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bring back antibiogram(), without deps

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dr. M.S. (Matthijs) Berends 2023-02-10 16:18:00 +01:00
parent 70a7ba0206
commit bc434db835
42 changed files with 11307 additions and 4734 deletions
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5
DESCRIPTION
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@ -1,5 +1,5 @@
Package: AMR
Version: 1.8.2.9113
Version: 1.8.2.9114
Date: 2023-02-10
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
@ -26,7 +26,8 @@ Authors@R: c(
person(family = "Salm", c("Jonas"), role = "ctb"),
person(family = "Schade", c("Rogier", "P."), role = "ctb"),
person(family = "Sinha", c("Bhanu", "N.", "M."), role = "ths", comment = c(ORCID = "0000-0003-1634-0010")),
person(family = "Underwood", c("Anthony"), role = "ctb", comment = c(ORCID = "0000-0002-8547-4277")))
person(family = "Underwood", c("Anthony"), role = "ctb", comment = c(ORCID = "0000-0002-8547-4277")),
person(family = "Williams", c("Anita"), role = "ctb", comment = c(ORCID = "0000-0002-5295-8451")))
Depends: R (>= 3.0.0)
Enhances:
cleaner,

4
NAMESPACE
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@ -73,6 +73,7 @@ S3method(asin,mic)
S3method(asinh,mic)
S3method(atan,mic)
S3method(atanh,mic)
S3method(barplot,antibiogram)
S3method(barplot,disk)
S3method(barplot,mic)
S3method(barplot,rsi)
@ -123,12 +124,14 @@ S3method(mean_amr_distance,mic)
S3method(mean_amr_distance,sir)
S3method(median,mic)
S3method(min,mic)
S3method(plot,antibiogram)
S3method(plot,disk)
S3method(plot,mic)
S3method(plot,resistance_predict)
S3method(plot,rsi)
S3method(plot,sir)
S3method(print,ab)
S3method(print,antibiogram)
S3method(print,av)
S3method(print,bug_drug_combinations)
S3method(print,custom_eucast_rules)
@ -216,6 +219,7 @@ export(aminoglycosides)
export(aminopenicillins)
export(amr_distance_from_row)
export(anti_join_microorganisms)
export(antibiogram)
export(antifungals)
export(antimicrobials_equal)
export(antimycobacterials)

2
NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.8.2.9113
# AMR 1.8.2.9114
*(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*

42
R/aa_helper_functions.R
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@ -386,7 +386,7 @@ import_fn <- function(name, pkg, error_on_fail = TRUE) {
getExportedValue(name = name, ns = asNamespace(pkg)),
error = function(e) {
if (isTRUE(error_on_fail)) {
stop_("function ", name, "() is not an exported object from package '", pkg,
stop_("function `", name, "()` is not an exported object from package '", pkg,
"'. Please create an issue at ", font_url("https://github.com/msberends/AMR/issues"), ". Many thanks!",
call = FALSE
)
@ -622,7 +622,7 @@ format_included_data_number <- function(data) {
} else {
rounder <- -1 # round on tens
}
paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = ","))
paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = " "))
}
# for eucast_rules() and mdro(), creates markdown output with URLs and names
@ -928,11 +928,9 @@ get_current_data <- function(arg_name, call) {
get_current_column <- function() {
# try dplyr::cur_columns() first
cur_column <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
if (!is.null(cur_column)) {
out <- tryCatch(cur_column(), error = function(e) NULL)
if (!is.null(out)) {
return(out)
}
out <- tryCatch(cur_column(), error = function(e) NULL)
if (!is.null(out)) {
return(out)
}
# cur_column() doesn't always work (only allowed for certain conditions set by dplyr), but it's probably still possible:
@ -965,8 +963,20 @@ get_current_column <- function() {
}
is_null_or_grouped_tbl <- function(x) {
# class "grouped_df" might change at one point, so only set in one place; here.
is.null(x) || inherits(x, "grouped_df")
# class "grouped_data" is from {poorman}, see aa_helper_pm_functions.R
# class "grouped_df" is from {dplyr} and might change at one point, so only set in one place; here.
is.null(x) || inherits(x, "grouped_data") || inherits(x, "grouped_df")
}
get_group_names <- function(x) {
if ("pm_groups" %in% names(attributes(x))) {
pm_get_groups(x)
} else if (!is.null(x) && is_null_or_grouped_tbl(x)) {
grps <- colnames(attributes(x)$groups)
grps[!grps %in% c(".group_id", ".rows")]
} else {
character(0)
}
}
unique_call_id <- function(entire_session = FALSE, match_fn = NULL) {
@ -1272,7 +1282,7 @@ create_pillar_column <- function(x, ...) {
new_pillar_shaft_simple(x, ...)
}
as_original_data_class <- function(df, old_class = NULL) {
as_original_data_class <- function(df, old_class = NULL, extra_class = NULL) {
if ("tbl_df" %in% old_class && pkg_is_available("tibble", also_load = FALSE)) {
# this will then also remove groups
fn <- import_fn("as_tibble", "tibble")
@ -1285,7 +1295,11 @@ as_original_data_class <- function(df, old_class = NULL) {
} else {
fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
}
fn(df)
out <- fn(df)
if (!is.null(extra_class)) {
class(out) <- c(extra_class, class(out))
}
out
}
# works exactly like round(), but rounds `round2(44.55, 1)` to 44.6 instead of 44.5
@ -1425,7 +1439,7 @@ add_MO_lookup_to_AMR_env <- function() {
}
trimws2 <- function(..., whitespace = "[\u0009\u000A\u000B\u000C\u000D\u0020\u0085\u00A0\u1680\u180E\u2000\u2001\u2002\u2003\u2004\u2005\u2006\u2007\u2008\u2009\u200A\u200B\u200C\u200D\u2028\u2029\u202F\u205F\u2060\u3000\uFEFF]") {
# this is even faster than trimws() itself which sets " \t\n\r".
# this is even faster than trimws() itself which sets "[ \t\r\n]".
trimws(..., whitespace = whitespace)
}
@ -1441,7 +1455,7 @@ readRDS2 <- function(file, refhook = NULL) {
match <- function(x, table, ...) {
chmatch <- import_fn("chmatch", "data.table", error_on_fail = FALSE)
if (!is.null(chmatch) && is.character(x) && is.character(table)) {
# data.table::chmatch() is 35% faster than base::match() for character
# data.table::chmatch() is much faster than base::match() for character
chmatch(x, table, ...)
} else {
base::match(x, table, ...)
@ -1450,7 +1464,7 @@ match <- function(x, table, ...) {
`%in%` <- function(x, table) {
chin <- import_fn("%chin%", "data.table", error_on_fail = FALSE)
if (!is.null(chin) && is.character(x) && is.character(table)) {
# data.table::`%chin%`() is 20-50% faster than base::`%in%`() for character
# data.table::`%chin%`() is much faster than base::`%in%`() for character
chin(x, table)
} else {
base::`%in%`(x, table)

