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# Page not found (404)
AMR.egg-info/PKG-INFO
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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.
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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
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rpy2
numpy
pandas
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AMR
AMR/__init__.py
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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
AMR/_engine.py
+93
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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
AMR/beta.py
+22
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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
AMR/datasets.py
+54
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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]
AMR/functions.py
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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)
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9091</small>
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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/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</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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<img src="logo.svg" class="logo" alt=""><h1>CLAUDE.md — AMR R Package</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/CLAUDE.md" class="external-link"><code>CLAUDE.md</code></a></small>
</div>
<div id="claudemd--amr-r-package" class="section level1">
<p>This file provides context for Claude Code when working in this repository.</p>
<div class="section level2">
<h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
<p><strong>AMR</strong> is a zero-dependency R package for antimicrobial resistance (AMR) data analysis using a One Health approach. It is peer-reviewed, used in 175+ countries, and supports 28 languages.</p>
<p>Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, and WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) and disk diffusion handling - Multilingual output (28 languages)</p>
</div>
<div class="section level2">
<h2 id="common-commands">Common Commands<a class="anchor" aria-label="anchor" href="#common-commands"></a></h2>
<p>All commands run inside an R session:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)</span></span>
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">document</span><span class="op">(</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Run all tests</span></span>
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">test</span><span class="op">(</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Full package check (CRAN-level: docs + tests + checks)</span></span>
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">check</span><span class="op">(</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Build pkgdown website locally</span></span>
<span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Code coverage report</span></span>
<span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p>From the shell:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
</div>
<div class="section level2">
<h2 id="repository-structure">Repository Structure<a class="anchor" aria-label="anchor" href="#repository-structure"></a></h2>
<pre><code>R/ # All R source files (62 files, ~28,000 lines)
man/ # Auto-generated .Rd documentation (do not edit manually)
tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
data/ # Pre-compiled .rda datasets
data-raw/ # Scripts used to generate data/ files
vignettes/ # Rmd vignette articles
inst/ # Installed files (translations, etc.)
_pkgdown.yml # pkgdown website configuration</code></pre>
</div>
<div class="section level2">
<h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
<p><strong>Naming conventions in <code>R/</code>:</strong></p>
<table class="table"><thead><tr><th>Prefix/Name</th>
<th>Purpose</th>
</tr></thead><tbody><tr><td><code>aa_*.R</code></td>
<td>Loaded first (helpers, globals, options, package docs)</td>
</tr><tr><td><code>zz_deprecated.R</code></td>
<td>Deprecated function wrappers</td>
</tr><tr><td><code>zzz.R</code></td>
<td>
<code>.onLoad</code> / <code>.onAttach</code> initialization</td>
</tr></tbody></table><p><strong>Key source files:</strong></p>
<ul><li>
<code>aa_helper_functions.R</code> / <code>aa_helper_pm_functions.R</code> — internal utility functions (large; ~63 KB and ~37 KB)</li>
<li>
<code>aa_globals.R</code> — global constants and breakpoint lookup structures</li>
<li>
<code>aa_options.R</code> — <code>amr_options()</code> / <code>get_AMR_option()</code> system</li>
<li>
<code>mo.R</code> / <code>mo_property.R</code> — microorganism lookup and properties</li>
<li>
<code>ab.R</code> / <code>ab_property.R</code> — antimicrobial drug functions</li>
<li>
<code>av.R</code> / <code>av_property.R</code> — antiviral drug functions</li>
<li>
<code>sir.R</code> / <code>sir_calc.R</code> / <code>sir_df.R</code> — SIR classification engine</li>
<li>
<code>mic.R</code> / <code>disk.R</code> — MIC and disk diffusion classes</li>
<li>
<code>antibiogram.R</code> — antibiogram generation (traditional, combined, syndromic, WISCA)</li>
<li>
<code>first_isolate.R</code> — first-isolate identification algorithms</li>
<li>
<code>mdro.R</code> — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)</li>
<li>
<code>amr_selectors.R</code> — tidyselect helpers for selecting AMR columns</li>
<li>
<code>interpretive_rules.R</code> / <code>custom_eucast_rules.R</code> — clinical interpretation rules</li>
<li>
<code>translate.R</code> — 28-language translation system</li>
<li>
<code>ggplot_sir.R</code> / <code>ggplot_pca.R</code> / <code>plotting.R</code> — visualisation functions</li>
</ul></div>
<div class="section level2">
<h2 id="code-style">Code Style<a class="anchor" aria-label="anchor" href="#code-style"></a></h2>
<p>Follow the <a href="https://style.tidyverse.org/" class="external-link">tidyverse style guide</a> precisely. Key rules:</p>
<ul><li>2-space indentation; no tabs</li>
<li>
<code>&lt;-</code> for assignment, not <code>=</code>
</li>
<li>Spaces around all binary operators and after commas; no spaces inside parentheses</li>
<li>When a function call must break across lines, place the first argument on a new line indented by 2 spaces, and put the closing <code>)</code> on its own line — <strong>never align arguments to the opening parenthesis</strong> (no hanging/forced mid-line indentation)</li>
</ul><div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># good</span></span>
<span><span class="fu">stop_</span><span class="op">(</span></span>
<span> <span class="st">"some long message part one "</span>,</span>
<span> <span class="st">"part two"</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="co"># bad — forces indentation to match the opening parenthesis</span></span>
<span><span class="fu">stop_</span><span class="op">(</span><span class="st">"some long message part one "</span>,</span>
<span> <span class="st">"part two"</span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
<p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
<table class="table"><thead><tr><th>Class</th>
<th>Created by</th>
<th>Represents</th>
</tr></thead><tbody><tr><td><code>&lt;mo&gt;</code></td>
<td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
<td>Microorganism code</td>
</tr><tr><td><code>&lt;ab&gt;</code></td>
<td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
<td>Antimicrobial drug code</td>
</tr><tr><td><code>&lt;av&gt;</code></td>
<td><code><a href="reference/as.av.html">as.av()</a></code></td>
<td>Antiviral drug code</td>
</tr><tr><td><code>&lt;sir&gt;</code></td>
<td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
<td>SIR value (S/I/R/SDD)</td>
</tr><tr><td><code>&lt;mic&gt;</code></td>
<td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
<td>Minimum inhibitory concentration</td>
</tr><tr><td><code>&lt;disk&gt;</code></td>
<td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
<td>Disk diffusion diameter</td>
</tr></tbody></table></div>
<div class="section level2">
<h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
<p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
<table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
<th>Contents</th>
</tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
<td>~79,000 microbial species with full taxonomy</td>
</tr><tr><td><code>antimicrobials.rda</code></td>
<td>~620 antimicrobial drugs with ATC codes</td>
</tr><tr><td><code>antivirals.rda</code></td>
<td>Antiviral drugs</td>
</tr><tr><td><code>clinical_breakpoints.rda</code></td>
<td>EUCAST + CLSI breakpoints (2011–2025)</td>
</tr><tr><td><code>intrinsic_resistant.rda</code></td>
<td>Intrinsic resistance patterns</td>
</tr><tr><td><code>example_isolates.rda</code></td>
<td>Example AMR dataset for documentation/testing</td>
</tr><tr><td><code>WHONET.rda</code></td>
<td>Example WHONET-format dataset</td>
</tr></tbody></table></div>
<div class="section level2">
<h2 id="zero-dependency-design">Zero-Dependency Design<a class="anchor" aria-label="anchor" href="#zero-dependency-design"></a></h2>
<p>The package has <strong>no <code>Imports</code></strong> in <code>DESCRIPTION</code>. All optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) are listed in <code>Suggests</code> and guarded with:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"pkg"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span> <span class="va">...</span> <span class="op">}</span></span></code></pre></div>
<p>Never add packages to <code>Imports</code>. If new functionality requires an external package, add it to <code>Suggests</code> and guard usage appropriately.</p>
</div>
<div class="section level2">
<h2 id="testing">Testing<a class="anchor" aria-label="anchor" href="#testing"></a></h2>
<ul><li>
<strong>Framework:</strong> <code>testthat</code> (R ≥ 3.1); legacy <code>tinytest</code> used for R 3.0–3.6 CI</li>
<li>
<strong>Test files:</strong> <code>tests/testthat/test-*.R</code>
</li>
<li>
<strong>Helpers:</strong> <code>tests/testthat/helper-functions.R</code>
</li>
<li>
<strong>CI matrix:</strong> GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 through oldrel-4</li>
<li>
<strong>Coverage:</strong> <code>covr</code> (some files excluded: <code>atc_online.R</code>, <code>mo_source.R</code>, <code>translate.R</code>, <code>resistance_predict.R</code>, <code>zz_deprecated.R</code>, helper files, <code>zzz.R</code>)</li>
</ul></div>
<div class="section level2">
<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
<ul><li>All exported functions use <strong>roxygen2</strong> blocks (<code>RoxygenNote: 7.3.3</code>, markdown enabled)</li>
<li>Run <code>devtools::document()</code> after any change to roxygen comments</li>
<li>Never edit files in <code>man/</code> directly — they are auto-generated</li>
<li>Vignettes live in <code>vignettes/</code> as <code>.Rmd</code> files</li>
<li>The pkgdown website is configured in <code>_pkgdown.yml</code>
</li>
</ul></div>
<div class="section level2">
<h2 id="versioning">Versioning<a class="anchor" aria-label="anchor" href="#versioning"></a></h2>
<p>Version format: <code>major.minor.patch.dev</code> (e.g., <code>3.0.1.9021</code>)</p>
<ul><li>Development versions use a <code>.9xxx</code> suffix</li>
<li>Stable CRAN releases drop the dev suffix (e.g., <code>3.0.1</code>)</li>
<li>
<code>NEWS.md</code> uses sections <strong>New</strong>, <strong>Fixes</strong>, <strong>Updates</strong> with GitHub issue references (<code>#NNN</code>)</li>
</ul><div class="section level3">
<h3 id="version-and-date-bump-required-for-every-pr">Version and date bump required for every PR<a class="anchor" aria-label="anchor" href="#version-and-date-bump-required-for-every-pr"></a></h3>
<p>All PRs are <strong>squash-merged</strong>, so each PR lands as exactly <strong>one commit</strong> on the default branch. Version numbers are kept in sync with the cumulative commit count since the last released tag. Therefore <strong>exactly one version bump is allowed per PR</strong>, regardless of how many intermediate commits are made on the branch.</p>
<div class="section level4">
<h4 id="computing-the-correct-version-number">Computing the correct version number<a class="anchor" aria-label="anchor" href="#computing-the-correct-version-number"></a></h4>
<p><strong>First, ensure <code>git</code> and <code>gh</code> are installed</strong> — both are required for the version computation and for pushing changes. Install them if missing before doing anything else:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">which</span> git <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> git</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="fu">which</span> gh <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> gh</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co"># Also ensure all tags are fetched so git describe works</span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> fetch <span class="at">--tags</span></span></code></pre></div>
<p>Then run the following from the repo root to determine the version string to use:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0<span class="va">)</span></span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="va">defaultbranch</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> branch <span class="kw">|</span> <span class="fu">cut</span> <span class="at">-c</span> 3- <span class="kw">|</span> <span class="fu">grep</span> <span class="at">-E</span> <span class="st">'^master$|^main$'</span><span class="va">)</span></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="fu">git</span> fetch origin <span class="va">${defaultbranch}</span> <span class="at">--quiet</span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..origin/<span class="va">${defaultbranch})</span></span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="va">currentversion</span><span class="op">=</span><span class="st">"</span><span class="va">${currenttag}</span><span class="st">.</span><span class="va">$((currentcommit</span> <span class="op">+</span> <span class="dv">9001</span> <span class="op">+</span> <span class="dv">1</span><span class="va">))</span><span class="st">"</span></span>
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="bu">echo</span> <span class="st">"</span><span class="va">$currentversion</span><span class="st">"</span></span></code></pre></div>
<p>The <code>+ 1</code> accounts for the fact that this PR’s squash commit is not yet on the default branch. Set <strong>both</strong> of these files to the resulting version string (and only once per PR, even across multiple commits):</p>
<ol style="list-style-type: decimal"><li><p><strong><code>DESCRIPTION</code></strong> — the <code>Version:</code> field</p></li>
<li>
<p><strong><code>NEWS.md</code></strong> — <strong>only replace line 1</strong> (the <code># AMR &lt;version&gt;</code> heading) with the new version number; do <strong>not</strong> create a new section. <code>NEWS.md</code> is a <strong>continuous log</strong> for the entire current <code>x.y.z.9nnn</code> development series: all changes since the last stable release accumulate under that single heading. After updating line 1, append the new change as a bullet under the appropriate sub-heading (<code>### New</code>, <code>### Fixes</code>, or <code>### Updates</code>).</p>
<p>Style rules for <code>NEWS.md</code> entries:</p>
<ul><li>Be <strong>extremely concise</strong> — one short line per item</li>
<li>Do <strong>not</strong> end with a full stop (period)</li>
<li>No verbose explanations; just the essential fact</li>
</ul></li>
</ol><p>If <code>git describe</code> fails (e.g. no tags exist in the environment), fall back to reading the current version from <code>DESCRIPTION</code> and adding 1 to the last numeric component — but only if no bump has already been made in this PR.</p>
</div>
<div class="section level4">
<h4 id="date-field">Date field<a class="anchor" aria-label="anchor" href="#date-field"></a></h4>
<p>The <code>Date:</code> field in <code>DESCRIPTION</code> must reflect the date of the <strong>last commit to the PR</strong> (not the first), in ISO format. Update it with every commit so it is always current:</p>
<pre><code><span><span class="va">Date</span><span class="op">:</span> <span class="fl">2026</span><span class="op">-</span><span class="fl">03</span><span class="op">-</span><span class="fl">07</span></span></code></pre>
</div>
</div>
</div>
<div class="section level2">
<h2 id="internal-state">Internal State<a class="anchor" aria-label="anchor" href="#internal-state"></a></h2>
<p>The package uses a private <code>AMR_env</code> environment (created in <code>aa_globals.R</code>) for caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). This avoids re-computation within a session.</p>
</div>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
</nav></aside></div>
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<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in the Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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CLAUDE.md
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# CLAUDE.md — AMR R Package
This file provides context for Claude Code when working in this
repository.
## Project Overview
**AMR** is a zero-dependency R package for antimicrobial resistance
(AMR) data analysis using a One Health approach. It is peer-reviewed,
used in 175+ countries, and supports 28 languages.
Key capabilities: - SIR (Susceptible/Intermediate/Resistant)
classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints -
Antibiogram generation: traditional, combined, syndromic, and WISCA -
Microorganism taxonomy database (~79,000 species) - Antimicrobial drug
database (~620 drugs) - Multi-drug resistant organism (MDRO)
classification - First-isolate identification - Minimum Inhibitory
Concentration (MIC) and disk diffusion handling - Multilingual output
(28 languages)
## Common Commands
All commands run inside an R session:
``` r
# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
devtools::document()
# Run all tests
devtools::test()
# Full package check (CRAN-level: docs + tests + checks)
devtools::check()
# Build pkgdown website locally
pkgdown::build_site()
# Code coverage report
covr::package_coverage()
```
From the shell:
``` bash
# CRAN check from parent directory
R CMD check AMR
```
## Repository Structure
R/ # All R source files (62 files, ~28,000 lines)
man/ # Auto-generated .Rd documentation (do not edit manually)
tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
data/ # Pre-compiled .rda datasets
data-raw/ # Scripts used to generate data/ files
vignettes/ # Rmd vignette articles
inst/ # Installed files (translations, etc.)
