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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 +1,320 @@
+<!DOCTYPE html>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>License</h1>
+
+    </div>
+
+<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
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+  - 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
+
+
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+NO WARRANTY
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+
+END OF TERMS AND CONDITIONS
+</pre>
+
+  </main></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 General Public License version 2.0 (GPL-2)</a>.<br>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.</p>
+</div>
+
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+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -1,226 +0,0 @@
-
-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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+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <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 Index</a>.</p>
+<p>This Python package is a wrapper round 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="install">Install<a class="anchor" aria-label="anchor" href="#install"></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="cb1"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" 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="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
+<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
+<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
+<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
+<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
+<span id="cb2-6"><a href="#cb2-6" 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="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" 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="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
+<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a></span>
+<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
+<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
+<span id="cb4-6"><a href="#cb4-6" 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="cb4-7"><a href="#cb4-7" 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="cb4-8"><a href="#cb4-8" tabindex="-1"></a>}</span>
+<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
+<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a></span>
+<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
+<span id="cb4-12"><a href="#cb4-12" 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="cb4-13"><a href="#cb4-13" 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="cb4-14"><a href="#cb4-14" tabindex="-1"></a></span>
+<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
+<span id="cb4-16"><a href="#cb4-16" 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="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> AMR</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
+<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
+<span id="cb5-6"><a href="#cb5-6" 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="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" 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="cb7-2"><a href="#cb7-2" 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="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" 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="cb8-2"><a href="#cb8-2" 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="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" 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="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" 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>
+  </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 General Public License version 2.0 (GPL-2)</a>.<br>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.</p>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
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+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
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+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
+    <li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul>
+</li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>AMR with tidymodels</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_with_tidymodels.Rmd" class="external-link"><code>vignettes/AMR_with_tidymodels.Rmd</code></a></small>
+      <div class="d-none name"><code>AMR_with_tidymodels.Rmd</code></div>
+    </div>
+
+    
+    
+<blockquote>
+<p>This page was entirely written by our <a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant" class="external-link">AMR for R
+Assistant</a>, a ChatGPT manually-trained model able to answer any
+question about the AMR package.</p>
+</blockquote>
+<p>Antimicrobial resistance (AMR) is a global health crisis, and
+understanding resistance patterns is crucial for managing effective
+treatments. The <code>AMR</code> R package provides robust tools for
+analysing AMR data, including convenient antibiotic selector functions
+like <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>. In
+this post, we will explore how to use the <code>tidymodels</code>
+framework to predict resistance patterns in the
+<code>example_isolates</code> dataset.</p>
+<p>By leveraging the power of <code>tidymodels</code> and the
+<code>AMR</code> 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.</p>
+<div class="section level3">
+<h3 id="objective">
+<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective"></a>
+</h3>
+<p>Our goal is to build a predictive model using the
+<code>tidymodels</code> framework to determine the Gramstain of the
+microorganism based on microbial data. We will:</p>
+<ol style="list-style-type: decimal">
+<li>Preprocess data using the selector functions
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>.</li>
+<li>Define a logistic regression model for prediction.</li>
+<li>Use a structured <code>tidymodels</code> workflow to preprocess,
+train, and evaluate the model.</li>
+</ol>
+</div>
+<div class="section level3">
+<h3 id="data-preparation">
+<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation"></a>
+</h3>
+<p>We begin by loading the required libraries and preparing the
+<code>example_isolates</code> dataset from the <code>AMR</code>
+package.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Load required libraries</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/AMR/">AMR</a></span><span class="op">)</span>          <span class="co"># For AMR data analysis</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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span>   <span class="co"># For machine learning workflows, and data manipulation (dplyr, tidyr, ...)</span></span>
+<span><span class="co">#&gt; ── <span style="font-weight: bold;">Attaching packages</span> ────────────────────────────────────── tidymodels 1.3.0 ──</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">broom       </span> 1.0.7     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">recipes     </span> 1.1.1</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">dials       </span> 1.4.0     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">rsample     </span> 1.2.1</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">dplyr       </span> 1.1.4     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">tibble      </span> 3.2.1</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">ggplot2     </span> 3.5.1     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">tidyr       </span> 1.3.1</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">infer       </span> 1.0.7     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">tune        </span> 1.3.0</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">modeldata   </span> 1.4.0     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">workflows   </span> 1.2.0</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">parsnip     </span> 1.3.1     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">workflowsets</span> 1.1.0</span></span>
+<span><span class="co">#&gt; <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">purrr       </span> 1.0.4     <span style="color: #00BB00;">✔</span> <span style="color: #0000BB;">yardstick   </span> 1.3.2</span></span>
+<span><span class="co">#&gt; ── <span style="font-weight: bold;">Conflicts</span> ───────────────────────────────────────── tidymodels_conflicts() ──</span></span>
+<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">purrr</span>::<span style="color: #00BB00;">discard()</span> masks <span style="color: #0000BB;">scales</span>::discard()</span></span>
+<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">filter()</span>  masks <span style="color: #0000BB;">stats</span>::filter()</span></span>