2
R/ab_property.R
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@ -335,7 +335,7 @@ ab_url <- function(x, open = FALSE, ...) {
ab_property <- function(x, property = "name", language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(property, is_in = colnames(AMR::antibiotics), has_length = 1)
meet_criteria(language, is_in = c(LANGUAGES_SUPPORTED, ""), has_length = 1, allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(ab_validate(x = x, property = property, ...), language = language)
}

90
R/antibiogram.R.bak → R/antibiogram.R
View File

@ -170,9 +170,11 @@
#' antibiotics = c(aminoglycosides(), carbapenems()),
#' syndromic_group = "ward")
#'
#' # now define a data set with only E. coli
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
#'
#' # with a custom language, though this will be determined automatically
#' # (i.e., this table will be in Spanish on Spanish systems)
#' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
#' antibiogram(ex1,
#' antibiotics = aminoglycosides(),
#' ab_transform = "name",
@ -313,46 +315,48 @@ antibiogram <- function(x,
}
antibiotics <- unlist(antibiotics)
} else {
if (identical(select, import_fn("select", "dplyr", error_on_fail = FALSE))) {
antibiotics <- suppressWarnings(x %>% select({{ antibiotics }}) %>% colnames())
} else {
antibiotics <- colnames(x[, antibiotics, drop = FALSE])
}
antibiotics <- colnames(suppressWarnings(x[, antibiotics, drop = FALSE]))
}
if (isTRUE(has_syndromic_group)) {
out <- x %>%
select(.syndromic_group, .mo, antibiotics) %>%
group_by(.syndromic_group)
out <- x %pm>%
pm_select(.syndromic_group, .mo, antibiotics) %pm>%
pm_group_by(.syndromic_group)
} else {
out <- x %>%
select(.mo, antibiotics)
out <- x %pm>%
pm_select(.mo, antibiotics)
}
# get numbers of S, I, R (per group)
out <- out %>%
out <- out %pm>%
bug_drug_combinations(col_mo = ".mo",
FUN = function(x) x)
counts <- out
out$numerator <- ifelse(isTRUE(combine_SI), out$S + out$I, out$S)
out$minimum <- minimum
# regroup for summarising
if (isTRUE(has_syndromic_group)) {
colnames(out)[1] <- "syndromic_group"
out <- out %>%
group_by(syndromic_group, mo, ab)
out <- out %pm>%
pm_group_by(syndromic_group, mo, ab)
} else {
out <- out %>%
group_by(mo, ab)
}
if (any(out$total < minimum, na.rm = TRUE)) {
message_("NOTE: ", sum(out$total < minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
out <- out %pm>%
pm_group_by(mo, ab)
}
out <- out %>%
summarise(SI = ifelse(total >= minimum, numerator / total, NA_real_)) %>%
filter(!is.na(SI))
if (isTRUE(combine_SI)) {
out$numerator <- out$S + out$I
} else {
out$numerator <- out$S
}
out$minimum <- minimum
if (any(out$total < out$minimum, na.rm = TRUE)) {
message_("NOTE: ", sum(out$total < out$minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
out <- out %pm>%
subset(total >= minimum)
}
out <- out %pm>%
pm_summarise(SI = numerator / total)
# transform names of antibiotics
ab_naming_function <- function(x, t, l, s) {
@ -378,16 +382,20 @@ antibiogram <- function(x,
# transform long to wide
long_to_wide <- function(object, digs) {
object <- object %>%
mutate(SI = round(SI * 100, digits = digs)) %>%
object$SI <- round(object$SI * 100, digits = digs)
object <- object %pm>%
# an unclassed data.frame is required for stats::reshape()
as.data.frame(stringsAsFactors = FALSE) %>%
as.data.frame(stringsAsFactors = FALSE) %pm>%
stats::reshape(direction = "wide", idvar = "mo", timevar = "ab", v.names = "SI")
colnames(object) <- gsub("^SI?[.]", "", colnames(object))
return(object)
}
long <- ungroup(out)
# ungroup for long -> wide transformation
attr(out, "pm_groups") <- NULL
attr(out, "groups") <- NULL
class(out) <- class(out)[!class(out) %in% c("grouped_df", "grouped_data")]
long <- out
if (isTRUE(has_syndromic_group)) {
grps <- unique(out$syndromic_group)
@ -401,14 +409,14 @@ antibiogram <- function(x,
}
}
# sort rows
new_df <- new_df %>% arrange(mo, syndromic_group)
new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
# sort columns
new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
} else {
new_df <- long_to_wide(out, digs = digits)
# sort rows
new_df <- new_df %>% arrange(mo)
new_df <- new_df %pm>% pm_arrange(mo)
# sort columns
new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
@ -417,16 +425,16 @@ antibiogram <- function(x,
# add total N if indicated
if (isTRUE(add_total_n)) {
if (isTRUE(has_syndromic_group)) {
n_per_mo <- counts %>%
group_by(mo, .syndromic_group) %>%
summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
n_per_mo <- counts %pm>%
pm_group_by(mo, .syndromic_group) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "syn", "count")
count_group <- n_per_mo$count[match(paste(new_df[[2]], new_df[[1]]), paste(n_per_mo$mo, n_per_mo$syn))]
edit_col <- 2
} else {
n_per_mo <- counts %>%
group_by(mo) %>%
summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
n_per_mo <- counts %pm>%
pm_group_by(mo) %pm>%
pm_summarise(paste0(min(total, na.rm = TRUE), "-", max(total, na.rm = TRUE)))
colnames(n_per_mo) <- c("mo", "count")
count_group <- n_per_mo$count[match(new_df[[1]], n_per_mo$mo)]
edit_col <- 1
@ -489,7 +497,7 @@ autoplot.antibiogram <- function(object, ...) {
} else {
NULL
}),
position = "dodge") +
position = ggplot2::position_dodge2(preserve = "single")) +
ggplot2::facet_wrap("mo") +
ggplot2::labs(y = ifelse(isTRUE(attributes(object)$combine_SI), "%SI", "%S"),
x = NULL,
@ -506,12 +514,14 @@ autoplot.antibiogram <- function(object, ...) {
#' @rdname antibiogram
print.antibiogram <- function(x, as_kable = !interactive(), ...) {
meet_criteria(as_kable, allow_class = "logical", has_length = 1)
if (isTRUE(as_kable) && !identical(Sys.getenv("IN_PKGDOWN"), "true")) {
if (isTRUE(as_kable) &&
# be sure not to run kable in pkgdown for our website generation
!identical(Sys.getenv("IN_PKGDOWN"), "true")) {
stop_ifnot_installed("knitr")
kable <- import_fn("kable", "knitr", error_on_fail = TRUE)
kable(x, ...)
} else {
# remove 'antibiogram' class and print as indicated
# remove 'antibiogram' class and print with default method
class(x) <- class(x)[class(x) != "antibiogram"]
print(x, ...)
}

