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 Metadata-Version: 2.4
 Name: AMR
 Version: 3.0.1.9091
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends
 Author-email: m.s.berends@umcg.nl
 License: GPL 2
 Project-URL: Bug Tracker, https://github.com/msberends/AMR/issues
 Classifier: Programming Language :: Python :: 3
 Classifier: Operating System :: OS Independent
 Requires-Python: >=3.6
 Description-Content-Type: text/markdown
 Requires-Dist: rpy2
 Requires-Dist: numpy
 Requires-Dist: pandas
 Dynamic: author
 Dynamic: author-email
 Dynamic: classifier
 Dynamic: description
 Dynamic: description-content-type
 Dynamic: home-page
 Dynamic: license
 Dynamic: project-url
 Dynamic: requires-dist
 Dynamic: requires-python
 Dynamic: summary
 The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python package](https://pypi.org/project/AMR/).
 This Python package is a wrapper around the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
 # Prerequisites
 This package was only tested with a [virtual environment (venv)](https://docs.python.org/3/library/venv.html). You can set up such an environment by running:
 ```python
 # linux and macOS:
 python -m venv /path/to/new/virtual/environment
 # Windows:
 python -m venv C:\path\to\new\virtual\environment
 ```
 Then you can [activate the environment](https://docs.python.org/3/library/venv.html#how-venvs-work), after which the venv is ready to work with.
 # Install AMR
 1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
    ```bash
    pip install AMR
    ```
 2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
    For Linux:
    ```bash
    # Ubuntu / Debian
    sudo apt install r-base
    # Fedora:
    sudo dnf install R
    # CentOS/RHEL
    sudo yum install R
    ```
    For macOS (using [Homebrew](https://brew.sh)):
    ```bash
    brew install r
    ```
    For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
 # Examples of Usage
 ## Cleaning Taxonomy
 Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
 ```python
 import pandas as pd
 import AMR
 # Sample data
 data = {
    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
 }
 df = pd.DataFrame(data)
 # Use AMR functions to clean microorganism and drug names
 df['MO_clean'] = AMR.mo_name(df['MOs'])
 df['Drug_clean'] = AMR.ab_name(df['Drug'])
 # Display the results
 print(df)
 ```
 | MOs         | Drug      | MO_clean           | Drug_clean    |
 |-------------|-----------|--------------------|---------------|
 | E. coli     | Cipro     | Escherichia coli   | Ciprofloxacin |
 | ESCCOL      | CIP       | Escherichia coli   | Ciprofloxacin |
 | esco        | J01MA02   | Escherichia coli   | Ciprofloxacin |
 | Esche coli  | Ciproxin  | Escherichia coli   | Ciprofloxacin |
 ### Explanation
 * **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 ## Calculating AMR
 ```python
 import AMR
 import pandas as pd
 df = AMR.example_isolates
 result = AMR.resistance(df["AMX"])
 print(result)
 ```
 ```
 [0.59555556]
 ```
 ## Generating Antibiograms
 One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
 ```python
 result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
 print(result2a)
 ```
 | Pathogen        | Amoxicillin     | Ciprofloxacin   | Piperacillin/tazobactam  |
 |-----------------|-----------------|-----------------|--------------------------|
 | CoNS            | 7% (10/142)     | 73% (183/252)   | 30% (10/33)              |
 | E. coli         | 50% (196/392)   | 88% (399/456)   | 94% (393/416)            |
 | K. pneumoniae   | 0% (0/58)       | 96% (53/55)     | 89% (47/53)              |
 | P. aeruginosa   | 0% (0/30)       | 100% (30/30)    | None                     |
 | P. mirabilis    | None            | 94% (34/36)     | None                     |
 | S. aureus       | 6% (8/131)      | 90% (171/191)   | None                     |
 | S. epidermidis  | 1% (1/91)       | 64% (87/136)    | None                     |
 | S. hominis      | None            | 80% (56/70)     | None                     |
 | S. pneumoniae   | 100% (112/112)  | None            | 100% (112/112)           |
 ```python
 result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
 print(result2b)
 ```
 | Pathogen       | Amoxicillin     | Ciprofloxacin    | Piperacillin/tazobactam  |
 |----------------|-----------------|------------------|--------------------------|
 | Gram-negative  | 36% (226/631)   | 91% (621/684)    | 88% (565/641)            |
 | Gram-positive  | 43% (305/703)   | 77% (560/724)    | 86% (296/345)            |
 In this example, we generate an antibiogram by selecting various antibiotics.
 ## Taxonomic Data Sets Now in Python!
 As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
 ```python
 AMR.microorganisms
 ```
 | mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
 |--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
 | B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
 | B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
 | B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
 | B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
 | B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
 | F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
 ```python
 AMR.antimicrobials
 ```
 | ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
 |-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
 | AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
 | ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
 | ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
 | ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
 | AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
 | ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
 | VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
 | VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
 | XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
 | ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
 | 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.
 By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
 Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
@@ -1,12 +0,0 @@
 README.md
 setup.py
 AMR/__init__.py
 AMR/_engine.py
 AMR/beta.py
 AMR/datasets.py
 AMR/functions.py
 AMR.egg-info/PKG-INFO
 AMR.egg-info/SOURCES.txt
 AMR.egg-info/dependency_links.txt
 AMR.egg-info/requires.txt
 AMR.egg-info/top_level.txt
@@ -1,3 +0,0 @@
 rpy2
 numpy
 pandas
@@ -1 +0,0 @@
 AMR
@@ -1,249 +0,0 @@
 import sys
 _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
 from .functions import custom_eucast_rules
 from .functions import ab_class
 from .functions import ab_selector
 from .functions import ab_from_text
 from .functions import ab_name
 from .functions import ab_cid
 from .functions import ab_synonyms
 from .functions import ab_tradenames
 from .functions import ab_group
 from .functions import ab_atc
 from .functions import ab_atc_group1
 from .functions import ab_atc_group2
 from .functions import ab_loinc
 from .functions import ab_ddd
 from .functions import ab_ddd_units
 from .functions import ab_info
 from .functions import ab_url
 from .functions import ab_property
 from .functions import add_custom_antimicrobials
 from .functions import clear_custom_antimicrobials
 from .functions import add_custom_microorganisms
 from .functions import clear_custom_microorganisms
 from .functions import age
 from .functions import age_groups
 from .functions import all_sir
 from .functions import all_sir_predictors
 from .functions import all_mic
 from .functions import all_mic_predictors
 from .functions import all_disk
 from .functions import all_disk_predictors
 from .functions import step_mic_log2
 from .functions import step_sir_numeric
 from .functions import amr_course
 from .functions import wisca
 from .functions import antibiogram
 from .functions import retrieve_wisca_parameters
 from .functions import wisca_plot
 from .functions import aminoglycosides
 from .functions import aminopenicillins
 from .functions import antifungals
 from .functions import antimycobacterials
 from .functions import betalactams
 from .functions import betalactams_with_inhibitor
 from .functions import carbapenems
 from .functions import cephalosporins
 from .functions import cephalosporins_1st
 from .functions import cephalosporins_2nd
 from .functions import cephalosporins_3rd
 from .functions import cephalosporins_4th
 from .functions import cephalosporins_5th
 from .functions import fluoroquinolones
 from .functions import glycopeptides
 from .functions import ionophores
 from .functions import isoxazolylpenicillins
 from .functions import lincosamides
 from .functions import lipoglycopeptides
 from .functions import macrolides
 from .functions import monobactams
 from .functions import nitrofurans
 from .functions import oxazolidinones
 from .functions import penicillins
 from .functions import peptides
 from .functions import phenicols
 from .functions import phosphonics
 from .functions import polymyxins
 from .functions import quinolones
 from .functions import rifamycins
 from .functions import spiropyrimidinetriones
 from .functions import streptogramins
 from .functions import sulfonamides
 from .functions import tetracyclines
 from .functions import trimethoprims
 from .functions import ureidopenicillins
 from .functions import amr_class
 from .functions import amr_selector
 from .functions import administrable_per_os
 from .functions import administrable_iv
 from .functions import not_intrinsic_resistant
 from .functions import as_ab
 from .functions import is_ab
 from .functions import ab_reset_session
 from .functions import as_av
 from .functions import is_av
 from .functions import as_disk
 from .functions import is_disk
 from .functions import as_mic
 from .functions import is_mic
 from .functions import rescale_mic
 from .functions import mic_p50
 from .functions import mic_p90
 from .functions import as_mo
 from .functions import is_mo
 from .functions import mo_uncertainties
 from .functions import mo_renamed
 from .functions import mo_failures
 from .functions import mo_reset_session
 from .functions import mo_cleaning_regex
 from .functions import as_sir
 from .functions import is_sir
 from .functions import is_sir_eligible
 from .functions import sir_interpretation_history
 from .functions import atc_online_property
 from .functions import atc_online_groups
 from .functions import atc_online_ddd
 from .functions import atc_online_ddd_units
 from .functions import av_from_text
 from .functions import av_name
 from .functions import av_cid
 from .functions import av_synonyms
 from .functions import av_tradenames
 from .functions import av_group
 from .functions import av_atc
 from .functions import av_loinc
 from .functions import av_ddd
 from .functions import av_ddd_units
 from .functions import av_info
 from .functions import av_url
 from .functions import av_property
 from .functions import availability
 from .functions import bug_drug_combinations
 from .functions import count_resistant
 from .functions import count_susceptible
 from .functions import count_S
 from .functions import count_SI
 from .functions import count_I
 from .functions import count_IR
 from .functions import count_R
 from .functions import count_all
 from .functions import n_sir
 from .functions import count_df
 from .functions import custom_interpretive_rules
 from .functions import custom_mdro_guideline
 from .functions import export_ncbi_biosample
 from .functions import first_isolate
 from .functions import filter_first_isolate
 from .functions import g_test
 from .functions import is_new_episode
 from .functions import ggplot_pca
 from .functions import ggplot_sir
 from .functions import geom_sir
 from .functions import guess_ab_col
 from .functions import interpretive_rules
 from .functions import eucast_rules
 from .functions import clsi_rules
 from .functions import eucast_dosage
 from .functions import italicise_taxonomy
 from .functions import italicize_taxonomy
 from .functions import inner_join_microorganisms
 from .functions import left_join_microorganisms
 from .functions import right_join_microorganisms
 from .functions import full_join_microorganisms
 from .functions import semi_join_microorganisms
 from .functions import anti_join_microorganisms
 from .functions import key_antimicrobials
 from .functions import all_antimicrobials
 from .functions import kurtosis
 from .functions import like
 from .functions import mdro
 from .functions import brmo
 from .functions import mrgn
 from .functions import mdr_tb
 from .functions import mdr_cmi2012
 from .functions import eucast_exceptional_phenotypes
 from .functions import mean_amr_distance
 from .functions import amr_distance_from_row
 from .functions import mo_matching_score
 from .functions import mo_name
 from .functions import mo_fullname
 from .functions import mo_shortname
 from .functions import mo_subspecies
 from .functions import mo_species
 from .functions import mo_genus
 from .functions import mo_family
 from .functions import mo_order
 from .functions import mo_class
 from .functions import mo_phylum
 from .functions import mo_kingdom
 from .functions import mo_domain
 from .functions import mo_type
 from .functions import mo_status
 from .functions import mo_pathogenicity
 from .functions import mo_gramstain
 from .functions import mo_is_gram_negative
 from .functions import mo_is_gram_positive
 from .functions import mo_is_yeast
 from .functions import mo_is_intrinsic_resistant
 from .functions import mo_oxygen_tolerance
 from .functions import mo_is_anaerobic
 from .functions import mo_morphology
 from .functions import mo_snomed
 from .functions import mo_ref
 from .functions import mo_authors
 from .functions import mo_year
 from .functions import mo_lpsn
 from .functions import mo_mycobank
 from .functions import mo_gbif
 from .functions import mo_rank
 from .functions import mo_taxonomy
 from .functions import mo_synonyms
 from .functions import mo_current
 from .functions import mo_group_members
 from .functions import mo_info
 from .functions import mo_url
 from .functions import mo_property
 from .functions import pca
 from .functions import theme_sir
 from .functions import labels_sir_count
 from .functions import resistance
 from .functions import susceptibility
 from .functions import sir_confidence_interval
 from .functions import proportion_R
 from .functions import proportion_IR
 from .functions import proportion_I
 from .functions import proportion_SI
 from .functions import proportion_S
 from .functions import proportion_df
 from .functions import sir_df
 from .functions import random_mic
 from .functions import random_disk
 from .functions import random_sir
 from .functions import resistance_predict
 from .functions import sir_predict
 from .functions import ggplot_sir_predict
 from .functions import skewness
 from .functions import top_n_microorganisms
 from .functions import reset_AMR_locale
 from .functions import translate_AMR
@@ -1,93 +0,0 @@
 import os
 import sys
 import importlib.metadata as metadata
 # Get the path to the virtual environment
 venv_path = sys.prefix
 r_lib_path = os.path.join(venv_path, "R_libs")
 os.makedirs(r_lib_path, exist_ok=True)
 # Set environment variable before importing rpy2
 os.environ['R_LIBS_SITE'] = r_lib_path
 from rpy2 import robjects
 from rpy2.robjects.vectors import StrVector
 from rpy2.robjects.packages import importr, isinstalled
 # Import base and utils once
 base = importr('base')
 utils = importr('utils')
 # Silence R console output entirely
 robjects.r('suppressMessages(suppressWarnings(sink(tempfile())))')
 base._libPaths(r_lib_path)
 _installed_source = None
 def _r_version():
    """Return the currently installed AMR R package version, or None."""
    try:
        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:
        return ''
 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
    )
 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:
                install_fn()
            except Exception as e:
                print(f"AMR: Could not update ({e})", 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
@@ -1,22 +0,0 @@
 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
@@ -1,54 +0,0 @@
 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)
    with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
        _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"))) {
            as.character(x)
        } else {
            x
        }
    })
    df <- df[, !sapply(df, is.list)]
    df
    ''')
    df['date'] = pd.to_datetime(df['date'])
    return df
 def get(name, source="cran"):
    """Retrieve a dataset by name, installing AMR if needed."""
    _load_datasets(source)
    return _cache[name]
@@ -1,984 +0,0 @@
 import functools
 import rpy2.robjects as robjects
 from rpy2.robjects.packages import importr
 from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector, DataFrame
 from rpy2.robjects.conversion import localconverter
 from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 import pandas as pd
 import numpy as np
 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):
    """Convert Python lists/tuples to typed R vectors.
    rpy2's default_converter passes Python lists to R as R lists, not as
    character/numeric vectors. This causes element-wise type-check functions
    such as is.mic(), is.sir(), and is.disk() to return a logical vector
    rather than a single logical, breaking R's scalar && operator.
    This helper converts Python lists and tuples to the appropriate R vector
    type based on the element types, so R always receives a proper vector."""
    if isinstance(value, (list, tuple)):
        if len(value) == 0:
            return StrVector([])
        # bool must be checked before int because bool is a subclass of int
        if all(isinstance(v, bool) for v in value):
            return robjects.vectors.BoolVector(value)
        if all(isinstance(v, int) for v in value):
            return IntVector(value)
        if all(isinstance(v, float) for v in value):
            return FloatVector(value)
        if all(isinstance(v, str) for v in value):
            return StrVector(value)
        # Mixed types: coerce all to string
        return StrVector([str(v) for v in value])
    return value
 def convert_to_python(r_output):
    # Check if it's a StrVector (R character vector)
    if isinstance(r_output, StrVector):
        return list(r_output)  # Convert to a Python list of strings
    # Check if it's a FactorVector (R factor)
    elif isinstance(r_output, FactorVector):
        return list(r_output)  # Convert to a list of integers (factor levels)
    # Check if it's an IntVector or FloatVector (numeric R vectors)
    elif isinstance(r_output, (IntVector, FloatVector)):
        return list(r_output)  # Convert to a Python list of integers or floats
    # Check if it's a pandas-compatible R data frame
    elif isinstance(r_output, (pd.DataFrame, DataFrame)):
        return r_output  # Return as pandas DataFrame (already converted by pandas2ri)
    # Check if the input is a NumPy array and has a string data type
    if isinstance(r_output, np.ndarray) and np.issubdtype(r_output.dtype, np.str_):
        return r_output.tolist()  # Convert to a regular Python list
    # Fall-back
    return r_output
 def r_to_python(r_func):
    """Decorator that converts Python list/tuple inputs to typed R vectors,
    runs the rpy2 function under a localconverter, and converts the output
    to a Python type."""
    @functools.wraps(r_func)
    def wrapper(*args, **kwargs):
        args = tuple(convert_to_r(a) for a in args)
        kwargs = {k: convert_to_r(v) for k, v in kwargs.items()}
        with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
            return convert_to_python(r_func(*args, **kwargs))
    return wrapper
@r_to_python
 def custom_eucast_rules(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.custom_eucast_rules(*args, **kwargs)
@r_to_python
 def ab_class(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_class(*args, **kwargs)
@r_to_python
 def ab_selector(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_selector(*args, **kwargs)
@r_to_python
 def ab_from_text(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_from_text(*args, **kwargs)
@r_to_python
 def ab_name(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_name(x, *args, **kwargs)
@r_to_python
 def ab_cid(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_cid(x, *args, **kwargs)
@r_to_python
 def ab_synonyms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_synonyms(x, *args, **kwargs)
@r_to_python
 def ab_tradenames(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_tradenames(x, *args, **kwargs)
@r_to_python
 def ab_group(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_group(x, *args, **kwargs)
@r_to_python
 def ab_atc(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_atc(x, *args, **kwargs)
@r_to_python
 def ab_atc_group1(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_atc_group1(x, *args, **kwargs)
@r_to_python
 def ab_atc_group2(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_atc_group2(x, *args, **kwargs)
@r_to_python
 def ab_loinc(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_loinc(x, *args, **kwargs)
@r_to_python
 def ab_ddd(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_ddd(x, *args, **kwargs)
@r_to_python
 def ab_ddd_units(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_ddd_units(x, *args, **kwargs)
@r_to_python
 def ab_info(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_info(x, *args, **kwargs)
@r_to_python
 def ab_url(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_url(x, *args, **kwargs)
@r_to_python
 def ab_property(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_property(x, *args, **kwargs)
@r_to_python
 def add_custom_antimicrobials(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.add_custom_antimicrobials(x)
@r_to_python
 def clear_custom_antimicrobials(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.clear_custom_antimicrobials(*args, **kwargs)
@r_to_python
 def add_custom_microorganisms(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.add_custom_microorganisms(x)
@r_to_python
 def clear_custom_microorganisms(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.clear_custom_microorganisms(*args, **kwargs)
@r_to_python
 def age(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.age(x, *args, **kwargs)
@r_to_python
 def age_groups(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.age_groups(x, *args, **kwargs)
@r_to_python
 def all_sir(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_sir(*args, **kwargs)
@r_to_python
 def all_sir_predictors(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_sir_predictors(*args, **kwargs)
@r_to_python
 def all_mic(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_mic(*args, **kwargs)
@r_to_python
 def all_mic_predictors(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_mic_predictors(*args, **kwargs)
@r_to_python
 def all_disk(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_disk(*args, **kwargs)
@r_to_python
 def all_disk_predictors(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_disk_predictors(*args, **kwargs)
@r_to_python
 def step_mic_log2(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.step_mic_log2(*args, **kwargs)
@r_to_python
 def step_sir_numeric(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.step_sir_numeric(*args, **kwargs)
@r_to_python
 def amr_course(github_repo, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.amr_course(github_repo, *args, **kwargs)
@r_to_python
 def wisca(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.wisca(*args, **kwargs)
@r_to_python
 def antibiogram(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.antibiogram(*args, **kwargs)
@r_to_python
 def retrieve_wisca_parameters(wisca_model, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.retrieve_wisca_parameters(wisca_model, *args, **kwargs)
@r_to_python
 def wisca_plot(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.wisca_plot(*args, **kwargs)
@r_to_python
 def aminoglycosides(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.aminoglycosides(*args, **kwargs)
@r_to_python
 def aminopenicillins(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.aminopenicillins(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def antifungals(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.antifungals(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def antimycobacterials(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.antimycobacterials(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def betalactams(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.betalactams(*args, **kwargs)
@r_to_python
 def betalactams_with_inhibitor(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.betalactams_with_inhibitor(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def carbapenems(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.carbapenems(*args, **kwargs)
@r_to_python
 def cephalosporins(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins(*args, **kwargs)
@r_to_python
 def cephalosporins_1st(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins_1st(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def cephalosporins_2nd(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins_2nd(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def cephalosporins_3rd(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins_3rd(*args, **kwargs)
@r_to_python
 def cephalosporins_4th(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins_4th(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def cephalosporins_5th(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.cephalosporins_5th(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def fluoroquinolones(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.fluoroquinolones(*args, **kwargs)
@r_to_python
 def glycopeptides(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.glycopeptides(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def ionophores(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ionophores(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def isoxazolylpenicillins(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.isoxazolylpenicillins(*args, **kwargs)
@r_to_python
 def lincosamides(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.lincosamides(*args, **kwargs)
@r_to_python
 def lipoglycopeptides(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.lipoglycopeptides(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def macrolides(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.macrolides(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def monobactams(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.monobactams(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def nitrofurans(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.nitrofurans(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def oxazolidinones(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.oxazolidinones(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def penicillins(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.penicillins(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def peptides(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.peptides(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def phenicols(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.phenicols(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def phosphonics(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.phosphonics(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def polymyxins(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.polymyxins(*args, **kwargs)
@r_to_python
 def quinolones(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.quinolones(*args, **kwargs)
@r_to_python
 def rifamycins(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.rifamycins(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def spiropyrimidinetriones(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.spiropyrimidinetriones(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def streptogramins(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.streptogramins(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def sulfonamides(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.sulfonamides(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def tetracyclines(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.tetracyclines(*args, **kwargs)
@r_to_python
 def trimethoprims(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.trimethoprims(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def ureidopenicillins(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ureidopenicillins(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def amr_class(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.amr_class(*args, **kwargs)
@r_to_python
 def amr_selector(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.amr_selector(*args, **kwargs)
@r_to_python
 def administrable_per_os(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.administrable_per_os(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def administrable_iv(only_sir_columns = False, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.administrable_iv(only_sir_columns = False, *args, **kwargs)
@r_to_python
 def not_intrinsic_resistant(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.not_intrinsic_resistant(*args, **kwargs)
@r_to_python
 def as_ab(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_ab(*args, **kwargs)
@r_to_python
 def is_ab(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_ab(x)
@r_to_python
 def ab_reset_session(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ab_reset_session(*args, **kwargs)
@r_to_python
 def as_av(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_av(x, *args, **kwargs)
@r_to_python
 def is_av(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_av(x)
@r_to_python
 def as_disk(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_disk(x, *args, **kwargs)
@r_to_python
 def is_disk(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_disk(x)
@r_to_python
 def as_mic(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_mic(x, *args, **kwargs)
@r_to_python
 def is_mic(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_mic(x)
@r_to_python
 def rescale_mic(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.rescale_mic(*args, **kwargs)
@r_to_python
 def mic_p50(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mic_p50(x, *args, **kwargs)
@r_to_python
 def mic_p90(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mic_p90(x, *args, **kwargs)
@r_to_python
 def as_mo(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_mo(*args, **kwargs)
@r_to_python
 def is_mo(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_mo(x)
@r_to_python
 def mo_uncertainties(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_uncertainties(*args, **kwargs)
@r_to_python
