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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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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/Preference animal species groups.xlsx Executable file
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6
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 #

336
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,
version = 2,
compress = "xz"
))
# 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,
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

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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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032dfd1b044cc838f0915b0eef919471
e8c57a559ad846e72c700c88929e880d

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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 #

6
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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 #

6
data-raw/reproduction_of_antibiotics.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 #

6
data-raw/reproduction_of_antivirals.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
data-raw/reproduction_of_clinical_breakpoints.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
data-raw/reproduction_of_dosage.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
data-raw/reproduction_of_example_isolates_unclean.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
data-raw/reproduction_of_intrinsic_resistant.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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data-raw/reproduction_of_microorganisms.groups.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
data-raw/salmonellae.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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