documentation update

.Rbuildignore

@@ -30,6 +30,7 @@
 ^vignettes/datasets\.Rmd$
 ^vignettes/EUCAST\.Rmd$
 ^vignettes/MDR\.Rmd$
+^vignettes/other_pkg.*\.Rmd$
 ^vignettes/PCA\.Rmd$
 ^vignettes/resistance_predict\.Rmd$
 ^vignettes/SPSS\.Rmd$

.github/prehooks/pre-commit

@@ -11,7 +11,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

.github/workflows/check-old.yaml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -48,7 +48,8 @@ jobs:
         config:
           # 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 versions).
+          # 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: ubuntu-latest, r: '3.4', allowfail: false}
           - {os: ubuntu-latest, r: '3.3', allowfail: false}
           - {os: ubuntu-latest, r: '3.2', allowfail: false}

.github/workflows/check-recent.yaml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -52,21 +52,21 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          # current development version:
+          # current development version, check all major OSes:
           - {os: macOS-latest,   r: 'devel', allowfail: false}
           - {os: windows-latest, r: 'devel', allowfail: false}
           - {os: ubuntu-latest,  r: 'devel', allowfail: false}
 
-          # current 'release' version:
-          - {os: macOS-latest,   r: '4.2', allowfail: false}
-          - {os: windows-latest, r: '4.2', allowfail: false}
-          - {os: ubuntu-latest,  r: '4.2', allowfail: false}
+          # current 'release' version, check all major OSes:
+          - {os: macOS-latest,   r: '4.3', allowfail: false}
+          - {os: windows-latest, r: '4.3', allowfail: false}
+          - {os: ubuntu-latest,  r: '4.3', allowfail: false}
 
           # older versions (see also check-old.yaml for even older versions):
+          - {os: ubuntu-latest, r: '4.2', allowfail: false}
           - {os: ubuntu-latest, r: '4.1', allowfail: false}
           - {os: ubuntu-latest, r: '4.0', allowfail: false}
-          - {os: ubuntu-latest, r: '3.6', allowfail: false}
-          - {os: ubuntu-latest, r: '3.5', allowfail: false} # when a new R releases, this one has to move to check-old.yaml
+          - {os: ubuntu-latest, r: '3.6', allowfail: false} # when a new R releases, this one has to move to check-old.yaml
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/codecovr.yaml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

.github/workflows/lintr.yaml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

.github/workflows/website.yaml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -65,7 +65,11 @@ jobs:
       - name: Set up R dependencies
         uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::pkgdown
+          # add extra packages for website articles:
+          extra-packages: |
+            any::pkgdown
+            any::tidymodels
+            any::data.table
 
       # Send updates to repo using GH Actions bot
       - name: Create website in separate branch

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.0.0
-Date: 2023-03-12
+Version: 2.0.0.9023
+Date: 2023-05-27
 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

NAMESPACE

@@ -330,6 +330,7 @@ export(mo_gbif)
 export(mo_genus)
 export(mo_gramstain)
 export(mo_info)
+export(mo_is_anaerobic)
 export(mo_is_gram_negative)
 export(mo_is_gram_positive)
 export(mo_is_intrinsic_resistant)
@@ -339,6 +340,7 @@ export(mo_lpsn)
 export(mo_matching_score)
 export(mo_name)
 export(mo_order)
+export(mo_oxygen_tolerance)
 export(mo_pathogenicity)
 export(mo_phylum)
 export(mo_property)

NEWS.md

@@ -1,3 +1,21 @@
+# AMR 2.0.0.9023
+
+## Changed
+* Added oxygen tolerance from BacDive to over 25,000 bacteria in the `microorganisms` data set
+  * Added `mo_oxygen_tolerance()` to retrieve the values
+  * Added `mo_is_anaerobic()` to determine which genera/species are obligate anaerobic bacteria
+* Added LPSN and GBIF identifiers, and oxygen tolerance to `mo_info()`
+* Added SAS Transport files (file extension `.xpt`) to [our download page](https://msberends.github.io/AMR/articles/datasets.html) to use in SAS software
+* Added microbial codes for Gram-negative/positive anaerobic bacteria
+* `mo_rank()` now returns `NA` for 'unknown' microorganisms (`B_ANAER`, `B_ANAER-NEG`, `B_ANAER-POS`, `B_GRAMN`, `B_GRAMP`, `F_FUNGUS`, `F_YEAST`, and `UNKNOWN`)
+* Fixed formatting for `sir_interpretation_history()`
+* Fixed some WHONET codes for microorganisms and consequently a couple of entries in `clinical_breakpoints`
+* Fixed a bug for `as.mo()` that led to coercion of `NA` values when using custom microorganism codes
+* Fixed usage of `icu_exclude` in `first_isolates()`
+* Improved `as.mo()` algorithm for searching on only species names
+* Updated the code table in `microorganisms.codes`
+
+
 # AMR 2.0.0
 
 This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.

R/aa_amr-package.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/aa_globals.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -81,6 +81,11 @@ TAXONOMY_VERSION <- list(
     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"
   ),
+  BacDive = list(
+    accessed_date = as.Date("2023-05-12"),
+    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"),
     citation = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12).",

R/aa_helper_functions.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -505,7 +505,7 @@ word_wrap <- function(...,
 
   # clean introduced whitespace between fullstops
   msg <- gsub("[.] +[.]", "..", msg)
-  # remove extra space that was introduced (e.g. "Smith et al., 2022")
+  # remove extra space that was introduced (e.g. "Smith et al. , 2022")
   msg <- gsub(". ,", ".,", msg, fixed = TRUE)
   msg <- gsub("[ ,", "[,", msg, fixed = TRUE)
   msg <- gsub("/ /", "//", msg, fixed = TRUE)
@@ -629,7 +629,12 @@ dataset_UTF8_to_ASCII <- function(df) {
 }
 
 documentation_date <- function(d) {
-  paste0(trimws(format(d, "%e")), " ", month.name[as.integer(format(d, "%m"))], ", ", format(d, "%Y"))
+  day <- as.integer(format(d, "%e"))
+  suffix <- rep("th", length(day))
+  suffix[day %in% c(1, 21, 31)] <- "st"
+  suffix[day %in% c(2, 22)] <- "nd"
+  suffix[day %in% c(3, 23)] <- "rd"
+  paste0(month.name[as.integer(format(d, "%m"))], " ", day, suffix, ", ", format(d, "%Y"))
 }
 
 format_included_data_number <- function(data) {
@@ -644,10 +649,13 @@ format_included_data_number <- function(data) {
     rounder <- -3 # round on thousands
   } else if (n > 1000) {
     rounder <- -2 # round on hundreds
+  } else if (n < 50) {
+    # do not round
+    rounder <- 0
   } else {
     rounder <- -1 # round on tens
   }
-  paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = " "))
+  paste0(ifelse(rounder == 0, "", "~"), format(round(n, rounder), decimal.mark = ".", big.mark = " "))
 }
 
