(v2.1.1.9070) fix for mo_current mo (no-verify)

add files (no-verify)
(v2.1.1.9068) fix for mo_url() and as.mo() for synonyms
2025-09-16 15:09:41 +02:00 · 2024-07-19 18:05:49 +02:00 · 2024-07-17 14:31:09 +02:00 · 2024-07-17 14:29:55 +02:00 · 2024-07-16 16:10:38 +02:00 · 2024-07-16 16:09:33 +02:00
199 changed files with 355754 additions and 123774 deletions
--- a/.github/prehooks/commit-msg
+++ b/.github/prehooks/commit-msg
@@ -8,9 +8,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -29,29 +29,10 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#######################################
-# This script runs on commit-msg hook #
-#######################################
-
-# Path to the commit message file
-COMMIT_MSG_FILE=$1
-
-# Read the current commit message
-COMMIT_MSG=$(cat "$COMMIT_MSG_FILE")
-# check the commit message, cancel commit if needed
-if [[ "$COMMIT_MSG" =~ \(no-check\)|\(no-checks\)|\(no-verify\) ]]; then
-  exit 0
-fi
-
-# Path to the DESCRIPTION file
-DESCRIPTION_FILE="DESCRIPTION"
-# Read the version number from the DESCRIPTION file
-if [ -f "$DESCRIPTION_FILE" ]; then
-  currentversion=$(grep "^Version:" "$DESCRIPTION_FILE" | awk '{print $2}')
-else
-  echo "Error: DESCRIPTION file not found. Unable to prepend version number."
-  exit 1
-fi
-
-# Prepend the version number to the commit message
-echo "(v${currentversion}) ${COMMIT_MSG}" > "$COMMIT_MSG_FILE"
+# always add these:
+git add data-raw/*
+git add man/*
+git add R/sysdata.rda
+git add NAMESPACE
+git add DESCRIPTION
+git add NEWS.md
--- a/.github/prehooks/pre-commit
+++ b/.github/prehooks/pre-commit
@@ -8,9 +8,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -29,93 +29,10 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-########################################
-# This script runs before every commit #
-########################################
-
-# try to fetch the commit message from the standard location
-COMMIT_MSG_PATH=".git/COMMIT_EDITMSG"
-if [ -f "$COMMIT_MSG_PATH" ]; then
-  COMMIT_MSG=$(cat "$COMMIT_MSG_PATH")
-else
-  echo "Commit message file not found."
-  exit 1
-fi
-# check the commit message, cancel commit if needed
-if [[ "$COMMIT_MSG" =~ \(no-check\)|\(no-checks\)|\(no-verify\) ]]; then
-  echo "Not running pre-commit checks:"
-  echo "Commit message contains '(no-check)', '(no-checks)', or '(no-verify)."
-  echo ""
-  exit 0
-fi
-
-echo "Running pre-commit checks..."
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-if command -v Rscript > /dev/null; then
-  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
-    Rscript -e "source('data-raw/_pre_commit_checks.R')"
-    currentpkg=$(Rscript -e "cat(pkgload::pkg_name())")
-    echo "- Adding changed files in ./data-raw and ./man to this commit"
-    git add data-raw/*
-    git add man/*
-    git add R/sysdata.rda
-    git add NAMESPACE
-  else
-    echo "- R package 'pkgload', 'devtools', 'dplyr', or 'styler' not installed!"
-    currentpkg="your"
-  fi
-else
-  echo "- R is not available on your system!"
-  currentpkg="your"
-fi
-echo ""
-
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-echo "Updating semantic versioning and date..."
-
-# get tags from remote, and remove tags not on remote:
-git fetch origin --prune --prune-tags --quiet
-currenttagfull=$(git describe --tags --abbrev=0)
-currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
-# assume main branch to be 'main' or 'master', pick the right name:
-defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
-if [ "$currenttag" = "" ]; then
-  # there is no tag, so set tag to 0.0.1 and commit index to current count
-  currenttag="0.0.1"
-  currentcommit=$(git rev-list --count ${defaultbranch})
-  echo "- no git tags found, create one in format 'v(x).(y).(z)' - curently ${currentcommit} previous commits in '${defaultbranch}'"
-else
-  # there is a tag, so base version number on that
-  currentcommit=$(git rev-list --count ${currenttagfull}..${defaultbranch})
-  echo "- latest tag is '${currenttagfull}', with ${currentcommit} previous commits in '${defaultbranch}'"
-fi
-# combine tag (e.g. 1.2.3) and commit number (like 5) increased by 9000 to indicate beta version
-currentversion="$currenttag.$((currentcommit + 9001))" # results in e.g. 1.2.3.9005
-echo "- ${currentpkg} pkg version set to ${currentversion}"
-
-# set version number and date to DESCRIPTION file
-sed -i -- "s/^Version: .*/Version: ${currentversion}/" DESCRIPTION
-sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION
-echo "- updated version number and date in ./DESCRIPTION"
-# remove leftover on macOS
-rm -f DESCRIPTION--
-# add to commit
+# always add these:
+git add data-raw/*
+git add man/*
+git add R/sysdata.rda
+git add NAMESPACE
 git add DESCRIPTION
-
-# set version number to NEWS file
-if [ -e "NEWS.md" ]; then
-  if [ "$currentpkg" = "your" ]; then
-    currentpkg=""
-  fi
-  sed -i -- "1s/.*/# ${currentpkg} ${currentversion}/" NEWS.md
-  echo "- updated version number in ./NEWS.md"
-  # remove leftover on macOS
-  rm -f NEWS.md--
-  # add to commit
-  git add NEWS.md
-else
-  echo "- no NEWS.md found!"
-fi
-echo ""
+git add NEWS.md
--- a/.github/prehooks/prepare-commit-msg
+++ b/.github/prehooks/prepare-commit-msg
@@ -0,0 +1,126 @@
+#!/bin/bash
+
+# ==================================================================== #
+# TITLE:                                                               #
+# AMR: An R Package for Working with Antimicrobial Resistance Data     #
+#                                                                      #
+# SOURCE CODE:                                                         #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# PLEASE CITE THIS SOFTWARE AS:                                        #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
+# https://doi.org/10.18637/jss.v104.i03                                #
+#                                                                      #
+# Developed at the University of Groningen and the University Medical  #
+# Center Groningen in The Netherlands, in collaboration with many      #
+# colleagues from around the world, see our website.                   #
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+########################################
+# This script runs before every commit #
+########################################
+
+COMMIT_MSG_FILE=".git/COMMIT_EDITMSG"
+
+# Read the commit message
+if [ -f "$COMMIT_MSG_FILE" ]; then
+  COMMIT_MSG=$(cat "$COMMIT_MSG_FILE")
+else
+  echo "Commit message file not found."
+  exit 1
+fi
+
+# Check the commit message and skip checks if needed
+if [[ "$COMMIT_MSG" =~ no-?checks?|no-?verify ]]; then
+  echo "Not running prehook:"
+  echo "Commit message contains 'no-check' or 'no-verify'."
+  echo ""
+  exit 0
+fi
+
+echo "Running prehook..."
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+if command -v Rscript > /dev/null; then
+  if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
+    Rscript -e "source('data-raw/_pre_commit_checks.R')"
+    currentpkg=$(Rscript -e "cat(pkgload::pkg_name())")
+    echo "- Adding changed files in ./data-raw and ./man to this commit"
+    git add data-raw/*
+    git add man/*
+    git add R/sysdata.rda
+    git add NAMESPACE
+  else
+    echo "- R package 'pkgload', 'devtools', or 'dplyr' not installed!"
+    currentpkg="your"
+  fi
+else
+  echo "- R is not available on your system!"
+  currentpkg="your"
+fi
+echo ""
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+echo "Updating semantic versioning and date..."
+
+# get tags from remote, and remove tags not on remote:
+git fetch origin --prune --prune-tags --quiet
+currenttagfull=$(git describe --tags --abbrev=0)
+currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
+# assume main branch to be 'main' or 'master', pick the right name:
+defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
+if [ "$currenttag" = "" ]; then
+  # there is no tag, so set tag to 0.0.1 and commit index to current count
+  currenttag="0.0.1"
+  currentcommit=$(git rev-list --count ${defaultbranch})
+  echo "- no git tags found, create one in format 'v(x).(y).(z)' - curently ${currentcommit} previous commits in '${defaultbranch}'"
+else
+  # there is a tag, so base version number on that
+  currentcommit=$(git rev-list --count ${currenttagfull}..${defaultbranch})
+  echo "- latest tag is '${currenttagfull}', with ${currentcommit} previous commits in '${defaultbranch}'"
+fi
+# combine tag (e.g. 1.2.3) and commit number (like 5) increased by 9000 to indicate beta version
+currentversion="$currenttag.$((currentcommit + 9001))" # results in e.g. 1.2.3.9005
+echo "- ${currentpkg} pkg version set to ${currentversion}"
+
+# set version number and date to DESCRIPTION file
+sed -i -- "s/^Version: .*/Version: ${currentversion}/" DESCRIPTION
+sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION
+echo "- updated version number and date in ./DESCRIPTION"
+# remove leftover on macOS
+rm -f DESCRIPTION--
+# add to commit
+git add DESCRIPTION
+
+# set version number to NEWS file
+if [ -e "NEWS.md" ]; then
+  if [ "$currentpkg" = "your" ]; then
+    currentpkg=""
+  fi
+  sed -i -- "1s/.*/# ${currentpkg} ${currentversion}/" NEWS.md
+  echo "- updated version number in ./NEWS.md"
+  # remove leftover on macOS
+  rm -f NEWS.md--
+  # add to commit
+  git add NEWS.md
+else
+  echo "- no NEWS.md found!"
+fi
+echo ""
+
+# Prepend the version number to the commit message
+echo "(v${currentversion}) ${COMMIT_MSG}" > "$COMMIT_MSG_FILE"
--- a/.github/workflows/check-old.yaml
+++ b/.github/workflows/check-old.yaml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/.github/workflows/check-recent.yaml
+++ b/.github/workflows/check-recent.yaml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/.github/workflows/codecovr.yaml
+++ b/.github/workflows/codecovr.yaml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/.github/workflows/lintr.yaml
+++ b/.github/workflows/lintr.yaml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/6
+++ b/6
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9050
-Date: 2024-06-15
+Version: 2.1.1.9070
+Date: 2024-07-19
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
  data analysis and to work with microbial and antimicrobial properties by
@@ -39,7 +39,6 @@ Suggests:
  data.table,
  dplyr,
  ggplot2,
-  janitor,
  knitr,
  progress,
  readxl,
@@ -49,7 +48,6 @@ Suggests:
  tibble,
  tidyselect,
  tinytest,
-  tsibble,
  vctrs,
  xml2
 VignetteBuilder: knitr,rmarkdown
--- a/2
+++ b/2
@@ -96,6 +96,7 @@ S3method(print,mo_renamed)
 S3method(print,mo_uncertainties)
 S3method(print,pca)
 S3method(print,sir)
+S3method(print,sir_log)
 S3method(quantile,mic)
 S3method(rep,ab)
 S3method(rep,av)
@@ -265,6 +266,7 @@ export(mo_is_yeast)
 export(mo_kingdom)
 export(mo_lpsn)
 export(mo_matching_score)
+export(mo_mycobank)
 export(mo_name)
 export(mo_order)
 export(mo_oxygen_tolerance)
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,8 +1,8 @@
-# AMR 2.1.1.9050
+# AMR 2.1.1.9070

-*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support!)*
+*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*

-#### A New Milestone: One Health Support (= Human + Veterinary + Environmental)
+#### A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
 This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.

 ## Breaking
@@ -15,19 +15,32 @@ This package now supports not only tools for AMR data analysis in clinical setti
  * The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well.
  * `ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q")
  * `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
-* `as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI. Also, to get quantitative values, `as.double()` or a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
-* The function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
-* Function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
-* Function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.
-* Clinical breakpoints and intrinsic resistance of EUCAST 2024 and CLSI 2024 have been added to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 (v14.0) is now the new default guideline for all MIC and disks diffusion interpretations.
+* Clinical breakpoints
+  * EUCAST 2024 and CLSI 2024 are now supported, by adding all of their over 4,000 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2024 is now the new default guideline for all MIC and disks diffusion interpretations.
+  * `as.sir()` now brings additional factor levels: "NI" for non-interpretable and "SDD" for susceptible dose-dependent. Currently, the `clinical_breakpoints` data set contains 24 breakpoints that can return the value "SDD" instead of "I".
+* MIC plotting and transforming
+  * New function group `scale_*_mic()`, namely: `scale_x_mic()`, `scale_y_mic()`, `scale_colour_mic()` and `scale_fill_mic()`. They are advanced ggplot2 extensions to allow easy plotting of MIC values. They allow for manual range definition and plotting missing intermediate log2 levels.
+  * New function `rescale_mic()`, which allows to rescale MIC values to a manually set range. This is the powerhouse behind the `scale_*_mic()` functions, but it can be used by users directly to e.g. compare equality in MIC distributions by rescaling them to the same range first.
+* Microbiological taxonomy (`microorganisms` data set) updated to June 2024, with some exciting new features:
+  * Added MycoBank as the primary taxonomic source for fungi
+    * The `microorganisms` data set now contains additional columns `mycobank`, `mycobank_parent`, and `mycobank_renamed_to`
+    * New function `mo_mycobank()` to get the MycoBank record number, analogous to existing functions `mo_lpsn()` and `mo_gbif()`
+  * We've welcomed over 2,000 records from 2023, over 900 from 2024, and many thousands of new fungi
+* Other
+  * New function `mo_group_members()` to retrieve the member microorganisms of a microorganism group. For example, `mo_group_members("Strep group C")` returns a vector of all microorganisms that are in that group.

 ## Changed
-* For SIR interpretation, it is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users.
-* Extended the antibiotic selectors with `nitrofurans()` and `rifamycins()`
-* `antibiotics` data set:
+* SIR interpretation
+  * It is now possible to use column names for argument `ab`, `mo`, and `uti`: `as.sir(..., ab = "column1", mo = "column2", uti = "column3")`. This greatly improves the flexibility for users.
+  * Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
+  * To get quantitative values, `as.double()` on a `sir` object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become `NA`). Other functions using `sir` classes (e.g., `summary()`) are updated to reflect the change to contain NI and SDD.
+* `antibiotics` data set
  * Added "clindamycin inducible screening" as `CLI1`. Since clindamycin is a lincosamide, the antibiotic selector `lincosamides()` now contains the argument `only_treatable = TRUE` (similar to other antibiotic selectors that contain non-treatable drugs)
  * Added Amorolfine (`AMO`, D01AE16), which is now also part of the `antifungals()` selector
-* For MICs:
+* Antibiotic selectors
+  * Added selectors `nitrofurans()` and `rifamycins()`
+  * When using antibiotic selectors such as `aminoglycosides()` that exclude non-treatable drugs like gentamicin-high, the function now always returns a warning that these can be included using `only_treatable = FALSE`
+* MICs
  * Added as valid levels: 4096, 6 powers of 0.0625, and 5 powers of 192 (192, 384, 576, 768, 960)
  * Added new argument `keep_operators` to `as.mic()`. This can be `"all"` (default), `"none"`, or `"edges"`. This argument is also available in the new `rescale_mic()` and `scale_*_mic()` functions.
  * Comparisons of MIC values are now more strict. For example, `>32` is higher than (and never equal to) `32`. Thus, `as.mic(">32") == as.mic(32)` now returns `FALSE`, and `as.mic(">32") > as.mic(32)` now returns `TRUE`.
@@ -42,8 +55,8 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * Fix for mapping 'high level' antibiotics in `as.ab()` (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high)
 * Improved overall algorithm of `as.ab()` for better performance and accuracy
 * Improved overall algorithm of `as.mo()` for better performance and accuracy. Specifically, more weight is given to genus and species combinations in cases where the subspecies is miswritten, so that the result will be the correct genus and species.
-* When using antibiotic selectors such as `aminoglycosides()` that exclude non-treatable drugs like gentamicin-high, the function now always returns a warning that these can be included using `only_treatable = FALSE`
 * Intermediate log2 levels used for MIC plotting are now more common values instead of following a strict dilution range
+* Fixed a bug for when `antibiogram()` returns an empty data set