 def mo_renamed(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_renamed(*args, **kwargs)
@r_to_python
 def mo_failures(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_failures(*args, **kwargs)
@r_to_python
 def mo_reset_session(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_reset_session(*args, **kwargs)
@r_to_python
 def mo_cleaning_regex(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_cleaning_regex(*args, **kwargs)
@r_to_python
 def as_sir(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.as_sir(x, *args, **kwargs)
@r_to_python
 def is_sir(x):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_sir(x)
@r_to_python
 def is_sir_eligible(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_sir_eligible(x, *args, **kwargs)
@r_to_python
 def sir_interpretation_history(clean):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.sir_interpretation_history(clean)
@r_to_python
 def atc_online_property(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.atc_online_property(*args, **kwargs)
@r_to_python
 def atc_online_groups(atc_code, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.atc_online_groups(atc_code, *args, **kwargs)
@r_to_python
 def atc_online_ddd(atc_code, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.atc_online_ddd(atc_code, *args, **kwargs)
@r_to_python
 def atc_online_ddd_units(atc_code, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.atc_online_ddd_units(atc_code, *args, **kwargs)
@r_to_python
 def av_from_text(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_from_text(*args, **kwargs)
@r_to_python
 def av_name(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_name(x, *args, **kwargs)
@r_to_python
 def av_cid(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_cid(x, *args, **kwargs)
@r_to_python
 def av_synonyms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_synonyms(x, *args, **kwargs)
@r_to_python
 def av_tradenames(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_tradenames(x, *args, **kwargs)
@r_to_python
 def av_group(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_group(x, *args, **kwargs)
@r_to_python
 def av_atc(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_atc(x, *args, **kwargs)
@r_to_python
 def av_loinc(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_loinc(x, *args, **kwargs)
@r_to_python
 def av_ddd(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_ddd(x, *args, **kwargs)
@r_to_python
 def av_ddd_units(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_ddd_units(x, *args, **kwargs)
@r_to_python
 def av_info(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_info(x, *args, **kwargs)
@r_to_python
 def av_url(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_url(x, *args, **kwargs)
@r_to_python
 def av_property(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.av_property(x, *args, **kwargs)
@r_to_python
 def availability(tbl, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.availability(tbl, *args, **kwargs)
@r_to_python
 def bug_drug_combinations(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.bug_drug_combinations(*args, **kwargs)
@r_to_python
 def count_resistant(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_resistant(*args, **kwargs)
@r_to_python
 def count_susceptible(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_susceptible(*args, **kwargs)
@r_to_python
 def count_S(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_S(*args, **kwargs)
@r_to_python
 def count_SI(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_SI(*args, **kwargs)
@r_to_python
 def count_I(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_I(*args, **kwargs)
@r_to_python
 def count_IR(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_IR(*args, **kwargs)
@r_to_python
 def count_R(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_R(*args, **kwargs)
@r_to_python
 def count_all(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_all(*args, **kwargs)
@r_to_python
 def n_sir(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.n_sir(*args, **kwargs)
@r_to_python
 def count_df(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.count_df(*args, **kwargs)
@r_to_python
 def custom_interpretive_rules(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.custom_interpretive_rules(*args, **kwargs)
@r_to_python
 def custom_mdro_guideline(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.custom_mdro_guideline(*args, **kwargs)
@r_to_python
 def export_ncbi_biosample(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.export_ncbi_biosample(*args, **kwargs)
@r_to_python
 def first_isolate(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.first_isolate(*args, **kwargs)
@r_to_python
 def filter_first_isolate(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.filter_first_isolate(*args, **kwargs)
@r_to_python
 def g_test(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.g_test(x, *args, **kwargs)
@r_to_python
 def is_new_episode(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.is_new_episode(x, *args, **kwargs)
@r_to_python
 def ggplot_pca(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ggplot_pca(*args, **kwargs)
@r_to_python
 def ggplot_sir(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ggplot_sir(*args, **kwargs)
@r_to_python
 def geom_sir(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.geom_sir(*args, **kwargs)
@r_to_python
 def guess_ab_col(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.guess_ab_col(*args, **kwargs)
@r_to_python
 def interpretive_rules(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.interpretive_rules(*args, **kwargs)
@r_to_python
 def eucast_rules(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.eucast_rules(*args, **kwargs)
@r_to_python
 def clsi_rules(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.clsi_rules(*args, **kwargs)
@r_to_python
 def eucast_dosage(ab, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.eucast_dosage(ab, *args, **kwargs)
@r_to_python
 def italicise_taxonomy(string, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.italicise_taxonomy(string, *args, **kwargs)
@r_to_python
 def italicize_taxonomy(string, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.italicize_taxonomy(string, *args, **kwargs)
@r_to_python
 def inner_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.inner_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def left_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.left_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def right_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.right_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def full_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.full_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def semi_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.semi_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def anti_join_microorganisms(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.anti_join_microorganisms(x, *args, **kwargs)
@r_to_python
 def key_antimicrobials(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.key_antimicrobials(*args, **kwargs)
@r_to_python
 def all_antimicrobials(x = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.all_antimicrobials(x = None, *args, **kwargs)
@r_to_python
 def kurtosis(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.kurtosis(x, *args, **kwargs)
@r_to_python
 def like(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.like(x, *args, **kwargs)
@r_to_python
 def mdro(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mdro(*args, **kwargs)
@r_to_python
 def brmo(x = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.brmo(x = None, *args, **kwargs)
@r_to_python
 def mrgn(x = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mrgn(x = None, *args, **kwargs)
@r_to_python
 def mdr_tb(x = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mdr_tb(x = None, *args, **kwargs)
@r_to_python
 def mdr_cmi2012(x = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mdr_cmi2012(x = None, *args, **kwargs)
@r_to_python
 def eucast_exceptional_phenotypes(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.eucast_exceptional_phenotypes(*args, **kwargs)
@r_to_python
 def mean_amr_distance(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mean_amr_distance(x, *args, **kwargs)
@r_to_python
 def amr_distance_from_row(amr_distance, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.amr_distance_from_row(amr_distance, *args, **kwargs)
@r_to_python
 def mo_matching_score(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_matching_score(x, *args, **kwargs)
@r_to_python
 def mo_name(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_name(*args, **kwargs)
@r_to_python
 def mo_fullname(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_fullname(*args, **kwargs)
@r_to_python
 def mo_shortname(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_shortname(*args, **kwargs)
@r_to_python
 def mo_subspecies(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_subspecies(*args, **kwargs)
@r_to_python
 def mo_species(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_species(*args, **kwargs)
@r_to_python
 def mo_genus(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_genus(*args, **kwargs)
@r_to_python
 def mo_family(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_family(*args, **kwargs)
@r_to_python
 def mo_order(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_order(*args, **kwargs)
@r_to_python
 def mo_class(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_class(*args, **kwargs)
@r_to_python
 def mo_phylum(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_phylum(*args, **kwargs)
@r_to_python
 def mo_kingdom(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_kingdom(*args, **kwargs)
@r_to_python
 def mo_domain(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_domain(*args, **kwargs)
@r_to_python
 def mo_type(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_type(*args, **kwargs)
@r_to_python
 def mo_status(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_status(*args, **kwargs)
@r_to_python
 def mo_pathogenicity(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_pathogenicity(*args, **kwargs)
@r_to_python
 def mo_gramstain(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_gramstain(*args, **kwargs)
@r_to_python
 def mo_is_gram_negative(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_is_gram_negative(*args, **kwargs)
@r_to_python
 def mo_is_gram_positive(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_is_gram_positive(*args, **kwargs)
@r_to_python
 def mo_is_yeast(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_is_yeast(*args, **kwargs)
@r_to_python
 def mo_is_intrinsic_resistant(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_is_intrinsic_resistant(*args, **kwargs)
@r_to_python
 def mo_oxygen_tolerance(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_oxygen_tolerance(*args, **kwargs)
@r_to_python
 def mo_is_anaerobic(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_is_anaerobic(*args, **kwargs)
@r_to_python
 def mo_morphology(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_morphology(*args, **kwargs)
@r_to_python
 def mo_snomed(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_snomed(*args, **kwargs)
@r_to_python
 def mo_ref(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_ref(*args, **kwargs)
@r_to_python
 def mo_authors(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_authors(*args, **kwargs)
@r_to_python
 def mo_year(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_year(*args, **kwargs)
@r_to_python
 def mo_lpsn(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_lpsn(*args, **kwargs)
@r_to_python
 def mo_mycobank(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_mycobank(*args, **kwargs)
@r_to_python
 def mo_gbif(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_gbif(*args, **kwargs)
@r_to_python
 def mo_rank(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_rank(*args, **kwargs)
@r_to_python
 def mo_taxonomy(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_taxonomy(*args, **kwargs)
@r_to_python
 def mo_synonyms(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_synonyms(*args, **kwargs)
@r_to_python
 def mo_current(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_current(x, *args, **kwargs)
@r_to_python
 def mo_group_members(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_group_members(*args, **kwargs)
@r_to_python
 def mo_info(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_info(*args, **kwargs)
@r_to_python
 def mo_url(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_url(*args, **kwargs)
@r_to_python
 def mo_property(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.mo_property(*args, **kwargs)
@r_to_python
 def pca(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.pca(*args, **kwargs)
@r_to_python
 def theme_sir(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.theme_sir(*args, **kwargs)
@r_to_python
 def labels_sir_count(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.labels_sir_count(*args, **kwargs)
@r_to_python
 def resistance(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.resistance(*args, **kwargs)
@r_to_python
 def susceptibility(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.susceptibility(*args, **kwargs)
@r_to_python
 def sir_confidence_interval(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.sir_confidence_interval(*args, **kwargs)
@r_to_python
 def proportion_R(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_R(*args, **kwargs)
@r_to_python
 def proportion_IR(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_IR(*args, **kwargs)
@r_to_python
 def proportion_I(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_I(*args, **kwargs)
@r_to_python
 def proportion_SI(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_SI(*args, **kwargs)
@r_to_python
 def proportion_S(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_S(*args, **kwargs)
@r_to_python
 def proportion_df(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.proportion_df(*args, **kwargs)
@r_to_python
 def sir_df(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.sir_df(*args, **kwargs)
@r_to_python
 def random_mic(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.random_mic(*args, **kwargs)
@r_to_python
 def random_disk(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.random_disk(*args, **kwargs)
@r_to_python
 def random_sir(size = None, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.random_sir(size = None, *args, **kwargs)
@r_to_python
 def resistance_predict(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.resistance_predict(*args, **kwargs)
@r_to_python
 def sir_predict(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.sir_predict(*args, **kwargs)
@r_to_python
 def ggplot_sir_predict(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.ggplot_sir_predict(*args, **kwargs)
@r_to_python
 def skewness(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.skewness(x, *args, **kwargs)
@r_to_python
 def top_n_microorganisms(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.top_n_microorganisms(*args, **kwargs)
@r_to_python
 def reset_AMR_locale(*args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.reset_AMR_locale(*args, **kwargs)
@r_to_python
 def translate_AMR(x, *args, **kwargs):
    """Please see our website of the R package for the full manual: https://amr-for-r.org"""
    return amr_r.translate_AMR(x, *args, **kwargs)
@@ -0,0 +1,280 @@
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    <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9075</small>
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    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
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      <img src="logo.svg" class="logo" alt=""><h1>CLAUDE.md — AMR R Package</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/CLAUDE.md" class="external-link"><code>CLAUDE.md</code></a></small>
    </div>
 <div id="claudemd--amr-r-package" class="section level1">
 <p>This file provides context for Claude Code when working in this repository.</p>
 <div class="section level2">
 <h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
 <p><strong>AMR</strong> is a zero-dependency R package for antimicrobial resistance (AMR) data analysis using a One Health approach. It is peer-reviewed, used in 175+ countries, and supports 28 languages.</p>
 <p>Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, and WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) and disk diffusion handling - Multilingual output (28 languages)</p>
 </div>
 <div class="section level2">
 <h2 id="common-commands">Common Commands<a class="anchor" aria-label="anchor" href="#common-commands"></a></h2>
 <p>All commands run inside an R session:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)</span></span>
 <span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">document</span><span class="op">(</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Run all tests</span></span>
 <span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">test</span><span class="op">(</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Full package check (CRAN-level: docs + tests + checks)</span></span>
 <span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">check</span><span class="op">(</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Build pkgdown website locally</span></span>
 <span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Code coverage report</span></span>
 <span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p>From the shell:</p>
 <div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span>
 <span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="repository-structure">Repository Structure<a class="anchor" aria-label="anchor" href="#repository-structure"></a></h2>
 <pre><code>R/              # All R source files (62 files, ~28,000 lines)
 man/            # Auto-generated .Rd documentation (do not edit manually)
 tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
 data/           # Pre-compiled .rda datasets
 data-raw/       # Scripts used to generate data/ files
 vignettes/      # Rmd vignette articles
 inst/           # Installed files (translations, etc.)
 _pkgdown.yml    # pkgdown website configuration</code></pre>
 </div>
 <div class="section level2">
 <h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
 <p><strong>Naming conventions in <code>R/</code>:</strong></p>
 <table class="table"><thead><tr><th>Prefix/Name</th>
 <th>Purpose</th>
 </tr></thead><tbody><tr><td><code>aa_*.R</code></td>
 <td>Loaded first (helpers, globals, options, package docs)</td>
 </tr><tr><td><code>zz_deprecated.R</code></td>
 <td>Deprecated function wrappers</td>
 </tr><tr><td><code>zzz.R</code></td>
 <td>
 <code>.onLoad</code> / <code>.onAttach</code> initialization</td>
 </tr></tbody></table><p><strong>Key source files:</strong></p>
 <ul><li>
 <code>aa_helper_functions.R</code> / <code>aa_helper_pm_functions.R</code> — internal utility functions (large; ~63 KB and ~37 KB)</li>
 <li>
 <code>aa_globals.R</code> — global constants and breakpoint lookup structures</li>
 <li>
 <code>aa_options.R</code> — <code>amr_options()</code> / <code>get_AMR_option()</code> system</li>
 <li>
 <code>mo.R</code> / <code>mo_property.R</code> — microorganism lookup and properties</li>
 <li>
 <code>ab.R</code> / <code>ab_property.R</code> — antimicrobial drug functions</li>
 <li>
 <code>av.R</code> / <code>av_property.R</code> — antiviral drug functions</li>
 <li>
 <code>sir.R</code> / <code>sir_calc.R</code> / <code>sir_df.R</code> — SIR classification engine</li>
 <li>
 <code>mic.R</code> / <code>disk.R</code> — MIC and disk diffusion classes</li>
 <li>
 <code>antibiogram.R</code> — antibiogram generation (traditional, combined, syndromic, WISCA)</li>
 <li>
 <code>first_isolate.R</code> — first-isolate identification algorithms</li>
 <li>
 <code>mdro.R</code> — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)</li>
 <li>
 <code>amr_selectors.R</code> — tidyselect helpers for selecting AMR columns</li>
 <li>
 <code>interpretive_rules.R</code> / <code>custom_eucast_rules.R</code> — clinical interpretation rules</li>
 <li>
 <code>translate.R</code> — 28-language translation system</li>
 <li>
 <code>ggplot_sir.R</code> / <code>ggplot_pca.R</code> / <code>plotting.R</code> — visualisation functions</li>
 </ul></div>
 <div class="section level2">
 <h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
 <p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
 <table class="table"><thead><tr><th>Class</th>
 <th>Created by</th>
 <th>Represents</th>
 </tr></thead><tbody><tr><td><code>&lt;mo&gt;</code></td>
 <td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
 <td>Microorganism code</td>
 </tr><tr><td><code>&lt;ab&gt;</code></td>
 <td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
 <td>Antimicrobial drug code</td>
 </tr><tr><td><code>&lt;av&gt;</code></td>
 <td><code><a href="reference/as.av.html">as.av()</a></code></td>
 <td>Antiviral drug code</td>
 </tr><tr><td><code>&lt;sir&gt;</code></td>
 <td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
 <td>SIR value (S/I/R/SDD)</td>
 </tr><tr><td><code>&lt;mic&gt;</code></td>
 <td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
 <td>Minimum inhibitory concentration</td>
 </tr><tr><td><code>&lt;disk&gt;</code></td>
 <td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
 <td>Disk diffusion diameter</td>
 </tr></tbody></table></div>
 <div class="section level2">
 <h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
 <p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
 <table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
 <th>Contents</th>
 </tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
 <td>~79,000 microbial species with full taxonomy</td>
 </tr><tr><td><code>antimicrobials.rda</code></td>
 <td>~620 antimicrobial drugs with ATC codes</td>
 </tr><tr><td><code>antivirals.rda</code></td>
 <td>Antiviral drugs</td>
 </tr><tr><td><code>clinical_breakpoints.rda</code></td>
 <td>EUCAST + CLSI breakpoints (2011–2025)</td>
 </tr><tr><td><code>intrinsic_resistant.rda</code></td>
 <td>Intrinsic resistance patterns</td>
 </tr><tr><td><code>example_isolates.rda</code></td>
 <td>Example AMR dataset for documentation/testing</td>
 </tr><tr><td><code>WHONET.rda</code></td>
 <td>Example WHONET-format dataset</td>
 </tr></tbody></table></div>
 <div class="section level2">
 <h2 id="zero-dependency-design">Zero-Dependency Design<a class="anchor" aria-label="anchor" href="#zero-dependency-design"></a></h2>
 <p>The package has <strong>no <code>Imports</code></strong> in <code>DESCRIPTION</code>. All optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) are listed in <code>Suggests</code> and guarded with:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"pkg"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span> <span class="va">...</span> <span class="op">}</span></span></code></pre></div>
 <p>Never add packages to <code>Imports</code>. If new functionality requires an external package, add it to <code>Suggests</code> and guard usage appropriately.</p>
 </div>
 <div class="section level2">
 <h2 id="testing">Testing<a class="anchor" aria-label="anchor" href="#testing"></a></h2>
 <ul><li>
 <strong>Framework:</strong> <code>testthat</code> (R ≥ 3.1); legacy <code>tinytest</code> used for R 3.0–3.6 CI</li>
 <li>
 <strong>Test files:</strong> <code>tests/testthat/test-*.R</code>
 </li>
 <li>
 <strong>Helpers:</strong> <code>tests/testthat/helper-functions.R</code>
 </li>
 <li>
 <strong>CI matrix:</strong> GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 through oldrel-4</li>
 <li>
 <strong>Coverage:</strong> <code>covr</code> (some files excluded: <code>atc_online.R</code>, <code>mo_source.R</code>, <code>translate.R</code>, <code>resistance_predict.R</code>, <code>zz_deprecated.R</code>, helper files, <code>zzz.R</code>)</li>
 </ul></div>
 <div class="section level2">
 <h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
 <ul><li>All exported functions use <strong>roxygen2</strong> blocks (<code>RoxygenNote: 7.3.3</code>, markdown enabled)</li>
 <li>Run <code>devtools::document()</code> after any change to roxygen comments</li>
 <li>Never edit files in <code>man/</code> directly — they are auto-generated</li>
 <li>Vignettes live in <code>vignettes/</code> as <code>.Rmd</code> files</li>
 <li>The pkgdown website is configured in <code>_pkgdown.yml</code>
 </li>
 </ul></div>
 <div class="section level2">
 <h2 id="versioning">Versioning<a class="anchor" aria-label="anchor" href="#versioning"></a></h2>
 <p>Version format: <code>major.minor.patch.dev</code> (e.g., <code>3.0.1.9021</code>)</p>
 <ul><li>Development versions use a <code>.9xxx</code> suffix</li>
 <li>Stable CRAN releases drop the dev suffix (e.g., <code>3.0.1</code>)</li>
 <li>
 <code>NEWS.md</code> uses sections <strong>New</strong>, <strong>Fixes</strong>, <strong>Updates</strong> with GitHub issue references (<code>#NNN</code>)</li>
 </ul><div class="section level3">
 <h3 id="version-and-date-bump-required-for-every-pr">Version and date bump required for every PR<a class="anchor" aria-label="anchor" href="#version-and-date-bump-required-for-every-pr"></a></h3>
 <p>All PRs are <strong>squash-merged</strong>, so each PR lands as exactly <strong>one commit</strong> on the default branch. Version numbers are kept in sync with the cumulative commit count since the last released tag. Therefore <strong>exactly one version bump is allowed per PR</strong>, regardless of how many intermediate commits are made on the branch.</p>
 <div class="section level4">
 <h4 id="computing-the-correct-version-number">Computing the correct version number<a class="anchor" aria-label="anchor" href="#computing-the-correct-version-number"></a></h4>
 <p><strong>First, ensure <code>git</code> and <code>gh</code> are installed</strong> — both are required for the version computation and for pushing changes. Install them if missing before doing anything else:</p>
 <div class="sourceCode" id="cb5"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">which</span> git <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> git</span>
 <span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">which</span> gh   <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> gh</span>
 <span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Also ensure all tags are fetched so git describe works</span></span>
 <span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="fu">git</span> fetch <span class="at">--tags</span></span></code></pre></div>
 <p>Then run the following from the repo root to determine the version string to use:</p>
 <div class="sourceCode" id="cb6"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
 <span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0<span class="va">)</span></span>
 <span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="va">defaultbranch</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> branch <span class="kw">|</span> <span class="fu">cut</span> <span class="at">-c</span> 3- <span class="kw">|</span> <span class="fu">grep</span> <span class="at">-E</span> <span class="st">'^master$|^main$'</span><span class="va">)</span></span>
 <span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> fetch origin <span class="va">${defaultbranch}</span> <span class="at">--quiet</span></span>
 <span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..origin/<span class="va">${defaultbranch})</span></span>
 <span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="va">currentversion</span><span class="op">=</span><span class="st">"</span><span class="va">${currenttag}</span><span class="st">.</span><span class="va">$((currentcommit</span> <span class="op">+</span> <span class="dv">9001</span> <span class="op">+</span> <span class="dv">1</span><span class="va">))</span><span class="st">"</span></span>
 <span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="bu">echo</span> <span class="st">"</span><span class="va">$currentversion</span><span class="st">"</span></span></code></pre></div>
 <p>The <code>+ 1</code> accounts for the fact that this PR’s squash commit is not yet on the default branch. Set <strong>both</strong> of these files to the resulting version string (and only once per PR, even across multiple commits):</p>
 <ol style="list-style-type: decimal"><li><p><strong><code>DESCRIPTION</code></strong> — the <code>Version:</code> field</p></li>
 <li>
 <p><strong><code>NEWS.md</code></strong> — <strong>only replace line 1</strong> (the <code># AMR &lt;version&gt;</code> heading) with the new version number; do <strong>not</strong> create a new section. <code>NEWS.md</code> is a <strong>continuous log</strong> for the entire current <code>x.y.z.9nnn</code> development series: all changes since the last stable release accumulate under that single heading. After updating line 1, append the new change as a bullet under the appropriate sub-heading (<code>### New</code>, <code>### Fixes</code>, or <code>### Updates</code>).</p>
 <p>Style rules for <code>NEWS.md</code> entries:</p>
 <ul><li>Be <strong>extremely concise</strong> — one short line per item</li>
 <li>Do <strong>not</strong> end with a full stop (period)</li>
 <li>No verbose explanations; just the essential fact</li>
 </ul></li>
 </ol><p>If <code>git describe</code> fails (e.g. no tags exist in the environment), fall back to reading the current version from <code>DESCRIPTION</code> and adding 1 to the last numeric component — but only if no bump has already been made in this PR.</p>
 </div>
 <div class="section level4">
 <h4 id="date-field">Date field<a class="anchor" aria-label="anchor" href="#date-field"></a></h4>
 <p>The <code>Date:</code> field in <code>DESCRIPTION</code> must reflect the date of the <strong>last commit to the PR</strong> (not the first), in ISO format. Update it with every commit so it is always current:</p>
 <pre><code><span><span class="va">Date</span><span class="op">:</span> <span class="fl">2026</span><span class="op">-</span><span class="fl">03</span><span class="op">-</span><span class="fl">07</span></span></code></pre>
 </div>
 </div>
 </div>
 <div class="section level2">
 <h2 id="internal-state">Internal State<a class="anchor" aria-label="anchor" href="#internal-state"></a></h2>
 <p>The package uses a private <code>AMR_env</code> environment (created in <code>aa_globals.R</code>) for caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). This avoids re-computation within a session.</p>
 </div>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside></div>
    <footer><div class="pkgdown-footer-left">
  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
 </div>
 <div class="pkgdown-footer-right">
  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
 </div>
    </footer></div>
  </body></html>
@@ -0,0 +1,238 @@
 # CLAUDE.md — AMR R Package
 This file provides context for Claude Code when working in this
 repository.