 # for eucast_rules() and mdro(), creates markdown output with URLs and names
@@ -670,10 +678,15 @@ create_eucast_ab_documentation <- function() {
   atcs <- ab_atc(ab, only_first = TRUE)
   # only keep ABx with an ATC code:
   ab <- ab[!is.na(atcs)]
+  atcs <- atcs[!is.na(atcs)]
+
+  # sort all vectors on name:
   ab_names <- ab_name(ab, language = NULL, tolower = TRUE)
   ab <- ab[order(ab_names)]
+  atcs <- atcs[order(ab_names)]
   ab_names <- ab_names[order(ab_names)]
-  atc_txt <- paste0("[", atcs[!is.na(atcs)], "](", ab_url(ab), ")")
+  # create the text:
+  atc_txt <- paste0("[", atcs, "](", ab_url(ab), ")")
   out <- paste0(ab_names, " (`", ab, "`, ", atc_txt, ")", collapse = ", ")
   substr(out, 1, 1) <- toupper(substr(out, 1, 1))
   out
@@ -996,7 +1009,7 @@ get_current_column <- function() {
 
   # cur_column() doesn't always work (only allowed for certain conditions set by dplyr), but it's probably still possible:
   frms <- lapply(sys.frames(), function(env) {
-    if (!is.null(env$i)) {
+    if (tryCatch(!is.null(env$i), error = function(e) FALSE)) {
       if (!is.null(env$tibble_vars)) {
         # for mutate_if()
         env$tibble_vars[env$i]

R/aa_helper_pm_functions.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/aa_options.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/ab.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/ab_from_text.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/ab_property.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -69,7 +69,7 @@
 #' ab_atc_group2("AMX")
 #' ab_url("AMX")
 #'
-#' # smart lowercase tranformation
+#' # smart lowercase transformation
 #' ab_name(x = c("AMC", "PLB"))
 #' ab_name(x = c("AMC", "PLB"), tolower = TRUE)
 #'

R/ab_selectors.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/age.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/antibiogram.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -224,7 +224,7 @@
 #' # in an Rmd file, you would just need to return `ureido` in a chunk,
 #' # but to be explicit here:
 #' if (requireNamespace("knitr")) {
-#'   knitr::knit_print(ureido)
+#'   cat(knitr::knit_print(ureido))
 #' }
 #'
 #'

R/atc_online.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/av.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/av_from_text.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/av_property.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -61,7 +61,7 @@
 #' av_group("ACI")
 #' av_url("ACI")
 #'
-#' # smart lowercase tranformation
+#' # lowercase transformation
 #' av_name(x = c("ACI", "VALA"))
 #' av_name(x = c("ACI", "VALA"), tolower = TRUE)
 #'

R/availability.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/bug_drug_combinations.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/count.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/custom_antimicrobials.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/custom_eucast_rules.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/custom_microorganisms.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -71,7 +71,8 @@
 #' @examples
 #' \donttest{
 #' # a combination of species is not formal taxonomy, so
-#' # this will result in only "Enterobacter asburiae":
+#' # this will result in "Enterobacter cloacae cloacae",
+#' # since it resembles the input best:
 #' mo_name("Enterobacter asburiae/cloacae")
 #'
 #' # now add a custom entry - it will be considered by as.mo() and
@@ -109,7 +110,7 @@
 #' mo_name("BACTEROIDES / PARABACTEROIDES")
 #' mo_rank("BACTEROIDES / PARABACTEROIDES")
 #'
-#' # taxonomy still works, although a slashline genus was given as input:
+#' # taxonomy still works, even though a slashline genus was given as input:
 #' mo_family("Bacteroides/Parabacteroides")
 #'
 #'
@@ -247,19 +248,14 @@ add_custom_microorganisms <- function(x) {
     "CUSTOM",
     seq.int(from = current + 1, to = current + nrow(x), by = 1),
     "_",
-    toupper(unname(abbreviate(
-      gsub(
-        " +", " _ ",
-        gsub(
-          "[^A-Za-z0-9-]", " ",
-          trimws2(paste(x$genus, x$species, x$subspecies))
-        )
-      ),
-      minlength = 10
-    )))
-  )
+    trimws(
+      paste(abbreviate_mo(x$genus, 5),
+            abbreviate_mo(x$species, 4, hyphen_as_space = TRUE),
+            abbreviate_mo(x$subspecies, 4, hyphen_as_space = TRUE),
+            sep = "_"),
+      whitespace = "_"))
   stop_if(anyDuplicated(c(as.character(AMR_env$MO_lookup$mo), x$mo)), "MO codes must be unique and not match existing MO codes of the AMR package")
-
+  
   # add to package ----
   AMR_env$custom_mo_codes <- c(AMR_env$custom_mo_codes, x$mo)
   class(AMR_env$MO_lookup$mo) <- "character"
@@ -306,3 +302,26 @@ clear_custom_microorganisms <- function() {
   AMR_env$mo_uncertainties <- AMR_env$mo_uncertainties[0, , drop = FALSE]
   message_("Cleared ", nr2char(n - n2), " custom record", ifelse(n - n2 > 1, "s", ""), " from the internal `microorganisms` data set.")
 }
+
+abbreviate_mo <- function(x, minlength = 5, prefix = "", hyphen_as_space = FALSE, ...) {
+  if (hyphen_as_space == TRUE) {
+    x <- gsub("-", " ", x, fixed = TRUE)
+  }
+  # keep a starting Latin ae
+  suppressWarnings(
+    gsub("(\u00C6|\u00E6)+",
+         "AE",
+         toupper(
+           paste0(prefix,
+                  abbreviate(
+                    gsub("^ae",
+                         "\u00E6\u00E6",
+                         x,
+                         ignore.case = TRUE),
+                    minlength = minlength,
+                    use.classes = TRUE,
+                    method = "both.sides",
+                    ...
+                  ))))
+  )
+}