+<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">lag()</span>     masks <span style="color: #0000BB;">stats</span>::lag()</span></span>
+<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">recipes</span>::<span style="color: #00BB00;">step()</span>  masks <span style="color: #0000BB;">stats</span>::step()</span></span>
+<span></span>
+<span><span class="co"># Select relevant columns for prediction</span></span>
+<span><span class="va">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="co"># select AB results dynamically</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">mo</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</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"># replace NAs with NI (not-interpretable)</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><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>                 <span class="op">~</span><span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>          <span class="co"># make factors of SIR columns</span></span>
+<span>          <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>                 <span class="va">as.integer</span><span class="op">)</span>,</span>
+<span>          <span class="co"># get Gramstain of microorganisms</span></span>
+<span>          mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</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"># drop NAs - the ones without a Gramstain (fungi, etc.)</span></span>
+<span>  <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>
+dynamically select columns for antimicrobials in these classes.</li>
+<li>
+<code>drop_na()</code> ensures the model receives complete cases for
+training.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="defining-the-workflow">
+<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow"></a>
+</h3>
+<p>We now define the <code>tidymodels</code> workflow, which consists of
+three steps: preprocessing, model specification, and fitting.</p>
+<div class="section level4">
+<h4 id="preprocessing-with-a-recipe">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe"></a>
+</h4>
+<p>We create a recipe to preprocess the data for modelling.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Define the recipe for data preprocessing</span></span>
+<span><span class="va">resistance_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">mo</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">data</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">step_corr</span><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="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, threshold <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
+<span><span class="va">resistance_recipe</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; outcome:    1</span></span>
+<span><span class="co">#&gt; predictor: 20</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">c(aminoglycosides(), betalactams())</span></span></span></code></pre></div>
+<p>For a recipe that includes at least one preprocessing operation, like
+we have with <code>step_corr()</code>, the necessary parameters can be
+estimated from a training set using <code>prep()</code>:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">prep</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; outcome:    1</span></span>
+<span><span class="co">#&gt; predictor: 20</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Training information</span></span>
+<span><span class="co">#&gt; Training data contained 1968 data points and no incomplete rows.</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">AMX</span> <span style="color: #0000BB;">CTX</span> | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>recipe(mo ~ ., data = data)</code> will take the
+<code>mo</code> column as outcome and all other columns as
+predictors.</li>
+<li>
+<code>step_corr()</code> removes predictors (i.e., antibiotic
+columns) that have a higher correlation than 90%.</li>
+</ul>
+<p>Notice how the recipe contains just the antibiotic selector functions
+- no need to define the columns specifically. In the preparation
+(retrieved with <code>prep()</code>) we can see that the columns or
+variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
+existing, other variables.</p>
+</div>
+<div class="section level4">
+<h4 id="specifying-the-model">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model"></a>
+</h4>
+<p>We define a logistic regression model since resistance prediction is
+a binary classification task.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Specify a logistic regression model</span></span>
+<span><span class="va">logistic_model</span> <span class="op">&lt;-</span> <span class="fu">logistic_reg</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="fu">set_engine</span><span class="op">(</span><span class="st">"glm"</span><span class="op">)</span> <span class="co"># Use the Generalized Linear Model engine</span></span>
+<span><span class="va">logistic_model</span></span>
+<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>logistic_reg()</code> sets up a logistic regression
+model.</li>
+<li>
+<code>set_engine("glm")</code> specifies the use of R’s built-in GLM
+engine.</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="building-the-workflow">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow"></a>
+</h4>
+<p>We bundle the recipe and model together into a <code>workflow</code>,
+which organizes the entire modeling process.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Combine the recipe and model into a workflow</span></span>
+<span><span class="va">resistance_workflow</span> <span class="op">&lt;-</span> <span class="fu">workflow</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="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe</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"># Add the preprocessing recipe</span></span>
+<span>  <span class="fu">add_model</span><span class="op">(</span><span class="va">logistic_model</span><span class="op">)</span> <span class="co"># Add the logistic regression model</span></span>
+<span><span class="va">resistance_workflow</span></span>
+<span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; 1 Recipe Step</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; • step_corr()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
+</div>
+</div>
+<div class="section level3">
+<h3 id="training-and-evaluating-the-model">
+<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model"></a>
+</h3>
+<p>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.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Split data into training and testing sets</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span> <span class="co"># For reproducibility</span></span>
+<span><span class="va">data_split</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span> <span class="co"># 80% training, 20% testing</span></span>
+<span><span class="va">training_data</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span> <span class="co"># Training set</span></span>
+<span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span>   <span class="co"># Testing set</span></span>
+<span></span>
+<span><span class="co"># Fit the workflow to the training data</span></span>
+<span><span class="va">fitted_workflow</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">fit</span><span class="op">(</span><span class="va">training_data</span><span class="op">)</span> <span class="co"># Train the model</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>initial_split()</code> splits the data into training and
+testing sets.</li>
+<li>
+<code>fit()</code> trains the workflow on the training set.</li>
+</ul>
+<p>Notice how in <code>fit()</code>, the antibiotic selector functions
+are internally called again. For training, these functions are called
+since they are stored in the recipe.</p>
+<p>Next, we evaluate the model on the testing data.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Make predictions on the testing set</span></span>
+<span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</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://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span>                <span class="co"># Generate predictions</span></span>
+<span><span class="va">probabilities</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</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://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</span><span class="op">)</span> <span class="co"># Generate probabilities</span></span>
+<span></span>
+<span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">predictions</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/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">probabilities</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/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span> <span class="co"># Combine with true labels</span></span>
