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new species groups, updated clinical breakpoints

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dr. M.S. (Matthijs) Berends
2023-07-08 17:30:05 +02:00
parent 2d97cca6d9
commit acb534102b
172 changed files with 44445 additions and 52835 deletions
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6
R/aa_amr-package.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

34
R/aa_globals.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -30,12 +30,12 @@
# add new version numbers here, and add the rules themselves to "data-raw/eucast_rules.tsv" and clinical_breakpoints
# (sourcing "data-raw/_pre_commit_hook.R" will process the TSV file)
EUCAST_VERSION_BREAKPOINTS <- list(
"13.0" = list(
version_txt = "v13.0",
year = 2023,
title = "'EUCAST Clinical Breakpoint Tables'",
url = "https://www.eucast.org/clinical_breakpoints/"
),
# "13.0" = list(
# version_txt = "v13.0",
# year = 2023,
# title = "'EUCAST Clinical Breakpoint Tables'",
# url = "https://www.eucast.org/clinical_breakpoints/"
# ),
"12.0" = list(
version_txt = "v12.0",
year = 2022,
@@ -75,14 +75,14 @@ EUCAST_VERSION_EXPERT_RULES <- list(
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
)
)
EUCAST_VERSION_RESISTANTPHENOTYPES <- list(
"1.2" = list(
version_txt = "v1.2",
year = 2023,
title = "'Expected Resistant Phenotypes'",
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
)
)
# EUCAST_VERSION_RESISTANTPHENOTYPES <- list(
# "1.2" = list(
# version_txt = "v1.2",
# year = 2023,
# title = "'Expected Resistant Phenotypes'",
# url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
# )
# )
TAXONOMY_VERSION <- list(
GBIF = list(

30
R/aa_helper_functions.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -1182,20 +1182,20 @@ is_dark <- function() {
}
isTRUE(AMR_env$is_dark_theme)
}
font_black <- function(..., collapse = " ") {
font_black <- function(..., collapse = " ", adapt = TRUE) {
before <- "\033[38;5;232m"
after <- "\033[39m"
if (is_dark()) {
if (isTRUE(adapt) && is_dark()) {
# white
before <- "\033[37m"
after <- "\033[39m"
}
try_colour(..., before = before, after = after, collapse = collapse)
}
font_white <- function(..., collapse = " ") {
font_white <- function(..., collapse = " ", adapt = TRUE) {
before <- "\033[37m"
after <- "\033[39m"
if (is_dark()) {
if (isTRUE(adapt) && is_dark()) {
# black
before <- "\033[38;5;232m"
after <- "\033[39m"
@@ -1283,7 +1283,7 @@ font_stripstyle <- function(x) {
x
}
progress_ticker <- function(n = 1, n_min = 0, print = TRUE, ...) {
progress_ticker <- function(n = 1, n_min = 0, print = TRUE, clear = TRUE, title = "", only_bar_percent = FALSE, ...) {
if (print == FALSE || n < n_min) {
# create fake/empty object
pb <- list()
@@ -1299,9 +1299,11 @@ progress_ticker <- function(n = 1, n_min = 0, print = TRUE, ...) {
progress_bar <- import_fn("progress_bar", "progress", error_on_fail = FALSE)
if (!is.null(progress_bar)) {
# so we use progress::progress_bar
# a close() method was also added, see below this function
# a close()-method was also added, see below for that
pb <- progress_bar$new(
format = "[:bar] :percent (:current/:total,:eta)",
format = paste0(title,
ifelse(only_bar_percent == TRUE, "[:bar] :percent", "[:bar] :percent (:current/:total,:eta)")),
clear = clear,
total = n
)
} else {
@@ -1487,11 +1489,11 @@ add_MO_lookup_to_AMR_env <- function() {
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
MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 1.25
MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 1.5
MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 2
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 3
# the fullname lowercase, important for the internal algorithms in as.mo()
MO_lookup$fullname_lower <- tolower(trimws(paste(

6
R/aa_helper_pm_functions.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/aa_options.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ab.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ab_from_text.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ab_property.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ab_selectors.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/age.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/antibiogram.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/atc_online.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/av.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/av_from_text.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/av_property.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/availability.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/bug_drug_combinations.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/count.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/custom_antimicrobials.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/custom_eucast_rules.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/custom_microorganisms.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