R/data.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -93,6 +93,7 @@
 #' - `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, e.g. "V`r "\u00e1\u0148ov\u00e1"`" becomes "Vanova".
 #' - `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$kingdom == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance.
 #' - `lpsn_parent`\cr LPSN identifier of the parent taxon
 #' - `lpsn_renamed_to`\cr LPSN identifier of the currently valid taxon
 #' - `gbif`\cr Identifier ('taxonID') of the Global Biodiversity Information Facility (GBIF)
@@ -120,12 +121,12 @@
 #' ### Manual additions
 #' For convenience, some entries were added manually:
 #'
-#' - `r format_included_data_number(which(microorganisms$genus == "Salmonella" & microorganisms$species == "enterica" & microorganisms$source == "manually added"))` entries for the city-like serovars of *Salmonellae*
-#' - 11 entries of *Streptococcus* (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
+#' - `r format_included_data_number(microorganisms[which(microorganisms$source == "manually added" & microorganisms$genus == "Salmonella"), , drop = FALSE])` entries of *Salmonella*, such as the city-like serovars and groups A to H
+#' - `r format_included_data_number(microorganisms[which(microorganisms$source == "manually added" & microorganisms$genus == "Streptococcus"), , drop = FALSE])` entries of *Streptococcus*, such as the beta-haemolytic groups A to K, viridans, and milleri
 #' - 2 entries of *Staphylococcus* (coagulase-negative (CoNS) and coagulase-positive (CoPS))
 #' - 1 entry of *Blastocystis* (*B.  hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993)
 #' - 1 entry of *Moraxella* (*M. catarrhalis*), which was formally named *Branhamella catarrhalis* (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
-#' - 6 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast, unknown fungus, and unknown anaerobic bacteria)
+#' - 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>.
 #'
@@ -140,6 +141,8 @@
 #'
 #' * `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
 #'
+#' * `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
+#' 
 #' * `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
 #'
 #' * Grimont *et al.* (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
@@ -161,6 +164,15 @@
 #' @seealso [as.mo()] [microorganisms]
 #' @examples
 #' microorganisms.codes
+#' 
+#' # 'ECO' or 'eco' is the WHONET code for E. coli:
+#' microorganisms.codes[microorganisms.codes$code == "ECO", ]
+#' 
+#' # and therefore, 'eco' will be understood as E. coli in this package:
+#' mo_info("eco")
+#' 
+#' # works for all AMR functions:
+#' mo_is_intrinsic_resistant("eco", ab = "vancomycin")
 "microorganisms.codes"
 
 #' Data Set with `r format(nrow(example_isolates), big.mark = " ")` Example Isolates

R/disk.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/eucast_rules.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/first_isolate.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -191,13 +191,14 @@ first_isolate <- function(x = NULL,
   }
   meet_criteria(col_specimen, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
   if (is.logical(col_icu)) {
-    meet_criteria(col_icu, allow_class = "logical", has_length = c(1, nrow(x)), allow_NULL = TRUE)
-    if (length(col_icu) == 1) {
-      col_icu <- rep(col_icu, nrow(x))
-    }
-  } else {
+    meet_criteria(col_icu, allow_class = "logical", has_length = c(1, nrow(x)), allow_NA = TRUE, allow_NULL = TRUE)
+    x$newvar_is_icu <- col_icu
+  } else if (!is.null(col_icu)) {
+    # add "logical" to the allowed classes here, since it may give an error in certain user input, and should then also say that logicals can be used too
     meet_criteria(col_icu, allow_class = c("character", "logical"), has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
-    col_icu <- x[, col_icu, drop = TRUE]
+    x$newvar_is_icu <- x[, col_icu, drop = TRUE]
+  } else {
+    x$newvar_is_icu <- NA
   }
   # method
   method <- coerce_method(method)
@@ -251,14 +252,13 @@ first_isolate <- function(x = NULL,
         "Determining first isolates ",
         ifelse(method %in% c("episode-based", "phenotype-based"),
           ifelse(is.infinite(episode_days),
-            "without a specified episode length",
-            paste("using an episode length of", episode_days, "days")
+            paste(font_bold("without"), " a specified episode length"),
+            paste("using an episode length of", font_bold(paste(episode_days, "days")))
           ),
           ""
         )
       ),
-      as_note = FALSE,
-      add_fn = font_black
+      add_fn = font_red
     )
   }
 
@@ -358,8 +358,7 @@ first_isolate <- function(x = NULL,
   # remove testcodes
   if (!is.null(testcodes_exclude) && isTRUE(info) && message_not_thrown_before("first_isolate", "excludingtestcodes")) {
     message_("Excluding test codes: ", vector_and(testcodes_exclude, quotes = TRUE),
-      add_fn = font_black,
-      as_note = FALSE
+      add_fn = font_red
     )
   }
 
@@ -372,8 +371,7 @@ first_isolate <- function(x = NULL,
     check_columns_existance(col_specimen, x)
     if (isTRUE(info) && message_not_thrown_before("first_isolate", "excludingspecimen")) {
       message_("Excluding other than specimen group '", specimen_group, "'",
-        add_fn = font_black,
-        as_note = FALSE
+        add_fn = font_red
       )
     }
   }
@@ -455,15 +453,13 @@ first_isolate <- function(x = NULL,
         message_("Basing inclusion on key antimicrobials, ",
           ifelse(ignore_I == FALSE, "not ", ""),
           "ignoring I",
-          add_fn = font_black,
-          as_note = FALSE
+          add_fn = font_red
         )
       }
       if (type == "points") {
         message_("Basing inclusion on all antimicrobial results, using a points threshold of ",
           points_threshold,
-          add_fn = font_black,
-          as_note = FALSE
+          add_fn = font_red
         )
       }
     }
@@ -505,34 +501,28 @@ first_isolate <- function(x = NULL,
       x$newvar_genus_species != "" &
       (x$other_pat_or_mo | x$more_than_episode_ago)
   }
-
+  
+  decimal.mark <- getOption("OutDec")
+  big.mark <- ifelse(decimal.mark != ",", ",", " ")
+  
   # first one as TRUE
   x[row.start, "newvar_first_isolate"] <- TRUE
   # no tests that should be included, or ICU
   if (!is.null(col_testcode)) {
     x[which(x[, col_testcode] %in% tolower(testcodes_exclude)), "newvar_first_isolate"] <- FALSE
   }
-
-  if (!is.null(col_icu)) {
+  if (any(!is.na(x$newvar_is_icu)) && any(x$newvar_is_icu == TRUE, na.rm = TRUE)) {
     if (icu_exclude == TRUE) {
       if (isTRUE(info)) {
-        message_("Excluding ", format(sum(col_icu, na.rm = TRUE), big.mark = " "), " isolates from ICU.",
-          add_fn = font_black,
-          as_note = FALSE
-        )
+        message_("Excluding ", format(sum(x$newvar_is_icu, na.rm = TRUE), decimal.mark = decimal.mark, big.mark = big.mark), " isolates from ICU.",
+                 add_fn = font_red)
       }
-      x[which(col_icu), "newvar_first_isolate"] <- FALSE
+      x[which(x$newvar_is_icu), "newvar_first_isolate"] <- FALSE
     } else if (isTRUE(info)) {
-      message_("Including isolates from ICU.",
-        add_fn = font_black,
-        as_note = FALSE
-      )
+      message_("Including isolates from ICU.")
     }
   }
 