2
R/av_property.R
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@ -252,7 +252,7 @@ av_url <- function(x, open = FALSE, ...) {
av_property <- function(x, property = "name", language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(property, is_in = colnames(AMR::antivirals), has_length = 1)
meet_criteria(language, is_in = c(LANGUAGES_SUPPORTED, ""), has_length = 1, allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(av_validate(x = x, property = property, ...), language = language)
}

9
R/bug_drug_combinations.R
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@ -44,6 +44,10 @@
#' @rdname bug_drug_combinations
#' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
#'
#' \donttest{
#' x <- bug_drug_combinations(example_isolates)
#' head(x)
@ -89,7 +93,7 @@ bug_drug_combinations <- function(x,
# select only groups and antibiotics
if (is_null_or_grouped_tbl(x.bak)) {
data_has_groups <- TRUE
groups <- setdiff(names(attributes(x.bak)$groups), ".rows")
groups <- get_group_names(x.bak)
x <- x[, c(groups, col_mo, colnames(x)[vapply(FUN.VALUE = logical(1), x, is.sir)]), drop = FALSE]
} else {
data_has_groups <- FALSE
@ -161,6 +165,7 @@ bug_drug_combinations <- function(x,
out <- run_it(x)
}
rownames(out) <- NULL
out <- out %>% pm_arrange(mo, ab)
out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
structure(out, class = c("bug_drug_combinations", ifelse(data_has_groups, "grouped", character(0)), class(out)))
}
@ -181,7 +186,7 @@ format.bug_drug_combinations <- function(x,
meet_criteria(x, allow_class = "data.frame")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(add_ab_group, allow_class = "logical", has_length = 1)
meet_criteria(remove_intrinsic_resistant, allow_class = "logical", has_length = 1)

30
R/data.R
View File

@ -27,7 +27,7 @@
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Data Sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = ",")` Antimicrobial Drugs
#' Data Sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = " ")` Antimicrobial Drugs
#'
#' Two data sets containing all antibiotics/antimycotics and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antibiotics] data set. Three identifiers are included in this data set: an antibiotic ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
#' @format
@ -82,10 +82,10 @@
#' @rdname antibiotics
"antivirals"
#' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Microorganisms
#'
#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()].
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables:
#' - `mo`\cr ID of microorganism as used by this package
#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
#' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status)`
@ -150,10 +150,10 @@
#' microorganisms
"microorganisms"
#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
#' Data Set with `r format(nrow(microorganisms.codes), big.mark = " ")` Common Microorganism Codes
#'
#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with [set_mo_source()]. They will all be searched when using [as.mo()] and consequently all the [`mo_*`][mo_property()] functions.
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = " ")` observations and `r ncol(microorganisms.codes)` variables:
#' - `code`\cr Commonly used code of a microorganism
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
#' @details
@ -163,10 +163,10 @@
#' microorganisms.codes
"microorganisms.codes"
#' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates
#' Data Set with `r format(nrow(example_isolates), big.mark = " ")` Example Isolates
#'
#' A data set containing `r format(nrow(example_isolates), big.mark = ",")` microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` variables:
#' A data set containing `r format(nrow(example_isolates), big.mark = " ")` microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates), big.mark = " ")` observations and `r ncol(example_isolates)` variables:
#' - `date`\cr Date of receipt at the laboratory
#' - `patient`\cr ID of the patient
#' - `age`\cr Age of the patient
@ -182,8 +182,8 @@
#' Data Set with Unclean Data
#'
#' A data set containing `r format(nrow(example_isolates_unclean), big.mark = ",")` microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables:
#' A data set containing `r format(nrow(example_isolates_unclean), big.mark = " ")` microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
#' @format A [tibble][tibble::tibble] with `r format(nrow(example_isolates_unclean), big.mark = " ")` observations and `r ncol(example_isolates_unclean)` variables:
#' - `patient_id`\cr ID of the patient
#' - `date`\cr date of receipt at the laboratory
#' - `hospital`\cr ID of the hospital, from A to C
@ -195,10 +195,10 @@
#' example_isolates_unclean
"example_isolates_unclean"
#' Data Set with `r format(nrow(WHONET), big.mark = ",")` Isolates - WHONET Example
#' Data Set with `r format(nrow(WHONET), big.mark = " ")` Isolates - WHONET Example
#'
#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antibiotic results are from our [example_isolates] data set. All patient names are created using online surname generators and are only in place for practice purposes.
#' @format A [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = " ")` observations and `r ncol(WHONET)` variables:
#' - `Identification number`\cr ID of the sample
#' - `Specimen number`\cr ID of the specimen
#' - `Organism`\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using [as.mo()].
@ -234,7 +234,7 @@
#' Data Set with Clinical Breakpoints for SIR Interpretation
#'
#' Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. Currently implemented guidelines are EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`). Use [as.sir()] to transform MICs or disks measurements to SIR values.
#' @format A [tibble][tibble::tibble] with `r format(nrow(clinical_breakpoints), big.mark = ",")` observations and `r ncol(clinical_breakpoints)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(clinical_breakpoints), big.mark = " ")` observations and `r ncol(clinical_breakpoints)` variables:
#' - `guideline`\cr Name of the guideline
#' - `method`\cr Either `r vector_or(clinical_breakpoints$method)`
#' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
@ -258,7 +258,7 @@
#' Data Set with Bacterial Intrinsic Resistance
#'
#' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
#' @format A [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = " ")` observations and `r ncol(intrinsic_resistant)` variables:
#' - `mo`\cr Microorganism ID
#' - `ab`\cr Antibiotic ID
#' @details
@ -275,7 +275,7 @@
#' Data Set with Treatment Dosages as Defined by EUCAST
#'
#' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()].
#' @format A [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables:
#' @format A [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = " ")` observations and `r ncol(dosage)` variables:
#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
#' - `name`\cr Official name of the antimicrobial drug as used by WHONET/EARS-Net or the WHO
#' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)`