 ## Project Overview
 **AMR** is a zero-dependency R package for antimicrobial resistance
 (AMR) data analysis using a One Health approach. It is peer-reviewed,
 used in 175+ countries, and supports 28 languages.
 Key capabilities: - SIR (Susceptible/Intermediate/Resistant)
 classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints -
 Antibiogram generation: traditional, combined, syndromic, and WISCA -
 Microorganism taxonomy database (~79,000 species) - Antimicrobial drug
 database (~620 drugs) - Multi-drug resistant organism (MDRO)
 classification - First-isolate identification - Minimum Inhibitory
 Concentration (MIC) and disk diffusion handling - Multilingual output
 (28 languages)
 ## Common Commands
 All commands run inside an R session:
 ``` r
 # Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
 devtools::document()
 # Run all tests
 devtools::test()
 # Full package check (CRAN-level: docs + tests + checks)
 devtools::check()
 # Build pkgdown website locally
 pkgdown::build_site()
 # Code coverage report
 covr::package_coverage()
 ```
 From the shell:
 ``` bash
 # CRAN check from parent directory
 R CMD check AMR
 ```
 ## Repository Structure
    R/              # All R source files (62 files, ~28,000 lines)
    man/            # Auto-generated .Rd documentation (do not edit manually)
    tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
    data/           # Pre-compiled .rda datasets
    data-raw/       # Scripts used to generate data/ files
    vignettes/      # Rmd vignette articles
    inst/           # Installed files (translations, etc.)
    _pkgdown.yml    # pkgdown website configuration
 ## R Source File Conventions
 **Naming conventions in `R/`:**
 | Prefix/Name       | Purpose                                                |
 |-------------------|--------------------------------------------------------|
 | `aa_*.R`          | Loaded first (helpers, globals, options, package docs) |
 | `zz_deprecated.R` | Deprecated function wrappers                           |
 | `zzz.R`           | `.onLoad` / `.onAttach` initialization                 |
 **Key source files:**
 - `aa_helper_functions.R` / `aa_helper_pm_functions.R` — internal
  utility functions (large; ~63 KB and ~37 KB)
 - `aa_globals.R` — global constants and breakpoint lookup structures
 - `aa_options.R` — `amr_options()` / `get_AMR_option()` system
 - `mo.R` / `mo_property.R` — microorganism lookup and properties
 - `ab.R` / `ab_property.R` — antimicrobial drug functions
 - `av.R` / `av_property.R` — antiviral drug functions
 - `sir.R` / `sir_calc.R` / `sir_df.R` — SIR classification engine
 - `mic.R` / `disk.R` — MIC and disk diffusion classes
 - `antibiogram.R` — antibiogram generation (traditional, combined,
  syndromic, WISCA)
 - `first_isolate.R` — first-isolate identification algorithms
 - `mdro.R` — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)
 - `amr_selectors.R` — tidyselect helpers for selecting AMR columns
 - `interpretive_rules.R` / `custom_eucast_rules.R` — clinical
  interpretation rules
 - `translate.R` — 28-language translation system
 - `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation
  functions
 ## Custom S3 Classes
 The package defines five S3 classes with full print/format/plot/vctrs
 support:
 | Class | Created by | Represents |
 |----|----|----|
 | `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
 | `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
 | `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
 | `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
 | `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
 | `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
 ## Data Files
 Pre-compiled in `data/` (do not edit directly; regenerate via
 `data-raw/` scripts):
 | File                       | Contents                                      |
 |----------------------------|-----------------------------------------------|
 | `microorganisms.rda`       | ~79,000 microbial species with full taxonomy  |
 | `antimicrobials.rda`       | ~620 antimicrobial drugs with ATC codes       |
 | `antivirals.rda`           | Antiviral drugs                               |
 | `clinical_breakpoints.rda` | EUCAST + CLSI breakpoints (2011–2025)         |
 | `intrinsic_resistant.rda`  | Intrinsic resistance patterns                 |
 | `example_isolates.rda`     | Example AMR dataset for documentation/testing |
 | `WHONET.rda`               | Example WHONET-format dataset                 |
 ## Zero-Dependency Design
 The package has **no `Imports`** in `DESCRIPTION`. All optional
 integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
 are listed in `Suggests` and guarded with:
 ``` r
 if (requireNamespace("pkg", quietly = TRUE)) { ... }
 ```
 Never add packages to `Imports`. If new functionality requires an
 external package, add it to `Suggests` and guard usage appropriately.
 ## Testing
 - **Framework:** `testthat` (R ≥ 3.1); legacy `tinytest` used for R
  3.0–3.6 CI
 - **Test files:** `tests/testthat/test-*.R`
 - **Helpers:** `tests/testthat/helper-functions.R`
 - **CI matrix:** GitHub Actions across Windows / macOS / Linux × R devel
  / release / oldrel-1 through oldrel-4
 - **Coverage:** `covr` (some files excluded: `atc_online.R`,
  `mo_source.R`, `translate.R`, `resistance_predict.R`,
  `zz_deprecated.R`, helper files, `zzz.R`)
 ## Documentation
 - All exported functions use **roxygen2** blocks (`RoxygenNote: 7.3.3`,
  markdown enabled)
 - Run `devtools::document()` after any change to roxygen comments
 - Never edit files in `man/` directly — they are auto-generated
 - Vignettes live in `vignettes/` as `.Rmd` files
 - The pkgdown website is configured in `_pkgdown.yml`
 ## Versioning
 Version format: `major.minor.patch.dev` (e.g., `3.0.1.9021`)
 - Development versions use a `.9xxx` suffix
 - Stable CRAN releases drop the dev suffix (e.g., `3.0.1`)
 - `NEWS.md` uses sections **New**, **Fixes**, **Updates** with GitHub
  issue references (`#NNN`)
 ### Version and date bump required for every PR
 All PRs are **squash-merged**, so each PR lands as exactly **one
 commit** on the default branch. Version numbers are kept in sync with
 the cumulative commit count since the last released tag. Therefore
 **exactly one version bump is allowed per PR**, regardless of how many
 intermediate commits are made on the branch.
 #### Computing the correct version number
 **First, ensure `git` and `gh` are installed** — both are required for
 the version computation and for pushing changes. Install them if missing
 before doing anything else:
 ``` bash
 which git || apt-get install -y git
 which gh   || apt-get install -y gh
 # Also ensure all tags are fetched so git describe works
 git fetch --tags
 ```
 Then run the following from the repo root to determine the version
 string to use:
 ``` bash
 currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
 currenttagfull=$(git describe --tags --abbrev=0)
 defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
 git fetch origin ${defaultbranch} --quiet
 currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
 currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
 echo "$currentversion"
 ```
 The `+ 1` accounts for the fact that this PR’s squash commit is not yet
 on the default branch. Set **both** of these files to the resulting
 version string (and only once per PR, even across multiple commits):
 1.  **`DESCRIPTION`** — the `Version:` field
 2.  **`NEWS.md`** — **only replace line 1** (the `# AMR <version>`
    heading) with the new version number; do **not** create a new
    section. `NEWS.md` is a **continuous log** for the entire current
    `x.y.z.9nnn` development series: all changes since the last stable
    release accumulate under that single heading. After updating line 1,
    append the new change as a bullet under the appropriate sub-heading
    (`### New`, `### Fixes`, or `### Updates`).
    Style rules for `NEWS.md` entries:
    - Be **extremely concise** — one short line per item
    - Do **not** end with a full stop (period)
    - No verbose explanations; just the essential fact
 If `git describe` fails (e.g. no tags exist in the environment), fall
 back to reading the current version from `DESCRIPTION` and adding 1 to
 the last numeric component — but only if no bump has already been made
 in this PR.
 #### Date field
 The `Date:` field in `DESCRIPTION` must reflect the date of the **last
 commit to the PR** (not the first), in ISO format. Update it with every
 commit so it is always current:
    Date: 2026-03-07
 ## Internal State
 The package uses a private `AMR_env` environment (created in
 `aa_globals.R`) for caching expensive lookups (e.g., microorganism
 matching scores, breakpoint tables). This avoids re-computation within a
 session.
@@ -0,0 +1 @@
 amr-for-r.org
@@ -0,0 +1,319 @@
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      <img src="logo.svg" class="logo" alt=""><h1>License</h1>
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 <pre>GNU GENERAL PUBLIC LICENSE
 Version 2, June 1991
 Copyright (C) 1989, 1991 Free Software Foundation, Inc., &lt;http://fsf.org/&gt;
  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
 A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
 This R package, with package name 'AMR':
 - May be used for commercial purposes
 - May be used for private purposes
 - May NOT be used for patent purposes
 - May be modified, although:
  - Modifications MUST be released under the same license when distributing the package
  - Changes made to the code MUST be documented
 - May be distributed, although:
  - Source code MUST be made available when the package is distributed
  - A copy of the license and copyright notice MUST be included with the package.
 - Comes with a LIMITATION of liability
 - Comes with NO warranty
 END OF THE SUMMARY
 GNU GENERAL PUBLIC LICENSE
 TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
 0. This License applies to any program or other work which contains
 a notice placed by the copyright holder saying it may be distributed
 under the terms of this General Public License. The "Program", below,
 refers to any such program or work, and a "work based on the Program"
 means either the Program or any derivative work under copyright law:
 that is to say, a work containing the Program or a portion of it,
 either verbatim or with modifications and/or translated into another
 language. (Hereinafter, translation is included without limitation in
 the term "modification".) Each licensee is addressed as "you".
 Activities other than copying, distribution and modification are not
 covered by this License; they are outside its scope. The act of
 running the Program is not restricted, and the output from the Program
 is covered only if its contents constitute a work based on the
 Program (independent of having been made by running the Program).
 Whether that is true depends on what the Program does.
 1. You may copy and distribute verbatim copies of the Program's
 source code as you receive it, in any medium, provided that you
 conspicuously and appropriately publish on each copy an appropriate
 copyright notice and disclaimer of warranty; keep intact all the
 notices that refer to this License and to the absence of any warranty;
 and give any other recipients of the Program a copy of this License
 along with the Program.
 You may charge a fee for the physical act of transferring a copy, and
 you may at your option offer warranty protection in exchange for a fee.
 2. You may modify your copy or copies of the Program or any portion
 of it, thus forming a work based on the Program, and copy and
 distribute such modifications or work under the terms of Section 1
 above, provided that you also meet all of these conditions:
    a) You must cause the modified files to carry prominent notices
    stating that you changed the files and the date of any change.
    b) You must cause any work that you distribute or publish, that in
    whole or in part contains or is derived from the Program or any
    part thereof, to be licensed as a whole at no charge to all third
    parties under the terms of this License.
    c) If the modified program normally reads commands interactively
    when run, you must cause it, when started running for such
    interactive use in the most ordinary way, to print or display an
    announcement including an appropriate copyright notice and a
    notice that there is no warranty (or else, saying that you provide
    a warranty) and that users may redistribute the program under
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    License.  (Exception: if the Program itself is interactive but
    does not normally print such an announcement, your work based on
    the Program is not required to print an announcement.)
 These requirements apply to the modified work as a whole. If
 identifiable sections of that work are not derived from the Program,
 and can be reasonably considered independent and separate works in
 themselves, then this License, and its terms, do not apply to those
 sections when you distribute them as separate works. But when you
 distribute the same sections as part of a whole which is a work based
 on the Program, the distribution of the whole must be on the terms of
 this License, whose permissions for other licensees extend to the
 entire whole, and thus to each and every part regardless of who wrote it.
 Thus, it is not the intent of this section to claim rights or contest
 your rights to work written entirely by you; rather, the intent is to
 exercise the right to control the distribution of derivative or
 collective works based on the Program.
 In addition, mere aggregation of another work not based on the Program
 with the Program (or with a work based on the Program) on a volume of
 a storage or distribution medium does not bring the other work under
 the scope of this License.
 3. You may copy and distribute the Program (or a work based on it,
 under Section 2) in object code or executable form under the terms of
 Sections 1 and 2 above provided that you also do one of the following:
    a) Accompany it with the complete corresponding machine-readable
    source code, which must be distributed under the terms of Sections
    1 and 2 above on a medium customarily used for software interchange; or,
    b) Accompany it with a written offer, valid for at least three
    years, to give any third party, for a charge no more than your
    cost of physically performing source distribution, a complete
    machine-readable copy of the corresponding source code, to be
    distributed under the terms of Sections 1 and 2 above on a medium
    customarily used for software interchange; or,
    c) Accompany it with the information you received as to the offer
    to distribute corresponding source code.  (This alternative is
    allowed only for noncommercial distribution and only if you
    received the program in object code or executable form with such
    an offer, in accord with Subsection b above.)
 The source code for a work means the preferred form of the work for
 making modifications to it. For an executable work, complete source
 code means all the source code for all modules it contains, plus any
 associated interface definition files, plus the scripts used to
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 special exception, the source code distributed need not include
 anything that is normally distributed (in either source or binary
 form) with the major components (compiler, kernel, and so on) of the
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 If distribution of executable or object code is made by offering
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 4. You may not copy, modify, sublicense, or distribute the Program
 except as expressly provided under this License. Any attempt
 otherwise to copy, modify, sublicense or distribute the Program is
 void, and will automatically terminate your rights under this License.
 However, parties who have received copies, or rights, from you under
 this License will not have their licenses terminated so long as such
 parties remain in full compliance.
 5. You are not required to accept this License, since you have not
 signed it. However, nothing else grants you permission to modify or
 distribute the Program or its derivative works. These actions are
 prohibited by law if you do not accept this License. Therefore, by
 modifying or distributing the Program (or any work based on the
 Program), you indicate your acceptance of this License to do so, and
 all its terms and conditions for copying, distributing or modifying
 the Program or works based on it.
 6. Each time you redistribute the Program (or any work based on the
 Program), the recipient automatically receives a license from the
 original licensor to copy, distribute or modify the Program subject to
 these terms and conditions. You may not impose any further
 restrictions on the recipients' exercise of the rights granted herein.
 You are not responsible for enforcing compliance by third parties to
 this License.
 7. If, as a consequence of a court judgment or allegation of patent
 infringement or for any other reason (not limited to patent issues),
 conditions are imposed on you (whether by court order, agreement or
 otherwise) that contradict the conditions of this License, they do not
 excuse you from the conditions of this License. If you cannot
 distribute so as to satisfy simultaneously your obligations under this
 License and any other pertinent obligations, then as a consequence you
 may not distribute the Program at all. For example, if a patent
 license would not permit royalty-free redistribution of the Program by
 all those who receive copies directly or indirectly through you, then
 the only way you could satisfy both it and this License would be to
 refrain entirely from distribution of the Program.
 If any portion of this section is held invalid or unenforceable under
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 It is not the purpose of this section to induce you to infringe any
 patents or other property right claims or to contest validity of any
 such claims; this section has the sole purpose of protecting the
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 implemented by public license practices. Many people have made
 generous contributions to the wide range of software distributed
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 system; it is up to the author/donor to decide if he or she is willing
 to distribute software through any other system and a licensee cannot
 impose that choice.
 This section is intended to make thoroughly clear what is believed to
 be a consequence of the rest of this License.
 8. If the distribution and/or use of the Program is restricted in
 certain countries either by patents or by copyrighted interfaces, the
 original copyright holder who places the Program under this License
 may add an explicit geographical distribution limitation excluding
 those countries, so that distribution is permitted only in or among
 countries not thus excluded. In such case, this License incorporates
 the limitation as if written in the body of this License.
 9. The Free Software Foundation may publish revised and/or new versions
 of the General Public License from time to time. Such new versions will
 be similar in spirit to the present version, but may differ in detail to
 address new problems or concerns.
 Each version is given a distinguishing version number. If the Program
 specifies a version number of this License which applies to it and "any
 later version", you have the option of following the terms and conditions
 either of that version or of any later version published by the Free
 Software Foundation. If the Program does not specify a version number of
 this License, you may choose any version ever published by the Free Software
 Foundation.
 10. If you wish to incorporate parts of the Program into other free
 programs whose distribution conditions are different, write to the author
 to ask for permission. For software which is copyrighted by the Free
 Software Foundation, write to the Free Software Foundation; we sometimes
 make exceptions for this. Our decision will be guided by the two goals
 of preserving the free status of all derivatives of our free software and
 of promoting the sharing and reuse of software generally.
 NO WARRANTY
 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
 FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
 OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
 PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
 OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
 MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
 TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
 PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
 REPAIR OR CORRECTION.
 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
 WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
 REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
 INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
 OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
 TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
 YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
 PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
 POSSIBILITY OF SUCH DAMAGES.
 END OF TERMS AND CONDITIONS
 </pre>
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@@ -0,0 +1,250 @@
 # License
    GNU GENERAL PUBLIC LICENSE
    Version 2, June 1991
    Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
      51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
    Everyone is permitted to copy and distribute verbatim copies
    of this license document, but changing it is not allowed.
    A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
    This R package, with package name 'AMR':
    - May be used for commercial purposes
    - May be used for private purposes
    - May NOT be used for patent purposes
    - May be modified, although:
      - Modifications MUST be released under the same license when distributing the package
      - Changes made to the code MUST be documented
    - May be distributed, although:
      - Source code MUST be made available when the package is distributed
      - A copy of the license and copyright notice MUST be included with the package.
    - Comes with a LIMITATION of liability
    - Comes with NO warranty
    END OF THE SUMMARY
    GNU GENERAL PUBLIC LICENSE
    TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
    0. This License applies to any program or other work which contains
    a notice placed by the copyright holder saying it may be distributed
    under the terms of this General Public License. The "Program", below,
    refers to any such program or work, and a "work based on the Program"
    means either the Program or any derivative work under copyright law:
    that is to say, a work containing the Program or a portion of it,
    either verbatim or with modifications and/or translated into another
    language. (Hereinafter, translation is included without limitation in
    the term "modification".) Each licensee is addressed as "you".
    Activities other than copying, distribution and modification are not
    covered by this License; they are outside its scope. The act of
    running the Program is not restricted, and the output from the Program
    is covered only if its contents constitute a work based on the
    Program (independent of having been made by running the Program).
    Whether that is true depends on what the Program does.
    1. You may copy and distribute verbatim copies of the Program's
    source code as you receive it, in any medium, provided that you
    conspicuously and appropriately publish on each copy an appropriate
    copyright notice and disclaimer of warranty; keep intact all the
    notices that refer to this License and to the absence of any warranty;
    and give any other recipients of the Program a copy of this License
    along with the Program.
    You may charge a fee for the physical act of transferring a copy, and
    you may at your option offer warranty protection in exchange for a fee.
    2. You may modify your copy or copies of the Program or any portion
    of it, thus forming a work based on the Program, and copy and
    distribute such modifications or work under the terms of Section 1
    above, provided that you also meet all of these conditions:
        a) You must cause the modified files to carry prominent notices
        stating that you changed the files and the date of any change.
        b) You must cause any work that you distribute or publish, that in
        whole or in part contains or is derived from the Program or any
        part thereof, to be licensed as a whole at no charge to all third
        parties under the terms of this License.
        c) If the modified program normally reads commands interactively
        when run, you must cause it, when started running for such
        interactive use in the most ordinary way, to print or display an
        announcement including an appropriate copyright notice and a
        notice that there is no warranty (or else, saying that you provide
        a warranty) and that users may redistribute the program under
        these conditions, and telling the user how to view a copy of this
        License.  (Exception: if the Program itself is interactive but
        does not normally print such an announcement, your work based on
        the Program is not required to print an announcement.)
    These requirements apply to the modified work as a whole. If
    identifiable sections of that work are not derived from the Program,
    and can be reasonably considered independent and separate works in
    themselves, then this License, and its terms, do not apply to those
    sections when you distribute them as separate works. But when you
    distribute the same sections as part of a whole which is a work based
    on the Program, the distribution of the whole must be on the terms of
    this License, whose permissions for other licensees extend to the
    entire whole, and thus to each and every part regardless of who wrote it.
    Thus, it is not the intent of this section to claim rights or contest
    your rights to work written entirely by you; rather, the intent is to
    exercise the right to control the distribution of derivative or
    collective works based on the Program.
    In addition, mere aggregation of another work not based on the Program
    with the Program (or with a work based on the Program) on a volume of
    a storage or distribution medium does not bring the other work under
    the scope of this License.
    3. You may copy and distribute the Program (or a work based on it,
    under Section 2) in object code or executable form under the terms of
    Sections 1 and 2 above provided that you also do one of the following:
        a) Accompany it with the complete corresponding machine-readable
        source code, which must be distributed under the terms of Sections
        1 and 2 above on a medium customarily used for software interchange; or,
        b) Accompany it with a written offer, valid for at least three
        years, to give any third party, for a charge no more than your
        cost of physically performing source distribution, a complete
        machine-readable copy of the corresponding source code, to be
        distributed under the terms of Sections 1 and 2 above on a medium
        customarily used for software interchange; or,
        c) Accompany it with the information you received as to the offer
        to distribute corresponding source code.  (This alternative is
        allowed only for noncommercial distribution and only if you
        received the program in object code or executable form with such
        an offer, in accord with Subsection b above.)
    The source code for a work means the preferred form of the work for
    making modifications to it. For an executable work, complete source
    code means all the source code for all modules it contains, plus any
    associated interface definition files, plus the scripts used to
    control compilation and installation of the executable. However, as a
    special exception, the source code distributed need not include
    anything that is normally distributed (in either source or binary
    form) with the major components (compiler, kernel, and so on) of the
    operating system on which the executable runs, unless that component
    itself accompanies the executable.
    If distribution of executable or object code is made by offering
    access to copy from a designated place, then offering equivalent
    access to copy the source code from the same place counts as
    distribution of the source code, even though third parties are not
    compelled to copy the source along with the object code.
    4. You may not copy, modify, sublicense, or distribute the Program
    except as expressly provided under this License. Any attempt
    otherwise to copy, modify, sublicense or distribute the Program is
    void, and will automatically terminate your rights under this License.
    However, parties who have received copies, or rights, from you under
    this License will not have their licenses terminated so long as such
    parties remain in full compliance.
    5. You are not required to accept this License, since you have not
    signed it. However, nothing else grants you permission to modify or
    distribute the Program or its derivative works. These actions are
    prohibited by law if you do not accept this License. Therefore, by
    modifying or distributing the Program (or any work based on the
    Program), you indicate your acceptance of this License to do so, and
    all its terms and conditions for copying, distributing or modifying
    the Program or works based on it.
    6. Each time you redistribute the Program (or any work based on the
    Program), the recipient automatically receives a license from the
    original licensor to copy, distribute or modify the Program subject to
    these terms and conditions. You may not impose any further
    restrictions on the recipients' exercise of the rights granted herein.
    You are not responsible for enforcing compliance by third parties to
    this License.
    7. If, as a consequence of a court judgment or allegation of patent
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 The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python package](https://pypi.org/project/AMR/).
 This Python package is a wrapper around the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
 # Prerequisites
 This package was only tested with a [virtual environment (venv)](https://docs.python.org/3/library/venv.html). You can set up such an environment by running:
 ```python
 # linux and macOS:
 python -m venv /path/to/new/virtual/environment
 # Windows:
 python -m venv C:\path\to\new\virtual\environment
 ```
 Then you can [activate the environment](https://docs.python.org/3/library/venv.html#how-venvs-work), after which the venv is ready to work with.