-  decimal.mark <- getOption("OutDec")
-  big.mark <- ifelse(decimal.mark != ",", ",", " ")
-
   if (isTRUE(info)) {
     # print group name if used in dplyr::group_by()
     cur_group <- import_fn("cur_group", "dplyr", error_on_fail = FALSE)
@@ -560,11 +550,12 @@ first_isolate <- function(x = NULL,
   # handle empty microorganisms
   if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) && isTRUE(info)) {
     message_(
-      ifelse(include_unknown == TRUE, "Included ", "Excluded "),
+      ifelse(include_unknown == TRUE, "Including ", "Excluding "),
       format(sum(x$newvar_mo == "UNKNOWN", na.rm = TRUE),
         decimal.mark = decimal.mark, big.mark = big.mark
       ),
-      " isolates with a microbial ID 'UNKNOWN' (in column '", font_bold(col_mo), "')"
+      " isolates with a microbial ID 'UNKNOWN' (in column '", font_bold(col_mo), "')",
+      add_fn = font_red
     )
   }
   x[which(x$newvar_mo == "UNKNOWN"), "newvar_first_isolate"] <- include_unknown
@@ -572,10 +563,11 @@ first_isolate <- function(x = NULL,
   # exclude all NAs
   if (anyNA(x$newvar_mo) && isTRUE(info)) {
     message_(
-      "Excluded ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
+      "Excluding ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
         decimal.mark = decimal.mark, big.mark = big.mark
       ),
-      " isolates with a microbial ID 'NA' (in column '", font_bold(col_mo), "')"
+      " isolates with a microbial ID `NA` (in column '", font_bold(col_mo), "')",
+      add_fn = font_red
     )
   }
   x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE

R/g.test.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/get_episode.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/ggplot_pca.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/ggplot_sir.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/guess_ab_col.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/italicise_taxonomy.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -57,7 +57,7 @@ italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
     before <- "*"
     after <- "*"
   } else if (type == "ansi") {
-    if (!has_colour()) {
+    if (!has_colour() && !identical(Sys.getenv("IN_PKGDOWN"), "true")) {
       return(string)
     }
     before <- "\033[3m"

R/join_microorganisms.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/key_antimicrobials.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/kurtosis.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/like.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -49,6 +49,9 @@
 #' @seealso [grepl()]
 
 #' @examples
+#' # data.table has a more limited version of %like%, so unload it:
+#' try(detach("package:data.table", unload = TRUE), silent = TRUE)
+#' 
 #' a <- "This is a test"
 #' b <- "TEST"
 #' a %like% b

