(v3.0.1.9085) website

When a genus+species abbreviation like "P. ovale" was used, the previous
bypass (Issue #288) checked sum(sp_exact) == 1, which failed if the species
also had subspecies sharing the epithet (ovale curtisi, ovale wallikeri).
The fix extends the bypass to fire whenever all exact species matches belong
to one genus, collapsing to the species-rank record (subspecies == "") for
genus+species queries and preserving the chosen row for explicit subspecies
queries.

Also extends the data-invariant test to cover all taxonomic rank columns
from domain to subspecies, not just the terminal three.


Claude-Session: https://claude.ai/code/session_01M4fqQYQYJ3drdudkDYNqAY

Co-authored-by: Claude <noreply@anthropic.com>

.github/workflows/check-old-tinytest.yaml

@@ -49,7 +49,7 @@ jobs:
           # Test all old versions of R >= 3.0, we support them all!
           # For these old versions, dependencies and vignettes will not be checked.
           # For recent R versions, see check-recent.yaml (r-lib and tidyverse support the latest 5 major R releases).
-          - {os: ubuntu-latest, r: '3.6', allowfail: false}
+          # - {os: ubuntu-latest, r: '3.6', allowfail: false}
           # - {os: windows-latest, r: '3.5', allowfail: false} # always fails, horrible with UTF-8
           # - {os: ubuntu-latest, r: '3.4', allowfail: false}  # 3.1-3.4 now always fails with Error in grep(warn_re, lines, invert = TRUE, value = TRUE) attempt to set index 46/46 in SET_STRING_ELT
           # - {os: ubuntu-latest, r: '3.3', allowfail: false}

.github/workflows/todo-tracker.yml

@@ -29,7 +29,6 @@
 
 on:
   push:
-    # only on main
     branches: "main"
 
 name: Update TODO Tracker
@@ -40,39 +39,227 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # full history required for git blame
 
-      - name: Generate TODO list from R/
+      - name: Generate TODO report
+        env:
+          GH_TOKEN: ${{ secrets.GH_REPO_SCOPE }}
         run: |
+          set -euo pipefail
           export TZ=Europe/Amsterdam
-          last_updated=$(date +"%e %B %Y %H:%M:%S %Z" | sed 's/^ *//')
-          echo "## \`TODO\` Report" > todo.md
-          echo "" >> todo.md
-          echo "**Last Updated: ${last_updated}**" >> todo.md
-          echo "" >> todo.md
-          echo "_This overview is automatically updated on each push to \`main\`. It provides an automated overview of all mentions of the text \`TODO\`._" >> todo.md
-          echo "" >> todo.md
-          todos=$(grep -rn --include=\*.{R,Rmd,yaml,yml,md,css,js}  --exclude={todo-tracker.yml,todo.md} "TODO" . || true)
-          if [ -z "$todos" ]; then
-            echo "✅ No TODOs found." >> todo.md
-          else
-            echo "$todos" | awk -F: -v repo="https://github.com/msberends/AMR/blob/main/" '
-              {
-                file = $1
-                gsub("^\\./", "", file)  # remove leading ./ if present
-                line = $2
-                text = substr($0, index($0,$3))
-                if (file != last_file) {
-                  if (last_file != "") print "```"
-                  print ""
-                  print "### [`" file "`](" repo file ")"
-                  print "```r"
-                  last_file = file
-                }
-                printf "L%s: %s\n", line, text
-              }
-            ' >> todo.md
-            echo "\`\`\`" >> todo.md
+
+          REPO="msberends/AMR"
+          REPO_URL="https://github.com/$REPO/blob/main"
+          NOW=$(date +%s)
+          LAST_UPDATED=$(date +"%e %B %Y %H:%M:%S %Z" | sed 's/^ *//')
+          STALE_DAYS=180
+
+          # ── helper: human-readable age ──────────────────────────────
+          format_age() {
+            local d=$1
+            if [ "$d" -lt 0 ] 2>/dev/null; then echo "unknown"; return; fi
+            local y=$((d / 365)) m=$(( (d % 365) / 30 ))
+            if [ "$y" -gt 0 ] && [ "$m" -gt 0 ]; then echo "${y}y ${m}m"
+            elif [ "$y" -gt 0 ]; then echo "${y}y"
+            elif [ "$m" -gt 0 ]; then echo "${m}m"
+            else echo "${d}d"
             fi
+          }
+
+          export -f format_age
+
+          # ── step 1: find all markers ────────────────────────────────
+          grep -rn \
+            --include='*.R' --include='*.Rmd' --include='*.yaml' \
+            --include='*.yml' --include='*.md' --include='*.css' \
+            --include='*.js' \
+            --exclude='todo-tracker.yml' --exclude='todo.md' \
+            -E '\b(TODO|FIXME|HACK|XXX)\b' . > /tmp/raw.txt || true
+
+          if [ ! -s /tmp/raw.txt ]; then
+            echo -e "## \`TODO\` Report\n\n**Last Updated: ${LAST_UPDATED}**\n\nNo markers found." > todo.md
+            exit 0
+          fi
+
+          # ── step 2: enrich with git blame & extract issue refs ──────
+          > /tmp/enriched.tsv
+          > /tmp/issues_seen.txt
+
+          while IFS= read -r match; do
+            clean=$(printf '%s\n' "$match" | sed 's|^\./||')
+            file=$(printf '%s\n' "$clean" | cut -d: -f1)
+            lineno=$(printf '%s\n' "$clean" | cut -d: -f2)
+            text=$(printf '%s\n' "$clean" | cut -d: -f3-)
+
+            # determine marker type (first match wins, TODO is default)
+            marker="TODO"
+            for m in FIXME HACK XXX; do
+              if printf '%s\n' "$text" | grep -qw "$m"; then marker="$m"; break; fi
+            done
+
+            # git blame timestamp
+            blame_ts=$(git blame -L "${lineno},${lineno}" --porcelain -- "$file" 2>/dev/null \
+              | awk '/^author-time/{print $2}' || echo "0")
+            blame_ts=${blame_ts:-0}
+
+            if [ "$blame_ts" -gt 0 ] 2>/dev/null; then
+              age_days=$(( (NOW - blame_ts) / 86400 ))
+            else
+              age_days=-1
+            fi
+
+            # extract issue references (#NNN)
+            issues=$(printf '%s\n' "$text" | grep -oE '#[0-9]+' | sed 's/#//' | tr '\n' ',' | sed 's/,$//' || true)
+            if [ -n "$issues" ]; then
+              for inum in $(echo "$issues" | tr ',' ' '); do
+                echo "$inum" >> /tmp/issues_seen.txt
+              done
+            fi
+
+            printf '%s\t%s\t%s\t%s\t%s\t%s\n' \
+              "$file" "$lineno" "$marker" "$age_days" "$issues" "$text" >> /tmp/enriched.tsv
+          done < /tmp/raw.txt
+
+          # ── step 3: query GitHub API for referenced issues ──────────
+          > /tmp/issue_info.tsv
+          if [ -s /tmp/issues_seen.txt ]; then
+            sort -un /tmp/issues_seen.txt | while read -r inum; do
+              info=$(gh api "/repos/$REPO/issues/$inum" \
+                --jq '"\(.state)\t\(.title)"' 2>/dev/null \
+                || echo "unknown	(could not fetch)")
+              printf '%s\t%s\n' "$inum" "$info" >> /tmp/issue_info.tsv
+            done
+          fi
+
+          # ── step 4: build the report ────────────────────────────────
+          {
+            # ── header ──
+            echo "## \`TODO\` Report"
+            echo ""
+            echo "**Last Updated: ${LAST_UPDATED}**"
+            echo ""
+            echo "_This overview is automatically updated on each push to \`main\`. It scans for \`TODO\`, \`FIXME\`, \`HACK\`, and \`XXX\` markers across the codebase._"
+            echo ""
+
+            # ── summary table ──
+            total=$(wc -l < /tmp/enriched.tsv | tr -d ' ')
+            files_affected=$(awk -F'\t' '{print $1}' /tmp/enriched.tsv | sort -u | wc -l | tr -d ' ')
+            todo_n=$(awk -F'\t' '$3=="TODO"' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            fixme_n=$(awk -F'\t' '$3=="FIXME"' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            hack_n=$(awk -F'\t' '$3=="HACK"' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            xxx_n=$(awk -F'\t' '$3=="XXX"' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            stale_n=$(awk -F'\t' -v s="$STALE_DAYS" '$4 > s' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            linked_n=$(awk -F'\t' '$5 != ""' /tmp/enriched.tsv | wc -l | tr -d ' ')
+            unlinked_n=$(awk -F'\t' '$5 == ""' /tmp/enriched.tsv | wc -l | tr -d ' ')
+
+            # oldest marker
+            oldest_line=$(awk -F'\t' '$4 >= 0' /tmp/enriched.tsv | sort -t$'\t' -k4 -rn | head -1)
+            oldest_days=$(echo "$oldest_line" | cut -f4)
+            oldest_file=$(echo "$oldest_line" | cut -f1)
+            oldest_lineno=$(echo "$oldest_line" | cut -f2)
+            oldest_age=$(format_age "$oldest_days")
+
+            echo "### Summary"
+            echo ""
+            echo "| Metric | Value |"
+            echo "|:---|---:|"
+            echo "| Total markers | **${total}** |"
+            [ "$todo_n" -gt 0 ] && echo "| \`TODO\` | ${todo_n} |"
+            [ "$fixme_n" -gt 0 ] && echo "| \`FIXME\` | ${fixme_n} |"
+            [ "$hack_n" -gt 0 ] && echo "| \`HACK\` | ${hack_n} |"
+            [ "$xxx_n" -gt 0 ] && echo "| \`XXX\` | ${xxx_n} |"
+            echo "| Files affected | ${files_affected} |"
+            echo "| Stale (> 6 months) | ${stale_n} |"
+            echo "| Oldest marker | ${oldest_age}, \`${oldest_file}\` L${oldest_lineno} |"
+            echo "| Linked to issues | ${linked_n} |"
+            echo "| Unlinked (no issue ref) | ${unlinked_n} |"
+            echo ""
+
+            # ── by referenced issue ──
+            if [ -s /tmp/issue_info.tsv ]; then
+              echo "### By Referenced Issue"
+              echo ""
+
+              has_closed=false
+
+              while IFS=$'\t' read -r inum state title; do
+                count=$(awk -F'\t' -v n="$inum" '$5 ~ "(^|,)"n"(,|$)"' /tmp/enriched.tsv | wc -l | tr -d ' ')
+                [ "$state" = "closed" ] && has_closed=true
+
+                state_icon=""
+                [ "$state" = "closed" ] && state_icon=" :warning:"
+
+                echo "<details><summary><b>#${inum}</b> (${state}): <i>${title}</i> &mdash; ${count} marker(s)${state_icon}</summary>"
+                echo ""
+
+                awk -F'\t' -v n="$inum" '$5 ~ "(^|,)"n"(,|$)"' /tmp/enriched.tsv \
+                | while IFS=$'\t' read -r f l m d refs txt; do
+                  age_str=$(format_age "$d")
+                  flag=""
+                  [ "$d" -gt "$STALE_DAYS" ] 2>/dev/null && flag=" :warning:"
+                  # re-read the actual source line and trim leading/trailing whitespace
+                  src_text=$(sed -n "${l}p" "$f" 2>/dev/null | sed 's/^[[:space:]]*//;s/[[:space:]]*$//' || true)
+                  echo "- [\`${f}\` L${l}](${REPO_URL}/${f}#L${l}) (${age_str} ago)${flag}"
+                  [ -n "$src_text" ] && echo "  \`${src_text}\`"
+                done
+
+                echo ""
+                echo "</details>"
+                echo ""
+              done < /tmp/issue_info.tsv
+
+              if [ "$has_closed" = true ]; then
+                echo "> **Warning:** some markers reference closed issues and may be stale."
+                echo ""
+              fi
+            fi
+
+            # ── by file ──
+            echo "### By File"
+            echo ""
+
+            prev_file=""
+            prev_lineno=-99
+
+            while IFS=$'\t' read -r file lineno marker age_days issues text; do
+              if [ "$file" != "$prev_file" ]; then
+                # close previous code block
+                if [ -n "$prev_file" ]; then
+                  echo '```'
+                  echo ""
+                fi
+
+                file_count=$(awk -F'\t' -v f="$file" '$1==f' /tmp/enriched.tsv | wc -l | tr -d ' ')
+                echo "#### [\`${file}\`](${REPO_URL}/${file}) &mdash; ${file_count} marker(s)"
+                echo '```r'
+
+                prev_lineno=-99
+              fi
+
+              # blank line between non-sequential lines (visual grouping)
+              if [ "$file" = "$prev_file" ] && [ $((lineno - prev_lineno)) -gt 1 ]; then
+                echo ""
+              fi
+
+              age_str=$(format_age "$age_days")
+              flag=""
+              [ "$age_days" -gt "$STALE_DAYS" ] 2>/dev/null && flag=" !!"
+
+              # re-read the actual source line to avoid TSV round-trip corruption
+              src_line=$(sed -n "${lineno}p" "$file" 2>/dev/null | sed 's/[[:space:]]*$//' || true)
+              printf 'L%s: %s  ◁ %s ago%s\n' "$lineno" "$src_line" "$age_str" "$flag"
+
+              prev_file="$file"
+              prev_lineno="$lineno"
+            done < <(sort -t$'\t' -k1,1 -k2,2n /tmp/enriched.tsv)
+
+            # close final code block
+            if [ -n "$prev_file" ]; then
+              echo '```'
+            fi
+
+          } > todo.md
 
       - name: Update GitHub issue
         uses: peter-evans/create-or-update-comment@v4

.gitignore

@@ -22,6 +22,7 @@ vignettes/*.R
 ^CRAN-RELEASE$
 packrat/lib*/
 packrat/src/
+*~$*
 data-raw/taxa.txt
 data-raw/taxon.tab
 data-raw/CLSI*.pdf

CLAUDE.md

@@ -85,6 +85,27 @@ _pkgdown.yml    # pkgdown website configuration
 - `translate.R` — 28-language translation system
 - `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation functions
 
+## Code Style
+
+Follow the [tidyverse style guide](https://style.tidyverse.org/) precisely. Key rules:
+
+- 2-space indentation; no tabs
+- `<-` for assignment, not `=`
+- Spaces around all binary operators and after commas; no spaces inside parentheses
+- When a function call must break across lines, place the first argument on a new line indented by 2 spaces, and put the closing `)` on its own line — **never align arguments to the opening parenthesis** (no hanging/forced mid-line indentation)
+
+```r
+# good
+stop_(
+  "some long message part one ",
+  "part two"
+)
+
+# bad — forces indentation to match the opening parenthesis
+stop_("some long message part one ",
+      "part two")
+```
+
 ## Custom S3 Classes
 
 The package defines five S3 classes with full print/format/plot/vctrs support:

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.1.9065
-Date: 2026-06-24
+Version: 3.0.1.9085
+Date: 2026-07-09
 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

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 3.0.1.9065
+# AMR 3.0.1.9085
 
 Planned as v3.1.0, end of June 2026.
 