 ## Other
 * Added Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input
--- a/R/aa_amr-package.R
+++ b/R/aa_amr-package.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/aa_globals.R
+++ b/R/aa_globals.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -86,26 +86,37 @@ EUCAST_VERSION_EXPERT_RULES <- list(

 TAXONOMY_VERSION <- list(
  GBIF = list(
-    accessed_date = as.Date("2024-01-08"),
+    name = "Global Biodiversity Information Facility (GBIF)",
+    accessed_date = as.Date("2024-06-24"),
    citation = "GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
    url = "https://www.gbif.org"
  ),
  LPSN = list(
-    accessed_date = as.Date("2022-12-11"),
+    name = "List of Prokaryotic names with Standing in Nomenclature (LPSN)",
+    accessed_date = as.Date("2024-06-24"),
    citation = "Parte, AC *et al.* (2020). **List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.** International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \\doi{10.1099/ijsem.0.004332}.",
    url = "https://lpsn.dsmz.de"
  ),
+  MycoBank = list(
+    name = "MycoBank",
+    accessed_date = as.Date("2024-06-24"),
+    citation = "Vincent, R *et al* (2013). **MycoBank gearing up for new horizons.** IMA Fungus, 4(2), 371-9; \\doi{10.5598/imafungus.2013.04.02.16}.",
+    url = "https://www.mycobank.org"
+  ),
  BacDive = list(
-    accessed_date = as.Date("2023-05-12"),
+    name = "BacDive",
+    accessed_date = as.Date("2024-07-16"),
    citation = "Reimer, LC *et al.* (2022). ***BacDive* in 2022: the knowledge base for standardized bacterial and archaeal data.** Nucleic Acids Res., 50(D1):D741-D74; \\doi{10.1093/nar/gkab961}.",
    url = "https://bacdive.dsmz.de"
  ),
  SNOMED = list(
-    accessed_date = as.Date("2021-07-01"),
+    name = "Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT)",
+    accessed_date = as.Date("2024-07-16"),
    citation = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12).",
    url = "https://phinvads.cdc.gov"
  ),
  LOINC = list(
+    name = "Logical Observation Identifiers Names and Codes (LOINC)",
    accessed_date = as.Date("2023-10-19"),
    citation = "Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023).",
    url = "https://loinc.org"
--- a/R/aa_helper_functions.R
+++ b/R/aa_helper_functions.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -573,6 +573,7 @@ warning_ <- function(...,
 # - wraps text to never break lines within words
 stop_ <- function(..., call = TRUE) {
  msg <- paste0(c(...), collapse = "")
+  msg_call <- ""
  if (!isFALSE(call)) {
    if (isTRUE(call)) {
      call <- as.character(sys.call(-1)[1])
@@ -580,10 +581,19 @@ stop_ <- function(..., call = TRUE) {
      # so you can go back more than 1 call, as used in sir_calc(), that now throws a reference to e.g. n_sir()
      call <- as.character(sys.call(call)[1])
    }
-    msg <- paste0("in ", call, "(): ", msg)
+    msg_call <- paste0("in ", call, "():")
  }
  msg <- trimws2(word_wrap(msg, add_fn = list(), as_note = FALSE))
-  stop(msg, call. = FALSE)
+  if (!is.null(AMR_env$cli_abort) && length(unlist(strsplit(msg, "\n", fixed = TRUE))) <= 1) {
+    if (is.character(call)) {
+      call <- as.call(str2lang(paste0(call, "()")))
+    } else {
+      call <- NULL
+    }
+    AMR_env$cli_abort(msg, call = call)
+  } else {
+    stop(paste(msg_call, msg), call. = FALSE)
+  }
 }

 stop_if <- function(expr, ..., call = TRUE) {
@@ -1021,10 +1031,10 @@ get_current_data <- function(arg_name, call) {
      fn <- as.character(sys.call(call + 1)[1])
      examples <- paste0(
        ", e.g.:\n",
-        "  your_data %>% select(", fn, "())\n",
-        "  your_data %>% select(column_a, column_b, ", fn, "())\n",
-        "  your_data[, ", fn, "()]\n",
-        '  your_data[, c("column_a", "column_b", ', fn, "())]"
+        " ", AMR_env$bullet_icon, " your_data %>% select(", fn, "())\n",
+        " ", AMR_env$bullet_icon, " your_data %>% select(column_a, column_b, ", fn, "())\n",
+        " ", AMR_env$bullet_icon, " your_data[, ", fn, "()]\n",
+        " ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", fn, "())]"
      )
    } else {
      examples <- ""
@@ -1340,23 +1350,24 @@ progress_ticker <- function(n = 1, n_min = 0, print = TRUE, clear = TRUE, title
    }
    set_clean_class(pb, new_class = "txtProgressBar")
  } else if (n >= n_min) {
-    # use `progress`, which also has a timer
+    title <- trimws2(title)
+    if (title != "") {
+      title <- paste0(title, " ")
+    }
    progress_bar <- import_fn("progress_bar", "progress", error_on_fail = FALSE)
    if (!is.null(progress_bar)) {
      # so we use progress::progress_bar
      # a close()-method was also added, see below for that
-      title <- trimws2(title)
-      if (title != "") {
-        title <- paste0(title, " ")
-      }
      pb <- progress_bar$new(
+        show_after = 0,
        format = paste0(title,
                        ifelse(only_bar_percent == TRUE, "[:bar] :percent", "[:bar] :percent (:current/:total,:eta)")),
        clear = clear,
        total = n
      )
    } else {
-      # use base R
+      # use base R's txtProgressBar
+      cat(title, "\n", sep = "")
      pb <- utils::txtProgressBar(max = n, style = 3)
      pb$tick <- function() {
        pb$up(pb$getVal() + 1)
@@ -1411,12 +1422,8 @@ as_original_data_class <- function(df, old_class = NULL, extra_class = NULL) {
  if ("tbl_df" %in% old_class && pkg_is_available("tibble")) {
    # this will then also remove groups
    fn <- import_fn("as_tibble", "tibble")
-  } else if ("tbl_ts" %in% old_class && pkg_is_available("tsibble")) {
-    fn <- import_fn("as_tsibble", "tsibble")
  } else if ("data.table" %in% old_class && pkg_is_available("data.table")) {
    fn <- import_fn("as.data.table", "data.table")
-  } else if ("tabyl" %in% old_class && pkg_is_available("janitor")) {
-    fn <- import_fn("as_tabyl", "janitor")
  } else {
    fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
  }
--- a/R/aa_helper_pm_functions.R
+++ b/R/aa_helper_pm_functions.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/aa_options.R
+++ b/R/aa_options.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ab.R
+++ b/R/ab.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ab_from_text.R
+++ b/R/ab_from_text.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ab_property.R
+++ b/R/ab_property.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ab_selectors.R
+++ b/R/ab_selectors.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -63,7 +63,7 @@
 #' # dplyr -------------------------------------------------------------------
 #' 
 #' if (require("dplyr")) {
-#'.  example_isolates %>% select(carbapenems())
+#'   example_isolates %>% select(carbapenems())
 #' }
 #'
 #' if (require("dplyr")) {
@@ -73,7 +73,7 @@
 #'
 #' if (require("dplyr")) {
 #'   # select only antibiotic columns with DDDs for oral treatment
-#'.  example_isolates %>% select(administrable_per_os())
+#'   example_isolates %>% select(administrable_per_os())
 #' }
 #'
 #' if (require("dplyr")) {
--- a/R/age.R
+++ b/R/age.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/antibiogram.R
+++ b/R/antibiogram.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -392,14 +392,17 @@ antibiogram <- function(x,
  } else {
    out$numerator <- out$S
  }
-  if (any(out$total < minimum, na.rm = TRUE)) {
+  if (all(out$total < minimum, na.rm = TRUE)) {
+    warning_("All combinations had less than `minimum = ", minimum, "` results, returning an empty antibiogram")
+    return(as_original_data_class(data.frame(), class(out), extra_class = "antibiogram"))
+  } else if (any(out$total < minimum, na.rm = TRUE)) {
    if (isTRUE(info)) {
      message_("NOTE: ", sum(out$total < minimum, na.rm = TRUE), " combinations had less than `minimum = ", minimum, "` results and were ignored", add_fn = font_red)
    }
    out <- out %pm>%
      subset(total >= minimum)
  }
-
+  
  # regroup for summarising
  if (isTRUE(has_syndromic_group)) {
    colnames(out)[1] <- "syndromic_group"
@@ -409,9 +412,10 @@ antibiogram <- function(x,
    out <- out %pm>%
      pm_group_by(mo, ab)
  }
+  
  out <- out %pm>%
    pm_summarise(SI = numerator / total)
-
+  
  # transform names of antibiotics
  ab_naming_function <- function(x, t, l, s) {
    x <- strsplit(x, s, fixed = TRUE)
@@ -515,6 +519,29 @@ antibiogram <- function(x,
  )
 }

+# will be exported in R/zzz.R
+tbl_sum.antibiogram <- function(x, ...) {
+  if (isTRUE(base::l10n_info()$`UTF-8`)) {
+    cross <- "\u00d7"
+  } else {
+    cross <- "x"
+  }
+  dims <- paste(format(NROW(x), big.mark = ","), cross, format(NCOL(x), big.mark = ","))
+  names(dims) <- "An Antibiogram"
+  dims
+}
+
+# will be exported in R/zzz.R
+tbl_format_footer.antibiogram <- function(x, ...) {
+  footer <- NextMethod()
+  if (NROW(x) == 0) {
+    return(footer)
+  }
+  c(footer, font_subtle(paste0("# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
+                               "# or use it directly in R Markdown or ",
+                               font_url("https://quarto.org", "Quarto"), ", see ", word_wrap("?antibiogram"))))
+}
+
 #' @export
 #' @rdname antibiogram
 plot.antibiogram <- function(x, ...) {
--- a/R/atc_online.R
+++ b/R/atc_online.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/av.R
+++ b/R/av.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/av_from_text.R
+++ b/R/av_from_text.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/av_property.R
+++ b/R/av_property.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/availability.R
+++ b/R/availability.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/bug_drug_combinations.R
+++ b/R/bug_drug_combinations.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/count.R
+++ b/R/count.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/custom_antimicrobials.R
+++ b/R/custom_antimicrobials.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/custom_eucast_rules.R
+++ b/R/custom_eucast_rules.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/custom_microorganisms.R
+++ b/R/custom_microorganisms.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/data.R
+++ b/R/data.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -32,9 +32,9 @@
 #' Two data sets containing all antibiotics/antimycotics and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antibiotics] data set. Three identifiers are included in this data set: an antibiotic ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
 #' @format
 #' ### For the [antibiotics] data set: a [tibble][tibble::tibble] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
-#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. *This is a unique identifier.*
-#' - `cid`\cr Compound ID as found in PubChem. *This is a unique identifier.*
-#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. *This is a unique identifier.*
+#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.***
+#' - `cid`\cr Compound ID as found in PubChem. ***This is a unique identifier.***
+#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. ***This is a unique identifier.***
 #' - `group`\cr A short and concise group name, based on WHONET and WHOCC definitions
 #' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, like `J01CR02`
 #' - `atc_group1`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like `"Macrolides, lincosamides and streptogramins"`
@@ -48,10 +48,10 @@
 #' - `loinc`\cr All codes associated with the name of the antimicrobial drug from `r TAXONOMY_VERSION$LOINC$citation` Use [ab_loinc()] to retrieve them quickly, see [ab_property()].
 #'
 #' ### For the [antivirals] data set: a [tibble][tibble::tibble] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
-#' - `av`\cr Antiviral ID as used in this package (such as `ACI`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. *This is a unique identifier.* Combinations are codes that contain a `+` to indicate this, such as `ATA+COBI` for atazanavir/cobicistat.
-#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. *This is a unique identifier.*
+#' - `av`\cr Antiviral ID as used in this package (such as `ACI`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.*** Combinations are codes that contain a `+` to indicate this, such as `ATA+COBI` for atazanavir/cobicistat.
+#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. ***This is a unique identifier.***
 #' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC
-#' - `cid`\cr Compound ID as found in PubChem. *This is a unique identifier.*
+#' - `cid`\cr Compound ID as found in PubChem. ***This is a unique identifier.***
 #' - `atc_group`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
 #' - `synonyms`\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
 #' - `oral_ddd`\cr Defined Daily Dose (DDD), oral treatment
@@ -82,35 +82,41 @@
 #' @rdname antibiotics
 "antivirals"