_pkgdown.yml # pkgdown website configuration
## R Source File Conventions
**Naming conventions in `R/`:**
| Prefix/Name | Purpose |
|-------------------|--------------------------------------------------------|
| `aa_*.R` | Loaded first (helpers, globals, options, package docs) |
| `zz_deprecated.R` | Deprecated function wrappers |
| `zzz.R` | `.onLoad` / `.onAttach` initialization |
**Key source files:**
- `aa_helper_functions.R` / `aa_helper_pm_functions.R` — internal
utility functions (large; ~63 KB and ~37 KB)
- `aa_globals.R` — global constants and breakpoint lookup structures
- `aa_options.R` — `amr_options()` / `get_AMR_option()` system
- `mo.R` / `mo_property.R` — microorganism lookup and properties
- `ab.R` / `ab_property.R` — antimicrobial drug functions
- `av.R` / `av_property.R` — antiviral drug functions
- `sir.R` / `sir_calc.R` / `sir_df.R` — SIR classification engine
- `mic.R` / `disk.R` — MIC and disk diffusion classes
- `antibiogram.R` — antibiogram generation (traditional, combined,
syndromic, WISCA)
- `first_isolate.R` — first-isolate identification algorithms
- `mdro.R` — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)
- `amr_selectors.R` — tidyselect helpers for selecting AMR columns
- `interpretive_rules.R` / `custom_eucast_rules.R` — clinical
interpretation rules
- `translate.R` — 28-language translation system
- `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation
functions
## Code Style
Follow the [tidyverse style guide](https://style.tidyverse.org/)
precisely. Key rules:
- 2-space indentation; no tabs
- `<-` for assignment, not `=`
- Spaces around all binary operators and after commas; no spaces inside
parentheses
- When a function call must break across lines, place the first argument
on a new line indented by 2 spaces, and put the closing `)` on its own
line — **never align arguments to the opening parenthesis** (no
hanging/forced mid-line indentation)
``` r
# good
stop_(
"some long message part one ",
"part two"
)
# bad — forces indentation to match the opening parenthesis
stop_("some long message part one ",
"part two")
```
## Custom S3 Classes
The package defines five S3 classes with full print/format/plot/vctrs
support:
| Class | Created by | Represents |
|----|----|----|
| `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
| `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
| `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
| `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
| `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
| `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
## Data Files
Pre-compiled in `data/` (do not edit directly; regenerate via
`data-raw/` scripts):
| File | Contents |
|----------------------------|-----------------------------------------------|
| `microorganisms.rda` | ~79,000 microbial species with full taxonomy |
| `antimicrobials.rda` | ~620 antimicrobial drugs with ATC codes |
| `antivirals.rda` | Antiviral drugs |
| `clinical_breakpoints.rda` | EUCAST + CLSI breakpoints (2011–2025) |
| `intrinsic_resistant.rda` | Intrinsic resistance patterns |
| `example_isolates.rda` | Example AMR dataset for documentation/testing |
| `WHONET.rda` | Example WHONET-format dataset |
## Zero-Dependency Design
The package has **no `Imports`** in `DESCRIPTION`. All optional
integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
are listed in `Suggests` and guarded with:
``` r
if (requireNamespace("pkg", quietly = TRUE)) { ... }
```
Never add packages to `Imports`. If new functionality requires an
external package, add it to `Suggests` and guard usage appropriately.
## Testing
- **Framework:** `testthat` (R ≥ 3.1); legacy `tinytest` used for R
3.0–3.6 CI
- **Test files:** `tests/testthat/test-*.R`
- **Helpers:** `tests/testthat/helper-functions.R`
- **CI matrix:** GitHub Actions across Windows / macOS / Linux × R devel
/ release / oldrel-1 through oldrel-4
- **Coverage:** `covr` (some files excluded: `atc_online.R`,
`mo_source.R`, `translate.R`, `resistance_predict.R`,
`zz_deprecated.R`, helper files, `zzz.R`)
## Documentation
- All exported functions use **roxygen2** blocks (`RoxygenNote: 7.3.3`,
markdown enabled)
- Run `devtools::document()` after any change to roxygen comments
- Never edit files in `man/` directly — they are auto-generated
- Vignettes live in `vignettes/` as `.Rmd` files
- The pkgdown website is configured in `_pkgdown.yml`
## Versioning
Version format: `major.minor.patch.dev` (e.g., `3.0.1.9021`)
- Development versions use a `.9xxx` suffix
- Stable CRAN releases drop the dev suffix (e.g., `3.0.1`)
- `NEWS.md` uses sections **New**, **Fixes**, **Updates** with GitHub
issue references (`#NNN`)
### Version and date bump required for every PR
All PRs are **squash-merged**, so each PR lands as exactly **one
commit** on the default branch. Version numbers are kept in sync with
the cumulative commit count since the last released tag. Therefore
**exactly one version bump is allowed per PR**, regardless of how many
intermediate commits are made on the branch.
#### Computing the correct version number
**First, ensure `git` and `gh` are installed** — both are required for
the version computation and for pushing changes. Install them if missing
before doing anything else:
``` bash
which git || apt-get install -y git
which gh || apt-get install -y gh
# Also ensure all tags are fetched so git describe works
git fetch --tags
```
Then run the following from the repo root to determine the version
string to use:
``` bash
currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
currenttagfull=$(git describe --tags --abbrev=0)
defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
git fetch origin ${defaultbranch} --quiet
currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
echo "$currentversion"
```
The `+ 1` accounts for the fact that this PR’s squash commit is not yet
on the default branch. Set **both** of these files to the resulting
version string (and only once per PR, even across multiple commits):
1. **`DESCRIPTION`** — the `Version:` field
2. **`NEWS.md`** — **only replace line 1** (the `# AMR <version>`
heading) with the new version number; do **not** create a new
section. `NEWS.md` is a **continuous log** for the entire current
`x.y.z.9nnn` development series: all changes since the last stable
release accumulate under that single heading. After updating line 1,
append the new change as a bullet under the appropriate sub-heading
(`### New`, `### Fixes`, or `### Updates`).
Style rules for `NEWS.md` entries:
- Be **extremely concise** — one short line per item
- Do **not** end with a full stop (period)
- No verbose explanations; just the essential fact
If `git describe` fails (e.g. no tags exist in the environment), fall
back to reading the current version from `DESCRIPTION` and adding 1 to
the last numeric component — but only if no bump has already been made
in this PR.
#### Date field
The `Date:` field in `DESCRIPTION` must reflect the date of the **last
commit to the PR** (not the first), in ISO format. Update it with every
commit so it is always current:
Date: 2026-03-07
## Internal State
The package uses a private `AMR_env` environment (created in
`aa_globals.R`) for caching expensive lookups (e.g., microorganism
matching scores, breakpoint tables). This avoids re-computation within a
session.
CNAME
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amr-for-r.org
LICENSE-text.html
-319
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@@ -1,319 +0,0 @@
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<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
- May be distributed, although:
- Source code MUST be made available when the package is distributed
- A copy of the license and copyright notice MUST be included with the package.
- Comes with a LIMITATION of liability
- Comes with NO warranty
END OF THE SUMMARY
GNU GENERAL PUBLIC LICENSE
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
a notice placed by the copyright holder saying it may be distributed
under the terms of this General Public License. The "Program", below,
refers to any such program or work, and a "work based on the Program"
means either the Program or any derivative work under copyright law:
that is to say, a work containing the Program or a portion of it,
either verbatim or with modifications and/or translated into another
language. (Hereinafter, translation is included without limitation in
the term "modification".) Each licensee is addressed as "you".
Activities other than copying, distribution and modification are not
covered by this License; they are outside its scope. The act of
running the Program is not restricted, and the output from the Program
is covered only if its contents constitute a work based on the
Program (independent of having been made by running the Program).
Whether that is true depends on what the Program does.
1. You may copy and distribute verbatim copies of the Program's
source code as you receive it, in any medium, provided that you
conspicuously and appropriately publish on each copy an appropriate
copyright notice and disclaimer of warranty; keep intact all the
notices that refer to this License and to the absence of any warranty;
and give any other recipients of the Program a copy of this License
along with the Program.
You may charge a fee for the physical act of transferring a copy, and
you may at your option offer warranty protection in exchange for a fee.
2. You may modify your copy or copies of the Program or any portion
of it, thus forming a work based on the Program, and copy and
distribute such modifications or work under the terms of Section 1
above, provided that you also meet all of these conditions:
a) You must cause the modified files to carry prominent notices
stating that you changed the files and the date of any change.
b) You must cause any work that you distribute or publish, that in
whole or in part contains or is derived from the Program or any
part thereof, to be licensed as a whole at no charge to all third
parties under the terms of this License.
c) If the modified program normally reads commands interactively
when run, you must cause it, when started running for such
interactive use in the most ordinary way, to print or display an
announcement including an appropriate copyright notice and a
notice that there is no warranty (or else, saying that you provide
a warranty) and that users may redistribute the program under
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License. (Exception: if the Program itself is interactive but
does not normally print such an announcement, your work based on
the Program is not required to print an announcement.)
These requirements apply to the modified work as a whole. If
identifiable sections of that work are not derived from the Program,
and can be reasonably considered independent and separate works in
themselves, then this License, and its terms, do not apply to those
sections when you distribute them as separate works. But when you
distribute the same sections as part of a whole which is a work based
on the Program, the distribution of the whole must be on the terms of
this License, whose permissions for other licensees extend to the
entire whole, and thus to each and every part regardless of who wrote it.
Thus, it is not the intent of this section to claim rights or contest
your rights to work written entirely by you; rather, the intent is to
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collective works based on the Program.
In addition, mere aggregation of another work not based on the Program
with the Program (or with a work based on the Program) on a volume of
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the scope of this License.
3. You may copy and distribute the Program (or a work based on it,
under Section 2) in object code or executable form under the terms of
Sections 1 and 2 above provided that you also do one of the following:
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may not distribute the Program at all. For example, if a patent
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integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
through that system in reliance on consistent application of that
system; it is up to the author/donor to decide if he or she is willing
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certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
the limitation as if written in the body of this License.
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of the General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
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specifies a version number of this License which applies to it and "any
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Foundation.
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to ask for permission. For software which is copyrighted by the Free
Software Foundation, write to the Free Software Foundation; we sometimes
make exceptions for this. Our decision will be guided by the two goals
of preserving the free status of all derivatives of our free software and
of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
</pre>
</main></div>
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LICENSE-text.md
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@@ -1,250 +0,0 @@
# License
GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
This R package, with package name 'AMR':
- May be used for commercial purposes
- May be used for private purposes
- May NOT be used for patent purposes
- May be modified, although:
- Modifications MUST be released under the same license when distributing the package
- Changes made to the code MUST be documented
- May be distributed, although:
- Source code MUST be made available when the package is distributed
- A copy of the license and copyright notice MUST be included with the package.
- Comes with a LIMITATION of liability
- Comes with NO warranty
END OF THE SUMMARY
GNU GENERAL PUBLIC LICENSE
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
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the term "modification".) Each licensee is addressed as "you".
Activities other than copying, distribution and modification are not
covered by this License; they are outside its scope. The act of
running the Program is not restricted, and the output from the Program
is covered only if its contents constitute a work based on the
Program (independent of having been made by running the Program).
Whether that is true depends on what the Program does.
1. You may copy and distribute verbatim copies of the Program's
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conspicuously and appropriately publish on each copy an appropriate
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and give any other recipients of the Program a copy of this License
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You may charge a fee for the physical act of transferring a copy, and
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Sections 1 and 2 above provided that you also do one of the following:
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1 and 2 above on a medium customarily used for software interchange; or,
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years, to give any third party, for a charge no more than your
cost of physically performing source distribution, a complete
machine-readable copy of the corresponding source code, to be
distributed under the terms of Sections 1 and 2 above on a medium
customarily used for software interchange; or,
c) Accompany it with the information you received as to the offer
to distribute corresponding source code. (This alternative is
allowed only for noncommercial distribution and only if you
received the program in object code or executable form with such
an offer, in accord with Subsection b above.)
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anything that is normally distributed (in either source or binary
form) with the major components (compiler, kernel, and so on) of the
operating system on which the executable runs, unless that component
itself accompanies the executable.
If distribution of executable or object code is made by offering
access to copy from a designated place, then offering equivalent
access to copy the source code from the same place counts as
distribution of the source code, even though third parties are not
compelled to copy the source along with the object code.
4. You may not copy, modify, sublicense, or distribute the Program
except as expressly provided under this License. Any attempt
otherwise to copy, modify, sublicense or distribute the Program is
void, and will automatically terminate your rights under this License.
However, parties who have received copies, or rights, from you under
this License will not have their licenses terminated so long as such
parties remain in full compliance.
5. You are not required to accept this License, since you have not
signed it. However, nothing else grants you permission to modify or
distribute the Program or its derivative works. These actions are
prohibited by law if you do not accept this License. Therefore, by
modifying or distributing the Program (or any work based on the
Program), you indicate your acceptance of this License to do so, and
all its terms and conditions for copying, distributing or modifying
the Program or works based on it.
6. Each time you redistribute the Program (or any work based on the
Program), the recipient automatically receives a license from the
original licensor to copy, distribute or modify the Program subject to
these terms and conditions. You may not impose any further
restrictions on the recipients' exercise of the rights granted herein.
You are not responsible for enforcing compliance by third parties to
this License.
7. If, as a consequence of a court judgment or allegation of patent
infringement or for any other reason (not limited to patent issues),
conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot
distribute so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you
may not distribute the Program at all. For example, if a patent
license would not permit royalty-free redistribution of the Program by
all those who receive copies directly or indirectly through you, then
the only way you could satisfy both it and this License would be to
refrain entirely from distribution of the Program.
If any portion of this section is held invalid or unenforceable under
any particular circumstance, the balance of the section is intended to
apply and the section as a whole is intended to apply in other
circumstances.
It is not the purpose of this section to induce you to infringe any
patents or other property right claims or to contest validity of any
such claims; this section has the sole purpose of protecting the
integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
through that system in reliance on consistent application of that
system; it is up to the author/donor to decide if he or she is willing
to distribute software through any other system and a licensee cannot
impose that choice.
This section is intended to make thoroughly clear what is believed to
be a consequence of the rest of this License.
8. If the distribution and/or use of the Program is restricted in
certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
the limitation as if written in the body of this License.
9. The Free Software Foundation may publish revised and/or new versions
of the General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the Program
specifies a version number of this License which applies to it and "any
later version", you have the option of following the terms and conditions
either of that version or of any later version published by the Free
Software Foundation. If the Program does not specify a version number of
this License, you may choose any version ever published by the Free Software
Foundation.
10. If you wish to incorporate parts of the Program into other free
programs whose distribution conditions are different, write to the author
to ask for permission. For software which is copyrighted by the Free
Software Foundation, write to the Free Software Foundation; we sometimes
make exceptions for this. Our decision will be guided by the two goals
of preserving the free status of all derivatives of our free software and
of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
README.md
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+226
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@@ -0,0 +1,226 @@
The `AMR` package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the [`AMR` Python package](https://pypi.org/project/AMR/).
This Python package is a wrapper around the `AMR` R package. It uses the `rpy2` package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.