57
R/data.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -122,8 +122,7 @@
#' For convenience, some entries were added manually:
#'
#' - `r format_included_data_number(microorganisms[which(microorganisms$source == "manually added" & microorganisms$genus == "Salmonella"), , drop = FALSE])` entries of *Salmonella*, such as the city-like serovars and groups A to H
#' - `r format_included_data_number(microorganisms[which(microorganisms$source == "manually added" & microorganisms$genus == "Streptococcus"), , drop = FALSE])` entries of *Streptococcus*, such as the beta-haemolytic groups A to K, viridans, and milleri
#' - 2 entries of *Staphylococcus* (coagulase-negative (CoNS) and coagulase-positive (CoPS))
#' - `r format_included_data_number(length(which(microorganisms$rank == "species group")))` species groups (such as the beta-haemolytic *Streptococcus* groups A to K, coagulase-negative *Staphylococcus* (CoNS), *Mycobacterium tuberculosis* complex, etc.), of which the group compositions are stored in the [microorganisms.groups] data set
#' - 1 entry of *Blastocystis* (*B. hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993)
#' - 1 entry of *Moraxella* (*M. catarrhalis*), which was formally named *Branhamella catarrhalis* (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
#' - 8 other 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, and unknown anaerobic Gram-pos/Gram-neg bacteria)
@@ -148,14 +147,14 @@
#' * Grimont *et al.* (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
#'
#' * Bartlett *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269}
#' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
#' @seealso [as.mo()], [mo_property()], [microorganisms.groups], [microorganisms.codes], [intrinsic_resistant]
#' @examples
#' microorganisms
"microorganisms"
#' 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.
#' A data set containing commonly used codes for microorganisms, from laboratory systems and [WHONET](https://whonet.org). 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:
#' - `code`\cr Commonly used code of a microorganism
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
@@ -175,6 +174,24 @@
#' mo_is_intrinsic_resistant("eco", ab = "vancomycin")
"microorganisms.codes"
#' Data Set with `r format(nrow(microorganisms.groups), big.mark = " ")` Microorganisms In Species Groups
#'
#' A data set containing species groups and microbiological complexes, which are used in [the clinical breakpoints table][clinial_breakpoints].
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.groups), big.mark = " ")` observations and `r ncol(microorganisms.groups)` variables:
#' - `mo_group`\cr ID of the species group / microbiological complex
#' - `mo`\cr ID of the microorganism belonging in the species group / microbiological complex
#' - `mo_group_name`\cr Name of the species group / microbiological complex, as retrieved with [mo_name()]
#' - `mo_name`\cr Name of the microorganism belonging in the species group / microbiological complex, as retrieved with [mo_name()]
#' @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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#' @seealso [as.mo()] [microorganisms]
#' @examples
#' microorganisms.groups
#'
#' # these are all species in the Bacteroides fragilis group, as per WHONET:
#' microorganisms.groups[microorganisms.groups$mo_group == "B_BCTRD_FRGL-C", ]
"microorganisms.groups"
#' 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).
@@ -248,23 +265,33 @@
#' 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:
#' - `guideline`\cr Name of the guideline
#' - `method`\cr Either `r vector_or(clinical_breakpoints$method)`
#' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
#' - `type`\cr Breakpoint type, either `r vector_or(clinical_breakpoints$type)`
#' - `method`\cr Testing method, either `r vector_or(clinical_breakpoints$method)`
#' - `site`\cr Body site for which the breakpoint must be applied, e.g. "Oral" or "Respiratory"
#' - `mo`\cr Microbial ID, see [as.mo()]
#' - `rank_index`\cr Taxonomic rank index of `mo` from 1 (subspecies/infraspecies) to 5 (unknown microorganism)
#' - `ab`\cr Antibiotic ID, see [as.ab()]
#' - `ab`\cr Antibiotic code as used by this package, EARS-Net and WHONET, see [as.ab()]
#' - `ref_tbl`\cr Info about where the guideline rule can be found
#' - `disk_dose`\cr Dose of the used disk diffusion method
#' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
#' - `ecoff`\cr Epidemiological cut-off (ECOFF) value, used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi (use [`as.sir(..., ecoff = TRUE)`][as.sir()] to interpret raw data using ECOFF values)
#' - `uti`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
#' @details
#' Clinical breakpoints are validated through [WHONET](https://whonet.org), a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on [their website](https://whonet.org).
#' ### Different types of breakpoints
#' Supported types of breakpoints are `r vector_and(clinical_breakpoints$type, quote = FALSE)`. ECOFF (Epidemiological cut-off) values are used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi.
#'
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#'
#' They **allow for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.
#' The default is `"human"`, which can also be set with the [package option][AMR-options] [`AMR_breakpoint_type`][AMR-options]. Use [`as.sir(..., breakpoint_type = ...)`][as.sir()] to interpret raw data using a specific breakpoint type, e.g. `as.sir(..., breakpoint_type = "ECOFF")` to use ECOFFs.
#'
#' ### Imported from WHONET
#' Clinical breakpoints in this package were validated through and imported from [WHONET](https://whonet.org), a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on [their website](https://whonet.org). The developers of WHONET and this `AMR` package have been in contact about sharing their work. We highly appreciate their development on the WHONET software.
#'
#' ### Response from CLSI and EUCAST
#' The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this `AMR` package in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, the WHO, and developers of WHONET and the `AMR` package.
#'
#' ### Download
#' 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 [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw). They allow for machine reading EUCAST and CLSI guidelines, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI, though initiatives have started to overcome these burdens.
#'
#' **NOTE:** this `AMR` package (and the WHONET software as well) contains internal methods to apply the guidelines, which is rather complex. For example, some breakpoints must be applied on certain species groups (which are in case of this package available through the [microorganisms.groups] data set). It is important that this is considered when using the breakpoints for own use.
#' @seealso [intrinsic_resistant]
#' @examples
#' clinical_breakpoints

6
R/disk.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

16
R/eucast_rules.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -65,7 +65,7 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' @param verbose a [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
#' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Can be `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), reverse = TRUE)`.
#' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), reverse = TRUE)`.
#' @param version_resistant_phenotypes the version number to use for the EUCAST Expected Resistant Phenotypes. Can be `r vector_or(names(EUCAST_VERSION_RESISTANTPHENOTYPES), reverse = TRUE)`.
# @param version_resistant_phenotypes the version number to use for the EUCAST Expected Resistant Phenotypes. Can be `r vector_or(names(EUCAST_VERSION_RESISTANTPHENOTYPES), reverse = TRUE)`.
#' @param ampc_cephalosporin_resistance a [character] value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is `NA`. Currently only works when `version_expertrules` is `3.2` and higher; these version of '*EUCAST Expert Rules on Enterobacterales*' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of `NA` (the default) for this argument will remove results for these three drugs, while e.g. a value of `"R"` will make the results for these drugs resistant. Use `NULL` or `FALSE` to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using `TRUE` is equal to using `"R"`. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(EUCAST_RULES_DF[which(EUCAST_RULES_DF$reference.version %in% c(3.2, 3.3) & EUCAST_RULES_DF$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
#' @param ... column name of an antibiotic, see section *Antibiotics* below
#' @param ab any (vector of) text that can be coerced to a valid antibiotic drug code with [as.ab()]
@@ -168,7 +168,7 @@ eucast_rules <- function(x,
verbose = FALSE,
version_breakpoints = 12.0,
version_expertrules = 3.3,
version_resistant_phenotypes = 1.2,
# TODO version_resistant_phenotypes = 1.2,
ampc_cephalosporin_resistance = NA,
only_sir_columns = FALSE,
custom_rules = NULL,
@@ -180,7 +180,7 @@ eucast_rules <- function(x,
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))
meet_criteria(version_expertrules, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_EXPERT_RULES)))
meet_criteria(version_resistant_phenotypes, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)))
# meet_criteria(version_resistant_phenotypes, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)))
meet_criteria(ampc_cephalosporin_resistance, allow_class = c("logical", "character", "sir"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(custom_rules, allow_class = "custom_eucast_rules", allow_NULL = TRUE)
@@ -208,7 +208,7 @@ eucast_rules <- function(x,
breakpoints_info <- EUCAST_VERSION_BREAKPOINTS[[which(as.double(names(EUCAST_VERSION_BREAKPOINTS)) == version_breakpoints)]]
expertrules_info <- EUCAST_VERSION_EXPERT_RULES[[which(as.double(names(EUCAST_VERSION_EXPERT_RULES)) == version_expertrules)]]
resistantphenotypes_info <- EUCAST_VERSION_RESISTANTPHENOTYPES[[which(as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)) == version_resistant_phenotypes)]]
# resistantphenotypes_info <- EUCAST_VERSION_RESISTANTPHENOTYPES[[which(as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)) == version_resistant_phenotypes)]]
# support old setting (until AMR v1.3.0)
if (missing(rules) && !is.null(getOption("AMR.eucast_rules"))) {
@@ -1189,7 +1189,7 @@ edit_sir <- function(x,
#' @rdname eucast_rules
#' @export
eucast_dosage <- function(ab, administration = "iv", version_breakpoints = 13.0) {
eucast_dosage <- function(ab, administration = "iv", version_breakpoints = 12.0) {
meet_criteria(ab, allow_class = c("character", "numeric", "integer", "factor"))
meet_criteria(administration, allow_class = "character", is_in = dosage$administration[!is.na(dosage$administration)], has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))

6
R/first_isolate.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/g.test.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/get_episode.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ggplot_pca.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/ggplot_sir.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/guess_ab_col.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/italicise_taxonomy.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/join_microorganisms.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/key_antimicrobials.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/kurtosis.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/like.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/mdro.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/mean_amr_distance.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

8
R/mic.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -165,7 +165,7 @@ valid_mic_levels <- c(
#' autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "uk") # Ukrainian
#' }
as.mic <- function(x, na.rm = FALSE) {
meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
meet_criteria(x, allow_NA = TRUE)
meet_criteria(na.rm, allow_class = "logical", has_length = 1)
if (is.mic(x)) {