R/mdro.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/mean_amr_distance.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/mic.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/mo.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -95,13 +95,14 @@
 #' 1. Berends MS *et al.* (2022). **AMR: An R Package for Working with Antimicrobial Resistance Data**. *Journal of Statistical Software*, 104(3), 1-31; \doi{10.18637/jss.v104.i03}
 #' 2. Becker K *et al.* (2014). **Coagulase-Negative Staphylococci.** *Clin Microbiol Rev.* 27(4): 870-926; \doi{10.1128/CMR.00109-13}
 #' 3. Becker K *et al.* (2019). **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** *Clin Microbiol Infect*; \doi{10.1016/j.cmi.2019.02.028}
-#' 4. Becker K *et al.* (2020). **Emergence of coagulase-negative staphylococci** *Expert Rev Anti Infect Ther.* 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
-#' 5. Lancefield RC (1933). **A serological differentiation of human and other groups of hemolytic streptococci**. *J Exp Med.* 57(4): 571-95; \doi{10.1084/jem.57.4.571}
-#' 6. Berends MS *et al.* (2022). **Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019** *Microorganisms* 10(9), 1801; \doi{10.3390/microorganisms10091801}
+#' 4. Becker K *et al.* (2020). **Emergence of coagulase-negative staphylococci.** *Expert Rev Anti Infect Ther.* 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
+#' 5. Lancefield RC (1933). **A serological differentiation of human and other groups of hemolytic streptococci.** *J Exp Med.* 57(4): 571-95; \doi{10.1084/jem.57.4.571}
+#' 6. Berends MS *et al.* (2022). **Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019/** *Micro.rganisms* 10(9), 1801; \doi{10.3390/microorganisms10091801}
 #' 7. `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
 #' 8. `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
-#' 9. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
-#' 10. Bartlett A *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269}
+#' 9. `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
+#' 10. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
+#' 11. Bartlett A *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269}
 #' @export
 #' @return A [character] [vector] with additional class [`mo`]
 #' @seealso [microorganisms] for the [data.frame] that is being used to determine ID's.
@@ -214,10 +215,10 @@ as.mo <- function(x,
   # From known codes ----
   out[is.na(out) & toupper(x) %in% AMR::microorganisms.codes$code] <- AMR::microorganisms.codes$mo[match(toupper(x)[is.na(out) & toupper(x) %in% AMR::microorganisms.codes$code], AMR::microorganisms.codes$code)]
   # From SNOMED ----
-  if (any(is.na(out) & !is.na(x)) && any(is.na(out) & x %in% unlist(AMR_env$MO_lookup$snomed), na.rm = TRUE)) {
-    # found this extremely fast gem here: https://stackoverflow.com/a/11002456/4575331
-    out[is.na(out) & x %in% unlist(AMR_env$MO_lookup$snomed)] <- AMR_env$MO_lookup$mo[rep(seq_along(AMR_env$MO_lookup$snomed), vapply(FUN.VALUE = double(1), AMR_env$MO_lookup$snomed, length))[match(x[is.na(out) & x %in% unlist(AMR_env$MO_lookup$snomed)], unlist(AMR_env$MO_lookup$snomed))]]
-  }
+  # based on this extremely fast gem: https://stackoverflow.com/a/11002456/4575331
+  snomeds <- unlist(AMR_env$MO_lookup$snomed)
+  snomeds <- snomeds[!is.na(snomeds)]
+  out[is.na(out) & x %in% snomeds] <- AMR_env$MO_lookup$mo[rep(seq_along(AMR_env$MO_lookup$snomed), vapply(FUN.VALUE = double(1), AMR_env$MO_lookup$snomed, length))[match(x[is.na(out) & x %in% snomeds], snomeds)]]
   # From other familiar output ----
   # such as Salmonella groups, colloquial names, etc.
   out[is.na(out)] <- convert_colloquial_input(x[is.na(out)])
@@ -282,9 +283,19 @@ as.mo <- function(x,
       # do a pre-match on first character (and if it contains a space, first chars of first two terms)
       if (length(x_parts) %in% c(2, 3)) {
         # for genus + species + subspecies
-        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) & (AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1)))
+        if (nchar(gsub("[^a-z]", "", x_parts[1], perl = TRUE)) <= 3) {
+          filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) &
+                           (AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) |
+                             AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) |
+                             AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1)))
+        } else {
+          filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) |
+                           AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) |
+                           AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) |
+                           AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1))
+        }
       } else if (length(x_parts) > 3) {
-        first_chars <- paste0("(^| )", "[", paste(substr(x_parts, 1, 1), collapse = ""), "]")
+        first_chars <- paste0("(^| )[", paste(substr(x_parts, 1, 1), collapse = ""), "]")
         filtr <- which(AMR_env$MO_lookup$full_first %like_case% first_chars)
       } else if (nchar(x_out) == 4) {
         # no space and 4 characters - probably a code such as STAU or ESCO
@@ -297,7 +308,10 @@ as.mo <- function(x,
         msg <- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on ", vector_and(c(gsub("[a-z]*", "(...)", first_part, fixed = TRUE), second_part), sort = FALSE)))
         filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", first_part, ".* ", second_part))
       } else {
-        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_out, 1, 1))
+        # for genus or species or subspecies
+        filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts, 1, 1) |
+                         AMR_env$MO_lookup$species_first == substr(x_parts, 1, 1) |
+                         AMR_env$MO_lookup$subspecies_first == substr(x_parts, 1, 1))
       }
       if (length(filtr) == 0) {
         mo_to_search <- AMR_env$MO_lookup$fullname
@@ -547,7 +561,7 @@ mo_cleaning_regex <- function() {
     "|",
     "([({]|\\[).+([})]|\\])",
     "|",
-    "(^| )(e?spp|e?ssp|e?ss|e?sp|e?subsp|sube?species|biovar|biotype|serovar|serogr.?up|e?species)[.]*( |$|(complex|group)$))"
+    "(^| )(e?spp|e?ssp|e?ss|e?sp|e?subsp|sube?species|biovar|biotype|serovar|var|serogr.?up|e?species)[.]*( |$|(complex|group)$))"
   )
 }
 
@@ -799,9 +813,13 @@ rep.mo <- function(x, ...) {
 #' @export
 #' @noRd
 print.mo_uncertainties <- function(x, n = 10, ...) {
+  more_than_50 <- FALSE
   if (NROW(x) == 0) {
     cat(word_wrap("No uncertainties to show. Only uncertainties of the last call of `as.mo()` or any `mo_*()` function are stored.\n\n", add_fn = font_blue))
     return(invisible(NULL))
+  } else if (NROW(x) > 50) {
+    more_than_50 <- TRUE
+    x <- x[1:50, , drop = FALSE]
   }
 
   cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.\n\n", add_fn = font_blue))
@@ -888,8 +906,6 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
         ),
         collapse = "\n"
       ),
-      # Add "Based on {input}" text if it differs from the original input
-      ifelse(x[i, ]$original_input != x[i, ]$input, paste0(strrep(" ", nchar(x[i, ]$original_input) + 6), "Based on input \"", x[i, ]$input, "\""), ""),
       # Add note if result was coerced to accepted taxonomic name
       ifelse(x[i, ]$keep_synonyms == FALSE & x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")],
         paste0(
@@ -911,6 +927,9 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
   if (isTRUE(any_maxed_out)) {
     cat(font_blue(word_wrap("\nOnly the first ", n, " other matches of each record are shown. Run `print(mo_uncertainties(), n = ...)` to view more entries, or save `mo_uncertainties()` to an object.")))
   }
+  if (isTRUE(more_than_50)) {
+    cat(font_blue(word_wrap("\nOnly the first 50 uncertainties are shown. Run `View(mo_uncertainties())` to view all entries, or save `mo_uncertainties()` to an object.")))
+  }
 }
 
 #' @method print mo_renamed
@@ -947,25 +966,25 @@ convert_colloquial_input <- function(x) {
   out <- rep(NA_character_, length(x))
 