@@ -20,6 +20,7 @@ Planned as v3.1.0, end of June 2026.
 * New `wisca_plot()` to assess the susceptibility and incidence distributions from the Monte Carlo simulations
 
 ### Fixed
+* Setting `options(AMR_guideline = "EUCAST 2012")` or any year-qualified value no longer causes errors or silent wrong behaviour in `interpretive_rules()`, `resistance()`, `susceptibility()`, `count_resistant()`, `count_susceptible()`, and SIR plotting/printing functions (#298)
 * `as.sir()`
   * On data frames: already-converted SIR columns no longer dropped on re-run (#278)
   * Metadata columns (e.g. `patient`, `ward`) no longer misidentified as antibiotic columns
@@ -31,12 +32,14 @@ Planned as v3.1.0, end of June 2026.
 * `as.mo()`: 
   * Input of the form `"X complex"` now falls back to `"X"` when the complex is not a distinct taxon in the database, preventing `NA` results for valid clinical descriptions such as `"Proteus vulgaris complex"` (#287)
   * Abbreviated-genus input (e.g. `"S. apiospermum"`) now correctly ranks candidates whose species epithet exactly matches the input above more-prevalent organisms whose species does not match; fixes `"S. apiospermum"` resolving to *Staphylococcus* instead of *Scedosporium apiospermum* (#288)
+  * Abbreviated-genus input for species that have subspecies (e.g. `"P. ovale"`) now collapses to the species-rank record instead of incorrectly matching a more-prevalent organism; explicit subspecies queries (e.g. `"P. ovale curtisi"`) are preserved (#288)
 * `get_author_year()` in the microorganism reproduction script now strips `emend.` and everything after it, so `ref` reflects the combination authority rather than the emendation author (e.g. *Rhodococcus equi* now returns "Goodfellow et al., 1977" instead of "Nouioui et al., 2018")
 * BRMO classification now includes bacterial complexes (#275)
 * Translation fixes for Italian CoNS/CoPS names (#256), Dutch antimicrobials, and `sir_df()` foreign-language output (#272)
 * Fixed some EUCAST Expert Rules, mostly on *S. pneumoniae*
 
 ### Updated
+* `top_n_microorganisms()`: new `property_for_each` argument for sub-grouping within top *n* groups; rank ordering enforced (only lower taxonomic ranks allowed); fixed `property = NULL` not being accepted; inner filter now tracks original row indices to prevent cross-group contamination
 * Taxonomic update for all microorganisms, now updated to June 2026
 * `mo_kingdom()` now returns the formal taxonomic kingdom; a one-time note per session explains the change when querying bacterial or archaeal records.
 * `mo_taxonomy()` and `mo_info()` gained `domain` for the list output
@@ -50,7 +53,8 @@ Planned as v3.1.0, end of June 2026.
 * `antimicrobials$group` is now a `list`, so that drugs belonging to multiple groups are fully represented; use `ab_group(all_groups = TRUE)` to retrieve all groups for a drug (#246)
 * Improved console messages with clickable links throughout, powered by `cli` if it is installed (#191, #265)
 * `as.disk()`: input validation is now more strict, rejecting values that are not recognisable as a numeric disk zone diameter
-
+* `as.sir()` gains an `enforce_method` argument (`"auto"`, `"mic"`, or `"disk"`) to force the interpretation method when S3 class information is lost, e.g. when called from Python (#291)
+* `AMR for Python` vignette: added sections on installation channels (stable CRAN vs. development GitHub via `AMR.beta`) and on using `enforce_method` in `as_sir()` from Python
 
 # AMR 3.0.1
 

R/amr_selectors.R

@@ -202,7 +202,7 @@
 #' # data.table --------------------------------------------------------------
 #'
 #' # data.table is supported as well, just use it in the same way as with
-#' # base R, but add `with = FALSE` if using a single AB selector.
+#' # base R, but add `with = FALSE` if using a single AMR selector.
 #'
 #' if (require("data.table")) {
 #'   dt <- as.data.table(example_isolates)
@@ -215,7 +215,7 @@
 #'   dt[, carbapenems(), with = FALSE]
 #' }
 #'
-#' # for multiple selections or AB selectors, `with = FALSE` is not needed:
+#' # for multiple selections or AMR selectors, `with = FALSE` is not needed:
 #' if (require("data.table")) {
 #'   dt[, c("mo", aminoglycosides())]
 #' }

R/count.R

@@ -126,6 +126,11 @@ count_resistant <- function(...,
                             only_all_tested = FALSE,
                             guideline = getOption("AMR_guideline", "EUCAST")) {
   # other arguments for meet_criteria are handled by sir_calc()
+  if (guideline %like% "EUCAST") {
+    guideline <- "EUCAST"
+  } else if (guideline %like% "CLSI") {
+    guideline <- "CLSI"
+  }
   meet_criteria(guideline, allow_class = "character", is_in = c("EUCAST", "CLSI"), has_length = 1)
   if (is.null(getOption("AMR_guideline")) && missing(guideline) && message_not_thrown_before("count_resistant", "eucast_default", entire_session = TRUE)) {
     message_("{.help [{.fun count_resistant}](AMR::count_resistant)} assumes the EUCAST guideline and thus considers the 'I' category susceptible. Set the {.arg guideline} argument or the {.code AMR_guideline} option to either \"CLSI\" or \"EUCAST\", see {.topic [AMR-options](AMR::AMR-options)}.")
@@ -150,6 +155,11 @@ count_susceptible <- function(...,
                               only_all_tested = FALSE,
                               guideline = getOption("AMR_guideline", "EUCAST")) {
   # other arguments for meet_criteria are handled by sir_calc()
+  if (guideline %like% "EUCAST") {
+    guideline <- "EUCAST"
+  } else if (guideline %like% "CLSI") {
+    guideline <- "CLSI"
+  }
   meet_criteria(guideline, allow_class = "character", is_in = c("EUCAST", "CLSI"), has_length = 1)
   if (is.null(getOption("AMR_guideline")) && missing(guideline) && message_not_thrown_before("count_susceptible", "eucast_default", entire_session = TRUE)) {
     message_("{.help [{.fun count_susceptible}](AMR::count_susceptible)} assumes the EUCAST guideline and thus considers the 'I' category susceptible. Set the {.arg guideline} argument or the {.code AMR_guideline} option to either \"CLSI\" or \"EUCAST\", see {.topic [AMR-options](AMR::AMR-options)}.")

R/data.R

@@ -143,11 +143,10 @@
 #' ### Manual additions
 #' For convenience, some entries were added manually:
 #'
+#' - All `r format_included_data_number(length(which(microorganisms$rank == "species group")))` groups and complexes of the [microorganisms.groups] data set, for cross-reference (examples include beta-haemolytic *Streptococcus* groups A to K, coagulase-negative *Staphylococcus* (CoNS), *Mycobacterium tuberculosis* complex, etc.)
 #' - `r format_included_data_number(microorganisms[which(microorganisms$source == "manually added" & microorganisms$genus == "Salmonella"), , drop = FALSE])` entries of *Salmonella*, such as the city-like serovars and groups A to H
-#' - `r format_included_data_number(length(which(microorganisms$rank == "species group")))` species groups (such as the beta-haemolytic *Streptococcus* groups A to K, coagulase-negative *Staphylococcus* (CoNS), *Mycobacterium tuberculosis* complex, etc.), of which the group compositions are stored in the [microorganisms.groups] data set
 #' - 1 entry of *Blastocystis* (*B.  hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993)
-#' - 1 entry of *Moraxella* (*M. catarrhalis*), which was formally named *Branhamella catarrhalis* (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
-#' - 8 other 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, and unknown anaerobic Gram-pos/Gram-neg bacteria)
+#' - `r sum(microorganisms$fullname %like% "unknown")` 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_scripts/reproduction_of_microorganisms.R).
 #' @inheritSection AMR Download Our Reference Data

R/interpretive_rules.R

@@ -110,13 +110,8 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
 #' @references
 #' - EUCAST Expert Rules. Version 2.0, 2012.\cr
 #'   Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60; \doi{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
-#' - EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf)
-#' - EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.2, 2020. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf)
-#' - EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.3, 2021. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2021/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.3_20211018.pdf)
-#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx)
-#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 10.0, 2020. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_10.0_Breakpoint_Tables.xlsx)
-#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 11.0, 2021. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_11.0_Breakpoint_Tables.xlsx)
-#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 12.0, 2022. [(link)](https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_12.0_Breakpoint_Tables.xlsx)
+#' - EUCAST Expected Phenotypes. [(link)](https://www.eucast.org/bacteria/important-additional-information/expected-phenotypes/)
+#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. [(link)](https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/clinical-breakpoint-tables/)
 #' @inheritSection AMR Download Our Reference Data
 #' @examples
 #' \donttest{
@@ -175,6 +170,11 @@ interpretive_rules <- function(x,
                                ...) {
   meet_criteria(x, allow_class = "data.frame")
   meet_criteria(col_mo, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
+  if (guideline %like% "EUCAST") {
+    guideline <- "EUCAST"
+  } else if (guideline %like% "CLSI") {
+    guideline <- "CLSI"
+  }
   meet_criteria(guideline, allow_class = "character", has_length = 1, is_in = c("EUCAST", "CLSI"))
   meet_criteria(info, allow_class = "logical", has_length = 1)
   meet_criteria(rules, allow_class = "character", has_length = c(1, 2, 3, 4, 5, 6), is_in = c("breakpoints", "expected_phenotypes", "expert", "other", "all", "custom"))
@@ -200,12 +200,6 @@ interpretive_rules <- function(x,
 
   add_MO_lookup_to_AMR_env()
 
-  if (guideline %like% "EUCAST") {
-    guideline <- "EUCAST"
-  } else if (guideline %like% "CLSI") {
-    guideline <- "CLSI"
-  }
-
   if ("custom" %in% rules && is.null(custom_rules)) {
     warning_("in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: no custom rules were set with the {.arg custom_rules} argument",
       immediate = TRUE

R/mo.R

@@ -352,16 +352,34 @@ as.mo <- function(x,
             (MO_lookup_current$species_first == substr(x_parts[2], 1, 1) |
               MO_lookup_current$subspecies_first == substr(x_parts[2], 1, 1) |
               MO_lookup_current$subspecies_first == substr(x_parts[3], 1, 1)))
-          # Issue #288: if the species (and subspecies) word(s) in the input exactly match
-          # exactly one candidate, use only that candidate and bypass the 0.55 cutoff.
-          # This prevents prevalent bacteria from outranking a rarer organism whose species
-          # epithet is an unambiguous exact match, e.g. "S. apiospermum" → Scedosporium.
+          # Issue #288 (extended): if the species (and subspecies) word(s) in the input
+          # exactly match candidates that all belong to one and the same genus, bypass the
+          # 0.55 cutoff. A species together with its subspecies/autonyms (e.g. Plasmodium
+          # ovale + curtisi + wallikeri) is the same taxon, so for a genus+species input we
+          # collapse to the species-rank record (subspecies == ""). This prevents prevalent
+          # bacteria from outranking a rarer organism whose species epithet is an
+          # unambiguous exact match, e.g. "S. apiospermum" -> Scedosporium, "P. ovale" ->
+          # Plasmodium ovale. If two different genera share the epithet, the genus check
+          # stays FALSE and the normal matching score arbitrates.
           sp_exact <- tolower(MO_lookup_current$species[filtr]) == x_parts[2]
           if (length(x_parts) == 3) {
             sp_exact <- sp_exact & tolower(MO_lookup_current$subspecies[filtr]) == x_parts[3]
           }
-          if (sum(sp_exact) == 1) {
-            filtr <- filtr[sp_exact]
+          exact_idx <- filtr[sp_exact]
+          if (length(exact_idx) >= 1 &&
+            length(unique(MO_lookup_current$genus_lower[exact_idx])) == 1) {
+            if (length(x_parts) == 2) {
+              # genus + species only: collapse to the species-rank record (subspecies == "")
+              is_species_rank <- MO_lookup_current$subspecies[exact_idx] == ""
+              if (any(is_species_rank)) {
+                filtr <- exact_idx[is_species_rank][1]
+              } else {
+                filtr <- exact_idx[1]
+              }
+            } else {
+              # explicit subspecies given, unambiguous within the genus
+              filtr <- exact_idx[1]
+            }
             minimum_matching_score <- 0
           }
         } else {

