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(v2.1.1.9064) update all microbial taxonomy, add mycobank, big documentation update

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dr. M.S. (Matthijs) Berends 2024-07-16 14:51:57 +02:00
parent 4f9db23684
commit 640888f408
191 changed files with 321091 additions and 89382 deletions
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8
.github/prehooks/prepare-commit-msg vendored
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@ -8,9 +8,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -64,7 +64,7 @@ if command -v Rscript > /dev/null; then
git add R/sysdata.rda
git add NAMESPACE
else
echo "- R package 'pkgload', 'devtools', 'dplyr', or 'styler' not installed!"
echo "- R package 'pkgload', 'devtools', or 'dplyr' not installed!"
currentpkg="your"
fi
else

6
.github/workflows/check-old.yaml vendored
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
.github/workflows/check-recent.yaml vendored
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
.github/workflows/codecovr.yaml vendored
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
.github/workflows/lintr.yaml vendored
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
.github/workflows/website.yaml vendored
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 2.1.1.9061
Date: 2024-06-19
Version: 2.1.1.9063
Date: 2024-07-16
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by
@ -39,7 +39,6 @@ Suggests:
data.table,
dplyr,
ggplot2,
janitor,
knitr,
progress,
readxl,
@ -49,7 +48,6 @@ Suggests:
tibble,
tidyselect,
tinytest,
tsibble,
vctrs,
xml2
VignetteBuilder: knitr,rmarkdown

1
NAMESPACE
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@ -266,6 +266,7 @@ export(mo_is_yeast)
export(mo_kingdom)
export(mo_lpsn)
export(mo_matching_score)
export(mo_mycobank)
export(mo_name)
export(mo_order)
export(mo_oxygen_tolerance)

14
NEWS.md
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@ -1,4 +1,4 @@
# AMR 2.1.1.9061
# AMR 2.1.1.9063
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
@ -19,10 +19,15 @@ This package now supports not only tools for AMR data analysis in clinical setti
* EUCAST 2024 and CLSI 2024 are now supported, by adding all of their over 4,000 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 is now the new default guideline for all MIC and disks diffusion interpretations.
* `as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Currently, the `clinical_breakpoints` data set contains 24 breakpoints that can return the value "SDD" instead of "I".
* MIC plotting and transforming
* The function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
* Function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
* New function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
* New function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
* Microbiological taxonomy (`microorganisms` data set) updated to June 2024, with some exciting new features:
* Added MycoBank as the primary taxonomic source for fungi
* The `microorganisms` data set now contains additional columns `mycobank`, `mycobank_parent`, and `mycobank_renamed_to`
* New function `mo_mycobank()` to get the MycoBank record number, analogous to existing functions `mo_lpsn()` and `mo_gbif()`
* We've welcomed over 2,000 records from 2023, over 900 from 2024, and many thousands of new fungi
* Other
* Function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.
* New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.
## Changed
* SIR interpretation
@ -51,6 +56,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
* Improved overall algorithm of `as.ab()` for better performance and accuracy
* Improved overall algorithm of `as.mo()` for better performance and accuracy. Specifically, more weight is given to genus and species combinations in cases where the subspecies is miswritten, so that the result will be the correct genus and species.
* Intermediate log2 levels used for MIC plotting are now more common values instead of following a strict dilution range
* Fixed a bug for when `antibiogram()` returns an empty data set
## Other
* Added Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input

6
R/aa_amr-package.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

25
R/aa_globals.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -86,26 +86,37 @@ EUCAST_VERSION_EXPERT_RULES <- list(
TAXONOMY_VERSION <- list(
GBIF = list(
accessed_date = as.Date("2024-01-08"),
name = "Global Biodiversity Information Facility (GBIF)",
accessed_date = as.Date("2024-06-24"),
citation = "GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
url = "https://www.gbif.org"
),
LPSN = list(
accessed_date = as.Date("2022-12-11"),
name = "List of Prokaryotic names with Standing in Nomenclature (LPSN)",
accessed_date = as.Date("2024-06-24"),
citation = "Parte, AC *et al.* (2020). **List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.** International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \\doi{10.1099/ijsem.0.004332}.",
url = "https://lpsn.dsmz.de"
),
MycoBank = list(
name = "MycoBank",
accessed_date = as.Date("2024-06-24"),
citation = "Vincent, R *et al* (2013). **MycoBank gearing up for new horizons.** IMA Fungus, 4(2), 371-9; \\doi{10.5598/imafungus.2013.04.02.16}.",
url = "https://www.mycobank.org"
),
BacDive = list(
accessed_date = as.Date("2023-05-12"),
name = "BacDive",
accessed_date = as.Date("2024-07-16"),
citation = "Reimer, LC *et al.* (2022). ***BacDive* in 2022: the knowledge base for standardized bacterial and archaeal data.** Nucleic Acids Res., 50(D1):D741-D74; \\doi{10.1093/nar/gkab961}.",
url = "https://bacdive.dsmz.de"
),
SNOMED = list(
accessed_date = as.Date("2021-07-01"),
name = "Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT)",
accessed_date = as.Date("2024-07-16"),
citation = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12).",
url = "https://phinvads.cdc.gov"
),
LOINC = list(
name = "Logical Observation Identifiers Names and Codes (LOINC)",
accessed_date = as.Date("2023-10-19"),
citation = "Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023).",
url = "https://loinc.org"

32
R/aa_helper_functions.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -573,6 +573,7 @@ warning_ <- function(...,
# - wraps text to never break lines within words
stop_ <- function(..., call = TRUE) {
msg <- paste0(c(...), collapse = "")
msg_call <- ""
if (!isFALSE(call)) {
if (isTRUE(call)) {
call <- as.character(sys.call(-1)[1])
@ -580,10 +581,19 @@ stop_ <- function(..., call = TRUE) {
# so you can go back more than 1 call, as used in sir_calc(), that now throws a reference to e.g. n_sir()
call <- as.character(sys.call(call)[1])
}
msg <- paste0("in ", call, "(): ", msg)
msg_call <- paste0("in ", call, "():")
}
msg <- trimws2(word_wrap(msg, add_fn = list(), as_note = FALSE))
stop(msg, call. = FALSE)
if (!is.null(AMR_env$cli_abort) && length(unlist(strsplit(msg, "\n", fixed = TRUE))) <= 1) {
if (is.character(call)) {
call <- as.call(str2lang(paste0(call, "()")))
} else {
call <- NULL
}
AMR_env$cli_abort(msg, call = call)
} else {
stop(paste(msg_call, msg), call. = FALSE)
}
}
stop_if <- function(expr, ..., call = TRUE) {
@ -1021,10 +1031,10 @@ get_current_data <- function(arg_name, call) {
fn <- as.character(sys.call(call + 1)[1])
examples <- paste0(
", e.g.:\n",
" your_data %>% select(", fn, "())\n",
" your_data %>% select(column_a, column_b, ", fn, "())\n",
" your_data[, ", fn, "()]\n",
' your_data[, c("column_a", "column_b", ', fn, "())]"
" ", AMR_env$bullet_icon, " your_data %>% select(", fn, "())\n",
" ", AMR_env$bullet_icon, " your_data %>% select(column_a, column_b, ", fn, "())\n",
" ", AMR_env$bullet_icon, " your_data[, ", fn, "()]\n",
" ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", fn, "())]"
)
} else {
examples <- ""
@ -1412,12 +1422,8 @@ as_original_data_class <- function(df, old_class = NULL, extra_class = NULL) {
if ("tbl_df" %in% old_class && pkg_is_available("tibble")) {
# this will then also remove groups
fn <- import_fn("as_tibble", "tibble")
} else if ("tbl_ts" %in% old_class && pkg_is_available("tsibble")) {
fn <- import_fn("as_tsibble", "tsibble")
} else if ("data.table" %in% old_class && pkg_is_available("data.table")) {
fn <- import_fn("as.data.table", "data.table")
} else if ("tabyl" %in% old_class && pkg_is_available("janitor")) {
fn <- import_fn("as_tabyl", "janitor")
} else {
fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
}

6
R/aa_helper_pm_functions.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/aa_options.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ab.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ab_from_text.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ab_property.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ab_selectors.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/age.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