+<span></span>
+<span><span class="va">predictions</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 394 × 24</span></span></span>
+<span><span class="co">#&gt;    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB</span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;fct&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;fct&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> Gram-positive              3.17<span style="color: #949494;">e</span><span style="color: #BB0000;">- 8</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     5     1</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> Gram-negative              9.99<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              1.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 3</span> Gram-n…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> Gram-negative              9.46<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              5.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 2</span> Gram-n…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     1     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Gram-negative              1   <span style="color: #949494;">e</span>+ 0              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span> Gram-n…     1     1</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> Gram-positive              6.05<span style="color: #949494;">e</span><span style="color: #BB0000;">-11</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 384 more rows</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 18 more variables: AMK &lt;int&gt;, KAN &lt;int&gt;, PEN &lt;int&gt;, OXA &lt;int&gt;, FLC &lt;int&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   AMX &lt;int&gt;, AMC &lt;int&gt;, AMP &lt;int&gt;, TZP &lt;int&gt;, CZO &lt;int&gt;, FEP &lt;int&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;int&gt;, FOX &lt;int&gt;, CTX &lt;int&gt;, CAZ &lt;int&gt;, CRO &lt;int&gt;, IPM &lt;int&gt;, MEM &lt;int&gt;</span></span></span>
+<span></span>
+<span><span class="co"># Evaluate model performance</span></span>
+<span><span class="va">metrics</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">mo</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span> <span class="co"># Calculate performance metrics</span></span>
+<span></span>
+<span><span class="va">metrics</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 3</span></span></span>
+<span><span class="co">#&gt;   .metric  .estimator .estimate</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></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.995</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.989</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> generates predictions on the testing
+set.</li>
+<li>
+<code>metrics()</code> computes evaluation metrics like accuracy and
+kappa.</li>
+</ul>
+<p>It appears we can predict the Gram based on AMR results with a 99.5%
+accuracy based on AMR results of aminoglycosides and beta-lactam
+antibiotics. The ROC curve looks like this:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</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://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" width="720"></p>
+</div>
+<div class="section level3">
+<h3 id="conclusion">
+<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
+</h3>
+<p>In this post, we demonstrated how to build a machine learning
+pipeline with the <code>tidymodels</code> framework and the
+<code>AMR</code> package. By combining selector functions like
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
+<code>tidymodels</code>, we efficiently prepared data, trained a model,
+and evaluated its performance.</p>
+<p>This workflow is extensible to other antibiotic classes and
+resistance patterns, empowering users to analyse AMR data systematically
+and reproducibly.</p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
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+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
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+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>How to 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_intrinsic_resistance/" class="external-link">on
+their website</a>:</p>
+<blockquote>
+<p><em>EUCAST expert rules are a tabulated collection of expert
+knowledge on intrinsic resistances, exceptional resistance phenotypes
+and interpretive rules that may be applied to antimicrobial
+susceptibility testing in order to 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 intrinsic resistance and unusual phenotypes (v3.1, 2016).</p>
+<p>Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function we use for this
+purpose can also apply additional rules, like forcing
+<help title="ATC: J01CA01">ampicillin</help> = R in isolates when
+<help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</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 impossible 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
+ampicillin 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
+(namely, <em>Klebsiella</em>). EUCAST expert rules solve this, that can
+be applied using <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>:</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"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</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"</span>,</span>
+<span>    <span class="st">"Escherichia"</span></span>
+<span>  <span class="op">)</span>,</span>
+<span>  ampicillin <span class="op">=</span> <span class="st">"S"</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">oops</span></span>
+<span><span class="co">#&gt;            mo ampicillin</span></span>
+<span><span class="co">#&gt; 1  Klebsiella          S</span></span>
+<span><span class="co">#&gt; 2 Escherichia          S</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/eucast_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><span class="op">)</span></span>
+<span><span class="co">#&gt;            mo ampicillin</span></span>
+<span><span class="co">#&gt; 1  Klebsiella          S</span></span>
+<span><span class="co">#&gt; 2 Escherichia          S</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"</span>, <span class="st">"Escherichia"</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"</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>, is basically a form of imputation, and is part of the
+<code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function as well:</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"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</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>  stringsAsFactors <span class="op">=</span> <span class="cn">FALSE</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/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</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">S</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">S</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">S</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">S</td>
+<td align="center">S</td>
+</tr>
+</tbody>
+</table>
+</div>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>How to determine multi-drug resistance (MDR)</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/MDR.Rmd" class="external-link"><code>vignettes/MDR.Rmd</code></a></small>
+      <div class="d-none name"><code>MDR.Rmd</code></div>
+    </div>
+
+    
+    
+<p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which
+micro-organisms are multi-drug resistant organisms (MDRO).</p>
+<div class="section level3">
+<h3 id="type-of-input">Type of input<a class="anchor" aria-label="anchor" href="#type-of-input"></a>
+</h3>
+<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a
+regular <code>data.frame</code>. It tries to automatically determine the
+right columns for info about your isolates, such as the name of the
+species and all columns with results of antimicrobial agents. See the
+help page for more info about how to set the right settings for your
+data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
+<p>For WHONET data (and most other data), all settings are automatically
+set correctly.</p>
+</div>
+<div class="section level3">
+<h3 id="guidelines">Guidelines<a class="anchor" aria-label="anchor" href="#guidelines"></a>
+</h3>
+<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can
+select a guideline with the <code>guideline</code> parameter. Currently
+supported guidelines are (case-insensitive):</p>
+<ul>
+<li>
+<p><code>guideline = "CMI2012"</code> (default)</p>
+<p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant,
+extensively drug-resistant and pandrug-resistant bacteria: an