# Prerequisites
This package was only tested with a [virtual environment (venv)](https://docs.python.org/3/library/venv.html). You can set up such an environment by running:
```python
# linux and macOS:
python -m venv /path/to/new/virtual/environment
# Windows:
python -m venv C:\path\to\new\virtual\environment
```
Then you can [activate the environment](https://docs.python.org/3/library/venv.html#how-venvs-work), after which the venv is ready to work with.
# Install AMR
1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
```bash
pip install AMR
```
2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
For Linux:
```bash
# Ubuntu / Debian
sudo apt install r-base
# Fedora:
sudo dnf install R
# CentOS/RHEL
sudo yum install R
```
For macOS (using [Homebrew](https://brew.sh)):
```bash
brew install r
```
For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
# Examples of Usage
## Cleaning Taxonomy
Here’s an example that demonstrates how to clean microorganism and drug names using the `AMR` Python package:
```python
import pandas as pd
import AMR
# Sample data
data = {
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)
# Use AMR functions to clean microorganism and drug names
df['MO_clean'] = AMR.mo_name(df['MOs'])
df['Drug_clean'] = AMR.ab_name(df['Drug'])
# Display the results
print(df)
```
| MOs | Drug | MO_clean | Drug_clean |
|-------------|-----------|--------------------|---------------|
| E. coli | Cipro | Escherichia coli | Ciprofloxacin |
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
### Explanation
* **mo_name:** This function standardises microorganism names. Here, different variations of *Escherichia coli* (such as "E. coli", "ESCCOL", "esco", and "Esche coli") are all converted into the correct, standardised form, "Escherichia coli".
* **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
## Calculating AMR
```python
import AMR
import pandas as pd
df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result)
```
```
[0.59555556]
```
## Generating Antibiograms
One of the core functions of the `AMR` package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:
```python
result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
print(result2a)
```
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|-----------------|-----------------|-----------------|--------------------------|
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
| P. mirabilis | None | 94% (34/36) | None |
| S. aureus | 6% (8/131) | 90% (171/191) | None |
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
| S. hominis | None | 80% (56/70) | None |
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
```python
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
print(result2b)
```
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|----------------|-----------------|------------------|--------------------------|
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
In this example, we generate an antibiogram by selecting various antibiotics.
## Taxonomic Data Sets Now in Python!
As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antimicrobials`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
```python
AMR.microorganisms
```
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
```python
AMR.antimicrobials
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| ... | ... | ... | ... | ... | ... | ... | ... |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
# Installation Channels
## Stable Release (CRAN)
The default `AMR` Python package uses the latest stable version of the `AMR` R package, published on CRAN. After running `pip install AMR`, import it as usual:
```python
import AMR
AMR.example_isolates
```
## Development Version (GitHub)
To use the latest development version of the `AMR` R package (sourced directly from GitHub), import the `beta` sub-package and alias it as `AMR`:
```python
import AMR.beta as AMR
AMR.example_isolates
```
Aliasing with `as AMR` keeps all downstream code identical to the stable import. Switching between the stable release and the development version requires changing only the import line — nothing else in your script needs to change.
# SIR Classification with `as_sir()`
## Using `enforce_method`
The `as_sir()` function in R uses S3 method dispatch to select the correct calculation method based on the input class: `<mic>` for MIC values and `<disk>` for disk diffusion values. Because Python objects do not carry R class attributes through the `rpy2` bridge, this automatic dispatch may not resolve correctly.
To explicitly specify the input type, use the `enforce_method` argument:
```python
# Treat the column as MIC values — maps to R's as.sir.mic()
AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
# Treat the column as disk diffusion values — maps to R's as.sir.disk()
AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
```
Without `enforce_method`, R falls back to class-based dispatch on the raw Python input, which may fail or return unexpected results. Always supply `enforce_method` when calling `as_sir()` from Python.
# Conclusion
With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
By just running `import AMR`, users can seamlessly integrate the robust features of the R `AMR` package into Python workflows.
Whether you're cleaning data or analysing resistance patterns, the `AMR` Python package makes it easy to work with AMR data in Python.
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<img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
<div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>The <code>AMR</code> package for R is a powerful tool for
antimicrobial resistance (AMR) analysis. It provides extensive features
for handling microbial and antimicrobial data. However, for those who
work primarily in Python, we now have a more intuitive option available:
the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
package</a>.</p>
<p>This Python package is a wrapper around the <code>AMR</code> R
package. It uses the <code>rpy2</code> package internally. Despite the
need to have R installed, Python users can now easily work with AMR data
directly through Python code.</p>
</div>
<div class="section level2">
<h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
</h2>
<p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
(venv)</a>. You can set up such an environment by running:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
<p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
the environment</a>, after which the venv is ready to work with.</p>
</div>
<div class="section level2">
<h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
</h2>
<ol style="list-style-type: decimal">
<li>
<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
just run:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
</li>
<li>
<p>Make sure you have R installed. There is <strong>no need to
install the <code>AMR</code> R package</strong>, as it will be installed
automatically.</p>
<p>For Linux:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
<p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
download page</a> to download and install R.</p>
</li>
</ol>
</div>
<div class="section level2">
<h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
</h2>
<div class="section level3">
<h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
</h3>
<p>Here’s an example that demonstrates how to clean microorganism and
drug names using the <code>AMR</code> Python package:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th>MOs</th>
<th>Drug</th>
<th>MO_clean</th>
<th>Drug_clean</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>E. coli</td>
<td>Cipro</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="even">
<td>ESCCOL</td>
<td>CIP</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="odd">
<td>esco</td>
<td>J01MA02</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="even">
<td>Esche coli</td>
<td>Ciproxin</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
</tbody>
</table>
<div class="section level4">
<h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
</h4>
<ul>
<li><p><strong>mo_name:</strong> This function standardises
microorganism names. Here, different variations of <em>Escherichia
coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
all converted into the correct, standardised form, “Escherichia
coli”.</p></li>
<li><p><strong>ab_name</strong>: Similarly, this function standardises
antimicrobial names. The different representations of ciprofloxacin
(e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
the standard name, “Ciprofloxacin”.</p></li>
</ul>
</div>
</div>
<div class="section level3">
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<pre><code>[0.59555556]</code></pre>
</div>
<div class="section level3">
<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
</h3>
<p>One of the core functions of the <code>AMR</code> package is
generating an antibiogram, a table that summarises the antimicrobial
susceptibility of bacterial isolates. Here’s how you can generate an
antibiogram from Python:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="22%">
<col width="22%">
<col width="22%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>CoNS</td>
<td>7% (10/142)</td>
<td>73% (183/252)</td>
<td>30% (10/33)</td>
</tr>
<tr class="even">
<td>E. coli</td>
<td>50% (196/392)</td>
<td>88% (399/456)</td>
<td>94% (393/416)</td>
</tr>
<tr class="odd">
<td>K. pneumoniae</td>
<td>0% (0/58)</td>
<td>96% (53/55)</td>
<td>89% (47/53)</td>
</tr>
<tr class="even">
<td>P. aeruginosa</td>
<td>0% (0/30)</td>
<td>100% (30/30)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>P. mirabilis</td>
<td>None</td>
<td>94% (34/36)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. aureus</td>
<td>6% (8/131)</td>
<td>90% (171/191)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. epidermidis</td>
<td>1% (1/91)</td>
<td>64% (87/136)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. hominis</td>
<td>None</td>
<td>80% (56/70)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. pneumoniae</td>
<td>100% (112/112)</td>
<td>None</td>
<td>100% (112/112)</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="20%">
<col width="22%">
<col width="23%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>Gram-negative</td>
<td>36% (226/631)</td>
<td>91% (621/684)</td>
<td>88% (565/641)</td>
</tr>
<tr class="even">
<td>Gram-positive</td>
<td>43% (305/703)</td>
<td>77% (560/724)</td>
<td>86% (296/345)</td>
</tr>
</tbody>
</table>
<p>In this example, we generate an antibiogram by selecting various
antibiotics.</p>
</div>
<div class="section level3">
<h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
</h3>
<p>As a Python user, you might like that the most important data sets of
the <code>AMR</code> R package, <code>microorganisms</code>,
<code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
<code>example_isolates</code>, are now available as regular Python data
frames:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
<table class="table">
<colgroup>
<col width="11%">
<col width="29%">
<col width="8%">
<col width="8%">
<col width="8%">
<col width="10%">
<col width="13%">
<col width="9%">
</colgroup>
<thead><tr class="header">
<th>mo</th>
<th>fullname</th>
<th>status</th>
<th>kingdom</th>
<th>gbif</th>
<th>gbif_parent</th>
<th>gbif_renamed_to</th>
<th>prevalence</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>B_GRAMN</td>
<td>(unknown Gram-negatives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_GRAMP</td>
<td>(unknown Gram-positives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ANAER-NEG</td>
<td>(unknown anaerobic Gram-negatives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ANAER-POS</td>
<td>(unknown anaerobic Gram-positives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ANAER</td>
<td>(unknown anaerobic bacteria)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
</tr>
<tr class="odd">
<td>B_ZYMMN_POMC</td>
<td>Zymomonas pomaceae</td>
<td>accepted</td>
<td>Bacteria</td>
<td>10744418</td>
<td>3221412</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ZYMPH</td>
<td>Zymophilus</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>9475166</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ZYMPH_PCVR</td>
<td>Zymophilus paucivorans</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ZYMPH_RFFN</td>
<td>Zymophilus raffinosivorans</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>F_ZYZYG</td>
<td>Zyzygomyces</td>
<td>unknown</td>
<td>Fungi</td>
<td>None</td>
<td>7581</td>
<td>None</td>
<td>2.0</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="12%">
<col width="20%">
<col width="25%">
<col width="9%">
<col width="11%">
<col width="7%">
<col width="9%">
</colgroup>
<thead><tr class="header">
<th>ab</th>
<th>cid</th>
<th>name</th>
<th>group</th>
<th>oral_ddd</th>
<th>oral_units</th>
<th>iv_ddd</th>
<th>iv_units</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>AMA</td>
<td>4649.0</td>
<td>4-aminosalicylic acid</td>
<td>Antimycobacterials</td>
<td>12.00</td>
<td>g</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ACM</td>
<td>6450012.0</td>
<td>Acetylmidecamycin</td>
<td>Macrolides/lincosamides</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>ASP</td>
<td>49787020.0</td>
<td>Acetylspiramycin</td>
<td>Macrolides/lincosamides</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ALS</td>
<td>8954.0</td>
<td>Aldesulfone sodium</td>
<td>Other antibacterials</td>
<td>0.33</td>
<td>g</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>AMK</td>
<td>37768.0</td>
<td>Amikacin</td>
<td>Aminoglycosides</td>
<td>NaN</td>
<td>None</td>
<td>1.0</td>
<td>g</td>
</tr>
<tr class="even">
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
</tr>
<tr class="odd">
<td>VIR</td>
<td>11979535.0</td>
<td>Virginiamycine</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>VOR</td>
<td>71616.0</td>
<td>Voriconazole</td>
<td>Antifungals/antimycotics</td>
<td>0.40</td>
<td>g</td>
<td>0.4</td>
<td>g</td>
</tr>
<tr class="odd">
<td>XBR</td>
<td>72144.0</td>
<td>Xibornol</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ZID</td>
<td>77846445.0</td>
<td>Zidebactam</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>ZFD</td>
<td>NaN</td>
<td>Zoliflodacin</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section level2">
<h2 id="installation-channels">Installation Channels<a class="anchor" aria-label="anchor" href="#installation-channels"></a>
</h2>
<div class="section level3">
<h3 id="stable-release-cran">Stable Release (CRAN)<a class="anchor" aria-label="anchor" href="#stable-release-cran"></a>
</h3>
<p>The default <code>AMR</code> Python package uses the latest stable
version of the <code>AMR</code> R package, published on CRAN. After
running <code>pip install AMR</code>, import it as usual:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>AMR.example_isolates</span></code></pre></div>
</div>
<div class="section level3">
<h3 id="development-version-github">Development Version (GitHub)<a class="anchor" aria-label="anchor" href="#development-version-github"></a>
</h3>
<p>To use the latest development version of the <code>AMR</code> R
package (sourced directly from GitHub), import the <code>beta</code>
sub-package and alias it as <code>AMR</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="im">import</span> AMR.beta <span class="im">as</span> AMR</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>AMR.example_isolates</span></code></pre></div>
<p>Aliasing with <code>as AMR</code> 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.</p>
</div>
</div>
<div class="section level2">
<h2 id="sir-classification-with-as_sir">SIR Classification with <code>as_sir()</code><a class="anchor" aria-label="anchor" href="#sir-classification-with-as_sir"></a>
</h2>
<div class="section level3">
<h3 id="using-enforce_method">Using <code>enforce_method</code><a class="anchor" aria-label="anchor" href="#using-enforce_method"></a>
</h3>
<p>The <code>as_sir()</code> function in R uses S3 method dispatch to
select the correct calculation method based on the input class:
<code>&lt;mic&gt;</code> for MIC values and <code>&lt;disk&gt;</code>
for disk diffusion values. Because Python objects do not carry R class
attributes through the <code>rpy2</code> bridge, this automatic dispatch
may not resolve correctly.</p>
<p>To explicitly specify the input type, use the
<code>enforce_method</code> argument:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="co"># Treat the column as MIC values — maps to R's as.sir.mic()</span></span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>AMR.as_sir(df[<span class="st">"MIC_col"</span>], mo<span class="op">=</span><span class="st">"E. coli"</span>, ab<span class="op">=</span><span class="st">"AMX"</span>, guideline<span class="op">=</span><span class="st">"EUCAST"</span>, enforce_method<span class="op">=</span><span class="st">"mic"</span>)</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a></span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a><span class="co"># Treat the column as disk diffusion values — maps to R's as.sir.disk()</span></span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>AMR.as_sir(df[<span class="st">"disk_col"</span>], mo<span class="op">=</span><span class="st">"E. coli"</span>, ab<span class="op">=</span><span class="st">"AMX"</span>, guideline<span class="op">=</span><span class="st">"EUCAST"</span>, enforce_method<span class="op">=</span><span class="st">"disk"</span>)</span></code></pre></div>
<p>Without <code>enforce_method</code>, R falls back to class-based
dispatch on the raw Python input, which may fail or return unexpected
results. Always supply <code>enforce_method</code> when calling
<code>as_sir()</code> from Python.</p>
</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 GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in the Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
</div>
<div class="pkgdown-footer-right">
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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</html>
articles/AMR_for_Python.md
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# AMR for Python
## Introduction
The `AMR` package for R is a powerful tool for antimicrobial resistance
(AMR) analysis. It provides extensive features for handling microbial
and antimicrobial data. However, for those who work primarily in Python,
we now have a more intuitive option available: the [`AMR` Python
package](https://pypi.org/project/AMR/).
This Python package is a wrapper around the `AMR` R package. It uses the
`rpy2` package internally. Despite the need to have R installed, Python
users can now easily work with AMR data directly through Python code.
## Prerequisites
This package was only tested with a [virtual environment
(venv)](https://docs.python.org/3/library/venv.html). You can set up
such an environment by running:
``` python
# linux and macOS:
python -m venv /path/to/new/virtual/environment
# Windows:
python -m venv C:\path\to\new\virtual\environment
```
Then you can [activate the
environment](https://docs.python.org/3/library/venv.html#how-venvs-work),
after which the venv is ready to work with.
