version bump

.github/workflows/check-old.yaml

@@ -49,7 +49,7 @@ jobs:
           # Test all old versions of R >= 3.0, we support them all!
           # For these old versions, dependencies and vignettes will not be checked.
           # For recent R versions, see check-recent.yaml (r-lib and tidyverse support the latest 5 major R releases).
-          - {os: windows-latest, r: '3.5', allowfail: true}
+          # - {os: windows-latest, r: '3.5', allowfail: true} # always fails, horrible with UTF-8
           - {os: ubuntu-latest, r: '3.4', allowfail: false}
           - {os: ubuntu-latest, r: '3.3', allowfail: false}
           - {os: ubuntu-latest, r: '3.2', allowfail: false}

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 2.0.0.9035
+Version: 2.0.0.9037
 Date: 2023-07-11
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.0.0.9035
+# AMR 2.0.0.9037
 
 ## New
 * Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added for `as.sir()`. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations

R/sir.R

@@ -941,7 +941,7 @@ as_sir_method <- function(method_short,
     mo_current <- df_unique[i, "mo", drop = TRUE]
     uti_current <- df_unique[i, "uti", drop = TRUE]
     if (is.na(uti_current)) {
-      # preference, so no filter on UTIs
+      # no preference, so no filter on UTIs
       rows <- which(df$mo == mo_current)
     } else {
       rows <- which(df$mo == mo_current & df$uti == uti_current)
@@ -957,10 +957,10 @@ as_sir_method <- function(method_short,
     mo_current_rank <- AMR_env$MO_lookup$rank[match(mo_current, AMR_env$MO_lookup$mo)]
     mo_current_name <- AMR_env$MO_lookup$fullname[match(mo_current, AMR_env$MO_lookup$mo)]
     if (mo_current %in% AMR::microorganisms.groups$mo) {
-      # get the species group
+      # get the species group (might be more than 1 entry)
-      mo_current_species_group <- AMR::microorganisms.groups$mo_group[match(mo_current, AMR::microorganisms.groups$mo)]
+      mo_current_species_group <- AMR::microorganisms.groups$mo_group[which(AMR::microorganisms.groups$mo == mo_current)]
     } else {
-      mo_current_species_group <- mo_current
+      mo_current_species_group <- NULL
     }
     mo_current_other <- structure("UNKNOWN", class = c("mo", "character"))
     # formatted for notes
@@ -977,7 +977,7 @@ as_sir_method <- function(method_short,
     # (this will prefer species breakpoints over order breakpoints)
     breakpoints_current <- breakpoints %pm>%
       subset(mo %in% c(
-        mo_current_genus, mo_current_family,
+        mo_current, mo_current_genus, mo_current_family,
         mo_current_order, mo_current_class,
         mo_current_species_group,
         mo_current_other

data-raw/reproduction_of_clinical_breakpoints.R

@@ -297,6 +297,9 @@ breakpoints_new[which(breakpoints_new$breakpoint_R == 257), "breakpoint_R"] <- 2
 breakpoints_new[which(breakpoints_new$breakpoint_R == 513), "breakpoint_R"] <- 512
 breakpoints_new[which(breakpoints_new$breakpoint_R == 1025), "breakpoint_R"] <- 1024
 
+# fix streptococci in WHONET table of EUCAST: Strep A, B, C and G now includes all streptococci:
+clinical_breakpoints$mo[clinical_breakpoints$mo == "B_STRPT" & clinical_breakpoints$ref_tbl %like% "strep.* a.* b.*c.*g"] <- as.mo("B_STRPT_ABCG")
+
 # WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
 # EUCAST 2022 guideline: S <= 8 and R > 8
 #           WHONET file: S <= 8 and R >= 16