-#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Microorganisms
+#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Taxonomic Records of Microorganisms
 #'
-#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()].
+#' @description
+#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date, TAXONOMY_VERSION$MycoBank$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms. This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()] and microorganism properties can be looked up using any of the [`mo_*`][mo_property()] functions.
+#' 
+#' This data set is carefully crafted, yet made 100% reproducible from public and authoritative taxonomic sources (using [this script](https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R)), namely: *`r TAXONOMY_VERSION$LPSN$name`* for bacteria, *`r TAXONOMY_VERSION$MycoBank$name`* for fungi, and *`r TAXONOMY_VERSION$GBIF$name`* for all others taxons.
 #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables:
-#' - `mo`\cr ID of microorganism as used by this package. *This is a unique identifier.*
-#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. *This is a unique identifier.*
+#' - `mo`\cr ID of microorganism as used by this package. ***This is a unique identifier.***
+#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon. ***This is a unique identifier.***
 #' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status)`
-#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism
+#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is equal to their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy.
 #' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"`
 #' - `ref`\cr Author(s) and year of related scientific publication. This contains only the *first surname* and year of the *latest* authors, e.g. "Wallis *et al.* 2006 *emend.* Smith and Jones 2018" becomes "Smith *et al.*, 2018". This field is directly retrieved from the source specified in the column `source`. Moreover, accents were removed to comply with CRAN that only allows ASCII characters.
-#' - `lpsn`\cr Identifier ('Record number') of the List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set.
 #' - `oxygen_tolerance` \cr Oxygen tolerance, either `r vector_or(microorganisms$oxygen_tolerance)`. These data were retrieved from BacDive (see *Source*). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently `r round(length(microorganisms$oxygen_tolerance[which(!is.na(microorganisms$oxygen_tolerance))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance.
+#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
+#' - `lpsn`\cr Identifier ('Record number') of `r TAXONOMY_VERSION$LPSN$name`. This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$lpsn)))` records.
 #' - `lpsn_parent`\cr LPSN identifier of the parent taxon
 #' - `lpsn_renamed_to`\cr LPSN identifier of the currently valid taxon
-#' - `gbif`\cr Identifier ('taxonID') of the Global Biodiversity Information Facility (GBIF)
+#' - `mycobank`\cr Identifier ('MycoBank #') of `r TAXONOMY_VERSION$MycoBank$name`. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$mycobank)))` records.
+#' - `mycobank_parent`\cr MycoBank identifier of the parent taxon
+#' - `mycobank_renamed_to`\cr MycoBank identifier of the currently valid taxon
+#' - `gbif`\cr Identifier ('taxonID') of `r TAXONOMY_VERSION$GBIF$name`. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$gbif)))` records.
 #' - `gbif_parent`\cr GBIF identifier of the parent taxon
 #' - `gbif_renamed_to`\cr GBIF identifier of the currently valid taxon
-#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
 #' - `prevalence`\cr Prevalence of the microorganism based on Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}), see [mo_matching_score()] for the full explanation
 #' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
 #' @details
-#' Please note that entries are only based on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
+#' Please note that entries are only based on LPSN, MycoBank, and GBIF (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
 #'
-#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not before 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
+#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not until 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
 #'
 #' @section Included Taxa:
-#' Included taxonomic data are:
+#' Included taxonomic data from [LPSN](`r TAXONOMY_VERSION$LPSN$url`), [MycoBank](`r TAXONOMY_VERSION$MycoBank$url`), and [GBIF](`r TAXONOMY_VERSION$GBIF$url`) are:
 #' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria")), , drop = FALSE])` (sub)species from the kingdoms of Archaea and Bacteria
-#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` (sub)species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
+#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
 #' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Protozoa"), , drop = FALSE])` (sub)species from the kingdom of Protozoa
 #' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
 #' - All `r format_included_data_number(microorganisms[which(microorganisms$status != "accepted"), , drop = FALSE])` previously accepted names of all included (sub)species (these were taxonomically renamed)
@@ -127,22 +133,28 @@
 #' - 1 entry of *Moraxella* (*M. catarrhalis*), which was formally named *Branhamella catarrhalis* (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
 #' - 8 other 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, and unknown anaerobic Gram-pos/Gram-neg bacteria)
 #'
-#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R>.
+#' The syntax used to transform the original data to a cleansed \R format, can be [found here](https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R).
 #'
 #' ### Direct download
 #' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
-#' @section About the Records from LPSN (see *Source*):
-#' LPSN is the main source for bacteriological taxonomy of this `AMR` package.
-#'
-#' The List of Prokaryotic names with Standing in Nomenclature (LPSN) provides comprehensive information on the nomenclature of prokaryotes. LPSN is a free to use service founded by Jean P. Euzeby in 1997 and later on maintained by Aidan C. Parte.
 #' @source
-#' * `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
-#'
-#' * `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)`.
+#' Taxonomic entries were imported in this order of importance:
+#' 1. `r TAXONOMY_VERSION$LPSN$name`:\cr\cr
+#'    `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
 #' 
-#' * `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
+#' 2. `r TAXONOMY_VERSION$MycoBank$name`:\cr\cr
+#'    `r TAXONOMY_VERSION$MycoBank$citation` Accessed from <`r TAXONOMY_VERSION$MycoBank$url`> on `r documentation_date(TAXONOMY_VERSION$MycoBank$accessed_date)`.
+#'
+#' 3. `r TAXONOMY_VERSION$GBIF$name`:\cr\cr
+#'    `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
+#'
+#' Furthermore, these sources were used for additional details:
+#' 
+#' * `r TAXONOMY_VERSION$BacDive$name`:\cr\cr
+#'   `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
+#' 
+#' * `r TAXONOMY_VERSION$SNOMED$name`:\cr\cr
+#'   `r TAXONOMY_VERSION$SNOMED$citation` Accessed from <`r TAXONOMY_VERSION$SNOMED$url`> on `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`.
 #'
 #' * Grimont *et al.* (2007). Antigenic Formulae of the Salmonella Serovars, 9th Edition. WHO Collaborating Centre for Reference and Research on *Salmonella* (WHOCC-SALM).
 #'
@@ -156,7 +168,7 @@
 #'
 #' A data set containing commonly used codes for microorganisms, from laboratory systems and [WHONET](https://whonet.org). Define your own with [set_mo_source()]. They will all be searched when using [as.mo()] and consequently all the [`mo_*`][mo_property()] functions.
 #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.codes), big.mark = " ")` observations and `r ncol(microorganisms.codes)` variables:
-#' - `code`\cr Commonly used code of a microorganism. *This is a unique identifier.*
+#' - `code`\cr Commonly used code of a microorganism. ***This is a unique identifier.***
 #' - `mo`\cr ID of the microorganism in the [microorganisms] data set
 #' @details
 #' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
@@ -283,6 +295,7 @@
 #' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
 #' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
 #' - `uti`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
+#' - `is_SDD`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the intermediate range between "S" and "R" should be interpreted as "SDD", instead of "I". This currently applies to `r sum(clinical_breakpoints$is_SDD)` breakpoints.
 #' @details
 #' ### Different types of breakpoints
 #' Supported types of breakpoints are `r vector_and(clinical_breakpoints$type, quote = FALSE)`. ECOFF (Epidemiological cut-off) values are used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi.
@@ -290,10 +303,10 @@
 #' The default is `"human"`, which can also be set with the [package option][AMR-options] [`AMR_breakpoint_type`][AMR-options]. Use [`as.sir(..., breakpoint_type = ...)`][as.sir()] to interpret raw data using a specific breakpoint type, e.g. `as.sir(..., breakpoint_type = "ECOFF")` to use ECOFFs.
 #' 
 #' ### Imported from WHONET
-#' Clinical breakpoints in this package were validated through and imported from [WHONET](https://whonet.org), a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on [their website](https://whonet.org). The developers of WHONET and this `AMR` package have been in contact about sharing their work. We highly appreciate their development on the WHONET software.
+#' Clinical breakpoints in this package were validated through and imported from [WHONET](https://whonet.org), a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on [their website](https://whonet.org). The developers of WHONET and this `AMR` package have been in contact about sharing their work. We highly appreciate their great development on the WHONET software.
 #' 
 #' ### Response from CLSI and EUCAST
-#' The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this `AMR` package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, the WHO, and developers of WHONET and the `AMR` package.
+#' The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this `AMR` package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, WHO, developers of WHONET software, and developers of this `AMR` package.
 #' 
 #' ### Download
 #' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw). They allow for machine reading EUCAST and CLSI guidelines, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI, though initiatives have started to overcome these burdens.
--- a/R/disk.R
+++ b/R/disk.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/eucast_rules.R
+++ b/R/eucast_rules.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/export_biosample.R
+++ b/R/export_biosample.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/first_isolate.R
+++ b/R/first_isolate.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/g.test.R
+++ b/R/g.test.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/get_episode.R
+++ b/R/get_episode.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ggplot_pca.R
+++ b/R/ggplot_pca.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/ggplot_sir.R
+++ b/R/ggplot_sir.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/guess_ab_col.R
+++ b/R/guess_ab_col.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/italicise_taxonomy.R
+++ b/R/italicise_taxonomy.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/join_microorganisms.R
+++ b/R/join_microorganisms.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/key_antimicrobials.R
+++ b/R/key_antimicrobials.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/kurtosis.R
+++ b/R/kurtosis.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/like.R
+++ b/R/like.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/mdro.R
+++ b/R/mdro.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/mean_amr_distance.R
+++ b/R/mean_amr_distance.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/mic.R
+++ b/R/mic.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -119,7 +119,7 @@ COMMON_MIC_VALUES <- c(0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064,
 #' quantile(mic_data)
 #' all(mic_data < 512)
 #' 
-#' # limit MICs using rescale_mic()
+#' # rescale MICs using rescale_mic()
 #' rescale_mic(mic_data, mic_range = c(4, 16))
 #'
 #' # interpret MIC values
--- a/R/mo.R
+++ b/R/mo.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -99,10 +99,11 @@
 #' 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$BacDive$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}
+#' 8. `r TAXONOMY_VERSION$MycoBank$citation` Accessed from <`r TAXONOMY_VERSION$MycoBank$url`> on `r documentation_date(TAXONOMY_VERSION$MycoBank$accessed_date)`.
+#' 9. `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
+#' 10. `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`.
+#' 11. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
+#' 12. 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.
@@ -418,20 +419,10 @@ as.mo <- function(x,
  } # end of loop over all yet unknowns
  
  # Keep or replace synonyms ----
-  lpsn_matches <- AMR_env$MO_lookup$lpsn_renamed_to[match(out, AMR_env$MO_lookup$mo)]
-  lpsn_matches[!lpsn_matches %in% AMR_env$MO_lookup$lpsn] <- NA
-  # GBIF only for non-bacteria, since we use LPSN as primary source for bacteria
-  # (an example is Strep anginosus, renamed according to GBIF, not according to LPSN)
-  gbif_matches <- AMR_env$MO_lookup$gbif_renamed_to[AMR_env$MO_lookup$kingdom != "Bacteria"][match(out, AMR_env$MO_lookup$mo[AMR_env$MO_lookup$kingdom != "Bacteria"])]
-  gbif_matches[!gbif_matches %in% AMR_env$MO_lookup$gbif] <- NA
-  AMR_env$mo_renamed <- list(
-    old = out[!is.na(gbif_matches) | !is.na(lpsn_matches)],
-    gbif_matches = gbif_matches[!is.na(gbif_matches) | !is.na(lpsn_matches)],
-    lpsn_matches = lpsn_matches[!is.na(gbif_matches) | !is.na(lpsn_matches)]
-  )
+  out_current <- synonym_mo_to_accepted_mo(out, fill_in_accepted = FALSE)
+  AMR_env$mo_renamed <- list(old = out[!is.na(out_current)])
  if (isFALSE(keep_synonyms)) {
-    out[which(!is.na(gbif_matches))] <- AMR_env$MO_lookup$mo[match(gbif_matches[which(!is.na(gbif_matches))], AMR_env$MO_lookup$gbif)]
-    out[which(!is.na(lpsn_matches))] <- AMR_env$MO_lookup$mo[match(lpsn_matches[which(!is.na(lpsn_matches))], AMR_env$MO_lookup$lpsn)]
+    out[!is.na(out_current)] <- out_current[!is.na(out_current)]
    if (isTRUE(info) && length(AMR_env$mo_renamed$old) > 0) {
      print(mo_renamed(), extra_txt = " (use `keep_synonyms = TRUE` to leave uncorrected)")
    }
@@ -1040,13 +1031,14 @@ convert_colloquial_input <- function(x) {
  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"
+  out[is.na(out) & x %like_case% "anaerob[a-z]+ bacter"] <- "B_ANAER"
  
  # coryneform bacteria
  out[x %like_case% "^coryneform"] <- "B_CORYNF"
  
  # yeasts and fungi
-  out[x %like_case% "^yeast?"] <- "F_YEAST"
-  out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS"
+  out[x %like_case% "(^| )yeast?"] <- "F_YEAST"
+  out[x %like_case% "(^| )fung(us|i)"] <- "F_FUNGUS"
  
  # trivial names known to the field
  out[x %like_case% "meningo[ck]o[ck]"] <- "B_NESSR_MNNG"
@@ -1251,19 +1243,39 @@ load_mo_uncertainties <- function(metadata) {
  AMR_env$mo_uncertainties <- metadata$uncertainties
 }

-synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE) {
-  x_gbif <- AMR_env$MO_lookup$gbif_renamed_to[match(x, AMR_env$MO_lookup$mo)]
-  x_lpsn <- AMR_env$MO_lookup$lpsn_renamed_to[match(x, AMR_env$MO_lookup$mo)]
-  x_gbif[!x_gbif %in% AMR_env$MO_lookup$gbif] <- NA
-  x_lpsn[!x_lpsn %in% AMR_env$MO_lookup$lpsn] <- NA
-  
-  out <- ifelse(is.na(x_lpsn),
-                AMR_env$MO_lookup$mo[match(x_gbif, AMR_env$MO_lookup$gbif)],
-                AMR_env$MO_lookup$mo[match(x_lpsn, AMR_env$MO_lookup$lpsn)]
-  )
-  if (isTRUE(fill_in_accepted)) {
-    x_accepted <- which(AMR_env$MO_lookup$status[match(x, AMR_env$MO_lookup$mo)] == "accepted")
-    out[x_accepted] <- x[x_accepted]
+synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE, dataset = AMR_env$MO_lookup) {
+  # `dataset` is an argument so that it can be used in the regeneration of the microorganisms data set
+  if (identical(dataset, AMR_env$MO_lookup)) {
+    add_MO_lookup_to_AMR_env()
+    dataset <- AMR_env$MO_lookup
  }
+
+  out <- x
+  is_still_synonym <- dataset$status[match(out, dataset$mo)] == "synonym"
+  limit <- 0
+  while(any(is_still_synonym, na.rm = TRUE) && limit < 5) {
+    limit <- limit + 1
+    
+    # make sure to get the latest name, e.g. Fusarium pulicaris robiniae was first renamed to Fusarium roseum, then to Fusarium sambucinum
+    # we need the MO of Fusarium pulicaris robiniae to return the MO of Fusarium sambucinum
+    must_be_corrected <- !is.na(is_still_synonym) & is_still_synonym
+    x_gbif <- dataset$gbif_renamed_to[match(out, dataset$mo)]
+    x_mycobank <- dataset$mycobank_renamed_to[match(out, dataset$mo)]
+    x_lpsn <- dataset$lpsn_renamed_to[match(out, dataset$mo)]
+
+    out[must_be_corrected & !is.na(x_gbif)] <- dataset$mo[match(x_gbif[must_be_corrected & !is.na(x_gbif)], dataset$gbif)]
+    out[must_be_corrected & !is.na(x_mycobank)] <- dataset$mo[match(x_mycobank[must_be_corrected & !is.na(x_mycobank)], dataset$mycobank)]
+    out[must_be_corrected & !is.na(x_lpsn)] <- dataset$mo[match(x_lpsn[must_be_corrected & !is.na(x_lpsn)], dataset$lpsn)]
+
+    is_still_synonym <- dataset$status[match(out, dataset$mo)] == "synonym"
+  }
+  
+  x_no_synonym <- dataset$status[match(x, dataset$mo)] != "synonym"
+  out[x_no_synonym] <- NA_character_
+  if (isTRUE(fill_in_accepted)) {
+    out[!is.na(x_no_synonym) & x_no_synonym] <- x[!is.na(x_no_synonym) & x_no_synonym]
+  }
+  
+  out[is.na(match(x, dataset$mo))] <- NA_character_
  out
 }
--- a/R/mo_matching_score.R
+++ b/R/mo_matching_score.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/mo_property.R
+++ b/R/mo_property.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -39,8 +39,8 @@
 #' @details All functions will, at default, **not** keep old taxonomic properties, as synonyms are automatically replaced with the current taxonomy. Take for example *Enterobacter aerogenes*, which was initially named in 1960 but renamed to *Klebsiella aerogenes* in 2017:
 #' - `mo_genus("Enterobacter aerogenes")` will return `"Klebsiella"` (with a note about the renaming)
 #' - `mo_genus("Enterobacter aerogenes", keep_synonyms = TRUE)` will return `"Enterobacter"` (with a once-per-session warning that the name is outdated)
-#' - `mo_ref("Enterobacter aerogenes")` will return `"Tindall et al., 2017"` (with a note)
-#' - `mo_ref("Enterobacter aerogenes", keep_synonyms = TRUE)` will return `"Hormaeche et al., 1960"` (with a warning)
+#' - `mo_ref("Enterobacter aerogenes")` will return `"Tindall et al., 2017"` (with a note about the renaming)
+#' - `mo_ref("Enterobacter aerogenes", keep_synonyms = TRUE)` will return `"Hormaeche et al., 1960"` (with a once-per-session warning that the name is outdated)
 #'
 #' The short name ([mo_shortname()]) returns the first character of the genus and the full species, such as `"E. coli"`, for species and subspecies. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will be considered *Escherichia coli*. As a result, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
 #'
@@ -50,15 +50,15 @@
 #'
 #' Determination of the Gram stain ([mo_gramstain()]) will be based on the taxonomic kingdom and phylum. Originally, Cavalier-Smith defined the so-called subkingdoms Negibacteria and Posibacteria (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318/)), and only considered these phyla as Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes. These phyla were later renamed to Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota (2021, [PMID 34694987](https://pubmed.ncbi.nlm.nih.gov/34694987/)). Bacteria in these phyla are considered Gram-positive in this `AMR` package, except for members of the class Negativicutes (within phylum Bacillota) which are Gram-negative. All other bacteria are considered Gram-negative. Species outside the kingdom of Bacteria will return a value `NA`. Functions [mo_is_gram_negative()] and [mo_is_gram_positive()] always return `TRUE` or `FALSE` (or `NA` when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
 #'
-#' 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 yeasts ([mo_is_yeast()]) will be based on the taxonomic kingdom and class. *Budding yeasts* are yeasts that reproduce asexually through a process called budding, where a new cell develops from a small protrusion on the parent cell. Taxonomically, these are members of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) or Pichiomycetes. *True yeasts* quite specifically refers to yeasts in the underlying order Saccharomycetales (such as *Saccharomyces cerevisiae*). Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes or Pichiomycetes, 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.
+#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. [This MycoBank URL](`r TAXONOMY_VERSION$MycoBank$url`) will be used for fungi wherever available , [this LPSN URL](`r TAXONOMY_VERSION$MycoBank$url`) for bacteria wherever available, and [this GBIF link](`r TAXONOMY_VERSION$GBIF$url`) otherwise.
 #'
-#' SNOMED codes ([mo_snomed()]) are from the version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`. See *Source* and the [microorganisms] data set for more info.
+#' SNOMED codes ([mo_snomed()]) was last updated on `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`. See *Source* and the [microorganisms] data set for more info.
 #'
 #' Old taxonomic names (so-called 'synonyms') can be retrieved with [mo_synonyms()] (which will have the scientific reference as [name][base::names()]), the current taxonomic name can be retrieved with [mo_current()]. Both functions return full names.
 #'
@@ -71,8 +71,9 @@
 #' @return
 #' - An [integer] in case of [mo_year()]
 #' - An [ordered factor][factor] in case of [mo_pathogenicity()]
-#' - A [list] in case of [mo_taxonomy()], [mo_synonyms()], [mo_snomed()] and [mo_info()]
-#' - A named [character] in case of [mo_url()]
+#' - A [list] in case of [mo_taxonomy()], [mo_synonyms()], [mo_snomed()], and [mo_info()]
+#' - A [logical] in case of [mo_is_anaerobic()], [mo_is_gram_negative()], [mo_is_gram_positive()], [mo_is_intrinsic_resistant()], and [mo_is_yeast()]
+#' - A named [character] in case of [mo_synonyms()] and [mo_url()]
 #' - A [character] in all other cases
 #' @export
 #' @seealso Data set [microorganisms]
@@ -107,8 +108,8 @@
 #' mo_url("Klebsiella pneumoniae")
 #' mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
 #' 
-#' mo_group_members("Streptococcus group A")
-#' mo_group_members(c("Streptococcus group C",
+#' mo_group_members(c("Streptococcus group A",
+#'                    "Streptococcus group C",
 #'                    "Streptococcus group G",
 #'                    "Streptococcus group L"))
 #' 
@@ -118,10 +119,13 @@
 #' mo_ref("Klebsiella aerogenes")
 #' mo_authors("Klebsiella aerogenes")
 #' mo_year("Klebsiella aerogenes")
+#' mo_synonyms("Klebsiella aerogenes")
 #' mo_lpsn("Klebsiella aerogenes")
 #' mo_gbif("Klebsiella aerogenes")
-#' mo_synonyms("Klebsiella aerogenes")
-#'
+#' mo_mycobank("Candida albicans")
+#' mo_mycobank("Candida krusei")
+#' mo_mycobank("Candida krusei", keep_synonyms = TRUE)
+#' 
 #'
 #' # abbreviations known in the field -----------------------------------------
 #'
@@ -214,7 +218,13 @@ mo_name <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A