35
R/antibiogram.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -392,7 +392,10 @@ antibiogram <- function(x,
} else {
out$numerator <- out$S
}
if (any(out$total < minimum, na.rm = TRUE)) {
if (all(out$total < minimum, na.rm = TRUE)) {
warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
} else if (any(out$total < minimum, na.rm = TRUE)) {
if (isTRUE(info)) {
message_("NOTE: ", sum(out$total < minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
}
@ -409,6 +412,7 @@ antibiogram <- function(x,
out <- out %pm>%
pm_group_by(mo, ab)
}
out <- out %pm>%
pm_summarise(SI = numerator / total)
@ -515,6 +519,29 @@ antibiogram <- function(x,
)
}
# will be exported in R/zzz.R
tbl_sum.antibiogram <- function(x, ...) {
if (isTRUE(base::l10n_info()$`UTF-8`)) {
cross <- "\u00d7"
} else {
cross <- "x"
}
dims <- paste(format(NROW(x), big.mark = ","), cross, format(NCOL(x), big.mark = ","))
names(dims) <- "An Antibiogram"
dims
}
# will be exported in R/zzz.R
tbl_format_footer.antibiogram <- function(x, ...) {
footer <- NextMethod()
if (NROW(x) == 0) {
return(footer)
}
c(footer, font_subtle(paste0("# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
"# or use it directly in R Markdown or ",
font_url("https://quarto.org", "Quarto"), ", see ", word_wrap("?antibiogram"))))
}
#' @export
#' @rdname antibiogram
plot.antibiogram <- function(x, ...) {

6
R/atc_online.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/av_property.R
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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/count.R
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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/custom_antimicrobials.R
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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/custom_eucast_rules.R
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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/custom_microorganisms.R
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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -32,9 +32,9 @@
#' Two data sets containing all antibiotics/antimycotics and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antibiotics] data set. Three identifiers are included in this data set: an antibiotic ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
#' @format
#' ### For the [antibiotics] data set: a [tibble][tibble::tibble] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. *This is a unique identifier.*
#' - `cid`\cr Compound ID as found in PubChem. *This is a unique identifier.*
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. *This is a unique identifier.*
#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.***
#' - `cid`\cr Compound ID as found in PubChem. ***This is a unique identifier.***
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. ***This is a unique identifier.***
#' - `group`\cr A short and concise group name, based on WHONET and WHOCC definitions
#' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, like `J01CR02`
#' - `atc_group1`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like `"Macrolides, lincosamides and streptogramins"`
@ -48,10 +48,10 @@
#' - `loinc`\cr All codes associated with the name of the antimicrobial drug from `r TAXONOMY_VERSION$LOINC$citation` Use [ab_loinc()] to retrieve them quickly, see [ab_property()].
#'
#' ### For the [antivirals] data set: a [tibble][tibble::tibble] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
#' - `av`\cr Antiviral ID as used in this package (such as `ACI`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. *This is a unique identifier.* Combinations are codes that contain a `+` to indicate this, such as `ATA+COBI` for atazanavir/cobicistat.
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. *This is a unique identifier.*
#' - `av`\cr Antiviral ID as used in this package (such as `ACI`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.*** Combinations are codes that contain a `+` to indicate this, such as `ATA+COBI` for atazanavir/cobicistat.
#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. ***This is a unique identifier.***
#' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
#' - `cid`\cr Compound ID as found in PubChem. *This is a unique identifier.*
#' - `cid`\cr Compound ID as found in PubChem. ***This is a unique identifier.***
#' - `atc_group`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
#' - `synonyms`\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
#' - `oral_ddd`\cr Defined Daily Dose (DDD), oral treatment
@ -82,35 +82,41 @@
#' @rdname antibiotics
"antivirals"
#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Microorganisms
#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Taxonomic Records of Microorganisms
#'
#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()].
#' @description
#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date, TAXONOMY_VERSION$MycoBank$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms. This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()] and microorganism properties can be looked up using any of the [`mo_*`][mo_property()] functions.
#'
#' This data set is carefully crafted, yet made 100% reproducible from public and authoritative taxonomic sources (using [this script](https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R)), namely: *`r TAXONOMY_VERSION$LPSN$name`* for bacteria, *`r TAXONOMY_VERSION$MycoBank$name`* for fungi, and *`r TAXONOMY_VERSION$GBIF$name`* for all others taxons.
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables:
#' - `mo`\cr ID of microorganism as used by this package. *This is a unique identifier.*
#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. *This is a unique identifier.*
#' - `mo`\cr ID of microorganism as used by this package. ***This is a unique identifier.***
#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. ***This is a unique identifier.***
#' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status)`
#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism
#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is equal to their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy.
#' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"`
#' - `ref`\cr Author(s) and year of related scientific publication. This contains only the *first surname* and year of the *latest* authors, e.g. "Wallis *et al.* 2006 *emend.* Smith and Jones 2018" becomes "Smith *et al.*, 2018". This field is directly retrieved from the source specified in the column `source`. Moreover, accents were removed to comply with CRAN that only allows ASCII characters.
#' - `lpsn`\cr Identifier ('Record number') of the List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set.
#' - `oxygen_tolerance` \cr Oxygen tolerance, either `r vector_or(microorganisms$oxygen_tolerance)`. These data were retrieved from BacDive (see *Source*). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently `r round(length(microorganisms$oxygen_tolerance[which(!is.na(microorganisms$oxygen_tolerance))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance.
#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
#' - `lpsn`\cr Identifier ('Record number') of `r TAXONOMY_VERSION$LPSN$name`. This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$lpsn)))` records.
#' - `lpsn_parent`\cr LPSN identifier of the parent taxon
#' - `lpsn_renamed_to`\cr LPSN identifier of the currently valid taxon
#' - `gbif`\cr Identifier ('taxonID') of the Global Biodiversity Information Facility (GBIF)
#' - `mycobank`\cr Identifier ('MycoBank #') of `r TAXONOMY_VERSION$MycoBank$name`. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$mycobank)))` records.
#' - `mycobank_parent`\cr MycoBank identifier of the parent taxon
#' - `mycobank_renamed_to`\cr MycoBank identifier of the currently valid taxon
#' - `gbif`\cr Identifier ('taxonID') of `r TAXONOMY_VERSION$GBIF$name`. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$gbif)))` records.
#' - `gbif_parent`\cr GBIF identifier of the parent taxon
#' - `gbif_renamed_to`\cr GBIF identifier of the currently valid taxon
#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
#' - `prevalence`\cr Prevalence of the microorganism based on Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}), see [mo_matching_score()] for the full explanation
#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
#' @details
#' Please note that entries are only based on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
#' Please note that entries are only based on LPSN, MycoBank, and GBIF (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
#'
#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not before 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not until 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
#'
#' @section Included Taxa:
#' Included taxonomic data are:
#' Included taxonomic data from [LPSN](`r TAXONOMY_VERSION$LPSN$url`), [MycoBank](`r TAXONOMY_VERSION$MycoBank$url`), and [GBIF](`r TAXONOMY_VERSION$GBIF$url`) are:
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria")), , drop = FALSE])` (sub)species from the kingdoms of Archaea and Bacteria
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Protozoa"), , drop = FALSE])` (sub)species from the kingdom of Protozoa
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
#' - All `r format_included_data_number(microorganisms[which(microorganisms$status != "accepted"), , drop = FALSE])` previously accepted names of all included (sub)species (these were taxonomically renamed)
@ -127,22 +133,28 @@
#' - 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)
#'
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R>.
#' The syntax used to transform the original data to a cleansed \R format, can be [found here](https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R).
#'
#' ### Direct 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).
#' @section About the Records from LPSN (see *Source*):
#' LPSN is the main source for bacteriological taxonomy of this `AMR` package.
#'
#' The List of Prokaryotic names with Standing in Nomenclature (LPSN) provides comprehensive information on the nomenclature of prokaryotes. LPSN is a free to use service founded by Jean P. Euzeby in 1997 and later on maintained by Aidan C. Parte.
#' @source
#' * `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
#' Taxonomic entries were imported in this order of importance:
#' 1. `r TAXONOMY_VERSION$LPSN$name`:\cr\cr
#' `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
#'
#' * `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
#' 2. `r TAXONOMY_VERSION$MycoBank$name`:\cr\cr
#' `r TAXONOMY_VERSION$MycoBank$citation` Accessed from <`r TAXONOMY_VERSION$MycoBank$url`> on `r documentation_date(TAXONOMY_VERSION$MycoBank$accessed_date)`.
#'
#' * `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
#' 3. `r TAXONOMY_VERSION$GBIF$name`:\cr\cr
#' `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
#'
#' * `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
#' Furthermore, these sources were used for additional details:
#'
#' * `r TAXONOMY_VERSION$BacDive$name`:\cr\cr
#' `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
#'
#' * `r TAXONOMY_VERSION$SNOMED$name`:\cr\cr
#' `r TAXONOMY_VERSION$SNOMED$citation` Accessed from <`r TAXONOMY_VERSION$SNOMED$url`> on `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`.
#'
#' * Grimont *et al.* (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
#'
@ -156,7 +168,7 @@
#'
#' 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. *This is a unique identifier.*
#' - `code`\cr Commonly used code of a microorganism. ***This is a unique identifier.***
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
#' @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).