   # Streptococci, like GBS = Group B Streptococci (B_STRPT_GRPB)
-  out[x %like_case% "^g[abcdfghkl]s$"] <- gsub("g([abcdfghkl])s",
+out[x %like_case% "^g[abcdefghijkl]s$"] <- gsub("g([abcdefghijkl])s",
     "B_STRPT_GRP\\U\\1",
-    x[x %like_case% "^g[abcdfghkl]s$"],
+    x[x %like_case% "^g[abcdefghijkl]s$"],
     perl = TRUE
   )
   # Streptococci in different languages, like "estreptococos grupo B"
-  out[x %like_case% "strepto[ck]o[ck].* [abcdfghkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdfghkl])$",
+  out[x %like_case% "strepto[ck]o[ck].* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$",
     "B_STRPT_GRP\\U\\1",
-    x[x %like_case% "strepto[ck]o[ck].* [abcdfghkl]$"],
+    x[x %like_case% "strepto[ck]o[ck].* [abcdefghijkl]$"],
     perl = TRUE
   )
-  out[x %like_case% "strep[a-z]* group [abcdfghkl]$"] <- gsub(".* ([abcdfghkl])$",
+  out[x %like_case% "strep[a-z]* group [abcdefghijkl]$"] <- gsub(".* ([abcdefghijkl])$",
     "B_STRPT_GRP\\U\\1",
-    x[x %like_case% "strep[a-z]* group [abcdfghkl]$"],
+    x[x %like_case% "strep[a-z]* group [abcdefghijkl]$"],
     perl = TRUE
   )
-  out[x %like_case% "group [abcdfghkl] strepto[ck]o[ck]"] <- gsub(".*group ([abcdfghkl]) strepto[ck]o[ck].*",
+  out[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"] <- gsub(".*group ([abcdefghijkl]) strepto[ck]o[ck].*",
     "B_STRPT_GRP\\U\\1",
-    x[x %like_case% "group [abcdfghkl] strepto[ck]o[ck]"],
+    x[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"],
     perl = TRUE
   )
   out[x %like_case% "ha?emoly.*strep"] <- "B_STRPT_HAEM"
@@ -975,14 +994,14 @@ convert_colloquial_input <- function(x) {
   out[x %like_case% "(viridans.* (strepto|^s).*|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
 
   # Salmonella in different languages, like "Salmonella grupo B"
-  out[x %like_case% "salmonella.* [abcd]$"] <- gsub(".*salmonella.* ([abcd])$",
+  out[x %like_case% "salmonella.* [abcdefgh]$"] <- gsub(".*salmonella.* ([abcdefgh])$",
     "B_SLMNL_GRP\\U\\1",
-    x[x %like_case% "salmonella.* [abcd]$"],
+    x[x %like_case% "salmonella.* [abcdefgh]$"],
     perl = TRUE
   )
-  out[x %like_case% "group [abcd] salmonella"] <- gsub(".*group ([abcd]) salmonella*",
+  out[x %like_case% "group [abcdefgh] salmonella"] <- gsub(".*group ([abcdefgh]) salmonella*",
     "B_SLMNL_GRP\\U\\1",
-    x[x %like_case% "group [abcd] salmonella"],
+    x[x %like_case% "group [abcdefgh] salmonella"],
     perl = TRUE
   )
 
@@ -995,8 +1014,10 @@ convert_colloquial_input <- function(x) {
   out[x %like_case% "( |^)gram[-]( |$)"] <- "B_GRAMN"
   out[x %like_case% "gram[ -]?pos.*"] <- "B_GRAMP"
   out[x %like_case% "( |^)gram[+]( |$)"] <- "B_GRAMP"
+  out[x %like_case% "anaerob[a-z]+ .*gram[ -]?neg.*"] <- "B_ANAER-NEG"
+  out[x %like_case% "anaerob[a-z]+ .*gram[ -]?pos.*"] <- "B_ANAER-POS"
   out[is.na(out) & x %like_case% "anaerob[a-z]+ (micro)?.*organism"] <- "B_ANAER"
-
+  
   # yeasts and fungi
   out[x %like_case% "^yeast?"] <- "F_YEAST"
   out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS"
@@ -1006,7 +1027,7 @@ convert_colloquial_input <- function(x) {
   out[x %like_case% "gono[ck]o[ck]"] <- "B_NESSR_GNRR"
   out[x %like_case% "pneumo[ck]o[ck]"] <- "B_STRPT_PNMN"
 
-  # unexisting names (xxx and con are WHONET codes)
+  # unexisting names (con is the WHONET code for contamination)
   out[x %in% c("con", "other", "none", "unknown") | x %like_case% "virus"] <- "UNKNOWN"
 
   # WHONET has a lot of E. coli and Vibrio cholerae names
@@ -1017,18 +1038,23 @@ convert_colloquial_input <- function(x) {
 }
 
 italicise <- function(x) {
+  if (!has_colour()) {
+    return(x)
+  }
   out <- font_italic(x, collapse = NULL)
+  # city-like serovars of Salmonella (start with a capital)
   out[x %like_case% "Salmonella [A-Z]"] <- paste(
     font_italic("Salmonella"),
     gsub("Salmonella ", "", x[x %like_case% "Salmonella [A-Z]"])
   )
+  # streptococcal groups
   out[x %like_case% "Streptococcus [A-Z]"] <- paste(
     font_italic("Streptococcus"),
     gsub("Streptococcus ", "", x[x %like_case% "Streptococcus [A-Z]"])
   )
-  if (has_colour()) {
-    out <- gsub("(Group|group|Complex|complex)(\033\\[23m)?", "\033[23m\\1", out, perl = TRUE)
-  }
+  # be sure not to make these italic
+  out <- gsub("([ -]*)(Group|group|Complex|complex)(\033\\[23m)?", "\033[23m\\1\\2", out, perl = TRUE)
+  out <- gsub("(\033\\[3m)?(Beta[-]haemolytic|Coagulase[-](postive|negative)) ", "\\2 \033[3m", out, perl = TRUE)
   out
 }
 

R/mo_matching_score.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/mo_property.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -53,6 +53,8 @@
 #' Determination of yeasts ([mo_is_yeast()]) will be based on the taxonomic kingdom and class. *Budding yeasts* are fungi of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes). *True yeasts* are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes, the function will return `TRUE`. It returns `FALSE` otherwise (or `NA` when the input is `NA` or the MO code is `UNKNOWN`).
 #'
 #' Determination of intrinsic resistance ([mo_is_intrinsic_resistant()]) will be based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(3.3)`. The [mo_is_intrinsic_resistant()] function can be vectorised over both argument `x` (input for microorganisms) and `ab` (input for antibiotics).
+#' 
+#' Determination of bacterial oxygen tolerance ([mo_oxygen_tolerance()]) will be based on BacDive, see *Source*. The function [mo_is_anaerobic()] only returns `TRUE` if the oxygen tolerance is `"anaerobe"`, indicting an obligate anaerobic species or genus. It always returns `FALSE` for species outside the taxonomic kingdom of Bacteria.
 #'
 #' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
 #'
@@ -480,7 +482,7 @@ mo_gramstain <- function(x, language = get_AMR_locale(), keep_synonyms = getOpti
     # but class Negativicutes (of phylum Bacillota) are Gram-negative!
     mo_class(x.mo, language = NULL, keep_synonyms = keep_synonyms) != "Negativicutes")
   # and of course our own ID for Gram-positives
-  | x.mo == "B_GRAMP"] <- "Gram-positive"
+  | x.mo %in% c("B_GRAMP", "B_ANAER-POS")] <- "Gram-positive"
 
   load_mo_uncertainties(metadata)
   translate_into_language(x, language = language, only_unknown = FALSE)
@@ -589,6 +591,40 @@ mo_is_intrinsic_resistant <- function(x, ab, language = get_AMR_locale(), keep_s
   paste(x, ab) %in% AMR_env$intrinsic_resistant
 }
 