 #' @rdname mo_property
 #' @export
-mo_fullname <- mo_name
+mo_fullname <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an 'mo' column
+    x <- find_mo_col(fn = "mo_fullname")
+  }
+  mo_name(x = x, language = language, keep_synonyms = keep_synonyms, ...)
+}

 #' @rdname mo_property
 #' @export
@@ -543,8 +553,7 @@ mo_is_yeast <- function(x, language = get_AMR_locale(), keep_synonyms = getOptio

  load_mo_uncertainties(metadata)

-  out <- rep(FALSE, length(x))
-  out[x.kingdom == "Fungi" & x.class == "Saccharomycetes"] <- TRUE
+  out <- x.mo == "F_YEAST" | (x.kingdom == "Fungi" & x.class %in% c("Saccharomycetes", "Pichiomycetes"))
  out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
  out
 }
@@ -697,6 +706,21 @@ mo_lpsn <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
  mo_validate(x = x, property = "lpsn", language = language, keep_synonyms = keep_synonyms, ...)
 }

+#' @rdname mo_property
+#' @export
+mo_mycobank <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an 'mo' column
+    x <- find_mo_col(fn = "mo_mycobank")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  language <- validate_language(language)
+  meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
+  
+  mo_validate(x = x, property = "mycobank", language = language, keep_synonyms = keep_synonyms, ...)
+}
+
+
 #' @rdname mo_property
 #' @export
 mo_gbif <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("AMR_keep_synonyms", FALSE), ...) {
@@ -796,7 +820,7 @@ mo_synonyms <- function(x, language = get_AMR_locale(), keep_synonyms = getOptio
 mo_current <- function(x, language = get_AMR_locale(), ...) {
  meet_criteria(x, allow_NA = TRUE)
  language <- validate_language(language)
-  x.mo <- suppressWarnings(as.mo(x, keep_synonyms = TRUE, ...))
+  x.mo <- suppressWarnings(as.mo(x, keep_synonyms = TRUE, info = FALSE, ...))
  out <- synonym_mo_to_accepted_mo(x.mo, fill_in_accepted = TRUE)
  mo_name(out, language = language)
 }
@@ -894,19 +918,23 @@ mo_url <- function(x, open = FALSE, language = get_AMR_locale(), keep_synonyms =

  x.rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
  x.name <- AMR_env$MO_lookup$fullname[match(x.mo, AMR_env$MO_lookup$mo)]
+  
  x.lpsn <- AMR_env$MO_lookup$lpsn[match(x.mo, AMR_env$MO_lookup$mo)]
+  x.mycobank <- AMR_env$MO_lookup$mycobank[match(x.mo, AMR_env$MO_lookup$mo)]
  x.gbif <- AMR_env$MO_lookup$gbif[match(x.mo, AMR_env$MO_lookup$mo)]

  u <- character(length(x))
  u[!is.na(x.gbif)] <- paste0(TAXONOMY_VERSION$GBIF$url, "/species/", x.gbif[!is.na(x.gbif)])
  # overwrite with LPSN:
  u[!is.na(x.lpsn)] <- paste0(TAXONOMY_VERSION$LPSN$url, "/", x.rank[!is.na(x.lpsn)], "/", gsub(" ", "-", tolower(x.name[!is.na(x.lpsn)]), fixed = TRUE))
+  # overwrite with MycoBank (bacteria from LPSN will not be overwritten since MycoBank has no bacteria)
+  u[!is.na(x.mycobank)] <- paste0(TAXONOMY_VERSION$MycoBank$url, "/mb/", gsub(" ", "%20", tolower(x.mycobank[!is.na(x.mycobank)]), fixed = TRUE))

  names(u) <- x.name

  if (isTRUE(open)) {
    if (length(u) > 1) {
-      warning_("in `mo_url()`: only the first URL will be opened, as `browseURL()` only suports one string.")
+      warning_("in `mo_url()`: only the first URL will be opened, as R's built-in function `browseURL()` only suports one string.")
    }
    utils::browseURL(u[1L])
  }
--- a/R/mo_source.R
+++ b/R/mo_source.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/pca.R
+++ b/R/pca.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/plot.R
+++ b/R/plot.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/proportion.R
+++ b/R/proportion.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/random.R
+++ b/R/random.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/resistance_predict.R
+++ b/R/resistance_predict.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/sir.R
+++ b/R/sir.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -50,6 +50,7 @@
 #' @param include_PKPD a [logical] to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`. Can also be set with the [package option][AMR-options] [`AMR_include_PKPD`][AMR-options].
 #' @param breakpoint_type the type of breakpoints to use, either `r vector_or(clinical_breakpoints$type)`. ECOFF stands for Epidemiological Cut-Off values. The default is `"human"`, which can also be set with the [package option][AMR-options] [`AMR_breakpoint_type`][AMR-options]. If `host` is set to values of veterinary species, this will automatically be set to `"animal"`.
 #' @param host a vector (or column name) with [character]s to indicate the host. Only useful for veterinary breakpoints, as it requires `breakpoint_type = "animal"`. The values can be any text resembling the animal species, even in any of the `r length(LANGUAGES_SUPPORTED)` supported languages of this package. For foreign languages, be sure to set the language with [set_AMR_locale()] (though it will be automatically guessed based on the system language).
+#' @param verbose a [logical] to indicate that all notes should be printed during interpretation of MIC values or disk diffusion values.
 #' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
 #' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*
 #' @param ... for using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
@@ -151,39 +152,93 @@
 #' - **CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type != "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type != "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m100/>.
 #' - **CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/veterinary-medicine/documents/vet01//>.
 #' - **EUCAST Breakpoint tables for interpretation of MICs and zone diameters**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`, *European Committee on Antimicrobial Susceptibility Testing* (EUCAST). <https://www.eucast.org/clinical_breakpoints>.
+#' - **WHONET** as a source for machine-reading the clinical breakpoints ([read more here](https://msberends.github.io/AMR/reference/clinical_breakpoints.html#imported-from-whonet)), 1989-`r max(as.integer(gsub("[^0-9]", "", AMR::clinical_breakpoints$guideline)))`, *WHO Collaborating Centre for Surveillance of Antimicrobial Resistance*. <https://whonet.org/>.
+#' 
 #' @inheritSection AMR Reference Data Publicly Available
 #' @examples
 #' example_isolates
 #' summary(example_isolates) # see all SIR results at a glance
 #'
 #' # For INTERPRETING disk diffusion and MIC values -----------------------
+#' 
+#' # example data sets, with combined MIC values and disk zones
+#' df_wide <- data.frame(
+#'   microorganism = "Escherichia coli",
+#'   amoxicillin = as.mic(8),
+#'   cipro = as.mic(0.256),
+#'   tobra = as.disk(16),
+#'   genta = as.disk(18),
+#'   ERY = "R"
+#' )
+#' df_long <- data.frame(
+#'   bacteria = rep("Escherichia coli", 4),
+#'   antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),
+#'   mics = as.mic(c(0.01, 1, 4, 8)),
+#'   disks = as.disk(c(6, 10, 14, 18))
+#' )
 #'
 #' \donttest{
 #' ## Using dplyr -------------------------------------------------
 #' if (require("dplyr")) {
 #'   # approaches that all work without additional arguments:
-#'   df %>% mutate_if(is.mic, as.sir)
-#'   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
-#'   df %>% mutate(across(where(is.mic), as.sir))
-#'   df %>% mutate_at(vars(AMP:TOB), as.sir)
-#'   df %>% mutate(across(AMP:TOB, as.sir))
+#'   df_wide %>% mutate_if(is.mic, as.sir)
+#'   df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
+#'   df_wide %>% mutate(across(where(is.mic), as.sir))
+#'   df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
+#'   df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
 #'   
 #'   # approaches that all work with additional arguments:
-#'   df %>% mutate_if(is.mic, as.sir, mo = "column1", guideline = "CLSI")
-#'   df %>% mutate(across(where(is.mic),
-#'                        function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
-#'   df %>% mutate_at(vars(AMP:TOB), as.sir, mo = "column1", guideline = "CLSI")
-#'   df %>% mutate(across(AMP:TOB,
-#'                        function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
+#'   df_long %>%
+#'     # given a certain data type, e.g. MIC values
+#'     mutate_if(is.mic, as.sir,
+#'               mo = "bacteria",
+#'               ab = "antibiotic",
+#'               guideline = "CLSI")
+#'   df_long %>%
+#'     mutate(across(where(is.mic),
+#'                   function(x) as.sir(x,
+#'                                      mo = "bacteria",
+#'                                      ab = "antibiotic",
+#'                                      guideline = "CLSI")))
+#'   df_wide %>%
+#'     # given certain columns, e.g. from 'cipro' to 'genta'
+#'     mutate_at(vars(cipro:genta), as.sir,
+#'               mo = "bacteria",
+#'               guideline = "CLSI")
+#'   df_wide %>%
+#'     mutate(across(cipro:genta,
+#'                        function(x) as.sir(x,
+#'                                           mo = "bacteria",
+#'                                           guideline = "CLSI")))
 #'                        
 #'   # for veterinary breakpoints, add 'host':
-#'   df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "species_column")
-#'   df %>% mutate_if(is.mic, as.sir, guideline = "CLSI", host = "horse")
-#'   df %>% mutate(across(where(is.mic),
-#'                        function(x) as.sir(x, guideline = "CLSI", host = "species_column")))
-#'   df %>% mutate_at(vars(AMP:TOB), as.sir, guideline = "CLSI", host = "species_column")
-#'   df %>% mutate(across(AMP:TOB,
-#'                        function(x) as.sir(x, mo = "column1", guideline = "CLSI")))
+#'   df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
+#'   df_long %>%
+#'     # given a certain data type, e.g. MIC values
+#'     mutate_if(is.mic, as.sir,
+#'               mo = "bacteria",
+#'               ab = "antibiotic",
+#'               host = "animal_species",
+#'               guideline = "CLSI")
+#'   df_long %>%
+#'     mutate(across(where(is.mic),
+#'                   function(x) as.sir(x,
+#'                                      mo = "bacteria",
+#'                                      ab = "antibiotic",
+#'                                      host = "animal_species",
+#'                                      guideline = "CLSI")))
+#'   df_wide %>%
+#'     mutate_at(vars(cipro:genta), as.sir,
+#'               mo = "bacteria",
+#'               ab = "antibiotic",
+#'               host = "animal_species",
+#'               guideline = "CLSI")
+#'   df_wide %>%
+#'     mutate(across(cipro:genta,
+#'                        function(x) as.sir(x,
+#'                                           mo = "bacteria",
+#'                                           host = "animal_species",
+#'                                           guideline = "CLSI")))
 #'   
 #'   # to include information about urinary tract infections (UTI)
 #'   data.frame(mo = "E. coli",
@@ -196,23 +251,14 @@
 #'              specimen = c("urine", "blood")) %>%
 #'     as.sir() # automatically determines urine isolates
 #'
-#'   df %>%
-#'     mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
+#'   df_wide %>%
+#'     mutate_at(vars(cipro:genta), as.sir, mo = "E. coli", uti = TRUE)
 #' }
 #'
 #'
 #' ## Using base R ------------------------------------------------
 #'
-#' # a whole data set, even with combined MIC values and disk zones
-#' df <- data.frame(
-#'   microorganism = "Escherichia coli",
-#'   AMP = as.mic(8),
-#'   CIP = as.mic(0.256),
-#'   GEN = as.disk(18),
-#'   TOB = as.disk(16),
-#'   ERY = "R"
-#' )
-#' as.sir(df)
+#' as.sir(df_wide)
 #'
 #' # return a 'logbook' about the results:
 #' sir_interpretation_history()
@@ -473,6 +519,7 @@ as.sir.mic <- function(x,
                       include_PKPD = getOption("AMR_include_PKPD", TRUE),
                       breakpoint_type = getOption("AMR_breakpoint_type", "human"),
                       host = NULL,
+                       verbose = FALSE,
                       ...) {
  as_sir_method(
    method_short = "mic",
@@ -489,6 +536,7 @@ as.sir.mic <- function(x,
    include_PKPD = include_PKPD,
    breakpoint_type = breakpoint_type,
    host = host,
+    verbose = verbose,
    ...
  )
 }
@@ -506,6 +554,7 @@ as.sir.disk <- function(x,
                        include_PKPD = getOption("AMR_include_PKPD", TRUE),
                        breakpoint_type = getOption("AMR_breakpoint_type", "human"),
                        host = NULL,
+                        verbose = FALSE,
                        ...) {
  as_sir_method(
    method_short = "disk",
@@ -522,6 +571,7 @@ as.sir.disk <- function(x,
    include_PKPD = include_PKPD,
    breakpoint_type = breakpoint_type,
    host = NULL,
+    verbose = verbose,
    ...
  )
 }
@@ -539,7 +589,8 @@ as.sir.data.frame <- function(x,
                              include_screening = getOption("AMR_include_screening", FALSE),
                              include_PKPD = getOption("AMR_include_PKPD", TRUE),
                              breakpoint_type = getOption("AMR_breakpoint_type", "human"),
-                              host = NULL) {
+                              host = NULL,
+                              verbose = FALSE) {
  meet_criteria(x, allow_class = "data.frame") # will also check for dimensions > 0
  meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), allow_NULL = TRUE)
  meet_criteria(guideline, allow_class = "character", has_length = 1)
@@ -551,6 +602,7 @@ as.sir.data.frame <- function(x,
  meet_criteria(include_PKPD, allow_class = "logical", has_length = 1)
  meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1)
  meet_criteria(host, allow_class = c("character", "factor"), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(verbose, allow_class = "logical", has_length = 1)
  x.bak <- x
  for (i in seq_len(ncol(x))) {
    # don't keep factors, overwriting them is hard
@@ -701,6 +753,7 @@ as.sir.data.frame <- function(x,
          include_PKPD = include_PKPD,
          breakpoint_type = breakpoint_type,
          host = host,
+          verbose = verbose,
          is_data.frame = TRUE
        )
    } else if (types[i] == "disk") {
@@ -720,6 +773,7 @@ as.sir.data.frame <- function(x,
          include_PKPD = include_PKPD,
          breakpoint_type = breakpoint_type,
          host = host,
+          verbose = verbose,
          is_data.frame = TRUE
        )
    } else if (types[i] == "sir") {
@@ -778,7 +832,7 @@ get_guideline <- function(guideline, reference_data) {
 }