## Install AMR
1. Since the Python package is available on the official [Python
Package Index](https://pypi.org/project/AMR/), you can just run:
``` bash
pip install AMR
```
2. Make sure you have R installed. There is **no need to install the
`AMR` R package**, as it will be installed automatically.
For Linux:
``` bash
# Ubuntu / Debian
sudo apt install r-base
# Fedora:
sudo dnf install R
# CentOS/RHEL
sudo yum install R
```
For macOS (using [Homebrew](https://brew.sh)):
``` bash
brew install r
```
For Windows, visit the [CRAN download
page](https://cran.r-project.org) to download and install R.
## Examples of Usage
### Cleaning Taxonomy
Here’s an example that demonstrates how to clean microorganism and drug
names using the `AMR` Python package:
``` python
import pandas as pd
import AMR
# Sample data
data = {
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)
# Use AMR functions to clean microorganism and drug names
df['MO_clean'] = AMR.mo_name(df['MOs'])
df['Drug_clean'] = AMR.ab_name(df['Drug'])
# Display the results
print(df)
```
| MOs | Drug | MO_clean | Drug_clean |
|------------|----------|------------------|---------------|
| E. coli | Cipro | Escherichia coli | Ciprofloxacin |
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
#### Explanation
- **mo_name:** This function standardises microorganism names. Here,
different variations of *Escherichia coli* (such as “E. coli”,
“ESCCOL”, “esco”, and “Esche coli”) are all converted into the
correct, standardised form, “Escherichia coli”.
- **ab_name**: Similarly, this function standardises antimicrobial
names. The different representations of ciprofloxacin (e.g., “Cipro”,
“CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard
name, “Ciprofloxacin”.
### Calculating AMR
``` python
import AMR
import pandas as pd
df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result)
```
[0.59555556]
### Generating Antibiograms
One of the core functions of the `AMR` package is generating an
antibiogram, a table that summarises the antimicrobial susceptibility of
bacterial isolates. Here’s how you can generate an antibiogram from
Python:
``` python
result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
print(result2a)
```
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|----------------|----------------|---------------|-------------------------|
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
| P. mirabilis | None | 94% (34/36) | None |
| S. aureus | 6% (8/131) | 90% (171/191) | None |
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
| S. hominis | None | 80% (56/70) | None |
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
``` python
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
print(result2b)
```
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|---------------|---------------|---------------|-------------------------|
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
In this example, we generate an antibiogram by selecting various
antibiotics.
### Taxonomic Data Sets Now in Python!
As a Python user, you might like that the most important data sets of
the `AMR` R package, `microorganisms`, `antimicrobials`,
`clinical_breakpoints`, and `example_isolates`, are now available as
regular Python data frames:
``` python
AMR.microorganisms
```
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|----|----|----|----|----|----|----|----|
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
| … | … | … | … | … | … | … | … |
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
``` python
AMR.antimicrobials
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|----|----|----|----|----|----|----|----|
| AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| … | … | … | … | … | … | … | … |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
## 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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# AMR with tidymodels
> This page was almost entirely written by our [AMR for R
> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
> model able to answer any question about the `AMR` package.
Antimicrobial resistance (AMR) is a global health crisis, and
understanding resistance patterns is crucial for managing effective
treatments. The `AMR` R package provides robust tools for analysing AMR
data, including convenient antimicrobial selector functions like
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
In this post, we will explore how to use the `tidymodels` framework to
predict resistance patterns in the `example_isolates` dataset in two
examples.
This post contains the following examples:
1. Using Antimicrobial Selectors
2. Predicting ESBL Presence Using Raw MICs
3. Predicting AMR Over Time
## Example 1: Using Antimicrobial Selectors
By leveraging the power of `tidymodels` and the `AMR` package, we’ll
build a reproducible machine learning workflow to predict the Gramstain
of the microorganism to two important antibiotic classes:
aminoglycosides and beta-lactams.
### **Objective**
Our goal is to build a predictive model using the `tidymodels` framework
to determine the Gramstain of the microorganism based on microbial data.
We will:
1. Preprocess data using the selector functions
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
2. Define a logistic regression model for prediction.
3. Use a structured `tidymodels` workflow to preprocess, train, and
evaluate the model.
### **Data Preparation**
We begin by loading the required libraries and preparing the
`example_isolates` dataset from the `AMR` package.
``` r
# Load required libraries
library(AMR) # For AMR data analysis
library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
```
Prepare the data:
``` r
# Your data could look like this:
example_isolates
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
#> # ℹ 1,990 more rows
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
# Select relevant columns for prediction
data <- example_isolates %>%
# select AB results dynamically
select(mo, aminoglycosides(), betalactams()) %>%
# replace NAs with NI (not-interpretable)
mutate(
across(
where(is.sir),
~ replace_na(.x, "NI")
),
# make factors of SIR columns
across(
where(is.sir),
as.integer
),
# get Gramstain of microorganisms
mo = as.factor(mo_gramstain(mo))
) %>%
# drop NAs - the ones without a Gramstain (fungi, etc.)
drop_na()
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
#> FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
#> AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
#> (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
#> (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
```
**Explanation:**
- [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
dynamically select columns for antimicrobials in these classes.
- `drop_na()` ensures the model receives complete cases for training.
### **Defining the Workflow**
We now define the `tidymodels` workflow, which consists of three steps:
preprocessing, model specification, and fitting.
#### 1. Preprocessing with a Recipe
We create a recipe to preprocess the data for modelling.
``` r
# Define the recipe for data preprocessing
resistance_recipe <- recipe(mo ~ ., data = data) %>%
step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
resistance_recipe
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 20
#>
#> ── Operations
#> • Correlation filter on: c(aminoglycosides(), betalactams())
```
For a recipe that includes at least one preprocessing operation, like we
have with `step_corr()`, the necessary parameters can be estimated from
a training set using `prep()`:
``` r
prep(resistance_recipe)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
#> FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
#> AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
#> (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
#> (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
#>
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#>
#>
#> ── Inputs
#>
#> Number of variables by role
#>
#> outcome: 1
#> predictor: 20
#>
#>
#>
#> ── Training information
#>
#> Training data contained 1968 data points and no incomplete rows.
#>
#>
#>
#> ── Operations
#>
#> • Correlation filter on: AMX CTX | Trained
```
**Explanation:**
- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and
all other columns as predictors.
- `step_corr()` removes predictors (i.e., antibiotic columns) that have
a higher correlation than 90%.
Notice how the recipe contains just the antimicrobial selector
functions - no need to define the columns specifically. In the
preparation (retrieved with `prep()`) we can see that the columns or
variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
existing, other variables.
#### 2. Specifying the Model
We define a logistic regression model since resistance prediction is a
binary classification task.
``` r
# Specify a logistic regression model
logistic_model <- logistic_reg() %>%
set_engine("glm") # Use the Generalised Linear Model engine
logistic_model
#> Logistic Regression Model Specification (classification)
#>
#> Computational engine: glm
```
**Explanation:**
- `logistic_reg()` sets up a logistic regression model.
- `set_engine("glm")` specifies the use of R’s built-in GLM engine.
#### 3. Building the Workflow
We bundle the recipe and model together into a `workflow`, which
organises the entire modelling process.
``` r
# Combine the recipe and model into a workflow
resistance_workflow <- workflow() %>%
add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
add_model(logistic_model) # Add the logistic regression model
resistance_workflow
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: logistic_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 1 Recipe Step
#>
#> • step_corr()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Logistic Regression Model Specification (classification)
#>
#> Computational engine: glm
```
### **Training and Evaluating the Model**
To train the model, we split the data into training and testing sets.
Then, we fit the workflow on the training set and evaluate its
performance.
``` r
# Split data into training and testing sets
set.seed(123) # For reproducibility
data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
training_data <- training(data_split) # Training set
testing_data <- testing(data_split) # Testing set
# Fit the workflow to the training data
fitted_workflow <- resistance_workflow %>%
fit(training_data) # Train the model
```
**Explanation:**
- `initial_split()` splits the data into training and testing sets.
- `fit()` trains the workflow on the training set.
Notice how in `fit()`, the antimicrobial selector functions are
internally called again. For training, these functions are called since
they are stored in the recipe.
Next, we evaluate the model on the testing data.
``` r
# Make predictions on the testing set
predictions <- fitted_workflow %>%
predict(testing_data) # Generate predictions
probabilities <- fitted_workflow %>%
predict(testing_data, type = "prob") # Generate probabilities
predictions <- predictions %>%
bind_cols(probabilities) %>%
bind_cols(testing_data) # Combine with true labels
predictions
#> # A tibble: 394 × 24
#> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB
#> <fct> <dbl> <dbl> <fct> <int> <int>
#> 1 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5
#> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1
#> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5
#> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5
#> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5
#> 6 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5
#> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5
#> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4
#> 9 Gram-negative 1 e+ 0 2.22 e-16 Gram-n… 1 1
#> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4
#> # ℹ 384 more rows
#> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>,
#> # AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>,
#> # CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>
# Evaluate model performance
metrics <- predictions %>%
metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
metrics
#> # A tibble: 2 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 accuracy binary 0.995
#> 2 kap binary 0.989
# To assess some other model properties, you can make our own `metrics()` function
our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value
metrics2 <- predictions %>%
our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function
metrics2
#> # A tibble: 4 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 accuracy binary 0.995
#> 2 kap binary 0.989
#> 3 ppv binary 0.987
#> 4 npv binary 1
```
**Explanation:**
- [`predict()`](https://rdrr.io/r/stats/predict.html) generates
predictions on the testing set.
- `metrics()` computes evaluation metrics like accuracy and kappa.
It appears we can predict the Gram stain with a 99.5% accuracy based on
AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
curve looks like this:
``` r
predictions %>%
roc_curve(mo, `.pred_Gram-negative`) %>%
autoplot()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png)
### **Conclusion**
In this example, we demonstrated how to build a machine learning
pipeline with the `tidymodels` framework and the `AMR` package. By
combining selector functions like
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
with `tidymodels`, we efficiently prepared data, trained a model, and
evaluated its performance.
This workflow is extensible to other antimicrobial classes and
resistance patterns, empowering users to analyse AMR data systematically
and reproducibly.
------------------------------------------------------------------------
## Example 2: Predicting ESBL Presence Using Raw MICs
In this second example, we demonstrate how to use `<mic>` columns
directly in `tidymodels` workflows using AMR-specific recipe steps. This
includes a transformation to `log2` scale using
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
which prepares MIC values for use in classification models.
This approach and idea formed the basis for the publication [DOI:
10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
to model the presence of extended-spectrum beta-lactamases (ESBL) based
on MIC values.
### **Objective**
Our goal is to:
1. Use raw MIC values to predict whether a bacterial isolate produces
ESBL.
2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
3. Train a classification model and evaluate its predictive
performance.
### **Data Preparation**
We use the `esbl_isolates` dataset that comes with the AMR package.
``` r
# Load required libraries
library(AMR)
library(tidymodels)
# View the esbl_isolates data set
esbl_isolates
#> # A tibble: 500 × 19
#> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT
#> <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
#> 1 FALSE Esch… 32 32 4 64 64 8.00 8.00 1 1 16.0 20
#> 2 FALSE Esch… 32 32 4 64 64 4.00 8.00 1 1 16.0 320
#> 3 FALSE Esch… 4 2 64 8 4 8.00 0.12 16 16 0.5 20
#> 4 FALSE Kleb… 32 32 16 64 64 8.00 8.00 1 1 0.5 20
#> 5 FALSE Esch… 32 32 4 4 4 0.25 2.00 1 1 16.0 320
#> 6 FALSE Citr… 32 32 16 64 64 64.00 32.00 1 1 0.5 20
#> 7 FALSE Morg… 32 32 4 64 64 16.00 2.00 1 1 0.5 20
#> 8 FALSE Prot… 16 32 4 1 4 8.00 0.12 1 1 16.0 320
#> 9 FALSE Ente… 32 32 8 64 64 32.00 4.00 1 1 0.5 20
#> 10 FALSE Citr… 32 32 32 64 64 8.00 64.00 1 1 16.0 320
#> # ℹ 490 more rows
#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
#> # COL <mic>
# Prepare a binary outcome and convert to ordered factor
data <- esbl_isolates %>%
mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
```
**Explanation:**
- `esbl_isolates`: Contains MIC test results and ESBL status for each
isolate.
- `mutate(esbl = ...)`: Converts the target column to an ordered factor
for classification.
### **Defining the Workflow**
#### 1. Preprocessing with a Recipe
We use our
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
function to log2-transform MIC values, ensuring that MICs are numeric
and properly scaled. All MIC predictors can easily and agnostically
selected using the new
[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
``` r
# Split into training and testing sets
set.seed(123)
split <- initial_split(data)
training_data <- training(split)
testing_data <- testing(split)
# Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
prep(mic_recipe)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 17
#> undeclared role: 1
#>
#> ── Training information
#> Training data contained 375 data points and no incomplete rows.
#>
#> ── Operations
#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
```
**Explanation:**
- `remove_role()`: Removes irrelevant variables like genus.
- [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md):
Applies `log2(as.numeric(...))` to all MIC predictors in one go.
- `prep()`: Finalises the recipe based on training data.
#### 2. Specifying the Model
We use a simple logistic regression to model ESBL presence, though
recent models such as xgboost ([link to `parsnip`
manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
could be much more precise.
``` r
# Define the model
model <- logistic_reg(mode = "classification") %>%
set_engine("glm")
model
#> Logistic Regression Model Specification (classification)
#>
#> Computational engine: glm
```
**Explanation:**
- `logistic_reg()`: Specifies a binary classification model.
- `set_engine("glm")`: Uses the base R GLM engine.
#### 3. Building the Workflow
``` r
# Create workflow
workflow_model <- workflow() %>%
add_recipe(mic_recipe) %>%
add_model(model)
workflow_model
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: logistic_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 1 Recipe Step
#>
#> • step_mic_log2()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Logistic Regression Model Specification (classification)
#>
#> Computational engine: glm
```
### **Training and Evaluating the Model**
``` r
# Fit the model
fitted <- fit(workflow_model, training_data)
# Generate predictions
predictions <- predict(fitted, testing_data) %>%
bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
bind_cols(testing_data)
# Evaluate model performance
our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
metrics
#> # A tibble: 7 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 accuracy binary 0.92
#> 2 recall binary 0.921
#> 3 precision binary 0.921
#> 4 sensitivity binary 0.921
#> 5 specificity binary 0.919
#> 6 ppv binary 0.921
#> 7 npv binary 0.919
```
**Explanation:**
- `fit()`: Trains the model on the processed training data.
- [`predict()`](https://rdrr.io/r/stats/predict.html): Produces
predictions for unseen test data.
- `metric_set()`: Allows evaluating multiple classification metrics.
This will make `our_metrics` to become a function that we can use to
check the predictions with.
It appears we can predict ESBL gene presence with a positive predictive
value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
using a simplistic logistic regression model.
### **Visualising Predictions**
We can visualise predictions by comparing predicted and actual ESBL
status.
``` r
library(ggplot2)
ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
geom_bar(position = "stack") +
labs(
title = "Predicted vs Actual ESBL Status",
x = "Actual ESBL",
y = "Count"
) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png)
And plot the certainties too - how certain were the actual predictions?