 # Install AMR
 1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
    ```bash
    pip install AMR
    ```
 2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
    For Linux:
    ```bash
    # Ubuntu / Debian
    sudo apt install r-base
    # Fedora:
    sudo dnf install R
    # CentOS/RHEL
    sudo yum install R
    ```
    For macOS (using [Homebrew](https://brew.sh)):
    ```bash
    brew install r
    ```
    For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
 # Examples of Usage
 ## Cleaning Taxonomy
 Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
 ```python
 import pandas as pd
 import AMR
 # Sample data
 data = {
    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
 }
 df = pd.DataFrame(data)
 # Use AMR functions to clean microorganism and drug names
 df['MO_clean'] = AMR.mo_name(df['MOs'])
 df['Drug_clean'] = AMR.ab_name(df['Drug'])
 # Display the results
 print(df)
 ```
 | MOs         | Drug      | MO_clean           | Drug_clean    |
 |-------------|-----------|--------------------|---------------|
 | E. coli     | Cipro     | Escherichia coli   | Ciprofloxacin |
 | ESCCOL      | CIP       | Escherichia coli   | Ciprofloxacin |
 | esco        | J01MA02   | Escherichia coli   | Ciprofloxacin |
 | Esche coli  | Ciproxin  | Escherichia coli   | Ciprofloxacin |
 ### Explanation
 * **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 ## Calculating AMR
 ```python
 import AMR
 import pandas as pd
 df = AMR.example_isolates
 result = AMR.resistance(df["AMX"])
 print(result)
 ```
 ```
 [0.59555556]
 ```
 ## Generating Antibiograms
 One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
 ```python
 result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
 print(result2a)
 ```
 | Pathogen        | Amoxicillin     | Ciprofloxacin   | Piperacillin/tazobactam  |
 |-----------------|-----------------|-----------------|--------------------------|
 | CoNS            | 7% (10/142)     | 73% (183/252)   | 30% (10/33)              |
 | E. coli         | 50% (196/392)   | 88% (399/456)   | 94% (393/416)            |
 | K. pneumoniae   | 0% (0/58)       | 96% (53/55)     | 89% (47/53)              |
 | P. aeruginosa   | 0% (0/30)       | 100% (30/30)    | None                     |
 | P. mirabilis    | None            | 94% (34/36)     | None                     |
 | S. aureus       | 6% (8/131)      | 90% (171/191)   | None                     |
 | S. epidermidis  | 1% (1/91)       | 64% (87/136)    | None                     |
 | S. hominis      | None            | 80% (56/70)     | None                     |
 | S. pneumoniae   | 100% (112/112)  | None            | 100% (112/112)           |
 ```python
 result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
 print(result2b)
 ```
 | Pathogen       | Amoxicillin     | Ciprofloxacin    | Piperacillin/tazobactam  |
 |----------------|-----------------|------------------|--------------------------|
 | Gram-negative  | 36% (226/631)   | 91% (621/684)    | 88% (565/641)            |
 | Gram-positive  | 43% (305/703)   | 77% (560/724)    | 86% (296/345)            |
 In this example, we generate an antibiogram by selecting various antibiotics.
 ## Taxonomic Data Sets Now in Python!
 As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
 ```python
 AMR.microorganisms
 ```
 | mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
 |--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
 | B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
 | ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
 | B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
 | B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
 | B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
 | B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
 | F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
 ```python
 AMR.antimicrobials
 ```
 | ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
 |-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
 | AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
 | ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
 | ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
 | ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
 | AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
 | ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
 | VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
 | VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
 | XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
 | ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
 | 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.
 By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
 Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
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      <img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
      <div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
    </div>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>The <code>AMR</code> package for R is a powerful tool for
 antimicrobial resistance (AMR) analysis. It provides extensive features
 for handling microbial and antimicrobial data. However, for those who
 work primarily in Python, we now have a more intuitive option available:
 the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
 package</a>.</p>
 <p>This Python package is a wrapper around the <code>AMR</code> R
 package. It uses the <code>rpy2</code> package internally. Despite the
 need to have R installed, Python users can now easily work with AMR data
 directly through Python code.</p>
 </div>
 <div class="section level2">
 <h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
 </h2>
 <p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
 (venv)</a>. You can set up such an environment by running:</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
 <span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
 <span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
 <span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
 <span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
 <p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
 the environment</a>, after which the venv is ready to work with.</p>
 </div>
 <div class="section level2">
 <h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
 </h2>
 <ol style="list-style-type: decimal">
 <li>
 <p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
 just run:</p>
 <div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
 </li>
 <li>
 <p>Make sure you have R installed. There is <strong>no need to
 install the <code>AMR</code> R package</strong>, as it will be installed
 automatically.</p>
 <p>For Linux:</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
 <span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
 <span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
 <span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
 <span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
 <span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
 <p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
 <div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
 <p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
 download page</a> to download and install R.</p>
 </li>
 </ol>
 </div>
 <div class="section level2">
 <h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
 </h2>
 <div class="section level3">
 <h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
 </h3>
 <p>Here’s an example that demonstrates how to clean microorganism and
 drug names using the <code>AMR</code> Python package:</p>
 <div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
 <span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
 <span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
 <span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
 <span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>    <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
 <span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>    <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
 <span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
 <span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
 <span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
 <span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
 <span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
 <span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
 <span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
 <span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
 <span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th>MOs</th>
 <th>Drug</th>
 <th>MO_clean</th>
 <th>Drug_clean</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>E. coli</td>
 <td>Cipro</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="even">
 <td>ESCCOL</td>
 <td>CIP</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="odd">
 <td>esco</td>
 <td>J01MA02</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="even">
 <td>Esche coli</td>
 <td>Ciproxin</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 </tbody>
 </table>
 <div class="section level4">
 <h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
 </h4>
 <ul>
 <li><p><strong>mo_name:</strong> This function standardises
 microorganism names. Here, different variations of <em>Escherichia
 coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
 all converted into the correct, standardised form, “Escherichia
 coli”.</p></li>
 <li><p><strong>ab_name</strong>: Similarly, this function standardises
 antimicrobial names. The different representations of ciprofloxacin
 (e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
 the standard name, “Ciprofloxacin”.</p></li>
 </ul>
 </div>
 </div>
 <div class="section level3">
 <h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
 </h3>
 <div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
 <span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
 <span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
 <span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
 <span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
 <pre><code>[0.59555556]</code></pre>
 </div>
 <div class="section level3">
 <h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
 </h3>
 <p>One of the core functions of the <code>AMR</code> package is
 generating an antibiogram, a table that summarises the antimicrobial
 susceptibility of bacterial isolates. Here’s how you can generate an
 antibiogram from Python:</p>
 <div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
 <span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="22%">
 <col width="22%">
 <col width="22%">
 <col width="33%">
 </colgroup>
 <thead><tr class="header">
 <th>Pathogen</th>
 <th>Amoxicillin</th>
 <th>Ciprofloxacin</th>
 <th>Piperacillin/tazobactam</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>CoNS</td>
 <td>7% (10/142)</td>
 <td>73% (183/252)</td>
 <td>30% (10/33)</td>
 </tr>
 <tr class="even">
 <td>E. coli</td>
 <td>50% (196/392)</td>
 <td>88% (399/456)</td>
 <td>94% (393/416)</td>
 </tr>
 <tr class="odd">
 <td>K. pneumoniae</td>
 <td>0% (0/58)</td>
 <td>96% (53/55)</td>
 <td>89% (47/53)</td>
 </tr>
 <tr class="even">
 <td>P. aeruginosa</td>
 <td>0% (0/30)</td>
 <td>100% (30/30)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>P. mirabilis</td>
 <td>None</td>
 <td>94% (34/36)</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>S. aureus</td>
 <td>6% (8/131)</td>
 <td>90% (171/191)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>S. epidermidis</td>
 <td>1% (1/91)</td>
 <td>64% (87/136)</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>S. hominis</td>
 <td>None</td>
 <td>80% (56/70)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>S. pneumoniae</td>
 <td>100% (112/112)</td>
 <td>None</td>
 <td>100% (112/112)</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
 <span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="20%">
 <col width="22%">
 <col width="23%">
 <col width="33%">
 </colgroup>
 <thead><tr class="header">
 <th>Pathogen</th>
 <th>Amoxicillin</th>
 <th>Ciprofloxacin</th>
 <th>Piperacillin/tazobactam</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>Gram-negative</td>
 <td>36% (226/631)</td>
 <td>91% (621/684)</td>
 <td>88% (565/641)</td>
 </tr>
 <tr class="even">
 <td>Gram-positive</td>
 <td>43% (305/703)</td>
 <td>77% (560/724)</td>
 <td>86% (296/345)</td>
 </tr>
 </tbody>
 </table>
 <p>In this example, we generate an antibiogram by selecting various
 antibiotics.</p>
 </div>
 <div class="section level3">
 <h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
 </h3>
 <p>As a Python user, you might like that the most important data sets of
 the <code>AMR</code> R package, <code>microorganisms</code>,
 <code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
 <code>example_isolates</code>, are now available as regular Python data
 frames:</p>
 <div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="11%">
 <col width="29%">
 <col width="8%">
 <col width="8%">
 <col width="8%">
 <col width="10%">
 <col width="13%">
 <col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th>mo</th>
 <th>fullname</th>
 <th>status</th>
 <th>kingdom</th>
 <th>gbif</th>
 <th>gbif_parent</th>
 <th>gbif_renamed_to</th>
 <th>prevalence</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>B_GRAMN</td>
 <td>(unknown Gram-negatives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_GRAMP</td>
 <td>(unknown Gram-positives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ANAER-NEG</td>
 <td>(unknown anaerobic Gram-negatives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ANAER-POS</td>
 <td>(unknown anaerobic Gram-positives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ANAER</td>
 <td>(unknown anaerobic bacteria)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 </tr>
 <tr class="odd">
 <td>B_ZYMMN_POMC</td>
 <td>Zymomonas pomaceae</td>
 <td>accepted</td>
 <td>Bacteria</td>
 <td>10744418</td>
 <td>3221412</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ZYMPH</td>
 <td>Zymophilus</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>9475166</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ZYMPH_PCVR</td>
 <td>Zymophilus paucivorans</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ZYMPH_RFFN</td>
 <td>Zymophilus raffinosivorans</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>F_ZYZYG</td>
 <td>Zyzygomyces</td>
 <td>unknown</td>
 <td>Fungi</td>
 <td>None</td>
 <td>7581</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
 <table style="width:100%;" class="table">
 <colgroup>
 <col width="4%">
 <col width="12%">
 <col width="20%">
 <col width="25%">
 <col width="9%">
 <col width="11%">
 <col width="7%">
 <col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th>ab</th>
 <th>cid</th>
 <th>name</th>
 <th>group</th>
 <th>oral_ddd</th>
 <th>oral_units</th>
 <th>iv_ddd</th>
 <th>iv_units</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>AMA</td>
 <td>4649.0</td>
 <td>4-aminosalicylic acid</td>
 <td>Antimycobacterials</td>
 <td>12.00</td>
 <td>g</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ACM</td>
 <td>6450012.0</td>
 <td>Acetylmidecamycin</td>
 <td>Macrolides/lincosamides</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>ASP</td>
 <td>49787020.0</td>
 <td>Acetylspiramycin</td>
 <td>Macrolides/lincosamides</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ALS</td>
 <td>8954.0</td>
 <td>Aldesulfone sodium</td>
 <td>Other antibacterials</td>
 <td>0.33</td>
 <td>g</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>AMK</td>
 <td>37768.0</td>
 <td>Amikacin</td>
 <td>Aminoglycosides</td>
 <td>NaN</td>
 <td>None</td>
 <td>1.0</td>
 <td>g</td>
 </tr>
 <tr class="even">
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 </tr>
 <tr class="odd">
 <td>VIR</td>
 <td>11979535.0</td>
 <td>Virginiamycine</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>VOR</td>
 <td>71616.0</td>
 <td>Voriconazole</td>
 <td>Antifungals/antimycotics</td>
 <td>0.40</td>
 <td>g</td>
 <td>0.4</td>
 <td>g</td>
 </tr>
 <tr class="odd">
 <td>XBR</td>
 <td>72144.0</td>
 <td>Xibornol</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ZID</td>
 <td>77846445.0</td>
 <td>Zidebactam</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>ZFD</td>
 <td>NaN</td>
 <td>Zoliflodacin</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 </tbody>
 </table>
 </div>
 </div>
 <div class="section level2">
 <h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
 </h2>
 <p>With the <code>AMR</code> Python package, Python users can now
 effortlessly call R functions from the <code>AMR</code> R package. This
 eliminates the need for complex <code>rpy2</code> 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.</p>
 <p>By just running <code>import AMR</code>, users can seamlessly
 integrate the robust features of the R <code>AMR</code> package into
 Python workflows.</p>
 <p>Whether you’re cleaning data or analysing resistance patterns, the
 <code>AMR</code> Python package makes it easy to work with AMR data in
 Python.</p>
 </div>
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    </nav></aside>
 </div>
    <footer><div class="pkgdown-footer-left">
  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
 </div>
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@@ -0,0 +1,217 @@
 # AMR for Python
 ## Introduction
 The `AMR` package for R is a powerful tool for antimicrobial resistance
 (AMR) analysis. It provides extensive features for handling microbial
 and antimicrobial data. However, for those who work primarily in Python,
 we now have a more intuitive option available: the [`AMR` Python
 package](https://pypi.org/project/AMR/).
 This Python package is a wrapper around the `AMR` R package. It uses the
 `rpy2` package internally. Despite the need to have R installed, Python
 users can now easily work with AMR data directly through Python code.
 ## Prerequisites
 This package was only tested with a [virtual environment
 (venv)](https://docs.python.org/3/library/venv.html). You can set up
 such an environment by running:
 ``` python
 # linux and macOS:
 python -m venv /path/to/new/virtual/environment
 # Windows:
 python -m venv C:\path\to\new\virtual\environment
 ```
 Then you can [activate the
 environment](https://docs.python.org/3/library/venv.html#how-venvs-work),
 after which the venv is ready to work with.
 ## Install AMR
 1.  Since the Python package is available on the official [Python
    Package Index](https://pypi.org/project/AMR/), you can just run:
    ``` bash
    pip install AMR
    ```
 2.  Make sure you have R installed. There is **no need to install the
    `AMR` R package**, as it will be installed automatically.
    For Linux:
    ``` bash
    # Ubuntu / Debian
    sudo apt install r-base
    # Fedora:
    sudo dnf install R
    # CentOS/RHEL
    sudo yum install R
    ```
    For macOS (using [Homebrew](https://brew.sh)):
    ``` bash
    brew install r
    ```
    For Windows, visit the [CRAN download
    page](https://cran.r-project.org) to download and install R.
 ## Examples of Usage
 ### Cleaning Taxonomy
 Here’s an example that demonstrates how to clean microorganism and drug
 names using the `AMR` Python package:
 ``` python
 import pandas as pd
 import AMR
 # Sample data
 data = {
    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
 }
 df = pd.DataFrame(data)
 # Use AMR functions to clean microorganism and drug names
 df['MO_clean'] = AMR.mo_name(df['MOs'])
 df['Drug_clean'] = AMR.ab_name(df['Drug'])
 # Display the results
 print(df)
 ```
 | MOs        | Drug     | MO_clean         | Drug_clean    |
 |------------|----------|------------------|---------------|
 | E. coli    | Cipro    | Escherichia coli | Ciprofloxacin |
 | ESCCOL     | CIP      | Escherichia coli | Ciprofloxacin |
 | esco       | J01MA02  | Escherichia coli | Ciprofloxacin |
 | Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
 #### Explanation
 - **mo_name:** This function standardises microorganism names. Here,
  different variations of *Escherichia coli* (such as “E. coli”,
  “ESCCOL”, “esco”, and “Esche coli”) are all converted into the
  correct, standardised form, “Escherichia coli”.
 - **ab_name**: Similarly, this function standardises antimicrobial
  names. The different representations of ciprofloxacin (e.g., “Cipro”,
  “CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard
  name, “Ciprofloxacin”.
 ### Calculating AMR
 ``` python
 import AMR
 import pandas as pd
 df = AMR.example_isolates
 result = AMR.resistance(df["AMX"])
 print(result)
 ```
    [0.59555556]
 ### Generating Antibiograms
 One of the core functions of the `AMR` package is generating an
 antibiogram, a table that summarises the antimicrobial susceptibility of
 bacterial isolates. Here’s how you can generate an antibiogram from
 Python:
 ``` python
 result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
 print(result2a)
 ```
 | Pathogen       | Amoxicillin    | Ciprofloxacin | Piperacillin/tazobactam |
 |----------------|----------------|---------------|-------------------------|
 | CoNS           | 7% (10/142)    | 73% (183/252) | 30% (10/33)             |
 | E. coli        | 50% (196/392)  | 88% (399/456) | 94% (393/416)           |
 | K. pneumoniae  | 0% (0/58)      | 96% (53/55)   | 89% (47/53)             |
 | P. aeruginosa  | 0% (0/30)      | 100% (30/30)  | None                    |
 | P. mirabilis   | None           | 94% (34/36)   | None                    |
 | S. aureus      | 6% (8/131)     | 90% (171/191) | None                    |
 | S. epidermidis | 1% (1/91)      | 64% (87/136)  | None                    |
 | S. hominis     | None           | 80% (56/70)   | None                    |
 | S. pneumoniae  | 100% (112/112) | None          | 100% (112/112)          |
 ``` python
 result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
 print(result2b)
 ```
 | Pathogen      | Amoxicillin   | Ciprofloxacin | Piperacillin/tazobactam |
 |---------------|---------------|---------------|-------------------------|
 | Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641)           |
 | Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345)           |
 In this example, we generate an antibiogram by selecting various
 antibiotics.
 ### Taxonomic Data Sets Now in Python!
 As a Python user, you might like that the most important data sets of
 the `AMR` R package, `microorganisms`, `antimicrobials`,
 `clinical_breakpoints`, and `example_isolates`, are now available as
 regular Python data frames:
 ``` python
 AMR.microorganisms
 ```
 | mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
 |----|----|----|----|----|----|----|----|
 | B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
 | B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
 | B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
 | B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
 | B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
 | … | … | … | … | … | … | … | … |
 | B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
 | B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
 | B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
 | B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
 | F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
 ``` python
 AMR.antimicrobials
 ```
 | ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
 |----|----|----|----|----|----|----|----|
 | AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
 | ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
 | ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
 | ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
 | AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
 | … | … | … | … | … | … | … | … |
 | VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
 | VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
 | XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
 | ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
 | ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
 ## 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.
 By just running `import AMR`, users can seamlessly integrate the robust
 features of the R `AMR` package into Python workflows.
 Whether you’re cleaning data or analysing resistance patterns, the `AMR`
 Python package makes it easy to work with AMR data in Python.
@@ -0,0 +1,903 @@
 # AMR with tidymodels
 > This page was almost entirely written by our [AMR for R
 > Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
 > model able to answer any question about the `AMR` package.
 Antimicrobial resistance (AMR) is a global health crisis, and
 understanding resistance patterns is crucial for managing effective
 treatments. The `AMR` R package provides robust tools for analysing AMR
 data, including convenient antimicrobial selector functions like
 [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
 and
 [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
 In this post, we will explore how to use the `tidymodels` framework to
 predict resistance patterns in the `example_isolates` dataset in two
 examples.
 This post contains the following examples:
 1.  Using Antimicrobial Selectors
 2.  Predicting ESBL Presence Using Raw MICs
 3.  Predicting AMR Over Time
 ## Example 1: Using Antimicrobial Selectors
 By leveraging the power of `tidymodels` and the `AMR` package, we’ll
 build a reproducible machine learning workflow to predict the Gramstain
 of the microorganism to two important antibiotic classes:
 aminoglycosides and beta-lactams.
 ### **Objective**
 Our goal is to build a predictive model using the `tidymodels` framework
 to determine the Gramstain of the microorganism based on microbial data.
 We will:
 1.  Preprocess data using the selector functions
    [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
    and
    [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
 2.  Define a logistic regression model for prediction.
 3.  Use a structured `tidymodels` workflow to preprocess, train, and
    evaluate the model.
 ### **Data Preparation**
 We begin by loading the required libraries and preparing the
 `example_isolates` dataset from the `AMR` package.
 ``` r
 # Load required libraries
 library(AMR) # For AMR data analysis
 library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
 ```
 Prepare the data:
 ``` r
 # Your data could look like this:
 example_isolates
 #> # A tibble: 2,000 × 46
 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
 #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
 #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
 #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
 #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
 #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
 #> # ℹ 1,990 more rows
 #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
 #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
 #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
 #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
 #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
 #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
 # Select relevant columns for prediction
 data <- example_isolates %>%
  # select AB results dynamically
  select(mo, aminoglycosides(), betalactams()) %>%
  # replace NAs with NI (not-interpretable)
  mutate(
    across(
      where(is.sir),
      ~ replace_na(.x, "NI")
    ),
    # make factors of SIR columns
    across(
      where(is.sir),
      as.integer
    ),
    # get Gramstain of microorganisms
    mo = as.factor(mo_gramstain(mo))
  ) %>%
  # drop NAs - the ones without a Gramstain (fungi, etc.)
  drop_na()
 #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
 #>   (amikacin), and KAN (kanamycin)
 #> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
 #>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
 #>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
 #>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
 #>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
 ```
 **Explanation:**
 - [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
  and
  [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
  dynamically select columns for antimicrobials in these classes.
 - `drop_na()` ensures the model receives complete cases for training.
 ### **Defining the Workflow**
 We now define the `tidymodels` workflow, which consists of three steps:
 preprocessing, model specification, and fitting.
 #### 1. Preprocessing with a Recipe
 We create a recipe to preprocess the data for modelling.
 ``` r
 # Define the recipe for data preprocessing
 resistance_recipe <- recipe(mo ~ ., data = data) %>%
  step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
 resistance_recipe
 #> 
 #> ── Recipe ──────────────────────────────────────────────────────────────────────
 #> 
 #> ── Inputs
 #> Number of variables by role
 #> outcome:    1
 #> predictor: 20
 #> 
 #> ── Operations
 #> • Correlation filter on: c(aminoglycosides(), betalactams())
 ```
 For a recipe that includes at least one preprocessing operation, like we
 have with `step_corr()`, the necessary parameters can be estimated from
 a training set using `prep()`:
 ``` r
 prep(resistance_recipe)
 #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
 #>   (amikacin), and KAN (kanamycin)
 #> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
 #>   FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
 #>   AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
 #>   (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
 #>   (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
 #> 
 #> 
 #> ── Recipe ──────────────────────────────────────────────────────────────────────
 #> 
 #> 
 #> 
 #> ── Inputs 
 #> 
 #> Number of variables by role
 #> 
 #> outcome:    1
 #> predictor: 20
 #> 
 #> 
 #> 
 #> ── Training information 
 #> 
 #> Training data contained 1968 data points and no incomplete rows.
 #> 
 #> 
 #> 
 #> ── Operations 
 #> 
 #> • Correlation filter on: AMX CTX | Trained
 ```
 **Explanation:**
 - `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and
  all other columns as predictors.
 - `step_corr()` removes predictors (i.e., antibiotic columns) that have
  a higher correlation than 90%.
 Notice how the recipe contains just the antimicrobial selector
 functions - no need to define the columns specifically. In the
 preparation (retrieved with `prep()`) we can see that the columns or
 variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
 existing, other variables.
 #### 2. Specifying the Model
 We define a logistic regression model since resistance prediction is a
 binary classification task.
 ``` r
 # Specify a logistic regression model
 logistic_model <- logistic_reg() %>%
  set_engine("glm") # Use the Generalised Linear Model engine
 logistic_model
 #> Logistic Regression Model Specification (classification)
 #> 
 #> Computational engine: glm
 ```
 **Explanation:**
 - `logistic_reg()` sets up a logistic regression model.