 convert_host <- function(x, lang = get_AMR_locale()) {
-  x <- trimws2(tolower(as.character(x)))
+  x <- gsub("[^a-zA-Z ]", "", trimws2(tolower(as.character(x))), perl = TRUE)
  x_out <- rep(NA_character_, length(x))
  x_out[trimws2(tolower(x)) == "human"] <- "human"
  x_out[trimws2(tolower(x)) == "ecoff"] <- "ecoff"
@@ -790,6 +844,32 @@ convert_host <- function(x, lang = get_AMR_locale()) {
  x_out[is.na(x_out) & (x %like% "horse|equine" | x %like% translate_AMR("horse|horses|equine", lang))] <- "horse"
  x_out[is.na(x_out) & (x %like% "aqua|fish" | x %like% translate_AMR("aquatic|fish", lang))] <- "aquatic"
  x_out[is.na(x_out) & (x %like% "bird|chicken|poultry|avia" | x %like% translate_AMR("bird|birds|poultry", lang))] <- "poultry"
+  # additional animals, not necessarily currently in breakpoint guidelines:
+  x_out[is.na(x_out) & (x %like% "camel|camelid" | x %like% translate_AMR("camel|camels|camelid", lang))] <- "camels"
+  x_out[is.na(x_out) & (x %like% "deer|cervine" | x %like% translate_AMR("deer|deers|cervine", lang))] <- "deer"
+  x_out[is.na(x_out) & (x %like% "donkey|asinine" | x %like% translate_AMR("donkey|donkeys|asinine", lang))] <- "donkeys"
+  x_out[is.na(x_out) & (x %like% "ferret|musteline" | x %like% translate_AMR("ferret|ferrets|musteline", lang))] <- "ferrets"
+  x_out[is.na(x_out) & (x %like% "goat|caprine" | x %like% translate_AMR("goat|goats|caprine", lang))] <- "goats"
+  x_out[is.na(x_out) & (x %like% "guinea pig|caviine" | x %like% translate_AMR("guinea pig|guinea pigs|caviine", lang))] <- "guinea pigs"
+  x_out[is.na(x_out) & (x %like% "hamster|cricetine" | x %like% translate_AMR("hamster|hamsters|cricetine", lang))] <- "hamsters"
+  x_out[is.na(x_out) & (x %like% "monkey|simian" | x %like% translate_AMR("monkey|monkeys|simian", lang))] <- "monkeys"
+  x_out[is.na(x_out) & (x %like% "mouse|murine" | x %like% translate_AMR("mouse|mice|murine", lang))] <- "mice"
+  x_out[is.na(x_out) & (x %like% "pig|porcine" | x %like% translate_AMR("pig|pigs|porcine", lang))] <- "pigs"
+  x_out[is.na(x_out) & (x %like% "rabbit|leporine" | x %like% translate_AMR("rabbit|rabbits|leporine", lang))] <- "rabbits"
+  x_out[is.na(x_out) & (x %like% "rat|ratine" | x %like% translate_AMR("rat|rats|ratine", lang))] <- "rats"
+  x_out[is.na(x_out) & (x %like% "sheep|ovine" | x %like% translate_AMR("sheep|sheeps|ovine", lang))] <- "sheep"
+  x_out[is.na(x_out) & (x %like% "snake|serpentine" | x %like% translate_AMR("snake|snakes|serpentine", lang))] <- "snakes"
+  x_out[is.na(x_out) & (x %like% "turkey|meleagrine" | x %like% translate_AMR("turkey|turkeys|meleagrine", lang))] <- "turkey"
+  if (any(x_out %in% c(NA_character_, "animal"))) {
+    x_out[is.na(x_out) & x == "animal"] <- AMR_env$host_preferred_order[1]
+    if (message_not_thrown_before("as.sir", "convert_host_missing")) {
+      message_(ifelse(any(is.na(x_out) & !is.na(x), na.rm = TRUE),
+                      paste0("The following host(s) are invalid: ", vector_and(x[is.na(x_out) & !is.na(x)]), ". "),
+                      ""),
+               "For missing animal hosts, assuming \"", AMR_env$host_preferred_order[1], "\", since these have the highest breakpoint availability.")
+    x_out[is.na(x_out)] <- AMR_env$host_preferred_order[1]
+    }
+  }
  x_out[x_out == "ecoff"] <- "ECOFF"
  x_out
 }
@@ -808,6 +888,7 @@ as_sir_method <- function(method_short,
                          include_PKPD,
                          breakpoint_type,
                          host,
+                          verbose,
                          ...) {
  meet_criteria(x, allow_NA = TRUE, .call_depth = -2)
  meet_criteria(mo, allow_class = c("mo", "character"), has_length = c(1, length(x)), allow_NULL = TRUE, .call_depth = -2)
@@ -822,6 +903,7 @@ as_sir_method <- function(method_short,
  check_reference_data(reference_data, .call_depth = -2)
  meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1, .call_depth = -2)
  meet_criteria(host, allow_class = c("character", "factor"), allow_NULL = TRUE, allow_NA = TRUE, .call_depth = -2)
+  meet_criteria(verbose, allow_class = "logical", has_length = 1, .call_depth = -2)
  
  # backward compatibilty
  dots <- list(...)
@@ -832,6 +914,13 @@ as_sir_method <- function(method_short,
  
  guideline_coerced <- get_guideline(guideline, reference_data)
  
+  if (message_not_thrown_before("as.sir", "sir_interpretation_history")) {
+    message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all the details of the breakpoint interpretations.\n\n", add_fn = font_green)
+  }
+  
+  current_df <- tryCatch(get_current_data(NA, 0), error = function(e) NULL)
+  
+  # get host
  if (breakpoint_type == "animal") {
    if (is.null(host)) {
      host <- AMR_env$host_preferred_order[1]
@@ -849,17 +938,26 @@ as_sir_method <- function(method_short,
      host <- breakpoint_type
    }
  }
-  host <- convert_host(host)
-  
-  if (message_not_thrown_before("as.sir", "sir_interpretation_history")) {
-    message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all the details of the breakpoint interpretations. Note that some ", ifelse(breakpoint_type == "animal", "animal hosts and ", ""), "microorganisms might not have breakpoints for each antimicrobial drug in ", guideline_coerced, ".\n\n")
+  if (!is.null(host) && !all(toupper(as.character(host)) %in% c("HUMAN", "ECOFF"))) {
+    if (!is.null(current_df) && length(host) == 1 && host %in% colnames(current_df) && any(current_df[[host]] %like% "[A-Z]", na.rm = TRUE)) {
+      host <- current_df[[host]]
+    } else if (length(host) != length(x)) {
+      # for dplyr's across()
+      cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
+      if (!is.null(cur_column_dplyr) && is.data.frame(current_df)) {
+        # try to get current column, which will only be available when in across()
+        host <- tryCatch(cur_column_dplyr(),
+                         error = function(e) host
+        )
+      }
+    }
  }
+  host.bak <- host
+  host <- convert_host(host)
  if (breakpoint_type == "animal" && message_not_thrown_before("as.sir", "host_preferred_order")) {
    message_("Please note that in the absence of specific veterinary breakpoints for certain animal hosts, breakpoints for dogs, cattle, swine, cats, horse, aquatic, and poultry, in that order, are used as substitutes.\n\n")
  }
  
-  current_df <- tryCatch(get_current_data(NA, 0), error = function(e) NULL)
-
  # get ab
  if (!is.null(current_df) && length(ab) == 1 && ab %in% colnames(current_df) && any(current_df[[ab]] %like% "[A-Z]", na.rm = TRUE)) {
    ab <- current_df[[ab]]
@@ -942,13 +1040,14 @@ as_sir_method <- function(method_short,
    stop_("No unambiguous name was supplied about the antibiotic (argument `ab`). See ?as.sir.", call = FALSE)
  }

-  ab.bak <- ab
+  ab.bak <- trimws2(ab)
  ab <- suppressWarnings(as.ab(ab))
  if (!is.null(list(...)$mo.bak)) {
    mo.bak <- list(...)$mo.bak
  } else {
    mo.bak <- mo
  }
+  mo.bak <- trimws2(mo.bak)
  # be sure to take current taxonomy, as the 'clinical_breakpoints' data set only contains current taxonomy
  mo <- suppressWarnings(suppressMessages(as.mo(mo, keep_synonyms = FALSE, info = FALSE)))
  if (all(is.na(ab))) {
@@ -1001,24 +1100,13 @@ as_sir_method <- function(method_short,
                             paste0(", ", font_bold(guideline_coerced)),
                             ""),
                      "... ")
-  
-  msg_note <- function(messages) {
-    messages <- unique(messages)
-    for (i in seq_len(length(messages))) {
-      messages[i] <- word_wrap(extra_indent = 5, messages[i])
-    }
-    message(
-      font_yellow_bg(paste0(" NOTE", ifelse(length(messages) > 1, "S", ""), " \n")),
-      paste0("  ", font_black(AMR_env$bullet_icon), " ", font_black(messages, collapse = NULL), collapse = "\n")
-    )
-  }

  method <- method_short

  metadata_mo <- get_mo_uncertainties()

  rise_warning <- FALSE
-  rise_note <- FALSE
+  rise_notes <- FALSE
  method_coerced <- toupper(method)
  ab_coerced <- as.ab(ab)
  
@@ -1070,14 +1158,14 @@ as_sir_method <- function(method_short,
      subset(mo != "UNKNOWN" & ref_tbl %unlike% "PK.*PD")
  }

-  msgs <- character(0)
+  notes <- character(0)

  if (guideline_coerced %like% "EUCAST") {
    any_is_intrinsic_resistant <- FALSE
    add_intrinsic_resistance_to_AMR_env()
  }
  
-  if (nrow(df_unique) < 10) {
+  if (nrow(df_unique) < 10 || nrow(breakpoints) == 0) {
    # only print intro under 10 items, otherwise progressbar will print this and then it will be printed double
    message_(intro_txt, appendLF = FALSE, as_note = FALSE)
  }
@@ -1097,19 +1185,38 @@ as_sir_method <- function(method_short,
    return(rep(NA_sir_, nrow(df)))
  }
  
+  vectorise_log_entry <- function(x, len) {
+    if (length(x) == 1 && len > 1) {
+      rep(x, len)
+    } else {
+      x
+    }
+  }
+  
  # run the rules (df_unique is a row combination per mo/ab/uti/host)
  for (i in seq_len(nrow(df_unique))) {
    p$tick()
    mo_current <- df_unique[i, "mo", drop = TRUE]
    ab_current <- df_unique[i, "ab", drop = TRUE]
    host_current <- df_unique[i, "host", drop = TRUE]
+    if (is.na(host_current)) {
+      # fall back to human
+      host_current <- "human"
+    }
    uti_current <- df_unique[i, "uti", drop = TRUE]
+    notes_current <- character(0)
    if (isFALSE(uti_current)) {
      # no preference, so no filter on UTIs
      rows <- which(df$mo == mo_current & df$ab == ab_current & df$host == host_current)
    } else {
      rows <- which(df$mo == mo_current & df$ab == ab_current & df$host == host_current & df$uti == uti_current)
    }
+    if (length(rows) == 0) {
+      notes_current <- c(notes_current, font_red("Returned an empty result, which is unexpected. Are all of `mo`, `ab`, and `host` set and available?"))
+      notes <- c(notes, notes_current)
+      rise_warning <- TRUE
+      next
+    }
    values <- df[rows, "values", drop = TRUE]
    new_sir <- rep(NA_sir_, length(rows))

@@ -1136,7 +1243,6 @@ as_sir_method <- function(method_short,
      suppressMessages(suppressWarnings(ab_name(ab_current, language = NULL, tolower = TRUE))),
      " (", ab_current, ")"
    )
-    notes <- character(0)
   
    # gather all available breakpoints for current MO
    breakpoints_current <- breakpoints %pm>%
@@ -1147,6 +1253,35 @@ as_sir_method <- function(method_short,
        mo_current_species_group,
        mo_current_other
      ))
+    
+    if (NROW(breakpoints_current) == 0) {
+      AMR_env$sir_interpretation_history <- rbind_AMR(
+        AMR_env$sir_interpretation_history,
+        # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
+        data.frame(
+          datetime = vectorise_log_entry(Sys.time(), length(rows)),
+          index = rows,
+          ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
+          mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
+          host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
+          ab = vectorise_log_entry(ab_current, length(rows)),
+          mo = vectorise_log_entry(mo_current, length(rows)),
+          host = vectorise_log_entry(host_current, length(rows)),
+          method = vectorise_log_entry(method_coerced, length(rows)),
+          input = vectorise_log_entry(as.double(values), length(rows)),
+          outcome = vectorise_log_entry(NA_sir_, length(rows)),
+          notes = vectorise_log_entry("NO BREAKPOINT AVAILABLE", length(rows)),
+          guideline = vectorise_log_entry(guideline_coerced, length(rows)),
+          ref_table = vectorise_log_entry(NA_character_, length(rows)),
+          uti = vectorise_log_entry(uti_current, length(rows)),
+          breakpoint_S_R = vectorise_log_entry(NA_character_, length(rows)),
+          stringsAsFactors = FALSE
+        )
+      )
+      notes <- c(notes, notes_current)
+      next
+    }
+    
    # set the host index according to most available breakpoints (see R/zzz.R where this is set in the pkg environment)
    breakpoints_current$host_index <- match(breakpoints_current$host, c("human", "ECOFF", AMR_env$host_preferred_order))
    
@@ -1154,6 +1289,7 @@ as_sir_method <- function(method_short,
    # (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
    if (all(uti_current == FALSE, na.rm = TRUE)) {
      breakpoints_current <- breakpoints_current %pm>%
+        #  `uti` is a column in the data set
        # this will put UTI = FALSE first, then UTI = NA, then UTI = TRUE
        pm_mutate(uti_index = ifelse(is.na(uti) & uti == FALSE, 1, 
                                     ifelse(is.na(uti), 2,
@@ -1167,11 +1303,6 @@ as_sir_method <- function(method_short,
        pm_arrange(host_index, rank_index)
    }
    
-    if (NROW(breakpoints_current) == 0) {
-      # no note about missing breakpoints - it's already in the header before the interpretation starts
-      next
-    }
-    
    # veterinary host check
    host_current <- unique(df_unique[i, "host", drop = TRUE])[1]
    breakpoints_current$host_match <- breakpoints_current$host == host_current
@@ -1181,12 +1312,11 @@ as_sir_method <- function(method_short,
          subset(host_match == TRUE)
      } else {
        # no breakpoint found for this host, so sort on mostly available guidelines
-        notes <- c(notes, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
-        # msgs <- c(msgs, paste0("No breakpoints available for ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " - using ", font_bold(breakpoints_current$host[1]), " instead."))
+        notes_current <- c(notes_current, paste0("Using ", font_bold(breakpoints_current$host[1]), " breakpoints since ", font_bold(host_current), " for ", ab_formatted, " in ", mo_formatted, " are not available."))
      }
    }
 
-    # throw notes for different body sites
+    # throw messages for different body sites
    site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
    if (is.na(site)) {
      site <- paste0("an unspecified body site")
@@ -1195,21 +1325,20 @@ as_sir_method <- function(method_short,
    }
    if (nrow(breakpoints_current) == 1 && all(breakpoints_current$uti == TRUE) && any(uti_current %in% c(FALSE, NA)) && message_not_thrown_before("as.sir", "uti", ab_current)) {
      # only UTI breakpoints available
-      warning_("in `as.sir()`: interpretation of ", font_bold(ab_formatted), " is only available for (uncomplicated) urinary tract infections (UTI) for some microorganisms, thus assuming `uti = TRUE`. See `?as.sir`.")
-      rise_warning <- TRUE
+      notes_current <- c(notes_current, paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI); assuming `uti = TRUE`."))
    } else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti_current)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_current, ab_current)) {
      # both UTI and Non-UTI breakpoints available
-      msgs <- c(msgs, paste0("Breakpoints for UTI ", font_bold("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, ". Use argument `uti` to set which isolates are from urine. See `?as.sir`."))
+      notes_current <- c(notes_current, paste0("Breakpoints for UTI ", font_bold("and"), " non-UTI available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, ". Use argument `uti` to set which isolates are from urine. See `?as.sir`."))
      breakpoints_current <- breakpoints_current %pm>%
        pm_filter(uti == FALSE)
    } else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && all(breakpoints_current$uti == FALSE, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteOther", mo_current, ab_current)) {
      # breakpoints for multiple body sites available
-      msgs <- c(msgs, paste0("Multiple breakpoints available for ", ab_formatted, " in ", mo_formatted, " - assuming ", site, "."))
+      notes_current <- c(notes_current, paste0("Multiple breakpoints available for ", font_bold(ab_formatted), " in ", mo_formatted, " - assuming ", site, "."))
    }
    