2
R/first_isolate.R
View File

@ -512,7 +512,7 @@ first_isolate <- function(x = NULL,
if (!is.null(col_icu)) {
if (icu_exclude == TRUE) {
if (isTRUE(info)) {
message_("Excluding ", format(sum(col_icu, na.rm = TRUE), big.mark = ","), " isolates from ICU.",
message_("Excluding ", format(sum(col_icu, na.rm = TRUE), big.mark = " "), " isolates from ICU.",
add_fn = font_black,
as_note = FALSE
)

22
R/get_episode.R
View File

@ -98,9 +98,27 @@
#' )
#' }
#'
#' # grouping on patients and microorganisms leads to the same
#' # results as first_isolate() when using 'episode-based':
#' if (require("dplyr")) {
#' # is_new_episode() has a lot more flexibility than first_isolate(),
#' # since you can group on anything that seems relevant:
#' x <- df %>%
#' filter_first_isolate(
#' include_unknown = TRUE,
#' method = "episode-based"
#' )
#'
#' y <- df %>%
#' group_by(patient, mo) %>%
#' filter(is_new_episode(date, 365)) %>%
#' ungroup()
#'
#' identical(x, y)
#' }
#'
#' # but is_new_episode() has a lot more flexibility than first_isolate(),
#' # since you can now group on anything that seems relevant:
#' if (require("dplyr")) {
#'
#' df %>%
#' group_by(patient, mo, ward) %>%
#' mutate(flag_episode = is_new_episode(date, 365)) %>%

6
R/ggplot_sir.R
View File

@ -202,7 +202,7 @@ ggplot_sir <- function(data,
meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
language <- validate_language(language)
meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
meet_criteria(colours, allow_class = c("character", "logical"))
@ -300,7 +300,7 @@ geom_sir <- function(position = NULL,
meet_criteria(x, allow_class = "character", has_length = 1)
meet_criteria(fill, allow_class = "character", has_length = 1)
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
language <- validate_language(language)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
@ -486,7 +486,7 @@ labels_sir_count <- function(position = NULL,
meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
meet_criteria(x, allow_class = "character", has_length = 1)
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
language <- validate_language(language)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)

6
R/mo.R
View File

@ -158,7 +158,7 @@ as.mo <- function(x,
meet_criteria(Becker, allow_class = c("logical", "character"), has_length = 1)
meet_criteria(Lancefield, allow_class = c("logical", "character"), has_length = 1)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
meet_criteria(minimum_matching_score, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE)
meet_criteria(minimum_matching_score, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(reference_df, allow_class = "data.frame", allow_NULL = TRUE)
meet_criteria(ignore_pattern, allow_class = "character", has_length = 1, allow_NULL = TRUE)
language <- validate_language(language)
@ -627,7 +627,7 @@ freq.mo <- function(x, ...) {
.add_header = list(
`Gram-negative` = paste0(
format(sum(grams == "Gram-negative", na.rm = TRUE),
big.mark = ",",
big.mark = " ",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-negative", na.rm = TRUE) / length(grams),
@ -637,7 +637,7 @@ freq.mo <- function(x, ...) {
),
`Gram-positive` = paste0(
format(sum(grams == "Gram-positive", na.rm = TRUE),
big.mark = ",",
big.mark = " ",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-positive", na.rm = TRUE) / length(grams),

8
R/mo_property.R
View File

@ -900,12 +900,16 @@ mo_validate <- function(x, property, language, keep_synonyms = keep_synonyms, ..
}
# get property reeaaally fast using match()
x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
if (property == "snomed") {
x <- lapply(x, function(y) unlist(AMR_env$MO_lookup$snomed[match(y, AMR_env$MO_lookup$mo)]))
} else {
x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
}
if (property == "mo") {
return(set_clean_class(x, new_class = c("mo", "character")))
} else if (property == "snomed") {
return(sort(as.character(eval(parse(text = x)))))
return(x)
} else if (property == "prevalence") {
return(as.double(x))
} else {

10
R/proportion.R
View File

@ -27,7 +27,7 @@
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Calculate Microbial Resistance
#' Calculate Antimicrobial Resistance
#'
#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
#'
@ -49,7 +49,7 @@
#'
#' Use [sir_confidence_interval()] to calculate the confidence interval, which relies on [binom.test()], i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial *resistance*. Change the `side` argument to "left"/"min" or "right"/"max" to return a single value, and change the `ab_result` argument to e.g. `c("S", "I")` to test for antimicrobial *susceptibility*, see Examples.
#'
#' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set.
#' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set with one of the four available algorithms.
#'
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
#'
@ -57,6 +57,7 @@
#' @section Combination Therapy:
#' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
#'
#'
#' ```
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
@ -77,11 +78,14 @@
#' ```
#'
#' Please note that, in combination therapies, for `only_all_tested = TRUE` applies that:
#'
#' ```
#' count_S() + count_I() + count_R() = count_all()
#' proportion_S() + proportion_I() + proportion_R() = 1
#' ```
#'
#' and that, in combination therapies, for `only_all_tested = FALSE` applies that:
#'
#' ```
#' count_S() + count_I() + count_R() >= count_all()
#' proportion_S() + proportion_I() + proportion_R() >= 1
@ -98,6 +102,8 @@
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
#'
#'
#' # base R ------------------------------------------------------------
#' # determines %R

