
# add a column with only "e coli"-like combinations
MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
# so arrange data on prevalence first, then full name
MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
}
create_intr_resistance <- function() {
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
paste(AMR::microorganisms[match(AMR::intrinsic_resistant$microorganism, AMR::microorganisms$fullname), "mo", drop = TRUE],
AMR::antibiotics[match(AMR::intrinsic_resistant$antibiotic, AMR::antibiotics$name), "ab", drop = TRUE])
}
# Save internal data sets to R/sysdata.rda --------------------------------
# See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
eucast_rules_file <- utils::read.delim(file = "data-raw/eucast_rules.tsv",
skip = 10,
sep = "\t",
@ -48,7 +144,7 @@ eucast_rules_file <- utils::read.delim(file = "data-raw/eucast_rules.tsv",
mutate(reference.rule_group = as.character(reference.rule_group)) %>%
select(-sorting_rule)
# Translations ----
# Translations
translations_file <- utils::read.delim(file = "data-raw/translations.tsv",
sep = "\t",
stringsAsFactors = FALSE,
@ -62,23 +158,42 @@ translations_file <- utils::read.delim(file = "data-raw/translations.tsv",
allowEscapes = TRUE, # else "\\1" will be imported as "\\\\1"
quote = "")
# Old microorganism codes -------------------------------------------------
# Old microorganism codes
microorganisms.translation <- readRDS("data-raw/microorganisms.translation.rds")
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
INTRINSIC_R <- create_intr_resistance()
# for checking input in `language` argument in e.g. mo_*() and ab_*() functions
LANGUAGES_SUPPORTED <- sort(c("en", unique(translations_file$lang)))
# vectors of CoNS and CoPS, improves speed in as.mo()
MO_CONS <- create_species_cons_cops("CoNS")
MO_COPS <- create_species_cons_cops("CoPS")
# reference data - they have additional columns compared to `antibiotics` and `microorganisms` to improve speed
AB_lookup <- create_AB_lookup()
MO_lookup <- create_MO_lookup()
MO.old_lookup <- create_MO.old_lookup()
# Export to package as internal data ----
usethis::use_data(eucast_rules_file, translations_file, microorganisms.translation,
usethis::use_data(eucast_rules_file,
translations_file,
microorganisms.translation,
INTRINSIC_R,
LANGUAGES_SUPPORTED,
MO_CONS,
MO_COPS,
AB_lookup,
MO_lookup,
MO.old_lookup,
internal = TRUE,
overwrite = TRUE,
version = 2,
compress = "xz")
# Remove from global environment ----
rm(eucast_rules_file)
rm(translations_file)
rm(microorganisms.translation)
# Export data sets to the repository in different formats -----------------
# Save to raw data to repository ----
write_md5 <- function(object) {
conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
writeLines(digest::digest(object, "md5"), conn)
@ -93,7 +208,7 @@ changed_md5 <- function(object) {
}, error = function(e) TRUE)
}
usethis::ui_done(paste0("Saving raw data to {usethis::ui_value('/data-raw/')}"))
devtools::load_all(quiet = TRUE)
# give official names to ABs and MOs
rsi <- dplyr::mutate(rsi_translation, ab = ab_name(ab), mo = mo_name(mo))
if (changed_md5(rsi)) {
@ -169,5 +284,7 @@ if (changed_md5(dosage)) {
try(openxlsx::write.xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
}
rm(write_md5)
rm(changed_md5)
# remove leftovers from global env
current_globalenv <- ls(envir = globalenv())
rm(list = current_globalenv[!current_globalenv %in% old_globalenv])
rm(current_globalenv)

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>
@ -193,7 +193,7 @@
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">29 December 2020</h4>
<h4 class="date">22 January 2021</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -228,7 +228,7 @@
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, more than <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">360,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">383,346 R-related questions</a> have already been asked on this platform (that covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
@ -243,7 +243,7 @@
<li>
<p><strong>R is (nowadays) the preferred analysis software in academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann <em>et al.</em>, 2017). For all the above reasons, the number of references to R as an analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/">is rising continuously</a> and has even surpassed SPSS for academic use (Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2020, I don’t see any reason why SPSS would be of any better use than R.</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2021, I don’t see any reason why SPSS would be of any better use than R.</p>
</li>
</ul>
<p>To demonstrate the first point:</p>

@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -24,14 +24,6 @@
# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
*/
@media (prefers-color-scheme: dark) {
.navbar-default {
background-color: #213730;
}
.navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus {
background-color: #355951;
}
}
/* R for Data Science (r4ds) */
#r4ds a {
@ -209,16 +201,6 @@ thead ~ tbody {
/* only when it has a header */
border-bottom: 2px solid black;
}
@media (prefers-color-scheme: dark) {
thead {
border-top: 2px solid white;
border-bottom: 2px solid white;
}
thead ~ tbody {
/* only when it has a header */
border-bottom: 2px solid white;
}
}
thead th {
text-align: inherit;
}