295
R/mo.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -169,7 +169,7 @@ as.mo <- function(x,
meet_criteria(cleaning_regex, allow_class = "character", has_length = 1, allow_NULL = TRUE)
language <- validate_language(language)
meet_criteria(info, allow_class = "logical", has_length = 1)
add_MO_lookup_to_AMR_env()
if (tryCatch(all(x %in% c(AMR_env$MO_lookup$mo, NA)), error = function(e) FALSE) &&
@@ -180,28 +180,23 @@ as.mo <- function(x,
# is.mo() won't work - MO codes might change between package versions
return(set_clean_class(x, new_class = c("mo", "character")))
}
# start off with replaced language-specific non-ASCII characters with ASCII characters
x <- parse_and_convert(x)
# replace mo codes used in older package versions
x <- replace_old_mo_codes(x, property = "mo")
# ignore cases that match the ignore pattern
x <- replace_ignore_pattern(x, ignore_pattern)
x_lower <- tolower(x)
complexes <- x[trimws2(x_lower) %like_case% " (complex|group)$"]
if (length(complexes) > 0 && identical(cleaning_regex, mo_cleaning_regex()) && !any(AMR_env$MO_lookup$fullname[which(AMR_env$MO_lookup$source == "Added by user")] %like% "(group|complex)", na.rm = TRUE)) {
warning_("in `as.mo()`: 'complex' and 'group' were ignored from the input in ", length(complexes), " case", ifelse(length(complexes) > 1, "s", ""), ", as they are currently not supported.\nYou can add your own microorganism with `add_custom_microorganisms()`.", call = FALSE)
}
# WHONET: xxx = no growth
x[x_lower %in% c("", "xxx", "na", "nan")] <- NA_character_
out <- rep(NA_character_, length(x))
# below we use base R's match(), known for powering '%in%', and incredibly fast!
# From reference_df ----
reference_df <- repair_reference_df(reference_df)
if (!is.null(reference_df)) {
@@ -233,33 +228,33 @@ as.mo <- function(x,
" for ", vector_and(x[is.na(old) & !is.na(new)]), ". Run `mo_reset_session()` to reset this. This note will be shown once per session for this input."
)
}
# For all other input ----
if (any(is.na(out) & !is.na(x))) {
# reset uncertainties
AMR_env$mo_uncertainties <- AMR_env$mo_uncertainties[0, ]
AMR_env$mo_failures <- NULL
# Laboratory systems: remove (translated) entries like "no growth", "not E. coli", etc.
x[trimws2(x) %like% translate_into_language("no .*growth", language = language)] <- NA_character_
x[trimws2(x) %like% paste0("^(", translate_into_language("no|not", language = language), ") ")] <- NA_character_
# groups are in our taxonomic table with a capital G
x <- gsub(" group( |$)", " Group\\1", x, perl = TRUE)
# run over all unique leftovers
x_unique <- unique(x[is.na(out) & !is.na(x)])
# set up progress bar
progress <- progress_ticker(n = length(x_unique), n_min = 10, print = info)
on.exit(close(progress))
msg <- character(0)
# run it
x_coerced <- vapply(FUN.VALUE = character(1), x_unique, function(x_search) {
progress$tick()
# some required cleaning steps
x_out <- trimws2(x_search)
# this applies the `cleaning_regex` argument, which defaults to mo_cleaning_regex()
@@ -269,28 +264,28 @@ as.mo <- function(x,
x_out <- tolower(x_out)
# when x_search_cleaned are only capitals (such as in codes), make them lowercase to increase matching score
x_search_cleaned[x_search_cleaned == toupper(x_search_cleaned)] <- x_out[x_search_cleaned == toupper(x_search_cleaned)]
# first check if cleaning led to an exact result, case-insensitive
if (x_out %in% AMR_env$MO_lookup$fullname_lower) {
return(as.character(AMR_env$MO_lookup$mo[match(x_out, AMR_env$MO_lookup$fullname_lower)]))
}
# input must not be too short
if (nchar(x_out) < 3) {
return("UNKNOWN")
}
# take out the parts, split by space
x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]]
# do a pre-match on first character (and if it contains a space, first chars of first two terms)
if (length(x_parts) %in% c(2, 3)) {
# for genus + species + subspecies
if (nchar(gsub("[^a-z]", "", x_parts[1], perl = TRUE)) <= 3) {
filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) &
(AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) |
AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) |
AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1)))
AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) |
AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1)))
} else {
filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) |
AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) |
@@ -325,7 +320,7 @@ as.mo <- function(x,
} else {
mo_to_search <- AMR_env$MO_lookup$fullname[filtr]
}
AMR_env$mo_to_search <- mo_to_search
# determine the matching score on the original search value
m <- mo_matching_score(x = x_search_cleaned, n = mo_to_search)
@@ -347,7 +342,7 @@ as.mo <- function(x,
m[m < minimum_matching_score] <- NA_real_
minimum_matching_score_current <- minimum_matching_score
}
top_hits <- mo_to_search[order(m, decreasing = TRUE, na.last = NA)] # na.last = NA will remove the NAs
if (length(top_hits) == 0) {
warning_("No hits found for \"", x_search, "\" with minimum_matching_score = ", ifelse(is.null(minimum_matching_score), paste0("NULL (=", round(min(minimum_matching_score_current, na.rm = TRUE), 3), ")"), minimum_matching_score), ". Try setting this value lower or even to 0.", call = FALSE)
@@ -380,12 +375,12 @@ as.mo <- function(x,
# the actual result:
as.character(result_mo)
})
# remove progress bar from console
close(progress)
# expand from unique again
out[is.na(out)] <- x_coerced[match(x[is.na(out)], x_unique)]
# Throw note about uncertainties ----
if (isTRUE(info) && NROW(AMR_env$mo_uncertainties) > 0) {
if (message_not_thrown_before("as.mo", "uncertainties", AMR_env$mo_uncertainties$original_input)) {
@@ -408,14 +403,14 @@ as.mo <- function(x,
"Microorganism translation was uncertain for ", examples,
". Run `mo_uncertainties()` to review ", plural[2], ", or use `add_custom_microorganisms()` to add custom entries."
))
for (m in msg) {
message_(m)
}
}
}
} # end of loop over all yet unknowns
# Keep or replace synonyms ----
lpsn_matches <- AMR_env$MO_lookup$lpsn_renamed_to[match(out, AMR_env$MO_lookup$mo)]
lpsn_matches[!lpsn_matches %in% AMR_env$MO_lookup$lpsn] <- NA
@@ -438,14 +433,14 @@ as.mo <- function(x,
# keep synonyms is TRUE, so check if any do have synonyms
warning_("Function `as.mo()` returned ", nr2char(length(unique(AMR_env$mo_renamed$old))), " old taxonomic name", ifelse(length(unique(AMR_env$mo_renamed$old)) > 1, "s", ""), ". Use `as.mo(..., keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic names, or set the R option `AMR_keep_synonyms` to `FALSE`. This warning will be shown once per session.", call = FALSE)
}
# Apply Becker ----
if (isTRUE(Becker) || Becker == "all") {
# warn when species found that are not in:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
# comment below code if all staphylococcal species are categorised as CoNS/CoPS
post_Becker <- paste(
"Staphylococcus",
@@ -454,13 +449,13 @@ as.mo <- function(x,
if (any(out %in% AMR_env$MO_lookup$mo[match(post_Becker, AMR_env$MO_lookup$fullname)])) {
if (message_not_thrown_before("as.mo", "becker")) {
warning_("in `as.mo()`: Becker ", font_italic("et al."), " (2014, 2019, 2020) does not contain these species named after their publication: ",
vector_and(font_italic(gsub("Staphylococcus", "S.", post_Becker, fixed = TRUE), collapse = NULL), quotes = FALSE),
". Categorisation to CoNS/CoPS was taken from the original scientific publication(s).",
immediate = TRUE, call = FALSE
vector_and(font_italic(gsub("Staphylococcus", "S.", post_Becker, fixed = TRUE), collapse = NULL), quotes = FALSE),
". Categorisation to CoNS/CoPS was taken from the original scientific publication(s).",
immediate = TRUE, call = FALSE
)
}
}