2
R/resistance_predict.R
View File

@ -125,7 +125,7 @@ resistance_predict <- function(x,
meet_criteria(year_min, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
meet_criteria(year_max, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
meet_criteria(year_every, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(model, allow_class = c("character", "function"), has_length = 1, allow_NULL = TRUE)
meet_criteria(I_as_S, allow_class = "logical", has_length = 1)
meet_criteria(preserve_measurements, allow_class = "logical", has_length = 1)

2
R/sir.R
View File

@ -89,7 +89,7 @@
#'
#' ### Machine-Readable Interpretation Guidelines
#'
#' The repository of this package [contains a machine-readable version](https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt) of all guidelines. This is a CSV file consisting of `r format(nrow(AMR::clinical_breakpoints), big.mark = ",")` rows and `r ncol(AMR::clinical_breakpoints)` columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. **This allows for easy implementation of these rules in laboratory information systems (LIS)**. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
#' The repository of this package [contains a machine-readable version](https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt) of all guidelines. This is a CSV file consisting of `r format(nrow(AMR::clinical_breakpoints), big.mark = " ")` rows and `r ncol(AMR::clinical_breakpoints)` columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. **This allows for easy implementation of these rules in laboratory information systems (LIS)**. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
#'
#' ### Other
#'

13
R/sir_calc.R
View File

@ -31,7 +31,8 @@ dots2vars <- function(...) {
# this function is to give more informative output about
# variable names in count_* and proportion_* functions
dots <- substitute(list(...))
as.character(dots)[2:length(dots)]
dots <- as.character(dots)[2:length(dots)]
paste0(dots[dots != "."], collapse = "+")
}
sir_calc <- function(...,
@ -41,7 +42,7 @@ sir_calc <- function(...,
only_all_tested = FALSE,
only_count = FALSE) {
meet_criteria(ab_result, allow_class = c("character", "numeric", "integer"), has_length = c(1, 2, 3))
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
meet_criteria(only_count, allow_class = "logical", has_length = 1)
@ -133,7 +134,7 @@ sir_calc <- function(...,
}
x_transposed <- as.list(as.data.frame(t(x), stringsAsFactors = FALSE))
if (only_all_tested == TRUE) {
if (isTRUE(only_all_tested)) {
# no NAs in any column
y <- apply(
X = as.data.frame(lapply(x, as.integer), stringsAsFactors = FALSE),
@ -224,8 +225,8 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1)
meet_criteria(data, allow_class = "data.frame", contains_column_class = "sir")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(confidence_level, allow_class = "numeric", has_length = 1)
@ -236,7 +237,7 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
# select only groups and antibiotics
if (is_null_or_grouped_tbl(data)) {
data_has_groups <- TRUE
groups <- setdiff(names(attributes(data)$groups), ".rows")
groups <- get_group_names(data)
data <- data[, c(groups, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.sir)]), drop = FALSE]
} else {
data_has_groups <- FALSE

3
R/translate.R
View File

@ -246,7 +246,8 @@ translate_into_language <- function(from,
}
lapply(
seq_len(nrow(df_trans)),
# starting from last row, since more general translations are on top, such as 'Group'
rev(seq_len(nrow(df_trans))),
function(i) {
from_unique_translated <<- gsub(
pattern = df_trans$pattern[i],

1
R/zzz.R
View File

@ -123,6 +123,7 @@ if (utf8_supported && !is_latex) {
s3_register("ggplot2::autoplot", "mic")
s3_register("ggplot2::autoplot", "disk")
s3_register("ggplot2::autoplot", "resistance_predict")
s3_register("ggplot2::autoplot", "antibiogram")
# Support for fortify from the ggplot2 package
s3_register("ggplot2::fortify", "sir")
s3_register("ggplot2::fortify", "mic")

9351
data-raw/AMR_intro.ai
View File

File diff suppressed because one or more lines are too long

4
data-raw/reproduction_of_microorganisms.R
View File

@ -1298,8 +1298,8 @@ taxonomy <- taxonomy %>%
message(
"\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = ","), " rows.\n",
"This was ", format(nrow(AMR::microorganisms), big.mark = ","), " rows.\n"
"\nCongratulations! The new taxonomic table will contain ", format(nrow(taxonomy), big.mark = " "), " rows.\n",
"This was ", format(nrow(AMR::microorganisms), big.mark = " "), " rows.\n"
)
# these are the new ones:

1
index.md
View File

@ -1,5 +1,6 @@
# The `AMR` Package for R <a href="https://msberends.github.io/AMR/"><img src="./logo.svg" align="right" height="139" /></a>
* Generates **antibiograms** - traditional, combined, syndromic, and even WISCA
* Provides the **full microbiological taxonomy** and data on **all antimicrobial drugs**
* Applies all recent **CLSI and EUCAST clinical breakpoints** for MICs and disk zones
* Corrects for duplicate isolates, **calculates and predicts AMR** per antibiotic class

1
inst/tinytest/test-zzz.R
View File

@ -81,6 +81,7 @@ call_functions <- c(
"labs" = "ggplot2",
"layer" = "ggplot2",
"position_fill" = "ggplot2",
"position_dodge2" = "ggplot2",
"scale_fill_manual" = "ggplot2",
"scale_y_continuous" = "ggplot2",
"theme" = "ggplot2",