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# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/eucast_rules.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/export_biosample.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/first_isolate.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/g.test.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/get_episode.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ggplot_pca.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/ggplot_sir.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/guess_ab_col.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/italicise_taxonomy.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/join_microorganisms.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/key_antimicrobials.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/kurtosis.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/like.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/mdro.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/mean_amr_distance.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

8
R/mic.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -119,7 +119,7 @@ COMMON_MIC_VALUES <- c(0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064,
#' quantile(mic_data)
#' all(mic_data < 512)
#'
#' # limit MICs using rescale_mic()
#' # rescale MICs using rescale_mic()
#' rescale_mic(mic_data, mic_range = c(4, 16))
#'
#' # interpret MIC values

39
R/mo.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -1251,19 +1251,32 @@ load_mo_uncertainties <- function(metadata) {
AMR_env$mo_uncertainties <- metadata$uncertainties
}
synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE) {
x_gbif <- AMR_env$MO_lookup$gbif_renamed_to[match(x, AMR_env$MO_lookup$mo)]
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
synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE, dataset = AMR_env$MO_lookup) {
if (identical(dataset, AMR_env$MO_lookup)) {
add_MO_lookup_to_AMR_env()
dataset <- AMR_env$MO_lookup
}
x_lpsn <- dataset$lpsn_renamed_to[match(x, dataset$mo)] %or% NA_character_
x_mycobank <- dataset$mycobank_renamed_to[match(x, dataset$mo)] %or% NA_character_
x_gbif <- dataset$gbif_renamed_to[match(x, dataset$mo)] %or% NA_character_
out <- ifelse(is.na(x_lpsn),
AMR_env$MO_lookup$mo[match(x_gbif, AMR_env$MO_lookup$gbif)],
AMR_env$MO_lookup$mo[match(x_lpsn, AMR_env$MO_lookup$lpsn)]
)
# Replace invalid values with NA
x_lpsn[!x_lpsn %in% dataset$lpsn] <- NA_character_
x_mycobank[!x_mycobank %in% dataset$mycobank] <- NA_character_
x_gbif[!x_gbif %in% dataset$gbif] <- NA_character_
# Create output vector using vectorized operations
out <- rep(NA_character_, length(x))
out[is.na(out) & !is.na(x_lpsn)] <- dataset$mo[match(x_lpsn[is.na(out) & !is.na(x_lpsn)], dataset$lpsn)]
out[is.na(out) & !is.na(x_mycobank)] <- dataset$mo[match(x_mycobank[is.na(out) & !is.na(x_mycobank)], dataset$mycobank)]
out[is.na(out) & !is.na(x_gbif)] <- dataset$mo[match(x_gbif[is.na(out) & !is.na(x_gbif)], dataset$gbif)]
out[dataset$status[match(x, dataset$mo)] == "accepted"] <- NA_character_
if (isTRUE(fill_in_accepted)) {
x_accepted <- which(AMR_env$MO_lookup$status[match(x, AMR_env$MO_lookup$mo)] == "accepted")
x_accepted <- which(dataset$status[match(x, dataset$mo)] == "accepted")
out[x_accepted] <- x[x_accepted]
}
out[is.na(match(x, dataset$mo))] <- NA_character_
out
}

6
R/mo_matching_score.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

30
R/mo_property.R
View File

@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -120,6 +120,7 @@
#' mo_year("Klebsiella aerogenes")
#' mo_lpsn("Klebsiella aerogenes")
#' mo_gbif("Klebsiella aerogenes")
#' mo_mycobank("Candida albicans")
#' mo_synonyms("Klebsiella aerogenes")
#'
#'
@ -214,7 +215,13 @@ mo_name <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
#' @rdname mo_property
#' @export
mo_fullname <- mo_name
mo_fullname <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
if (missing(x)) {
# this tries to find the data and an 'mo' column
x <- find_mo_col(fn = "mo_fullname")
}
mo_name(x = x, language = language, keep_synonyms = keep_synonyms, ...)
}
#' @rdname mo_property
#' @export
@ -697,6 +704,21 @@ mo_lpsn <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
mo_validate(x = x, property = "lpsn", language = language, keep_synonyms = keep_synonyms, ...)
}
#' @rdname mo_property
#' @export
mo_mycobank <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
if (missing(x)) {
# this tries to find the data and an 'mo' column
x <- find_mo_col(fn = "mo_mycobank")
}
meet_criteria(x, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
mo_validate(x = x, property = "mycobank", language = language, keep_synonyms = keep_synonyms, ...)
}
#' @rdname mo_property
#' @export
mo_gbif <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {

6
R/mo_source.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/pca.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/plot.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/proportion.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/random.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/resistance_predict.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/sir.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/sir_calc.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/sir_df.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/skewness.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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R/sysdata.rda
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6
R/translate.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/vctrs.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/whocc.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
R/zz_deprecated.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

11
R/zzz.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -81,11 +81,12 @@ AMR_env$is_dark_theme <- NULL
AMR_env$chmatch <- import_fn("chmatch", "data.table", error_on_fail = FALSE)
AMR_env$chin <- import_fn("%chin%", "data.table", error_on_fail = FALSE)
# take cli symbols if available
# take cli symbols and error function if available
AMR_env$info_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$info %or% "i"
AMR_env$bullet_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$bullet %or% "*"
AMR_env$dots <- import_fn("symbol", "cli", error_on_fail = FALSE)$ellipsis %or% "..."
AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %or% "*"
AMR_env$cli_abort <- import_fn("cli_abort", "cli", error_on_fail = FALSE)
.onLoad <- function(lib, pkg) {
# Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
@ -104,6 +105,8 @@ AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %o
s3_register("pillar::type_sum", "mo")
s3_register("pillar::type_sum", "sir")
s3_register("pillar::type_sum", "mic")
s3_register("pillar::tbl_sum", "antibiogram")
s3_register("pillar::tbl_format_footer", "antibiogram")
# Support for frequency tables from the cleaner package
s3_register("cleaner::freq", "mo")
s3_register("cleaner::freq", "sir")