R/mo_property.R

@@ -83,6 +83,7 @@
 #' @examples
 #' # taxonomic tree -----------------------------------------------------------
 #'
+#' mo_domain("Klebsiella pneumoniae")
 #' mo_kingdom("Klebsiella pneumoniae")
 #' mo_phylum("Klebsiella pneumoniae")
 #' mo_class("Klebsiella pneumoniae")
@@ -92,6 +93,8 @@
 #' mo_species("Klebsiella pneumoniae")
 #' mo_subspecies("Klebsiella pneumoniae")
 #'
+#' # all in one go
+#' mo_taxonomy("Klebsiella pneumoniae")
 #'
 #' # full names and short names -----------------------------------------------
 #'
@@ -112,6 +115,7 @@
 #' mo_url("Klebsiella pneumoniae")
 #' mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
 #'
+#' mo_group_members("Streptococcus group A")
 #' mo_group_members(c(
 #'   "Streptococcus group A",
 #'   "Streptococcus group C",
@@ -155,6 +159,7 @@
 #'
 #' mo_fullname("Staph epidermidis")
 #' mo_fullname("Staph epidermidis", Becker = TRUE)
+#'
 #' mo_shortname("Staph epidermidis")
 #' mo_shortname("Staph epidermidis", Becker = TRUE)
 #'
@@ -163,6 +168,7 @@
 #'
 #' mo_fullname("Strep agalactiae")
 #' mo_fullname("Strep agalactiae", Lancefield = TRUE)
+#'
 #' mo_shortname("Strep agalactiae")
 #' mo_shortname("Strep agalactiae", Lancefield = TRUE)
 #'
@@ -175,10 +181,10 @@
 #' mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek
 #' mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian
 #'
-#' # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated
-#' mo_kingdom("Klebsiella pneumoniae")
+#' # mo_type is equal to mo_domain, but mo_domain will remain untranslated
+#' mo_domain("Klebsiella pneumoniae")
 #' mo_type("Klebsiella pneumoniae")
-#' mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect
+#' mo_domain("Klebsiella pneumoniae", language = "zh") # Chinese, no effect
 #' mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated
 #'
 #' mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de")
@@ -807,19 +813,19 @@ mo_taxonomy <- function(x, language = get_AMR_locale(), keep_synonyms = getOptio
   language <- validate_language(language)
   meet_criteria(keep_synonyms, allow_class = "logical", has_length = 1)
 
-  x <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
+  x.mo <- as.mo(x, language = language, keep_synonyms = keep_synonyms, ...)
   metadata <- get_mo_uncertainties()
 
   out <- list(
-    domain = mo_domain(x, language = language, keep_synonyms = keep_synonyms),
-    kingdom = mo_kingdom(x, language = language, keep_synonyms = keep_synonyms),
-    phylum = mo_phylum(x, language = language, keep_synonyms = keep_synonyms),
-    class = mo_class(x, language = language, keep_synonyms = keep_synonyms),
-    order = mo_order(x, language = language, keep_synonyms = keep_synonyms),
-    family = mo_family(x, language = language, keep_synonyms = keep_synonyms),
-    genus = mo_genus(x, language = language, keep_synonyms = keep_synonyms),
-    species = mo_species(x, language = language, keep_synonyms = keep_synonyms),
-    subspecies = mo_subspecies(x, language = language, keep_synonyms = keep_synonyms)
+    domain = mo_domain(x.mo, language = language, keep_synonyms = keep_synonyms),
+    kingdom = suppressMessages(mo_kingdom(x.mo, language = language, keep_synonyms = keep_synonyms)),
+    phylum = mo_phylum(x.mo, language = language, keep_synonyms = keep_synonyms),
+    class = mo_class(x.mo, language = language, keep_synonyms = keep_synonyms),
+    order = mo_order(x.mo, language = language, keep_synonyms = keep_synonyms),
+    family = mo_family(x.mo, language = language, keep_synonyms = keep_synonyms),
+    genus = mo_genus(x.mo, language = language, keep_synonyms = keep_synonyms),
+    species = mo_species(x.mo, language = language, keep_synonyms = keep_synonyms),
+    subspecies = mo_subspecies(x.mo, language = language, keep_synonyms = keep_synonyms)
   )
 
   load_mo_uncertainties(metadata)

R/plotting.R

@@ -482,7 +482,7 @@ scale_x_sir <- function(colours_SIR = c(
                           R = "#ED553B"
                         ),
                         language = get_AMR_locale(),
-                        eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+                        eucast_I = getOption("AMR_guideline", "EUCAST") %like% "EUCAST",
                         ...) {
   meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
@@ -499,7 +499,7 @@ scale_colour_sir <- function(colours_SIR = c(
                                R = "#ED553B"
                              ),
                              language = get_AMR_locale(),
-                             eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+                             eucast_I = getOption("AMR_guideline", "EUCAST") %like% "EUCAST",
                              ...) {
   meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
@@ -528,7 +528,7 @@ scale_fill_sir <- function(colours_SIR = c(
                              R = "#ED553B"
                            ),
                            language = get_AMR_locale(),
-                           eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+                           eucast_I = getOption("AMR_guideline", "EUCAST") %like% "EUCAST",
                            ...) {
   meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)

R/proportion.R

@@ -236,6 +236,11 @@ resistance <- function(...,
                        only_all_tested = FALSE,
                        guideline = getOption("AMR_guideline", "EUCAST")) {
   # other arguments for meet_criteria are handled by sir_calc()
+  if (guideline %like% "EUCAST") {
+    guideline <- "EUCAST"
+  } else if (guideline %like% "CLSI") {
+    guideline <- "CLSI"
+  }
   meet_criteria(guideline, allow_class = "character", is_in = c("EUCAST", "CLSI"), has_length = 1)
   if (is.null(getOption("AMR_guideline")) && missing(guideline) && message_not_thrown_before("resistance", "eucast_default", entire_session = TRUE)) {
     message_("{.help [{.fun resistance}](AMR::resistance)} assumes the EUCAST guideline and thus considers the 'I' category susceptible. Set the {.arg guideline} argument or the {.code AMR_guideline} option to either \"CLSI\" or \"EUCAST\", see {.topic [AMR-options](AMR::AMR-options)}.")
@@ -264,6 +269,11 @@ susceptibility <- function(...,
                            only_all_tested = FALSE,
                            guideline = getOption("AMR_guideline", "EUCAST")) {
   # other arguments for meet_criteria are handled by sir_calc()
+  if (guideline %like% "EUCAST") {
+    guideline <- "EUCAST"
+  } else if (guideline %like% "CLSI") {
+    guideline <- "CLSI"
+  }
   meet_criteria(guideline, allow_class = "character", is_in = c("EUCAST", "CLSI"), has_length = 1)
   if (is.null(getOption("AMR_guideline")) && missing(guideline) && message_not_thrown_before("susceptibility", "eucast_default", entire_session = TRUE)) {
     message_("{.help [{.fun susceptibility}](AMR::susceptibility)} assumes the EUCAST guideline and thus considers the 'I' category susceptible. Set the {.arg guideline} argument or the {.code AMR_guideline} option to either \"CLSI\" or \"EUCAST\", see {.topic [AMR-options](AMR::AMR-options)}.")

R/sir.R

@@ -73,6 +73,7 @@ VALID_SIR_LEVELS <- c("S", "SDD", "I", "R", "NI", "WT", "NWT", "NS")
 #' @param threshold Maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*.
 #' @param conserve_capped_values Deprecated, use `capped_mic_handling` instead.
 #' @param ... For using on a [data.frame]: selection of columns to apply `as.sir()` to. Supports [tidyselect language][tidyselect::starts_with()] such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be [antimicrobial selectors][amr_selector()], e.g. `as.sir(df, penicillins())`.
+#' @param enforce_method A [character] string to force interpretation as a specific method, useful when the S3 class of `x` is lost (e.g., when called from Python via rpy2). Must be one of `"auto"` (default), `"mic"`, or `"disk"`.
 #'
 #' Otherwise: arguments passed on to methods.
 #' @details
@@ -385,9 +386,16 @@ VALID_SIR_LEVELS <- c("S", "SDD", "I", "R", "NI", "WT", "NWT", "NS")
 #'   #   mutate(across(where(is_sir_eligible), as.sir))
 #' }
 #' }
-as.sir <- function(x, ...) {
+as.sir <- function(x, ..., enforce_method = "auto") {
+  meet_criteria(enforce_method, allow_class = "character", has_length = 1, is_in = c("auto", "mic", "disk"))
+  if (enforce_method == "mic") {
+    as.sir.mic(x, ...)
+  } else if (enforce_method == "disk") {
+    as.sir.disk(x, ...)
+  } else {
     UseMethod("as.sir")
   }
+}
 
 as_sir_structure <- function(x,
                              guideline = NULL,
@@ -525,7 +533,7 @@ as.sir.default <- function(x,
   } else if (!all(is.na(x)) && !identical(levels(x), VALID_SIR_LEVELS) && !all(x %in% c(VALID_SIR_LEVELS, NA))) {
     if (all(x %unlike% "(S|I|R)", na.rm = TRUE) && !all(x %in% c(1, 2, 3, 4, 5), na.rm = TRUE)) {
       # check if they are actually MICs or disks
-      if (all_valid_mics(x) && !(all_valid_disks(x) && identical(x, floor(x)))) {
+      if (all_valid_mics(x) && !(all_valid_disks(x) && identical(x, tryCatch(floor(x), error = function(e) NULL)))) {
         warning_("in {.help [{.fun as.sir}](AMR::as.sir)}: input values were guessed to be MIC values - preferably transform them with {.help [{.fun as.mic}](AMR::as.mic)} before running {.help [{.fun as.sir}](AMR::as.sir)}.")
         return(as.sir(as.mic(x), ...))
       } else if (all_valid_disks(x)) {
@@ -2111,7 +2119,7 @@ pillar_shaft.sir <- function(x, ...) {
     out[is.na(x)] <- pillar::style_subtle("  NA")
     out[x == "S"] <- font_green_bg("  S  ") # has font_black internally
     out[x == "SDD"] <- font_green_lighter_bg(" SDD ") # has font_black internally
-    if (getOption("AMR_guideline", "EUCAST")[1] == "EUCAST") {
+    if (getOption("AMR_guideline", "EUCAST")[1] %like% "EUCAST") {
       out[x == "I"] <- font_green_lighter_bg("  I  ") # has font_black internally
     } else {
       out[x == "I"] <- font_orange_bg("  I  ") # has font_black internally

R/tidymodels.R

@@ -126,7 +126,8 @@ step_mic_log2 <- function(
   trained = FALSE,
   columns = NULL,
   skip = FALSE,
-    id = recipes::rand_id("mic_log2")) {
+  id = recipes::rand_id("mic_log2")
+) {
   recipes::add_step(
     recipe,
     step_mic_log2_new(
@@ -201,7 +202,8 @@ step_sir_numeric <- function(
   trained = FALSE,
   columns = NULL,
   skip = FALSE,
-    id = recipes::rand_id("sir_numeric")) {
+  id = recipes::rand_id("sir_numeric")
+) {
   recipes::add_step(
     recipe,
     step_sir_numeric_new(

R/top_n_microorganisms.R

@@ -29,73 +29,88 @@
 
 #' Filter Top *n* Microorganisms
 #'
-#' This function filters a data set to include only the top *n* microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, or to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
+#' Filters a data set to include only the top *n* microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
 #' @param x A data frame containing microbial data.
-#' @param n An integer specifying the maximum number of unique values of the `property` to include in the output.
-#' @param property A character string indicating the microorganism property to use for filtering. Must be one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, documentation = TRUE)`. If `NULL`, the raw values from `col_mo` will be used without transformation. When using `"species"` (default) or `"subpecies"`, the genus will be added to make sure each (sub)species still belongs to the right genus.
-#' @param n_for_each An optional integer specifying the maximum number of rows to retain for each value of the selected property. If `NULL`, all rows within the top *n* groups will be included.
+#' @param n A positive whole number specifying the maximum number of unique values of `property` to include in the output.
+#' @param property A character string indicating the microorganism property to use for filtering. Must be one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, documentation = TRUE)`. If `NULL`, the raw values from `col_mo` will be used without transformation. When using `"species"` (default) or `"subspecies"`, the genus is prepended to ensure each name is unambiguous.
+#' @param n_for_each An optional positive whole number specifying the maximum number of distinct microorganism groups at the level of `property_for_each` to retain within each of the top *n* groups. Only used when `property_for_each` is also set.
+#' @param property_for_each The microorganism property to use for sub-grouping within each top *n* group. Must be one of the column names of the [microorganisms] data set and at a strictly lower taxonomic rank than `property` (allowed order: domain > kingdom > phylum > class > order > family > genus > species > subspecies). Defaults to `"species"`. Only relevant when `n_for_each` is set.
 #' @param col_mo A character string indicating the column in `x` that contains microorganism names or codes. Defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
 #' @param ... Additional arguments passed on to [mo_property()] when `property` is not `NULL`.
-#' @details This function is useful for preprocessing data before creating [antibiograms][antibiogram()] or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
+#' @details This function is useful for preprocessing data before creating [antibiograms][antibiogram()] or other analyses that require focused subsets of microbial data.
 #' @export
 #' @seealso [mo_property()], [as.mo()], [antibiogram()]
 #' @examples
 #' # filter to the top 3 species:
-#' top_n_microorganisms(example_isolates,
-#'   n = 3
-#' )
+#' top_n_microorganisms(example_isolates, n = 3)
 #'
 #' # filter to any species in the top 5 genera:
-#' top_n_microorganisms(example_isolates,
-#'   n = 5, property = "genus"
-#' )
+#' top_n_microorganisms(example_isolates, n = 5, property = "genus")
 #'
 #' # filter to the top 3 species in each of the top 5 genera:
 #' top_n_microorganisms(example_isolates,
 #'   n = 5, property = "genus", n_for_each = 3
 #' )
-top_n_microorganisms <- function(x, n, property = "species", n_for_each = NULL, col_mo = NULL, ...) {
+#'
+#' # filter to the top 2 genera in each of the top 3 families:
+#' top_n_microorganisms(example_isolates,
+#'   n = 3, property = "family", n_for_each = 2, property_for_each = "genus"
+#' )
+top_n_microorganisms <- function(x, n, property = "species", n_for_each = NULL, property_for_each = "species", col_mo = NULL, ...) {
   meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
   meet_criteria(n, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
-  meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms))
+  meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms), allow_NULL = TRUE)
   meet_criteria(n_for_each, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE, allow_NULL = TRUE)
+  meet_criteria(property_for_each, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms), allow_NULL = TRUE)
   meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+
   if (is.null(col_mo)) {
     col_mo <- search_type_in_df(x = x, type = "mo", info = TRUE)
     stop_if(is.null(col_mo), "{.arg col_mo} must be set")
   }
 