# 'MO_CONS' and 'MO_COPS' are 'mo' vectors created in R/_pre_commit_hook.R
out[out %in% MO_CONS] <- "B_STPHY_CONS"
out[out %in% MO_COPS] <- "B_STPHY_COPS"
@@ -468,11 +463,11 @@ as.mo <- function(x,
out[out == "B_STPHY_AURS"] <- "B_STPHY_COPS"
}
}
# Apply Lancefield ----
if (isTRUE(Lancefield) || Lancefield == "all") {
# (using `%like_case%` to also match subspecies)
# group A - S. pyogenes
out[out %like_case% "^B_STRPT_PYGN(_|$)"] <- "B_STRPT_GRPA"
# group B - S. agalactiae
@@ -493,17 +488,17 @@ as.mo <- function(x,
out[out %like_case% "^B_STRPT_SLVR(_|$)"] <- "B_STRPT_GRPK"
# group L - only S. dysgalactiae which is also group C & G, so ignore it here
}
# All unknowns ----
out[is.na(out) & !is.na(x)] <- "UNKNOWN"
AMR_env$mo_failures <- unique(x[out == "UNKNOWN" & !x %in% c("UNKNOWN", "con") & !x %like_case% "^[(]unknown [a-z]+[)]$" & !is.na(x)])
AMR_env$mo_failures <- unique(x[out == "UNKNOWN" & !toupper(x) %in% c("UNKNOWN", "CON", "UNK") & !x %like_case% "^[(]unknown [a-z]+[)]$" & !is.na(x)])
if (length(AMR_env$mo_failures) > 0) {
warning_("The following input could not be coerced and was returned as \"UNKNOWN\": ", vector_and(AMR_env$mo_failures, quotes = TRUE), ".\nYou can retrieve this list with `mo_failures()`.", call = FALSE)
}
# Return class ----
set_clean_class(out,
new_class = c("mo", "character")
new_class = c("mo", "character")
)
}
@@ -526,13 +521,13 @@ mo_uncertainties <- function() {
mo_renamed <- function() {
add_MO_lookup_to_AMR_env()
x <- AMR_env$mo_renamed
x$new <- synonym_mo_to_accepted_mo(x$old)
mo_old <- AMR_env$MO_lookup$fullname[match(x$old, AMR_env$MO_lookup$mo)]
mo_new <- AMR_env$MO_lookup$fullname[match(x$new, AMR_env$MO_lookup$mo)]
ref_old <- AMR_env$MO_lookup$ref[match(x$old, AMR_env$MO_lookup$mo)]
ref_new <- AMR_env$MO_lookup$ref[match(x$new, AMR_env$MO_lookup$mo)]
df_renamed <- data.frame(
old = mo_old,
new = mo_new,
@@ -572,7 +567,7 @@ mo_cleaning_regex <- function() {
"|",
"([({]|\\[).+([})]|\\])",
"|",
"(^| )(e?spp|e?ssp|e?ss|e?sp|e?subsp|sube?species|biovar|biotype|serovar|var|serogr.?up|e?species)[.]*( |$|(complex|group)$))"
"(^| )(e?spp|e?ssp|e?ss|e?sp|e?subsp|sube?species|biovar|biotype|serovar|var|serogr.?up|e?species|titer|dummy)[.]*|( Ig[ADEGM])( |$))"
)
}
@@ -586,30 +581,30 @@ pillar_shaft.mo <- function(x, ...) {
out[!is.na(x)] <- gsub("^([A-Z]+_)(.*)", paste0(font_subtle("\\1"), "\\2"), out[!is.na(x)], perl = TRUE)
# and grey out every _
out[!is.na(x)] <- gsub("_", font_subtle("_"), out[!is.na(x)])
# markup NA and UNKNOWN
out[is.na(x)] <- font_na(" NA")
out[x == "UNKNOWN"] <- font_na(" UNKNOWN")
# markup manual codes
out[x %in% AMR_env$MO_lookup$mo & !x %in% AMR::microorganisms$mo] <- font_blue(out[x %in% AMR_env$MO_lookup$mo & !x %in% AMR::microorganisms$mo], collapse = NULL)
df <- tryCatch(get_current_data(arg_name = "x", call = 0),
error = function(e) NULL
error = function(e) NULL
)
if (!is.null(df)) {
mo_cols <- vapply(FUN.VALUE = logical(1), df, is.mo)
} else {
mo_cols <- NULL
}
all_mos <- c(AMR_env$MO_lookup$mo, NA)
if (!all(x %in% all_mos) ||
(!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) {
(!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) {
# markup old mo codes
out[!x %in% all_mos] <- font_italic(
font_na(x[!x %in% all_mos],
collapse = NULL
collapse = NULL
),
collapse = NULL
)
@@ -625,15 +620,15 @@ pillar_shaft.mo <- function(x, ...) {
call = FALSE
)
}
# make it always fit exactly
max_char <- max(nchar(x))
if (is.na(max_char)) {
max_char <- 12
}
create_pillar_column(out,
align = "left",
width = max_char + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0)
align = "left",
width = max_char + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0)
)
}
@@ -656,21 +651,21 @@ freq.mo <- function(x, ...) {
.add_header = list(
`Gram-negative` = paste0(
format(sum(grams == "Gram-negative", na.rm = TRUE),
big.mark = " ",
decimal.mark = "."
big.mark = " ",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-negative", na.rm = TRUE) / length(grams),
digits = digits
digits = digits
),
")"
),
`Gram-positive` = paste0(
format(sum(grams == "Gram-positive", na.rm = TRUE),
big.mark = " ",
decimal.mark = "."
big.mark = " ",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-positive", na.rm = TRUE) / length(grams),
digits = digits
digits = digits
),
")"
),
@@ -832,26 +827,26 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
more_than_50 <- TRUE
x <- x[1:50, , drop = FALSE]
}
cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.\n\n", add_fn = font_blue))
add_MO_lookup_to_AMR_env()
col_red <- function(x) font_rose_bg(font_black(x, collapse = NULL), collapse = NULL)
col_orange <- function(x) font_orange_bg(font_black(x, collapse = NULL), collapse = NULL)
col_yellow <- function(x) font_yellow_bg(font_black(x, collapse = NULL), collapse = NULL)
col_green <- function(x) font_green_bg(font_black(x, collapse = NULL), collapse = NULL)
col_red <- function(x) font_rose_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
col_orange <- function(x) font_orange_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
col_yellow <- function(x) font_yellow_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
col_green <- function(x) font_green_bg(font_black(x, collapse = NULL, adapt = FALSE), collapse = NULL)
if (has_colour()) {
cat(word_wrap("Colour keys: ",
col_red(" 0.000-0.549 "),
col_orange(" 0.550-0.649 "),
col_yellow(" 0.650-0.749 "),
col_green(" 0.750-1.000"),
add_fn = font_blue
col_red(" 0.000-0.549 "),
col_orange(" 0.550-0.649 "),
col_yellow(" 0.650-0.749 "),
col_green(" 0.750-1.000"),
add_fn = font_blue
), font_green_bg(" "), "\n", sep = "")
}
score_set_colour <- function(text, scores) {
# set colours to scores
text[scores >= 0.75] <- col_green(text[scores >= 0.75])
@@ -860,7 +855,7 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
text[scores < 0.55] <- col_red(text[scores < 0.55])
text
}
txt <- ""
any_maxed_out <- FALSE
for (i in seq_len(nrow(x))) {
@@ -872,15 +867,15 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
}
scores <- mo_matching_score(x = x[i, ]$input, n = candidates)
n_candidates <- length(candidates)
candidates_formatted <- italicise(candidates)
scores_formatted <- trimws(formatC(round(scores, 3), format = "f", digits = 3))
scores_formatted <- score_set_colour(scores_formatted, scores)
# sort on descending scores
candidates_formatted <- candidates_formatted[order(1 - scores)]
scores_formatted <- scores_formatted[order(1 - scores)]
candidates <- word_wrap(
paste0(
"Also matched: ",
@@ -898,42 +893,42 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
} else {
candidates <- ""
}
score <- mo_matching_score(
x = x[i, ]$input,
n = x[i, ]$fullname
)
score_formatted <- trimws(formatC(round(score, 3), format = "f", digits = 3))
txt <- paste(txt,
paste0(
paste0(
"", strrep(font_grey("-"), times = getOption("width", 100)), "\n",
'"', x[i, ]$original_input, '"',
" -> ",
paste0(
font_bold(italicise(x[i, ]$fullname)),
" (", x[i, ]$mo, ", ", score_set_colour(score_formatted, score), ")"
)
),
collapse = "\n"
),
# Add note if result was coerced to accepted taxonomic name
ifelse(x[i, ]$keep_synonyms == FALSE & x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")],
paste0(
strrep(" ", nchar(x[i, ]$original_input) + 6),
font_red(paste0("This old taxonomic name was converted to ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", synonym_mo_to_accepted_mo(x[i, ]$mo), ")."), collapse = NULL)
),
""
),
candidates,
sep = "\n"
paste0(
paste0(
"", strrep(font_grey("-"), times = getOption("width", 100)), "\n",
'"', x[i, ]$original_input, '"',
" -> ",
paste0(
font_bold(italicise(x[i, ]$fullname)),
" (", x[i, ]$mo, ", ", score_set_colour(score_formatted, score), ")"
)
),
collapse = "\n"
),