 - `set_engine("glm")` specifies the use of R’s built-in GLM engine.
 #### 3. Building the Workflow
 We bundle the recipe and model together into a `workflow`, which
 organises the entire modelling process.
 ``` r
 # Combine the recipe and model into a workflow
 resistance_workflow <- workflow() %>%
  add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
  add_model(logistic_model) # Add the logistic regression model
 resistance_workflow
 #> ══ Workflow ════════════════════════════════════════════════════════════════════
 #> Preprocessor: Recipe
 #> Model: logistic_reg()
 #> 
 #> ── Preprocessor ────────────────────────────────────────────────────────────────
 #> 1 Recipe Step
 #> 
 #> • step_corr()
 #> 
 #> ── Model ───────────────────────────────────────────────────────────────────────
 #> Logistic Regression Model Specification (classification)
 #> 
 #> Computational engine: glm
 ```
 ### **Training and Evaluating the Model**
 To train the model, we split the data into training and testing sets.
 Then, we fit the workflow on the training set and evaluate its
 performance.
 ``` r
 # Split data into training and testing sets
 set.seed(123) # For reproducibility
 data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
 training_data <- training(data_split) # Training set
 testing_data <- testing(data_split) # Testing set
 # Fit the workflow to the training data
 fitted_workflow <- resistance_workflow %>%
  fit(training_data) # Train the model
 ```
 **Explanation:**
 - `initial_split()` splits the data into training and testing sets.
 - `fit()` trains the workflow on the training set.
 Notice how in `fit()`, the antimicrobial selector functions are
 internally called again. For training, these functions are called since
 they are stored in the recipe.
 Next, we evaluate the model on the testing data.
 ``` r
 # Make predictions on the testing set
 predictions <- fitted_workflow %>%
  predict(testing_data) # Generate predictions
 probabilities <- fitted_workflow %>%
  predict(testing_data, type = "prob") # Generate probabilities
 predictions <- predictions %>%
  bind_cols(probabilities) %>%
  bind_cols(testing_data) # Combine with true labels
 predictions
 #> # A tibble: 394 × 24
 #>    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB
 #>    <fct>                         <dbl>                 <dbl> <fct>   <int> <int>
 #>  1 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
 #>  2 Gram-positive              3.17e- 8             1.000e+ 0 Gram-p…     5     1
 #>  3 Gram-negative              9.99e- 1             1.42 e- 3 Gram-n…     5     5
 #>  4 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     5     5
 #>  5 Gram-negative              9.46e- 1             5.42 e- 2 Gram-n…     5     5
 #>  6 Gram-positive              1.07e- 1             8.93 e- 1 Gram-p…     5     5
 #>  7 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     1     5
 #>  8 Gram-positive              2.22e-16             1    e+ 0 Gram-p…     4     4
 #>  9 Gram-negative              1   e+ 0             2.22 e-16 Gram-n…     1     1
 #> 10 Gram-positive              6.05e-11             1.000e+ 0 Gram-p…     4     4
 #> # ℹ 384 more rows
 #> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>,
 #> #   AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>,
 #> #   CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>
 # Evaluate model performance
 metrics <- predictions %>%
  metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
 metrics
 #> # A tibble: 2 × 3
 #>   .metric  .estimator .estimate
 #>   <chr>    <chr>          <dbl>
 #> 1 accuracy binary         0.995
 #> 2 kap      binary         0.989
 # To assess some other model properties, you can make our own `metrics()` function
 our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value
 metrics2 <- predictions %>%
  our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function
 metrics2
 #> # A tibble: 4 × 3
 #>   .metric  .estimator .estimate
 #>   <chr>    <chr>          <dbl>
 #> 1 accuracy binary         0.995
 #> 2 kap      binary         0.989
 #> 3 ppv      binary         0.987
 #> 4 npv      binary         1
 ```
 **Explanation:**
 - [`predict()`](https://rdrr.io/r/stats/predict.html) generates
  predictions on the testing set.
 - `metrics()` computes evaluation metrics like accuracy and kappa.
 It appears we can predict the Gram stain with a 99.5% accuracy based on
 AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
 curve looks like this:
 ``` r
 predictions %>%
  roc_curve(mo, `.pred_Gram-negative`) %>%
  autoplot()
 ```
 ![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png)
 ### **Conclusion**
 In this example, we demonstrated how to build a machine learning
 pipeline with the `tidymodels` framework and the `AMR` package. By
 combining selector functions like
 [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
 and
 [`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
 with `tidymodels`, we efficiently prepared data, trained a model, and
 evaluated its performance.
 This workflow is extensible to other antimicrobial classes and
 resistance patterns, empowering users to analyse AMR data systematically
 and reproducibly.
 ------------------------------------------------------------------------
 ## Example 2: Predicting ESBL Presence Using Raw MICs
 In this second example, we demonstrate how to use `<mic>` columns
 directly in `tidymodels` workflows using AMR-specific recipe steps. This
 includes a transformation to `log2` scale using
 [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
 which prepares MIC values for use in classification models.
 This approach and idea formed the basis for the publication [DOI:
 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
 to model the presence of extended-spectrum beta-lactamases (ESBL) based
 on MIC values.
 ### **Objective**
 Our goal is to:
 1.  Use raw MIC values to predict whether a bacterial isolate produces
    ESBL.
 2.  Apply AMR-aware preprocessing in a `tidymodels` recipe.
 3.  Train a classification model and evaluate its predictive
    performance.
 ### **Data Preparation**
 We use the `esbl_isolates` dataset that comes with the AMR package.
 ``` r
 # Load required libraries
 library(AMR)
 library(tidymodels)
 # View the esbl_isolates data set
 esbl_isolates
 #> # A tibble: 500 × 19
 #>    esbl  genus   AMC   AMP   TZP   CXM   FOX   CTX   CAZ   GEN   TOB   TMP   SXT
 #>    <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
 #>  1 FALSE Esch…    32    32     4    64    64  8.00  8.00     1     1  16.0    20
 #>  2 FALSE Esch…    32    32     4    64    64  4.00  8.00     1     1  16.0   320
 #>  3 FALSE Esch…     4     2    64     8     4  8.00  0.12    16    16   0.5    20
 #>  4 FALSE Kleb…    32    32    16    64    64  8.00  8.00     1     1   0.5    20
 #>  5 FALSE Esch…    32    32     4     4     4  0.25  2.00     1     1  16.0   320
 #>  6 FALSE Citr…    32    32    16    64    64 64.00 32.00     1     1   0.5    20
 #>  7 FALSE Morg…    32    32     4    64    64 16.00  2.00     1     1   0.5    20
 #>  8 FALSE Prot…    16    32     4     1     4  8.00  0.12     1     1  16.0   320
 #>  9 FALSE Ente…    32    32     8    64    64 32.00  4.00     1     1   0.5    20
 #> 10 FALSE Citr…    32    32    32    64    64  8.00 64.00     1     1  16.0   320
 #> # ℹ 490 more rows
 #> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
 #> #   COL <mic>
 # Prepare a binary outcome and convert to ordered factor
 data <- esbl_isolates %>%
  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
 ```
 **Explanation:**
 - `esbl_isolates`: Contains MIC test results and ESBL status for each
  isolate.
 - `mutate(esbl = ...)`: Converts the target column to an ordered factor
  for classification.
 ### **Defining the Workflow**
 #### 1. Preprocessing with a Recipe
 We use our
 [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
 function to log2-transform MIC values, ensuring that MICs are numeric
 and properly scaled. All MIC predictors can easily and agnostically
 selected using the new
 [`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
 ``` r
 # Split into training and testing sets
 set.seed(123)
 split <- initial_split(data)
 training_data <- training(split)
 testing_data <- testing(split)
 # Define the recipe
 mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
  remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
  step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
 prep(mic_recipe)
 #> 
 #> ── Recipe ──────────────────────────────────────────────────────────────────────
 #> 
 #> ── Inputs
 #> Number of variables by role
 #> outcome:          1
 #> predictor:       17
 #> undeclared role:  1
 #> 
 #> ── Training information
 #> Training data contained 375 data points and no incomplete rows.
 #> 
 #> ── Operations
 #> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
 ```
 **Explanation:**
 - `remove_role()`: Removes irrelevant variables like genus.
 - [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md):
  Applies `log2(as.numeric(...))` to all MIC predictors in one go.
 - `prep()`: Finalises the recipe based on training data.
 #### 2. Specifying the Model
 We use a simple logistic regression to model ESBL presence, though
 recent models such as xgboost ([link to `parsnip`
 manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
 could be much more precise.
 ``` r
 # Define the model
 model <- logistic_reg(mode = "classification") %>%
  set_engine("glm")
 model
 #> Logistic Regression Model Specification (classification)
 #> 
 #> Computational engine: glm
 ```
 **Explanation:**
 - `logistic_reg()`: Specifies a binary classification model.
 - `set_engine("glm")`: Uses the base R GLM engine.
 #### 3. Building the Workflow
 ``` r
 # Create workflow
 workflow_model <- workflow() %>%
  add_recipe(mic_recipe) %>%
  add_model(model)
 workflow_model
 #> ══ Workflow ════════════════════════════════════════════════════════════════════
 #> Preprocessor: Recipe
 #> Model: logistic_reg()
 #> 
 #> ── Preprocessor ────────────────────────────────────────────────────────────────
 #> 1 Recipe Step
 #> 
 #> • step_mic_log2()
 #> 
 #> ── Model ───────────────────────────────────────────────────────────────────────
 #> Logistic Regression Model Specification (classification)
 #> 
 #> Computational engine: glm
 ```
 ### **Training and Evaluating the Model**
 ``` r
 # Fit the model
 fitted <- fit(workflow_model, training_data)
 # Generate predictions
 predictions <- predict(fitted, testing_data) %>%
  bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
  bind_cols(testing_data)
 # Evaluate model performance
 our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
 metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
 metrics
 #> # A tibble: 7 × 3
 #>   .metric     .estimator .estimate
 #>   <chr>       <chr>          <dbl>
 #> 1 accuracy    binary         0.92 
 #> 2 recall      binary         0.921
 #> 3 precision   binary         0.921
 #> 4 sensitivity binary         0.921
 #> 5 specificity binary         0.919
 #> 6 ppv         binary         0.921
 #> 7 npv         binary         0.919
 ```
 **Explanation:**
 - `fit()`: Trains the model on the processed training data.
 - [`predict()`](https://rdrr.io/r/stats/predict.html): Produces
  predictions for unseen test data.
 - `metric_set()`: Allows evaluating multiple classification metrics.
  This will make `our_metrics` to become a function that we can use to
  check the predictions with.
 It appears we can predict ESBL gene presence with a positive predictive
 value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
 using a simplistic logistic regression model.
 ### **Visualising Predictions**
 We can visualise predictions by comparing predicted and actual ESBL
 status.
 ``` r
 library(ggplot2)
 ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
  geom_bar(position = "stack") +
  labs(
    title = "Predicted vs Actual ESBL Status",
    x = "Actual ESBL",
    y = "Count"
  ) +
  theme_minimal()
 ```
 ![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png)
 And plot the certainties too - how certain were the actual predictions?
 ``` r
 predictions %>%
  mutate(
    certainty = ifelse(.pred_class == "FALSE",
      .pred_FALSE,
      .pred_TRUE
    ),
    correct = ifelse(esbl == .pred_class, "Right", "Wrong")
  ) %>%
  ggplot(aes(
    x = seq_len(nrow(predictions)),
    y = certainty,
    colour = correct
  )) +
  scale_colour_manual(
    values = c(Right = "green3", Wrong = "red2"),
    name = "Correct?"
  ) +
  geom_point() +
  scale_y_continuous(
    labels = function(x) paste0(x * 100, "%"),
    limits = c(0.5, 1)
  ) +
  theme_minimal()
 ```
 ![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
 ### **Conclusion**
 In this example, we showcased how the new `AMR`-specific recipe steps
 simplify working with `<mic>` columns in `tidymodels`. The
 [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
 transformation converts MICs (with or without operators) to
 log2-transformed numerics, improving compatibility with classification
 models.
 This pipeline enables realistic, reproducible, and interpretable
 modelling of antimicrobial resistance data.
 ------------------------------------------------------------------------
 ## Example 3: Predicting AMR Over Time
 In this third example, we aim to predict antimicrobial resistance (AMR)
 trends over time using `tidymodels`. We will model resistance to three
 antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and
 ciprofloxacin `CIP`), based on historical data grouped by year and
 hospital ward.
 ### **Objective**
 Our goal is to:
 1.  Prepare the dataset by aggregating resistance data over time.
 2.  Define a regression model to predict AMR trends.
 3.  Use `tidymodels` to preprocess, train, and evaluate the model.
 ### **Data Preparation**
 We start by transforming the `example_isolates` dataset into a
 structured time-series format.
 ``` r
 # Load required libraries
 library(AMR)
 library(tidymodels)
 # Transform dataset
 data_time <- example_isolates %>%
  top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
  mutate(
    year = as.integer(format(date, "%Y")), # Extract year from date
    gramstain = mo_gramstain(mo)
  ) %>% # Get taxonomic names
  group_by(year, gramstain) %>%
  summarise(
    across(c(AMX, AMC, CIP),
      function(x) resistance(x, minimum = 0),
      .names = "res_{.col}"
    ),
    .groups = "drop"
  ) %>%
  filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
 #> ℹ Using column mo as input for `col_mo`.
 #> 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.
 data_time
 #> # A tibble: 32 × 5
 #>     year gramstain     res_AMX res_AMC res_CIP
 #>    <int> <chr>           <dbl>   <dbl>   <dbl>
 #>  1  2002 Gram-negative   1      0.105   0.0606
 #>  2  2002 Gram-positive   0.838  0.182   0.162 
 #>  3  2003 Gram-negative   1      0.0714  0     
 #>  4  2003 Gram-positive   0.714  0.244   0.154 
 #>  5  2004 Gram-negative   0.464  0.0938  0     
 #>  6  2004 Gram-positive   0.849  0.299   0.244 
 #>  7  2005 Gram-negative   0.412  0.132   0.0588
 #>  8  2005 Gram-positive   0.882  0.382   0.154 
 #>  9  2006 Gram-negative   0.379  0       0.1   
 #> 10  2006 Gram-positive   0.778  0.333   0.353 
 #> # ℹ 22 more rows
 ```
 **Explanation:**
 - `mo_name(mo)`: Converts microbial codes into proper species names.
 - [`resistance()`](https://amr-for-r.org/reference/proportion.md):
  Converts AMR results into numeric values (proportion of resistant
  isolates).
 - `group_by(year, ward, species)`: Aggregates resistance rates by year
  and ward.
 ### **Defining the Workflow**
 We now define the modelling workflow, which consists of a preprocessing
 step, a model specification, and the fitting process.
 #### 1. Preprocessing with a Recipe
 ``` r
 # Define the recipe
 resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
  step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
  step_normalize(year) %>% # Normalise year for better model performance
  step_nzv(all_predictors()) # Remove near-zero variance predictors
 resistance_recipe_time
 #> 
 #> ── Recipe ──────────────────────────────────────────────────────────────────────
 #> 
 #> ── Inputs
 #> Number of variables by role
 #> outcome:   1
 #> predictor: 2
 #> 
 #> ── Operations
 #> • Dummy variables from: gramstain
 #> • Centering and scaling for: year
 #> • Sparse, unbalanced variable filter on: all_predictors()
 ```
 **Explanation:**
 - `step_dummy()`: Encodes categorical variables (`ward`, `species`) as
  numerical indicators.
 - `step_normalize()`: Normalises the `year` variable.
 - `step_nzv()`: Removes near-zero variance predictors.
 #### 2. Specifying the Model
 We use a linear regression model to predict resistance trends.
 ``` r
 # Define the linear regression model
 lm_model <- linear_reg() %>%
  set_engine("lm") # Use linear regression
 lm_model
 #> Linear Regression Model Specification (regression)
 #> 
 #> Computational engine: lm
 ```
 **Explanation:**
 - `linear_reg()`: Defines a linear regression model.
 - `set_engine("lm")`: Uses R’s built-in linear regression engine.
 #### 3. Building the Workflow
 We combine the preprocessing recipe and model into a workflow.
 ``` r
 # Create workflow
 resistance_workflow_time <- workflow() %>%
  add_recipe(resistance_recipe_time) %>%
  add_model(lm_model)
 resistance_workflow_time
 #> ══ Workflow ════════════════════════════════════════════════════════════════════
 #> Preprocessor: Recipe
 #> Model: linear_reg()
 #> 
 #> ── Preprocessor ────────────────────────────────────────────────────────────────
 #> 3 Recipe Steps
 #> 
 #> • step_dummy()
 #> • step_normalize()
 #> • step_nzv()
 #> 
 #> ── Model ───────────────────────────────────────────────────────────────────────
 #> Linear Regression Model Specification (regression)
 #> 
 #> Computational engine: lm
 ```
 ### **Training and Evaluating the Model**
 We split the data into training and testing sets, fit the model, and
 evaluate performance.
 ``` r
 # Split the data
 set.seed(123)
 data_split_time <- initial_split(data_time, prop = 0.8)
 train_time <- training(data_split_time)
 test_time <- testing(data_split_time)
 # Train the model
 fitted_workflow_time <- resistance_workflow_time %>%
  fit(train_time)
 # Make predictions
 predictions_time <- fitted_workflow_time %>%
  predict(test_time) %>%
  bind_cols(test_time)
 # Evaluate model
 metrics_time <- predictions_time %>%
  metrics(truth = res_AMX, estimate = .pred)
 metrics_time
 #> # A tibble: 3 × 3
 #>   .metric .estimator .estimate
 #>   <chr>   <chr>          <dbl>
 #> 1 rmse    standard      0.0774
 #> 2 rsq     standard      0.711 
 #> 3 mae     standard      0.0704
 ```
 **Explanation:**
 - `initial_split()`: Splits data into training and testing sets.
 - `fit()`: Trains the workflow.
 - [`predict()`](https://rdrr.io/r/stats/predict.html): Generates
  resistance predictions.
 - `metrics()`: Evaluates model performance.
 ### **Visualising Predictions**
 We plot resistance trends over time for amoxicillin.
 ``` r
 library(ggplot2)
 # Plot actual vs predicted resistance over time
 ggplot(predictions_time, aes(x = year)) +
  geom_point(aes(y = res_AMX, color = "Actual")) +
  geom_line(aes(y = .pred, color = "Predicted")) +
  labs(
    title = "Predicted vs Actual AMX Resistance Over Time",
    x = "Year",
    y = "Resistance Proportion"
  ) +
  theme_minimal()
 ```
 ![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png)
 Additionally, we can visualise resistance trends in `ggplot2` and
 directly add linear models there:
 ``` r
 ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
  geom_line() +
  labs(
    title = "AMX Resistance Trends",
    x = "Year",
    y = "Resistance Proportion"
  ) +
  # add a linear model directly in ggplot2:
  geom_smooth(
    method = "lm",
    formula = y ~ x,
    alpha = 0.25
  ) +
  theme_minimal()
 ```
 ![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png)
 ### **Conclusion**
 In this example, we demonstrated how to analyze AMR trends over time
 using `tidymodels`. By aggregating resistance rates by year and hospital
 ward, we built a predictive model to track changes in resistance to
 amoxicillin (`AMX`), amoxicillin-clavulanic acid (`AMC`), and
 ciprofloxacin (`CIP`).
 This method can be extended to other antibiotics and resistance
 patterns, providing valuable insights into AMR dynamics in healthcare
 settings.
@@ -0,0 +1,337 @@
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      <img src="../logo.svg" class="logo" alt=""><h1>Apply EUCAST rules</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
      <div class="d-none name"><code>EUCAST.Rmd</code></div>
    </div>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>What are EUCAST rules? The European Committee on Antimicrobial
 Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
 their website</a>:</p>
 <blockquote>
 <p><em>EUCAST expert rules (see below) are a tabulated collection of
 expert knowledge on interpretive rules, expected resistant phenotypes
 and expected susceptible phenotypes which should be applied to
 antimicrobial susceptibility testing in order to reduce testing, reduce
 errors and make appropriate recommendations for reporting particular
 resistances.</em></p>
 </blockquote>
 <p>In Europe, a lot of medical microbiological laboratories already
 apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
 <em>et al.</em>, 2015</a>). Our package features their latest insights
 on expected resistant phenotypes (v1.2, 2023).</p>
 </div>
 <div class="section level2">
 <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
 </h2>
 <p>These rules can be used to discard improbable bug-drug combinations
 in your data. For example, <em>Klebsiella</em> produces beta-lactamase
 that prevents ampicillin (or amoxicillin) from working against it. In
 other words, practically every strain of <em>Klebsiella</em> is
 resistant to ampicillin.</p>
 <p>Sometimes, laboratory data can still contain such strains with
 <em>Klebsiella</em> being susceptible to ampicillin. This could be
 because an antibiogram is available before an identification is
 available, and the antibiogram is then not re-interpreted based on the
 identification. The <code><a href="../reference/interpretive_rules.html">eucast_rules()</a></code> function resolves this,
 by applying the latest ‘EUCAST Expected Resistant Phenotypes’
 guideline:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
 <span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>    <span class="st">"Escherichia coli"</span></span>
 <span>  <span class="op">)</span>,</span>
 <span>  ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">oops</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
 <span><span class="co">#&gt;   mo                    ampicillin</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;">  S  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
 <span><span class="co">#&gt;   mo                    ampicillin</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FF5F5F;">  R  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span></code></pre></div>
 <p>A more convenient function is
 <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
 but allows to check for one or more specific microorganisms or
 antimicrobials:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
 <span>  <span class="st">"ampicillin"</span></span>
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; [1]  TRUE FALSE</span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
 <span>  <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; [1]  TRUE FALSE</span></span></code></pre></div>
 <p>EUCAST rules can not only be used for correction, they can also be
 used for filling in known resistance and susceptibility based on results
 of other antimicrobials drugs. This process is called <em>interpretive
 reading</em>, and is basically a form of imputation:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
 <span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>    <span class="st">"Staphylococcus aureus"</span>,</span>
 <span>    <span class="st">"Enterococcus faecalis"</span>,</span>
 <span>    <span class="st">"Escherichia coli"</span>,</span>
 <span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>    <span class="st">"Pseudomonas aeruginosa"</span></span>
 <span>  <span class="op">)</span>,</span>
 <span>  VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
 <span>  AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
 <span>  COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
 <span>  CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
 <span>  CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
 <span>  PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
 <span>  FOX <span class="op">=</span> <span class="st">"S"</span> <span class="co"># Cefoxitin</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">mo</th>
 <th align="center">VAN</th>
 <th align="center">AMX</th>
 <th align="center">COL</th>
 <th align="center">CAZ</th>
 <th align="center">CXM</th>
 <th align="center">PEN</th>
 <th align="center">FOX</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Staphylococcus aureus</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Enterococcus faecalis</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Escherichia coli</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Klebsiella pneumoniae</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Pseudomonas aeruginosa</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">mo</th>
 <th align="center">VAN</th>
 <th align="center">AMX</th>
 <th align="center">COL</th>
 <th align="center">CAZ</th>
 <th align="center">CXM</th>
 <th align="center">PEN</th>
 <th align="center">FOX</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Staphylococcus aureus</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Enterococcus faecalis</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">R</td>
 </tr>
 <tr class="odd">
 <td align="left">Escherichia coli</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Klebsiella pneumoniae</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Pseudomonas aeruginosa</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">R</td>
 </tr>
 </tbody>
 </table>
 </div>
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    </nav></aside>
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  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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@@ -0,0 +1,131 @@
 # Apply EUCAST rules
 ## Introduction
 What are EUCAST rules? The European Committee on Antimicrobial
 Susceptibility Testing (EUCAST) states [on their
 website](https://www.eucast.org/expert_rules_and_expected_phenotypes):
 > *EUCAST expert rules (see below) are a tabulated collection of expert
 > knowledge on interpretive rules, expected resistant phenotypes and
 > expected susceptible phenotypes which should be applied to
 > antimicrobial susceptibility testing in order to reduce testing,
 > reduce errors and make appropriate recommendations for reporting
 > particular resistances.*
 In Europe, a lot of medical microbiological laboratories already apply
 these rules ([Brown *et al.*,
 2015](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008)).