-  x.bak <- x
+  .taxonomic_ranks <- c("domain", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies")
+  if (!is.null(n_for_each) && !is.null(property) && !is.null(property_for_each)) {
+    prop_rank <- match(property, .taxonomic_ranks)
+    each_rank <- match(property_for_each, .taxonomic_ranks)
+    if (!is.na(prop_rank) && !is.na(each_rank) && each_rank <= prop_rank) {
+      stop_(
+        "`property_for_each` (\"", property_for_each, "\") must be at a lower ",
+        "taxonomic rank than `property` (\"", property, "\")"
+      )
+    }
+  }
 
+  x.bak <- x
   x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE], keep_synonyms = TRUE)
 
-  if (is.null(property)) {
-    x$prop_val <- x[[col_mo]]
-  } else if (property == "species") {
-    x$prop_val <- paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...))
-  } else if (property == "subspecies") {
-    x$prop_val <- paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...), mo_subspecies(x[[col_mo]], ...))
+  get_prop_val <- function(prop) {
+    if (is.null(prop)) {
+      x[[col_mo]]
+    } else if (prop == "species") {
+      paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...))
+    } else if (prop == "subspecies") {
+      paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...), mo_subspecies(x[[col_mo]], ...))
     } else {
-    x$prop_val <- mo_property(x[[col_mo]], property = property, ...)
+      mo_property(x[[col_mo]], property = prop, ...)
+    }
   }
-  counts <- sort(table(x$prop_val), decreasing = TRUE)
 
-  n <- as.integer(n)
-  if (length(counts) < n) {
-    n <- length(counts)
-  }
-  count_values <- names(counts)[seq_len(n)]
-  filtered_rows <- which(x$prop_val %in% count_values)
+  x$prop_val <- get_prop_val(property)
+  counts <- sort(table(x$prop_val), decreasing = TRUE)
+  n <- min(as.integer(n), length(counts))
+  filtered_rows <- which(x$prop_val %in% names(counts)[seq_len(n)])
 
   if (!is.null(n_for_each)) {
     n_for_each <- as.integer(n_for_each)
+    x$prop_val_each <- get_prop_val(property_for_each)
     filtered_x <- x[filtered_rows, , drop = FALSE]
+    filtered_x$.orig_row <- filtered_rows
     filtered_rows <- do.call(
       c,
       lapply(split(filtered_x, filtered_x$prop_val), function(group) {
-        top_values <- names(sort(table(group[[col_mo]]), decreasing = TRUE)[seq_len(n_for_each)])
-        top_values <- top_values[!is.na(top_values)]
-        which(x[[col_mo]] %in% top_values)
+        top_each <- names(sort(table(group$prop_val_each), decreasing = TRUE)[seq_len(n_for_each)])
+        group$.orig_row[group$prop_val_each %in% top_each[!is.na(top_each)]]
       })
     )
   }