6
_pkgdown.yml
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

6
codecov.yml
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

4
data-raw/AMR_vet.qmd
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@ -13,7 +13,6 @@ format:
library(dplyr)
library(readr)
library(tidyr)
library(janitor)
# WHONET version of 16th Feb 2024
whonet_breakpoints <- read_tsv("WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"),
@ -42,8 +41,7 @@ whonet_breakpoints |>
filter(BREAKPOINT_TYPE == "Animal") |>
count(YEAR, HOST, REFERENCE_TABLE = gsub("VET[0-9]+ ", "", REFERENCE_TABLE)) |>
pivot_wider(names_from = YEAR, values_from = n, values_fill = list(n = 0)) |>
arrange(HOST, REFERENCE_TABLE) |>
adorn_totals(name = "TOTAL")
arrange(HOST, REFERENCE_TABLE)
```
### Cats only

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data-raw/_language_update.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

334
data-raw/_pre_commit_checks.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
@ -36,12 +36,14 @@ devtools::load_all(quiet = TRUE)
suppressMessages(set_AMR_locale("English"))
old_globalenv <- ls(envir = globalenv())
pre_commit_lst <- list()
# Save internal data to R/sysdata.rda -------------------------------------
usethis::ui_info(paste0("Updating internal package data"))
# See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
EUCAST_RULES_DF <- utils::read.delim(
pre_commit_lst$EUCAST_RULES_DF <- utils::read.delim(
file = "data-raw/eucast_rules.tsv",
skip = 9,
sep = "\t",
@ -67,7 +69,7 @@ EUCAST_RULES_DF <- utils::read.delim(
mutate(reference.rule_group = as.character(reference.rule_group)) %>%
select(-sorting_rule)
TRANSLATIONS <- utils::read.delim(
pre_commit_lst$TRANSLATIONS <- utils::read.delim(
file = "data-raw/translations.tsv",
sep = "\t",
stringsAsFactors = FALSE,
@ -82,15 +84,15 @@ TRANSLATIONS <- utils::read.delim(
quote = ""
)
LANGUAGES_SUPPORTED_NAMES <- c(
pre_commit_lst$LANGUAGES_SUPPORTED_NAMES <- c(
list(en = list(exonym = "English", endonym = "English")),
lapply(
TRANSLATIONS[, which(nchar(colnames(TRANSLATIONS)) == 2), drop = FALSE],
TRANSLATIONS[, which(nchar(colnames(pre_commit_lst$TRANSLATIONS)) == 2), drop = FALSE],
function(x) list(exonym = x[1], endonym = x[2])
)
)
LANGUAGES_SUPPORTED <- names(LANGUAGES_SUPPORTED_NAMES)
pre_commit_lst$LANGUAGES_SUPPORTED <- names(pre_commit_lst$LANGUAGES_SUPPORTED_NAMES)
# vectors of CoNS and CoPS, improves speed in as.mo()
create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
@ -147,115 +149,223 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
]
}
}
MO_CONS <- create_species_cons_cops("CoNS")
MO_COPS <- create_species_cons_cops("CoPS")
MO_STREP_ABCG <- AMR::microorganisms$mo[which(AMR::microorganisms$genus == "Streptococcus" &
pre_commit_lst$MO_CONS <- create_species_cons_cops("CoNS")
pre_commit_lst$MO_COPS <- create_species_cons_cops("CoPS")
pre_commit_lst$MO_STREP_ABCG <- AMR::microorganisms$mo[which(AMR::microorganisms$genus == "Streptococcus" &
tolower(AMR::microorganisms$species) %in% c(
"pyogenes", "agalactiae", "dysgalactiae", "equi", "canis",
"group a", "group b", "group c", "group g"
))]
MO_LANCEFIELD <- AMR::microorganisms$mo[which(AMR::microorganisms$mo %like% "^(B_STRPT_PYGN(_|$)|B_STRPT_AGLC(_|$)|B_STRPT_(DYSG|EQUI)(_|$)|B_STRPT_ANGN(_|$)|B_STRPT_(DYSG|CANS)(_|$)|B_STRPT_SNGN(_|$)|B_STRPT_SLVR(_|$))")]
MO_PREVALENT_GENERA <- c(
"Absidia", "Acanthamoeba", "Acremonium", "Aedes", "Alternaria", "Amoeba", "Ancylostoma", "Angiostrongylus",
"Anisakis", "Anopheles", "Apophysomyces", "Arthroderma", "Aspergillus", "Aureobasidium", "Basidiobolus", "Beauveria",
"Blastocystis", "Blastomyces", "Candida", "Capillaria", "Chaetomium", "Chrysonilia", "Chrysosporium", "Cladophialophora",
"Cladosporium", "Conidiobolus", "Contracaecum", "Cordylobia", "Cryptococcus", "Curvularia", "Demodex",
"Dermatobia", "Dientamoeba", "Diphyllobothrium", "Dirofilaria", "Echinostoma", "Entamoeba", "Enterobius",
"Exophiala", "Exserohilum", "Fasciola", "Fonsecaea", "Fusarium", "Geotrichum", "Giardia", "Haloarcula", "Halobacterium",
"Halococcus", "Hendersonula", "Heterophyes", "Histomonas", "Histoplasma", "Hymenolepis", "Hypomyces",
"Hysterothylacium", "Kloeckera", "Kodamaea", "Leishmania", "Lichtheimia", "Lodderomyces",
"Malassezia", "Malbranchea", "Metagonimus", "Meyerozyma", "Microsporidium",
"Microsporum", "Millerozyma", "Mortierella", "Mucor", "Mycocentrospora", "Necator", "Nectria", "Ochroconis", "Oesophagostomum",
"Oidiodendron", "Opisthorchis", "Paecilomyces", "Pediculus", "Penicillium", "Phlebotomus", "Phoma", "Pichia", "Piedraia", "Pithomyces",
"Pityrosporum", "Pneumocystis", "Pseudallescheria", "Pseudoterranova", "Pulex", "Rhizomucor", "Rhizopus",
"Rhodotorula", "Saccharomyces", "Saprochaete", "Sarcoptes", "Scedosporium", "Scolecobasidium", "Scopulariopsis", "Scytalidium", "Spirometra",
"Sporobolomyces", "Sporotrichum", "Stachybotrys", "Strongyloides", "Syngamus", "Taenia", "Talaromyces", "Toxocara", "Trichinella",
"Trichobilharzia", "Trichoderma", "Trichomonas", "Trichophyton", "Trichosporon", "Trichostrongylus", "Trichuris",
"Tritirachium", "Trombicula", "Trypanosoma", "Tunga", "Verticillium", "Wuchereria"
pre_commit_lst$MO_LANCEFIELD <- AMR::microorganisms$mo[which(AMR::microorganisms$mo %like% "^(B_STRPT_PYGN(_|$)|B_STRPT_AGLC(_|$)|B_STRPT_(DYSG|EQUI)(_|$)|B_STRPT_ANGN(_|$)|B_STRPT_(DYSG|CANS)(_|$)|B_STRPT_SNGN(_|$)|B_STRPT_SLVR(_|$))")]
pre_commit_lst$MO_PREVALENT_GENERA <- c(
"Absidia",
"Acanthamoeba",
"Acremonium",
"Aedes",
"Alternaria",
"Amoeba",
"Ancylostoma",
"Angiostrongylus",
"Anisakis",
"Anopheles",
"Apophysomyces",
"Arthroderma",
"Aspergillus",
"Aureobasidium",
"Basidiobolus",
"Beauveria",
"Blastocystis",
"Blastomyces",
"Candida",
"Capillaria",
"Chaetomium",
"Chrysonilia",
"Chrysosporium",
"Cladophialophora",
"Cladosporium",
"Conidiobolus",
"Contracaecum",
"Cordylobia",
"Cryptococcus",
"Curvularia",
"Demodex",
"Dermatobia",
"Dientamoeba",
"Diphyllobothrium",
"Dirofilaria",
"Echinostoma",
"Entamoeba",
"Enterobius",
"Exophiala",
"Exserohilum",
"Fasciola",
"Fonsecaea",
"Fusarium",
"Geotrichum",
"Giardia",
"Haloarcula",
"Halobacterium",
"Halococcus",
"Hansenula",
"Hendersonula",
"Heterophyes",
"Histomonas",
"Histoplasma",
"Hymenolepis",
"Hypomyces",
"Hysterothylacium",
"Kloeckera",
"Kluyveromyces",
"Kodamaea",
"Leishmania",
"Lichtheimia",
"Lodderomyces",
"Lomentospora",
"Malassezia",
"Malbranchea",
"Metagonimus",
"Meyerozyma",
"Microsporidium",
"Microsporum",
"Millerozyma",
"Mortierella",
"Mucor",
"Mycocentrospora",
"Necator",
"Nectria",
"Ochroconis",
"Oesophagostomum",
"Oidiodendron",
"Opisthorchis",
"Paecilomyces",
"Pediculus",
"Penicillium",
"Phlebotomus",
"Phoma",
"Pichia",
"Piedraia",
"Pithomyces",
"Pityrosporum",
"Pneumocystis",
"Pseudallescheria",
"Pseudoscopulariopsis",
"Pseudoterranova",
"Pulex",
"Rhizomucor",
"Rhizopus",
"Rhodotorula",
"Saccharomyces",
"Saprochaete",
"Sarcoptes",
"Scedosporium",
"Scolecobasidium",
"Scopulariopsis",
"Scytalidium",
"Spirometra",
"Sporobolomyces",
"Sporotrichum",
"Stachybotrys",
"Strongyloides",
"Syngamus",
"Taenia",
"Talaromyces",
"Toxocara",
"Trichinella",
"Trichobilharzia",
"Trichoderma",
"Trichomonas",
"Trichophyton",
"Trichosporon",
"Trichostrongylus",
"Trichuris",
"Tritirachium",
"Trombicula",
"Trypanosoma",
"Tunga",
"Verticillium",
"Wuchereria"
)
# antibiotic groups
# (these will also be used for eucast_rules() and understanding data-raw/eucast_rules.tsv)
globalenv_before_ab <- c(ls(envir = globalenv()), "globalenv_before_ab")
AB_AMINOGLYCOSIDES <- antibiotics %>%
pre_commit_lst$AB_AMINOGLYCOSIDES <- antibiotics %>%
filter(group %like% "aminoglycoside") %>%
pull(ab)
AB_AMINOPENICILLINS <- as.ab(c("AMP", "AMX"))
AB_ANTIFUNGALS <- antibiotics %>%
pre_commit_lst$AB_AMINOPENICILLINS <- as.ab(c("AMP", "AMX"))
pre_commit_lst$AB_ANTIFUNGALS <- antibiotics %>%
filter(group %like% "antifungal") %>%
pull(ab)
AB_ANTIMYCOBACTERIALS <- antibiotics %>%
pre_commit_lst$AB_ANTIMYCOBACTERIALS <- antibiotics %>%
filter(group %like% "antimycobacterial") %>%
pull(ab)
AB_CARBAPENEMS <- antibiotics %>%
pre_commit_lst$AB_CARBAPENEMS <- antibiotics %>%
filter(group %like% "carbapenem") %>%
pull(ab)
AB_CEPHALOSPORINS <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS <- antibiotics %>%
filter(group %like% "cephalosporin") %>%
pull(ab)
AB_CEPHALOSPORINS_1ST <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_1ST <- antibiotics %>%
filter(group %like% "cephalosporin.*1") %>%
pull(ab)
AB_CEPHALOSPORINS_2ND <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_2ND <- antibiotics %>%
filter(group %like% "cephalosporin.*2") %>%
pull(ab)
AB_CEPHALOSPORINS_3RD <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_3RD <- antibiotics %>%
filter(group %like% "cephalosporin.*3") %>%