``` r
predictions %>%
mutate(
certainty = ifelse(.pred_class == "FALSE",
.pred_FALSE,
.pred_TRUE
),
correct = ifelse(esbl == .pred_class, "Right", "Wrong")
) %>%
ggplot(aes(
x = seq_len(nrow(predictions)),
y = certainty,
colour = correct
)) +
scale_colour_manual(
values = c(Right = "green3", Wrong = "red2"),
name = "Correct?"
) +
geom_point() +
scale_y_continuous(
labels = function(x) paste0(x * 100, "%"),
limits = c(0.5, 1)
) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png)
### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps
simplify working with `<mic>` columns in `tidymodels`. The
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
transformation converts MICs (with or without operators) to
log2-transformed numerics, improving compatibility with classification
models.
This pipeline enables realistic, reproducible, and interpretable
modelling of antimicrobial resistance data.
------------------------------------------------------------------------
## Example 3: Predicting AMR Over Time
In this third example, we aim to predict antimicrobial resistance (AMR)
trends over time using `tidymodels`. We will model resistance to three
antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and
ciprofloxacin `CIP`), based on historical data grouped by year and
hospital ward.
### **Objective**
Our goal is to:
1. Prepare the dataset by aggregating resistance data over time.
2. Define a regression model to predict AMR trends.
3. Use `tidymodels` to preprocess, train, and evaluate the model.
### **Data Preparation**
We start by transforming the `example_isolates` dataset into a
structured time-series format.
``` r
# Load required libraries
library(AMR)
library(tidymodels)
# Transform dataset
data_time <- example_isolates %>%
top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
mutate(
year = as.integer(format(date, "%Y")), # Extract year from date
gramstain = mo_gramstain(mo)
) %>% # Get taxonomic names
group_by(year, gramstain) %>%
summarise(
across(c(AMX, AMC, CIP),
function(x) resistance(x, minimum = 0),
.names = "res_{.col}"
),
.groups = "drop"
) %>%
filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
#> ℹ Using column mo as input for `col_mo`.
data_time
#> # A tibble: 32 × 5
#> year gramstain res_AMX res_AMC res_CIP
#> <int> <chr> <dbl> <dbl> <dbl>
#> 1 2002 Gram-negative 1 0.105 0.0606
#> 2 2002 Gram-positive 0.838 0.182 0.162
#> 3 2003 Gram-negative 1 0.0714 0
#> 4 2003 Gram-positive 0.714 0.244 0.154
#> 5 2004 Gram-negative 0.464 0.0938 0
#> 6 2004 Gram-positive 0.849 0.299 0.244
#> 7 2005 Gram-negative 0.412 0.132 0.0588
#> 8 2005 Gram-positive 0.882 0.382 0.154
#> 9 2006 Gram-negative 0.379 0 0.1
#> 10 2006 Gram-positive 0.778 0.333 0.353
#> # ℹ 22 more rows
```
**Explanation:**
- `mo_name(mo)`: Converts microbial codes into proper species names.
- [`resistance()`](https://amr-for-r.org/reference/proportion.md):
Converts AMR results into numeric values (proportion of resistant
isolates).
- `group_by(year, ward, species)`: Aggregates resistance rates by year
and ward.
### **Defining the Workflow**
We now define the modelling workflow, which consists of a preprocessing
step, a model specification, and the fitting process.
#### 1. Preprocessing with a Recipe
``` r
# Define the recipe
resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
step_normalize(year) %>% # Normalise year for better model performance
step_nzv(all_predictors()) # Remove near-zero variance predictors
resistance_recipe_time
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 2
#>
#> ── Operations
#> • Dummy variables from: gramstain
#> • Centering and scaling for: year
#> • Sparse, unbalanced variable filter on: all_predictors()
```
**Explanation:**
- `step_dummy()`: Encodes categorical variables (`ward`, `species`) as
numerical indicators.
- `step_normalize()`: Normalises the `year` variable.
- `step_nzv()`: Removes near-zero variance predictors.
#### 2. Specifying the Model
We use a linear regression model to predict resistance trends.
``` r
# Define the linear regression model
lm_model <- linear_reg() %>%
set_engine("lm") # Use linear regression
lm_model
#> Linear Regression Model Specification (regression)
#>
#> Computational engine: lm
```
**Explanation:**
- `linear_reg()`: Defines a linear regression model.
- `set_engine("lm")`: Uses R’s built-in linear regression engine.
#### 3. Building the Workflow
We combine the preprocessing recipe and model into a workflow.
``` r
# Create workflow
resistance_workflow_time <- workflow() %>%
add_recipe(resistance_recipe_time) %>%
add_model(lm_model)
resistance_workflow_time
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 3 Recipe Steps
#>
#> • step_dummy()
#> • step_normalize()
#> • step_nzv()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Linear Regression Model Specification (regression)
#>
#> Computational engine: lm
```
### **Training and Evaluating the Model**
We split the data into training and testing sets, fit the model, and
evaluate performance.
``` r
# Split the data
set.seed(123)
data_split_time <- initial_split(data_time, prop = 0.8)
train_time <- training(data_split_time)
test_time <- testing(data_split_time)
# Train the model
fitted_workflow_time <- resistance_workflow_time %>%
fit(train_time)
# Make predictions
predictions_time <- fitted_workflow_time %>%
predict(test_time) %>%
bind_cols(test_time)
# Evaluate model
metrics_time <- predictions_time %>%
metrics(truth = res_AMX, estimate = .pred)
metrics_time
#> # A tibble: 3 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 rmse standard 0.0774
#> 2 rsq standard 0.711
#> 3 mae standard 0.0704
```
**Explanation:**
- `initial_split()`: Splits data into training and testing sets.
- `fit()`: Trains the workflow.
- [`predict()`](https://rdrr.io/r/stats/predict.html): Generates
resistance predictions.
- `metrics()`: Evaluates model performance.
### **Visualising Predictions**
We plot resistance trends over time for amoxicillin.
``` r
library(ggplot2)
# Plot actual vs predicted resistance over time
ggplot(predictions_time, aes(x = year)) +
geom_point(aes(y = res_AMX, color = "Actual")) +
geom_line(aes(y = .pred, color = "Predicted")) +
labs(
title = "Predicted vs Actual AMX Resistance Over Time",
x = "Year",
y = "Resistance Proportion"
) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png)
Additionally, we can visualise resistance trends in `ggplot2` and
directly add linear models there:
``` r
ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
geom_line() +
labs(
title = "AMX Resistance Trends",
x = "Year",
y = "Resistance Proportion"
) +
# add a linear model directly in ggplot2:
geom_smooth(
method = "lm",
formula = y ~ x,
alpha = 0.25
) +
theme_minimal()
```
![](AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png)
### **Conclusion**
In this example, we demonstrated how to analyze AMR trends over time
using `tidymodels`. By aggregating resistance rates by year and hospital
ward, we built a predictive model to track changes in resistance to
amoxicillin (`AMX`), amoxicillin-clavulanic acid (`AMC`), and
ciprofloxacin (`CIP`).
This method can be extended to other antibiotics and resistance
patterns, providing valuable insights into AMR dynamics in healthcare
settings.
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<img src="../logo.svg" class="logo" alt=""><h1>Apply EUCAST rules</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
<div class="d-none name"><code>EUCAST.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>What are EUCAST rules? The European Committee on Antimicrobial
Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
their website</a>:</p>
<blockquote>
<p><em>EUCAST expert rules (see below) are a tabulated collection of
expert knowledge on interpretive rules, expected resistant phenotypes
and expected susceptible phenotypes which should be applied to
antimicrobial susceptibility testing in order to reduce testing, reduce
errors and make appropriate recommendations for reporting particular
resistances.</em></p>
</blockquote>
<p>In Europe, a lot of medical microbiological laboratories already
apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
<em>et al.</em>, 2015</a>). Our package features their latest insights
on expected resistant phenotypes (v1.2, 2023).</p>
</div>
<div class="section level2">
<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
</h2>
<p>These rules can be used to discard improbable bug-drug combinations
in your data. For example, <em>Klebsiella</em> produces beta-lactamase
that prevents ampicillin (or amoxicillin) from working against it. In
other words, practically every strain of <em>Klebsiella</em> is
resistant to ampicillin.</p>
<p>Sometimes, laboratory data can still contain such strains with
<em>Klebsiella</em> being susceptible to ampicillin. This could be
because an antibiogram is available before an identification is
available, and the antibiogram is then not re-interpreted based on the
identification. The <code><a href="../reference/interpretive_rules.html">eucast_rules()</a></code> function resolves this,
by applying the latest ‘EUCAST Expected Resistant Phenotypes’
guideline:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
<span> mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
<span> <span class="st">"Escherichia coli"</span></span>
<span> <span class="op">)</span>,</span>
<span> ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
<span><span class="op">)</span></span>
<span><span class="va">oops</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
<span><span class="co">#&gt; mo ampicillin</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;"> S </span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
<span></span>
<span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
<span><span class="co">#&gt; mo ampicillin</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FF5F5F;"> R </span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span></code></pre></div>
<p>A more convenient function is
<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
but allows to check for one or more specific microorganisms or
antimicrobials:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
<span> <span class="st">"ampicillin"</span></span>
<span><span class="op">)</span></span>
<span><span class="co">#&gt; [1] TRUE FALSE</span></span>
<span></span>
<span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
<span><span class="op">)</span></span>
<span><span class="co">#&gt; [1] TRUE FALSE</span></span></code></pre></div>
<p>EUCAST rules can not only be used for correction, they can also be
used for filling in known resistance and susceptibility based on results
of other antimicrobials drugs. This process is called <em>interpretive
reading</em>, and is basically a form of imputation:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
<span> mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
<span> <span class="st">"Staphylococcus aureus"</span>,</span>
<span> <span class="st">"Enterococcus faecalis"</span>,</span>
<span> <span class="st">"Escherichia coli"</span>,</span>
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
<span> <span class="st">"Pseudomonas aeruginosa"</span></span>
<span> <span class="op">)</span>,</span>
<span> VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
<span> AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
<span> COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
<span> CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
<span> CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
<span> PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
<span> FOX <span class="op">=</span> <span class="st">"S"</span> <span class="co"># Cefoxitin</span></span>
<span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
<th align="center">VAN</th>
<th align="center">AMX</th>
<th align="center">COL</th>
<th align="center">CAZ</th>
<th align="center">CXM</th>
<th align="center">PEN</th>
<th align="center">FOX</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Staphylococcus aureus</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Enterococcus faecalis</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Escherichia coli</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Klebsiella pneumoniae</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Pseudomonas aeruginosa</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">mo</th>
<th align="center">VAN</th>
<th align="center">AMX</th>
<th align="center">COL</th>
<th align="center">CAZ</th>
<th align="center">CXM</th>
<th align="center">PEN</th>
<th align="center">FOX</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Staphylococcus aureus</td>
<td align="center">-</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Enterococcus faecalis</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="left">Escherichia coli</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="left">Klebsiella pneumoniae</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">S</td>
</tr>
<tr class="odd">
<td align="left">Pseudomonas aeruginosa</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
</tr>
</tbody>
</table>
</div>
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articles/EUCAST.md
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# Apply EUCAST rules
## Introduction
What are EUCAST rules? The European Committee on Antimicrobial
Susceptibility Testing (EUCAST) states [on their
website](https://www.eucast.org/expert_rules_and_expected_phenotypes):
> *EUCAST expert rules (see below) are a tabulated collection of expert
> knowledge on interpretive rules, expected resistant phenotypes and
> expected susceptible phenotypes which should be applied to
> antimicrobial susceptibility testing in order to reduce testing,
> reduce errors and make appropriate recommendations for reporting
> particular resistances.*
In Europe, a lot of medical microbiological laboratories already apply
these rules ([Brown *et al.*,
2015](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008)).
Our package features their latest insights on expected resistant
phenotypes (v1.2, 2023).
## Examples
These rules can be used to discard improbable bug-drug combinations in
your data. For example, *Klebsiella* produces beta-lactamase that
prevents ampicillin (or amoxicillin) from working against it. In other
words, practically every strain of *Klebsiella* is resistant to
ampicillin.