3
man/AMR.Rd
View File

@ -32,7 +32,7 @@ The \code{AMR} package is a \href{https://msberends.github.io/AMR/#copyright}{fr
This work was published in the Journal of Statistical Software (Volume 104(3); \href{https://doi.org/10.18637/jss.v104.i03}{DOI 10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\href{https://doi.org/10.33612/diss.177417131}{DOI 10.33612/diss.177417131} and \href{https://doi.org/10.33612/diss.192486375}{DOI 10.33612/diss.192486375}).
After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52,000}} (updated December 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52 000}} (updated December 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
}
@ -79,6 +79,7 @@ Other contributors:
\item Rogier P. Schade [contributor]
\item Bhanu N. M. Sinha (\href{https://orcid.org/0000-0003-1634-0010}{ORCID}) [thesis advisor]
\item Anthony Underwood (\href{https://orcid.org/0000-0002-8547-4277}{ORCID}) [contributor]
\item Anita Williams (\href{https://orcid.org/0000-0002-5295-8451}{ORCID}) [contributor]
}
}

236
man/antibiogram.Rd Normal file
View File

@ -0,0 +1,236 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/antibiogram.R
\name{antibiogram}
\alias{antibiogram}
\alias{plot.antibiogram}
\alias{autoplot.antibiogram}
\alias{print.antibiogram}
\title{Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA)}
\source{
\itemize{
\item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
\item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
}
\usage{
antibiogram(
x,
antibiotics = where(is.sir),
mo_transform = "shortname",
ab_transform = NULL,
syndromic_group = NULL,
add_total_n = TRUE,
only_all_tested = FALSE,
digits = 0,
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + "
)
\method{plot}{antibiogram}(x, ...)
\method{autoplot}{antibiogram}(object, ...)
\method{print}{antibiogram}(x, as_kable = !interactive(), ...)
}
\arguments{
\item{x}{a \link{data.frame} containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see \code{\link[=as.sir]{as.sir()}})}
\item{antibiotics}{vector of column names, or (any combinations of) \link[=antibiotic_class_selectors]{antibiotic selectors} such as \code{\link[=aminoglycosides]{aminoglycosides()}} or \code{\link[=carbapenems]{carbapenems()}}. For combination antibiograms, this can also be column names separated with \code{"+"}, such as "TZP+TOB" given that the data set contains columns "TZP" and "TOB". See \emph{Examples}.}
\item{mo_transform}{a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the \link{microorganisms} data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" or "snomed". Can also be \code{NULL} to not transform the input.}
\item{ab_transform}{a character to transform antibiotic input - must be one of the column names of the \link{antibiotics} data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units" or "loinc". Can also be \code{NULL} to not transform the input.}
\item{syndromic_group}{a column name of \code{x}, or values calculated to split rows of \code{x}, e.g. by using \code{\link[=ifelse]{ifelse()}} or \code{\link[dplyr:case_when]{case_when()}}. See \emph{Examples}.}
\item{add_total_n}{a \link{logical} to indicate whether total available numbers per pathogen should be added to the table (defaults to \code{TRUE}). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for \emph{E. coli} 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").}
\item{only_all_tested}{(for combination antibiograms): a \link{logical} to indicate that isolates must be tested for all antibiotics, see \emph{Details}}
\item{digits}{number of digits to use for rounding}
\item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
\item{language}{language to translate text, which defaults to the system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}})}
\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
\item{combine_SI}{a \link{logical} to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (defaults to \code{TRUE})}
\item{sep}{a separating character for antibiotic columns in combination antibiograms}
\item{...}{method extensions}
\item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
\item{as_kable}{a \link{logical} to indicate whether the printing should be done using \code{\link[knitr:kable]{knitr::kable()}} (which is the default in non-interactive sessions)}
}
\description{
Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}) and Barbieri \emph{et al.} (2021, \doi{10.1186/s13756-021-00939-2}), and allow reporting in e.g. R Markdown and Quarto as well.
}
\details{
This function returns a table with values between 0 and 100 for \emph{susceptibility}, not resistance.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
There are four antibiogram types, as proposed by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}:
\enumerate{
\item \strong{Traditional Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to piperacillin/tazobactam (TZP)
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = "TZP")
}\if{html}{\out{</div>}}
\item \strong{Combination Antibiogram}
Case example: Additional susceptibility of \emph{Pseudomonas aeruginosa} to TZP + tobramycin versus TZP alone
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
}\if{html}{\out{</div>}}
\item \strong{Syndromic Antibiogram}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respiratory specimens (obtained among ICU patients only)
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = penicillins(),
syndromic_group = "ward")
}\if{html}{\out{</div>}}
\item \strong{Weighted-Incidence Syndromic Combination Antibiogram (WISCA)}
Case example: Susceptibility of \emph{Pseudomonas aeruginosa} to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure
Code example:
\if{html}{\out{<div class="sourceCode r">}}\preformatted{antibiogram(your_data,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male",
"Group 1", "Group 2"))
}\if{html}{\out{</div>}}
}
All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using \code{\link[ggplot2:autoplot]{ggplot2::autoplot()}} or base \R \code{\link[=plot]{plot()}}/\code{\link[=barplot]{barplot()}}) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of \code{\link[=antibiogram]{antibiogram()}} to your needs, e.g. \code{flextable::as_flextable()} or \code{gt::gt()}.
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the \code{only_all_tested} argument (defaults to \code{FALSE}). See this example for two antibiotics, Drug A and Drug B, about how \code{\link[=antibiogram]{antibiogram()}} works to calculate the \%SI:
\if{html}{\out{<div class="sourceCode">}}\preformatted{--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
}\if{html}{\out{</div>}}
Printing the antibiogram in non-interactive sessions will be done by \code{\link[knitr:kable]{knitr::kable()}}, with support for \link[knitr:kable]{all their implemented formats}, such as "markdown". The knitr format will be automatically determined if printed inside a knitr document (LaTeX, HTML, etc.).
}
\examples{
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates
# Traditional antibiogram ----------------------------------------------
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()))
antibiogram(example_isolates,
antibiotics = aminoglycosides(),
ab_transform = "atc",
mo_transform = "gramstain")
antibiogram(example_isolates,
antibiotics = carbapenems(),
ab_transform = "name",
mo_transform = "name")
# Combined antibiogram -------------------------------------------------
# combined antibiotics yield higher empiric coverage
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain")
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB"),
mo_transform = "gramstain",
ab_transform = "name",
sep = " & ")
# Syndromic antibiogram ------------------------------------------------
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward")
# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
# with a custom language, though this will be determined automatically
# (i.e., this table will be in Spanish on Spanish systems)
antibiogram(ex1,
antibiotics = aminoglycosides(),
ab_transform = "name",
syndromic_group = ifelse(ex1$ward == "ICU",
"UCI", "No UCI"),
language = "es")
# Weighted-incidence syndromic combination antibiogram (WISCA) ---------
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
minimum = 10, # this should be >= 30, but now just as example
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"))
# Generate plots with ggplot2 or base R --------------------------------
ab1 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain")
ab2 <- antibiogram(example_isolates,
antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
syndromic_group = "ward")
plot(ab1)
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab1)
}
plot(ab2)
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab2)
}
}