data-raw/reproduction_of_microorganisms.groups.R

@@ -112,7 +112,7 @@ microorganisms.groups <- whonet_organisms %>%
                    mo = microorganisms$mo[which(microorganisms$mo %like% "^B_STRPT_SLVR(_|$)")])) %>%
   # and for EUCAST: Strep group A, B, C, G
   bind_rows(tibble(mo_group = as.mo("Streptococcus Group A, B, C, G"),
-                   mo = microorganisms$mo[which(microorganisms$mo %like% "^B_STRPT_(PYGN|AGLC|DYSG|EQUI|CANS)(_|$)")])) %>%
+                   mo = microorganisms$mo[which(microorganisms$mo %like% "^B_STRPT_(PYGN|AGLC|DYSG|EQUI|CANS|GRPA|GRPB|GRPC|GRPG)(_|$)")])) %>%
   # HACEK is:
   # - Haemophilus species
   # - Aggregatibacter species
@@ -133,8 +133,8 @@ microorganisms.groups <- whonet_organisms %>%
   bind_rows(tibble(mo_group = as.mo("B_MYCBC_RGM"),
                    mo = paste("Mycobacterium", c( "abscessus abscessus", "abscessus bolletii", "abscessus massiliense", "agri", "aichiense", "algericum", "alvei", "anyangense", "arabiense", "aromaticivorans", "aubagnense", "aubagnense", "aurum", "austroafricanum", "bacteremicum", "boenickei", "bourgelatii", "brisbanense", "brumae", "canariasense", "celeriflavum", "chelonae", "chitae", "chlorophenolicum", "chubuense", "confluentis", "cosmeticum", "crocinum", "diernhoferi", "duvalii", "elephantis", "fallax", "flavescens", "fluoranthenivorans", "fortuitum", "franklinii", "frederiksbergense", "gadium", "gilvum", "goodii", "hassiacum", "hippocampi", "hodleri", "holsaticum", "houstonense", "immunogenum", "insubricum", "iranicum", "komossense", "litorale", "llatzerense", "madagascariense", "mageritense", "monacense", "moriokaense", "mucogenicum", "mucogenicum", "murale", "neoaurum", "neworleansense", "novocastrense", "obuense", "pallens", "parafortuitum", "peregrinum", "phlei", "phocaicum", "phocaicum", "porcinum", "poriferae", "psychrotolerans", "pyrenivorans", "rhodesiae", "rufum", "rutilum", "salmoniphilum", "sediminis", "senegalense", "septicum", "setense", "smegmatis", "sphagni", "thermoresistibile", "tokaiense", "vaccae", "vanbaalenii", "wolinskyi")) %>% as.mo(keep_synonyms = TRUE))) %>%
   # add full names
-  mutate(mo_group_name = mo_name(mo_group, keep_synonyms = TRUE),
+  mutate(mo_group_name = mo_name(mo_group, keep_synonyms = TRUE, language = NULL),
-         mo_name = mo_name(mo, keep_synonyms = TRUE)) %>% 
+         mo_name = mo_name(mo, keep_synonyms = TRUE, language = NULL)) %>% 
   arrange(mo_group_name, mo_name) %>% 
   filter(mo_group != mo) %>% 
   distinct() %>% 
@@ -146,30 +146,3 @@ class(microorganisms.groups$mo) <- c("mo", "character")
 usethis::use_data(microorganisms.groups, internal = FALSE, overwrite = TRUE, compress = "xz", version = 2)
 rm(microorganisms.groups)
 devtools::load_all()
-
-
-# 
-# microorganisms <- microorganisms %>% 
-#   mutate(mo = as.character(mo)) %>% 
-#   bind_rows(
-#     microorganisms %>% filter(mo == "B_STRPT_HAEM") %>% 
-#       mutate(mo = as.character(mo),
-#              mo = "B_STRPT_ABCG",
-#              fullname = "Streptococcus Group A, B, C, G",
-#              species = "Group A, B, C, G")) %>% 
-#   arrange(fullname) %>% 
-#   dataset_UTF8_to_ASCII()
-# 
-# microorganisms$rank[which(microorganisms$fullname %like% "^Streptococcus Group")] <- "species group"
-# microorganisms$lpsn_parent[which(microorganisms$fullname %like% "^Streptococcus Group")] <- 517118
-# microorganisms$gbif_parent[which(microorganisms$fullname %like% "^Streptococcus Group")] <- 3223465
-# microorganisms$ref[which(microorganisms$fullname %like% "^Streptococcus Group")] <- "Lancefield, 1933"
-# microorganisms$prevalence[which(microorganisms$fullname %like% "^Streptococcus Group")] <- 1.5
-# microorganisms$oxygen_tolerance[which(microorganisms$fullname %like% "^Streptococcus Group")] <- "likely facultative anaerobe"
-# 
-
-
-class(microorganisms$mo) <- c("mo", "character")
-usethis::use_data(microorganisms, internal = FALSE, overwrite = TRUE, compress = "xz", version = 2)
-rm(microorganisms)
-devtools::load_all()

man/AMR.Rd

@@ -32,7 +32,7 @@ The \code{AMR} package is a \href{https://msberends.github.io/AMR/#copyright}{fr
 
 This work was published in the Journal of Statistical Software (Volume 104(3); \doi{jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
 
-After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52 000 microorganisms}} (updated December 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
+After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52 000 microorganisms}} (updated december 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
 
 The \code{AMR} package is available in English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
 }

man/microorganisms.groups.Rd

@@ -3,9 +3,9 @@
 \docType{data}
 \name{microorganisms.groups}
 \alias{microorganisms.groups}
-\title{Data Set with 444 Microorganisms In Species Groups}
+\title{Data Set with 448 Microorganisms In Species Groups}
 \format{
-A \link[tibble:tibble]{tibble} with 444 observations and 4 variables:
+A \link[tibble:tibble]{tibble} with 448 observations and 4 variables:
 \itemize{
 \item \code{mo_group}\cr ID of the species group / microbiological complex
 \item \code{mo}\cr ID of the microorganism belonging in the species group / microbiological complex

vignettes/datasets.Rmd

@@ -49,7 +49,6 @@ download_txt <- function(filename) {
   excel <- paste0(filename, ".xlsx")
   feather <- paste0(filename, ".feather")
   parquet <- paste0(filename, ".parquet")
-  sas <- paste0(filename, ".sas")
   xpt <- paste0(filename, ".xpt")
   spss <- paste0(filename, ".sav")
   stata <- paste0(filename, ".dta")
@@ -70,7 +69,6 @@ download_txt <- function(filename) {
     file.exists(excel),
     file.exists(feather),
     file.exists(parquet),
-    file.exists(sas),
     file.exists(xpt),
     file.exists(spss),
     file.exists(stata)
@@ -82,7 +80,6 @@ download_txt <- function(filename) {
       create_txt(excel, "xlsx", "Microsoft Excel workbook", file.exists(excel)),
       create_txt(feather, "feather", "Apache Feather file", file.exists(feather)),
       create_txt(parquet, "parquet", "Apache Parquet file", file.exists(parquet)),
-      create_txt(sas, "sas", "SAS data (SAS) file", file.exists(sas)),
       create_txt(xpt, "xpt", "SAS transport (XPT) file", file.exists(xpt)),
       create_txt(spss, "sav", "IBM SPSS Statistics data file", file.exists(spss)),
       create_txt(stata, "dta", "Stata DTA file", file.exists(stata))