pull(ab)
AB_CEPHALOSPORINS_4TH <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_4TH <- antibiotics %>%
filter(group %like% "cephalosporin.*4") %>%
pull(ab)
AB_CEPHALOSPORINS_5TH <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_5TH <- antibiotics %>%
filter(group %like% "cephalosporin.*5") %>%
pull(ab)
AB_CEPHALOSPORINS_EXCEPT_CAZ <- AB_CEPHALOSPORINS[AB_CEPHALOSPORINS != "CAZ"]
AB_FLUOROQUINOLONES <- antibiotics %>%
pre_commit_lst$AB_CEPHALOSPORINS_EXCEPT_CAZ <- pre_commit_lst$AB_CEPHALOSPORINS[pre_commit_lst$AB_CEPHALOSPORINS != "CAZ"]
pre_commit_lst$AB_FLUOROQUINOLONES <- antibiotics %>%
filter(atc_group2 %like% "fluoroquinolone" | (group %like% "quinolone" & is.na(atc_group2))) %>%
pull(ab)
AB_GLYCOPEPTIDES <- antibiotics %>%
pre_commit_lst$AB_GLYCOPEPTIDES <- antibiotics %>%
filter(group %like% "glycopeptide") %>%
pull(ab)
AB_LIPOGLYCOPEPTIDES <- as.ab(c("DAL", "ORI", "TLV")) # dalba/orita/tela
AB_GLYCOPEPTIDES_EXCEPT_LIPO <- AB_GLYCOPEPTIDES[!AB_GLYCOPEPTIDES %in% AB_LIPOGLYCOPEPTIDES]
AB_LINCOSAMIDES <- antibiotics %>%
pre_commit_lst$AB_LIPOGLYCOPEPTIDES <- as.ab(c("DAL", "ORI", "TLV")) # dalba/orita/tela
pre_commit_lst$AB_GLYCOPEPTIDES_EXCEPT_LIPO <- pre_commit_lst$AB_GLYCOPEPTIDES[!pre_commit_lst$AB_GLYCOPEPTIDES %in% pre_commit_lst$AB_LIPOGLYCOPEPTIDES]
pre_commit_lst$AB_LINCOSAMIDES <- antibiotics %>%
filter(atc_group2 %like% "lincosamide" | (group %like% "lincosamide" & is.na(atc_group2))) %>%
pull(ab)
AB_MACROLIDES <- antibiotics %>%
pre_commit_lst$AB_MACROLIDES <- antibiotics %>%
filter(atc_group2 %like% "macrolide" | (group %like% "macrolide" & is.na(atc_group2) & name %unlike% "screening|inducible")) %>%
pull(ab)
AB_NITROFURANS <- antibiotics %>%
pre_commit_lst$AB_NITROFURANS <- antibiotics %>%
filter(name %like% "^furaz|nitrofura" | atc_group2 %like% "nitrofuran") %>%
pull(ab)
AB_OXAZOLIDINONES <- antibiotics %>%
pre_commit_lst$AB_OXAZOLIDINONES <- antibiotics %>%
filter(group %like% "oxazolidinone") %>%
pull(ab)
AB_PENICILLINS <- antibiotics %>%
pre_commit_lst$AB_PENICILLINS <- antibiotics %>%
filter(group %like% "penicillin") %>%
pull(ab)
AB_POLYMYXINS <- antibiotics %>%
pre_commit_lst$AB_POLYMYXINS <- antibiotics %>%
filter(group %like% "polymyxin") %>%
pull(ab)
AB_QUINOLONES <- antibiotics %>%
pre_commit_lst$AB_QUINOLONES <- antibiotics %>%
filter(group %like% "quinolone") %>%
pull(ab)
AB_RIFAMYCINS <- antibiotics %>%
pre_commit_lst$AB_RIFAMYCINS <- antibiotics %>%
filter(name %like% "Rifampi|Rifabutin|Rifapentine|rifamy") %>%
pull(ab)
AB_STREPTOGRAMINS <- antibiotics %>%
pre_commit_lst$AB_STREPTOGRAMINS <- antibiotics %>%
filter(atc_group2 %like% "streptogramin") %>%
pull(ab)
AB_TETRACYCLINES <- antibiotics %>%
pre_commit_lst$AB_TETRACYCLINES <- antibiotics %>%
filter(group %like% "tetracycline") %>%
pull(ab)
AB_TETRACYCLINES_EXCEPT_TGC <- AB_TETRACYCLINES[AB_TETRACYCLINES != "TGC"]
AB_TRIMETHOPRIMS <- antibiotics %>%
pre_commit_lst$AB_TETRACYCLINES_EXCEPT_TGC <- pre_commit_lst$AB_TETRACYCLINES[pre_commit_lst$AB_TETRACYCLINES != "TGC"]
pre_commit_lst$AB_TRIMETHOPRIMS <- antibiotics %>%
filter(group %like% "trimethoprim") %>%
pull(ab)
AB_UREIDOPENICILLINS <- as.ab(c("PIP", "TZP", "AZL", "MEZ"))
AB_BETALACTAMS <- c(AB_PENICILLINS, AB_CEPHALOSPORINS, AB_CARBAPENEMS)
pre_commit_lst$AB_UREIDOPENICILLINS <- as.ab(c("PIP", "TZP", "AZL", "MEZ"))
pre_commit_lst$AB_BETALACTAMS <- c(pre_commit_lst$AB_PENICILLINS, pre_commit_lst$AB_CEPHALOSPORINS, pre_commit_lst$AB_CARBAPENEMS)
# this will be used for documentation:
DEFINED_AB_GROUPS <- ls(envir = globalenv())
DEFINED_AB_GROUPS <- DEFINED_AB_GROUPS[!DEFINED_AB_GROUPS %in% globalenv_before_ab]
pre_commit_lst$DEFINED_AB_GROUPS <- sort(names(pre_commit_lst)[names(pre_commit_lst) %like% "^AB_" & names(pre_commit_lst) != "AB_LOOKUP"])
create_AB_AV_lookup <- function(df) {
new_df <- df
new_df$generalised_name <- generalise_antibiotic_name(new_df$name)
@ -282,62 +392,26 @@ create_AB_AV_lookup <- function(df) {
))
new_df[, colnames(new_df)[colnames(new_df) %like% "^generalised"]]
}
AB_LOOKUP <- create_AB_AV_lookup(AMR::antibiotics)
AV_LOOKUP <- create_AB_AV_lookup(AMR::antivirals)
pre_commit_lst$AB_LOOKUP <- create_AB_AV_lookup(AMR::antibiotics)
pre_commit_lst$AV_LOOKUP <- create_AB_AV_lookup(AMR::antivirals)
# Export to package as internal data ----
usethis::ui_info(paste0("Updating internal package data"))
suppressMessages(usethis::use_data(EUCAST_RULES_DF,
TRANSLATIONS,
LANGUAGES_SUPPORTED_NAMES,
LANGUAGES_SUPPORTED,
MO_CONS,
MO_COPS,
MO_STREP_ABCG,
MO_LANCEFIELD,
MO_PREVALENT_GENERA,
AB_LOOKUP,
AV_LOOKUP,
AB_AMINOGLYCOSIDES,
AB_AMINOPENICILLINS,
AB_ANTIFUNGALS,
AB_ANTIMYCOBACTERIALS,
AB_CARBAPENEMS,
AB_CEPHALOSPORINS,
AB_CEPHALOSPORINS_1ST,
AB_CEPHALOSPORINS_2ND,
AB_CEPHALOSPORINS_3RD,
AB_CEPHALOSPORINS_4TH,
AB_CEPHALOSPORINS_5TH,
AB_CEPHALOSPORINS_EXCEPT_CAZ,
AB_FLUOROQUINOLONES,
AB_LIPOGLYCOPEPTIDES,
AB_GLYCOPEPTIDES,
AB_GLYCOPEPTIDES_EXCEPT_LIPO,
AB_LINCOSAMIDES,
AB_MACROLIDES,
AB_NITROFURANS,
AB_OXAZOLIDINONES,
AB_PENICILLINS,
AB_POLYMYXINS,
AB_QUINOLONES,
AB_RIFAMYCINS,
AB_STREPTOGRAMINS,
AB_TETRACYCLINES,
AB_TETRACYCLINES_EXCEPT_TGC,
AB_TRIMETHOPRIMS,
AB_UREIDOPENICILLINS,
AB_BETALACTAMS,
DEFINED_AB_GROUPS,
internal = TRUE,
overwrite = TRUE,
# usethis::use_data() must receive unquoted object names, which is not flexible at all.
# we'll use good old base::save() instead
save(list = names(pre_commit_lst),
file = "R/sysdata.rda",
envir = as.environment(pre_commit_lst),
compress = "xz",
version = 2,
compress = "xz"
))
ascii = FALSE)
usethis::ui_done("Saved to {usethis::ui_value('R/sysdata.rda')}")
# Export data sets to the repository in different formats -----------------
for (pkg in c("haven", "openxlsx", "arrow")) {
for (pkg in c("haven", "openxlsx2", "arrow")) {
if (!pkg %in% rownames(utils::installed.packages())) {
message("NOTE: package '", pkg, "' not installed! Ignoring export where this package is required.")
}
@ -378,7 +452,7 @@ if (changed_md5(clin_break)) {
try(haven::write_xpt(clin_break, "data-raw/clinical_breakpoints.xpt"), silent = TRUE)
try(haven::write_sav(clin_break, "data-raw/clinical_breakpoints.sav"), silent = TRUE)
try(haven::write_dta(clin_break, "data-raw/clinical_breakpoints.dta"), silent = TRUE)
try(openxlsx::write.xlsx(clin_break, "data-raw/clinical_breakpoints.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(clin_break, "data-raw/clinical_breakpoints.xlsx"), silent = TRUE)
try(arrow::write_feather(clin_break, "data-raw/clinical_breakpoints.feather"), silent = TRUE)
try(arrow::write_parquet(clin_break, "data-raw/clinical_breakpoints.parquet"), silent = TRUE)
}
@ -396,7 +470,7 @@ if (changed_md5(microorganisms)) {
try(haven::write_dta(mo, "data-raw/microorganisms.dta"), silent = TRUE)
mo_all_snomed <- microorganisms %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
try(write.table(mo_all_snomed, "data-raw/microorganisms.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
try(openxlsx::write.xlsx(mo_all_snomed, "data-raw/microorganisms.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(mo_all_snomed, "data-raw/microorganisms.xlsx"), silent = TRUE)
try(arrow::write_feather(microorganisms, "data-raw/microorganisms.feather"), silent = TRUE)
try(arrow::write_parquet(microorganisms, "data-raw/microorganisms.parquet"), silent = TRUE)
}
@ -409,7 +483,7 @@ if (changed_md5(microorganisms.codes)) {
try(haven::write_xpt(microorganisms.codes, "data-raw/microorganisms.codes.xpt"), silent = TRUE)
try(haven::write_sav(microorganisms.codes, "data-raw/microorganisms.codes.sav"), silent = TRUE)
try(haven::write_dta(microorganisms.codes, "data-raw/microorganisms.codes.dta"), silent = TRUE)
try(openxlsx::write.xlsx(microorganisms.codes, "data-raw/microorganisms.codes.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(microorganisms.codes, "data-raw/microorganisms.codes.xlsx"), silent = TRUE)
try(arrow::write_feather(microorganisms.codes, "data-raw/microorganisms.codes.feather"), silent = TRUE)
try(arrow::write_parquet(microorganisms.codes, "data-raw/microorganisms.codes.parquet"), silent = TRUE)
}
@ -422,7 +496,7 @@ if (changed_md5(microorganisms.groups)) {
try(haven::write_xpt(microorganisms.groups, "data-raw/microorganisms.groups.xpt"), silent = TRUE)
try(haven::write_sav(microorganisms.groups, "data-raw/microorganisms.groups.sav"), silent = TRUE)
try(haven::write_dta(microorganisms.groups, "data-raw/microorganisms.groups.dta"), silent = TRUE)
try(openxlsx::write.xlsx(microorganisms.groups, "data-raw/microorganisms.groups.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(microorganisms.groups, "data-raw/microorganisms.groups.xlsx"), silent = TRUE)
try(arrow::write_feather(microorganisms.groups, "data-raw/microorganisms.groups.feather"), silent = TRUE)