+international expert proposal for interim standard definitions for
+acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
+</li>
+<li>
+<p><code>guideline = "EUCAST3.2"</code> (or simply
+<code>guideline = "EUCAST"</code>)</p>
+<p>The European international guideline - EUCAST Expert Rules Version
+3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
+</li>
+<li>
+<p><code>guideline = "EUCAST3.1"</code></p>
+<p>The European international guideline - EUCAST Expert Rules Version
+3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
+</li>
+<li>
+<p><code>guideline = "TB"</code></p>
+<p>The international guideline for multi-drug resistant tuberculosis -
+World Health Organization “Companion handbook to the WHO guidelines for
+the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
+</li>
+<li>
+<p><code>guideline = "MRGN"</code></p>
+<p>The German national guideline - Mueller <em>et al.</em> (2015)
+Antimicrobial Resistance and Infection Control 4:7. DOI:
+10.1186/s13756-015-0047-6</p>
+</li>
+<li>
+<p><code>guideline = "BRMO"</code></p>
+<p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en
+Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen)
+(ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
+</li>
+</ul>
+<p>Please suggest your own (country-specific) guidelines by letting us
+know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
+<div class="section level4">
+<h4 id="custom-guidelines">Custom Guidelines<a class="anchor" aria-label="anchor" href="#custom-guidelines"></a>
+</h4>
+<p>You can also use your own custom guideline. Custom guidelines can be
+set with the <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function. This is of
+great importance if you have custom rules to determine MDROs in your
+hospital, e.g., rules that are dependent on ward, state of contact
+isolation or other variables in your data.</p>
+<p>If you are familiar with <code><a href="https://dplyr.tidyverse.org/reference/case_when.html" class="external-link">case_when()</a></code> of the
+<code>dplyr</code> package, you will recognise the input method to set
+your own rules. Rules must be set using what R considers to be the
+‘formula notation’:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">custom</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">custom_mdro_guideline</a></span><span class="op">(</span></span>
+<span>  <span class="va">CIP</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type A"</span>,</span>
+<span>  <span class="va">ERY</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type B"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p>If a row/an isolate matches the first rule, the value after the first
+<code>~</code> (in this case <em>‘Elderly Type A’</em>) will be set as
+MDRO value. Otherwise, the second rule will be tried and so on. The
+maximum number of rules is unlimited.</p>
+<p>You can print the rules set in the console for an overview. Colours
+will help reading it if your console supports colours.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">custom</span></span>
+<span><span class="co">#&gt; A set of custom MDRO rules:</span></span>
+<span><span class="co">#&gt;   1. <span style="font-weight: bold;">If </span><span style="color: #0000BB;">CIP</span><span style="color: #080808;"> is </span><span style="color: #080808; background-color: #FF5F5F;"> R </span><span style="color: #080808; font-weight: bold;"> and </span><span style="color: #0000BB;">age</span><span style="color: #080808;"> is higher than </span><span style="color: #0000BB;">60</span><span style="font-weight: bold;"> then: </span><span style="color: #BB0000;">Elderly Type A</span></span></span>
+<span><span class="co">#&gt;   2. <span style="font-weight: bold;">If </span><span style="color: #0000BB;">ERY</span><span style="color: #080808;"> is </span><span style="color: #080808; background-color: #FF5F5F;"> R </span><span style="color: #080808; font-weight: bold;"> and </span><span style="color: #0000BB;">age</span><span style="color: #080808;"> is higher than </span><span style="color: #0000BB;">60</span><span style="font-weight: bold;"> then: </span><span style="color: #BB0000;">Elderly Type B</span></span></span>
+<span><span class="co">#&gt;   3. <span style="font-weight: bold;">Otherwise: </span><span style="color: #BB0000;">Negative</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Unmatched rows will return <span style="color: #BB0000;">NA</span>.</span></span>
+<span><span class="co">#&gt; Results will be of class 'factor', with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></span></code></pre></div>
+<p>The outcome of the function can be used for the
+<code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">example_isolates</span>, guideline <span class="op">=</span> <span class="va">custom</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
+<span><span class="co">#&gt; x</span></span>
+<span><span class="co">#&gt;       Negative Elderly Type A Elderly Type B </span></span>
+<span><span class="co">#&gt;           1070            198            732</span></span></code></pre></div>
+<p>The rules set (the <code>custom</code> object in this case) could be
+exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you
+collaborate with multiple users. The custom rules set could then be
+imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
+</div>
+</div>
+<div class="section level3">
+<h3 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h3>
+<p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered
+<code>factor</code> for predefined guidelines. For example, the output
+of the default guideline by Magiorakos <em>et al.</em> returns a
+<code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in
+that order.</p>
+<p>The next example uses the <code>example_isolates</code> data set.
+This is a data set included with this package and contains full
+antibiograms of 2,000 microbial isolates. It reflects reality and can be
+used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
+on this data set, we get:</p>
+<div class="sourceCode" id="cb4"><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"># to support pipes: %&gt;%</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>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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="../reference/mdro.html">mdro</a></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="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span></span>
+<span><span class="co">#&gt; Warning: in <span style="background-color: #EEEEEE;">mdro()</span>: NA introduced for isolates where the available percentage of</span></span>
+<span><span class="co">#&gt; antimicrobial classes was below 50% (set with <span style="background-color: #EEEEEE;">pct_required_classes</span>)</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: factor &gt; ordered (numeric)<br>
+Length: 2,000<br>
+Levels: 4: Negative &lt; Multi-drug-resistant (MDR) &lt; Extensively
+drug-resistant …<br>
+Available: 1,745 (87.25%, NA: 255 = 12.75%)<br>
+Unique: 2</p>
+<table style="width:100%;" class="table">
+<colgroup>
+<col width="4%">
+<col width="38%">
+<col width="9%">
+<col width="12%">
+<col width="16%">
+<col width="19%">
+</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">Negative</td>
+<td align="right">1617</td>
+<td align="right">92.66%</td>
+<td align="right">1617</td>
+<td align="right">92.66%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">Multi-drug-resistant (MDR)</td>
+<td align="right">128</td>
+<td align="right">7.34%</td>
+<td align="right">1745</td>
+<td align="right">100.00%</td>
+</tr>
+</tbody>
+</table>
+<p>For another example, I will create a data set to determine multi-drug
+resistant TB:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># random_sir() is a helper function to generate</span></span>
+<span><span class="co"># a random vector with values S, I and R</span></span>
+<span><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p>Because all column names are automatically verified for valid drug
+names or codes, this would have worked exactly the same way:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  PZA <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  MFX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
+<span>  KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p>The data set now looks like this:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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">my_TB_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt;   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
+<span><span class="co">#&gt; 1          I         R            S          S            S            S</span></span>
+<span><span class="co">#&gt; 2          S         S            I          R            R            S</span></span>