README.Rmd

@@ -11,6 +11,7 @@ knitr::opts_chunk$set(
   # fig.path = "man/figures/README-",
   out.width = "100%"
 )
+options(width = 100)
 AMR:::reset_all_thrown_messages()
 ```
 
@@ -21,7 +22,7 @@ Please visit our comprehensive package website <https://amr-for-r.org> to read m
 Overview:
 
 * Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
-* **Peer-reviewed**, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
+* **Peer-reviewed**, used in over 175 countries, cited over 100 times, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
 * Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
 * Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs
 * Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
@@ -31,7 +32,9 @@ Overview:
 
 ----
 
-The `AMR` package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+The `AMR` package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.
+
+**Our aim has always been to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
 
 The `AMR` package supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
 

README.md

@@ -10,8 +10,8 @@ Overview:
 
 - Provides an **all-in-one solution** for antimicrobial resistance (AMR)
   data analysis in a One Health approach
-- **Peer-reviewed**, used in over 175 countries, available in 28
-  languages
+- **Peer-reviewed**, used in over 175 countries, cited over 100 times,
+  available in 28 languages
 - Generates **antibiograms** - WISCA for empiric coverage estimates, or
   traditional/syndromic for AMR surveillance
 - Provides the **full microbiological taxonomy** of ~97 000 distinct
@@ -32,10 +32,11 @@ The `AMR` package is a peer-reviewed, free and open-source R package
 with zero dependencies to simplify the analysis and prediction of
 Antimicrobial Resistance (AMR) and to work with microbial and
 antimicrobial data and properties, by using evidence-based methods.
-**Our aim is to provide a standard** for clean and reproducible AMR data
-analysis, that can therefore empower epidemiological analyses to
-continuously enable surveillance and treatment evaluation in any
-setting.
+
+**Our aim has always been to provide a standard** for clean and
+reproducible AMR data analysis, that can therefore empower
+epidemiological analyses to continuously enable surveillance and
+treatment evaluation in any setting.
 
 The `AMR` package supports and can read any data format, including
 WHONET data. This package works on Windows, macOS and Linux with all

data-raw/_generate_python_wrapper.sh

@@ -33,18 +33,20 @@
 rm -rf ../PythonPackage/AMR/*
 mkdir -p ../PythonPackage/AMR/AMR
 
-# Output Python file
+# Output files
 setup_file="../PythonPackage/AMR/setup.py"
-functions_file="../PythonPackage/AMR/AMR/functions.py"
-datasets_file="../PythonPackage/AMR/AMR/datasets.py"
 init_file="../PythonPackage/AMR/AMR/__init__.py"
+engine_file="../PythonPackage/AMR/AMR/_engine.py"
+datasets_file="../PythonPackage/AMR/AMR/datasets.py"
+functions_file="../PythonPackage/AMR/AMR/functions.py"
+beta_file="../PythonPackage/AMR/AMR/beta.py"
 description_file="../DESCRIPTION"
 
-# Write header to the datasets Python file, including the convert_to_python function
-cat <<EOL > "$datasets_file"
+# ---- _engine.py: R environment setup and installation logic ---- #
+
+cat <<'EOL' > "$engine_file"
 import os
 import sys
-import pandas as pd
 import importlib.metadata as metadata
 
 # Get the path to the virtual environment
@@ -56,48 +58,127 @@ os.makedirs(r_lib_path, exist_ok=True)
 os.environ['R_LIBS_SITE'] = r_lib_path
 
 from rpy2 import robjects
-from rpy2.robjects.conversion import localconverter
-from rpy2.robjects import default_converter, numpy2ri, pandas2ri
+from rpy2.robjects.vectors import StrVector
 from rpy2.robjects.packages import importr, isinstalled
 
-# Import base and utils
+# Import base and utils once
 base = importr('base')
 utils = importr('utils')
 
-base.options(warn=-1)
-
-# Ensure library paths explicitly
+# Silence R console output entirely
+robjects.r('suppressMessages(suppressWarnings(sink(tempfile())))')
 base._libPaths(r_lib_path)
 
-# Check if the AMR package is installed in R
-if not isinstalled('AMR', lib_loc=r_lib_path):
-    print(f"AMR: Installing latest AMR R package to {r_lib_path}...", flush=True)
-    utils.install_packages('AMR', repos='beta.amr-for-r.org', quiet=True)
+_installed_source = None
 
-# Retrieve Python AMR version
+def _r_version():
+    """Return the currently installed AMR R package version, or None."""
     try:
-    python_amr_version = str(metadata.version('AMR'))
+        return str(robjects.r(
+            f'as.character(packageVersion("AMR", lib.loc = "{r_lib_path}"))')[0])
+    except Exception:
+        return None
+
+def _py_version():
+    """Return the Python AMR package version from metadata, or empty string."""
+    try:
+        return str(metadata.version('AMR'))
     except metadata.PackageNotFoundError:
-    python_amr_version = str('')
+        return ''
 
-# Retrieve R AMR version
-r_amr_version = robjects.r(f'as.character(packageVersion("AMR", lib.loc = "{r_lib_path}"))')
-r_amr_version = str(r_amr_version[0])
+def _install_cran():
+    """Install AMR from CRAN into the isolated library."""
+    print("AMR: Installing from CRAN...", flush=True)
+    utils.install_packages(
+        'AMR',
+        repos='https://cloud.r-project.org',
+        lib=r_lib_path,
+        quiet=True
+    )
 
-# Compare R and Python package versions
-if r_amr_version != python_amr_version:
+def _install_github():
+    """Install AMR development version from GitHub into the isolated library."""
+    print("AMR: Installing development version from GitHub...", flush=True)
+    utils.install_packages(
+        StrVector(['remotes', 'desc']),
+        repos='https://cloud.r-project.org',
+        lib=r_lib_path,
+        quiet=True
+    )
+    remotes = importr('remotes', lib_loc=r_lib_path)
+    remotes.install_github('msberends/AMR', lib=r_lib_path, quiet=True)
+
+def ensure_amr(source="cran"):
+    """Ensure AMR is installed from the requested source. Idempotent per source."""
+    global _installed_source
+    
+    if _installed_source == source:
+        return
+    
+    install_fn = _install_github if source == "github" else _install_cran
+    
+    if not isinstalled('AMR', lib_loc=r_lib_path):
+        install_fn()
+    else:
+        # Check for version mismatch and update if needed
+        r_ver = _r_version()
+        py_ver = _py_version()
+        if r_ver != py_ver:
             try:
-        print(f"AMR: Updating AMR package in {r_lib_path}...", flush=True)
-        utils.install_packages('AMR', repos='beta.amr-for-r.org', quiet=True)
+                install_fn()
             except Exception as e:
-        print(f"AMR: Could not update: {e}", flush=True)
+                print(f"AMR: Could not update ({e})", flush=True)
     
-print(f"AMR: Setting up R environment and AMR datasets...", flush=True)
+    print(f"AMR: R package version {_r_version()} ready.", flush=True)
+    _installed_source = source
+
+def restore_sink():
+    """Restore R console output after setup is complete."""
+    try:
+        robjects.r('sink()')
+    except Exception:
+        pass
+EOL
+
+# ---- datasets.py: only dataset loading ---- #
+
+cat <<'EOL' > "$datasets_file"
+import pandas as pd
+from rpy2 import robjects
+from rpy2.robjects.conversion import localconverter
+from rpy2.robjects import default_converter, numpy2ri, pandas2ri
+
+from ._engine import ensure_amr, restore_sink
+
+_cache = {}
+_loaded_source = None
+
+def _load_datasets(source="cran"):
+    """Load all AMR datasets into the module cache."""
+    global _loaded_source
+    
+    if _cache and _loaded_source == source:
+        return
+    
+    if _cache and _loaded_source != source:
+        _cache.clear()
+    
+    ensure_amr(source)
     
-# Activate the automatic conversion between R and pandas DataFrames
     with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
-    # example_isolates
-    example_isolates = robjects.r('''
+        _cache['example_isolates'] = _load_example_isolates()
+        _cache['microorganisms'] = robjects.r(
+            'AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]')
+        _cache['antimicrobials'] = robjects.r(
+            'AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]')
+        _cache['clinical_breakpoints'] = robjects.r(
+            'AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]')
+    
+    restore_sink()
+    _loaded_source = source
+
+def _load_example_isolates():
+    df = robjects.r('''
     df <- AMR::example_isolates
     df[] <- lapply(df, function(x) {
         if (inherits(x, c("Date", "POSIXt", "factor"))) {
@@ -109,26 +190,72 @@ with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter
     df <- df[, !sapply(df, is.list)]
     df
     ''')
-    example_isolates['date'] = pd.to_datetime(example_isolates['date'])
+    df['date'] = pd.to_datetime(df['date'])
+    return df
 
-    # microorganisms
-    microorganisms = robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]')
-    antimicrobials = robjects.r('AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]')
-    clinical_breakpoints = robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]')
-
-base.options(warn = 0)
-
-print(f"AMR: Done.", flush=True)
+def get(name, source="cran"):
+    """Retrieve a dataset by name, installing AMR if needed."""
+    _load_datasets(source)
+    return _cache[name]
 EOL
 
-echo "from .datasets import example_isolates" >> $init_file
-echo "from .datasets import microorganisms" >> $init_file
-echo "from .datasets import antimicrobials" >> $init_file
-echo "from .datasets import clinical_breakpoints" >> $init_file
+# ---- __init__.py: lazy module, CRAN by default ---- #
 
+cat <<'EOL' > "$init_file"
+import sys
 
-# Write header to the functions Python file, including the convert_to_python function
-cat <<EOL > "$functions_file"
+_DATASETS = frozenset({
+    'example_isolates', 'microorganisms',
+    'antimicrobials', 'clinical_breakpoints'
+})
+
+class _AMRModule(type(sys.modules[__name__])):
+    """Lazy-loading module: nothing runs until an attribute is accessed."""
+    
+    def __getattr__(self, name):
+        if name in _DATASETS:
+            from .datasets import get
+            return get(name, source="cran")
+        try:
+            from . import functions
+            return getattr(functions, name)
+        except AttributeError:
+            raise AttributeError(
+                f"module 'AMR' has no attribute '{name}'")
+
+sys.modules[__name__].__class__ = _AMRModule
+EOL
+
+# ---- beta.py: GitHub development version ---- #
+
+cat <<'EOL' > "$beta_file"
+import sys
+
+_DATASETS = frozenset({
+    'example_isolates', 'microorganisms',
+    'antimicrobials', 'clinical_breakpoints'
+})
+
+class _BetaModule(type(sys.modules[__name__])):
+    """Lazy-loading module: installs AMR from GitHub on first access."""
+    
+    def __getattr__(self, name):
+        if name in _DATASETS:
+            from .datasets import get
+            return get(name, source="github")
+        try:
+            from . import functions
+            return getattr(functions, name)
+        except AttributeError:
+            raise AttributeError(
+                f"module 'AMR.beta' has no attribute '{name}'")
+
+sys.modules[__name__].__class__ = _BetaModule
+EOL
+
+# ---- functions.py: R-to-Python wrapper functions ---- #
+
+cat <<'EOL' > "$functions_file"
 import functools
 import rpy2.robjects as robjects
 from rpy2.robjects.packages import importr
@@ -138,7 +265,10 @@ from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 import pandas as pd
 import numpy as np
 
-# Import the AMR R package
+from ._engine import ensure_amr
+
+# Ensure AMR is available before importing it in R
+ensure_amr("cran")
 amr_r = importr('AMR')
 
 def convert_to_r(value):
@@ -204,12 +334,11 @@ def r_to_python(r_func):
     return wrapper
 EOL
 
-# Directory where the .Rd files are stored (update path as needed)
+# ---- Generate wrapper functions from .Rd files ---- #
+
 rd_dir="../man"
 
-# Iterate through each .Rd file in the man directory
 for rd_file in "$rd_dir"/*.Rd; do
-    # Extract function names and their arguments from the .Rd files
     awk '
     BEGIN {
         usage_started = 0
@@ -292,18 +421,19 @@ for rd_file in "$rd_dir"/*.Rd; do
     ' "$rd_file"
 done
 
-# Output completion message
 echo "Python wrapper functions generated in $functions_file."
 echo "Python wrapper functions listed in $init_file."
 
+# ---- README ---- #
+
 cp ../vignettes/AMR_for_Python.Rmd ../PythonPackage/AMR/README.md
 sed -i '1,/^# Introduction$/d' ../PythonPackage/AMR/README.md
-echo "README copied"
+echo "README copied."
+
+# ---- setup.py ---- #
 
-# Extract the relevant fields from DESCRIPTION
 version=$(grep "^Version:" "$description_file" | awk '{print $2}')
 
-# Write the setup.py file
 cat <<EOL > "$setup_file"
 from setuptools import setup, find_packages
 
@@ -334,10 +464,10 @@ setup(
 )
 EOL
 
-# Output completion message
-echo "setup.py has been generated in $setup_file."
+echo "setup.py generated."
+
+# ---- Build ---- #
 
 cd ../PythonPackage/AMR
 pip3 install build
 python3 -m build
-# python3 setup.py sdist bdist_wheel

data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints.R

@@ -503,6 +503,72 @@ dim(breakpoints_new)
 dim(clinical_breakpoints)
 
 
+# Correct anaerobic bacteria in EUCAST ----
+
+eucast_anaerobe_corrections <- tibble::tribble(
+  ~guideline, ~type, ~host, ~method, ~site, ~mo, ~rank_index, ~ab, ~ref_tbl, ~disk_dose, ~breakpoint_S, ~breakpoint_R, ~uti, ~is_SDD,
+  
+  # Prevotella spp.
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Prevotella"), 3, as.ab("AMP"), "Prevotella", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Prevotella"), 3, as.ab("AMP"), "Prevotella", "2 mcg",        25,    25,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Prevotella"), 3, as.ab("SAM"), "Prevotella", "10/10 mcg",    33,    33,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Prevotella"), 3, as.ab("AMX"), "Prevotella", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Prevotella"), 3, as.ab("AMC"), "Prevotella", "2/1 mcg",      24,    24,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Prevotella"), 3, as.ab("ETP"), "Prevotella", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Prevotella"), 3, as.ab("ETP"), "Prevotella", "10 mcg",       29,    29,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Prevotella"), 3, as.ab("IPM"), "Prevotella", NA,             0.125, 0.125, FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Prevotella"), 3, as.ab("IPM"), "Prevotella", "10 mcg",       35,    35,    FALSE, FALSE,
+  
+  # Fusobacterium necrophorum
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("AMP"), "F. necrophorum", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("AMP"), "F. necrophorum", "2 mcg",        27,    27,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("SAM"), "F. necrophorum", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("SAM"), "F. necrophorum", "10/10 mcg",    33,    33,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("AMX"), "F. necrophorum", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("AMC"), "F. necrophorum", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("AMC"), "F. necrophorum", "2/1 mcg",      23,    23,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("ETP"), "F. necrophorum", NA,             0.06,  0.06,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("ETP"), "F. necrophorum", "10 mcg",       35,    35,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("IPM"), "F. necrophorum", NA,             0.125, 0.125, FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Fusobacterium necrophorum"), 2, as.ab("IPM"), "F. necrophorum", "10 mcg",       36,    36,    FALSE, FALSE,
+  
+  # Clostridium perfringens
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("AMP"), "C. perfringens", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Clostridium perfringens"), 2, as.ab("AMP"), "C. perfringens", "2 mcg",        23,    23,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("SAM"), "C. perfringens", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Clostridium perfringens"), 2, as.ab("SAM"), "C. perfringens", "10/10 mcg",    27,    27,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("AMX"), "C. perfringens", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("AMC"), "C. perfringens", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Clostridium perfringens"), 2, as.ab("AMC"), "C. perfringens", "2/1 mcg",      23,    23,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("ETP"), "C. perfringens", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Clostridium perfringens"), 2, as.ab("ETP"), "C. perfringens", "10 mcg",       24,    24,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Clostridium perfringens"), 2, as.ab("IPM"), "C. perfringens", NA,             0.5,   0.5,   FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Clostridium perfringens"), 2, as.ab("IPM"), "C. perfringens", "10 mcg",       25,    25,    FALSE, FALSE,
+  
+  # Cutibacterium acnes
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("AMP"), "C. acnes", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("AMP"), "C. acnes", "2 mcg",        23,    23,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("SAM"), "C. acnes", "10/10 mcg",    33,    33,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("AMX"), "C. acnes", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("AMC"), "C. acnes", "2/1 mcg",      24,    24,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("CTX"), "C. acnes", "5 mcg",        26,    26,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("CRO"), "C. acnes", NA,             0.06,  0.06,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("CRO"), "C. acnes", "30 mcg",       33,    33,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("ETP"), "C. acnes", NA,             0.25,  0.25,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("ETP"), "C. acnes", "10 mcg",       28,    28,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("IPM"), "C. acnes", NA,             0.03,  0.03,  FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("IPM"), "C. acnes", "10 mcg",       39,    39,    FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "MIC",  NA, as.mo("Cutibacterium acnes"), 2, as.ab("LNZ"), "C. acnes", NA,             2,     2,     FALSE, FALSE,
+  "EUCAST 2025", "human", "human", "DISK", NA, as.mo("Cutibacterium acnes"), 2, as.ab("LNZ"), "C. acnes", "10 mcg",       34,    34,    FALSE, FALSE
+)
+
+breakpoints_new <- clinical_breakpoints |>
+  bind_rows(eucast_anaerobe_corrections) |>
+  bind_rows(eucast_anaerobe_corrections |> mutate(guideline = "EUCAST 2026")) |>
+  bind_rows(eucast_anaerobe_corrections |> mutate(guideline = "EUCAST 2023")) |>
+  bind_rows(eucast_anaerobe_corrections |> mutate(guideline = "EUCAST 2024"))
+
+
 # SAVE TO PACKAGE ----
 
 # determine rank again now that some changes were made on taxonomic level (genus -> species)

data-raw/clin_break.md5

@@ -1 +1 @@
-7bcb6eaf7e2da23ac552acbfd12b3e62
+634c5e23bed1e92783eeb4739c0d1486

data-raw/interpretive_rules.tsv

@@ -90,7 +90,7 @@ EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	NOR	S	CIP, LVX, MFX
 EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
 EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	ERY	I	AZM, CLR, RXT	I	
 EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
-EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
+EUCAST	Breakpoints	10	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	10	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	S	
 EUCAST	Breakpoints	10	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	I	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	I	
 EUCAST	Breakpoints	10	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	R	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	R	
@@ -199,7 +199,7 @@ EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	NOR	S	CIP, LVX, MFX
 EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
 EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	ERY	I	AZM, CLR, RXT	I	
 EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
-EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
+EUCAST	Breakpoints	11	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	11	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S	S	CTX, CRO	I	
 EUCAST	Breakpoints	11	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	S	
 EUCAST	Breakpoints	11	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	I	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	I	
@@ -260,7 +260,7 @@ EUCAST	Breakpoints	12	Enterococcus	genus	is	Enterococcus	AMP	R	AMP, SAM, AMX, AM
 EUCAST	Breakpoints	12	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	12	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	12	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	12	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	12	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))])); sort(x[x %in% penicillins()]) |> toString()
 EUCAST	Breakpoints	12	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	12	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	12	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -331,6 +331,9 @@ EUCAST	Breakpoints	12	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-
 EUCAST	Breakpoints	12	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
 EUCAST	Breakpoints	12	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	12	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S	S	CTX, CRO	I	
+EUCAST	Breakpoints	12	Staphylococcus	genus	is	Staphylococcus	TCY-S	R	DOX, MNO	R	
+EUCAST	Breakpoints	12	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
+EUCAST	Breakpoints	12	Staphylococcus	genus	is	Staphylococcus	NOR-S	R	CIP, MFX, LVX	R	
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	S	penicillins	S	
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	I	penicillins	I	
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	R	penicillins	R	
@@ -351,8 +354,8 @@ EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	12	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
-EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
+EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
 EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	12	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -381,7 +384,6 @@ EUCAST	Breakpoints	12	Viridans group streptococci	genus_species	one_of	Viridans
 EUCAST	Breakpoints	12	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	S	AMX, AMC, SAM, PIP, TZP	S	paste("Streptococcus", mo_species(microorganisms.groups$mo[which(microorganisms.groups$mo_group == "B_STRPT_VIRI")]), collapse = ", ")
 EUCAST	Breakpoints	12	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	I	AMX, AMC, SAM, PIP, TZP	I	paste("Streptococcus", mo_species(microorganisms.groups$mo[which(microorganisms.groups$mo_group == "B_STRPT_VIRI")]), collapse = ", ")