Sometimes, laboratory data can still contain such strains with
*Klebsiella* being susceptible to ampicillin. This could be because an
antibiogram is available before an identification is available, and the
antibiogram is then not re-interpreted based on the identification. The
[`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
function resolves this, by applying the latest ‘EUCAST Expected
Resistant Phenotypes’ guideline:
``` r
oops <- tibble::tibble(
mo = c(
"Klebsiella pneumoniae",
"Escherichia coli"
),
ampicillin = as.sir("S")
)
oops
#> # A tibble: 2 × 2
#> mo ampicillin
#> <chr> <sir>
#> 1 Klebsiella pneumoniae S
#> 2 Escherichia coli S
eucast_rules(oops, info = FALSE, overwrite = TRUE)
#> # A tibble: 2 × 2
#> mo ampicillin
#> <chr> <sir>
#> 1 Klebsiella pneumoniae R
#> 2 Escherichia coli S
```
A more convenient function is
[`mo_is_intrinsic_resistant()`](https://amr-for-r.org/reference/mo_property.md)
that uses the same guideline, but allows to check for one or more
specific microorganisms or antimicrobials:
``` r
mo_is_intrinsic_resistant(
c("Klebsiella pneumoniae", "Escherichia coli"),
"ampicillin"
)
#> [1] TRUE FALSE
mo_is_intrinsic_resistant(
"Klebsiella pneumoniae",
c("ampicillin", "kanamycin")
)
#> [1] TRUE FALSE
```
EUCAST rules can not only be used for correction, they can also be used
for filling in known resistance and susceptibility based on results of
other antimicrobials drugs. This process is called *interpretive
reading*, and is basically a form of imputation:
``` r
data <- tibble::tibble(
mo = c(
"Staphylococcus aureus",
"Enterococcus faecalis",
"Escherichia coli",
"Klebsiella pneumoniae",
"Pseudomonas aeruginosa"
),
VAN = "-", # Vancomycin
AMX = "-", # Amoxicillin
COL = "-", # Colistin
CAZ = "-", # Ceftazidime
CXM = "-", # Cefuroxime
PEN = "S", # Benzylenicillin
FOX = "S" # Cefoxitin
)
```
``` r
data
```
| mo | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Staphylococcus aureus | \- | \- | \- | \- | \- | S | S |
| Enterococcus faecalis | \- | \- | \- | \- | \- | S | S |
| Escherichia coli | \- | \- | \- | \- | \- | S | S |
| Klebsiella pneumoniae | \- | \- | \- | \- | \- | S | S |
| Pseudomonas aeruginosa | \- | \- | \- | \- | \- | S | S |
``` r
eucast_rules(data, overwrite = TRUE)
```
| mo | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Staphylococcus aureus | \- | S | R | R | S | S | S |
| Enterococcus faecalis | \- | \- | R | R | R | S | R |
| Escherichia coli | R | \- | \- | \- | \- | R | S |
| Klebsiella pneumoniae | R | R | \- | \- | \- | R | S |
| Pseudomonas aeruginosa | R | R | \- | \- | R | R | R |
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<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Conduct principal component analysis (PCA) for AMR</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
<div class="d-none name"><code>PCA.Rmd</code></div>
</div>
<p><strong>NOTE: This page will be updated soon, as the pca() function
is currently being developed.</strong></p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
</div>
<div class="section level2">
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h2>
<p>For PCA, we need to transform our AMR data first. This is what the
<code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
<span><span class="co">#&gt; Rows: 2,000</span></span>
<span><span class="co">#&gt; Columns: 46</span></span>
<span><span class="co">#&gt; $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
<span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
<span><span class="co">#&gt; $ age <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
<span><span class="co">#&gt; $ gender <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
<span><span class="co">#&gt; $ ward <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
<span><span class="co">#&gt; $ mo <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ PEN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ OXA <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ FLC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
<span><span class="co">#&gt; $ AMX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ AMC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ AMP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ TZP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ CZO <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ FEP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ CXM <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
<span><span class="co">#&gt; $ FOX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ CTX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
<span><span class="co">#&gt; $ CAZ <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
<span><span class="co">#&gt; $ CRO <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
<span><span class="co">#&gt; $ GEN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ TOB <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ AMK <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ KAN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ TMP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
<span><span class="co">#&gt; $ SXT <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ NIT <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ FOS <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ LNZ <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ CIP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
<span><span class="co">#&gt; $ MFX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ VAN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
<span><span class="co">#&gt; $ TEC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
<span><span class="co">#&gt; $ TCY <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
<span><span class="co">#&gt; $ TGC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ DOX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ ERY <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ CLI <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ AZM <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
<span><span class="co">#&gt; $ IPM <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
<span><span class="co">#&gt; $ MEM <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ MTR <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ CHL <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ COL <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
<span><span class="co">#&gt; $ MUP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
<span><span class="co">#&gt; $ RIF <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
<p>Now to transform this to a data set with only resistance percentages
per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
<span> order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
<span> genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
<span> <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># and genus as we do here</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
<span> <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
<span> <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
<span> <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
<span></span>
<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># Groups: order [5]</span></span></span>
<span><span class="co">#&gt; order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (unknown order) (unknown ge… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Actinomycetales Schaalia <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Bacteroidales Bacteroides <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Caryophanales Gemella <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> Caryophanales Listeria <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h2>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
that contain numeric values in all selected variables, so we now only
need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Columns selected for PCA: <span style="color: #0000BB;">"\033[1mAMC\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCAZ\033[22m"</span>,</span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">"\033[1mCTX\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCXM\033[22m"</span>, <span style="color: #0000BB;">"\033[1mGEN\033[22m"</span>,</span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">"\033[1mSXT\033[22m"</span>, <span style="color: #0000BB;">"\033[1mTMP\033[22m"</span>, and <span style="color: #0000BB;">"\033[1mTOB\033[22m"</span>. Total</span></span>
<span><span class="co">#&gt; observations available: 7.</span></span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
<span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
<span><span class="co">#&gt; [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></span>
<span><span class="co">#&gt; Importance of components:</span></span>
<span><span class="co">#&gt; PC1 PC2 PC3 PC4 PC5 PC6 PC7</span></span>
<span><span class="co">#&gt; Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
<span><span class="co">#&gt; Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
<span><span class="co">#&gt; Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
<pre><code><span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
<span><span class="co">#&gt; [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></span></code></pre>
<p>Good news. The first two components explain a total of 93.3% of the
variance (see the PC1 and PC2 values of the <em>Proportion of
Variance</em>. We can create a so-called biplot with the base R
<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
per drug explain the difference per microorganism.</p>
</div>
<div class="section level2">
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
</h2>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" alt="" width="750"></p>
<p>But we can’t see the explanation of the points. Perhaps this works
better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
<span> <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="750"></p>
</div>
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articles/PCA.md
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# Conduct principal component analysis (PCA) for AMR
**NOTE: This page will be updated soon, as the pca() function is
currently being developed.**
## Introduction
## Transforming
For PCA, we need to transform our AMR data first. This is what the
`example_isolates` data set in this package looks like:
``` r
library(AMR)
library(dplyr)
glimpse(example_isolates)
#> Rows: 2,000
#> Columns: 46
#> $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
#> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
#> $ age <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
#> $ gender <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
#> $ ward <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
#> $ mo <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
#> $ PEN <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
#> $ OXA <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ FLC <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
#> $ AMX <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
#> $ AMC <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
#> $ AMP <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
#> $ TZP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ CZO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
#> $ FEP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ CXM <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
#> $ FOX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
#> $ CTX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
#> $ CAZ <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
#> $ CRO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
#> $ GEN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ TOB <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
#> $ AMK <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ KAN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ TMP <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
#> $ SXT <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
#> $ NIT <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
#> $ FOS <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ LNZ <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
#> $ CIP <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
#> $ MFX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ VAN <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
#> $ TEC <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
#> $ TCY <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
#> $ TGC <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
#> $ DOX <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
#> $ ERY <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
#> $ CLI <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
#> $ AZM <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
#> $ IPM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
#> $ MEM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ MTR <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ CHL <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ COL <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
#> $ MUP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ RIF <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
```
Now to transform this to a data set with only resistance percentages per
taxonomic order and genus:
``` r
resistance_data <- example_isolates %>%
group_by(
order = mo_order(mo), # group on anything, like order
genus = mo_genus(mo)
) %>% # and genus as we do here
summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
select(
order, genus, AMC, CXM, CTX,
CAZ, GEN, TOB, TMP, SXT
) # and select only relevant columns
head(resistance_data)
#> # A tibble: 6 × 10
#> # Groups: order [5]
#> order genus AMC CXM CTX CAZ GEN TOB TMP SXT
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA
#> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA
#> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA
#> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA
#> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA
#> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA
```
## Perform principal component analysis
The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
automatically filter on rows that contain numeric values in all selected
variables, so we now only need to do:
``` r
pca_result <- pca(resistance_data)
#> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
#> "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
#> "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
#> observations available: 7.
```
The result can be reviewed with the good old
[`summary()`](https://rdrr.io/r/base/summary.html) function:
``` r
summary(pca_result)
#> Groups (n=4, named as 'order'):
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
#> Importance of components:
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7
#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
```
#> Groups (n=4, named as 'order'):
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
Good news. The first two components explain a total of 93.3% of the
variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
can create a so-called biplot with the base R
[`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
antimicrobial resistance per drug explain the difference per
microorganism.
## Plotting the results
``` r
biplot(pca_result)
```
![](PCA_files/figure-html/unnamed-chunk-5-1.png)
But we can’t see the explanation of the points. Perhaps this works
better with our new
[`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
function, that automatically adds the right labels and even groups:
``` r
ggplot_pca(pca_result)
```
![](PCA_files/figure-html/unnamed-chunk-6-1.png)
You can also print an ellipse per group, and edit the appearance:
``` r
ggplot_pca(pca_result, ellipse = TRUE) +
ggplot2::labs(title = "An AMR/PCA biplot!")
```
![](PCA_files/figure-html/unnamed-chunk-7-1.png)
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<img src="../logo.svg" class="logo" alt=""><h1>Work with WHONET data</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
<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://amr-for-r.org/reference/WHONET.html">example data set
<code>WHONET</code></a>. We will use it for this analysis.</p>
</div>
<div class="section level3">
<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
</h3>
<p>First, load the relevant packages if you did not yet did this. I use
the tidyverse for all of my analyses. All of them. If you don’t know it
yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># this package</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
<p>We will have to transform some variables to simplify and automate the
analysis:</p>
<ul>
<li>Microorganisms should be transformed to our own microorganism codes
(called an <code>mo</code>) using <a href="https://amr-for-r.org/reference/catalogue_of_life">our Catalogue
of Life reference data set</a>, which contains all ~70,000
microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
recognises almost all WHONET abbreviations of microorganisms.</li>
<li>Antimicrobial results or interpretations have to be clean and valid.
In other words, they should only contain values <code>"S"</code>,
<code>"I"</code> or <code>"R"</code>. That is exactly where the
<code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
</ul>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># transform variables</span></span>
<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="co"># get microbial ID based on given organism</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
<p>No errors or warnings, so all values are transformed succesfully.</p>
<p>We also created a package dedicated to data cleaning and checking,
called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
function can be used to create frequency tables.</p>
<p>So let’s check our data, with a couple of frequency tables:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 500<br>
Available: 500 (100%, NA: 0 = 0%)<br>
Unique: 38</p>
<p>Shortest: 11<br>
Longest: 40</p>
<table class="table">
<colgroup>
<col width="4%">
<col width="47%">
<col width="7%">
<col width="10%">
<col width="13%">
<col width="15%">
</colgroup>
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">245</td>
<td align="right">49.0%</td>
<td align="right">245</td>
<td align="right">49.0%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
<td align="right">74</td>
<td align="right">14.8%</td>
<td align="right">319</td>
<td align="right">63.8%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Staphylococcus epidermidis</td>
<td align="right">38</td>
<td align="right">7.6%</td>
<td align="right">357</td>
<td align="right">71.4%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">31</td>
<td align="right">6.2%</td>
<td align="right">388</td>
<td align="right">77.6%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Staphylococcus hominis</td>
<td align="right">21</td>
<td align="right">4.2%</td>
<td align="right">409</td>
<td align="right">81.8%</td>
</tr>
<tr class="even">
<td align="left">6</td>
<td align="left">Proteus mirabilis</td>
<td align="right">9</td>
<td align="right">1.8%</td>
<td align="right">418</td>
<td align="right">83.6%</td>
</tr>
<tr class="odd">
<td align="left">7</td>
<td align="left">Enterococcus faecium</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">426</td>
<td align="right">85.2%</td>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">Staphylococcus capitis urealyticus</td>
<td align="right">8</td>
<td align="right">1.6%</td>
<td align="right">434</td>
<td align="right">86.8%</td>
</tr>
<tr class="odd">
<td align="left">9</td>
<td align="left">Enterobacter cloacae</td>
<td align="right">5</td>
<td align="right">1.0%</td>
<td align="right">439</td>
<td align="right">87.8%</td>
</tr>
<tr class="even">
<td align="left">10</td>
<td align="left">Enterococcus columbae</td>
<td align="right">4</td>
<td align="right">0.8%</td>
<td align="right">443</td>
<td align="right">88.6%</td>
</tr>
</tbody>
</table>
<p>(omitted 28 entries, n = 57 [11.4%])</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
<span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered &gt; sir (numeric)<br>
Length: 500<br>
Levels: 8: S &lt; SDD &lt; I &lt; R &lt; NI &lt; WT &lt; NWT &lt;
NS<br>
Available: 481 (96.2%, NA: 19 = 3.8%)<br>
Unique: 3</p>
<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
Drug group: Aminopenicillins<br>
%SI: 78.59%</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">S</td>
<td align="right">356</td>
<td align="right">74.01%</td>
<td align="right">356</td>
<td align="right">74.01%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">R</td>
<td align="right">103</td>
<td align="right">21.41%</td>
<td align="right">459</td>
<td align="right">95.43%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">I</td>
<td align="right">22</td>
<td align="right">4.57%</td>
<td align="right">481</td>
<td align="right">100.00%</td>
</tr>
</tbody>
</table>
</div>
<div class="section level3">
<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
</h3>
<p>An easy <code>ggplot</code> will already give a lot of information,
using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
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articles/WHONET.md
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# Work with WHONET data
### Import of data
This tutorial assumes you already imported the WHONET data with e.g. the
[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
Choose the option ‘From Excel’ and select your exported file. Make sure
date fields are imported correctly.
An example syntax could look like this:
``` r
library(readxl)
data <- read_excel(path = "path/to/your/file.xlsx")
```
This package comes with an [example data set
`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
for this analysis.
### Preparation
First, load the relevant packages if you did not yet did this. I use the
tidyverse for all of my analyses. All of them. If you don’t know it yet,
I suggest you read about it on their website:
<https://www.tidyverse.org/>.
``` r
library(dplyr) # part of tidyverse
library(ggplot2) # part of tidyverse
library(AMR) # this package
library(cleaner) # to create frequency tables
```
We will have to transform some variables to simplify and automate the
analysis:
- Microorganisms should be transformed to our own microorganism codes
(called an `mo`) using [our Catalogue of Life reference data
set](https://amr-for-r.org/reference/catalogue_of_life), which
contains all ~70,000 microorganisms from the taxonomic kingdoms
Bacteria, Fungi and Protozoa. We do the tranformation with
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
also recognises almost all WHONET abbreviations of microorganisms.
- Antimicrobial results or interpretations have to be clean and valid.
In other words, they should only contain values `"S"`, `"I"` or `"R"`.
That is exactly where the
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
for.
``` r
# transform variables
data <- WHONET %>%
# get microbial ID based on given organism
mutate(mo = as.mo(Organism)) %>%
# transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
```
No errors or warnings, so all values are transformed succesfully.
We also created a package dedicated to data cleaning and checking,
called the `cleaner` package. Its
[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables:
``` r
# our newly created `mo` variable, put in the mo_name() function
data %>% freq(mo_name(mo), nmax = 10)
```
**Frequency table**
Class: character
Length: 500
Available: 500 (100%, NA: 0 = 0%)
Unique: 38
Shortest: 11
Longest: 40
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|:---|:---|---:|---:|---:|---:|
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
(omitted 28 entries, n = 57 \[11.4%\])
``` r
# our transformed antibiotic columns
# amoxicillin/clavulanic acid (J01CR02) as an example
data %>% freq(AMC_ND2)
```
**Frequency table**
Class: factor \> ordered \> sir (numeric)
Length: 500
Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS
Available: 481 (96.2%, NA: 19 = 3.8%)
Unique: 3
Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)
Drug group: Aminopenicillins
%SI: 78.59%
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|:----|:-----|------:|--------:|-----------:|-------------:|
| 1 | S | 356 | 74.01% | 356 | 74.01% |
| 2 | R | 103 | 21.41% | 459 | 95.43% |
| 3 | I | 22 | 4.57% | 481 | 100.00% |
### A first glimpse at results
An easy `ggplot` will already give a lot of information, using the
included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
function:
``` r
data %>%
group_by(Country) %>%
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
```
![](WHONET_files/figure-html/unnamed-chunk-7-1.png)
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<img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
<div class="d-none name"><code>WISCA.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
</h2>
<p>When a clinician starts empirical antimicrobial therapy, the
causative pathogen is unknown. The question they need answered is not
<em>“what proportion of</em> E. coli <em>is susceptible to
ciprofloxacin?“</em> but rather <em>“what is the probability that this
regimen will adequately cover whatever pathogen turns out to be causing
my patient’s infection?”</em></p>
<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
cannot answer that question. It presents susceptibility percentages per
species per antibiotic, but:</p>
<ul>
<li>
<strong>It fragments information by organism.</strong> The clinician
must mentally combine susceptibility rates across multiple species,
weighting by how often each species causes the syndrome, a calculation
nobody does at the bedside.</li>
<li>
<strong>It ignores pathogen incidence.</strong> A species that
causes 2% of infections is given the same visual weight as one that
causes 60%.</li>
<li>
<strong>It does not evaluate combination regimens.</strong> Much
empirical therapy consists of two or more agents, but the traditional
antibiogram only shows monotherapy per organism.</li>
<li>
<strong>It provides no measure of uncertainty.</strong> A reported
“90% susceptible” based on 50 isolates has a 95% confidence interval of
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
point estimate without context.</li>
</ul>
<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
Antibiogram) resolves all four limitations. It estimates the probability
that a regimen will provide adequate empirical coverage for a given
infection syndrome, weighted by local pathogen incidence, with full
uncertainty quantification via Bayesian inference.</p>
<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
demonstrated that traditional antibiogram susceptibility rates could be
misleading: ciprofloxacin appeared 84% effective against <em>E.
coli</em> in the traditional antibiogram, but WISCA revealed only 62%
coverage for UTI and 37% for abdominal infections, because enterococci
(intrinsically resistant) and other species contribute substantially to
these syndromes. Randhawa <em>et al.</em> (2014) showed that
WISCA-guided regimen selection could improve time-to-adequate-coverage
on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
Bayesian framework now used in this package, enabling credible intervals
and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
globally across 52 hospitals in 23 countries.</p>
</div>
<div class="section level2">
<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
</h2>
<p>WISCA asks:</p>
<blockquote>
<p>“What is the <strong>probability</strong> that this regimen
<strong>will cover</strong> the pathogen, given the syndrome?”</p>
</blockquote>
<p>This means combining two quantities:</p>
<ul>
<li>
<strong>Pathogen incidence</strong> in the syndrome (how often each
species causes it),</li>
<li>
<strong>Susceptibility</strong> of each pathogen to the
regimen.</li>
</ul>
<p>We can write this as:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
<p>For example, suppose in your hospital:</p>
<ul>
<li>
<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
susceptible to a drug.</li>
<li>
<em>Klebsiella</em> causes 40% of UTIs, and 70% of
<em>Klebsiella</em> are susceptible.</li>
</ul>
<p>Then:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
<p>That 82% is a far more clinically meaningful number than the
species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
reported separately in a traditional antibiogram, because it directly
answers the question the clinician actually faces.</p>
<p>But in real data, both incidence and susceptibility are
<strong>estimated from finite samples</strong>, so they carry
uncertainty. A sample of 50 isolates is not a census. WISCA models this
uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
distributions.</p>
</div>
<div class="section level2">
<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
</h2>
<div class="section level3">
<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
</h3>
<p>Let:</p>
<ul>
<li>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
be the number of pathogens,</li>
<li>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
be a
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
prior (uniform, non-informative),</li>
<li>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
be the observed isolate counts per species.</li>
</ul>
<p>Then the posterior incidence is:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
<p>To simulate from this, we use:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
<p>The Dirichlet is the conjugate prior for multinomial data. With the
non-informative prior
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
the posterior is dominated by the data once sample sizes are reasonable.