+<span><span class="co">#&gt; 3          R         I            I          I            R            I</span></span>
+<span><span class="co">#&gt; 4          I         S            S          S            S            S</span></span>
+<span><span class="co">#&gt; 5          I         I            I          S            I            S</span></span>
+<span><span class="co">#&gt; 6          R         S            R          S            I            I</span></span>
+<span><span class="co">#&gt;   kanamycin</span></span>
+<span><span class="co">#&gt; 1         R</span></span>
+<span><span class="co">#&gt; 2         I</span></span>
+<span><span class="co">#&gt; 3         S</span></span>
+<span><span class="co">#&gt; 4         I</span></span>
+<span><span class="co">#&gt; 5         I</span></span>
+<span><span class="co">#&gt; 6         I</span></span></code></pre></div>
+<p>We can now add the interpretation of MDR-TB to our data set. You can
+use:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></span></code></pre></div>
+<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ No column found as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">col_mo</span><span style="color: #0000BB;">, </span><span style="color: #0000BB; font-weight: bold;">assuming all rows contain</span></span></span>
+<span><span class="co"><span style="color: #0000BB; font-weight: bold;">#&gt;   </span><span style="color: #0000BB; font-weight: bold; font-style: italic;">Mycobacterium tuberculosis</span><span style="color: #0000BB; font-weight: bold;">.</span></span></span></code></pre></div>
+<p>Create a frequency table of the results:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: factor &gt; ordered (numeric)<br>
+Length: 5,000<br>
+Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt;
+Multi-drug-resistant &lt;…<br>
+Available: 5,000 (100%, NA: 0 = 0%)<br>
+Unique: 5</p>
+<table style="width:100%;" class="table">
+<colgroup>
+<col width="4%">
+<col width="38%">
+<col width="9%">
+<col width="12%">
+<col width="16%">
+<col width="19%">
+</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">Mono-resistant</td>
+<td align="right">3223</td>
+<td align="right">64.46%</td>
+<td align="right">3223</td>
+<td align="right">64.46%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">Negative</td>
+<td align="right">967</td>
+<td align="right">19.34%</td>
+<td align="right">4190</td>
+<td align="right">83.80%</td>
+</tr>
+<tr class="odd">
+<td align="left">3</td>
+<td align="left">Multi-drug-resistant</td>
+<td align="right">454</td>
+<td align="right">9.08%</td>
+<td align="right">4644</td>
+<td align="right">92.88%</td>
+</tr>
+<tr class="even">
+<td align="left">4</td>
+<td align="left">Poly-resistant</td>
+<td align="right">245</td>
+<td align="right">4.90%</td>
+<td align="right">4889</td>
+<td align="right">97.78%</td>
+</tr>
+<tr class="odd">
+<td align="left">5</td>
+<td align="left">Extensively drug-resistant</td>
+<td align="right">111</td>
+<td align="right">2.22%</td>
+<td align="right">5000</td>
+<td align="right">100.00%</td>
+</tr>
+</tbody>
+</table>
+</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 General Public License version 2.0 (GPL-2)</a>.<br>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.</p>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
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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>
+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
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+    <li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>How to 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://msberends.github.io/AMR/">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, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…</span></span>
+<span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…</span></span>
+<span><span class="co">#&gt; $ age     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…</span></span>
+<span><span class="co">#&gt; $ gender  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…</span></span>
+<span><span class="co">#&gt; $ ward    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…</span></span>
+<span><span class="co">#&gt; $ mo      <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…</span></span>
+<span><span class="co">#&gt; $ PEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…</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: #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 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; $ FLC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, S, S, R, S, S, S, <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>, R, 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: #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>, R, R, <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>, R, <span style="color: #BB0000;">NA</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, I, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, I, I, R, I, I, <span style="color: #BB0000;">NA</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: #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>, R, R, <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>, R, <span style="color: #BB0000;">NA</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: #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 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; $ CZO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</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 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>, R, <span style="color: #BB0000;">NA</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: #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 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; $ CXM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I, I, R, R, R, R, S, S, R, S, S, S, S, S, <span style="color: #BB0000;">NA</span>, S, S, R, R, S, 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: #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 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>, R, <span style="color: #BB0000;">NA</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: #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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, S, 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: #BB0000;">NA</span>, R, R, R, R, R, R, R, R, R, R, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, R, R, …</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: #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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, S, 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: #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 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; $ TOB     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</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>, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <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; $ AMK     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</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 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 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: #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 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; $ TMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, S, S, R, R, R, R, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, S, S, S, R, R, R, …</span></span>
+<span><span class="co">#&gt; $ SXT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, S, S, S, <span style="color: #BB0000;">NA</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: #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 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>, R,…</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: #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 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; $ LNZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, <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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, R, <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: #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>, S, S, <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>, S, 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: #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 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; $ VAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, S, S, S, S, S, S, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, R, S, S, S, …</span></span>