# Add note if result was coerced to accepted taxonomic name
ifelse(x[i, ]$keep_synonyms == FALSE & x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")],
paste0(
strrep(" ", nchar(x[i, ]$original_input) + 6),
font_red(paste0("This old taxonomic name was converted to ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", synonym_mo_to_accepted_mo(x[i, ]$mo), ")."), collapse = NULL)
),
""
),
candidates,
sep = "\n"
)
txt <- gsub("[\n]+", "\n", txt)
# remove first and last break
txt <- gsub("(^[\n]|[\n]$)", "", txt)
txt <- paste0("\n", txt, "\n")
}
cat(txt)
if (isTRUE(any_maxed_out)) {
cat(font_blue(word_wrap("\nOnly the first ", n, " other matches of each record are shown. Run `print(mo_uncertainties(), n = ...)` to view more entries, or save `mo_uncertainties()` to an object.")))
@@ -951,19 +946,19 @@ print.mo_renamed <- function(x, extra_txt = "", n = 25, ...) {
cat(word_wrap("No renamed taxonomy to show. Only renamed taxonomy of the last call of `as.mo()` or any `mo_*()` function are stored.\n", add_fn = font_blue))
return(invisible(NULL))
}
x$ref_old[!is.na(x$ref_old)] <- paste0(" (", gsub("et al.", font_italic("et al."), x$ref_old[!is.na(x$ref_old)], fixed = TRUE), ")")
x$ref_new[!is.na(x$ref_new)] <- paste0(" (", gsub("et al.", font_italic("et al."), x$ref_new[!is.na(x$ref_new)], fixed = TRUE), ")")
x$ref_old[is.na(x$ref_old)] <- " (author unknown)"
x$ref_new[is.na(x$ref_new)] <- " (author unknown)"
rows <- seq_len(min(NROW(x), n))
message_(
"The following microorganism", ifelse(NROW(x) > 1, "s were", " was"), " taxonomically renamed", extra_txt, ":\n",
paste0(" ", AMR_env$bullet_icon, " ", font_italic(x$old[rows], collapse = NULL), x$ref_old[rows],
" -> ", font_italic(x$new[rows], collapse = NULL), x$ref_new[rows],
collapse = "\n"
" -> ", font_italic(x$new[rows], collapse = NULL), x$ref_new[rows],
collapse = "\n"
),
ifelse(NROW(x) > n, paste0("\n\nOnly the first ", n, " (out of ", NROW(x), ") are shown. Run `print(mo_renamed(), n = ...)` to view more entries (might be slow), or save `mo_renamed()` to an object."), "")
)
@@ -975,51 +970,51 @@ convert_colloquial_input <- function(x) {
x.bak <- trimws2(x)
x <- trimws2(tolower(x))
out <- rep(NA_character_, length(x))
# Streptococci, like GBS = Group B Streptococci (B_STRPT_GRPB)
out[x %like_case% "^g[abcdefghijkl]s$"] <- gsub("g([abcdefghijkl])s",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "^g[abcdefghijkl]s$"],
perl = TRUE
out[x %like_case% "^g[abcdefghijkl]s$"] <- gsub("g([abcdefghijkl])s",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "^g[abcdefghijkl]s$"],
perl = TRUE
)
# Streptococci in different languages, like "estreptococos grupo B"
out[x %like_case% "strepto[ck]o[ck].* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "strepto[ck]o[ck].* [abcdefghijkl]$"],
perl = TRUE
out[x %like_case% "strepto[ck]o[ck][a-zA-Z]* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "strepto[ck]o[ck][a-zA-Z]* [abcdefghijkl]$"],
perl = TRUE
)
out[x %like_case% "strep[a-z]* group [abcdefghijkl]$"] <- gsub(".* ([abcdefghijkl])$",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "strep[a-z]* group [abcdefghijkl]$"],
perl = TRUE
"B_STRPT_GRP\\U\\1",
x[x %like_case% "strep[a-z]* group [abcdefghijkl]$"],
perl = TRUE
)
out[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"] <- gsub(".*group ([abcdefghijkl]) strepto[ck]o[ck].*",
"B_STRPT_GRP\\U\\1",
x[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"],
perl = TRUE
"B_STRPT_GRP\\U\\1",
x[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"],
perl = TRUE
)
out[x %like_case% "ha?emoly.*strep"] <- "B_STRPT_HAEM"
out[x %like_case% "(strepto.* mil+er+i|^mgs[^a-z]*$)"] <- "B_STRPT_MILL"
out[x %like_case% "mil+er+i gr"] <- "B_STRPT_MILL"
out[x %like_case% "((strepto|^s).* viridans|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
out[x %like_case% "(viridans.* (strepto|^s).*|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
# Salmonella in different languages, like "Salmonella grupo B"
out[x %like_case% "salmonella.* [abcdefgh]$"] <- gsub(".*salmonella.* ([abcdefgh])$",
"B_SLMNL_GRP\\U\\1",
x[x %like_case% "salmonella.* [abcdefgh]$"],
perl = TRUE
"B_SLMNL_GRP\\U\\1",
x[x %like_case% "salmonella.* [abcdefgh]$"],
perl = TRUE
)
out[x %like_case% "group [abcdefgh] salmonella"] <- gsub(".*group ([abcdefgh]) salmonella*",
"B_SLMNL_GRP\\U\\1",
x[x %like_case% "group [abcdefgh] salmonella"],
perl = TRUE
"B_SLMNL_GRP\\U\\1",
x[x %like_case% "group [abcdefgh] salmonella"],
perl = TRUE
)
# CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese)
out[x %like_case% "([ck]oagulas[ea].negatie?[vf]|^[ck]o?ns[^a-z]*$)"] <- "B_STPHY_CONS"
out[x %like_case% "([ck]oagulas[ea].positie?[vf]|^[ck]o?ps[^a-z]*$)"] <- "B_STPHY_COPS"
# Gram stains
out[x %like_case% "gram[ -]?neg.*"] <- "B_GRAMN"
out[x %like_case% "( |^)gram[-]( |$)"] <- "B_GRAMN"
@@ -1032,19 +1027,19 @@ out[x %like_case% "^g[abcdefghijkl]s$"] <- gsub("g([abcdefghijkl])s",
# yeasts and fungi
out[x %like_case% "^yeast?"] <- "F_YEAST"
out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS"
# trivial names known to the field
out[x %like_case% "meningo[ck]o[ck]"] <- "B_NESSR_MNNG"
out[x %like_case% "gono[ck]o[ck]"] <- "B_NESSR_GNRR"
out[x %like_case% "pneumo[ck]o[ck]"] <- "B_STRPT_PNMN"
# unexisting names (con is the WHONET code for contamination)
out[x %in% c("con", "other", "none", "unknown") | x %like_case% "virus"] <- "UNKNOWN"
# WHONET has a lot of E. coli and Vibrio cholerae names
out[x %like_case% "escherichia coli"] <- "B_ESCHR_COLI"
out[x %like_case% "vibrio cholerae"] <- "B_VIBRI_CHLR"
out
}
@@ -1136,7 +1131,7 @@ replace_old_mo_codes <- function(x, property) {
name <- gsub(" .*", " ", name, fixed = TRUE)
name <- paste0("^", name)
results <- AMR_env$MO_lookup$mo[AMR_env$MO_lookup$kingdom %like_case% kingdom &
AMR_env$MO_lookup$fullname_lower %like_case% name]
AMR_env$MO_lookup$fullname_lower %like_case% name]
if (length(results) > 1) {
all_direct_matches <<- FALSE
}
@@ -1173,8 +1168,8 @@ replace_old_mo_codes <- function(x, property) {
"to ", ifelse(n_solved == 1, "a ", ""),
"currently used MO code", ifelse(n_solved == 1, "", "s"),
ifelse(n_unsolved > 0,
paste0(" and ", n_unsolved, " old MO code", ifelse(n_unsolved == 1, "", "s"), " could not be updated."),
"."
paste0(" and ", n_unsolved, " old MO code", ifelse(n_unsolved == 1, "", "s"), " could not be updated."),
"."
)
)
}
@@ -1203,19 +1198,19 @@ repair_reference_df <- function(reference_df) {
# has valid own reference_df
reference_df <- reference_df %pm>%
pm_filter(!is.na(mo))
# keep only first two columns, second must be mo
if (colnames(reference_df)[1] == "mo") {
reference_df <- reference_df %pm>% pm_select(2, "mo")
} else {
reference_df <- reference_df %pm>% pm_select(1, "mo")
}
# remove factors, just keep characters
colnames(reference_df)[1] <- "x"
reference_df[, "x"] <- as.character(reference_df[, "x", drop = TRUE])
reference_df[, "mo"] <- as.character(reference_df[, "mo", drop = TRUE])
# some MO codes might be old
reference_df[, "mo"] <- as.mo(reference_df[, "mo", drop = TRUE], reference_df = NULL)
reference_df
@@ -1237,10 +1232,10 @@ synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE) {
x_lpsn <- AMR_env$MO_lookup$lpsn_renamed_to[match(x, AMR_env$MO_lookup$mo)]
x_gbif[!x_gbif %in% AMR_env$MO_lookup$gbif] <- NA
x_lpsn[!x_lpsn %in% AMR_env$MO_lookup$lpsn] <- NA
out <- ifelse(is.na(x_lpsn),
AMR_env$MO_lookup$mo[match(x_gbif, AMR_env$MO_lookup$gbif)],
AMR_env$MO_lookup$mo[match(x_lpsn, AMR_env$MO_lookup$lpsn)]
AMR_env$MO_lookup$mo[match(x_gbif, AMR_env$MO_lookup$gbif)],
AMR_env$MO_lookup$mo[match(x_lpsn, AMR_env$MO_lookup$lpsn)]
)
if (isTRUE(fill_in_accepted)) {
x_accepted <- which(AMR_env$MO_lookup$status[match(x, AMR_env$MO_lookup$mo)] == "accepted")