 EUCAST	Breakpoints	12	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	R	AMX, AMC, SAM, PIP, TZP	R	paste("Streptococcus", mo_species(microorganisms.groups$mo[which(microorganisms.groups$mo_group == "B_STRPT_VIRI")]), collapse = ", ")
-EUCAST	Breakpoints	13	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S	S	CTX, CRO	I	
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	AMP	S	AMX	S	
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	AMP	I	AMX	I	
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	AMP	R	AMX	R	
@@ -391,19 +393,19 @@ EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	14	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	14	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	14	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & mo_genus(clinical_breakpoints$mo) == "Prevotella" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusibacterium necrophorum") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Clostridium perfringens") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	14	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -439,9 +441,9 @@ EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	AMP	I	A
 EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	AMP	R	AMX	R	
 EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	LEX	S	CZO	I	
 EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	CFR	S	CZO	I	
-EUCAST	Breakpoints	14	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	S	CIP	S	
-EUCAST	Breakpoints	14	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	I	CIP	I	
-EUCAST	Breakpoints	14	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	R	CIP	R	
+EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	S	fluoroquinolones	S	
+EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	I	fluoroquinolones	I	
+EUCAST	Breakpoints	14	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	R	fluoroquinolones	R	
 EUCAST	Breakpoints	14	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	S	DOX	S	
 EUCAST	Breakpoints	14	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	I	DOX	I	
 EUCAST	Breakpoints	14	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	R	DOX	R	
@@ -451,7 +453,7 @@ EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	AMP	R	AMP, SAM, AMX, AM
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	14	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> sort() |> toString()
+EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Haemophilus influenzae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% penicillins()] |> sort() |> toString()
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	14	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -521,8 +523,11 @@ EUCAST	Breakpoints	14	Staphylococcus	genus_species	is	Staphylococcus coagulase-n
 EUCAST	Breakpoints	14	Staphylococcus	genus_species	is	Staphylococcus aureus	VAN	S	DAL, ORI	S	
 EUCAST	Breakpoints	14	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S, VAN	R, S	TLV	S	MRSA isolates are in this file safely denoted as FOX resistant
 EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
-EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
+EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
 EUCAST	Breakpoints	14	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S	S	CTX, CRO	I	
+EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
+EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
+EUCAST	Breakpoints	14	Staphylococcus	genus	is	Staphylococcus	NOR-S	R	CIP, MFX, LVX	R	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	S	penicillins	S	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	I	penicillins	I	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	R	penicillins	R	
@@ -543,8 +548,8 @@ EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	14	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
-EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(c("SAM", "PIP", "TZP", "PHN", "IMR", "MEV", clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))])); x[x %in% betalactams()] |> sort() |> toString() AND REMOVE CEC
+EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	14	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -582,19 +587,19 @@ EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	15	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & mo_genus(clinical_breakpoints$mo) == "Prevotella" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Fusibacterium necrophorum") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Clostridium perfringens") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	15	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -630,9 +635,9 @@ EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	AMP	I	A
 EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	AMP	R	AMX	R	
 EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	LEX	S	CZO	I	
 EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	CFR	S	CZO	I	
-EUCAST	Breakpoints	15	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	S	CIP	S	
-EUCAST	Breakpoints	15	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	I	CIP	I	
-EUCAST	Breakpoints	15	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	R	CIP	R	
+EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	S	fluoroquinolones	S	
+EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	I	fluoroquinolones	I	
+EUCAST	Breakpoints	15	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	R	fluoroquinolones	R	
 EUCAST	Breakpoints	15	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	S	DOX	S	
 EUCAST	Breakpoints	15	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	I	DOX	I	
 EUCAST	Breakpoints	15	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	R	DOX	R	
@@ -643,7 +648,7 @@ EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	AMP	R	AMX, AMC	R
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	15	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("Haemophilus influenzae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% penicillins()] |> sort() |> toString()
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	15	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -708,9 +713,12 @@ EUCAST	Breakpoints	15	Staphylococcus	genus_species	one_of	Staphylococcus pseudin
 EUCAST	Breakpoints	15	Staphylococcus	genus_species	one_of	Staphylococcus pseudintermedius, Staphylococcus schleiferi, Staphylococcus coagulans	OXA-S	R	carbapenems	R	Not explicitly mentioned in guidelines in this section, but previous section about these 3 species do mention OXA as preferred method
 EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	NOR-S	S	MFX	S	
 EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	NOR-S	S	CIP, LVX	I	
+EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	NOR-S	R	CIP, MFX, LVX	R	
 EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
-EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
+EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
 EUCAST	Breakpoints	15	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S	S	CTX, CRO	I	
+EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
+EUCAST	Breakpoints	15	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	S	penicillins	S	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	I	penicillins	I	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	R	penicillins	R	
@@ -731,8 +739,8 @@ EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	15	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
-EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, FPE, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV", "FPE")) |> toString()
+EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, FPE, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV", "FPE")) |> toString()
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	15	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -770,19 +778,19 @@ EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	16	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2024" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Fusibacterium necrophorum") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringens"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringens") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	16	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -818,9 +826,9 @@ EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	AMP	I	A
 EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	AMP	R	AMX	R	
 EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	LEX	S	CZO	I	
 EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	CFR	S	CZO	I	
-EUCAST	Breakpoints	16	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	S	CIP	S	
-EUCAST	Breakpoints	16	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	I	CIP	I	
-EUCAST	Breakpoints	16	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	R	CIP	R	
+EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	S	fluoroquinolones	S	
+EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	I	fluoroquinolones	I	
+EUCAST	Breakpoints	16	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	R	fluoroquinolones	R	
 EUCAST	Breakpoints	16	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	S	DOX	S	
 EUCAST	Breakpoints	16	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	I	DOX	I	
 EUCAST	Breakpoints	16	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	R	DOX	R	
@@ -831,7 +839,7 @@ EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	AMP	R	AMX, AMC	R
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	16	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CAZ, CEC, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TEM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Haemophilus influenzae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% penicillins()] |> sort() |> toString()
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	16	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -897,9 +905,12 @@ EUCAST	Breakpoints	16	Staphylococcus	genus_species	one_of	Staphylococcus pseudin
 EUCAST	Breakpoints	16	Staphylococcus	genus_species	one_of	Staphylococcus pseudintermedius, Staphylococcus schleiferi, Staphylococcus coagulans	OXA-S	I	carbapenems	I	Not explicitly mentioned in guidelines in this section, but previous section about these 3 species do mention OXA as preferred method
 EUCAST	Breakpoints	16	Staphylococcus	genus_species	one_of	Staphylococcus pseudintermedius, Staphylococcus schleiferi, Staphylococcus coagulans	OXA-S	R	carbapenems	R	Not explicitly mentioned in guidelines in this section, but previous section about these 3 species do mention OXA as preferred method
 EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	NOR-S	S	MFX	S	
+EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	NOR-S	R	CIP, MFX, LVX	R	
 EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	NOR-S	S	CIP, LVX	I	
 EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
-EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
+EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
+EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
+EUCAST	Breakpoints	16	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	S	penicillins	S	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	I	penicillins	I	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	R	penicillins	R	
@@ -920,8 +931,8 @@ EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Strep
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	16	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
-EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, BPR, CFM, CPD, CPT, CRO, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PHN, PIP, PIP, SAM, TZP, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, FPE, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV", "FPE")) |> toString()
+EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, FPE, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV", "FPE")) |> toString()
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	16	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -959,19 +970,19 @@ EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococc
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	S	fluoroquinolones	S	
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	I	fluoroquinolones	I	
 EUCAST	Breakpoints	13.1	Aerococcus sanguinicola/urinae	genus_species	is	Aerococcus sanguinicola, Aerococcus urinae	NOR-S	R	fluoroquinolones	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusibacterium necrophorum") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Clostridium perfringensm"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Clostridium perfringens") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2026" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()]) |> toString()
+EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% betalactams()] |> sort() |> toString()
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
 EUCAST	Breakpoints	13.1	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
@@ -1003,9 +1014,9 @@ EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	AMP	I
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	AMP	R	AMX	R	
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	LEX	S	CZO	I	
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	CFR	S	CZO	I	
-EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	S	CIP	S	
-EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	I	CIP	I	
-EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus	is	Salmonella	PEF-S	R	CIP	R	
+EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	S	fluoroquinolones	S	
+EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	I	fluoroquinolones	I	
+EUCAST	Breakpoints	13.1	Enterobacterales (Order)	order	is	Enterobacterales	PEF-S	R	fluoroquinolones	R	
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	S	DOX	S	
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	I	DOX	I	
 EUCAST	Breakpoints	13.1	Enterobacterales (Order)	genus_species	is	Yersinia enterocolitica	TCY	R	DOX	R	
@@ -1015,7 +1026,7 @@ EUCAST	Breakpoints	13.1	Enterococcus	genus	is	Enterococcus	AMP	R	AMP, SAM, AMX,
 EUCAST	Breakpoints	13.1	Enterococcus	genus	is	Enterococcus	NOR-S	S	CIP, LVX	S	
 EUCAST	Breakpoints	13.1	Enterococcus	genus	is	Enterococcus	NOR-S	I	CIP, LVX	I	
 EUCAST	Breakpoints	13.1	Enterococcus	genus	is	Enterococcus	NOR-S	R	CIP, LVX	R	
-EUCAST	Breakpoints	13.1	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, CFM, CPD, CPT, CRO, CTB, CTX, CXM, CZT, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("H. influenzae"))]), "IMR", "MEV"); sort(x[x %in% betalactams()])
+EUCAST	Breakpoints	13.1	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S	S	AMC, AMP, AMX, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Haemophilus influenzae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); x[x %in% penicillins()] |> sort() |> toString()
 EUCAST	Breakpoints	13.1	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	PEN-S, BLA-S	R, R	AMP, AMX, PIP	R	
 EUCAST	Breakpoints	13.1	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	S	SAM	S	
 EUCAST	Breakpoints	13.1	Haemophilus influenzae	genus_species	is	Haemophilus influenzae	AMC	I	SAM	I	
@@ -1084,7 +1095,10 @@ EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	NOR-S	S	CIP, LVX
 EUCAST	Breakpoints	13.1	Staphylococcus	genus_species	is	Staphylococcus aureus	VAN	S	DAL, ORI	S	
 EUCAST	Breakpoints	13.1	Staphylococcus	genus_species	is	Staphylococcus aureus	FOX-S, VAN	R, S	TLV	S	MRSA isolates are in this file safely denoted as FOX resistant
 EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	
-EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	TCY-S	S	DOX, MNO	S	
+EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO	S	
+EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
+EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	
+EUCAST	Breakpoints	13.1	Staphylococcus	genus	is	Staphylococcus	NOR-S	R	CIP, MFX, LVX	R	
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	S	penicillins	S	
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	I	penicillins	I	
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group C, Streptococcus Group G	PEN	R	penicillins	R	
@@ -1105,8 +1119,8 @@ EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Str
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	ERY	R	AZM, CLR, RXT	R	
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	S	DOX, MNO	S	
 EUCAST	Breakpoints	13.1	Streptococcus groups A, B, C, G	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	TCY-S	R	DOX, MNO	R	
-EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
-EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, OXA, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2022" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV"))
+EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
+EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	AMC, AMP, AMX, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IMR, IPM, MEM, MEV, PEN, PHN, PIP, SAM, TZP	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$ab != as.ab("cefaclor"))]); sort(c(x[x %in% betalactams()], "SAM", "PIP", "TZP", "PHN", "IMR", "MEV")) |> toString()
 EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	S	CEC	I	
 EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	PEN	S	CEC	I	
 EUCAST	Breakpoints	13.1	Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA-S	R	PEN, PHN	R	from flowchart: when OXA < 20 or PEN > 0.06
@@ -1135,25 +1149,6 @@ EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridan
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	S	AMX, AMC, SAM, PIP, TZP	S	will be expanded in eucast_rules()
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	I	AMX, AMC, SAM, PIP, TZP	I	will be expanded in eucast_rules()
 EUCAST	Breakpoints	13.1	Viridans group streptococci	genus_species	one_of	Viridans Group Streptococcus (VGS)	AMP	R	AMX, AMC, SAM, PIP, TZP	R	will be expanded in eucast_rules()
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & mo_genus(clinical_breakpoints$mo) == "Prevotella")]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	S	AMX	S	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	I	AMX	I	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus	is	Prevotella	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	S	AMX	S	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	I	AMX	I	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Fusobacterium necrophorum	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	PEN	S	AMC, AMP, AMX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Fusobacterium necrophorum"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	S	AMX	S	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	I	AMX	I	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Clostridium perfringens	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	PEN	S	AMC, AMP, AMX, CRO, CTX, ETP, IPM, MEM, PEN, PIP, SAM, TZP	S	x <- c(unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2023" & clinical_breakpoints$mo == as.mo("Cutibacterium acnes"))]), "AMP", "SAM", "AMX", "AMC", "PIP", "ETP", "IPM", "TZP", "CTX", "CRO"); sort(x[x %in% betalactams()])
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	S	AMX	S	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	I	AMX	I	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	AMP	R	AMX	R	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	S	CRO	S	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	I	CRO	I	
-EUCAST	Breakpoints	13.0	Anaerobic bacteria	genus_species	is	Cutibacterium acnes	CTX	R	CRO	R	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	I	AMX	S	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	R	AMX	R	
 EUCAST	Breakpoints		Corynebacterium diphtheriae/ulcerans	genus_species	one_of	Corynebacterium diphtheriae, Corynebacterium ulcerans	PEN	S	CTX	S	
@@ -1455,13 +1450,7 @@ EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	M
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	TCY	S	DOX, MNO, TGC	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	TCY	R	DOX, MNO	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Staphylococcus	genus	is	Staphylococcus	LNZ	S	TZD	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, cephalosporins, carbapenems	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	S	aminopenicillins, CTX, CRO	S	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	R	aminopenicillins, CTX, CRO	R	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	LVX	I	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	MFX	S	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	R	LVX, MFX	R	
-EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA	S	AMC, AMP, AMX, CEC, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IPM, MEM, PEN	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)) & clinical_breakpoints$mo == as.mo("S. pneumoniae"))]); x[x %in% betalactams()] |> toString()
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	OXA	S	AMC, AMP, AMX, CEC, CPD, CPT, CRO, CTX, CXM, DOR, ETP, FEP, IPM, MEM, PEN	S	x <- unique(clinical_breakpoints$ab[which(clinical_breakpoints$guideline == "EUCAST 2025" & clinical_breakpoints$mo == as.mo("S. pneumoniae") & clinical_breakpoints$host == "human" & (clinical_breakpoints$site != "Screen" | is.na(clinical_breakpoints$site)))]); sort(c(x[x %in% betalactams()])) |> toString()
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	S	LVX	I	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	NOR	S	MFX	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	ERY, CLI	S, S	macrolides, lincosamides	S	
@@ -1470,6 +1459,12 @@ EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	i
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	MFX	R	fluoroquinolones	R	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	TCY	S	DOX, MNO	S	
 EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus pneumoniae	genus_species	is	Streptococcus pneumoniae	TCY	R	DOX, MNO	R	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	PEN	S	aminopenicillins, cephalosporins, carbapenems	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	S	aminopenicillins, CTX, CRO	S	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Viridans Group Streptococcus (VGS)	PEN	R	aminopenicillins, CTX, CRO	R	Weird that this rules in in the Strep A/B/C/G document, while it's not in the Strep viridans document - document title itself it for "S. species"
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	LVX	I	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	S	MFX	S	
+EUCAST	Expert Rules	3.3	Expert Rules on Streptococcus species	genus_species	one_of	Streptococcus Group A, Streptococcus Group B, Streptococcus Group C, Streptococcus Group G	NOR	R	LVX, MFX	R	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	order	is	Enterobacterales			PEN, glycopeptides_except_lipo, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	fullname	like	^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)			aminopenicillins, TIC	R	
 EUCAST	Expert Rules	3.3	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	fullname	like	^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX	R	

index.Rmd

@@ -13,13 +13,14 @@ knitr::opts_chunk$set(
   fig.path = "pkgdown/assets/",
   out.width = "100%"
 )
+options(width = 100)
 AMR:::reset_all_thrown_messages()
 ```
 