+<span><span class="co">#&gt; $ TEC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, <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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, R, <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, R, S, S, S, S, S, S, S, I, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, I, R, R, S, I, R, …</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: #BB0000;">NA</span>, S, S, S, S, S, S, S, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, S, <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: #BB0000;">NA</span>, S, S, S, S, S, S, S, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, S, <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, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…</span></span>
+<span><span class="co">#&gt; $ CLI     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, <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>, R, R, R, R, R, <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, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…</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: #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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, S, S, <span style="color: #BB0000;">NA</span>, S, 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: #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 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; $ MTR     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</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 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 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: #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 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; $ COL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, R, R, R, R, R, R, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, …</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: #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 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; $ RIF     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, <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 style="color: #BB0000;">NA</span>, <span style="color: #BB0000;">NA</span>, R, R, R, R, R, <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>
+<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: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></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" 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" 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" width="750"></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
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+      <img src="../logo.svg" class="logo" alt=""><h1>How to 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>
+      <div class="d-none name"><code>WHONET.Rmd</code></div>
+    </div>
+
+    
+    
+<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://msberends.github.io/AMR/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://msberends.github.io/AMR/">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://msberends.github.io/AMR/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></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: factor &gt; ordered &gt; sir (numeric)<br>
+Length: 500<br>
+Levels: 5: S &lt; SDD &lt; I &lt; R &lt; NI<br>
+Available: 481 (96.2%, NA: 19 = 3.8%)<br>
+Unique: 3</p>
+<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)<br>
+Drug group: Beta-lactams/penicillins<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>
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+    <li><a class="dropdown-item" href="../articles/MDR.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
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+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
+    <li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
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+      <h3>All vignettes</h3>
+      <div class="section-desc"></div>
+
+      <dl><dt><a href="AMR_for_Python.html">AMR for Python</a></dt>
+        <dd>
+        </dd><dt><a href="AMR_with_tidymodels.html">AMR with tidymodels</a></dt>
+        <dd>
+        </dd><dt><a href="AMR.html">How to conduct AMR data analysis</a></dt>
+        <dd>
+        </dd><dt><a href="datasets.html">Data sets for download / own use</a></dt>
+        <dd>
+        </dd><dt><a href="EUCAST.html">How to apply EUCAST rules</a></dt>
+        <dd>
+        </dd><dt><a href="MDR.html">How to determine multi-drug resistance (MDR)</a></dt>
+        <dd>
+        </dd><dt><a href="PCA.html">How to conduct principal component analysis (PCA) for AMR</a></dt>
+        <dd>
+        </dd><dt><a href="resistance_predict.html">How to predict antimicrobial resistance</a></dt>
+        <dd>
+        </dd><dt><a href="welcome_to_AMR.html">Welcome to the `AMR` package</a></dt>
+        <dd>
+        </dd><dt><a href="WHONET.html">How to work with WHONET data</a></dt>
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+<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script>
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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="">2.1.1.9201</small>
+
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="../articles/resistance_predict.html"><span class="fa fa-dice"></span> Predict Antimicrobial Resistance</a></li>
+    <li><a class="dropdown-item" href="../articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <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>
+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="../articles/MDR.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="../articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
+    <li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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+</form></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul>
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+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>How to predict antimicrobial resistance</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/resistance_predict.Rmd" class="external-link"><code>vignettes/resistance_predict.Rmd</code></a></small>
+      <div class="d-none name"><code>resistance_predict.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
+</h2>
+<p>As with many uses in R, we need some additional packages for AMR data
+analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a>. The
+tidyverse tremendously improves the way we conduct data science - it
+allows for a very natural way of writing syntaxes and creating beautiful
+plots in R.</p>
+<p>Our <code>AMR</code> package depends on these packages and even
+extends their use and functions.</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://dplyr.tidyverse.org" class="external-link">dplyr</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://ggplot2.tidyverse.org" class="external-link">ggplot2</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://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># (if not yet installed, install with:)</span></span>
+<span><span class="co"># install.packages(c("tidyverse", "AMR"))</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="prediction-analysis">Prediction analysis<a class="anchor" aria-label="anchor" href="#prediction-analysis"></a>
+</h2>
+<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>,
+which takes the same input as functions for <a href="./AMR.html">other
+AMR data analysis</a>. Based on a date column, it calculates cases per
+year and uses a regression model to predict antimicrobial
+resistance.</p>
+<p>It is basically as easy as:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
+<span><span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>tbl <span class="op">=</span> <span class="va">example_isolates</span>, col_date <span class="op">=</span> <span class="st">"date"</span>, col_ab <span class="op">=</span> <span class="st">"TZP"</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># or:</span></span>
+<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="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span></span>
+<span>    col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
+<span>    model <span class="op">=</span> <span class="st">"binomial"</span></span>
+<span>  <span class="op">)</span></span>
+<span></span>
+<span><span class="co"># to bind it to object 'predict_TZP' for example:</span></span>
+<span><span class="va">predict_TZP</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="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span></span>
+<span>    col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
+<span>    model <span class="op">=</span> <span class="st">"binomial"</span></span>
+<span>  <span class="op">)</span></span></code></pre></div>
+<p>The function will look for a date column itself if
+<code>col_date</code> is not set.</p>