try(arrow::write_parquet(microorganisms.groups, "data-raw/microorganisms.groups.parquet"), silent = TRUE)
}
@ -437,7 +511,7 @@ if (changed_md5(ab)) {
try(haven::write_dta(ab, "data-raw/antibiotics.dta"), silent = TRUE)
ab_lists <- antibiotics %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
try(write.table(ab_lists, "data-raw/antibiotics.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
try(openxlsx::write.xlsx(ab_lists, "data-raw/antibiotics.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(ab_lists, "data-raw/antibiotics.xlsx"), silent = TRUE)
try(arrow::write_feather(antibiotics, "data-raw/antibiotics.feather"), silent = TRUE)
try(arrow::write_parquet(antibiotics, "data-raw/antibiotics.parquet"), silent = TRUE)
}
@ -452,7 +526,7 @@ if (changed_md5(av)) {
try(haven::write_dta(av, "data-raw/antivirals.dta"), silent = TRUE)
av_lists <- antivirals %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
try(write.table(av_lists, "data-raw/antivirals.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
try(openxlsx::write.xlsx(av_lists, "data-raw/antivirals.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(av_lists, "data-raw/antivirals.xlsx"), silent = TRUE)
try(arrow::write_feather(antivirals, "data-raw/antivirals.feather"), silent = TRUE)
try(arrow::write_parquet(antivirals, "data-raw/antivirals.parquet"), silent = TRUE)
}
@ -471,7 +545,7 @@ if (changed_md5(intrinsicR)) {
try(haven::write_xpt(intrinsicR, "data-raw/intrinsic_resistant.xpt"), silent = TRUE)
try(haven::write_sav(intrinsicR, "data-raw/intrinsic_resistant.sav"), silent = TRUE)
try(haven::write_dta(intrinsicR, "data-raw/intrinsic_resistant.dta"), silent = TRUE)
try(openxlsx::write.xlsx(intrinsicR, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(intrinsicR, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
try(arrow::write_feather(intrinsicR, "data-raw/intrinsic_resistant.feather"), silent = TRUE)
try(arrow::write_parquet(intrinsicR, "data-raw/intrinsic_resistant.parquet"), silent = TRUE)
}
@ -484,18 +558,13 @@ if (changed_md5(dosage)) {
try(haven::write_xpt(dosage, "data-raw/dosage.xpt"), silent = TRUE)
try(haven::write_sav(dosage, "data-raw/dosage.sav"), silent = TRUE)
try(haven::write_dta(dosage, "data-raw/dosage.dta"), silent = TRUE)
try(openxlsx::write.xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
try(openxlsx2::write_xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
try(arrow::write_feather(dosage, "data-raw/dosage.feather"), silent = TRUE)
try(arrow::write_parquet(dosage, "data-raw/dosage.parquet"), silent = TRUE)
}
suppressMessages(reset_AMR_locale())
# remove leftovers from global env
current_globalenv <- ls(envir = globalenv())
rm(list = current_globalenv[!current_globalenv %in% old_globalenv])
rm(current_globalenv)
devtools::load_all(quiet = TRUE)
suppressMessages(set_AMR_locale("English"))
@ -509,19 +578,6 @@ usethis::ui_info("Documenting package")
suppressMessages(devtools::document(quiet = TRUE))
# Style pkg ---------------------------------------------------------------
if (!"styler" %in% rownames(utils::installed.packages())) {
message("Package 'styler' not installed!")
} else if (interactive()) {
# only when sourcing this file ourselves
# usethis::ui_info("Styling package")
# styler::style_pkg(
# style = styler::tidyverse_style,
# filetype = c("R", "Rmd")
# )
}
# Finished ----------------------------------------------------------------
usethis::ui_done("All done")
suppressMessages(reset_AMR_locale())

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data-raw/clin_break.md5
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@ -1 +1 @@
91ebec60b7f84f55ec0b756964d7c5b6
c79b6e112dc3ab478b990f0689b685b6

BIN
data-raw/clinical_breakpoints.dta
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BIN
data-raw/clinical_breakpoints.feather
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data-raw/clinical_breakpoints.rds
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data-raw/clinical_breakpoints.txt
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@ -192,23 +192,23 @@
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "SXT" "Brucella melitensis" 0.125 0.125 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "TCY" "Brucella melitensis" "30ug" 42 42 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "TCY" "Brucella melitensis" 0.5 0.5 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "CIP" "Brucella melitensis" "5ug" 50 27 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "CIP" "Brucella melitensis" 1e-04 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Meningitis" "B_BRCLL_OVIS" 2 "CRO" "Brucella melitensis" "30ug" 30 30 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "Meningitis" "B_BRCLL_OVIS" 2 "CRO" "Brucella melitensis" 2 2 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "DOX" "Brucella melitensis" 0.25 0.25 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "GEN" "Brucella melitensis" "10ug" 23 23 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "GEN" "Brucella melitensis" 0.5 0.5 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "LVX" "Brucella melitensis" "5ug" 50 28 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "LVX" "Brucella melitensis" 1e-04 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "RIF" "Brucella melitensis" "5ug" 20 20 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "RIF" "Brucella melitensis" 2 2 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "STR" "Brucella melitensis" "10ug" 15 15 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "STR" "Brucella melitensis" 1 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "SXT" "Brucella melitensis" "1.25ug/23.75ug" 29 29 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "SXT" "Brucella melitensis" 0.125 0.125 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_OVIS" 2 "TCY" "Brucella melitensis" "30ug" 42 42 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_OVIS" 2 "TCY" "Brucella melitensis" 0.5 0.5 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "CIP" "Brucella melitensis" "5ug" 50 27 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "CIP" "Brucella melitensis" 1e-04 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Meningitis" "B_BRCLL_MLTN" 2 "CRO" "Brucella melitensis" "30ug" 30 30 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "Meningitis" "B_BRCLL_MLTN" 2 "CRO" "Brucella melitensis" 2 2 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "DOX" "Brucella melitensis" 0.25 0.25 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "GEN" "Brucella melitensis" "10ug" 23 23 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "GEN" "Brucella melitensis" 0.5 0.5 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "LVX" "Brucella melitensis" "5ug" 50 28 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "LVX" "Brucella melitensis" 1e-04 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "RIF" "Brucella melitensis" "5ug" 20 20 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "RIF" "Brucella melitensis" 2 2 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "STR" "Brucella melitensis" "10ug" 15 15 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "STR" "Brucella melitensis" 1 1 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "SXT" "Brucella melitensis" "1.25ug/23.75ug" 29 29 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "SXT" "Brucella melitensis" 0.125 0.125 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRCLL_MLTN" 2 "TCY" "Brucella melitensis" "30ug" 42 42 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRCLL_MLTN" 2 "TCY" "Brucella melitensis" 0.5 0.5 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_BRKHL_PSDM" 2 "AMC" "ECOFF" "20/10ug" 22 22 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_BRKHL_PSDM" 2 "AMC" "B. pseudomallei" "20ug/10ug" 50 22 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_BRKHL_PSDM" 2 "AMC" "B. pseudomallei" 1e-04 8 FALSE FALSE
@ -1155,8 +1155,8 @@
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_PSTRL_MLTC" 2 "PEN" "ECOFF" "1 unit" 15 15 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_PSTRL_MLTC" 2 "SXT" "ECOFF" "1.25/23.75ug" 18 18 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_PSTRL_MLTC" 2 "TCY" "ECOFF" "30ug" 21 21 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2024" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2024" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2024" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SERRT_MRCS" 2 "AMP" "ECOFF" "10ug" 14 14 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SERRT_MRCS" 2 "CAZ" "ECOFF" "10ug" 20 20 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SERRT_MRCS" 2 "CIP" "ECOFF" "5ug" 22 22 FALSE FALSE
@ -1185,7 +1185,7 @@