 # The `AMR` Package for R <a href="https://amr-for-r.org/"><img src="./logo.svg" align="right" height="139" /></a>
 
 * Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
-* **Peer-reviewed**, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
+* **Peer-reviewed**, used in over 175 countries, cited over 100 times, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
 * Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
 * Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs
 * Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
@@ -40,9 +41,9 @@ AMR:::reset_all_thrown_messages()
 
 ## Introduction
 
-The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project!
+The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.
 
-This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
+**Our aim has always been to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project! The `AMR` package was already cited [over 100 times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC) in scientific research.
 
 After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](./reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
 

index.md

@@ -5,8 +5,8 @@
 
 - Provides an **all-in-one solution** for antimicrobial resistance (AMR)
   data analysis in a One Health approach
-- **Peer-reviewed**, used in over 175 countries, available in 28
-  languages
+- **Peer-reviewed**, used in over 175 countries, cited over 100 times,
+  available in 28 languages
 - Generates **antibiograms** - WISCA for empiric coverage estimates, or
   traditional/syndromic for AMR surveillance
 - Provides the **full microbiological taxonomy** of ~97 000 distinct
@@ -27,12 +27,9 @@
 <div style="display: flex; font-size: 0.8em;">
 
 <p style="text-align:left; width: 50%;">
-
 <small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
 </p>
-
 <p style="text-align:right; width: 50%;">
-
 <small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
 </p>
 
@@ -49,22 +46,20 @@ R package with [zero
 dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify
 the analysis and prediction of Antimicrobial Resistance (AMR) and to
 work with microbial and antimicrobial data and properties, by using
-evidence-based methods. **Our aim is to provide a standard** for clean
-and reproducible AMR data analysis, that can therefore empower
+evidence-based methods.
+
+**Our aim has always been to provide a standard** for clean and
+reproducible AMR data analysis, that can therefore empower
 epidemiological analyses to continuously enable surveillance and
 treatment evaluation in any setting. We are a team of [many different
 researchers](./authors.html) from around the globe to make this a
-successful and durable project!
-
-This work was published in the Journal of Statistical Software (Volume
-104(3); [DOI
-10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and
-formed the basis of two PhD theses ([DOI
-10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and
-[DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
+successful and durable project! The `AMR` package was already cited
+[over 100
+times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC)
+in scientific research.
 
 After installing this package, R knows [**~97 000 distinct microbial
-species**](./reference/microorganisms.html) (updated May 2026) and all
+species**](./reference/microorganisms.html) (updated mei 2026) and all
 [**~620 antimicrobial and antiviral
 drugs**](./reference/antimicrobials.html) by name and code (including
 ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all
@@ -175,11 +170,13 @@ example_isolates %>%
 #> ℹ Using column mo as input for `mo_fullname()`
 #> ℹ Using column mo as input for `mo_is_gram_negative()`
 #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
-#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant
-#>   Phenotypes' v1.2 (2023). This note will be shown once per session.
-#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
-#>   (amikacin), and KAN (kanamycin)
-#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
+#> ℹ Determining intrinsic resistance based on 'EUCAST Expected
+#>   Resistant Phenotypes' v1.2 (2023). This note will be shown
+#>   once per session.
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
+#>   (tobramycin), AMK (amikacin), and KAN (kanamycin)
+#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
+#>   (meropenem)
 #> # A tibble: 35 × 7
 #>    bacteria                     GEN   TOB   AMK   KAN   IPM   MEM  
 #>    <chr>                        <sir> <sir> <sir> <sir> <sir> <sir>
@@ -229,8 +226,8 @@ wisca(example_isolates,
 ```
 
 | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
-|:---|:---|:---|
-| 70.1% (64.9-75.7%) | 93.6% (92.2-95%) | 89.8% (86.7-92.3%) |
+|:------------------------|:-------------------------------------|:-------------------------------------|
+| 70% (64.8-75.1%)        | 93.6% (92.1-95%)                     | 89.9% (86.9-92.3%)                   |
 
 WISCA supports stratification by any clinical variable, so you can
 generate syndrome-specific or ward-specific coverage estimates:
@@ -244,10 +241,10 @@ wisca(example_isolates,
 ```
 
 | Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
-|:---|:---|:---|:---|
-| Clinical | 74.5% (69.3-80.1%) | 93.7% (92-95.1%) | 90.5% (87.1-93.1%) |
-| ICU | 56.7% (48-65.5%) | 86.7% (83.4-89.8%) | 82.9% (78.2-87.3%) |
-| Outpatient | 57.8% (46.4-69.7%) | 76.5% (70.1-82.2%) | 67.9% (57.9-77.5%) |
+|:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
+| Clinical        | 74.7% (69-80.3%)        | 93.6% (92-95.2%)                     | 90.4% (86.8-93.1%)                   |
+| ICU             | 56.9% (48.7-66%)        | 86.8% (83.6-90%)                     | 82.8% (78.3-87.3%)                   |
+| Outpatient      | 57.2% (46-68.2%)        | 76.5% (70.3-82.2%)                   | 67.7% (57.3-77.2%)                   |
 
 **For AMR surveillance**, traditional antibiograms remain the right tool
 for tracking resistance per species over time:
@@ -256,11 +253,12 @@ for tracking resistance per species over time:
 antibiogram(example_isolates,
             mo_transform = "gramstain",
             antimicrobials = c("AMC", carbapenems(), "TZP"))
-#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
+#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
+#>   (meropenem)
 ```
 
 | Pathogen      | Amoxicillin/clavulanic acid | Imipenem            | Meropenem            | Piperacillin/tazobactam |
-|:---|:---|:---|:---|:---|
+|:--------------|:----------------------------|:--------------------|:---------------------|:------------------------|
 | Gram-negative | 76% (73-79%,N=726)          | 99% (98-100%,N=631) | 100% (99-100%,N=626) | 88% (85-91%,N=641)      |
 | Gram-positive | 76% (74-79%,N=1138)         | 81% (75-85%,N=257)  | 77% (70-82%,N=203)   | 86% (82-89%,N=345)      |
 
@@ -274,7 +272,7 @@ antibiogram(example_isolates,
 ```
 
 | Pathogen      | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
-|:---|:---|:---|:---|
+|:--------------|:------------------------|:-------------------------------------|:-------------------------------------|
 | Gram-negative | 88% (85-91%,N=641)      | 99% (97-99%,N=691)                   | 98% (97-99%,N=693)                   |
 | Gram-positive | 86% (82-89%,N=345)      | 98% (96-98%,N=1044)                  | 95% (93-97%,N=550)                   |
 
@@ -349,10 +347,6 @@ example_isolates %>%
   summarise(across(c(GEN, TOB),
                    list(total_R = resistance,
                         conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
-#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
-#>   category susceptible. Set the `guideline` argument or the `AMR_guideline`
-#>   option to either "CLSI" or "EUCAST", see `?AMR-options`.
-#> ℹ This message will be shown once per session.
 #> # A tibble: 3 × 5
 #>   ward       GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
 #>   <chr>            <dbl> <chr>              <dbl> <chr>       
@@ -375,15 +369,16 @@ out <- example_isolates %>%
   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
   summarise(across(c(aminoglycosides(), polymyxins()),
             resistance))
-#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
-#>   (amikacin), and KAN (kanamycin)
+#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
+#>   (tobramycin), AMK (amikacin), and KAN (kanamycin)
 #> ℹ For `polymyxins()` using column COL (colistin)
 #> Warning: There was 1 warning in `summarise()`.
-#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
+#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
+#>   resistance)`.
 #> ℹ In group 3: `ward = "Outpatient"`.
 #> Caused by warning:
-#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient"
-#> (whilst `minimum = 30`).
+#> ! Introducing NA: only 23 results available for KAN in group:
+#> ward = "Outpatient" (whilst `minimum = 30`).
 out
 #> # A tibble: 3 × 6
 #>   ward         GEN   TOB   AMK   KAN   COL

man/AMR.Rd

@@ -12,7 +12,7 @@ The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyrigh
 
 This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
 
-After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated May 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
+After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated mei 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
 
 The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
 }

man/antimicrobial_selectors.Rd

@@ -437,7 +437,7 @@ example_isolates[, amr_selector(oral_ddd > 1 & oral_units == "g")]
 # data.table --------------------------------------------------------------
 
 # data.table is supported as well, just use it in the same way as with
-# base R, but add `with = FALSE` if using a single AB selector.
+# base R, but add `with = FALSE` if using a single AMR selector.
 
 if (require("data.table")) {
   dt <- as.data.table(example_isolates)
@@ -450,7 +450,7 @@ if (require("data.table")) {
   dt[, carbapenems(), with = FALSE]
 }
 
-# for multiple selections or AB selectors, `with = FALSE` is not needed:
+# for multiple selections or AMR selectors, `with = FALSE` is not needed:
 if (require("data.table")) {
   dt[, c("mo", aminoglycosides())]
 }

man/as.sir.Rd

@@ -14,7 +14,7 @@
 \alias{sir_interpretation_history}
 \title{Interpret MIC and Disk Diffusion as SIR, or Clean Existing SIR Data}
 \usage{
-as.sir(x, ...)
+as.sir(x, ..., enforce_method = "auto")
 
 NA_sir_
 
@@ -108,7 +108,9 @@ sir_interpretation_history(clean = FALSE)
 \arguments{
 \item{x}{Vector of values (for class \code{\link{mic}}: MIC values in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimetres).}
 
-\item{...}{For using on a \link{data.frame}: selection of columns to apply \code{as.sir()} to. Supports \link[tidyselect:starts_with]{tidyselect language} such as \code{where(is.mic)}, \code{starts_with(...)}, or \code{column1:column4}, and can thus also be \link[=amr_selector]{antimicrobial selectors}, e.g. \code{as.sir(df, penicillins())}.
+\item{...}{For using on a \link{data.frame}: selection of columns to apply \code{as.sir()} to. Supports \link[tidyselect:starts_with]{tidyselect language} such as \code{where(is.mic)}, \code{starts_with(...)}, or \code{column1:column4}, and can thus also be \link[=amr_selector]{antimicrobial selectors}, e.g. \code{as.sir(df, penicillins())}.}
+
+\item{enforce_method}{A \link{character} string to force interpretation as a specific method, useful when the S3 class of \code{x} is lost (e.g., when called from Python via rpy2). Must be one of \code{"auto"} (default), \code{"mic"}, or \code{"disk"}.
 
 Otherwise: arguments passed on to methods.}
 

man/clinical_breakpoints.Rd

@@ -5,7 +5,7 @@
 \alias{clinical_breakpoints}
 \title{Data Set with Clinical Breakpoints for SIR Interpretation}
 \format{
-A \link[tibble:tibble]{tibble} with 45 555 observations and 14 variables:
+A \link[tibble:tibble]{tibble} with 45 735 observations and 14 variables:
 \itemize{
 \item \code{guideline}\cr Name of the guideline
 \item \code{type}\cr Breakpoint type, either \code{"ECOFF"}, \code{"animal"}, or \code{"human"}

man/g.test.Rd

@@ -46,7 +46,7 @@ A list with class \code{"htest"} containing the following
     \code{(observed - expected) / sqrt(expected)}.}
   \item{stdres}{standardized residuals,
     \code{(observed - expected) / sqrt(V)}, where \code{V} is the
-    residual cell variance (Agresti, 2007, section 2.4.5
+    residual cell variance {(\if{html}{\out{<a href="#reference+chisq.test.Rd+R+3AAgresti+3A2007" class="citation">}}Agresti 2007\if{html}{\out{</a>}}, section 2.4.5)}
     for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
 }
 \description{

man/ggplot_pca.Rd

@@ -59,8 +59,9 @@ ggplot_pca(
   }
 
 \item{pc.biplot}{
-    If true, use what Gabriel (1971) refers to as a "principal component
-    biplot", with \code{lambda = 1} and observations scaled up by sqrt(n) and
+    If true, use what {\if{html}{\cite{}\out{<a href="#reference+biplot.princomp.Rd+R+3AGabriel+3A1971" class="citation">}}Gabriel (1971)\if{html}{\out{</a>}}} refers to as a
+    \dQuote{principal component biplot},
+    with \code{lambda = 1} and observations scaled up by sqrt(n) and
     variables scaled down by sqrt(n).  Then inner products between
     variables approximate covariances and distances between observations
     approximate Mahalanobis distance.

man/interpretive_rules.Rd

@@ -173,12 +173,7 @@ eucast_dosage(c("tobra", "genta", "cipro"), "iv", version_breakpoints = 10)
 \itemize{
 \item EUCAST Expert Rules. Version 2.0, 2012.\cr
 Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60; \doi{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
-\item EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{(link)}
-\item EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.2, 2020. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf}{(link)}
-\item EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.3, 2021. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2021/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.3_20211018.pdf}{(link)}
-\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx}{(link)}
-\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 10.0, 2020. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_10.0_Breakpoint_Tables.xlsx}{(link)}
-\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 11.0, 2021. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_11.0_Breakpoint_Tables.xlsx}{(link)}
-\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 12.0, 2022. \href{https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_12.0_Breakpoint_Tables.xlsx}{(link)}
+\item EUCAST Expected Phenotypes. \href{https://www.eucast.org/bacteria/important-additional-information/expected-phenotypes/}{(link)}
+\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. \href{https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/clinical-breakpoint-tables/}{(link)}
 }
 }

man/microorganisms.Rd

@@ -59,11 +59,10 @@ Included taxonomic data from \href{https://lpsn.dsmz.de}{LPSN}, \href{https://ww
 