    # first check if mo is intrinsic resistant
    if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_current, ab_current) %in% AMR_env$intrinsic_resistant) {
-      msgs <- c(msgs, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
+      notes_current <- c(notes_current, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
      new_sir <- rep(as.sir("R"), length(rows))
    } else if (nrow(breakpoints_current) == 0) {
      # no rules available
@@ -1219,10 +1348,10 @@ as_sir_method <- function(method_short,
      breakpoints_current <- breakpoints_current[1L, , drop = FALSE]

      if (any(breakpoints_current$mo == "UNKNOWN", na.rm = TRUE) | any(breakpoints_current$ref_tbl %like% "PK.*PD", na.rm = TRUE)) {
-        msgs <- c(msgs, "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this")
+        notes_current <- c(notes_current, "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this")
      }
      if (any(breakpoints_current$site %like% "screen", na.rm = TRUE) | any(breakpoints_current$ref_tbl %like% "screen", na.rm = TRUE)) {
-        msgs <- c(msgs, "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this")
+        notes_current <- c(notes_current, "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this")
      }

      if (method == "mic") {
@@ -1231,8 +1360,9 @@ as_sir_method <- function(method_short,
          values <= breakpoints_current$breakpoint_S ~ as.sir("S"),
          guideline_coerced %like% "EUCAST" & values > breakpoints_current$breakpoint_R ~ as.sir("R"),
          guideline_coerced %like% "CLSI" & values >= breakpoints_current$breakpoint_R ~ as.sir("R"),
-          # return "I" when breakpoints are in the middle
-          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) ~ as.sir("I"),
+          # return "I" or "SDD" when breakpoints are in the middle
+          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == FALSE ~ as.sir("I"),
+          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == TRUE ~ as.sir("SDD"),
          # and NA otherwise
          TRUE ~ NA_sir_
        )
@@ -1242,60 +1372,70 @@ as_sir_method <- function(method_short,
          as.double(values) >= as.double(breakpoints_current$breakpoint_S) ~ as.sir("S"),
          guideline_coerced %like% "EUCAST" & as.double(values) < as.double(breakpoints_current$breakpoint_R) ~ as.sir("R"),
          guideline_coerced %like% "CLSI" & as.double(values) <= as.double(breakpoints_current$breakpoint_R) ~ as.sir("R"),
-          # return "I" when breakpoints are in the middle
-          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) ~ as.sir("I"),
+          # return "I" or "SDD" when breakpoints are in the middle
+          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == FALSE ~ as.sir("I"),
+          !is.na(breakpoints_current$breakpoint_S) & !is.na(breakpoints_current$breakpoint_R) & breakpoints_current$is_SDD == TRUE ~ as.sir("SDD"),
          # and NA otherwise
          TRUE ~ NA_sir_
        )
      }
-
+      
      # write to verbose output
      AMR_env$sir_interpretation_history <- rbind_AMR(
        AMR_env$sir_interpretation_history,
-        # recycling 1 to 2 rows does not seem to work, which is why rep() was added
+        # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
        data.frame(
-          datetime = rep(Sys.time(), length(rows)),
+          datetime = vectorise_log_entry(Sys.time(), length(rows)),
          index = rows,
-          ab_user = rep(ab.bak[match(ab_current, df$ab)][1], length(rows)),
-          mo_user = rep(mo.bak[match(mo_current, df$mo)][1], length(rows)),
-          ab = rep(ab_current, length(rows)),
-          mo = rep(breakpoints_current[, "mo", drop = TRUE], length(rows)),
-          method = rep(method_coerced, length(rows)),
-          input = as.double(values),
-          outcome = as.sir(new_sir),
-          host = rep(breakpoints_current[, "host", drop = TRUE], length(rows)),
-          notes = rep(paste0(notes, collapse = " "), length(rows)),
-          guideline = rep(guideline_coerced, length(rows)),
-          ref_table = rep(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
-          uti = rep(breakpoints_current[, "uti", drop = TRUE], length(rows)),
-          breakpoint_S_R = rep(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
+          ab_given = vectorise_log_entry(ab.bak[match(ab_current, df$ab)][1], length(rows)),
+          mo_given = vectorise_log_entry(mo.bak[match(mo_current, df$mo)][1], length(rows)),
+          host_given = vectorise_log_entry(host.bak[match(host_current, df$host)][1], length(rows)),
+          ab = vectorise_log_entry(breakpoints_current[, "ab", drop = TRUE], length(rows)),
+          mo = vectorise_log_entry(breakpoints_current[, "mo", drop = TRUE], length(rows)),
+          host = vectorise_log_entry(breakpoints_current[, "host", drop = TRUE], length(rows)),
+          method = vectorise_log_entry(method_coerced, length(rows)),
+          input = vectorise_log_entry(as.double(values), length(rows)),
+          outcome = vectorise_log_entry(as.sir(new_sir), length(rows)),
+          notes = vectorise_log_entry(paste0(font_stripstyle(notes_current), collapse = "\n"), length(rows)),
+          guideline = vectorise_log_entry(guideline_coerced, length(rows)),
+          ref_table = vectorise_log_entry(breakpoints_current[, "ref_tbl", drop = TRUE], length(rows)),
+          uti = vectorise_log_entry(breakpoints_current[, "uti", drop = TRUE], length(rows)),
+          breakpoint_S_R = vectorise_log_entry(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
          stringsAsFactors = FALSE
        )
      )
    }

+    notes <- c(notes, notes_current)
    df[rows, "result"] <- new_sir
  }
  
  close(p)
-
  # printing messages
  if (has_progress_bar == TRUE) {
    # the progress bar has overwritten the intro text, so:
    message_(intro_txt, appendLF = FALSE, as_note = FALSE)
  }
-  if (isTRUE(rise_warning)) {
-    message(font_rose_bg(" WARNING "))
-  } else if (length(notes) > 0) {
-    message(font_yellow_bg(" NOTES "))
-  } else if (length(msgs) == 0) {
-    message(font_green_bg(" OK "))
+  if (length(notes) > 0) {
+    if (isTRUE(rise_warning)) {
+      message(font_rose_bg(" WARNING "))
+    } else {
+      message(font_yellow_bg(" NOTE "))
+    }
+    notes <- unique(notes)
+    if (isTRUE(verbose) || length(notes) == 1 || NROW(AMR_env$sir_interpretation_history) == 0) {
+      for (i in seq_len(length(notes))) {
+        message(word_wrap("  ", AMR_env$bullet_icon, " ", notes[i], add_fn = font_black))
+      }
+    } else {
+      message(word_wrap("  ", AMR_env$bullet_icon, " There were multiple notes. Print or View `sir_interpretation_history()` to examine them, or use `as.sir(..., verbose = TRUE)` next time to directly print them here.", add_fn = font_black))
+    }
  } else {
-    msg_note(sort(msgs))
+    message(font_green_bg(" OK "))
  }
-
+  
  load_mo_uncertainties(metadata_mo)
-
+  
  df$result
 }

@@ -1306,24 +1446,32 @@ sir_interpretation_history <- function(clean = FALSE) {
  meet_criteria(clean, allow_class = "logical", has_length = 1)

  out <- AMR_env$sir_interpretation_history
-  if (NROW(out) == 0) {
-    message_("No results to return. Run `as.sir()` on MIC values or disk diffusion zones first to see a 'logbook' data set here.")
-    return(invisible(NULL))
-  }
  out$outcome <- as.sir(out$outcome)
-  # keep stored for next use
+  if (NROW(out) > 0) {
+    # sort descending on time
+    out <- out[order(format(out$datetime, "%Y%m%d%H%M"), out$index, decreasing = TRUE), , drop = FALSE]
+  }
+  
  if (isTRUE(clean)) {
    AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
  }
  
-  # sort descending on time
-  out <- out[order(out$datetime, decreasing = TRUE), , drop = FALSE]
-
  if (pkg_is_available("tibble")) {
-    import_fn("as_tibble", "tibble")(out)
-  } else {
-    out
+    out <- import_fn("as_tibble", "tibble")(out)
  }
+  structure(out, class = c("sir_log", class(out)))
+}
+
+#' @method print sir_log
+#' @export
+#' @noRd
+print.sir_log <- function(x, ...) {
+  if (NROW(x) == 0) {
+    message_("No results to print. Run `as.sir()` on MIC values or disk diffusion zones first to print a 'logbook' data set here.")
+    return(invisible(NULL))
+  }
+  class(x) <- class(x)[class(x) != "sir_log"]
+  print(x, ...)
 }

 # will be exported using s3_register() in R/zzz.R
--- a/R/sir_calc.R
+++ b/R/sir_calc.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/sir_df.R
+++ b/R/sir_df.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/skewness.R
+++ b/R/skewness.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/sysdata.rda
+++ b/R/sysdata.rda
--- a/R/translate.R
+++ b/R/translate.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/vctrs.R
+++ b/R/vctrs.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/whocc.R
+++ b/R/whocc.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/zz_deprecated.R
+++ b/R/zz_deprecated.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -58,14 +58,15 @@ AMR_env$av_previously_coerced <- data.frame(
 AMR_env$sir_interpretation_history <- data.frame(
  datetime = Sys.time()[0],
  index = integer(0),
-  ab_user = character(0),
-  mo_user = character(0),
+  ab_given = character(0),
+  mo_given = character(0),
+  host_given = character(0),
  ab = set_clean_class(character(0), c("ab", "character")),
  mo = set_clean_class(character(0), c("mo", "character")),
+  host = character(0),
  method = character(0),
  input = double(0),
  outcome = NA_sir_[0],
-  host = character(0),
  notes = character(0),
  guideline = character(0),
  ref_table = character(0),
@@ -80,11 +81,12 @@ AMR_env$is_dark_theme <- NULL
 AMR_env$chmatch <- import_fn("chmatch", "data.table", error_on_fail = FALSE)
 AMR_env$chin <- import_fn("%chin%", "data.table", error_on_fail = FALSE)

-# take cli symbols if available
+# take cli symbols and error function if available
 AMR_env$info_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$info %or% "i"
 AMR_env$bullet_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$bullet %or% "*"
 AMR_env$dots <- import_fn("symbol", "cli", error_on_fail = FALSE)$ellipsis %or% "..."
 AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %or% "*"
+AMR_env$cli_abort <- import_fn("cli_abort", "cli", error_on_fail = FALSE)

 .onLoad <- function(lib, pkg) {
  # Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
@@ -103,6 +105,8 @@ AMR_env$sup_1_icon <- import_fn("symbol", "cli", error_on_fail = FALSE)$sup_1 %o
  s3_register("pillar::type_sum", "mo")
  s3_register("pillar::type_sum", "sir")
  s3_register("pillar::type_sum", "mic")
+  s3_register("pillar::tbl_sum", "antibiogram")
+  s3_register("pillar::tbl_format_footer", "antibiogram")
  # Support for frequency tables from the cleaner package
  s3_register("cleaner::freq", "mo")
  s3_register("cleaner::freq", "sir")
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -242,16 +242,16 @@ reference:
      Some pages about our package and its external sources. Be sure to read our [How To's](./../articles/index.html)
      for more information about how to work with functions in this package.
    contents:
-    - "`example_isolates`"
    - "`microorganisms`"
+    - "`antibiotics`"
+    - "`clinical_breakpoints`"
+    - "`example_isolates`"
    - "`microorganisms.codes`"
    - "`microorganisms.groups`"
-    - "`antibiotics`"
    - "`intrinsic_resistant`"
    - "`dosage`"
    - "`WHOCC`"
    - "`example_isolates_unclean`"
-    - "`clinical_breakpoints`"
    - "`WHONET`"

  - title: "Other: miscellaneous functions"
--- a/codecov.yml
+++ b/codecov.yml
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/data-raw/AMR_vet.qmd
+++ b/data-raw/AMR_vet.qmd
@@ -13,7 +13,6 @@ format:
 library(dplyr)
 library(readr)
 library(tidyr)
-library(janitor)