@ -43,7 +43,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9007</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>
@ -236,13 +236,13 @@
<small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="amr-1509007" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9007">
<a href="#amr-1509007" class="anchor"></a>AMR 1.5.0.9007<small> Unreleased </small>
<div id="amr-1509008" class="section level1">
<h1 class="page-header" data-toc-text="1.5.0.9008">
<a href="#amr-1509008" class="anchor"></a>AMR 1.5.0.9008<small> Unreleased </small>
</h1>
<div id="last-updated-18-january-2021" class="section level2">
<div id="last-updated-22-january-2021" class="section level2">
<h2 class="hasAnchor">
<a href="#last-updated-18-january-2021" class="anchor"></a><small>Last updated: 18 January 2021</small>
<a href="#last-updated-22-january-2021" class="anchor"></a><small>Last updated: 22 January 2021</small>
</h2>
<div id="new" class="section level3">
<h3 class="hasAnchor">
@ -285,7 +285,9 @@
</li>
<li>Added Pretomanid (PMD, J04AK08) to the <code>antibiotics</code> data set</li>
<li>WHONET code <code>"PNV"</code> will now correctly be interpreted as <code>PHN</code>, the antibiotic code for phenoxymethylpenicillin (‘peni V’)</li>
<li>Fix for verbose output of <code><a href="../reference/mdro.html">mdro(..., verbose = TRUE)</a></code> for German guideline (3MGRN and 4MGRN) and <em>P. aeruginosa</em> in Dutch guideline (BRMO)</li>
<li>Fix for verbose output of <code><a href="../reference/mdro.html">mdro(..., verbose = TRUE)</a></code> for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only <em>P. aeruginosa</em>)</li>
<li>
<code><a href="../reference/as.rsi.html">is.rsi.eligible()</a></code> now returns <code>FALSE</code> immediately if the input does not contain any of the values “R”, “S” or “I”. This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.</li>
</ul>
</div>
<div id="other" class="section level3">
@ -293,6 +295,7 @@
<a href="#other" class="anchor"></a>Other</h3>
<ul>
<li>Big documentation updates</li>
<li>Loading the package (i.e., <code><a href="https://msberends.github.io/AMR/">library(AMR)</a></code>) now is ~50 times faster than before</li>
</ul>
</div>
</div>

@ -12,7 +12,7 @@ articles:
datasets: datasets.html
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
last_built: 2021-01-18T17:45Z
last_built: 2021-01-22T08:54Z
urls:
reference: https://msberends.github.io/AMR//reference
article: https://msberends.github.io/AMR//articles

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>

@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9008</span>
</span>
</div>
@ -346,14 +346,14 @@
<p>The <code>as.rsi()</code> function works in four ways:</p><ol>
<li><p>For <strong>cleaning raw / untransformed data</strong>. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as <code>"&lt;0.25; S"</code> will be coerced to <code>"S"</code>. Combined interpretations for multiple test methods (as seen in laboratory records) such as <code>"S; S"</code> will be coerced to <code>"S"</code>, but a value like <code>"S; I"</code> will return <code>NA</code> with a warning that the input is unclear.</p></li>
<li><p>For <strong>interpreting minimum inhibitory concentration (MIC) values</strong> according to EUCAST or CLSI. You must clean your MIC values first using <code><a href='as.mic.html'>as.mic()</a></code>, that also gives your columns the new data class <code><a href='as.mic.html'>mic</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul>
<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.mic</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
<span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='fu'>where</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.mic</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
<span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><span class='fu'><a href='https://dplyr.tidyverse.org/reference/across.html'>across</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>is.mic</span><span class='op'>)</span>, <span class='va'>as.rsi</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># since dplyr 1.0.0</span>
</pre></li>
<li><p>Operators like "&lt;=" will be stripped before interpretation. When using <code>conserve_capped_values = TRUE</code>, an MIC value of e.g. "&gt;2" will always return "R", even if the breakpoint according to the chosen guideline is "&gt;=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (<code>conserve_capped_values = FALSE</code>) considers "&gt;2" to be lower than "&gt;=4" and might in this case return "S" or "I".</p></li>
</ul></li>
<li><p>For <strong>interpreting disk diffusion diameters</strong> according to EUCAST or CLSI. You must clean your disk zones first using <code><a href='as.disk.html'>as.disk()</a></code>, that also gives your columns the new data class <code><a href='as.disk.html'>disk</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul>
<li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><pre><span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate_all.html'>mutate_if</a></span><span class='op'>(</span><span class='va'>is.disk</span>, <span class='va'>as.rsi</span><span class='op'>)</span> <span class='co'># until dplyr 1.0.0</span>
<span class='va'>your_data</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span><