 For convenience, some entries were added manually:
 \itemize{
+\item All 37 groups and complexes of the \link{microorganisms.groups} data set, for cross-reference (examples include beta-haemolytic \emph{Streptococcus} groups A to K, coagulase-negative \emph{Staphylococcus} (CoNS), \emph{Mycobacterium tuberculosis} complex, etc.)
 \item ~1 500 entries of \emph{Salmonella}, such as the city-like serovars and groups A to H
-\item 37 species groups (such as the beta-haemolytic \emph{Streptococcus} groups A to K, coagulase-negative \emph{Staphylococcus} (CoNS), \emph{Mycobacterium tuberculosis} complex, etc.), of which the group compositions are stored in the \link{microorganisms.groups} data set
 \item 1 entry of \emph{Blastocystis} (\emph{B.  hominis}), although it officially does not exist (Noel \emph{et al.} 2005, PMID 15634993)
-\item 1 entry of \emph{Moraxella} (\emph{M. catarrhalis}), which was formally named \emph{Branhamella catarrhalis} (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
-\item 8 other 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, and unknown anaerobic Gram-pos/Gram-neg bacteria)
+\item 9 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 \href{https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R}{found here}.

man/mo_property.Rd

@@ -401,6 +401,7 @@ Visit \href{https://amr-for-r.org/articles/datasets.html}{our website for direct
 \examples{
 # taxonomic tree -----------------------------------------------------------
 
+mo_domain("Klebsiella pneumoniae")
 mo_kingdom("Klebsiella pneumoniae")
 mo_phylum("Klebsiella pneumoniae")
 mo_class("Klebsiella pneumoniae")
@@ -410,6 +411,8 @@ mo_genus("Klebsiella pneumoniae")
 mo_species("Klebsiella pneumoniae")
 mo_subspecies("Klebsiella pneumoniae")
 
+# all in one go
+mo_taxonomy("Klebsiella pneumoniae")
 
 # full names and short names -----------------------------------------------
 
@@ -430,6 +433,7 @@ mo_rank("Klebsiella pneumoniae")
 mo_url("Klebsiella pneumoniae")
 mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
 
+mo_group_members("Streptococcus group A")
 mo_group_members(c(
   "Streptococcus group A",
   "Streptococcus group C",
@@ -473,6 +477,7 @@ mo_shortname("K. pneu rh")
 
 mo_fullname("Staph epidermidis")
 mo_fullname("Staph epidermidis", Becker = TRUE)
+
 mo_shortname("Staph epidermidis")
 mo_shortname("Staph epidermidis", Becker = TRUE)
 
@@ -481,6 +486,7 @@ mo_shortname("Staph epidermidis", Becker = TRUE)
 
 mo_fullname("Strep agalactiae")
 mo_fullname("Strep agalactiae", Lancefield = TRUE)
+
 mo_shortname("Strep agalactiae")
 mo_shortname("Strep agalactiae", Lancefield = TRUE)
 
@@ -493,10 +499,10 @@ mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish
 mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek
 mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian
 
-# mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated
-mo_kingdom("Klebsiella pneumoniae")
+# mo_type is equal to mo_domain, but mo_domain will remain untranslated
+mo_domain("Klebsiella pneumoniae")
 mo_type("Klebsiella pneumoniae")
-mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect
+mo_domain("Klebsiella pneumoniae", language = "zh") # Chinese, no effect
 mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated
 
 mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de")

man/plot.Rd

@@ -36,21 +36,21 @@ scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
 scale_x_sir(
   colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
   language = get_AMR_locale(),
-  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+  eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
   ...
 )
 
 scale_colour_sir(
   colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
   language = get_AMR_locale(),
-  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+  eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
   ...
 )
 
 scale_fill_sir(
   colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
   language = get_AMR_locale(),
-  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
+  eucast_I = getOption("AMR_guideline", "EUCAST") \%like\% "EUCAST",
   ...
 )
 

man/top_n_microorganisms.Rd

@@ -9,6 +9,7 @@ top_n_microorganisms(
   n,
   property = "species",
   n_for_each = NULL,
+  property_for_each = "species",
   col_mo = NULL,
   ...
 )
@@ -16,37 +17,40 @@ top_n_microorganisms(
 \arguments{
 \item{x}{A data frame containing microbial data.}
 
-\item{n}{An integer specifying the maximum number of unique values of the \code{property} to include in the output.}
+\item{n}{A positive whole number specifying the maximum number of unique values of \code{property} to include in the output.}
 
-\item{property}{A character string indicating the microorganism property to use for filtering. Must be one of the column names of the \link{microorganisms} data set: \code{"mo"}, \code{"fullname"}, \code{"status"}, \code{"domain"}, \code{"kingdom"}, \code{"phylum"}, \code{"class"}, \code{"order"}, \code{"family"}, \code{"genus"}, \code{"species"}, \code{"subspecies"}, \code{"rank"}, \code{"ref"}, \code{"oxygen_tolerance"}, \code{"morphology"}, \code{"source"}, \code{"lpsn"}, \code{"lpsn_parent"}, \code{"lpsn_renamed_to"}, \code{"mycobank"}, \code{"mycobank_parent"}, \code{"mycobank_renamed_to"}, \code{"gbif"}, \code{"gbif_parent"}, \code{"gbif_renamed_to"}, \code{"prevalence"}, or \code{"snomed"}. If \code{NULL}, the raw values from \code{col_mo} will be used without transformation. When using \code{"species"} (default) or \code{"subpecies"}, the genus will be added to make sure each (sub)species still belongs to the right genus.}
+\item{property}{A character string indicating the microorganism property to use for filtering. Must be one of the column names of the \link{microorganisms} data set: \code{"mo"}, \code{"fullname"}, \code{"status"}, \code{"domain"}, \code{"kingdom"}, \code{"phylum"}, \code{"class"}, \code{"order"}, \code{"family"}, \code{"genus"}, \code{"species"}, \code{"subspecies"}, \code{"rank"}, \code{"ref"}, \code{"oxygen_tolerance"}, \code{"morphology"}, \code{"source"}, \code{"lpsn"}, \code{"lpsn_parent"}, \code{"lpsn_renamed_to"}, \code{"mycobank"}, \code{"mycobank_parent"}, \code{"mycobank_renamed_to"}, \code{"gbif"}, \code{"gbif_parent"}, \code{"gbif_renamed_to"}, \code{"prevalence"}, or \code{"snomed"}. If \code{NULL}, the raw values from \code{col_mo} will be used without transformation. When using \code{"species"} (default) or \code{"subspecies"}, the genus is prepended to ensure each name is unambiguous.}
 
-\item{n_for_each}{An optional integer specifying the maximum number of rows to retain for each value of the selected property. If \code{NULL}, all rows within the top \emph{n} groups will be included.}
+\item{n_for_each}{An optional positive whole number specifying the maximum number of distinct microorganism groups at the level of \code{property_for_each} to retain within each of the top \emph{n} groups. Only used when \code{property_for_each} is also set.}
+
+\item{property_for_each}{The microorganism property to use for sub-grouping within each top \emph{n} group. Must be one of the column names of the \link{microorganisms} data set and at a strictly lower taxonomic rank than \code{property} (allowed order: domain > kingdom > phylum > class > order > family > genus > species > subspecies). Defaults to \code{"species"}. Only relevant when \code{n_for_each} is set.}
 
 \item{col_mo}{A character string indicating the column in \code{x} that contains microorganism names or codes. Defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
 \item{...}{Additional arguments passed on to \code{\link[=mo_property]{mo_property()}} when \code{property} is not \code{NULL}.}
 }
 \description{
-This function filters a data set to include only the top \emph{n} microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, or to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
+Filters a data set to include only the top \emph{n} microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
 }
 \details{
-This function is useful for preprocessing data before creating \link[=antibiogram]{antibiograms} or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
+This function is useful for preprocessing data before creating \link[=antibiogram]{antibiograms} or other analyses that require focused subsets of microbial data.
 }
 \examples{
 # filter to the top 3 species:
-top_n_microorganisms(example_isolates,
-  n = 3
-)
+top_n_microorganisms(example_isolates, n = 3)
 
 # filter to any species in the top 5 genera:
-top_n_microorganisms(example_isolates,
-  n = 5, property = "genus"
-)
+top_n_microorganisms(example_isolates, n = 5, property = "genus")
 
 # filter to the top 3 species in each of the top 5 genera:
 top_n_microorganisms(example_isolates,
   n = 5, property = "genus", n_for_each = 3
 )
+
+# filter to the top 2 genera in each of the top 3 families:
+top_n_microorganisms(example_isolates,
+  n = 3, property = "family", n_for_each = 2, property_for_each = "genus"
+)
 }
 \seealso{
 \code{\link[=mo_property]{mo_property()}}, \code{\link[=as.mo]{as.mo()}}, \code{\link[=antibiogram]{antibiogram()}}

tests/testthat/test-amr_selectors.R

@@ -88,7 +88,7 @@ test_that("test-amr selectors.R", {
   expect_equal(nrow(example_isolates[any(carbapenems() != "R"), ]), 910, tolerance = 0.5)
   expect_equal(nrow(example_isolates[carbapenems() != "R", ]), 704, tolerance = 0.5)
 
-  # filter with multiple antibiotic selectors using c()
+  # filter with multiple antimicrobial selectors using c()
   expect_equal(nrow(example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]), 26, tolerance = 0.5)
 
   # filter + select in one go: get penicillins in carbapenems-resistant strains

tests/testthat/test-data.R

@@ -142,3 +142,9 @@ test_that("test-data.R", {
   # x <- check_non_ascii() %>%
   #   filter(file %unlike% "^(data-raw|docs|git_)")
 })
+
+test_that("taxonomic name columns contain no NA (empty string is used instead)", {
+  for (col in c("domain", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies")) {
+    expect_false(anyNA(microorganisms[[col]]), info = col)
+  }
+})

tests/testthat/test-mo.R

@@ -337,4 +337,21 @@ test_that("test-mo.R", {
       c("skim_type", "skim_variable", "n_missing", "complete_rate", "mo.n_unique", "mo.gram_negative", "mo.gram_positive", "mo.yeast", "mo.top_genus", "mo.top_species")
     )
   }
+
+  # "P. knowlesi" must resolve to Plasmodium knowlesi, not a Pseudomonas species,
+  # even though P. knowlesi has subspecies (curtisi, wallikeri) sharing the epithet.
+  expect_identical(
+    as.mo("P. knowlesi", keep_synonyms = TRUE, info = FALSE),
+    as.mo("Plasmodium knowlesi", keep_synonyms = TRUE, info = FALSE)
+  )
+  expect_identical(
+    mo_name("P. knowlesi", keep_synonyms = TRUE, language = NULL),
+    "Plasmodium knowlesi"
+  )
+
+  # Non-regression: the original #288 example must still work.
+  expect_identical(
+    mo_genus("S. apiospermum", keep_synonyms = TRUE, language = NULL),
+    "Scedosporium"
+  )
 })

vignettes/AMR.Rmd

@@ -220,9 +220,9 @@ our_data_1st %>%
   count(mo_name(bacteria), sort = TRUE)
 ```
 
-## Select and filter with antibiotic selectors
+## Select and filter with antimicrobial selectors
 
-Using so-called antibiotic class selectors, you can select or filter columns based on the antibiotic class that your antibiotic results are in:
+Using so-called antimicrobial class selectors, you can select or filter columns based on the antimicrobial class that your antimicrobial results are in:
 
 ```{r bug_drg 2a}
 our_data_1st %>%
@@ -234,7 +234,7 @@ our_data_1st %>%
 our_data_1st %>%
   select(bacteria, where(is.sir))
 
-# filtering using AB selectors is also possible:
+# filtering using antimicrobial selectors is also possible:
 our_data_1st %>%
   filter(any(aminoglycosides() == "R"))
 

vignettes/AMR_for_Python.Rmd

@@ -200,6 +200,48 @@ AMR.antimicrobials
 | ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
 
 
+# Installation Channels
+
+## Stable Release (CRAN)
+
+The default `AMR` Python package uses the latest stable version of the `AMR` R package, published on CRAN. After running `pip install AMR`, import it as usual:
+
+```python
+import AMR
+
+AMR.example_isolates
+```
+
+## Development Version (GitHub)
+
+To use the latest development version of the `AMR` R package (sourced directly from GitHub), import the `beta` sub-package and alias it as `AMR`:
+
+```python
+import AMR.beta as AMR
+
+AMR.example_isolates
+```
+
+Aliasing with `as AMR` keeps all downstream code identical to the stable import. Switching between the stable release and the development version requires changing only the import line — nothing else in your script needs to change.
+
+# SIR Classification with `as_sir()`
+
+## Using `enforce_method`
+
+The `as_sir()` function in R uses S3 method dispatch to select the correct calculation method based on the input class: `<mic>` for MIC values and `<disk>` for disk diffusion values. Because Python objects do not carry R class attributes through the `rpy2` bridge, this automatic dispatch may not resolve correctly.
+
+To explicitly specify the input type, use the `enforce_method` argument:
+
+```python
+# Treat the column as MIC values — maps to R's as.sir.mic()
+AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
+
+# Treat the column as disk diffusion values — maps to R's as.sir.disk()
+AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
+```
+
+Without `enforce_method`, R falls back to class-based dispatch on the raw Python input, which may fail or return unexpected results. Always supply `enforce_method` when calling `as_sir()` from Python.
+
 # Conclusion
 
 With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.