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SLMNL_ENTR_ENTR" 1 "SXT" "ECOFF" "1.25/23.75ug" 21 21 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SLMNL_ENTR_ENTR" 1 "TCY" "ECOFF" "30ug" 17 17 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_SLMNL_ENTR_ENTR" 1 "TMP" "ECOFF" "5ug" 23 23 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_STNTR_MLTP" 2 "SXT" "ECOFF" "1.25/23.75ug" 16 16 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" "1.25ug/23.75ug" 50 16 FALSE FALSE
"EUCAST 2024" "human" "human" "MIC" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" 1e-04 4 FALSE FALSE
@ -1379,7 +1379,7 @@
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_STPHY_LGDN" 2 "TOB" "ECOFF" "10ug" 22 22 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Screen" "B_STPHY_PSDN" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Screen" "B_STPHY_SCHL" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Screen" "B_STPHY_SCHL_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "Screen" "B_STPHY_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_STPHY_SPRP" 2 "AMP" "ECOFF" "2ug" 17 17 FALSE FALSE
"EUCAST 2024" "human" "human" "DISK" "B_STPHY_SPRP" 2 "AMP" "Staphs" "2ug" 18 18 FALSE FALSE
"EUCAST 2024" "ECOFF" "ECOFF" "DISK" "B_STPHY_SPRP" 2 "CFR" "ECOFF" "30ug" 19 19 FALSE FALSE
@ -2866,15 +2866,15 @@
"EUCAST 2023" "human" "human" "MIC" "B_PSTRL" 3 "SXT" "Pasteurella spp." 0.25 0.25 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_PSTRL" 3 "TCY" "Pasteurella spp." "30ug" 24 24 FALSE FALSE
"EUCAST 2023" "animal" "cattle" "MIC" "Respiratory" "B_PSTRL_MLTC" 2 "FLR" "Pasteurella multocida" 2 4 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2023" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2023" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2023" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "DISK" "B_SHGLL_FLXN" 2 "PEF" "ECOFF" "5ug" 24 24 FALSE FALSE
"EUCAST 2023" "human" "human" "MIC" "B_SLMNL" 3 "CIP" "Enterobacteriaceae" 0.064 0.064 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_SLMNL" 3 "PEF" "Enterobacteriaceae" "5ug" 24 24 FALSE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "ECOFF" 32 32 FALSE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" "1.25ug/23.75ug" 50 16 FALSE FALSE
"EUCAST 2023" "human" "human" "MIC" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" 1e-04 4 FALSE FALSE
"EUCAST 2023" "human" "human" "MIC" "B_STPHY" 3 "AZM" "Staphs" 2 2 FALSE FALSE
@ -3012,7 +3012,7 @@
"EUCAST 2023" "human" "human" "MIC" "B_STPHY_LGDN" 2 "PEN" "Staphs" 0.125 0.125 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_STPHY_PSDN" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_STPHY_SCHL" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_STPHY_SCHL_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Screen" "B_STPHY_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "B_STPHY_SPRP" 2 "AMP" "Staphs" "2ug" 18 18 FALSE FALSE
"EUCAST 2023" "human" "human" "MIC" "Screen" "B_STPHY_SPRP" 2 "FOX" "Staphs" 8 8 FALSE FALSE
"EUCAST 2023" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_STPHY_SPRP" 2 "NIT" "Staphs" "100ug" 13 13 TRUE FALSE
@ -4298,15 +4298,15 @@
"EUCAST 2022" "human" "human" "DISK" "B_PSTRL_MLTC" 2 "SXT" "Pasteurella multocida" "1.25ug/23.75ug" 23 23 FALSE FALSE
"EUCAST 2022" "human" "human" "MIC" "B_PSTRL_MLTC" 2 "SXT" "Pasteurella multocida" 0.25 0.25 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_PSTRL_MLTC" 2 "TCY" "Pasteurella multocida" "30ug" 24 24 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2022" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2022" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2022" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "DISK" "B_SHGLL_FLXN" 2 "PEF" "ECOFF" "5ug" 24 24 FALSE FALSE
"EUCAST 2022" "human" "human" "MIC" "B_SLMNL" 3 "CIP" "Enterobacteriaceae" 0.064 0.064 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_SLMNL" 3 "PEF" "Enterobacteriaceae" "5ug" 24 24 FALSE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "ECOFF" 32 32 FALSE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" "1.25ug/23.75ug" 50 16 FALSE FALSE
"EUCAST 2022" "human" "human" "MIC" "B_STNTR_MLTP" 2 "SXT" "Stenotrophomonas maltophilia" 1e-04 4 FALSE FALSE
"EUCAST 2022" "human" "human" "MIC" "B_STPHY" 3 "AZM" "Staphs" 2 2 FALSE FALSE
@ -4446,7 +4446,7 @@
"EUCAST 2022" "human" "human" "MIC" "B_STPHY_LGDN" 2 "PEN" "Staphs" 0.125 0.125 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_STPHY_PSDN" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_STPHY_SCHL" 2 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_STPHY_SCHL_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Screen" "B_STPHY_CGLN" 1 "OXA" "Staphs" "1 unit" 20 20 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "B_STPHY_SPRP" 2 "AMP" "Staphs" "2ug" 18 18 FALSE FALSE
"EUCAST 2022" "human" "human" "MIC" "Screen" "B_STPHY_SPRP" 2 "FOX" "Staphs" 8 8 FALSE FALSE
"EUCAST 2022" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_STPHY_SPRP" 2 "NIT" "Staphs" "100ug" 13 13 TRUE FALSE
@ -6152,8 +6152,8 @@
"EUCAST 2021" "human" "human" "MIC" "B_PSTRL_MLTC" 2 "SXT" "Pasteurella multocida" 0.25 0.25 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_PSTRL_MLTC" 2 "TCY" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Screen" "B_PSTRL_MLTC" 2 "TCY" "Pasteurella multocida" "30ug" 24 24 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2021" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_RLTLL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2021" "human" "human" "DISK" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" "10ug" 15 15 TRUE FALSE
"EUCAST 2021" "human" "human" "MIC" "Uncomplicated urinary tract infection" "B_KLBSL" 3 "MEC" "Enterobacteriaceae" 8 8 TRUE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SERRT" 3 "CAZ" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SERRT" 3 "CTX" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SERRT" 3 "DOR" "ECOFF" 0.5 0.5 FALSE FALSE
@ -6225,11 +6225,11 @@
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "AMP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "CXM" "ECOFF" 16 16 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "AMP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "CXM" "ECOFF" 16 16 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "DOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "MNO" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "SXT" "ECOFF" 2 2 FALSE FALSE
@ -6486,7 +6486,7 @@
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STPHY_LGDN" 2 "VAN" "ECOFF" 4 4 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Screen" "B_STPHY_PSDN" 2 "OXA" "Staphs" "1ug" 20 20 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Screen" "B_STPHY_SCHL" 2 "OXA" "Staphs" "1ug" 20 20 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Screen" "B_STPHY_SCHL_CGLN" 1 "OXA" "Staphs" "1ug" 20 20 FALSE FALSE
"EUCAST 2021" "human" "human" "DISK" "Screen" "B_STPHY_CGLN" 1 "OXA" "Staphs" "1ug" 20 20 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STPHY_SCIR" 2 "CIP" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STPHY_SMLN" 2 "CIP" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STPHY_SMLN" 2 "VAN" "ECOFF" 4 4 FALSE FALSE
@ -6593,11 +6593,11 @@
"EUCAST 2021" "human" "human" "MIC" "B_STRPT_ANGN" 2 "TZD" "Viridans strept" 0.5 0.5 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_ANGN" 2 "TZP" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_ANGN" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "LNZ" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "LVX" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "LNZ" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "LVX" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2021" "human" "human" "MIC" "B_STRPT_CNST" 2 "DFX" "Viridans strept" 0.032 0.032 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_CNST" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2021" "ECOFF" "ECOFF" "MIC" "B_STRPT_CNST" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
@ -8354,11 +8354,11 @@
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "AMP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "CXM" "ECOFF" 16 16 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "AMP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "CXM" "ECOFF" 16 16 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "FOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.125 0.125 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "TZP" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "DOX" "ECOFF" 8 8 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "MNO" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STNTR_MLTP" 2 "SXT" "ECOFF" 2 2 FALSE FALSE