+<p>When running any of these commands, a summary of the regression model
+will be printed unless using
+<code>resistance_predict(..., info = FALSE)</code>.</p>
+<p>This text is only a printed summary - the actual result (output) of
+the function is a <code>data.frame</code> containing for each year: the
+number of observations, the actual observed resistance, the estimated
+resistance and the standard error below and above the estimation:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predict_TZP</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 34 × 7</span></span></span>
+<span><span class="co">#&gt;     year  value se_min se_max observations observed estimated</span></span>
+<span><span class="co">#&gt;  <span style="color: #BCBCBC;">*</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;int&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>  <span style="text-decoration: underline;">2</span>002 0.2        <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           15   0.2       0.056<span style="text-decoration: underline;">2</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>  <span style="text-decoration: underline;">2</span>003 0.062<span style="text-decoration: underline;">5</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           32   0.062<span style="text-decoration: underline;">5</span>    0.061<span style="text-decoration: underline;">6</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>  <span style="text-decoration: underline;">2</span>004 0.085<span style="text-decoration: underline;">4</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           82   0.085<span style="text-decoration: underline;">4</span>    0.067<span style="text-decoration: underline;">6</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>  <span style="text-decoration: underline;">2</span>005 0.05       <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           60   0.05      0.074<span style="text-decoration: underline;">1</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>  <span style="text-decoration: underline;">2</span>006 0.050<span style="text-decoration: underline;">8</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           59   0.050<span style="text-decoration: underline;">8</span>    0.081<span style="text-decoration: underline;">2</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>  <span style="text-decoration: underline;">2</span>007 0.121      <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           66   0.121     0.088<span style="text-decoration: underline;">9</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>  <span style="text-decoration: underline;">2</span>008 0.041<span style="text-decoration: underline;">7</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           72   0.041<span style="text-decoration: underline;">7</span>    0.097<span style="text-decoration: underline;">2</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>  <span style="text-decoration: underline;">2</span>009 0.016<span style="text-decoration: underline;">4</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           61   0.016<span style="text-decoration: underline;">4</span>    0.106 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>  <span style="text-decoration: underline;">2</span>010 0.056<span style="text-decoration: underline;">6</span>     <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           53   0.056<span style="text-decoration: underline;">6</span>    0.116 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>  <span style="text-decoration: underline;">2</span>011 0.183      <span style="color: #BB0000;">NA</span>     <span style="color: #BB0000;">NA</span>           93   0.183     0.127 </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 24 more rows</span></span></span></code></pre></div>
+<p>The function <code>plot</code> is available in base R, and can be
+extended by other packages to depend the output based on the type of
+input. We extended its function to cope with resistance predictions:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
+<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
+<p>This is the fastest way to plot the result. It automatically adds the
+right axes, error bars, titles, number of available observations and
+type of model.</p>
+<p>We also support the <code>ggplot2</code> package with our custom
+function <code><a href="../reference/resistance_predict.html">ggplot_sir_predict()</a></code> to create more appealing
+plots:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
+<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># choose for error bars instead of a ribbon</span></span>
+<span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
+<div class="section level3">
+<h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
+</h3>
+<p>Resistance is not easily predicted; if we look at vancomycin
+resistance in Gram-positive bacteria, the spread (i.e. standard error)
+is enormous:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</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/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</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/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
+<p>Vancomycin resistance could be 100% in ten years, but might remain
+very low.</p>
+<p>You can define the model with the <code>model</code> parameter. The
+model chosen above is a generalised linear regression model using a
+binomial distribution, assuming that a period of zero resistance was
+followed by a period of increasing resistance leading slowly to more and
+more resistance.</p>
+<p>Valid values are:</p>
+<table class="table">
+<colgroup>
+<col width="32%">
+<col width="25%">
+<col width="42%">
+</colgroup>
+<thead><tr class="header">
+<th>Input values</th>
+<th>Function used by R</th>
+<th>Type of model</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>
+<code>"binomial"</code> or <code>"binom"</code> or
+<code>"logit"</code>
+</td>
+<td><code>glm(..., family = binomial)</code></td>
+<td>Generalised linear model with binomial distribution</td>
+</tr>
+<tr class="even">
+<td>
+<code>"loglin"</code> or <code>"poisson"</code>
+</td>
+<td><code>glm(..., family = poisson)</code></td>
+<td>Generalised linear model with poisson distribution</td>
+</tr>
+<tr class="odd">
+<td>
+<code>"lin"</code> or <code>"linear"</code>
+</td>
+<td><code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code></td>
+<td>Linear model</td>
+</tr>
+</tbody>
+</table>
+<p>For the vancomycin resistance in Gram-positive bacteria, a linear
+model might be more appropriate:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</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/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</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/resistance_predict.html">ggplot_sir_predict</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
+<p>The model itself is also available from the object, as an
+<code>attribute</code>:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">$</span><span class="va">family</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Family: binomial </span></span>
+<span><span class="co">#&gt; Link function: logit</span></span>
+<span></span>
+<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">model</span><span class="op">)</span><span class="op">$</span><span class="va">coefficients</span></span>
+<span><span class="co">#&gt;                  Estimate  Std. Error   z value     Pr(&gt;|z|)</span></span>
+<span><span class="co">#&gt; (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span></span>
+<span><span class="co">#&gt; year           0.09883005  0.02295317  4.305725 1.664395e-05</span></span></code></pre></div>
+</div>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Welcome to the `AMR` package</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/welcome_to_AMR.Rmd" class="external-link"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
+      <div class="d-none name"><code>welcome_to_AMR.Rmd</code></div>
+    </div>
+
+    
+    
+<p>Note: to keep the package size as small as possible, we only include
+this vignette on CRAN. You can read more vignettes on our website about
+how to conduct AMR data analysis, determine MDROs, find explanation of
+EUCAST and CLSI breakpoints, and much more: <a href="https://msberends.github.io/AMR/articles/" class="uri">https://msberends.github.io/AMR/articles/</a>.</p>
+<hr>
+<p>The <code>AMR</code> package is a <a href="https://msberends.github.io/AMR/#copyright">free and
+open-source</a> R package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
+dependencies</a> to simplify the analysis and prediction of
+Antimicrobial Resistance (AMR) and to work with microbial and
+antimicrobial data and properties, by using evidence-based methods.