 # WHONET version of 16th Feb 2024
 whonet_breakpoints <- read_tsv("WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"),
@@ -42,8 +41,7 @@ whonet_breakpoints |>
  filter(BREAKPOINT_TYPE == "Animal") |>
  count(YEAR, HOST, REFERENCE_TABLE = gsub("VET[0-9]+ ", "", REFERENCE_TABLE)) |>
  pivot_wider(names_from = YEAR, values_from = n, values_fill = list(n = 0)) |>
-  arrange(HOST, REFERENCE_TABLE) |> 
-  adorn_totals(name = "TOTAL")
+  arrange(HOST, REFERENCE_TABLE)
 ```

 ### Cats only
--- a/data-raw/Preference
+++ b/data-raw/Preference
--- a/data-raw/_language_update.R
+++ b/data-raw/_language_update.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/data-raw/_pre_commit_checks.R
+++ b/data-raw/_pre_commit_checks.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
@@ -36,12 +36,14 @@ devtools::load_all(quiet = TRUE)

 suppressMessages(set_AMR_locale("English"))

-old_globalenv <- ls(envir = globalenv())
+pre_commit_lst <- list()

 # Save internal data to R/sysdata.rda -------------------------------------

+usethis::ui_info(paste0("Updating internal package data"))
+
 # See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
-EUCAST_RULES_DF <- utils::read.delim(
+pre_commit_lst$EUCAST_RULES_DF <- utils::read.delim(
  file = "data-raw/eucast_rules.tsv",
  skip = 9,
  sep = "\t",
@@ -67,7 +69,7 @@ EUCAST_RULES_DF <- utils::read.delim(
  mutate(reference.rule_group = as.character(reference.rule_group)) %>%
  select(-sorting_rule)

-TRANSLATIONS <- utils::read.delim(
+pre_commit_lst$TRANSLATIONS <- utils::read.delim(
  file = "data-raw/translations.tsv",
  sep = "\t",
  stringsAsFactors = FALSE,
@@ -82,15 +84,15 @@ TRANSLATIONS <- utils::read.delim(
  quote = ""
 )

-LANGUAGES_SUPPORTED_NAMES <- c(
+pre_commit_lst$LANGUAGES_SUPPORTED_NAMES <- c(
  list(en = list(exonym = "English", endonym = "English")),
  lapply(
-    TRANSLATIONS[, which(nchar(colnames(TRANSLATIONS)) == 2), drop = FALSE],
+    TRANSLATIONS[, which(nchar(colnames(pre_commit_lst$TRANSLATIONS)) == 2), drop = FALSE],
    function(x) list(exonym = x[1], endonym = x[2])
  )
 )

-LANGUAGES_SUPPORTED <- names(LANGUAGES_SUPPORTED_NAMES)
+pre_commit_lst$LANGUAGES_SUPPORTED <- names(pre_commit_lst$LANGUAGES_SUPPORTED_NAMES)

 # vectors of CoNS and CoPS, improves speed in as.mo()
 create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
@@ -120,7 +122,8 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
        "vitulinus", "vitulus", "warneri", "xylosus",
        "caledonicus", "canis",
        "durrellii", "lloydii",
-        "ratti", "taiwanensis", "veratri", "urealyticus"
+        "ratti", "taiwanensis", "veratri", "urealyticus",
+        "americanisciuri", "marylandisciuri", "shinii", "brunensis"
      ) |
        # old, now renamed to S. schleiferi (but still as synonym in our data of course):
        (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
@@ -147,115 +150,223 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
    ]
  }
 }
-MO_CONS <- create_species_cons_cops("CoNS")
-MO_COPS <- create_species_cons_cops("CoPS")
-MO_STREP_ABCG <- AMR::microorganisms$mo[which(AMR::microorganisms$genus == "Streptococcus" &
+pre_commit_lst$MO_CONS <- create_species_cons_cops("CoNS")
+pre_commit_lst$MO_COPS <- create_species_cons_cops("CoPS")
+pre_commit_lst$MO_STREP_ABCG <- AMR::microorganisms$mo[which(AMR::microorganisms$genus == "Streptococcus" &
  tolower(AMR::microorganisms$species) %in% c(
    "pyogenes", "agalactiae", "dysgalactiae", "equi", "canis",
    "group a", "group b", "group c", "group g"
  ))]
-MO_LANCEFIELD <- AMR::microorganisms$mo[which(AMR::microorganisms$mo %like% "^(B_STRPT_PYGN(_|$)|B_STRPT_AGLC(_|$)|B_STRPT_(DYSG|EQUI)(_|$)|B_STRPT_ANGN(_|$)|B_STRPT_(DYSG|CANS)(_|$)|B_STRPT_SNGN(_|$)|B_STRPT_SLVR(_|$))")]
-MO_PREVALENT_GENERA <- c(
-  "Absidia", "Acanthamoeba", "Acremonium", "Aedes", "Alternaria", "Amoeba", "Ancylostoma", "Angiostrongylus",
-  "Anisakis", "Anopheles", "Apophysomyces", "Arthroderma", "Aspergillus", "Aureobasidium", "Basidiobolus", "Beauveria",
-  "Blastocystis", "Blastomyces", "Candida", "Capillaria", "Chaetomium", "Chrysonilia", "Chrysosporium", "Cladophialophora",
-  "Cladosporium", "Conidiobolus", "Contracaecum", "Cordylobia", "Cryptococcus", "Curvularia", "Demodex",
-  "Dermatobia", "Dientamoeba", "Diphyllobothrium", "Dirofilaria", "Echinostoma", "Entamoeba", "Enterobius",
-  "Exophiala", "Exserohilum", "Fasciola", "Fonsecaea", "Fusarium", "Geotrichum", "Giardia", "Haloarcula", "Halobacterium",
-  "Halococcus", "Hendersonula", "Heterophyes", "Histomonas", "Histoplasma", "Hymenolepis", "Hypomyces",
-  "Hysterothylacium", "Kloeckera", "Kodamaea", "Leishmania", "Lichtheimia", "Lodderomyces",
-  "Malassezia", "Malbranchea", "Metagonimus", "Meyerozyma", "Microsporidium",
-  "Microsporum", "Millerozyma", "Mortierella", "Mucor", "Mycocentrospora", "Necator", "Nectria", "Ochroconis", "Oesophagostomum",
-  "Oidiodendron", "Opisthorchis", "Paecilomyces", "Pediculus", "Penicillium", "Phlebotomus", "Phoma", "Pichia", "Piedraia", "Pithomyces",
-  "Pityrosporum", "Pneumocystis", "Pseudallescheria", "Pseudoterranova", "Pulex", "Rhizomucor", "Rhizopus",
-  "Rhodotorula", "Saccharomyces", "Saprochaete", "Sarcoptes", "Scedosporium", "Scolecobasidium", "Scopulariopsis", "Scytalidium", "Spirometra",
-  "Sporobolomyces", "Sporotrichum", "Stachybotrys", "Strongyloides", "Syngamus", "Taenia", "Talaromyces", "Toxocara", "Trichinella",
-  "Trichobilharzia", "Trichoderma", "Trichomonas", "Trichophyton", "Trichosporon", "Trichostrongylus", "Trichuris",
-  "Tritirachium", "Trombicula", "Trypanosoma", "Tunga", "Verticillium", "Wuchereria"
+pre_commit_lst$MO_LANCEFIELD <- AMR::microorganisms$mo[which(AMR::microorganisms$mo %like% "^(B_STRPT_PYGN(_|$)|B_STRPT_AGLC(_|$)|B_STRPT_(DYSG|EQUI)(_|$)|B_STRPT_ANGN(_|$)|B_STRPT_(DYSG|CANS)(_|$)|B_STRPT_SNGN(_|$)|B_STRPT_SLVR(_|$))")]
+pre_commit_lst$MO_PREVALENT_GENERA <- c(
+  "Absidia",
+  "Acanthamoeba",
+  "Acremonium",
+  "Aedes",
+  "Alternaria",
+  "Amoeba",
+  "Ancylostoma",
+  "Angiostrongylus",
+  "Anisakis",
+  "Anopheles",
+  "Apophysomyces",
+  "Arthroderma",
+  "Aspergillus",
+  "Aureobasidium",
+  "Basidiobolus",
+  "Beauveria",
+  "Blastocystis",
+  "Blastomyces",
+  "Candida",
+  "Capillaria",
+  "Chaetomium",
+  "Chrysonilia",
+  "Chrysosporium",
+  "Cladophialophora",
+  "Cladosporium",
+  "Conidiobolus",
+  "Contracaecum",
+  "Cordylobia",
+  "Cryptococcus",
+  "Curvularia",
+  "Demodex",
+  "Dermatobia",
+  "Dientamoeba",
+  "Diphyllobothrium",
+  "Dirofilaria",
+  "Echinostoma",
+  "Entamoeba",
+  "Enterobius",
+  "Exophiala",
+  "Exserohilum",
+  "Fasciola",
+  "Fonsecaea",
+  "Fusarium",
+  "Geotrichum",
+  "Giardia",
+  "Haloarcula",
+  "Halobacterium",
+  "Halococcus",
+  "Hansenula",
+  "Hendersonula",
+  "Heterophyes",
+  "Histomonas",
+  "Histoplasma",
+  "Hymenolepis",
+  "Hypomyces",
+  "Hysterothylacium",
+  "Kloeckera",
+  "Kluyveromyces",
+  "Kodamaea",
+  "Leishmania",
+  "Lichtheimia",
+  "Lodderomyces",
+  "Lomentospora",
+  "Malassezia",
+  "Malbranchea",
+  "Metagonimus",
+  "Meyerozyma",
+  "Microsporidium",
+  "Microsporum",
+  "Millerozyma",
+  "Mortierella",
+  "Mucor",
+  "Mycocentrospora",
+  "Necator",
+  "Nectria",
+  "Ochroconis",
+  "Oesophagostomum",
+  "Oidiodendron",
+  "Opisthorchis",
+  "Paecilomyces",
+  "Pediculus",
+  "Penicillium",
+  "Phlebotomus",
+  "Phoma",
+  "Pichia",
+  "Piedraia",
+  "Pithomyces",
+  "Pityrosporum",
+  "Pneumocystis",
+  "Pseudallescheria",
+  "Pseudoscopulariopsis",
+  "Pseudoterranova",
+  "Pulex",
+  "Rhizomucor",
+  "Rhizopus",
+  "Rhodotorula",
+  "Saccharomyces",
+  "Saprochaete",
+  "Sarcoptes",
+  "Scedosporium",
+  "Scolecobasidium",
+  "Scopulariopsis",
+  "Scytalidium",
+  "Spirometra",
+  "Sporobolomyces",
+  "Sporotrichum",
+  "Stachybotrys",
+  "Strongyloides",
+  "Syngamus",
+  "Taenia",
+  "Talaromyces",
+  "Toxocara",
+  "Trichinella",
+  "Trichobilharzia",
+  "Trichoderma",
+  "Trichomonas",
+  "Trichophyton",
+  "Trichosporon",
+  "Trichostrongylus",
+  "Trichuris",
+  "Tritirachium",
+  "Trombicula",
+  "Trypanosoma",
+  "Tunga",
+  "Verticillium",
+  "Wuchereria"
 )

 # antibiotic groups
 # (these will also be used for eucast_rules() and understanding data-raw/eucast_rules.tsv)
-globalenv_before_ab <- c(ls(envir = globalenv()), "globalenv_before_ab")
-AB_AMINOGLYCOSIDES <- antibiotics %>%
+pre_commit_lst$AB_AMINOGLYCOSIDES <- antibiotics %>%
  filter(group %like% "aminoglycoside") %>%
  pull(ab)
-AB_AMINOPENICILLINS <- as.ab(c("AMP", "AMX"))
-AB_ANTIFUNGALS <- antibiotics %>%
+pre_commit_lst$AB_AMINOPENICILLINS <- as.ab(c("AMP", "AMX"))
+pre_commit_lst$AB_ANTIFUNGALS <- antibiotics %>%
  filter(group %like% "antifungal") %>%
  pull(ab)
-AB_ANTIMYCOBACTERIALS <- antibiotics %>%
+pre_commit_lst$AB_ANTIMYCOBACTERIALS <- antibiotics %>%
  filter(group %like% "antimycobacterial") %>%
  pull(ab)
-AB_CARBAPENEMS <- antibiotics %>%
+pre_commit_lst$AB_CARBAPENEMS <- antibiotics %>%
  filter(group %like% "carbapenem") %>%
  pull(ab)
-AB_CEPHALOSPORINS <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS <- antibiotics %>%
  filter(group %like% "cephalosporin") %>%
  pull(ab)
-AB_CEPHALOSPORINS_1ST <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_1ST <- antibiotics %>%
  filter(group %like% "cephalosporin.*1") %>%
  pull(ab)
-AB_CEPHALOSPORINS_2ND <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_2ND <- antibiotics %>%
  filter(group %like% "cephalosporin.*2") %>%
  pull(ab)
-AB_CEPHALOSPORINS_3RD <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_3RD <- antibiotics %>%
  filter(group %like% "cephalosporin.*3") %>%
  pull(ab)
-AB_CEPHALOSPORINS_4TH <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_4TH <- antibiotics %>%
  filter(group %like% "cephalosporin.*4") %>%
  pull(ab)
-AB_CEPHALOSPORINS_5TH <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_5TH <- antibiotics %>%
  filter(group %like% "cephalosporin.*5") %>%
  pull(ab)
-AB_CEPHALOSPORINS_EXCEPT_CAZ <- AB_CEPHALOSPORINS[AB_CEPHALOSPORINS != "CAZ"]
-AB_FLUOROQUINOLONES <- antibiotics %>%
+pre_commit_lst$AB_CEPHALOSPORINS_EXCEPT_CAZ <- pre_commit_lst$AB_CEPHALOSPORINS[pre_commit_lst$AB_CEPHALOSPORINS != "CAZ"]
+pre_commit_lst$AB_FLUOROQUINOLONES <- antibiotics %>%
  filter(atc_group2 %like% "fluoroquinolone" | (group %like% "quinolone" & is.na(atc_group2))) %>%
  pull(ab)
-AB_GLYCOPEPTIDES <- antibiotics %>%
+pre_commit_lst$AB_GLYCOPEPTIDES <- antibiotics %>%
  filter(group %like% "glycopeptide") %>%
  pull(ab)
-AB_LIPOGLYCOPEPTIDES <- as.ab(c("DAL", "ORI", "TLV")) # dalba/orita/tela
-AB_GLYCOPEPTIDES_EXCEPT_LIPO <- AB_GLYCOPEPTIDES[!AB_GLYCOPEPTIDES %in% AB_LIPOGLYCOPEPTIDES]
-AB_LINCOSAMIDES <- antibiotics %>%
+pre_commit_lst$AB_LIPOGLYCOPEPTIDES <- as.ab(c("DAL", "ORI", "TLV")) # dalba/orita/tela
+pre_commit_lst$AB_GLYCOPEPTIDES_EXCEPT_LIPO <- pre_commit_lst$AB_GLYCOPEPTIDES[!pre_commit_lst$AB_GLYCOPEPTIDES %in% pre_commit_lst$AB_LIPOGLYCOPEPTIDES]
+pre_commit_lst$AB_LINCOSAMIDES <- antibiotics %>%
  filter(atc_group2 %like% "lincosamide" | (group %like% "lincosamide" & is.na(atc_group2))) %>%
  pull(ab)
-AB_MACROLIDES <- antibiotics %>%
+pre_commit_lst$AB_MACROLIDES <- antibiotics %>%
  filter(atc_group2 %like% "macrolide" | (group %like% "macrolide" & is.na(atc_group2) & name %unlike% "screening|inducible")) %>%
  pull(ab)
-AB_NITROFURANS <- antibiotics %>%
+pre_commit_lst$AB_NITROFURANS <- antibiotics %>%
  filter(name %like% "^furaz|nitrofura" | atc_group2 %like% "nitrofuran") %>%
  pull(ab)
-AB_OXAZOLIDINONES <- antibiotics %>%
+pre_commit_lst$AB_OXAZOLIDINONES <- antibiotics %>%
  filter(group %like% "oxazolidinone") %>%
  pull(ab)
-AB_PENICILLINS <- antibiotics %>%
+pre_commit_lst$AB_PENICILLINS <- antibiotics %>%
  filter(group %like% "penicillin") %>%
  pull(ab)
-AB_POLYMYXINS <- antibiotics %>%
+pre_commit_lst$AB_POLYMYXINS <- antibiotics %>%
  filter(group %like% "polymyxin") %>%
  pull(ab)
-AB_QUINOLONES <- antibiotics %>%
+pre_commit_lst$AB_QUINOLONES <- antibiotics %>%
  filter(group %like% "quinolone") %>%
  pull(ab)
-AB_RIFAMYCINS <- antibiotics %>%
+pre_commit_lst$AB_RIFAMYCINS <- antibiotics %>%
  filter(name %like% "Rifampi|Rifabutin|Rifapentine|rifamy") %>%
  pull(ab)
-AB_STREPTOGRAMINS <- antibiotics %>%
+pre_commit_lst$AB_STREPTOGRAMINS <- antibiotics %>%
  filter(atc_group2 %like% "streptogramin") %>%
  pull(ab)
-AB_TETRACYCLINES <- antibiotics %>%
+pre_commit_lst$AB_TETRACYCLINES <- antibiotics %>%
  filter(group %like% "tetracycline") %>%
  pull(ab)
-AB_TETRACYCLINES_EXCEPT_TGC <- AB_TETRACYCLINES[AB_TETRACYCLINES != "TGC"]
-AB_TRIMETHOPRIMS <- antibiotics %>%
+pre_commit_lst$AB_TETRACYCLINES_EXCEPT_TGC <- pre_commit_lst$AB_TETRACYCLINES[pre_commit_lst$AB_TETRACYCLINES != "TGC"]
+pre_commit_lst$AB_TRIMETHOPRIMS <- antibiotics %>%
  filter(group %like% "trimethoprim") %>%
  pull(ab)
-AB_UREIDOPENICILLINS <- as.ab(c("PIP", "TZP", "AZL", "MEZ"))
-AB_BETALACTAMS <- c(AB_PENICILLINS, AB_CEPHALOSPORINS, AB_CARBAPENEMS)
+pre_commit_lst$AB_UREIDOPENICILLINS <- as.ab(c("PIP", "TZP", "AZL", "MEZ"))
+pre_commit_lst$AB_BETALACTAMS <- c(pre_commit_lst$AB_PENICILLINS, pre_commit_lst$AB_CEPHALOSPORINS, pre_commit_lst$AB_CARBAPENEMS)
 # this will be used for documentation:
-DEFINED_AB_GROUPS <- ls(envir = globalenv())
-DEFINED_AB_GROUPS <- DEFINED_AB_GROUPS[!DEFINED_AB_GROUPS %in% globalenv_before_ab]
+pre_commit_lst$DEFINED_AB_GROUPS <- sort(names(pre_commit_lst)[names(pre_commit_lst) %like% "^AB_" & names(pre_commit_lst) != "AB_LOOKUP"])
 create_AB_AV_lookup <- function(df) {
  new_df <- df