With small samples, the posterior is appropriately more diffuse,
reflecting genuine uncertainty, and the resulting credible intervals
will be wider.</p>
</div>
<div class="section level3">
<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
</h3>
<p>Each pathogen-regimen pair has a prior and observed data:</p>
<ul>
<li>Default prior:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
(Jeffreys prior)</li>
<li>Intrinsically resistant pairs:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
forcing near-zero susceptibility regardless of observed data (based on
EUCAST Expected Resistant Phenotypes)</li>
<li>Data:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
susceptible out of
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
tested</li>
</ul>
<p>The
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
category could also include values SDD (susceptible, dose-dependent) and
I (intermediate [CLSI], or susceptible, increased exposure
[EUCAST]).</p>
<p>Then the posterior is:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
</div>
<div class="section level3">
<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
</h3>
<p>Putting it together:</p>
<ol style="list-style-type: decimal">
<li>Simulate pathogen incidence:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
</li>
<li>Simulate susceptibility:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
</li>
<li>Combine:</li>
</ol>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
<ul>
<li>
<strong>Mean</strong> = expected coverage</li>
<li>
<strong>Quantiles</strong> = credible interval (95% by default)</li>
</ul>
<p>Because each simulation draws from the full posterior, the resulting
distribution of coverage estimates naturally captures the joint
uncertainty in both pathogen incidence and susceptibility. The credible
interval tells you how confident you can be in the coverage estimate,
something a traditional antibiogram never provides.</p>
</div>
</div>
<div class="section level2">
<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
</h2>
<table class="table">
<thead><tr class="header">
<th>Goal</th>
<th>Recommended approach</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>Guide empirical therapy decisions</td>
<td><strong>WISCA</strong></td>
</tr>
<tr class="even">
<td>Compare regimens for a syndrome</td>
<td><strong>WISCA</strong></td>
</tr>
<tr class="odd">
<td>Evaluate combination regimens</td>
<td><strong>WISCA</strong></td>
</tr>
<tr class="even">
<td>Antimicrobial stewardship (A-team)</td>
<td><strong>WISCA</strong></td>
</tr>
<tr class="odd">
<td>Track resistance trends per species</td>
<td>Traditional / Combination</td>
</tr>
<tr class="even">
<td>AMR surveillance reporting</td>
<td>Traditional / Syndromic</td>
</tr>
<tr class="odd">
<td>Understand species-level epidemiology</td>
<td>Traditional</td>
</tr>
</tbody>
</table>
<p>In short: if the end goal involves a <em>patient</em> who does not
yet have a culture result, WISCA is the appropriate tool. If the end
goal is <em>surveillance</em> of resistance at the species level, the
traditional antibiogram remains fit for purpose.</p>
</div>
<div class="section level2">
<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
</h2>
<div class="section level3">
<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
</h3>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span></span>
<span></span>
<span><span class="co"># Structure of our data</span></span>
<span><span class="va">data</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
<span><span class="co">#&gt; date patient age gender ward mo PEN OXA FLC AMX </span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515 79 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #949494;"> NA</span> </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
<span></span>
<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># Keep only 10 most common microorganisms</span></span>
<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span><span class="va">data</span>, n <span class="op">=</span> <span class="fl">10</span>, property <span class="op">=</span> <span class="st">"species"</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span></span></code></pre></div>
</div>
<div class="section level3">
<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
</h3>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
<span><span class="op">)</span></span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Ciprofloxacin</th>
<th align="left">Gentamicin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">76.8% (74.7-79.1%)</td>
<td align="left">81.5% (78.9-84.1%)</td>
<td align="left">82.9% (81-84.8%)</td>
</tr></tbody>
</table>
</div>
<div class="section level3">
<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
</h3>
<p>Combination regimens are specified with a <code>+</code> separator.
WISCA evaluates whether <em>at least one</em> agent in the combination
covers the pathogen:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
<span><span class="op">)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="24%">
<col width="38%">
<col width="36%">
</colgroup>
<thead><tr class="header">
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
</tr></thead>
<tbody><tr class="odd">
<td align="left">76.8% (74.6-78.9%)</td>
<td align="left">89.6% (88-91.1%)</td>
<td align="left">93.7% (92.5-94.9%)</td>
</tr></tbody>
</table>
</div>
<div class="section level3">
<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
</h3>
<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
per clinical stratum. You can pass a column name or any expression:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">wisca_out</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
<span><span class="op">)</span></span>
<span><span class="va">wisca_out</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="12%">
<col width="21%">
<col width="34%">
<col width="31%">
</colgroup>
<thead><tr class="header">
<th align="left">Syndromic Group</th>
<th align="left">Amoxicillin/clavulanic acid</th>
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Non-UTI</td>
<td align="left">72.5% (69.9-75.1%)</td>
<td align="left">86.9% (84.8-89%)</td>
<td align="left">91.4% (89.5-93%)</td>
</tr>
<tr class="even">
<td align="left">UTI</td>
<td align="left">86% (82.5-89%)</td>
<td align="left">94.8% (92.5-96.6%)</td>
<td align="left">97.9% (96.3-99%)</td>
</tr>
</tbody>
</table>
<p>The <code>AMR</code> package is available in 28 languages, which can
all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
<span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
<span> language <span class="op">=</span> <span class="st">"Spanish"</span></span>
<span><span class="op">)</span></span></code></pre></div>
<table class="table">
<colgroup>
<col width="12%">
<col width="21%">
<col width="34%">
<col width="31%">
</colgroup>
<thead><tr class="header">
<th align="left">Grupo sindrómico</th>
<th align="left">Amoxicilina/ácido clavulánico</th>
<th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
<th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">Non-UCI</td>
<td align="left">72.6% (69.9-75.3%)</td>
<td align="left">87% (84.9-89.1%)</td>
<td align="left">91.4% (89.7-92.9%)</td>
</tr>
<tr class="even">
<td align="left">UCI</td>
<td align="left">86% (82.7-89%)</td>
<td align="left">94.8% (92.7-96.4%)</td>
<td align="left">97.9% (96.5-99%)</td>
</tr>
</tbody>
</table>
</div>
<div class="section level3">
<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
</h3>
<p>Each row shows the estimated empirical coverage for a regimen, with a
95% credible interval. When comparing regimens:</p>
<ul>
<li>
<strong>Overlapping credible intervals</strong> mean there is no
statistically significant difference in coverage. If a narrower-spectrum
regimen overlaps with a broader one, the narrower-spectrum option can be
preferred on stewardship grounds.</li>
<li>
<strong>Non-overlapping credible intervals</strong> indicate a
clinically meaningful difference in coverage.</li>
</ul>
</div>
<div class="section level3">
<h3 id="plotting">Plotting<a class="anchor" aria-label="anchor" href="#plotting"></a>
</h3>
<p>WISCA results can be visualised in several ways. All plot functions
work on the output of <code><a href="../reference/antibiogram.html">wisca()</a></code> (or
<code>antibiogram(..., wisca = TRUE)</code>).</p>
<p>Below we use the <code>wisca_out</code> object that was generated
above.</p>
<div class="section level4">
<h4 id="coverage-with-credible-intervals">Coverage with credible intervals<a class="anchor" aria-label="anchor" href="#coverage-with-credible-intervals"></a>
</h4>
<p>The extended <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> method from the
<code>ggplot2()</code> package produces a point-and-interval plot
showing the coverage estimate and 95% credible interval for each
regimen, grouped by syndromic stratum. This is the most direct way to
compare regimens: overlapping intervals suggest clinical
non-inferiority, non-overlapping intervals indicate a meaningful
difference.</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
<p><img src="WISCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="720"></p>
</div>
<div class="section level4">
<h4 id="susceptibility-vs--incidence-weight">Susceptibility vs. incidence weight<a class="anchor" aria-label="anchor" href="#susceptibility-vs--incidence-weight"></a>
</h4>
<p><code><a href="../reference/antibiogram.html">wisca_plot()</a></code> produces a scatter plot of the Monte Carlo
simulation draws, showing each pathogen’s susceptibility (x-axis)
against its incidence weight (y-axis) for each regimen. Each dot
represents one of 1,000 simulated draws, so the spread reflects
posterior uncertainty. This plot reveals <em>why</em> a regimen achieves
its coverage: you can see which pathogens dominate the syndrome (high on
the y-axis), how susceptible they are (position on the x-axis), and how
uncertain both estimates are (spread of the cloud). The dashed vertical
lines denote the point estimates, i.e., the coverage percentages. The
ribbon behind the dashed lines denote the credible interval, which is
95% at default.</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
<p><img src="WISCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
</div>
<div class="section level4">
<h4 id="posterior-coverage-distributions">Posterior coverage distributions<a class="anchor" aria-label="anchor" href="#posterior-coverage-distributions"></a>
</h4>
<p>Setting <code>wisca_plot_type = "posterior_coverage"</code> shows the
full posterior distribution of coverage for each regimen as a density
curve. This is the most complete representation of what the Bayesian
model produces: each curve shows the relative likelihood of each
coverage value across all 1,000 simulations. Narrow, tall peaks indicate
high certainty; wide, flat curves indicate greater uncertainty. Where
two curves overlap, the regimens cannot be confidently
distinguished.</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span>, wisca_plot_type <span class="op">=</span> <span class="st">"posterior_coverage"</span><span class="op">)</span></span></code></pre></div>
<p><img src="WISCA_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
</div>
</div>
</div>
<div class="section level2">
<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
</h2>
<ul>
<li>
<code>simulations = 1000</code>: number of Monte Carlo draws</li>
<li>
<code>conf_interval = 0.95</code>: coverage interval width</li>
<li>
<code>combine_SI = TRUE</code>: count “I” and “SDD” as
susceptible</li>
</ul>
</div>
<div class="section level2">
<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
</h2>
<ul>
<li>
<strong>First isolates only</strong>: always deduplicate using
<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
introduce bias.</li>
<li>
<strong>Pathogen selection</strong>: consider filtering with
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
(e.g. CoNS without clinical context) can distort estimates and may
artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
<li>
<strong>Sample size</strong>: coverage estimates become reliable
with approximately 100+ isolates. For smaller datasets, consider pooling
data from multiple sites, but only after verifying that pathogen
distributions are sufficiently similar (Bielicki <em>et al.</em>,
2016).</li>
<li>
<strong>Culture request bias</strong>: WISCA is only as good as the
data it is based on. If cultures are selectively requested (e.g. only
after treatment failure), the dataset will be biased towards resistant
isolates. A robust culture policy is essential for reliable
estimates.</li>
</ul>
</div>
<div class="section level2">
<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
</h2>
<ul>
<li>It assumes your data are representative of the patient population
you are treating</li>
<li>No direct adjustment for patient-level covariates, although these
can be passed onto the <code>syndromic_group</code> argument for
stratification</li>
<li>WISCA does not model resistance trends over time; for that, you
might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
basic introduction</a>
</li>
</ul>
</div>
<div class="section level2">
<h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
</h2>
<p>WISCA enables:</p>
<ul>
<li>
<strong>Empirical regimen comparison</strong>, answering the
clinician’s actual question</li>
<li>
<strong>Syndrome-specific coverage estimation</strong>, stratifiable
by any clinical variable</li>
<li>
<strong>Fully probabilistic interpretation</strong>, with credible
intervals that honestly communicate uncertainty</li>
</ul>
<p>It is available in the <code>AMR</code> package via either:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
</h2>
<ol style="list-style-type: decimal">
<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
Demonstration of the weighted-incidence syndromic combination
antibiogram: an empiric prescribing decision aid. <em>Infect Control
Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
</li>
<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
Weighted-incidence syndromic combination antibiograms to guide empiric
treatment of critical care infections: a retrospective cohort study.
<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
</li>
<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
Selecting appropriate empirical antibiotic regimens for paediatric
bloodstream infections: application of a Bayesian decision model to
local and pooled antimicrobial resistance surveillance data. <em>J
Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
</li>
<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
prescribing in pediatric bloodstream infections: a potential application
of weighted-incidence syndromic combination antibiograms (WISCA).
<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
</li>
</ol>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
</nav></aside>
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articles/WISCA.md
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# Estimating Empirical Coverage with WISCA
## Why WISCA?
When a clinician starts empirical antimicrobial therapy, the causative
pathogen is unknown. The question they need answered is not *“what
proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
*“what is the probability that this regimen will adequately cover
whatever pathogen turns out to be causing my patient’s infection?”*
The traditional cumulative antibiogram, as standardised by CLSI M39,
cannot answer that question. It presents susceptibility percentages per
species per antibiotic, but:
- **It fragments information by organism.** The clinician must mentally
combine susceptibility rates across multiple species, weighting by how
often each species causes the syndrome, a calculation nobody does at
the bedside.
- **It ignores pathogen incidence.** A species that causes 2% of
infections is given the same visual weight as one that causes 60%.
- **It does not evaluate combination regimens.** Much empirical therapy
consists of two or more agents, but the traditional antibiogram only
shows monotherapy per organism.
- **It provides no measure of uncertainty.** A reported “90%
susceptible” based on 50 isolates has a 95% confidence interval of
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
point estimate without context.
**WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
resolves all four limitations. It estimates the probability that a
regimen will provide adequate empirical coverage for a given infection
syndrome, weighted by local pathogen incidence, with full uncertainty
quantification via Bayesian inference.