2
man/as.sir.Rd
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@ -156,7 +156,7 @@ After using \code{\link[=as.sir]{as.sir()}}, you can use the \code{\link[=eucast
\subsection{Machine-Readable Interpretation Guidelines}{
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 18,308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 18 308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
}
\subsection{Other}{

4
man/bug_drug_combinations.Rd
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@ -58,6 +58,10 @@ Determine antimicrobial resistance (AMR) of all bug-drug combinations in your da
The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination. Use \code{combine_SI = TRUE} (default) to test R vs. S+I and \code{combine_SI = FALSE} to test R+I vs. S.
}
\examples{
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates
\donttest{
x <- bug_drug_combinations(example_isolates)
head(x)

2
man/clinical_breakpoints.Rd
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@ -5,7 +5,7 @@
\alias{clinical_breakpoints}
\title{Data Set with Clinical Breakpoints for SIR Interpretation}
\format{
A \link[tibble:tibble]{tibble} with 18,308 observations and 11 variables:
A \link[tibble:tibble]{tibble} with 18 308 observations and 11 variables:
\itemize{
\item \code{guideline}\cr Name of the guideline
\item \code{method}\cr Either "DISK" or "MIC"

6
man/example_isolates.Rd
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@ -3,9 +3,9 @@
\docType{data}
\name{example_isolates}
\alias{example_isolates}
\title{Data Set with 2,000 Example Isolates}
\title{Data Set with 2 000 Example Isolates}
\format{
A \link[tibble:tibble]{tibble} with 2,000 observations and 46 variables:
A \link[tibble:tibble]{tibble} with 2 000 observations and 46 variables:
\itemize{
\item \code{date}\cr Date of receipt at the laboratory
\item \code{patient}\cr ID of the patient
@ -20,7 +20,7 @@ A \link[tibble:tibble]{tibble} with 2,000 observations and 46 variables:
example_isolates
}
\description{
A data set containing 2,000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read \href{https://msberends.github.io/AMR/articles/AMR.html}{the tutorial on our website}.
A data set containing 2 000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please read \href{https://msberends.github.io/AMR/articles/AMR.html}{the tutorial on our website}.
}
\details{
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.

4
man/example_isolates_unclean.Rd
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@ -5,7 +5,7 @@
\alias{example_isolates_unclean}
\title{Data Set with Unclean Data}
\format{
A \link[tibble:tibble]{tibble} with 3,000 observations and 8 variables:
A \link[tibble:tibble]{tibble} with 3 000 observations and 8 variables:
\itemize{
\item \code{patient_id}\cr ID of the patient
\item \code{date}\cr date of receipt at the laboratory
@ -18,7 +18,7 @@ A \link[tibble:tibble]{tibble} with 3,000 observations and 8 variables:
example_isolates_unclean
}
\description{
A data set containing 3,000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
A data set containing 3 000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
}
\details{
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.

22
man/get_episode.Rd
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@ -85,9 +85,27 @@ if (require("dplyr")) {
)
}
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
if (require("dplyr")) {
# is_new_episode() has a lot more flexibility than first_isolate(),
# since you can group on anything that seems relevant:
x <- df \%>\%
filter_first_isolate(
include_unknown = TRUE,
method = "episode-based"
)
y <- df \%>\%
group_by(patient, mo) \%>\%
filter(is_new_episode(date, 365)) \%>\%
ungroup()
identical(x, y)
}
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
if (require("dplyr")) {
df \%>\%
group_by(patient, mo, ward) \%>\%
mutate(flag_episode = is_new_episode(date, 365)) \%>\%

2
man/intrinsic_resistant.Rd
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@ -5,7 +5,7 @@
\alias{intrinsic_resistant}
\title{Data Set with Bacterial Intrinsic Resistance}
\format{
A \link[tibble:tibble]{tibble} with 134,634 observations and 2 variables:
A \link[tibble:tibble]{tibble} with 134 634 observations and 2 variables:
\itemize{
\item \code{mo}\cr Microorganism ID
\item \code{ab}\cr Antibiotic ID

16
man/microorganisms.Rd
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@ -3,9 +3,9 @@
\docType{data}
\name{microorganisms}
\alias{microorganisms}
\title{Data Set with 52,142 Microorganisms}
\title{Data Set with 52 142 Microorganisms}
\format{
A \link[tibble:tibble]{tibble} with 52,142 observations and 22 variables:
A \link[tibble:tibble]{tibble} with 52 142 observations and 22 variables:
\itemize{
\item \code{mo}\cr ID of microorganism as used by this package
\item \code{fullname}\cr Full name, like \code{"Escherichia coli"}. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
@ -48,11 +48,11 @@ For example, \emph{Staphylococcus pettenkoferi} was described for the first time
Included taxonomic data are:
\itemize{
\item All ~36,000 (sub)species from the kingdoms of Archaea and Bacteria
\item ~7,900 (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
\item ~5,100 (sub)species from the kingdom of Protozoa
\item ~1,400 (sub)species from ~40 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
\item All ~9,800 previously accepted names of all included (sub)species (these were taxonomically renamed)
\item All ~36 000 (sub)species from the kingdoms of Archaea and Bacteria
\item ~7 900 (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
\item ~5 100 (sub)species from the kingdom of Protozoa
\item ~1 400 (sub)species from ~40 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
\item All ~9 800 previously accepted names of all included (sub)species (these were taxonomically renamed)
\item The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
\item The identifier of the parent taxons
\item The year and first author of the related scientific publication
@ -61,7 +61,7 @@ Included taxonomic data are:
For convenience, some entries were added manually:
\itemize{
\item ~1,500 entries for the city-like serovars of \emph{Salmonellae}
\item ~1 500 entries for the city-like serovars of \emph{Salmonellae}
\item 11 entries of \emph{Streptococcus} (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
\item 2 entries of \emph{Staphylococcus} (coagulase-negative (CoNS) and coagulase-positive (CoPS))
\item 1 entry of \emph{Blastocystis} (\emph{B. hominis}), although it officially does not exist (Noel \emph{et al.} 2005, PMID 15634993)