@ -8729,11 +8729,11 @@
"EUCAST 2020" "human" "human" "MIC" "B_STRPT_ANGN" 2 "TZD" "Viridans strept" 0.25 0.25 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_ANGN" 2 "TZP" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_ANGN" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "LNZ" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "LVX" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_BOVS" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "LNZ" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "LVX" "ECOFF" 2 2 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_EQNS" 2 "VAN" "ECOFF" 1 1 FALSE FALSE
"EUCAST 2020" "human" "human" "MIC" "B_STRPT_CNST" 2 "DFX" "Viridans strept" 0.032 0.032 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_CNST" 2 "MFX" "ECOFF" 0.5 0.5 FALSE FALSE
"EUCAST 2020" "ECOFF" "ECOFF" "MIC" "B_STRPT_CNST" 2 "PEN" "ECOFF" 0.25 0.25 FALSE FALSE
@ -17058,15 +17058,15 @@
"CLSI 2024" "human" "human" "MIC" "B_SLMNL" 3 "SXT" "Table 2A-2" 2 4 FALSE FALSE
"CLSI 2024" "human" "human" "DISK" "B_SLMNL" 3 "TCY" "Table 2A-2" "30ug" 15 11 FALSE FALSE
"CLSI 2024" "human" "human" "MIC" "B_SLMNL" 3 "TCY" "Table 2A-2" 4 16 FALSE FALSE
"CLSI 2024" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2024" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2024" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2024" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2024" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2024" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "ECOFF" 32 32 FALSE FALSE
"CLSI 2024" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A-2" "15ug" 13 12 FALSE FALSE
"CLSI 2024" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A-2" 16 32 FALSE FALSE
"CLSI 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"CLSI 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2024" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2024" "human" "human" "MIC" "B_SMYCS" 3 "AMC" "M24 Table 7" 8 32 FALSE FALSE
"CLSI 2024" "human" "human" "MIC" "B_SMYCS" 3 "AMK" "M24 Table 7" 8 16 FALSE FALSE
"CLSI 2024" "human" "human" "MIC" "B_SMYCS" 3 "CIP" "M24 Table 7" 1 4 FALSE FALSE
@ -19258,16 +19258,16 @@
"CLSI 2023" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "LVX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2023" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2023" "human" "human" "DISK" "B_SLMNL" 3 "PEF" "Table 2A" "5ug" 24 23 FALSE FALSE
"CLSI 2023" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2023" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2023" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2023" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2023" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2023" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "ECOFF" 32 32 FALSE FALSE
"CLSI 2023" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" "15ug" 13 12 FALSE FALSE
"CLSI 2023" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" 16 32 FALSE FALSE
"CLSI 2023" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR_ENTR" 1 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"CLSI 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2023" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2023" "human" "human" "MIC" "B_SMYCS" 3 "AMC" "M24 Table 7" 8 32 FALSE FALSE
"CLSI 2023" "human" "human" "MIC" "B_SMYCS" 3 "AMK" "M24 Table 7" 8 16 FALSE FALSE
"CLSI 2023" "human" "human" "MIC" "B_SMYCS" 3 "CIP" "M24 Table 7" 1 4 FALSE FALSE
@ -21286,16 +21286,16 @@
"CLSI 2022" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "LVX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2022" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2022" "human" "human" "DISK" "B_SLMNL" 3 "PEF" "Table 2A" "5ug" 24 23 FALSE FALSE
"CLSI 2022" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2022" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2022" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2022" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2022" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2022" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "ECOFF" 32 32 FALSE FALSE
"CLSI 2022" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" "15ug" 13 12 FALSE FALSE
"CLSI 2022" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" 16 32 FALSE FALSE
"CLSI 2022" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR_ENTR" 1 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "FOX" "ECOFF" 8 8 FALSE FALSE
"CLSI 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR_ENTR" 1 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_RTDS" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2022" "ECOFF" "ECOFF" "MIC" "B_SLMNL_ENTR" 2 "MEM" "ECOFF" 0.064 0.064 FALSE FALSE
"CLSI 2022" "human" "human" "MIC" "Parenteral" "B_STNTR_MLTP" 2 "CAZ" "Table 2B-4" 8 32 FALSE FALSE
"CLSI 2022" "human" "human" "MIC" "B_STNTR_MLTP" 2 "CHL" "Table 2B-4" 8 32 FALSE FALSE
"CLSI 2022" "human" "human" "DISK" "Parenteral" "B_STNTR_MLTP" 2 "FDC" "Table 2B-4" "30ug" 15 15 FALSE FALSE
@ -22941,9 +22941,9 @@
"CLSI 2021" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "LVX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2021" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2021" "human" "human" "DISK" "B_SLMNL" 3 "PEF" "Table 2A" "5ug" 24 23 FALSE FALSE
"CLSI 2021" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2021" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2021" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2021" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
"CLSI 2021" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2021" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET01 Table 2A" 2 8 FALSE FALSE
"CLSI 2021" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" "15ug" 13 12 FALSE FALSE
"CLSI 2021" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" 16 32 FALSE FALSE
"CLSI 2021" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR_ENTR" 1 "FLR" "VET01 Table 2A" 4 16 FALSE FALSE
@ -24478,9 +24478,9 @@
"CLSI 2020" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "LVX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2020" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2020" "human" "human" "DISK" "B_SLMNL" 3 "PEF" "Table 2A" "5ug" 24 23 FALSE FALSE
"CLSI 2020" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE
"CLSI 2020" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET08 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2020" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET08 Table 2A" 2 8 FALSE FALSE
"CLSI 2020" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE
"CLSI 2020" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET08 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2020" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET08 Table 2A" 2 8 FALSE FALSE
"CLSI 2020" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" "15ug" 13 12 FALSE FALSE
"CLSI 2020" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" 16 32 FALSE FALSE
"CLSI 2020" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR_ENTR" 1 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE
@ -25988,9 +25988,9 @@
"CLSI 2019" "human" "human" "MIC" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2019" "human" "human" "MIC" "Extraintestinal" "B_SLMNL" 3 "OFX" "Table 2A" 0.125 2 FALSE FALSE
"CLSI 2019" "human" "human" "DISK" "B_SLMNL" 3 "PEF" "Table 2A" "5ug" 24 23 FALSE FALSE
"CLSI 2019" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE
"CLSI 2019" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET08 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2019" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_CHLR" 2 "TIO" "VET08 Table 2A" 2 8 FALSE FALSE
"CLSI 2019" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE
"CLSI 2019" "animal" "swine" "DISK" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET08 Table 2A" "30ug" 21 17 FALSE FALSE
"CLSI 2019" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR" 2 "TIO" "VET08 Table 2A" 2 8 FALSE FALSE
"CLSI 2019" "human" "human" "DISK" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" "15ug" 13 12 FALSE FALSE
"CLSI 2019" "human" "human" "MIC" "B_SLMNL_ENTR_ENTR" 1 "AZM" "Table 2A" 16 32 FALSE FALSE
"CLSI 2019" "animal" "swine" "MIC" "Respiratory" "B_SLMNL_ENTR_ENTR" 1 "FLR" "VET08 Table 2A" 4 16 FALSE FALSE

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data-raw/loinc.R
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@ -6,9 +6,9 @@
# https://github.com/msberends/AMR #
# #
# 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. #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #

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