+#' @rdname mo_property
+#' @export
+mo_oxygen_tolerance <- 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_oxygen_tolerance")
+  }
+  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 = "oxygen_tolerance", language = language, keep_synonyms = keep_synonyms, ...)
+}
+
+#' @rdname mo_property
+#' @export
+mo_is_anaerobic <- 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_is_anaerobic")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  language <- validate_language(language)
+  meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
+  
+  x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
+  metadata <- get_mo_uncertainties()
+  oxygen <- mo_oxygen_tolerance(x.mo, language = NULL, keep_synonyms = keep_synonyms)
+  load_mo_uncertainties(metadata)
+  out <- oxygen == "anaerobe" & !is.na(oxygen)
+  out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
+  out
+}
+
 #' @rdname mo_property
 #' @export
 mo_snomed <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
@@ -791,9 +827,12 @@ mo_info <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
         status = mo_status(y, language = language, keep_synonyms = keep_synonyms),
         synonyms = mo_synonyms(y, keep_synonyms = keep_synonyms),
         gramstain = mo_gramstain(y, language = language, keep_synonyms = keep_synonyms),
+        oxygen_tolerance = mo_oxygen_tolerance(y, language = language, keep_synonyms = keep_synonyms),
         url = unname(mo_url(y, open = FALSE, keep_synonyms = keep_synonyms)),
         ref = mo_ref(y, keep_synonyms = keep_synonyms),
-        snomed = unlist(mo_snomed(y, keep_synonyms = keep_synonyms))
+        snomed = unlist(mo_snomed(y, keep_synonyms = keep_synonyms)),
+        lpsn = mo_lpsn(y, language = language, keep_synonyms = keep_synonyms),
+        gbif = mo_gbif(y, language = language, keep_synonyms = keep_synonyms)
       )
     )
   })

R/mo_source.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/pca.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/plot.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/proportion.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/random.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/resistance_predict.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/sir.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -759,7 +759,7 @@ as_sir_method <- function(method_short,
   if (is.null(mo)) {
     stop_("No information was supplied about the microorganisms (missing argument `mo` and no column of class 'mo' found). See ?as.sir.\n\n",
       "To transform certain columns with e.g. mutate(), use `data %>% mutate(across(..., as.sir, mo = x))`, where x is your column with microorganisms.\n",
-      "To tranform all ", method_long, " in a data set, use `data %>% as.sir()` or `data %>% mutate_if(is.", method_short, ", as.sir)`.",
+      "To transform all ", method_long, " in a data set, use `data %>% as.sir()` or `data %>% mutate_if(is.", method_short, ", as.sir)`.",
       call = FALSE
     )
   }

R/sir_calc.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/sir_df.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/skewness.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/translate.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/vctrs.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/whocc.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/zz_deprecated.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

R/zzz.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -59,16 +59,15 @@ AMR_env$sir_interpretation_history <- data.frame(
   datetime = Sys.time()[0],
   index = integer(0),
   ab_input = character(0),
-  ab_considered = character(0),
+  ab_guideline = set_clean_class(character(0), c("ab", "character")),
   mo_input = character(0),
-  mo_considered = character(0),
+  mo_guideline = set_clean_class(character(0), c("mo", "character")),
   guideline = character(0),
   ref_table = character(0),
   method = character(0),
-  breakpoint_S = double(0),
-  breakpoint_R = double(0),
   input = double(0),
-  interpretation = character(0),
+  outcome = NA_sir_[0],
+  breakpoint_S_R = character(0),
   stringsAsFactors = FALSE
 )
 AMR_env$custom_ab_codes <- character(0)

_pkgdown.yml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -70,9 +70,9 @@ home:
 navbar:
   title: "AMR (for R)"
   left:
-    - text: "Home"
-      icon: "fa-home"
-      href: "index.html"
+    # - text: "Home"
+    #   icon: "fa-home"
+    #   href: "index.html"
     - text: "How to"
       icon: "fa-question-circle"
       menu:
@@ -100,9 +100,9 @@ navbar:
       - text: "Work with WHONET Data"
         icon: "fa-globe-americas"
         href: "articles/WHONET.html"
-      - text: "Import Data From SPSS/SAS/Stata"
-        icon: "fa-file-upload"
-        href: "articles/SPSS.html"
+      # - text: "Import Data From SPSS/SAS/Stata"
+      #   icon: "fa-file-upload"
+      #   href: "articles/SPSS.html"
       - text: "Apply Eucast Rules"
         icon: "fa-exchange-alt"
         href: "articles/EUCAST.html"
@@ -115,16 +115,31 @@ navbar:
       - text: "Get Properties of an Antiviral Drug"
         icon: "fa-capsules"
         href: "reference/av_property.html" # reference instead of an article
+    - text: "With other pkgs"
+      icon: "fa-layer-group"
+      menu:
+      - text: "AMR & dplyr/tidyverse"
+        icon: "fa-layer-group"
+        href: "articles/other_pkg.html"
+      - text: "AMR & data.table"
+        icon: "fa-layer-group"
+        href: "articles/other_pkg.html"
+      - text: "AMR & tidymodels"
+        icon: "fa-layer-group"
+        href: "articles/other_pkg.html"
+      - text: "AMR & base R"
+        icon: "fa-layer-group"
+        href: "articles/other_pkg.html"
     - text: "Manual"
       icon: "fa-book-open"
       href: "reference/index.html"
     - text: "Authors"
       icon: "fa-users"
       href: "authors.html"
+  right:
     - text: "Changelog"
       icon: "far fa-newspaper"
       href: "news/index.html"
-  right:
     - text: "Source Code"
       icon: "fab fa-github"
       href: "https://github.com/msberends/AMR"