10
R/mo_matching_score.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -50,7 +50,7 @@
#' * \eqn{l_n} is the length of \eqn{n};
#' * \eqn{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance) (counting any insertion as 1, and any deletion or substitution as 2) that is needed to change \eqn{x} into \eqn{n};
#' * \eqn{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
#' * \eqn{k_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Archaea = 2, others = 3.
#'
#' The grouping into human pathogenic prevalence \eqn{p} is based on recent work from Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:
#'
@@ -62,7 +62,7 @@
#' - Any genus present in the **established** list also has `prevalence = 1.0` in the [microorganisms] data set;
#' - Any other genus present in the **putative** list has `prevalence = 1.25` in the [microorganisms] data set;
#' - Any other species or subspecies of which the genus is present in the two aforementioned groups, has `prevalence = 1.5` in the [microorganisms] data set;
#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.5` in the [microorganisms] data set: `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`;
#' - Any *non-bacterial* genus, species or subspecies of which the genus is present in the following list, has `prevalence = 1.25` in the [microorganisms] data set: `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`;
#' - All other records have `prevalence = 2.0` in the [microorganisms] data set.
#'
#' When calculating the matching score, all characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.

6
R/mo_property.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/mo_source.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/pca.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/plot.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/proportion.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/random.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/resistance_predict.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