4
man/microorganisms.codes.Rd
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@ -3,9 +3,9 @@
\docType{data}
\name{microorganisms.codes}
\alias{microorganisms.codes}
\title{Data Set with 5,910 Common Microorganism Codes}
\title{Data Set with 5 910 Common Microorganism Codes}
\format{
A \link[tibble:tibble]{tibble} with 5,910 observations and 2 variables:
A \link[tibble:tibble]{tibble} with 5 910 observations and 2 variables:
\itemize{
\item \code{code}\cr Commonly used code of a microorganism
\item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set

6
man/proportion.Rd
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@ -13,7 +13,7 @@
\alias{proportion_S}
\alias{proportion_df}
\alias{sir_df}
\title{Calculate Microbial Resistance}
\title{Calculate Antimicrobial Resistance}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
@ -98,7 +98,7 @@ The function \code{\link[=resistance]{resistance()}} is equal to the function \c
Use \code{\link[=sir_confidence_interval]{sir_confidence_interval()}} to calculate the confidence interval, which relies on \code{\link[=binom.test]{binom.test()}}, i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial \emph{resistance}. Change the \code{side} argument to "left"/"min" or "right"/"max" to return a single value, and change the \code{ab_result} argument to e.g. \code{c("S", "I")} to test for antimicrobial \emph{susceptibility}, see Examples.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set.
\strong{Remember that you should filter your data to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set with one of the four available algorithms.
These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[=count]{count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can influence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} argument).}
@ -162,6 +162,8 @@ This AMR package honours this insight. Use \code{\link[=susceptibility]{suscepti
\examples{
# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates
# base R ------------------------------------------------------------
# determines \%R

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vignettes/AMR.Rmd
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@ -157,7 +157,7 @@ Using the `left_join()` function from the `dplyr` package, we can 'map' the gend
data <- data %>% left_join(patients_table)
```
The resulting data set contains `r format(nrow(data), big.mark = ",")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set:
The resulting data set contains `r format(nrow(data), big.mark = " ")` blood culture isolates. With the `head()` function we can preview the first 6 rows of this data set:
```{r preview data set 1, eval = FALSE}
head(data)
@ -251,7 +251,7 @@ data_1st <- data %>%
filter_first_isolate()
```
So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analysis. Now our data looks like:
So we end up with `r format(nrow(data_1st), big.mark = " ")` isolates for analysis. Now our data looks like:
```{r preview data set 3, eval = FALSE}
head(data_1st)
@ -362,7 +362,7 @@ data_1st %>%
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX)) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the `n_sir()` can be used, which works exactly like `n_distinct()` from the `dplyr` package. It counts all isolates available for every group (i.e. values S, I or R):
@ -382,7 +382,7 @@ data_1st %>%
amoxicillin = resistance(AMX),
available = n_sir(AMX)
) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
@ -404,7 +404,7 @@ data_1st %>%
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN)
) %>%
knitr::kable(align = "c", big.mark = ",")
knitr::kable(align = "c", big.mark = " ")
```
Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as `penicillins()`, which automatically will include the columns `AMX` and `AMC` of our data:

2
vignettes/SPSS.Rmd
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@ -49,7 +49,7 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
* **R has a huge community.**
Many R users just ask questions on websites like [StackOverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, [`r format(suppressWarnings(read.csv("https://data.stackexchange.com/stackoverflow/csv/1674647", quote = '"'))[[1]], big.mark = ",")` R-related questions](https://stackoverflow.com/questions/tagged/r?sort=votes) 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.
Many R users just ask questions on websites like [StackOverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, [`r format(suppressWarnings(read.csv("https://data.stackexchange.com/stackoverflow/csv/1674647", quote = '"'))[[1]], big.mark = " ")` R-related questions](https://stackoverflow.com/questions/tagged/r?sort=votes) 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.
* **R understands any data type, including SPSS/SAS/Stata.**

2
vignettes/benchmarks.Rmd.not
View File

@ -126,7 +126,7 @@ run_it <- microbenchmark(mo_name(x),
print(run_it, unit = "ms", signif = 3)
```
So getting official taxonomic names of `r format(length(x), big.mark = ",")` (!!) items consisting of `r n_distinct(x)` unique values only takes `r round(median(run_it$time, na.rm = TRUE) / 1e9, 3)` seconds. That is `r round(median(run_it$time, na.rm = TRUE) / length(x), 0)` nanoseconds on average. You only lose time on your unique input values.
So getting official taxonomic names of `r format(length(x), big.mark = " ")` (!!) items consisting of `r n_distinct(x)` unique values only takes `r round(median(run_it$time, na.rm = TRUE) / 1e9, 3)` seconds. That is `r round(median(run_it$time, na.rm = TRUE) / length(x), 0)` nanoseconds on average. You only lose time on your unique input values.
### Precalculated results

4
vignettes/datasets.Rmd
View File

@ -30,7 +30,7 @@ options(knitr.kable.NA = "")
structure_txt <- function(dataset) {
paste0(
"A data set with ",
format(nrow(dataset), big.mark = ","), " rows and ",
format(nrow(dataset), big.mark = " "), " rows and ",
ncol(dataset), " columns, containing the following column names: \n",
AMR:::vector_or(colnames(dataset), quotes = "*", last_sep = " and ", sort = FALSE), "."
)
@ -142,7 +142,7 @@ Included (sub)species per taxonomic kingdom:
```{r, echo = FALSE}
microorganisms %>%
count(kingdom) %>%
mutate(n = format(n, big.mark = ",")) %>%
mutate(n = format(n, big.mark = " ")) %>%
setNames(c("Kingdom", "Number of (sub)species")) %>%
print_df()
```