codecov.yml

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

data-raw/_language_update.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

data-raw/_pre_commit_hook.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #
@@ -31,6 +31,7 @@
 # source("data-raw/_pre_commit_hook.R")
 
 library(dplyr, warn.conflicts = FALSE)
+try(detach("package:data.table", unload = TRUE), silent = TRUE) # to prevent like() to precede over AMR::like
 devtools::load_all(quiet = TRUE)
 
 suppressMessages(set_AMR_locale("English"))
@@ -164,12 +165,12 @@ MO_PREVALENT_GENERA <- c(
   "Halococcus", "Hendersonula", "Heterophyes", "Histomonas", "Histoplasma", "Hymenolepis", "Hypomyces",
   "Hysterothylacium", "Leishmania", "Malassezia", "Malbranchea", "Metagonimus", "Meyerozyma", "Microsporidium",
   "Microsporum", "Mortierella", "Mucor", "Mycocentrospora", "Necator", "Nectria", "Ochroconis", "Oesophagostomum",
-  "Oidiodendron", "Opisthorchis", "Pediculus", "Phlebotomus", "Phoma", "Pichia", "Piedraia", "Pithomyces",
+  "Oidiodendron", "Opisthorchis", "Pediculus", "Penicillium", "Phlebotomus", "Phoma", "Pichia", "Piedraia", "Pithomyces",
   "Pityrosporum", "Pneumocystis", "Pseudallescheria", "Pseudoterranova", "Pulex", "Rhizomucor", "Rhizopus",
   "Rhodotorula", "Saccharomyces", "Sarcoptes", "Scolecobasidium", "Scopulariopsis", "Scytalidium", "Spirometra",
-  "Sporobolomyces", "Stachybotrys", "Strongyloides", "Syngamus", "Taenia", "Toxocara", "Trichinella", "Trichobilharzia",
-  "Trichoderma", "Trichomonas", "Trichophyton", "Trichosporon", "Trichostrongylus", "Trichuris", "Tritirachium",
-  "Trombicula", "Trypanosoma", "Tunga", "Wuchereria"
+  "Sporobolomyces", "Stachybotrys", "Strongyloides", "Syngamus", "Taenia", "Talaromyces", "Toxocara", "Trichinella",
+  "Trichobilharzia", "Trichoderma", "Trichomonas", "Trichophyton", "Trichosporon", "Trichostrongylus", "Trichuris",
+  "Tritirachium", "Trombicula", "Trypanosoma", "Tunga", "Wuchereria"
 )
 
 # antibiotic groups
@@ -365,7 +366,7 @@ if (changed_md5(clin_break)) {
   write_md5(clin_break)
   try(saveRDS(clin_break, "data-raw/clinical_breakpoints.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(clin_break, "data-raw/clinical_breakpoints.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(clin_break, "data-raw/clinical_breakpoints.sas"), silent = TRUE)
+  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)
@@ -381,7 +382,7 @@ if (changed_md5(microorganisms)) {
   mo <- microorganisms
   mo$snomed <- max_50_snomed
   mo <- dplyr::mutate_if(mo, ~ !is.numeric(.), as.character)
-  try(haven::write_sas(mo, "data-raw/microorganisms.sas"), silent = TRUE)
+  try(haven::write_xpt(mo, "data-raw/microorganisms.xpt"), silent = TRUE)
   try(haven::write_sav(mo, "data-raw/microorganisms.sav"), silent = TRUE)
   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 = ","))
@@ -396,7 +397,7 @@ if (changed_md5(ab)) {
   usethis::ui_info(paste0("Saving {usethis::ui_value('antibiotics')} to {usethis::ui_value('data-raw/')}"))
   write_md5(ab)
   try(saveRDS(antibiotics, "data-raw/antibiotics.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(haven::write_sas(ab, "data-raw/antibiotics.sas"), silent = TRUE)
+  try(haven::write_xpt(ab, "data-raw/antibiotics.xpt"), silent = TRUE)
   try(haven::write_sav(ab, "data-raw/antibiotics.sav"), silent = TRUE)
   try(haven::write_dta(ab, "data-raw/antibiotics.dta"), silent = TRUE)
   ab_lists <- antibiotics %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
@@ -411,7 +412,7 @@ if (changed_md5(av)) {
   usethis::ui_info(paste0("Saving {usethis::ui_value('antivirals')} to {usethis::ui_value('data-raw/')}"))
   write_md5(av)
   try(saveRDS(antivirals, "data-raw/antivirals.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(haven::write_sas(av, "data-raw/antivirals.sas"), silent = TRUE)
+  try(haven::write_xpt(av, "data-raw/antivirals.xpt"), silent = TRUE)
   try(haven::write_sav(av, "data-raw/antivirals.sav"), silent = TRUE)
   try(haven::write_dta(av, "data-raw/antivirals.dta"), silent = TRUE)
   av_lists <- antivirals %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
@@ -432,7 +433,7 @@ if (changed_md5(intrinsicR)) {
   write_md5(intrinsicR)
   try(saveRDS(intrinsicR, "data-raw/intrinsic_resistant.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(intrinsicR, "data-raw/intrinsic_resistant.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(intrinsicR, "data-raw/intrinsic_resistant.sas"), silent = TRUE)
+  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)
@@ -445,7 +446,7 @@ if (changed_md5(dosage)) {
   write_md5(dosage)
   try(saveRDS(dosage, "data-raw/dosage.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(dosage, "data-raw/dosage.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(dosage, "data-raw/dosage.sas"), silent = TRUE)
+  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)

data-raw/clin_break.md5

@@ -1 +1 @@
-68467f5179638ac5622281df53a5ea75
+0a9ea3545d68b95108a28096d975388f

data-raw/loinc.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

data-raw/microorganisms.md5

@@ -1 +1 @@
-7846247d4113c4e8f550cfd2cb87467f
+63cc9e5166dc50c7b474bb809557c392

data-raw/poorman_prepend.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

data-raw/read_EUCAST.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #

data-raw/reproduction_of_antibiotics.R

@@ -9,7 +9,7 @@
 # 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.                 #
-# doi:10.18637/jss.v104.i03                                            #
+# https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
 # Center Groningen in The Netherlands, in collaboration with many      #