 Our package features their latest insights on expected resistant
 phenotypes (v1.2, 2023).
 ## Examples
 These rules can be used to discard improbable bug-drug combinations in
 your data. For example, *Klebsiella* produces beta-lactamase that
 prevents ampicillin (or amoxicillin) from working against it. In other
 words, practically every strain of *Klebsiella* is resistant to
 ampicillin.
 Sometimes, laboratory data can still contain such strains with
 *Klebsiella* being susceptible to ampicillin. This could be because an
 antibiogram is available before an identification is available, and the
 antibiogram is then not re-interpreted based on the identification. The
 [`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
 function resolves this, by applying the latest ‘EUCAST Expected
 Resistant Phenotypes’ guideline:
 ``` r
 oops <- tibble::tibble(
  mo = c(
    "Klebsiella pneumoniae",
    "Escherichia coli"
  ),
  ampicillin = as.sir("S")
 )
 oops
 #> # A tibble: 2 × 2
 #>   mo                    ampicillin
 #>   <chr>                 <sir>     
 #> 1 Klebsiella pneumoniae   S       
 #> 2 Escherichia coli        S  
 eucast_rules(oops, info = FALSE, overwrite = TRUE)
 #> # A tibble: 2 × 2
 #>   mo                    ampicillin
 #>   <chr>                 <sir>     
 #> 1 Klebsiella pneumoniae   R       
 #> 2 Escherichia coli        S  
 ```
 A more convenient function is
 [`mo_is_intrinsic_resistant()`](https://amr-for-r.org/reference/mo_property.md)
 that uses the same guideline, but allows to check for one or more
 specific microorganisms or antimicrobials:
 ``` r
 mo_is_intrinsic_resistant(
  c("Klebsiella pneumoniae", "Escherichia coli"),
  "ampicillin"
 )
 #> [1]  TRUE FALSE
 mo_is_intrinsic_resistant(
  "Klebsiella pneumoniae",
  c("ampicillin", "kanamycin")
 )
 #> [1]  TRUE FALSE
 ```
 EUCAST rules can not only be used for correction, they can also be used
 for filling in known resistance and susceptibility based on results of
 other antimicrobials drugs. This process is called *interpretive
 reading*, and is basically a form of imputation:
 ``` r
 data <- tibble::tibble(
  mo = c(
    "Staphylococcus aureus",
    "Enterococcus faecalis",
    "Escherichia coli",
    "Klebsiella pneumoniae",
    "Pseudomonas aeruginosa"
  ),
  VAN = "-", # Vancomycin
  AMX = "-", # Amoxicillin
  COL = "-", # Colistin
  CAZ = "-", # Ceftazidime
  CXM = "-", # Cefuroxime
  PEN = "S", # Benzylenicillin
  FOX = "S" # Cefoxitin
 )
 ```
 ``` r
 data
 ```
 | mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
 |:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 | Staphylococcus aureus  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
 | Enterococcus faecalis  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
 | Escherichia coli       | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
 | Klebsiella pneumoniae  | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
 | Pseudomonas aeruginosa | \-  | \-  | \-  | \-  | \-  |  S  |  S  |
 ``` r
 eucast_rules(data, overwrite = TRUE)
 ```
 | mo                     | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
 |:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 | Staphylococcus aureus  | \-  |  S  |  R  |  R  |  S  |  S  |  S  |
 | Enterococcus faecalis  | \-  | \-  |  R  |  R  |  R  |  S  |  R  |
 | Escherichia coli       |  R  | \-  | \-  | \-  | \-  |  R  |  S  |
 | Klebsiella pneumoniae  |  R  |  R  | \-  | \-  | \-  |  R  |  S  |
 | Pseudomonas aeruginosa |  R  |  R  | \-  | \-  |  R  |  R  |  R  |
@@ -0,0 +1,254 @@
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      <img src="../logo.svg" class="logo" alt=""><h1>Conduct principal component analysis (PCA) for AMR</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
      <div class="d-none name"><code>PCA.Rmd</code></div>
    </div>
 <p><strong>NOTE: This page will be updated soon, as the pca() function
 is currently being developed.</strong></p>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 </div>
 <div class="section level2">
 <h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
 </h2>
 <p>For PCA, we need to transform our AMR data first. This is what the
 <code>example_isolates</code> data set in this package looks like:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Rows: 2,000</span></span>
 <span><span class="co">#&gt; Columns: 46</span></span>
 <span><span class="co">#&gt; $ date    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
 <span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
 <span><span class="co">#&gt; $ age     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
 <span><span class="co">#&gt; $ gender  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
 <span><span class="co">#&gt; $ ward    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
 <span><span class="co">#&gt; $ mo      <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ PEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ OXA     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ FLC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
 <span><span class="co">#&gt; $ AMX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ AMC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ AMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ TZP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CZO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ FEP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CXM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ FOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ CTX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ CAZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ CRO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ GEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ TOB     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ AMK     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ KAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ TMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ SXT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ NIT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ FOS     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ LNZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ CIP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ MFX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ VAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ TEC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ TCY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ TGC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ DOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ ERY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ CLI     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ AZM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ IPM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ MEM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ MTR     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CHL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ COL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ MUP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ RIF     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
 <p>Now to transform this to a data set with only resistance percentages
 per taxonomic order and genus:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
 <span>    order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
 <span>    genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
 <span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co">#  and genus as we do here</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
 <span>    <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
 <span>    <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
 <span>  <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
 <span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
 <span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># Groups:   order [5]</span></span></span>
 <span><span class="co">#&gt;   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (unknown order)   (unknown ge…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Actinomycetales   Schaalia        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Bacteroidales     Bacteroides     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Caryophanales     Gemella         <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> Caryophanales     Listeria        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
 </h2>
 <p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
 that contain numeric values in all selected variables, so we now only
 need to do:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Columns selected for PCA: <span style="color: #0000BB;">"\033[1mAMC\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCAZ\033[22m"</span>,</span></span>
 <span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mCTX\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCXM\033[22m"</span>, <span style="color: #0000BB;">"\033[1mGEN\033[22m"</span>,</span></span>
 <span><span class="co">#&gt;   <span style="color: #0000BB;">"\033[1mSXT\033[22m"</span>, <span style="color: #0000BB;">"\033[1mTMP\033[22m"</span>, and <span style="color: #0000BB;">"\033[1mTOB\033[22m"</span>. Total</span></span>
 <span><span class="co">#&gt;   observations available: 7.</span></span></code></pre></div>
 <p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
 function:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
 <span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span>
 <span><span class="co">#&gt; Importance of components:</span></span>
 <span><span class="co">#&gt;                           PC1    PC2    PC3     PC4     PC5     PC6       PC7</span></span>
 <span><span class="co">#&gt; Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
 <span><span class="co">#&gt; Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
 <span><span class="co">#&gt; Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
 <pre><code><span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
 <span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span></code></pre>
 <p>Good news. The first two components explain a total of 93.3% of the
 variance (see the PC1 and PC2 values of the <em>Proportion of
 Variance</em>. We can create a so-called biplot with the base R
 <code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
 per drug explain the difference per microorganism.</p>
 </div>
 <div class="section level2">
 <h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
 </h2>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" alt="" width="750"></p>
 <p>But we can’t see the explanation of the points. Perhaps this works
 better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
 automatically adds the right labels and even groups:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="750"></p>
 <p>You can also print an ellipse per group, and edit the appearance:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="750"></p>
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 # Conduct principal component analysis (PCA) for AMR
 **NOTE: This page will be updated soon, as the pca() function is
 currently being developed.**
 ## Introduction
 ## Transforming
 For PCA, we need to transform our AMR data first. This is what the
 `example_isolates` data set in this package looks like:
 ``` r
 library(AMR)
 library(dplyr)
 glimpse(example_isolates)
 #> Rows: 2,000
 #> Columns: 46
 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
 #> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
 #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
 #> $ gender  <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
 #> $ ward    <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
 #> $ mo      <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
 #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
 #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
 #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
 #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
 #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
 #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
 #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
 #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
 #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
 #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
 #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
 #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
 #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
 #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
 #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
 #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
 #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
 #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
 #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
 #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
 #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
 #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
 #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
 #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
 #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
 #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
 #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
 #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
 #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
 ```
 Now to transform this to a data set with only resistance percentages per
 taxonomic order and genus:
 ``` r
 resistance_data <- example_isolates %>%
  group_by(
    order = mo_order(mo), # group on anything, like order
    genus = mo_genus(mo)
  ) %>% #  and genus as we do here
  summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
  select(
    order, genus, AMC, CXM, CTX,
    CAZ, GEN, TOB, TMP, SXT
  ) # and select only relevant columns
 #> 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.
 head(resistance_data)
 #> # A tibble: 6 × 10
 #> # Groups:   order [5]
 #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT
 #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA
 #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA
 #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA
 #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA
 #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA
 #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA
 ```
 ## Perform principal component analysis
 The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
 automatically filter on rows that contain numeric values in all selected
 variables, so we now only need to do:
 ``` r
 pca_result <- pca(resistance_data)
 #> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
 #>   "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
 #>   "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
 #>   observations available: 7.
 ```
 The result can be reviewed with the good old
 [`summary()`](https://rdrr.io/r/base/summary.html) function:
 ``` r
 summary(pca_result)
 #> Groups (n=4, named as 'order'):
 #> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
 #> Importance of components:
 #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7
 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
 ```
    #> Groups (n=4, named as 'order'):
    #> [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
 Good news. The first two components explain a total of 93.3% of the
 variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
 can create a so-called biplot with the base R
 [`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
 antimicrobial resistance per drug explain the difference per
 microorganism.
 ## Plotting the results
 ``` r
 biplot(pca_result)
 ```
 ![](PCA_files/figure-html/unnamed-chunk-5-1.png)
 But we can’t see the explanation of the points. Perhaps this works
 better with our new
 [`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
 function, that automatically adds the right labels and even groups:
 ``` r
 ggplot_pca(pca_result)
 ```
 ![](PCA_files/figure-html/unnamed-chunk-6-1.png)
 You can also print an ellipse per group, and edit the appearance:
 ``` r
 ggplot_pca(pca_result, ellipse = TRUE) +
  ggplot2::labs(title = "An AMR/PCA biplot!")
 ```
 ![](PCA_files/figure-html/unnamed-chunk-7-1.png)
@@ -0,0 +1,343 @@
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      <img src="../logo.svg" class="logo" alt=""><h1>Work with WHONET data</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
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 <div class="section level3">
 <h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
 </h3>
 <p>This tutorial assumes you already imported the WHONET data with
 e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
 package</a>. In RStudio, this can be done using the menu button ‘Import
 Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
 select your exported file. Make sure date fields are imported
 correctly.</p>
 <p>An example syntax could look like this:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></span></code></pre></div>
 <p>This package comes with an <a href="https://amr-for-r.org/reference/WHONET.html">example data set
 <code>WHONET</code></a>. We will use it for this analysis.</p>
 </div>
 <div class="section level3">
 <h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
 </h3>
 <p>First, load the relevant packages if you did not yet did this. I use
 the tidyverse for all of my analyses. All of them. If you don’t know it
 yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># this package</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
 <p>We will have to transform some variables to simplify and automate the
 analysis:</p>
 <ul>
 <li>Microorganisms should be transformed to our own microorganism codes
 (called an <code>mo</code>) using <a href="https://amr-for-r.org/reference/catalogue_of_life">our Catalogue
 of Life reference data set</a>, which contains all ~70,000
 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
 We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
 recognises almost all WHONET abbreviations of microorganisms.</li>
 <li>Antimicrobial results or interpretations have to be clean and valid.
 In other words, they should only contain values <code>"S"</code>,
 <code>"I"</code> or <code>"R"</code>. That is exactly where the
 <code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
 </ul>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># transform variables</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># get microbial ID based on given organism</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
 <p>No errors or warnings, so all values are transformed succesfully.</p>
 <p>We also created a package dedicated to data cleaning and checking,
 called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
 function can be used to create frequency tables.</p>
 <p>So let’s check our data, with a couple of frequency tables:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
 <span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: character<br>
 Length: 500<br>
 Available: 500 (100%, NA: 0 = 0%)<br>
 Unique: 38</p>
 <p>Shortest: 11<br>
 Longest: 40</p>
 <table class="table">
 <colgroup>
 <col width="4%">
 <col width="47%">
 <col width="7%">
 <col width="10%">
 <col width="13%">
 <col width="15%">
 </colgroup>
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Escherichia coli</td>
 <td align="right">245</td>
 <td align="right">49.0%</td>
 <td align="right">245</td>
 <td align="right">49.0%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
 <td align="right">74</td>
 <td align="right">14.8%</td>
 <td align="right">319</td>
 <td align="right">63.8%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Staphylococcus epidermidis</td>
 <td align="right">38</td>
 <td align="right">7.6%</td>
 <td align="right">357</td>
 <td align="right">71.4%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Streptococcus pneumoniae</td>
 <td align="right">31</td>
 <td align="right">6.2%</td>
 <td align="right">388</td>
 <td align="right">77.6%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Staphylococcus hominis</td>
 <td align="right">21</td>
 <td align="right">4.2%</td>
 <td align="right">409</td>
 <td align="right">81.8%</td>
 </tr>
 <tr class="even">
 <td align="left">6</td>
 <td align="left">Proteus mirabilis</td>
 <td align="right">9</td>
 <td align="right">1.8%</td>
 <td align="right">418</td>
 <td align="right">83.6%</td>
 </tr>
 <tr class="odd">
 <td align="left">7</td>
 <td align="left">Enterococcus faecium</td>
 <td align="right">8</td>
 <td align="right">1.6%</td>
 <td align="right">426</td>
 <td align="right">85.2%</td>
 </tr>
 <tr class="even">
 <td align="left">8</td>
 <td align="left">Staphylococcus capitis urealyticus</td>
 <td align="right">8</td>
 <td align="right">1.6%</td>
 <td align="right">434</td>
 <td align="right">86.8%</td>
 </tr>
 <tr class="odd">
 <td align="left">9</td>
 <td align="left">Enterobacter cloacae</td>
 <td align="right">5</td>
 <td align="right">1.0%</td>
 <td align="right">439</td>
 <td align="right">87.8%</td>
 </tr>
 <tr class="even">
 <td align="left">10</td>
 <td align="left">Enterococcus columbae</td>
 <td align="right">4</td>
 <td align="right">0.8%</td>
 <td align="right">443</td>
 <td align="right">88.6%</td>
 </tr>
 </tbody>
 </table>
 <p>(omitted 28 entries, n = 57 [11.4%])</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
 <span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
 <span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
 <span><span class="co">#&gt;   category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
 <span><span class="co">#&gt;   option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: factor &gt; ordered &gt; sir (numeric)<br>
 Length: 500<br>
 Levels: 8: S &lt; SDD &lt; I &lt; R &lt; NI &lt; WT &lt; NWT &lt;
 NS<br>
 Available: 481 (96.2%, NA: 19 = 3.8%)<br>
 Unique: 3</p>
 <p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
 Drug group: Aminopenicillins<br>
 %SI: 78.59%</p>
 <table class="table">
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">S</td>
 <td align="right">356</td>
 <td align="right">74.01%</td>
 <td align="right">356</td>
 <td align="right">74.01%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">R</td>
 <td align="right">103</td>
 <td align="right">21.41%</td>
 <td align="right">459</td>
 <td align="right">95.43%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">I</td>
 <td align="right">22</td>
 <td align="right">4.57%</td>
 <td align="right">481</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
 </h3>
 <p>An easy <code>ggplot</code> will already give a lot of information,
 using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
 <p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside>
 </div>
    <footer><div class="pkgdown-footer-left">
  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
 </div>
 <div class="pkgdown-footer-right">
  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
 </div>
    </footer>
 </div>
  </body>
 </html>
@@ -0,0 +1,147 @@
 # Work with WHONET data
 ### Import of data
 This tutorial assumes you already imported the WHONET data with e.g. the
 [`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
 be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
 Choose the option ‘From Excel’ and select your exported file. Make sure
 date fields are imported correctly.
 An example syntax could look like this:
 ``` r
 library(readxl)
 data <- read_excel(path = "path/to/your/file.xlsx")
 ```
 This package comes with an [example data set
 `WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
 for this analysis.
 ### Preparation
 First, load the relevant packages if you did not yet did this. I use the
 tidyverse for all of my analyses. All of them. If you don’t know it yet,
 I suggest you read about it on their website:
 <https://www.tidyverse.org/>.
 ``` r
 library(dplyr) # part of tidyverse
 library(ggplot2) # part of tidyverse
 library(AMR) # this package
 library(cleaner) # to create frequency tables
 ```
 We will have to transform some variables to simplify and automate the
 analysis:
 - Microorganisms should be transformed to our own microorganism codes
  (called an `mo`) using [our Catalogue of Life reference data
  set](https://amr-for-r.org/reference/catalogue_of_life), which
  contains all ~70,000 microorganisms from the taxonomic kingdoms
  Bacteria, Fungi and Protozoa. We do the tranformation with
  [`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
  also recognises almost all WHONET abbreviations of microorganisms.
 - Antimicrobial results or interpretations have to be clean and valid.
  In other words, they should only contain values `"S"`, `"I"` or `"R"`.
  That is exactly where the
  [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
  for.
 ``` r
 # transform variables
 data <- WHONET %>%
  # get microbial ID based on given organism
  mutate(mo = as.mo(Organism)) %>%
  # transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
  mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
 ```
 No errors or warnings, so all values are transformed succesfully.
 We also created a package dedicated to data cleaning and checking,
 called the `cleaner` package. Its
 [`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
 function can be used to create frequency tables.
 So let’s check our data, with a couple of frequency tables:
 ``` r
 # our newly created `mo` variable, put in the mo_name() function
 data %>% freq(mo_name(mo), nmax = 10)
 ```
 **Frequency table**
 Class: character  
 Length: 500  
 Available: 500 (100%, NA: 0 = 0%)  
 Unique: 38
 Shortest: 11  
 Longest: 40
 |  | Item | Count | Percent | Cum. Count | Cum. Percent |
 |:---|:---|---:|---:|---:|---:|
 | 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
 | 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
 | 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
 | 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
 | 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
 | 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
 | 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
 | 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
 | 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
 | 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
 (omitted 28 entries, n = 57 \[11.4%\])
 ``` r
 # our transformed antibiotic columns
 # amoxicillin/clavulanic acid (J01CR02) as an example
 data %>% freq(AMC_ND2)
 #> ℹ `susceptibility()` 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.
 ```
 **Frequency table**
 Class: factor \> ordered \> sir (numeric)  
 Length: 500  
 Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS  
 Available: 481 (96.2%, NA: 19 = 3.8%)  
 Unique: 3
 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)  
 Drug group: Aminopenicillins  
 %SI: 78.59%
 |     | Item | Count | Percent | Cum. Count | Cum. Percent |
 |:----|:-----|------:|--------:|-----------:|-------------:|
 | 1   | S    |   356 |  74.01% |        356 |       74.01% |
 | 2   | R    |   103 |  21.41% |        459 |       95.43% |
 | 3   | I    |    22 |   4.57% |        481 |      100.00% |
 ### A first glimpse at results
 An easy `ggplot` will already give a lot of information, using the
 included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
 function:
 ``` r
 data %>%
  group_by(Country) %>%
  select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
  ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
 ```
 ![](WHONET_files/figure-html/unnamed-chunk-7-1.png)
@@ -0,0 +1,672 @@
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      <img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
      <div class="d-none name"><code>WISCA.Rmd</code></div>
    </div>
 <div class="section level2">
 <h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
 </h2>
 <p>When a clinician starts empirical antimicrobial therapy, the
 causative pathogen is unknown. The question they need answered is not
 <em>“what proportion of</em> E. coli <em>is susceptible to
 ciprofloxacin?“</em> but rather <em>“what is the probability that this
 regimen will adequately cover whatever pathogen turns out to be causing
 my patient’s infection?”</em></p>
 <p>The traditional cumulative antibiogram, as standardised by CLSI M39,
 cannot answer that question. It presents susceptibility percentages per
 species per antibiotic, but:</p>
 <ul>
 <li>
 <strong>It fragments information by organism.</strong> The clinician
 must mentally combine susceptibility rates across multiple species,
 weighting by how often each species causes the syndrome, a calculation
 nobody does at the bedside.</li>
 <li>
 <strong>It ignores pathogen incidence.</strong> A species that
 causes 2% of infections is given the same visual weight as one that
 causes 60%.</li>
 <li>
 <strong>It does not evaluate combination regimens.</strong> Much
 empirical therapy consists of two or more agents, but the traditional
 antibiogram only shows monotherapy per organism.</li>
 <li>
 <strong>It provides no measure of uncertainty.</strong> A reported
 “90% susceptible” based on 50 isolates has a 95% confidence interval of
 roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
 point estimate without context.</li>
 </ul>
 <p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
 Antibiogram) resolves all four limitations. It estimates the probability
 that a regimen will provide adequate empirical coverage for a given
 infection syndrome, weighted by local pathogen incidence, with full
 uncertainty quantification via Bayesian inference.</p>
 <p>The concept was introduced by Hebert <em>et al.</em> (2012), who
 demonstrated that traditional antibiogram susceptibility rates could be
 misleading: ciprofloxacin appeared 84% effective against <em>E.
 coli</em> in the traditional antibiogram, but WISCA revealed only 62%
 coverage for UTI and 37% for abdominal infections, because enterococci
 (intrinsically resistant) and other species contribute substantially to
 these syndromes. Randhawa <em>et al.</em> (2014) showed that
 WISCA-guided regimen selection could improve time-to-adequate-coverage
 on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
 Bayesian framework now used in this package, enabling credible intervals
 and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
 globally across 52 hospitals in 23 countries.</p>
 </div>
 <div class="section level2">
 <h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
 </h2>
 <p>WISCA asks:</p>
 <blockquote>
 <p>“What is the <strong>probability</strong> that this regimen
 <strong>will cover</strong> the pathogen, given the syndrome?”</p>
 </blockquote>
 <p>This means combining two quantities:</p>
 <ul>
 <li>
 <strong>Pathogen incidence</strong> in the syndrome (how often each
 species causes it),</li>
 <li>
 <strong>Susceptibility</strong> of each pathogen to the
 regimen.</li>
 </ul>
 <p>We can write this as:</p>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
 <p>For example, suppose in your hospital:</p>
 <ul>
 <li>
 <em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
 susceptible to a drug.</li>
 <li>
 <em>Klebsiella</em> causes 40% of UTIs, and 70% of
 <em>Klebsiella</em> are susceptible.</li>
 </ul>
 <p>Then:</p>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
 <p>That 82% is a far more clinically meaningful number than the
 species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
 reported separately in a traditional antibiogram, because it directly
 answers the question the clinician actually faces.</p>
 <p>But in real data, both incidence and susceptibility are
 <strong>estimated from finite samples</strong>, so they carry
 uncertainty. A sample of 50 isolates is not a census. WISCA models this
 uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
 distributions.</p>
 </div>
 <div class="section level2">
 <h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
 </h2>
 <div class="section level3">
 <h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
 </h3>
 <p>Let:</p>
 <ul>
 <li>
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
 be the number of pathogens,</li>
 <li>
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
 be a
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
 prior (uniform, non-informative),</li>
 <li>
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
 be the observed isolate counts per species.</li>
 </ul>
 <p>Then the posterior incidence is:</p>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
 <p>To simulate from this, we use:</p>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
 <p>The Dirichlet is the conjugate prior for multinomial data. With the
 non-informative prior
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
 the posterior is dominated by the data once sample sizes are reasonable.