+<strong>Our aim is to provide a standard</strong> for clean and
+reproducible AMR data analysis, that can therefore empower
+epidemiological analyses to continuously enable surveillance and
+treatment evaluation in any setting. <a href="https://msberends.github.io/AMR/authors.html">Many different
+researchers</a> from around the globe are continually helping us to make
+this a successful and durable project!</p>
+<p>This work was published in the Journal of Statistical Software
+(Volume 104(3); <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">DOI
+10.18637/jss.v104.i03</a>) and formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
+10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
+10.33612/diss.192486375</a>).</p>
+<p>After installing this package, R knows ~79 000 distinct microbial
+species and all ~620 antibiotic, antimycotic and antiviral drugs by name
+and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED
+CT), and knows all about valid SIR and MIC values. The integral
+breakpoint guidelines from CLSI and EUCAST are included from the last 10
+years. It supports and can read any data format, including WHONET
+data.</p>
+<p>With the help of contributors from all corners of the world, the
+<code>AMR</code> package is available in English, Czech, Chinese,
+Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese,
+Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish,
+Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial
+microorganism names are provided in these languages.</p>
+<p>This package is fully independent of any other R package and works on
+Windows, macOS and Linux with all versions of R since R-3.0 (April
+2013). <strong>It was designed to work in any setting, including those
+with very limited resources</strong>. Since its first public release in
+early 2018, this package has been downloaded from more than 175
+countries.</p>
+<p>This package can be used for:</p>
+<ul>
+<li>Reference for the taxonomy of microorganisms, since the package
+contains all microbial (sub)species from the List of Prokaryotic names
+with Standing in Nomenclature (LPSN) and the Global Biodiversity
+Information Facility (GBIF)</li>
+<li>Interpreting raw MIC and disk diffusion values, based on the latest
+CLSI or EUCAST guidelines</li>
+<li>Retrieving antimicrobial drug names, doses and forms of
+administration from clinical health care records</li>
+<li>Determining first isolates to be used for AMR data analysis</li>
+<li>Calculating antimicrobial resistance</li>
+<li>Determining multi-drug resistance (MDR) / multi-drug resistant
+organisms (MDRO)</li>
+<li>Calculating (empirical) susceptibility of both mono therapy and
+combination therapies</li>
+<li>Predicting future antimicrobial resistance using regression
+models</li>
+<li>Getting properties for any microorganism (like Gram stain, species,
+genus or family)</li>
+<li>Getting properties for any antibiotic (like name, code of
+EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
+<li>Plotting antimicrobial resistance</li>
+<li>Applying EUCAST expert rules</li>
+<li>Getting SNOMED codes of a microorganism, or getting properties of a
+microorganism based on a SNOMED code</li>
+<li>Getting LOINC codes of an antibiotic, or getting properties of an
+antibiotic based on a LOINC code</li>
+<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
+translate MIC values and disk diffusion diameters to SIR</li>
+<li>Principal component analysis for AMR</li>
+</ul>
+<p>All reference data sets (about microorganisms, antimicrobials, SIR
+interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
+publicly and freely available. We continually export our data sets to
+formats for use in R, SPSS, Stata and Excel. We also supply flat files
+that are machine-readable and suitable for input in any software
+program, such as laboratory information systems. Please find <a href="https://msberends.github.io/AMR/articles/datasets.html">all
+download links on our website</a>, which is automatically updated with
+every code change.</p>
+<p>This R package was created for both routine data analysis and
+academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
+with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
+Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a>, and
+is being <a href="https://msberends.github.io/AMR/news/">actively and
+durably maintained</a> by two public healthcare organisations in the
+Netherlands.</p>
+<hr>
+<p><small> This AMR package for R is free, open-source software and
+licensed under the <a href="https://msberends.github.io/AMR/LICENSE-text.html">GNU General
+Public License v2.0 (GPL-2)</a>. These requirements are consequently
+legally binding: modifications must be released under the same license
+when distributing the package, changes made to the code must be
+documented, source code must be made available when the package is
+distributed, and a copy of the license and copyright notice must be
+included with the package. </small></p>
+  </main>
+</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 General Public License version 2.0 (GPL-2)</a>.<br>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.</p>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/resistance_predict.html"><span class="fa fa-dice"></span> Predict Antimicrobial Resistance</a></li>
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+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="articles/MDR.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>Authors and Citation</h1>
+    </div>
+
+    <div class="section level2">
+      <h2>Authors</h2>
+
+      <ul class="list-unstyled"><li>
+          <p><strong>Matthijs S. Berends</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7620-1800" 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>Dennis Souverein</strong>. Author, contributor. <a href="https://orcid.org/0000-0003-0455-0336" 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>Erwin E. A. Hassing</strong>. Author, contributor.
+          </p>
+        </li>
+        <li>
+          <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>
+        </li>
+        <li>
+          <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>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anita Williams</strong>. Contributor. <a href="https://orcid.org/0000-0002-5295-8451" 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>Annick Lenglet</strong>. Contributor. <a href="https://orcid.org/0000-0003-2013-8405" 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>Anthony Underwood</strong>. Contributor. <a href="https://orcid.org/0000-0002-8547-4277" 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>Anton Mymrikov</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Bart C. Meijer</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <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>
+          </p>
+        </li>
+        <li>
+          <p><strong>Dmytro Mykhailenko</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <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>
+          </p>
+        </li>
+        <li>
+          <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>
+          </p>
+        </li>
+        <li>
+          <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>
+        </li>
+        <li>
+          <p><strong>Jonas Salm</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Judith M. Fonville</strong>. Contributor.
+          </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.
+          </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.
+          </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>
+
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+
+    <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 General Public License version 2.0 (GPL-2)</a>.<br>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.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/assets/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/assets/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -0,0 +1,54 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJV37Nv7g.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJVT7Nv7g.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJVz7Nv7g.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJVP7Nv7g.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJV77Nv7g.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Fira Code';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(uU9eCBsR6Z2vfE9aq3bL0fxyUs4tcw4W_D1sJVD7Ng.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
@@ -0,0 +1,18 @@
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(S6uyw4BMUTPHjxAwXjeu.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(S6uyw4BMUTPHjx4wXg.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
@@ -0,0 +1,54 @@
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6u8w4BMUTPHjxsAXC-q.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6uyw4BMUTPHjxAwXjeu.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6uyw4BMUTPHjx4wXg.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
--- a/Show More
+++ b/Show More