132
R/sir.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -30,6 +30,8 @@
#' Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data
#'
#' @description Interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing SIR values. This transforms the input to a new class [`sir`], which is an ordered [factor] with levels `S < I < R`.
#'
#' Currently available **breakpoint guidelines** are EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI")$guideline)))`, and available **breakpoint types** are `r vector_and(clinical_breakpoints$type)`.
#'
#' All breakpoints used for interpretation are publicly available in the [clinical_breakpoints] data set.
#' @rdname as.sir
@@ -43,11 +45,13 @@
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a [logical] to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.3)`.
#' @param include_screening a [logical] to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the [package option][AMR-options] [`AMR_include_screening`][AMR-options].
#' @param include_PKPD a [logical] to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`. Can also be set with the [package option][AMR-options] [`AMR_include_PKPD`][AMR-options].
#' @param ecoff a [logical] to indicate that ECOFF (Epidemiological Cut-Off) values must be used **instead** of other clinical breakpoints - the default is `FALSE`. Can also be set with the [package option][AMR-options] [`AMR_ecoff`][AMR-options].
#' @param breakpoint_type the type of breakpoints to use, either `r vector_or(clinical_breakpoints$type)`. ECOFF stands for Epidemiological Cut-Off values. The default is `"human"`, which can also be set with the [package option][AMR-options] [`AMR_breakpoint_type`][AMR-options].
#' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*
#' @param ... for using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
#' @details
#' *Note: The clinical breakpoints in this package were validated through and imported from [WHONET](https://whonet.org) and the public use of this `AMR` package has been endorsed by CLSI and EUCAST, please see [clinical_breakpoints] for more information.*
#'
#' ### How it Works
#'
#' The [as.sir()] function works in four ways:
@@ -429,7 +433,7 @@ as.sir.mic <- function(x,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
...) {
as_sir_method(
method_short = "mic",
@@ -444,7 +448,7 @@ as.sir.mic <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
breakpoint_type = breakpoint_type,
...
)
}
@@ -460,7 +464,7 @@ as.sir.disk <- function(x,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
...) {
as_sir_method(
method_short = "disk",
@@ -475,7 +479,7 @@ as.sir.disk <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
breakpoint_type = breakpoint_type,
...
)
}
@@ -492,7 +496,7 @@ as.sir.data.frame <- function(x,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE)) {
breakpoint_type = getOption("AMR_breakpoint_type", "human")) {
meet_criteria(x, allow_class = "data.frame") # will also check for dimensions > 0
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
@@ -502,7 +506,7 @@ as.sir.data.frame <- function(x,
meet_criteria(reference_data, allow_class = "data.frame")
meet_criteria(include_screening, allow_class = "logical", has_length = 1)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1)
meet_criteria(ecoff, allow_class = "logical", has_length = 1)
meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1)
x.bak <- x
for (i in seq_len(ncol(x))) {
@@ -634,7 +638,7 @@ as.sir.data.frame <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
breakpoint_type = breakpoint_type,
is_data.frame = TRUE
)
} else if (types[i] == "disk") {
@@ -652,7 +656,7 @@ as.sir.data.frame <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
breakpoint_type = breakpoint_type,
is_data.frame = TRUE
)
} else if (types[i] == "sir") {
@@ -722,7 +726,7 @@ as_sir_method <- function(method_short,
reference_data,
include_screening,
include_PKPD,
ecoff,
breakpoint_type,
...) {
meet_criteria(x, allow_NA = TRUE, .call_depth = -2)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE, .call_depth = -2)
@@ -734,8 +738,8 @@ as_sir_method <- function(method_short,
meet_criteria(reference_data, allow_class = "data.frame", .call_depth = -2)
meet_criteria(include_screening, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(ecoff, allow_class = "logical", has_length = 1, .call_depth = -2)
check_reference_data(reference_data, .call_depth = -2)
meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1, .call_depth = -2)
# for dplyr's across()
cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
@@ -792,8 +796,8 @@ as_sir_method <- function(method_short,
mo <- suppressWarnings(suppressMessages(as.mo(mo, keep_synonyms = FALSE, inf0 = FALSE)))
guideline_coerced <- get_guideline(guideline, reference_data)
if (is.na(ab)) {
message_("Returning NAs for unknown drug: '", font_bold(ab.bak),
"'. Rename this column to a drug name or code, and check the output with `as.ab()`.",
message_("Returning NAs for unknown antibiotic: '", font_bold(ab.bak),
"'. Rename this column to a valid name or code, and check the output with `as.ab()`.",
add_fn = font_red,
as_note = FALSE
)
@@ -830,28 +834,23 @@ as_sir_method <- function(method_short,
), agent_name, ")"
)
}
message_("=> Interpreting ", method_long, " of ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
agent_formatted,
mo_var_found,
" according to ",
ifelse(isTRUE(ecoff),
"ECOFF values of ",
""),
ifelse(identical(reference_data, AMR::clinical_breakpoints),
font_bold(guideline_coerced),
"manually defined 'reference_data'"
),
"... ",
appendLF = FALSE,
as_note = FALSE
)
# this intro text will also be printed in the progress bar in the `progress` package is installed
intro_txt <- paste0("Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
agent_formatted,
mo_var_found,
ifelse(identical(reference_data, AMR::clinical_breakpoints),
paste0(", ", font_bold(guideline_coerced)),
""),
"... ")
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
msg_note <- function(messages) {
for (i in seq_len(length(messages))) {
messages[i] <- word_wrap(extra_indent = 5, messages[i])
}
message(
font_green(font_bold(" Note:\n")),
font_yellow(font_bold(paste0(" Note", ifelse(length(messages) > 1, "s", ""), ":\n"))),
paste0(" ", font_black(AMR_env$bullet_icon), " ", font_black(messages, collapse = NULL), collapse = "\n")
)
}
@@ -894,6 +893,9 @@ as_sir_method <- function(method_short,
subset(method == method_coerced & ab == ab_coerced)
}
breakpoints <- breakpoints %pm>%
subset(type == breakpoint_type)
if (isFALSE(include_screening)) {
# remove screening rules from the breakpoints table
breakpoints <- breakpoints %pm>%
@@ -904,17 +906,16 @@ as_sir_method <- function(method_short,
breakpoints <- breakpoints %pm>%
subset(mo != "UNKNOWN" & ref_tbl %unlike% "PK.*PD")
}
if (isFALSE(ecoff)) {
# remove ECOFF interpretations from the breakpoints table
if (all(uti == FALSE, na.rm = TRUE)) {
# remove UTI breakpoints
breakpoints <- breakpoints %pm>%
subset(ref_tbl != "ECOFF")