 With small samples, the posterior is appropriately more diffuse,
 reflecting genuine uncertainty, and the resulting credible intervals
 will be wider.</p>
 </div>
 <div class="section level3">
 <h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
 </h3>
 <p>Each pathogen-regimen pair has a prior and observed data:</p>
 <ul>
 <li>Default prior:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
 (Jeffreys prior)</li>
 <li>Intrinsically resistant pairs:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
 forcing near-zero susceptibility regardless of observed data (based on
 EUCAST Expected Resistant Phenotypes)</li>
 <li>Data:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
 susceptible out of
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
 tested</li>
 </ul>
 <p>The
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
 category could also include values SDD (susceptible, dose-dependent) and
 I (intermediate [CLSI], or susceptible, increased exposure
 [EUCAST]).</p>
 <p>Then the posterior is:</p>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
 </div>
 <div class="section level3">
 <h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
 </h3>
 <p>Putting it together:</p>
 <ol style="list-style-type: decimal">
 <li>Simulate pathogen incidence:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
 </li>
 <li>Simulate susceptibility:
 <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
 </li>
 <li>Combine:</li>
 </ol>
 <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
 <p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
 <ul>
 <li>
 <strong>Mean</strong> = expected coverage</li>
 <li>
 <strong>Quantiles</strong> = credible interval (95% by default)</li>
 </ul>
 <p>Because each simulation draws from the full posterior, the resulting
 distribution of coverage estimates naturally captures the joint
 uncertainty in both pathogen incidence and susceptibility. The credible
 interval tells you how confident you can be in the coverage estimate,
 something a traditional antibiogram never provides.</p>
 </div>
 </div>
 <div class="section level2">
 <h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
 </h2>
 <table class="table">
 <thead><tr class="header">
 <th>Goal</th>
 <th>Recommended approach</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>Guide empirical therapy decisions</td>
 <td><strong>WISCA</strong></td>
 </tr>
 <tr class="even">
 <td>Compare regimens for a syndrome</td>
 <td><strong>WISCA</strong></td>
 </tr>
 <tr class="odd">
 <td>Evaluate combination regimens</td>
 <td><strong>WISCA</strong></td>
 </tr>
 <tr class="even">
 <td>Antimicrobial stewardship (A-team)</td>
 <td><strong>WISCA</strong></td>
 </tr>
 <tr class="odd">
 <td>Track resistance trends per species</td>
 <td>Traditional / Combination</td>
 </tr>
 <tr class="even">
 <td>AMR surveillance reporting</td>
 <td>Traditional / Syndromic</td>
 </tr>
 <tr class="odd">
 <td>Understand species-level epidemiology</td>
 <td>Traditional</td>
 </tr>
 </tbody>
 </table>
 <p>In short: if the end goal involves a <em>patient</em> who does not
 yet have a culture result, WISCA is the appropriate tool. If the end
 goal is <em>surveillance</em> of resistance at the species level, the
 traditional antibiogram remains fit for purpose.</p>
 </div>
 <div class="section level2">
 <h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
 </h2>
 <div class="section level3">
 <h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
 </h3>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span></span>
 <span></span>
 <span><span class="co"># Structure of our data</span></span>
 <span><span class="va">data</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
 <span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span>  <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FF5F5F;">  R  </span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;">  R  </span> <span style="color: #949494;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #949494;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
 <span></span>
 <span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
 <span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Keep only 10 most common microorganisms</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span><span class="va">data</span>, n <span class="op">=</span> <span class="fl">10</span>, property <span class="op">=</span> <span class="st">"species"</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span></span></code></pre></div>
 </div>
 <div class="section level3">
 <h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
 </h3>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
 <span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">Amoxicillin/clavulanic acid</th>
 <th align="left">Ciprofloxacin</th>
 <th align="left">Gentamicin</th>
 </tr></thead>
 <tbody><tr class="odd">
 <td align="left">76.8% (74.7-79.1%)</td>
 <td align="left">81.5% (78.9-84.1%)</td>
 <td align="left">82.9% (81-84.8%)</td>
 </tr></tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
 </h3>
 <p>Combination regimens are specified with a <code>+</code> separator.
 WISCA evaluates whether <em>at least one</em> agent in the combination
 covers the pathogen:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
 <span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="24%">
 <col width="38%">
 <col width="36%">
 </colgroup>
 <thead><tr class="header">
 <th align="left">Amoxicillin/clavulanic acid</th>
 <th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
 <th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
 </tr></thead>
 <tbody><tr class="odd">
 <td align="left">76.8% (74.6-78.9%)</td>
 <td align="left">89.6% (88-91.1%)</td>
 <td align="left">93.7% (92.5-94.9%)</td>
 </tr></tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
 </h3>
 <p>Use <code>syndromic_group</code> to produce separate WISCA estimates
 per clinical stratum. You can pass a column name or any expression:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">wisca_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
 <span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
 <span>  syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">wisca_out</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="12%">
 <col width="21%">
 <col width="34%">
 <col width="31%">
 </colgroup>
 <thead><tr class="header">
 <th align="left">Syndromic Group</th>
 <th align="left">Amoxicillin/clavulanic acid</th>
 <th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
 <th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Non-UTI</td>
 <td align="left">72.5% (69.9-75.1%)</td>
 <td align="left">86.9% (84.8-89%)</td>
 <td align="left">91.4% (89.5-93%)</td>
 </tr>
 <tr class="even">
 <td align="left">UTI</td>
 <td align="left">86% (82.5-89%)</td>
 <td align="left">94.8% (92.5-96.6%)</td>
 <td align="left">97.9% (96.3-99%)</td>
 </tr>
 </tbody>
 </table>
 <p>The <code>AMR</code> package is available in 28 languages, which can
 all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
 <span>  antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
 <span>  syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
 <span>  language <span class="op">=</span> <span class="st">"Spanish"</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="12%">
 <col width="21%">
 <col width="34%">
 <col width="31%">
 </colgroup>
 <thead><tr class="header">
 <th align="left">Grupo sindrómico</th>
 <th align="left">Amoxicilina/ácido clavulánico</th>
 <th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
 <th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Non-UCI</td>
 <td align="left">72.6% (69.9-75.3%)</td>
 <td align="left">87% (84.9-89.1%)</td>
 <td align="left">91.4% (89.7-92.9%)</td>
 </tr>
 <tr class="even">
 <td align="left">UCI</td>
 <td align="left">86% (82.7-89%)</td>
 <td align="left">94.8% (92.7-96.4%)</td>
 <td align="left">97.9% (96.5-99%)</td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
 </h3>
 <p>Each row shows the estimated empirical coverage for a regimen, with a
 95% credible interval. When comparing regimens:</p>
 <ul>
 <li>
 <strong>Overlapping credible intervals</strong> mean there is no
 statistically significant difference in coverage. If a narrower-spectrum
 regimen overlaps with a broader one, the narrower-spectrum option can be
 preferred on stewardship grounds.</li>
 <li>
 <strong>Non-overlapping credible intervals</strong> indicate a
 clinically meaningful difference in coverage.</li>
 </ul>
 </div>
 <div class="section level3">
 <h3 id="plotting">Plotting<a class="anchor" aria-label="anchor" href="#plotting"></a>
 </h3>
 <p>WISCA results can be visualised in several ways. All plot functions
 work on the output of <code><a href="../reference/antibiogram.html">wisca()</a></code> (or
 <code>antibiogram(..., wisca = TRUE)</code>).</p>
 <p>Below we use the <code>wisca_out</code> object that was generated
 above.</p>
 <div class="section level4">
 <h4 id="coverage-with-credible-intervals">Coverage with credible intervals<a class="anchor" aria-label="anchor" href="#coverage-with-credible-intervals"></a>
 </h4>
 <p>The extended <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> method from the
 <code>ggplot2()</code> package produces a point-and-interval plot
 showing the coverage estimate and 95% credible interval for each
 regimen, grouped by syndromic stratum. This is the most direct way to
 compare regimens: overlapping intervals suggest clinical
 non-inferiority, non-overlapping intervals indicate a meaningful
 difference.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
 <p><img src="WISCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 <div class="section level4">
 <h4 id="susceptibility-vs--incidence-weight">Susceptibility vs. incidence weight<a class="anchor" aria-label="anchor" href="#susceptibility-vs--incidence-weight"></a>
 </h4>
 <p><code><a href="../reference/antibiogram.html">wisca_plot()</a></code> produces a scatter plot of the Monte Carlo
 simulation draws, showing each pathogen’s susceptibility (x-axis)
 against its incidence weight (y-axis) for each regimen. Each dot
 represents one of 1,000 simulated draws, so the spread reflects
 posterior uncertainty. This plot reveals <em>why</em> a regimen achieves
 its coverage: you can see which pathogens dominate the syndrome (high on
 the y-axis), how susceptible they are (position on the x-axis), and how
 uncertain both estimates are (spread of the cloud). The dashed vertical
 lines denote the point estimates, i.e., the coverage percentages. The
 ribbon behind the dashed lines denote the credible interval, which is
 95% at default.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
 <p><img src="WISCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 <div class="section level4">
 <h4 id="posterior-coverage-distributions">Posterior coverage distributions<a class="anchor" aria-label="anchor" href="#posterior-coverage-distributions"></a>
 </h4>
 <p>Setting <code>wisca_plot_type = "posterior_coverage"</code> shows the
 full posterior distribution of coverage for each regimen as a density
 curve. This is the most complete representation of what the Bayesian
 model produces: each curve shows the relative likelihood of each
 coverage value across all 1,000 simulations. Narrow, tall peaks indicate
 high certainty; wide, flat curves indicate greater uncertainty. Where
 two curves overlap, the regimens cannot be confidently
 distinguished.</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span>, wisca_plot_type <span class="op">=</span> <span class="st">"posterior_coverage"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="WISCA_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
 </div>
 </div>
 </div>
 <div class="section level2">
 <h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
 </h2>
 <ul>
 <li>
 <code>simulations = 1000</code>: number of Monte Carlo draws</li>
 <li>
 <code>conf_interval = 0.95</code>: coverage interval width</li>
 <li>
 <code>combine_SI = TRUE</code>: count “I” and “SDD” as
 susceptible</li>
 </ul>
 </div>
 <div class="section level2">
 <h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
 </h2>
 <ul>
 <li>
 <strong>First isolates only</strong>: always deduplicate using
 <code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
 introduce bias.</li>
 <li>
 <strong>Pathogen selection</strong>: consider filtering with
 <code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
 (e.g. CoNS without clinical context) can distort estimates and may
 artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
 <li>
 <strong>Sample size</strong>: coverage estimates become reliable
 with approximately 100+ isolates. For smaller datasets, consider pooling
 data from multiple sites, but only after verifying that pathogen
 distributions are sufficiently similar (Bielicki <em>et al.</em>,
 2016).</li>
 <li>
 <strong>Culture request bias</strong>: WISCA is only as good as the
 data it is based on. If cultures are selectively requested (e.g. only
 after treatment failure), the dataset will be biased towards resistant
 isolates. A robust culture policy is essential for reliable
 estimates.</li>
 </ul>
 </div>
 <div class="section level2">
 <h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
 </h2>
 <ul>
 <li>It assumes your data are representative of the patient population
 you are treating</li>
 <li>No direct adjustment for patient-level covariates, although these
 can be passed onto the <code>syndromic_group</code> argument for
 stratification</li>
 <li>WISCA does not model resistance trends over time; for that, you
 might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
 basic introduction</a>
 </li>
 </ul>
 </div>
 <div class="section level2">
 <h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
 </h2>
 <p>WISCA enables:</p>
 <ul>
 <li>
 <strong>Empirical regimen comparison</strong>, answering the
 clinician’s actual question</li>
 <li>
 <strong>Syndrome-specific coverage estimation</strong>, stratifiable
 by any clinical variable</li>
 <li>
 <strong>Fully probabilistic interpretation</strong>, with credible
 intervals that honestly communicate uncertainty</li>
 </ul>
 <p>It is available in the <code>AMR</code> package via either:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
 <ol style="list-style-type: decimal">
 <li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
 Demonstration of the weighted-incidence syndromic combination
 antibiogram: an empiric prescribing decision aid. <em>Infect Control
 Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
 </li>
 <li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
 Weighted-incidence syndromic combination antibiograms to guide empiric
 treatment of critical care infections: a retrospective cohort study.
 <em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
 </li>
 <li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
 Selecting appropriate empirical antibiotic regimens for paediatric
 bloodstream infections: application of a Bayesian decision model to
 local and pooled antimicrobial resistance surveillance data. <em>J
 Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
 </li>
 <li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
 prescribing in pediatric bloodstream infections: a potential application
 of weighted-incidence syndromic combination antibiograms (WISCA).
 <em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
 </li>
 </ol>
 </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside>
 </div>
    <footer><div class="pkgdown-footer-left">
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@@ -0,0 +1,443 @@
 # Estimating Empirical Coverage with WISCA
 ## Why WISCA?
 When a clinician starts empirical antimicrobial therapy, the causative
 pathogen is unknown. The question they need answered is not *“what
 proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
 *“what is the probability that this regimen will adequately cover
 whatever pathogen turns out to be causing my patient’s infection?”*
 The traditional cumulative antibiogram, as standardised by CLSI M39,
 cannot answer that question. It presents susceptibility percentages per
 species per antibiotic, but:
 - **It fragments information by organism.** The clinician must mentally
  combine susceptibility rates across multiple species, weighting by how
  often each species causes the syndrome, a calculation nobody does at
  the bedside.
 - **It ignores pathogen incidence.** A species that causes 2% of
  infections is given the same visual weight as one that causes 60%.
 - **It does not evaluate combination regimens.** Much empirical therapy
  consists of two or more agents, but the traditional antibiogram only
  shows monotherapy per organism.
 - **It provides no measure of uncertainty.** A reported “90%
  susceptible” based on 50 isolates has a 95% confidence interval of
  roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
  point estimate without context.
 **WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
 resolves all four limitations. It estimates the probability that a
 regimen will provide adequate empirical coverage for a given infection
 syndrome, weighted by local pathogen incidence, with full uncertainty
 quantification via Bayesian inference.
 The concept was introduced by Hebert *et al.* (2012), who demonstrated
 that traditional antibiogram susceptibility rates could be misleading:
 ciprofloxacin appeared 84% effective against *E. coli* in the
 traditional antibiogram, but WISCA revealed only 62% coverage for UTI
 and 37% for abdominal infections, because enterococci (intrinsically
 resistant) and other species contribute substantially to these
 syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
 selection could improve time-to-adequate-coverage on the ICU by over
 40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
 in this package, enabling credible intervals and multi-centre pooling.
 Cook *et al.* (2022) applied it globally across 52 hospitals in 23
 countries.
 ## The idea
 WISCA asks:
 > “What is the **probability** that this regimen **will cover** the
 > pathogen, given the syndrome?”
 This means combining two quantities:
 - **Pathogen incidence** in the syndrome (how often each species causes
  it),
 - **Susceptibility** of each pathogen to the regimen.
 We can write this as:
 ``` math
 \text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
 ```
 For example, suppose in your hospital:
 - *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
  a drug.
 - *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
  susceptible.
 Then:
 ``` math
 \text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
 ```
 That 82% is a far more clinically meaningful number than the
 species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
 separately in a traditional antibiogram, because it directly answers the
 question the clinician actually faces.
 But in real data, both incidence and susceptibility are **estimated from
 finite samples**, so they carry uncertainty. A sample of 50 isolates is
 not a census. WISCA models this uncertainty **probabilistically**, using
 conjugate Bayesian distributions.
 ## The Bayesian engine
 ### Pathogen incidence
 Let:
 - $`K`$ be the number of pathogens,
 - $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
  prior (uniform, non-informative),
 - $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
  per species.
 Then the posterior incidence is:
 ``` math
 \boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
 ```
 To simulate from this, we use:
 ``` math
 x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
 ```
 The Dirichlet is the conjugate prior for multinomial data. With the
 non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
 posterior is dominated by the data once sample sizes are reasonable.
 With small samples, the posterior is appropriately more diffuse,
 reflecting genuine uncertainty, and the resulting credible intervals
 will be wider.
 ### Susceptibility
 Each pathogen-regimen pair has a prior and observed data:
 - Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
 - Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
  near-zero susceptibility regardless of observed data (based on EUCAST
  Expected Resistant Phenotypes)
 - Data: $`S`$ susceptible out of $`N`$ tested
 The $`S`$ category could also include values SDD (susceptible,
 dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
 exposure \[EUCAST\]).
 Then the posterior is:
 ``` math
 \theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
 ```
 ### Final coverage estimate
 Putting it together:
 1.  Simulate pathogen incidence:
    $`\boldsymbol{p} \sim \text{Dirichlet}`$
 2.  Simulate susceptibility:
    $`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
 3.  Combine:
 ``` math
 \text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
 ```
 Repeat this simulation (e.g., 1000 times) and summarise:
 - **Mean** = expected coverage
 - **Quantiles** = credible interval (95% by default)
 Because each simulation draws from the full posterior, the resulting
 distribution of coverage estimates naturally captures the joint
 uncertainty in both pathogen incidence and susceptibility. The credible
 interval tells you how confident you can be in the coverage estimate,
 something a traditional antibiogram never provides.
 ## When to use WISCA vs. traditional antibiograms
 | Goal                                  | Recommended approach      |
 |---------------------------------------|---------------------------|
 | Guide empirical therapy decisions     | **WISCA**                 |
 | Compare regimens for a syndrome       | **WISCA**                 |
 | Evaluate combination regimens         | **WISCA**                 |
 | Antimicrobial stewardship (A-team)    | **WISCA**                 |
 | Track resistance trends per species   | Traditional / Combination |
 | AMR surveillance reporting            | Traditional / Syndromic   |
 | Understand species-level epidemiology | Traditional               |
 In short: if the end goal involves a *patient* who does not yet have a
 culture result, WISCA is the appropriate tool. If the end goal is
 *surveillance* of resistance at the species level, the traditional
 antibiogram remains fit for purpose.
 ## Practical use in the `AMR` package
 ### Prepare data
 ``` r
 library(AMR)
 data <- example_isolates
 # Structure of our data
 data
 #> # A tibble: 2,000 × 46
 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
 #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
 #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA 
 #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
 #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R  
 #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA 
 #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA 
 #> # ℹ 1,990 more rows
 #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
 #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
 #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
 #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
 #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
 #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
 # Add a synthetic syndrome column for demonstration
 data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
 # Keep only 10 most common microorganisms
 data <- top_n_microorganisms(data, n = 10, property = "species")
 #> ℹ Using column mo as input for `col_mo`.
 ```
 ### Basic WISCA
 ``` r
 wisca(data,
  antimicrobials = c("AMC", "CIP", "GEN")
 )
 ```
 | Amoxicillin/clavulanic acid | Ciprofloxacin      | Gentamicin       |
 |:----------------------------|:-------------------|:-----------------|
 | 76.8% (74.7-79.1%)          | 81.5% (78.9-84.1%) | 82.9% (81-84.8%) |
 ### Use combination regimens
 Combination regimens are specified with a `+` separator. WISCA evaluates
 whether *at least one* agent in the combination covers the pathogen:
 ``` r
 wisca(data,
  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
 )
 ```
 | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
 |:---|:---|:---|
 | 76.8% (74.6-78.9%) | 89.6% (88-91.1%) | 93.7% (92.5-94.9%) |
 ### Stratify by syndrome
 Use `syndromic_group` to produce separate WISCA estimates per clinical
 stratum. You can pass a column name or any expression:
 ``` r
 wisca_out <- wisca(data,
  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
  syndromic_group = "syndrome"
 )
 wisca_out
 ```
 | Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
 |:---|:---|:---|:---|
 | Non-UTI | 72.5% (69.9-75.1%) | 86.9% (84.8-89%) | 91.4% (89.5-93%) |
 | UTI | 86% (82.5-89%) | 94.8% (92.5-96.6%) | 97.9% (96.3-99%) |
 The `AMR` package is available in 28 languages, which can all be used
 for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
 function too:
 ``` r
 wisca(data,
  antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
  syndromic_group = gsub("UTI", "UCI", data$syndrome),
  language = "Spanish"
 )
 ```
 | Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
 |:---|:---|:---|:---|
 | Non-UCI | 72.6% (69.9-75.3%) | 87% (84.9-89.1%) | 91.4% (89.7-92.9%) |
 | UCI | 86% (82.7-89%) | 94.8% (92.7-96.4%) | 97.9% (96.5-99%) |
 ### Interpreting the output
 Each row shows the estimated empirical coverage for a regimen, with a
 95% credible interval. When comparing regimens:
 - **Overlapping credible intervals** mean there is no statistically
  significant difference in coverage. If a narrower-spectrum regimen
  overlaps with a broader one, the narrower-spectrum option can be
  preferred on stewardship grounds.
 - **Non-overlapping credible intervals** indicate a clinically
  meaningful difference in coverage.
 ### Plotting
 WISCA results can be visualised in several ways. All plot functions work
 on the output of
 [`wisca()`](https://amr-for-r.org/reference/antibiogram.md) (or
 `antibiogram(..., wisca = TRUE)`).
 Below we use the `wisca_out` object that was generated above.
 #### Coverage with credible intervals
 The extended
 [`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
 method from the `ggplot2()` package produces a point-and-interval plot
 showing the coverage estimate and 95% credible interval for each
 regimen, grouped by syndromic stratum. This is the most direct way to
 compare regimens: overlapping intervals suggest clinical
 non-inferiority, non-overlapping intervals indicate a meaningful
 difference.
 ``` r
 ggplot2::autoplot(wisca_out)
 ```
 ![](WISCA_files/figure-html/unnamed-chunk-6-1.png)
 #### Susceptibility vs. incidence weight
 [`wisca_plot()`](https://amr-for-r.org/reference/antibiogram.md)
 produces a scatter plot of the Monte Carlo simulation draws, showing
 each pathogen’s susceptibility (x-axis) against its incidence weight
 (y-axis) for each regimen. Each dot represents one of 1,000 simulated
 draws, so the spread reflects posterior uncertainty. This plot reveals
 *why* a regimen achieves its coverage: you can see which pathogens
 dominate the syndrome (high on the y-axis), how susceptible they are
 (position on the x-axis), and how uncertain both estimates are (spread
 of the cloud). The dashed vertical lines denote the point estimates,
 i.e., the coverage percentages. The ribbon behind the dashed lines
 denote the credible interval, which is 95% at default.
 ``` r
 wisca_plot(wisca_out)
 ```
 ![](WISCA_files/figure-html/unnamed-chunk-7-1.png)
 #### Posterior coverage distributions
 Setting `wisca_plot_type = "posterior_coverage"` shows the full
 posterior distribution of coverage for each regimen as a density curve.
 This is the most complete representation of what the Bayesian model
 produces: each curve shows the relative likelihood of each coverage
 value across all 1,000 simulations. Narrow, tall peaks indicate high
 certainty; wide, flat curves indicate greater uncertainty. Where two
 curves overlap, the regimens cannot be confidently distinguished.
 ``` r
 wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
 ```
 ![](WISCA_files/figure-html/unnamed-chunk-8-1.png)
 ## Sensible defaults, which can be customised
 - `simulations = 1000`: number of Monte Carlo draws
 - `conf_interval = 0.95`: coverage interval width
 - `combine_SI = TRUE`: count “I” and “SDD” as susceptible
 ## Practical considerations
 - **First isolates only**: always deduplicate using
  [`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
  before running WISCA. Repeat isolates introduce bias.