  new_df$generalised_name <- generalise_antibiotic_name(new_df$name)
@@ -282,62 +393,26 @@ create_AB_AV_lookup <- function(df) {
  ))
  new_df[, colnames(new_df)[colnames(new_df) %like% "^generalised"]]
 }
-AB_LOOKUP <- create_AB_AV_lookup(AMR::antibiotics)
-AV_LOOKUP <- create_AB_AV_lookup(AMR::antivirals)
+pre_commit_lst$AB_LOOKUP <- create_AB_AV_lookup(AMR::antibiotics)
+pre_commit_lst$AV_LOOKUP <- create_AB_AV_lookup(AMR::antivirals)

 # Export to package as internal data ----
-usethis::ui_info(paste0("Updating internal package data"))
-suppressMessages(usethis::use_data(EUCAST_RULES_DF,
-  TRANSLATIONS,
-  LANGUAGES_SUPPORTED_NAMES,
-  LANGUAGES_SUPPORTED,
-  MO_CONS,
-  MO_COPS,
-  MO_STREP_ABCG,
-  MO_LANCEFIELD,
-  MO_PREVALENT_GENERA,
-  AB_LOOKUP,
-  AV_LOOKUP,
-  AB_AMINOGLYCOSIDES,
-  AB_AMINOPENICILLINS,
-  AB_ANTIFUNGALS,
-  AB_ANTIMYCOBACTERIALS,
-  AB_CARBAPENEMS,
-  AB_CEPHALOSPORINS,
-  AB_CEPHALOSPORINS_1ST,
-  AB_CEPHALOSPORINS_2ND,
-  AB_CEPHALOSPORINS_3RD,
-  AB_CEPHALOSPORINS_4TH,
-  AB_CEPHALOSPORINS_5TH,
-  AB_CEPHALOSPORINS_EXCEPT_CAZ,
-  AB_FLUOROQUINOLONES,
-  AB_LIPOGLYCOPEPTIDES,
-  AB_GLYCOPEPTIDES,
-  AB_GLYCOPEPTIDES_EXCEPT_LIPO,
-  AB_LINCOSAMIDES,
-  AB_MACROLIDES,
-  AB_NITROFURANS,
-  AB_OXAZOLIDINONES,
-  AB_PENICILLINS,
-  AB_POLYMYXINS,
-  AB_QUINOLONES,
-  AB_RIFAMYCINS,
-  AB_STREPTOGRAMINS,
-  AB_TETRACYCLINES,
-  AB_TETRACYCLINES_EXCEPT_TGC,
-  AB_TRIMETHOPRIMS,
-  AB_UREIDOPENICILLINS,
-  AB_BETALACTAMS,
-  DEFINED_AB_GROUPS,
-  internal = TRUE,
-  overwrite = TRUE,
-  version = 2,
-  compress = "xz"
-))
+# usethis::use_data() must receive unquoted object names, which is not flexible at all.
+# we'll use good old base::save() instead
+save(list = names(pre_commit_lst),
+     file = "R/sysdata.rda",
+     envir = as.environment(pre_commit_lst),
+     compress = "xz",
+     version = 2,
+     ascii = FALSE)
+usethis::ui_done("Saved to {usethis::ui_value('R/sysdata.rda')}")
+
+
+

 # Export data sets to the repository in different formats -----------------

-for (pkg in c("haven", "openxlsx", "arrow")) {
+for (pkg in c("haven", "openxlsx2", "arrow")) {
  if (!pkg %in% rownames(utils::installed.packages())) {
    message("NOTE: package '", pkg, "' not installed! Ignoring export where this package is required.")
  }
@@ -378,7 +453,7 @@ if (changed_md5(clin_break)) {
  try(haven::write_xpt(clin_break, "data-raw/clinical_breakpoints.xpt"), silent = TRUE)
  try(haven::write_sav(clin_break, "data-raw/clinical_breakpoints.sav"), silent = TRUE)
  try(haven::write_dta(clin_break, "data-raw/clinical_breakpoints.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(clin_break, "data-raw/clinical_breakpoints.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(clin_break, "data-raw/clinical_breakpoints.xlsx"), silent = TRUE)
  try(arrow::write_feather(clin_break, "data-raw/clinical_breakpoints.feather"), silent = TRUE)
  try(arrow::write_parquet(clin_break, "data-raw/clinical_breakpoints.parquet"), silent = TRUE)
 }
@@ -396,7 +471,7 @@ if (changed_md5(microorganisms)) {
  try(haven::write_dta(mo, "data-raw/microorganisms.dta"), silent = TRUE)
  mo_all_snomed <- microorganisms %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
  try(write.table(mo_all_snomed, "data-raw/microorganisms.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(openxlsx::write.xlsx(mo_all_snomed, "data-raw/microorganisms.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(mo_all_snomed, "data-raw/microorganisms.xlsx"), silent = TRUE)
  try(arrow::write_feather(microorganisms, "data-raw/microorganisms.feather"), silent = TRUE)
  try(arrow::write_parquet(microorganisms, "data-raw/microorganisms.parquet"), silent = TRUE)
 }
@@ -409,7 +484,7 @@ if (changed_md5(microorganisms.codes)) {
  try(haven::write_xpt(microorganisms.codes, "data-raw/microorganisms.codes.xpt"), silent = TRUE)
  try(haven::write_sav(microorganisms.codes, "data-raw/microorganisms.codes.sav"), silent = TRUE)
  try(haven::write_dta(microorganisms.codes, "data-raw/microorganisms.codes.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(microorganisms.codes, "data-raw/microorganisms.codes.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(microorganisms.codes, "data-raw/microorganisms.codes.xlsx"), silent = TRUE)
  try(arrow::write_feather(microorganisms.codes, "data-raw/microorganisms.codes.feather"), silent = TRUE)
  try(arrow::write_parquet(microorganisms.codes, "data-raw/microorganisms.codes.parquet"), silent = TRUE)
 }
@@ -422,7 +497,7 @@ if (changed_md5(microorganisms.groups)) {
  try(haven::write_xpt(microorganisms.groups, "data-raw/microorganisms.groups.xpt"), silent = TRUE)
  try(haven::write_sav(microorganisms.groups, "data-raw/microorganisms.groups.sav"), silent = TRUE)
  try(haven::write_dta(microorganisms.groups, "data-raw/microorganisms.groups.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(microorganisms.groups, "data-raw/microorganisms.groups.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(microorganisms.groups, "data-raw/microorganisms.groups.xlsx"), silent = TRUE)
  try(arrow::write_feather(microorganisms.groups, "data-raw/microorganisms.groups.feather"), silent = TRUE)
  try(arrow::write_parquet(microorganisms.groups, "data-raw/microorganisms.groups.parquet"), silent = TRUE)
 }
@@ -437,7 +512,7 @@ if (changed_md5(ab)) {
  try(haven::write_dta(ab, "data-raw/antibiotics.dta"), silent = TRUE)
  ab_lists <- antibiotics %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
  try(write.table(ab_lists, "data-raw/antibiotics.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(openxlsx::write.xlsx(ab_lists, "data-raw/antibiotics.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(ab_lists, "data-raw/antibiotics.xlsx"), silent = TRUE)
  try(arrow::write_feather(antibiotics, "data-raw/antibiotics.feather"), silent = TRUE)
  try(arrow::write_parquet(antibiotics, "data-raw/antibiotics.parquet"), silent = TRUE)
 }
@@ -452,7 +527,7 @@ if (changed_md5(av)) {
  try(haven::write_dta(av, "data-raw/antivirals.dta"), silent = TRUE)
  av_lists <- antivirals %>% mutate_if(is.list, function(x) sapply(x, paste, collapse = ","))
  try(write.table(av_lists, "data-raw/antivirals.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(openxlsx::write.xlsx(av_lists, "data-raw/antivirals.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(av_lists, "data-raw/antivirals.xlsx"), silent = TRUE)
  try(arrow::write_feather(antivirals, "data-raw/antivirals.feather"), silent = TRUE)
  try(arrow::write_parquet(antivirals, "data-raw/antivirals.parquet"), silent = TRUE)
 }
@@ -471,7 +546,7 @@ if (changed_md5(intrinsicR)) {
  try(haven::write_xpt(intrinsicR, "data-raw/intrinsic_resistant.xpt"), silent = TRUE)
  try(haven::write_sav(intrinsicR, "data-raw/intrinsic_resistant.sav"), silent = TRUE)
  try(haven::write_dta(intrinsicR, "data-raw/intrinsic_resistant.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(intrinsicR, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(intrinsicR, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
  try(arrow::write_feather(intrinsicR, "data-raw/intrinsic_resistant.feather"), silent = TRUE)
  try(arrow::write_parquet(intrinsicR, "data-raw/intrinsic_resistant.parquet"), silent = TRUE)
 }
@@ -484,18 +559,13 @@ if (changed_md5(dosage)) {
  try(haven::write_xpt(dosage, "data-raw/dosage.xpt"), silent = TRUE)
  try(haven::write_sav(dosage, "data-raw/dosage.sav"), silent = TRUE)
  try(haven::write_dta(dosage, "data-raw/dosage.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
+  try(openxlsx2::write_xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
  try(arrow::write_feather(dosage, "data-raw/dosage.feather"), silent = TRUE)
  try(arrow::write_parquet(dosage, "data-raw/dosage.parquet"), silent = TRUE)
 }

 suppressMessages(reset_AMR_locale())

-# remove leftovers from global env
-current_globalenv <- ls(envir = globalenv())
-rm(list = current_globalenv[!current_globalenv %in% old_globalenv])
-rm(current_globalenv)
-
 devtools::load_all(quiet = TRUE)
 suppressMessages(set_AMR_locale("English"))

@@ -509,19 +579,6 @@ usethis::ui_info("Documenting package")
 suppressMessages(devtools::document(quiet = TRUE))


-# Style pkg ---------------------------------------------------------------
-if (!"styler" %in% rownames(utils::installed.packages())) {
-  message("Package 'styler' not installed!")
-} else if (interactive()) {
-  # only when sourcing this file ourselves
-  # usethis::ui_info("Styling package")
-  # styler::style_pkg(
-  #   style = styler::tidyverse_style,
-  #   filetype = c("R", "Rmd")
-  # )
-}
-
-
 # Finished ----------------------------------------------------------------
 usethis::ui_done("All done")
 suppressMessages(reset_AMR_locale())
--- a/data-raw/bacdive.csv
+++ b/data-raw/bacdive.csv
--- a/data-raw/clin_break.md5
+++ b/data-raw/clin_break.md5
@@ -1 +1 @@
-5d90ad7fe89682bfc58700682c562207
+c79b6e112dc3ab478b990f0689b685b6
--- a/data-raw/clinical_breakpoints.dta
+++ b/data-raw/clinical_breakpoints.dta
--- a/data-raw/clinical_breakpoints.feather
+++ b/data-raw/clinical_breakpoints.feather
--- a/data-raw/clinical_breakpoints.rds
+++ b/data-raw/clinical_breakpoints.rds
--- a/data-raw/clinical_breakpoints.sav
+++ b/data-raw/clinical_breakpoints.sav
--- a/data-raw/clinical_breakpoints.txt
+++ b/data-raw/clinical_breakpoints.txt
--- a/data-raw/clinical_breakpoints.xlsx
+++ b/data-raw/clinical_breakpoints.xlsx
--- a/data-raw/clinical_breakpoints.xpt
+++ b/data-raw/clinical_breakpoints.xpt
--- a/data-raw/intrinsicR.md5
+++ b/data-raw/intrinsicR.md5
@@ -1 +1 @@
-032dfd1b044cc838f0915b0eef919471
+e8c57a559ad846e72c700c88929e880d
--- a/data-raw/intrinsic_resistant.dta
+++ b/data-raw/intrinsic_resistant.dta
--- a/data-raw/intrinsic_resistant.feather
+++ b/data-raw/intrinsic_resistant.feather
--- a/data-raw/intrinsic_resistant.parquet
+++ b/data-raw/intrinsic_resistant.parquet
--- a/data-raw/intrinsic_resistant.rds
+++ b/data-raw/intrinsic_resistant.rds
--- a/data-raw/intrinsic_resistant.sav
+++ b/data-raw/intrinsic_resistant.sav
--- a/data-raw/intrinsic_resistant.txt
+++ b/data-raw/intrinsic_resistant.txt
--- a/data-raw/intrinsic_resistant.xlsx
+++ b/data-raw/intrinsic_resistant.xlsx
--- a/data-raw/intrinsic_resistant.xpt
+++ b/data-raw/intrinsic_resistant.xpt
--- a/data-raw/loinc.R
+++ b/data-raw/loinc.R
@@ -6,9 +6,9 @@
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # PLEASE CITE THIS SOFTWARE AS:                                        #
-# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
-# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
-# Data. Journal of Statistical Software, 104(3), 1-31.                 #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
 # https://doi.org/10.18637/jss.v104.i03                                #
 #                                                                      #
 # Developed at the University of Groningen and the University Medical  #
--- a/data-raw/microorganisms.codes.dta
+++ b/data-raw/microorganisms.codes.dta
--- a/data-raw/microorganisms.codes.feather
+++ b/data-raw/microorganisms.codes.feather
--- a/data-raw/microorganisms.codes.md5
+++ b/data-raw/microorganisms.codes.md5
@@ -1 +1 @@
-89e85bfa66228cfba526e375dad7ff37
+c9ba186023a0003c8153215646983621
--- a/data-raw/microorganisms.codes.rds
+++ b/data-raw/microorganisms.codes.rds
--- a/data-raw/microorganisms.codes.sav
+++ b/data-raw/microorganisms.codes.sav
--- a/data-raw/microorganisms.codes.txt
+++ b/data-raw/microorganisms.codes.txt
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
dr. M.S. (Matthijs) Berends	573c0346ed	(v2.1.1.9070) fix for mo_current mo (no-verify)	2024-07-19 18:05:49 +02:00
dr. M.S. (Matthijs) Berends	83907c9c65	add files (no-verify)	2024-07-17 14:31:09 +02:00
dr. M.S. (Matthijs) Berends	7258a491b9	(v2.1.1.9068) fix for mo_url() and as.mo() for synonyms	2024-07-17 14:29:55 +02:00
dr. M.S. (Matthijs) Berends	63f6790c58	add (no-verify)	2024-07-16 16:10:38 +02:00
dr. M.S. (Matthijs) Berends	b94dac770c	(v2.1.1.9066) git hook fix?	2024-07-16 16:09:33 +02:00
dr. M.S. (Matthijs) Berends	ff03bb6471	(v2.1.1.9065) unit tests 2	2024-07-16 15:58:18 +02:00
dr. M.S. (Matthijs) Berends	7f344836ea	(v2.1.1.9064) unit tests	2024-07-16 15:55:58 +02:00
dr. M.S. (Matthijs) Berends	640888f408	(v2.1.1.9064) update all microbial taxonomy, add mycobank, big documentation update	2024-07-16 14:53:17 +02:00
dr. M.S. (Matthijs) Berends	4f9db23684	check for windows unit test (no-check)	2024-06-23 16:18:42 +02:00
dr. M.S. (Matthijs) Berends	82585901a7	(v2.1.1.9061) animal host fix	2024-06-19 15:41:45 +02:00
dr. M.S. (Matthijs) Berends	c67d003e9e	(v2.1.1.9060) SDD results now in `as.sir()`	2024-06-19 15:08:23 +02:00
dr. M.S. (Matthijs) Berends	0c3d81f32e	(v2.1.1.9059) documentation fix	2024-06-17 22:26:05 +02:00
dr. M.S. (Matthijs) Berends	1bc2e04e1c	(v2.1.1.9058) fix hosts, add translations	2024-06-17 22:19:38 +02:00
dr. M.S. (Matthijs) Berends	a4dc37a4e4	(v2.1.1.9057) fix for missing breakpoints	2024-06-17 16:52:12 +02:00
dr. M.S. (Matthijs) Berends	d9e66fb118	(v2.1.1.9056) example fix	2024-06-17 14:52:18 +02:00
dr. M.S. (Matthijs) Berends	1e65b5a289	(v2.1.1.9055) example fix	2024-06-17 14:36:40 +02:00
dr. M.S. (Matthijs) Berends	13baf8d7be	(v2.1.1.9054) fix examples	2024-06-17 13:52:02 +02:00
dr. M.S. (Matthijs) Berends	2dee1d71dc	(v2.1.1.9053) add `verbose` to `as.sir()`, unit test fix	2024-06-17 10:38:45 +02:00
dr. M.S. (Matthijs) Berends	68f7795481	(v2.1.1.9052) unit test fix	2024-06-16 20:53:50 +02:00
dr. M.S. (Matthijs) Berends	4ffac7e22d	(v2.1.1.9051) fix VGS mo code	2024-06-16 11:28:56 +02:00