} else {
# keep only ECOFF interpretations from the breakpoints table
subset(is.na(uti) | uti == FALSE)
} else if (all(uti == TRUE, na.rm = TRUE)) {
# remove UTI breakpoints
breakpoints <- breakpoints %pm>%
subset(ref_tbl == "ECOFF") %pm>%
pm_mutate(breakpoint_S = ecoff, breakpoint_R = ecoff)
subset(uti == TRUE)
}
msgs <- character(0)
if (nrow(breakpoints) == 0) {
# apparently no breakpoints found
@@ -931,33 +932,33 @@ as_sir_method <- function(method_short,
any_is_intrinsic_resistant <- FALSE
add_intrinsic_resistance_to_AMR_env()
}
p <- progress_ticker(n = length(unique(df$mo)), n_min = 10, title = font_blue(intro_txt), only_bar_percent = TRUE)
on.exit(close(p))
# run the rules
for (mo_unique in unique(df$mo)) {
rows <- which(df$mo == mo_unique)
for (mo_currrent in unique(df$mo)) {
p$tick()
rows <- which(df$mo == mo_currrent)
values <- df[rows, "values", drop = TRUE]
uti <- df[rows, "uti", drop = TRUE]
new_sir <- rep(NA_sir_, length(rows))
# find different mo properties
mo_current_genus <- as.mo(mo_genus(mo_unique, language = NULL))
mo_current_family <- as.mo(mo_family(mo_unique, language = NULL))
mo_current_order <- as.mo(mo_order(mo_unique, language = NULL))
mo_current_class <- as.mo(mo_class(mo_unique, language = NULL))
if (mo_genus(mo_unique, language = NULL) == "Staphylococcus") {
mo_current_becker <- as.mo(mo_unique, Becker = TRUE)
mo_current_genus <- as.mo(mo_genus(mo_currrent, language = NULL))
mo_current_family <- as.mo(mo_family(mo_currrent, language = NULL))
mo_current_order <- as.mo(mo_order(mo_currrent, language = NULL))
mo_current_class <- as.mo(mo_class(mo_currrent, language = NULL))
if (mo_currrent %in% AMR::microorganisms.groups$mo) {
# get the species group
mo_current_species_group <- AMR::microorganisms.groups$mo_group[match(mo_currrent, AMR::microorganisms.groups$mo)]
} else {
mo_current_becker <- mo_unique
}
if (mo_genus(mo_unique, language = NULL) == "Streptococcus") {
mo_current_lancefield <- as.mo(mo_unique, Lancefield = TRUE)
} else {
mo_current_lancefield <- mo_unique
mo_current_species_group <- mo_currrent
}
mo_current_other <- as.mo("UNKNOWN")
# formatted for notes
mo_formatted <- suppressMessages(suppressWarnings(mo_fullname(mo_unique, language = NULL, keep_synonyms = FALSE)))
if (!mo_rank(mo_unique) %in% c("kingdom", "phylum", "class", "order")) {
mo_formatted <- suppressMessages(suppressWarnings(mo_fullname(mo_currrent, language = NULL, keep_synonyms = FALSE)))
if (!mo_rank(mo_currrent) %in% c("kingdom", "phylum", "class", "order")) {
mo_formatted <- font_italic(mo_formatted)
}
ab_formatted <- paste0(
@@ -971,7 +972,7 @@ as_sir_method <- function(method_short,
subset(mo %in% c(
mo_current_genus, mo_current_family,
mo_current_order, mo_current_class,
mo_current_becker, mo_current_lancefield,
mo_current_species_group,
mo_current_other
))
@@ -991,12 +992,12 @@ as_sir_method <- function(method_short,
# only UTI breakpoints available
warning_("in `as.sir()`: interpretation of ", font_bold(ab_formatted), " is only available for (uncomplicated) urinary tract infections (UTI) for some microorganisms, thus assuming `uti = TRUE`. See `?as.sir`.")
rise_warning <- TRUE
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_unique, ab_coerced)) {
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_currrent, ab_coerced)) {
# both UTI and Non-UTI breakpoints available
msgs <- c(msgs, paste0("Breakpoints for UTI ", font_underline("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming non-UTI. Use argument `uti` to set which isolates are from urine. See `?as.sir`."))
breakpoints_current <- breakpoints_current %pm>%
pm_filter(uti == FALSE)
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_unique, ab_coerced)) {
} else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_currrent, ab_coerced)) {
# breakpoints for multiple body sites available
site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
if (is.na(site)) {
@@ -1008,7 +1009,7 @@ as_sir_method <- function(method_short,
}
# first check if mo is intrinsic resistant
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_unique, ab_coerced) %in% AMR_env$intrinsic_resistant) {
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_currrent, ab_coerced) %in% AMR_env$intrinsic_resistant) {
msgs <- c(msgs, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
new_sir <- rep(as.sir("R"), length(rows))
} else if (nrow(breakpoints_current) == 0) {
@@ -1058,7 +1059,7 @@ as_sir_method <- function(method_short,
index = rows,
ab_input = rep(ab.bak, length(rows)),
ab_guideline = rep(ab_coerced, length(rows)),
mo_input = rep(mo.bak[match(mo_unique, df$mo)][1], length(rows)),
mo_input = rep(mo.bak[match(mo_currrent, df$mo)][1], length(rows)),
mo_guideline = rep(breakpoints_current[, "mo", drop = TRUE], length(rows)),
guideline = rep(guideline_coerced, length(rows)),
ref_table = rep(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
@@ -1073,7 +1074,14 @@ as_sir_method <- function(method_short,
df[rows, "result"] <- new_sir
}
close(p)
# printing messages
if (!is.null(import_fn("progress_bar", "progress", error_on_fail = FALSE))) {
# the progress bar has overwritten the intro text, so:
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
}
if (isTRUE(rise_warning)) {
message(font_yellow(font_bold(" * WARNING *")))
} else if (length(msgs) == 0) {

6
R/sir_calc.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/sir_df.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/skewness.R
View File

@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/translate.R
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# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/vctrs.R
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# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/whocc.R
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# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

6
R/zz_deprecated.R
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# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #

20
R/zzz.R
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@@ -1,11 +1,11 @@
# ==================================================================== #
# TITLE #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
@@ -75,15 +75,11 @@ AMR_env$custom_mo_codes <- character(0)
AMR_env$is_dark_theme <- NULL
# determine info icon for messages
utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)
is_latex <- tryCatch(import_fn("is_latex_output", "knitr", error_on_fail = FALSE)(),
error = function(e) FALSE
)
if (utf8_supported && !is_latex) {
# \u2139 is a symbol officially named 'information source'
AMR_env$info_icon <- "\u2139"
AMR_env$bullet_icon <- "\u2022"
AMR_env$dots <- "\u2026"
if (pkg_is_available("cli")) {
# let cli do the determination of supported symbols
AMR_env$info_icon <- import_fn("symbol", "cli")$info
AMR_env$bullet_icon <- import_fn("symbol", "cli")$bullet
AMR_env$dots <- import_fn("symbol", "cli")$ellipsis
} else {
AMR_env$info_icon <- "i"
AMR_env$bullet_icon <- "*"