 - **Pathogen selection**: consider filtering with
  [`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
  Including rare contaminants (e.g. CoNS without clinical context) can
  distort estimates and may artificially lower coverage (Cook *et al.*,
  2022).
 - **Sample size**: coverage estimates become reliable with approximately
  100+ isolates. For smaller datasets, consider pooling data from
  multiple sites, but only after verifying that pathogen distributions
  are sufficiently similar (Bielicki *et al.*, 2016).
 - **Culture request bias**: WISCA is only as good as the data it is
  based on. If cultures are selectively requested (e.g. only after
  treatment failure), the dataset will be biased towards resistant
  isolates. A robust culture policy is essential for reliable estimates.
 ## Limitations
 - It assumes your data are representative of the patient population you
  are treating
 - No direct adjustment for patient-level covariates, although these can
  be passed onto the `syndromic_group` argument for stratification
 - WISCA does not model resistance trends over time; for that, you might
  want to use `tidymodels`, for which we [wrote a basic
  introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
 ## Summary
 WISCA enables:
 - **Empirical regimen comparison**, answering the clinician’s actual
  question
 - **Syndrome-specific coverage estimation**, stratifiable by any
  clinical variable
 - **Fully probabilistic interpretation**, with credible intervals that
  honestly communicate uncertainty
 It is available in the `AMR` package via either:
 ``` r
 wisca(...)
 antibiogram(..., wisca = TRUE)
 ```
 ## References
 1.  Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
    Demonstration of the weighted-incidence syndromic combination
    antibiogram: an empiric prescribing decision aid. *Infect Control
    Hosp Epidemiol.* 2012;33(4):381-388.
    <https://doi.org/10.1086/664768>
 2.  Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
    Weighted-incidence syndromic combination antibiograms to guide
    empiric treatment of critical care infections: a retrospective
    cohort study. *Crit Care.* 2014;18(3):R112.
    <https://doi.org/10.1186/cc13901>
 3.  Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
    Selecting appropriate empirical antibiotic regimens for paediatric
    bloodstream infections: application of a Bayesian decision model to
    local and pooled antimicrobial resistance surveillance data. *J
    Antimicrob Chemother.* 2016;71(3):794-802.
    <https://doi.org/10.1093/jac/dkv397>
 4.  Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
    prescribing in pediatric bloodstream infections: a potential
    application of weighted-incidence syndromic combination antibiograms
    (WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
    <https://doi.org/10.1080/14787210.2021.1967145>
@@ -0,0 +1,565 @@
 # Download data sets for download / own use
 All reference data (about microorganisms, antimicrobials, SIR
 interpretation, EUCAST rules, etc.) in this `AMR` package are reliable,
 up-to-date and freely available. We continually export our data sets to
 formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS,
 and Stata. We also provide tab-separated text files that are
 machine-readable and suitable for input in any software program, such as
 laboratory information systems.
 > If you are working in Python, be sure to use our [AMR for
 > Python](https://amr-for-r.org/articles/AMR_for_Python.html) package.
 > It allows all relevant AMR data sets to be natively available in
 > Python.
 ## `microorganisms`: Full Microbial Taxonomy
 A data set with 96 982 rows and 28 columns, containing the following
 column names:  
 *mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
 *order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
 *oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
 *lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
 *gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
 This data set is in R available as `microorganisms`, after you load the
 `AMR` package.
 It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/microorganisms.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
  (2.2 MB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
  (23.1 MB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
  (11.4 MB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
  (11 MB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
  (4.6 MB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
  (35.2 MB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
  (96.6 MB)
 **NOTE: The exported files for SPSS and Stata contain only the first 50
 SNOMED codes per record, as their file size would otherwise exceed 100
 MB; the file size limit of GitHub.** Their file structures and
 compression techniques are very inefficient. Advice? Use R instead. It’s
 free and much better in many ways.
 The tab-separated text file and Microsoft Excel workbook both contain
 all SNOMED codes as comma separated values.
 **Example content**
 Included (sub)species per taxonomic kingdom:
 |      Kingdom      | Number of (sub)species |
 |:-----------------:|:----------------------:|
 |                   |           20           |
 | (unknown kingdom) |           8            |
 |     Animalia      |         2 015          |
 |      Archaea      |          150           |
 |     Bacillati     |         24 200         |
 |     Bacteria      |           2            |
 First 6 rows when filtering on genus *Escherichia*:
 | mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia |  |  | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 |  |  |  |  | CS33H | CRYWR |  | 1 | 407310004, 407251000, 407281008, … |
 | B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata |  | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 |  |  |  | CS33J | CS33H | 3SVX6 | 1 |  |
 | B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii |  | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 |  |  |  |  | 3BGTB | CS33H |  | 1 | 419388003 |
 | B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae |  | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 |  |  |  | CS33K | CS33H | 4X4P7 | 1 |  |
 | B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli |  | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 |  |  |  |  | NT3L7 | CS33H |  | 1 | 1095001000112106, 715307006, 737528008, … |
 | B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies |  |  |  | GBIF |  | 776057 |  |  |  |  | 12233256 | NT3L7 |  | 1 |  |
 ------------------------------------------------------------------------
 ## `antimicrobials`: Antibiotic and Antifungal Drugs
 A data set with 505 rows and 14 columns, containing the following column
 names:  
 *ab*, *cid*, *name*, *group*, *atc*, *atc_group1*, *atc_group2*,
 *abbreviations*, *synonyms*, *oral_ddd*, *oral_units*, *iv_ddd*,
 *iv_units*, and *loinc*.
 This data set is in R available as `antimicrobials`, after you load the
 `AMR` package.
 It was last updated on 23 June 2026 12:38:59 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/antimicrobials.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds)
  (44 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt)
  (0.1 MB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx)
  (79 kB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather)
  (0.1 MB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet)
  (94 kB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav)
  (0.4 MB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.dta)
  (10 kB)
 The tab-separated text, Microsoft Excel, SPSS, and Stata files all
 contain the ATC codes, common abbreviations, trade names and LOINC codes
 as comma separated values.
 **Example content**
 | ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … |  |  | 1.0 | g | 101493-5, 11-7, 12-5, … |
 | AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
 | AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g |  |
 | AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
 | AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
 | PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … |  |  | 3.6 | g |  |
 ------------------------------------------------------------------------
 ## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
 A data set with 45 555 rows and 14 columns, containing the following
 column names:  
 *guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
 *ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
 *is_SDD*.
 This data set is in R available as `clinical_breakpoints`, after you
 load the `AMR` package.
 It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/clinical_breakpoints.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds)
  (92 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt)
  (4.2 MB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx)
  (2.7 MB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather)
  (2 MB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
  (0.1 MB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
  (7.5 MB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta)
  (12.6 MB)
 **Example content**
 | guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
 | EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans |  | 1.000 | 4.000 | FALSE | FALSE |
 | EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
 | EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans |  | 0.125 | 0.125 | FALSE | FALSE |
 | EUCAST 2026 | human | human | DISK |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
 | EUCAST 2026 | human | human | MIC |  | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans |  | 4.000 | 4.000 | FALSE | FALSE |
 ------------------------------------------------------------------------
 ## `microorganisms.groups`: Species Groups and Microbiological Complexes
 A data set with 530 rows and 4 columns, containing the following column
 names:  
 *mo_group*, *mo*, *mo_group_name*, and *mo_name*.
 This data set is in R available as `microorganisms.groups`, after you
 load the `AMR` package.
 It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/microorganisms.groups.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.rds)
  (6 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.txt)
  (50 kB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
  (19 kB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
  (19 kB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.parquet)
  (13 kB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
  (64 kB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
  (82 kB)
 **Example content**
 | mo_group | mo | mo_group_name | mo_name |
 |:--:|:--:|:--:|:--:|
 | B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
 | B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
 | B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
 | B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
 | B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
 | B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
 ------------------------------------------------------------------------
 ## `intrinsic_resistant`: Intrinsic Bacterial Resistance
 A data set with 294 079 rows and 2 columns, containing the following
 column names:  
 *mo* and *ab*.
 This data set is in R available as `intrinsic_resistant`, after you load
 the `AMR` package.
 It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/intrinsic_resistant.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.rds)
  (0.1 MB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
  (10.9 MB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
  (3.1 MB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
  (2.5 MB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet)
  (0.3 MB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
  (16 MB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
  (28.6 MB)
 **Example content**
 Example rows when filtering on *Enterobacter cloacae*:
 |    microorganism     |         antibiotic          |
 |:--------------------:|:---------------------------:|
 | Enterobacter cloacae |      Acetylmidecamycin      |
 | Enterobacter cloacae |      Acetylspiramycin       |
 | Enterobacter cloacae |         Amoxicillin         |
 | Enterobacter cloacae | Amoxicillin/clavulanic acid |
 | Enterobacter cloacae |         Ampicillin          |
 | Enterobacter cloacae |    Ampicillin/sulbactam     |
 | Enterobacter cloacae |          Avoparcin          |
 | Enterobacter cloacae |        Azithromycin         |
 | Enterobacter cloacae |      Benzylpenicillin       |
 | Enterobacter cloacae |          Bleomycin          |
 | Enterobacter cloacae |          Cadazolid          |
 | Enterobacter cloacae |         Cefadroxil          |
 | Enterobacter cloacae |          Cefalexin          |
 | Enterobacter cloacae |          Cefalotin          |
 | Enterobacter cloacae |          Cefazolin          |
 | Enterobacter cloacae |          Cefoxitin          |
 | Enterobacter cloacae |       Clarithromycin        |
 | Enterobacter cloacae |         Clindamycin         |
 | Enterobacter cloacae |         Cycloserine         |
 | Enterobacter cloacae |         Dalbavancin         |
 | Enterobacter cloacae |        Dirithromycin        |
 | Enterobacter cloacae |        Erythromycin         |
 | Enterobacter cloacae |       Flurithromycin        |
 | Enterobacter cloacae |        Fusidic acid         |
 | Enterobacter cloacae |        Gamithromycin        |
 | Enterobacter cloacae |          Josamycin          |
 | Enterobacter cloacae |         Kitasamycin         |
 | Enterobacter cloacae |         Lincomycin          |
 | Enterobacter cloacae |          Linezolid          |
 | Enterobacter cloacae |         Meleumycin          |
 | Enterobacter cloacae |         Midecamycin         |
 | Enterobacter cloacae |         Miocamycin          |
 | Enterobacter cloacae |        Nafithromycin        |
 | Enterobacter cloacae |        Norvancomycin        |
 | Enterobacter cloacae |        Oleandomycin         |
 | Enterobacter cloacae |         Oritavancin         |
 | Enterobacter cloacae |        Ostreogrycin         |
 | Enterobacter cloacae |         Pirlimycin          |
 | Enterobacter cloacae |          Primycin           |
 | Enterobacter cloacae |        Pristinamycin        |
 | Enterobacter cloacae |  Quinupristin/dalfopristin  |
 | Enterobacter cloacae |         Ramoplanin          |
 | Enterobacter cloacae |         Rifampicin          |
 | Enterobacter cloacae |         Rokitamycin         |
 | Enterobacter cloacae |        Roxithromycin        |
 | Enterobacter cloacae |        Solithromycin        |
 | Enterobacter cloacae |         Spiramycin          |
 | Enterobacter cloacae |          Tedizolid          |
 | Enterobacter cloacae |         Teicoplanin         |
 | Enterobacter cloacae |         Telavancin          |
 | Enterobacter cloacae |        Telithromycin        |
 | Enterobacter cloacae |        Thiacetazone         |
 | Enterobacter cloacae |        Tildipirosin         |
 | Enterobacter cloacae |         Tilmicosin          |
 | Enterobacter cloacae |       Troleandomycin        |
 | Enterobacter cloacae |        Tulathromycin        |
 | Enterobacter cloacae |           Tylosin           |
 | Enterobacter cloacae |         Tylvalosin          |
 | Enterobacter cloacae |         Vancomycin          |
 | Enterobacter cloacae |       Virginiamycine        |
 | Enterobacter cloacae |         Zorbamycin          |
 ------------------------------------------------------------------------
 ## `dosage`: Dosage Guidelines from EUCAST
 A data set with 759 rows and 9 columns, containing the following column
 names:  
 *ab*, *name*, *type*, *dose*, *dose_times*, *administration*, *notes*,
 *original_txt*, and *eucast_version*.
 This data set is in R available as `dosage`, after you load the `AMR`
 package.
 It was last updated on 20 April 2025 10:55:31 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/dosage.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.rds)
  (4 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.txt)
  (66 kB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.xlsx)
  (37 kB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.feather)
  (28 kB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.parquet)
  (9 kB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.sav)
  (97 kB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.dta)
  (0.2 MB)
 **Example content**
 | ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv |  | 25-30 mg/kg x 1 iv | 15 |
 | AMX | Amoxicillin | high_dosage | 2 g | 6 | iv |  | 2 g x 6 iv | 15 |
 | AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv |  | 1 g x 3-4 iv | 15 |
 | AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral |  | 0.75-1 g x 3 oral | 15 |
 | AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
 | AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral |  | 0.5 g x 3 oral | 15 |
 ------------------------------------------------------------------------
 ## `example_isolates`: Example Data for Practice
 A data set with 2 000 rows and 46 columns, containing the following
 column names:  
 *date*, *patient*, *age*, *gender*, *ward*, *mo*, *PEN*, *OXA*, *FLC*,
 *AMX*, *AMC*, *AMP*, *TZP*, *CZO*, *FEP*, *CXM*, *FOX*, *CTX*, *CAZ*,
 *CRO*, *GEN*, *TOB*, *AMK*, *KAN*, *TMP*, *SXT*, *NIT*, *FOS*, *LNZ*,
 *CIP*, *MFX*, *VAN*, *TEC*, *TCY*, *TGC*, *DOX*, *ERY*, *CLI*, *AZM*,
 *IPM*, *MEM*, *MTR*, *CHL*, *COL*, *MUP*, and *RIF*.
 This data set is in R available as `example_isolates`, after you load
 the `AMR` package.
 It was last updated on 24 June 2026 16:36:47 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/example_isolates.html).
 **Example content**
 | date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
 | 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R |  |  |  | I |  |  |  |  | I |  |  |  |  |  |  |  |  | R | R |  |  | R |  |  | R | R | R |  |  | R | R | R |  |  |  |  |  |  | R |
 | 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
 | 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | S | S |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
 | 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R |  | R |  |  |  |  | R |  |  |
 | 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R |  | R |  |  |  |  |  |  | R |  |  | R |  |  |  |  |  | R |  |  |  |  |  |  | S |  | S | S | S | R | R | R |  |  |  |  | R |  |  |
 ------------------------------------------------------------------------
 ## `example_isolates_unclean`: Example Data for Practice
 A data set with 3 000 rows and 8 columns, containing the following
 column names:  
 *patient_id*, *hospital*, *date*, *bacteria*, *AMX*, *AMC*, *CIP*, and
 *GEN*.
 This data set is in R available as `example_isolates_unclean`, after you
 load the `AMR` package.
 It was last updated on 27 August 2022 18:49:37 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/example_isolates_unclean.html).
 **Example content**
 | patient_id | hospital |    date    |   bacteria    | AMX | AMC | CIP | GEN |
 |:----------:|:--------:|:----------:|:-------------:|:---:|:---:|:---:|:---:|
 |     J3     |    A     | 2012-11-21 |    E. coli    |  R  |  I  |  S  |  S  |
 |     R7     |    A     | 2018-04-03 | K. pneumoniae |  R  |  I  |  S  |  S  |
 |     P3     |    A     | 2014-09-19 |    E. coli    |  R  |  S  |  S  |  S  |
 |    P10     |    A     | 2015-12-10 |    E. coli    |  S  |  I  |  S  |  S  |
 |     B7     |    A     | 2015-03-02 |    E. coli    |  S  |  S  |  S  |  S  |
 |     W3     |    A     | 2018-03-31 |   S. aureus   |  R  |  S  |  R  |  S  |
 ------------------------------------------------------------------------
 ## `microorganisms.codes`: Common Laboratory Codes
 A data set with 6 029 rows and 2 columns, containing the following
 column names:  
 *code* and *mo*.
 This data set is in R available as `microorganisms.codes`, after you
 load the `AMR` package.
 It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
 the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/microorganisms.codes.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
  (27 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
  (0.1 MB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.xlsx)
  (98 kB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.feather)
  (0.1 MB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
  (68 kB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
  (0.2 MB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.dta)
  (0.2 MB)
 **Example content**
 | code |      mo      |
 |:----:|:------------:|
 | 1011 |   B_GRAMP    |
 | 1012 |   B_GRAMP    |
 | 1013 |   B_GRAMN    |
 | 1014 |   B_GRAMN    |
 | 1015 |   F_YEAST    |
 | 103  | B_ESCHR_COLI |
 ------------------------------------------------------------------------
 ## `antivirals`: Antiviral Drugs
 A data set with 120 rows and 11 columns, containing the following column
 names:  
 *av*, *name*, *atc*, *cid*, *atc_group*, *synonyms*, *oral_ddd*,
 *oral_units*, *iv_ddd*, *iv_units*, and *loinc*.
 This data set is in R available as `antivirals`, after you load the
 `AMR` package.
 It was last updated on 20 October 2023 12:51:48 UTC. Find more info
 about the contents, (scientific) source, and structure of this [data set
 here](https://amr-for-r.org/reference/antimicrobials.html).
 **Direct download links:**
 - Download as [original R Data Structure (RDS)
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.rds)
  (6 kB)  
 - Download as [tab-separated text
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.txt)
  (17 kB)  
 - Download as [Microsoft Excel
  workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.xlsx)
  (16 kB)  
 - Download as [Apache Feather
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.feather)
  (16 kB)  
 - Download as [Apache Parquet
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.parquet)
  (13 kB)  
 - Download as [IBM SPSS Statistics data
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.sav)
  (32 kB)  
 - Download as [Stata DTA
  file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.dta)
  (78 kB)
 The tab-separated text, Microsoft Excel, SPSS, and Stata files all
 contain the trade names and LOINC codes as comma separated values.
 **Example content**
 | av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g |  |  | 29113-8, 30273-7, 30287-7, … |
 | ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g |  |
 | ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg |  |  |  |
 | AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g |  |  |  |
 | AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g |  |  | 29114-6, 30296-8, 30297-6, … |
 | ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g |  |  |  |
@@ -0,0 +1,89 @@
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 # Articles
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  use](https://amr-for-r.org/articles/datasets.md):
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@@ -0,0 +1,215 @@
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      <ul class="list-unstyled"><li>
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          <p><strong>Erwin E. A. Hassing</strong>. Author, contributor.
          </p>
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          <p><strong>Aislinn Cook</strong>. Contributor. <a href="https://orcid.org/0000-0002-9189-7815" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
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          <p><strong>Andrew P. Norgan</strong>. Contributor. <a href="https://orcid.org/0000-0002-2955-2066" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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          <p><strong>Bart C. Meijer</strong>. Contributor.
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          <p><strong>Christian F. Luz</strong>. Contributor. <a href="https://orcid.org/0000-0001-5809-5995" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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          <p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
          </p>
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          <p><strong>Gwen Knight</strong>. Contributor. <a href="https://orcid.org/0000-0002-7263-9896" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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          <p><strong>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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          <p><strong>Jason Stull</strong>. Contributor. <a href="https://orcid.org/0000-0002-9028-8153" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
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          <p><strong>Javier Sanchez</strong>. Contributor. <a href="https://orcid.org/0000-0003-2605-8094" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
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          <p><strong>Jonas Salm</strong>. Contributor.
          </p>
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          <p><strong>Judith M. Fonville</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Matthew Saab</strong>. Contributor. <a href="https://orcid.org/0009-0008-6626-7919" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Rogier P. Schade</strong>. Contributor. <a href="https://orcid.org/0000-0002-9487-4467" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
      </ul></div>
    <div class="section level2">
      <h2 id="citation">Citation</h2>
      <p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
      <p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
 “AMR: An R Package for Working with Antimicrobial Resistance Data.”
 <em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
 <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
 </p>
      <pre>@Article{,
  title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
  author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
  journal = {Journal of Statistical Software},
  year = {2022},
  volume = {104},
  number = {3},
  pages = {1--31},
  doi = {10.18637/jss.v104.i03},
 }</pre>
    </div>
  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
    </nav></aside></div>
    <footer><div class="pkgdown-footer-left">
  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
 </div>
 <div class="pkgdown-footer-right">
  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
 </div>
    </footer></div>
  </body></html>
@@ -0,0 +1,106 @@
 # Authors and Citation
 ## Authors
 - **Matthijs S. Berends**. Author, maintainer.
  [](https://orcid.org/0000-0001-7620-1800)
 - **Dennis Souverein**. Author, contributor.
  [](https://orcid.org/0000-0003-0455-0336)
 - **Erwin E. A. Hassing**. Author, contributor.
 - **Aislinn Cook**. Contributor.
  [](https://orcid.org/0000-0002-9189-7815)
 - **Andrew P. Norgan**. Contributor.
  [](https://orcid.org/0000-0002-2955-2066)
 - **Anita Williams**. Contributor.
  [](https://orcid.org/0000-0002-5295-8451)
 - **Annick Lenglet**. Contributor.
  [](https://orcid.org/0000-0003-2013-8405)
 - **Anthony Underwood**. Contributor.
  [](https://orcid.org/0000-0002-8547-4277)
 - **Anton Mymrikov**. Contributor.
 - **Bart C. Meijer**. Contributor.
 - **Christian F. Luz**. Contributor.
  [](https://orcid.org/0000-0001-5809-5995)
 - **Dmytro Mykhailenko**. Contributor.
 - **Eric H. L. C. M. Hazenberg**. Contributor.
 - **Gwen Knight**. Contributor.
  [](https://orcid.org/0000-0002-7263-9896)
 - **Jane Hawkey**. Contributor.
  [](https://orcid.org/0000-0001-9661-5293)
 - **Jason Stull**. Contributor.
  [](https://orcid.org/0000-0002-9028-8153)
 - **Javier Sanchez**. Contributor.
  [](https://orcid.org/0000-0003-2605-8094)
 - **Jonas Salm**. Contributor.
 - **Judith M. Fonville**. Contributor.
 - **Kathryn Holt**. Contributor.
  [](https://orcid.org/0000-0003-3949-2471)
 - **Larisse Bolton**. Contributor.
  [](https://orcid.org/0000-0001-7879-2173)
 - **Matthew Saab**. Contributor.
  [](https://orcid.org/0009-0008-6626-7919)
 - **Natacha Couto**. Contributor.
  [](https://orcid.org/0000-0002-9152-5464)
 - **Peter Dutey-Magni**. Contributor.
  [](https://orcid.org/0000-0002-8942-9836)
 - **Rogier P. Schade**. Contributor.
  [](https://orcid.org/0000-0002-9487-4467)
 - **Sofia Ny**. Contributor. [](https://orcid.org/0000-0002-2017-1363)
 - **Alex W. Friedrich**. Thesis advisor.
  [](https://orcid.org/0000-0003-4881-038X)
 - **Bhanu N. M. Sinha**. Thesis advisor.
  [](https://orcid.org/0000-0003-1634-0010)
 - **Casper J. Albers**. Thesis advisor.
  [](https://orcid.org/0000-0002-9213-6743)
 - **Corinna Glasner**. Thesis advisor.
  [](https://orcid.org/0000-0003-1241-1328)
 ## Citation
 Source:
 [`inst/CITATION`](https://github.com/msberends/AMR/blob/main/inst/CITATION)
 Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C
 (2022). “AMR: An R Package for Working with Antimicrobial Resistance
 Data.” *Journal of Statistical Software*, **104**(3), 1–31.
 [doi:10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03).
    @Article{,
      title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
      author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
      journal = {Journal of Statistical Software},
      year = {2022},
      volume = {104},
      number = {3},
      pages = {1--31},
      doi = {10.18637/jss.v104.i03},
    }
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