The concept was introduced by Hebert *et al.* (2012), who demonstrated
that traditional antibiogram susceptibility rates could be misleading:
ciprofloxacin appeared 84% effective against *E. coli* in the
traditional antibiogram, but WISCA revealed only 62% coverage for UTI
and 37% for abdominal infections, because enterococci (intrinsically
resistant) and other species contribute substantially to these
syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
selection could improve time-to-adequate-coverage on the ICU by over
40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
in this package, enabling credible intervals and multi-centre pooling.
Cook *et al.* (2022) applied it globally across 52 hospitals in 23
countries.
## The idea
WISCA asks:
> “What is the **probability** that this regimen **will cover** the
> pathogen, given the syndrome?”
This means combining two quantities:
- **Pathogen incidence** in the syndrome (how often each species causes
it),
- **Susceptibility** of each pathogen to the regimen.
We can write this as:
``` math
\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
```
For example, suppose in your hospital:
- *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
a drug.
- *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
susceptible.
Then:
``` math
\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
```
That 82% is a far more clinically meaningful number than the
species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
separately in a traditional antibiogram, because it directly answers the
question the clinician actually faces.
But in real data, both incidence and susceptibility are **estimated from
finite samples**, so they carry uncertainty. A sample of 50 isolates is
not a census. WISCA models this uncertainty **probabilistically**, using
conjugate Bayesian distributions.
## The Bayesian engine
### Pathogen incidence
Let:
- $`K`$ be the number of pathogens,
- $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
prior (uniform, non-informative),
- $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
per species.
Then the posterior incidence is:
``` math
\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
```
To simulate from this, we use:
``` math
x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
```
The Dirichlet is the conjugate prior for multinomial data. With the
non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
posterior is dominated by the data once sample sizes are reasonable.
With small samples, the posterior is appropriately more diffuse,
reflecting genuine uncertainty, and the resulting credible intervals
will be wider.
### Susceptibility
Each pathogen-regimen pair has a prior and observed data:
- Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
- Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
near-zero susceptibility regardless of observed data (based on EUCAST
Expected Resistant Phenotypes)
- Data: $`S`$ susceptible out of $`N`$ tested
The $`S`$ category could also include values SDD (susceptible,
dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
exposure \[EUCAST\]).
Then the posterior is:
``` math
\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
```
### Final coverage estimate
Putting it together:
1. Simulate pathogen incidence:
$`\boldsymbol{p} \sim \text{Dirichlet}`$
2. Simulate susceptibility:
$`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
3. Combine:
``` math
\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
```
Repeat this simulation (e.g., 1000 times) and summarise:
- **Mean** = expected coverage
- **Quantiles** = credible interval (95% by default)
Because each simulation draws from the full posterior, the resulting
distribution of coverage estimates naturally captures the joint
uncertainty in both pathogen incidence and susceptibility. The credible
interval tells you how confident you can be in the coverage estimate,
something a traditional antibiogram never provides.
## When to use WISCA vs. traditional antibiograms
| Goal | Recommended approach |
|---------------------------------------|---------------------------|
| Guide empirical therapy decisions | **WISCA** |
| Compare regimens for a syndrome | **WISCA** |
| Evaluate combination regimens | **WISCA** |
| Antimicrobial stewardship (A-team) | **WISCA** |
| Track resistance trends per species | Traditional / Combination |
| AMR surveillance reporting | Traditional / Syndromic |
| Understand species-level epidemiology | Traditional |
In short: if the end goal involves a *patient* who does not yet have a
culture result, WISCA is the appropriate tool. If the end goal is
*surveillance* of resistance at the species level, the traditional
antibiogram remains fit for purpose.
## Practical use in the `AMR` package
### Prepare data
``` r
library(AMR)
data <- example_isolates
# Structure of our data
data
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
#> # ℹ 1,990 more rows
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
# Add a synthetic syndrome column for demonstration
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
# Keep only 10 most common microorganisms
data <- top_n_microorganisms(data, n = 10, property = "species")
#> ℹ Using column mo as input for `col_mo`.
```
### Basic WISCA
``` r
wisca(data,
antimicrobials = c("AMC", "CIP", "GEN")
)
```
| Amoxicillin/clavulanic acid | Ciprofloxacin | Gentamicin |
|:----------------------------|:-------------------|:-----------------|
| 76.8% (74.7-79.1%) | 81.5% (78.9-84.1%) | 82.9% (81-84.8%) |
### Use combination regimens
Combination regimens are specified with a `+` separator. WISCA evaluates
whether *at least one* agent in the combination covers the pathogen:
``` r
wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
)
```
| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|:---|:---|:---|
| 76.8% (74.6-78.9%) | 89.6% (88-91.1%) | 93.7% (92.5-94.9%) |
### Stratify by syndrome
Use `syndromic_group` to produce separate WISCA estimates per clinical
stratum. You can pass a column name or any expression:
``` r
wisca_out <- wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
syndromic_group = "syndrome"
)
wisca_out
```
| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|:---|:---|:---|:---|
| Non-UTI | 72.5% (69.9-75.1%) | 86.9% (84.8-89%) | 91.4% (89.5-93%) |
| UTI | 86% (82.5-89%) | 94.8% (92.5-96.6%) | 97.9% (96.3-99%) |
The `AMR` package is available in 28 languages, which can all be used
for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
function too:
``` r
wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
syndromic_group = gsub("UTI", "UCI", data$syndrome),
language = "Spanish"
)
```
| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
|:---|:---|:---|:---|
| Non-UCI | 72.6% (69.9-75.3%) | 87% (84.9-89.1%) | 91.4% (89.7-92.9%) |
| UCI | 86% (82.7-89%) | 94.8% (92.7-96.4%) | 97.9% (96.5-99%) |
### Interpreting the output
Each row shows the estimated empirical coverage for a regimen, with a
95% credible interval. When comparing regimens:
- **Overlapping credible intervals** mean there is no statistically
significant difference in coverage. If a narrower-spectrum regimen
overlaps with a broader one, the narrower-spectrum option can be
preferred on stewardship grounds.
- **Non-overlapping credible intervals** indicate a clinically
meaningful difference in coverage.
### Plotting
WISCA results can be visualised in several ways. All plot functions work
on the output of
[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) (or
`antibiogram(..., wisca = TRUE)`).
Below we use the `wisca_out` object that was generated above.
#### Coverage with credible intervals
The extended
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
method from the `ggplot2()` package produces a point-and-interval plot
showing the coverage estimate and 95% credible interval for each
regimen, grouped by syndromic stratum. This is the most direct way to
compare regimens: overlapping intervals suggest clinical
non-inferiority, non-overlapping intervals indicate a meaningful
difference.
``` r
ggplot2::autoplot(wisca_out)
```
![](WISCA_files/figure-html/unnamed-chunk-6-1.png)
#### Susceptibility vs. incidence weight
[`wisca_plot()`](https://amr-for-r.org/reference/antibiogram.md)
produces a scatter plot of the Monte Carlo simulation draws, showing
each pathogen’s susceptibility (x-axis) against its incidence weight
(y-axis) for each regimen. Each dot represents one of 1,000 simulated
draws, so the spread reflects posterior uncertainty. This plot reveals
*why* a regimen achieves its coverage: you can see which pathogens
dominate the syndrome (high on the y-axis), how susceptible they are
(position on the x-axis), and how uncertain both estimates are (spread
of the cloud). The dashed vertical lines denote the point estimates,
i.e., the coverage percentages. The ribbon behind the dashed lines
denote the credible interval, which is 95% at default.
``` r
wisca_plot(wisca_out)
```
![](WISCA_files/figure-html/unnamed-chunk-7-1.png)
#### Posterior coverage distributions
Setting `wisca_plot_type = "posterior_coverage"` shows the full
posterior distribution of coverage for each regimen as a density curve.
This is the most complete representation of what the Bayesian model
produces: each curve shows the relative likelihood of each coverage
value across all 1,000 simulations. Narrow, tall peaks indicate high
certainty; wide, flat curves indicate greater uncertainty. Where two
curves overlap, the regimens cannot be confidently distinguished.
``` r
wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
```
![](WISCA_files/figure-html/unnamed-chunk-8-1.png)
## Sensible defaults, which can be customised
- `simulations = 1000`: number of Monte Carlo draws
- `conf_interval = 0.95`: coverage interval width
- `combine_SI = TRUE`: count “I” and “SDD” as susceptible
## Practical considerations
- **First isolates only**: always deduplicate using
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
before running WISCA. Repeat isolates introduce bias.
- **Pathogen selection**: consider filtering with
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
Including rare contaminants (e.g. CoNS without clinical context) can
distort estimates and may artificially lower coverage (Cook *et al.*,
2022).
- **Sample size**: coverage estimates become reliable with approximately
100+ isolates. For smaller datasets, consider pooling data from
multiple sites, but only after verifying that pathogen distributions
are sufficiently similar (Bielicki *et al.*, 2016).
- **Culture request bias**: WISCA is only as good as the data it is
based on. If cultures are selectively requested (e.g. only after
treatment failure), the dataset will be biased towards resistant
isolates. A robust culture policy is essential for reliable estimates.
## Limitations
- It assumes your data are representative of the patient population you
are treating
- No direct adjustment for patient-level covariates, although these can
be passed onto the `syndromic_group` argument for stratification
- WISCA does not model resistance trends over time; for that, you might
want to use `tidymodels`, for which we [wrote a basic
introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
## Summary
WISCA enables:
- **Empirical regimen comparison**, answering the clinician’s actual
question
- **Syndrome-specific coverage estimation**, stratifiable by any
clinical variable
- **Fully probabilistic interpretation**, with credible intervals that
honestly communicate uncertainty
It is available in the `AMR` package via either:
``` r
wisca(...)
antibiogram(..., wisca = TRUE)
```
## References
1. Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
Demonstration of the weighted-incidence syndromic combination
antibiogram: an empiric prescribing decision aid. *Infect Control
Hosp Epidemiol.* 2012;33(4):381-388.
<https://doi.org/10.1086/664768>
2. Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
Weighted-incidence syndromic combination antibiograms to guide
empiric treatment of critical care infections: a retrospective
cohort study. *Crit Care.* 2014;18(3):R112.
<https://doi.org/10.1186/cc13901>
3. Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
Selecting appropriate empirical antibiotic regimens for paediatric
bloodstream infections: application of a Bayesian decision model to
local and pooled antimicrobial resistance surveillance data. *J
Antimicrob Chemother.* 2016;71(3):794-802.
<https://doi.org/10.1093/jac/dkv397>
4. Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
prescribing in pediatric bloodstream infections: a potential
application of weighted-incidence syndromic combination antibiograms
(WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
<https://doi.org/10.1080/14787210.2021.1967145>
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# Download data sets for download / own use
All reference data (about microorganisms, antimicrobials, SIR
interpretation, EUCAST rules, etc.) in this `AMR` package are reliable,
up-to-date and freely available. We continually export our data sets to
formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS,
and Stata. We also provide tab-separated text files that are
machine-readable and suitable for input in any software program, such as
laboratory information systems.
> If you are working in Python, be sure to use our [AMR for
> Python](https://amr-for-r.org/articles/AMR_for_Python.html) package.
> It allows all relevant AMR data sets to be natively available in
> Python.
## `microorganisms`: Full Microbial Taxonomy
A data set with 96 982 rows and 28 columns, containing the following
column names:
*mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
*order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
*oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
*lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
*gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
This data set is in R available as `microorganisms`, after you load the
`AMR` package.
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/microorganisms.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
(2.2 MB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
(23.1 MB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
(11.4 MB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
(11 MB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
(4.6 MB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
(35.2 MB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
(96.6 MB)
**NOTE: The exported files for SPSS and Stata contain only the first 50
SNOMED codes per record, as their file size would otherwise exceed 100
MB; the file size limit of GitHub.** Their file structures and
compression techniques are very inefficient. Advice? Use R instead. It’s
free and much better in many ways.
The tab-separated text file and Microsoft Excel workbook both contain
all SNOMED codes as comma separated values.
**Example content**
Included (sub)species per taxonomic kingdom:
| Kingdom | Number of (sub)species |
|:-----------------:|:----------------------:|
| | 20 |
| (unknown kingdom) | 8 |
| Animalia | 2 015 |
| Archaea | 150 |
| Bacillati | 24 200 |
| Bacteria | 2 |
First 6 rows when filtering on genus *Escherichia*:
| mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 | | | | | CS33H | CRYWR | | 1 | 407310004, 407251000, 407281008, … |
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 | | | | CS33J | CS33H | 3SVX6 | 1 | |
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 | | | | | 3BGTB | CS33H | | 1 | 419388003 |
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 | | | | CS33K | CS33H | 4X4P7 | 1 | |
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 | | | | | NT3L7 | CS33H | | 1 | 1095001000112106, 715307006, 737528008, … |
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | | GBIF | | 776057 | | | | | 12233256 | NT3L7 | | 1 | |
------------------------------------------------------------------------
## `antimicrobials`: Antibiotic and Antifungal Drugs
A data set with 505 rows and 14 columns, containing the following column
names:
*ab*, *cid*, *name*, *group*, *atc*, *atc_group1*, *atc_group2*,
*abbreviations*, *synonyms*, *oral_ddd*, *oral_units*, *iv_ddd*,
*iv_units*, and *loinc*.
This data set is in R available as `antimicrobials`, after you load the
`AMR` package.
It was last updated on 13 August 2026 09:12:12 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/antimicrobials.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds)
(44 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt)
(0.1 MB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx)
(79 kB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather)
(0.1 MB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet)
(94 kB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav)
(0.4 MB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.dta)
(10 kB)
The tab-separated text, Microsoft Excel, SPSS, and Stata files all
contain the ATC codes, common abbreviations, trade names and LOINC codes
as comma separated values.
**Example content**
| ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … | | | 1.0 | g | 101493-5, 11-7, 12-5, … |
| AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
| AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g | |
| AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
| AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
| PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … | | | 3.6 | g | |
------------------------------------------------------------------------
## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
A data set with 45 735 rows and 14 columns, containing the following
column names:
*guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
*ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
*is_SDD*.
This data set is in R available as `clinical_breakpoints`, after you
load the `AMR` package.
It was last updated on 9 July 2026 15:14:59 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/clinical_breakpoints.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds)
(93 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt)
(4.2 MB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx)
(2.7 MB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather)
(2 MB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
(0.2 MB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
(7.5 MB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta)
(12.6 MB)
**Example content**
| guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | | 1.000 | 4.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | | 0.125 | 0.125 | FALSE | FALSE |
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | | 4.000 | 4.000 | FALSE | FALSE |
------------------------------------------------------------------------
## `microorganisms.groups`: Species Groups and Microbiological Complexes
A data set with 530 rows and 4 columns, containing the following column
names:
*mo_group*, *mo*, *mo_group_name*, and *mo_name*.
This data set is in R available as `microorganisms.groups`, after you
load the `AMR` package.
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/microorganisms.groups.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.rds)
(6 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.txt)
(50 kB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
(19 kB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
(19 kB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.parquet)
(13 kB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
(64 kB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
(82 kB)
**Example content**
| mo_group | mo | mo_group_name | mo_name |
|:--:|:--:|:--:|:--:|
| B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
| B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
| B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
| B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
| B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
| B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
------------------------------------------------------------------------
## `intrinsic_resistant`: Intrinsic Bacterial Resistance
A data set with 294 079 rows and 3 columns, containing the following
column names:
*mo*, *ab*, and *version*.
This data set is in R available as `intrinsic_resistant`, after you load
the `AMR` package.
It was last updated on 3 September 2026 10:14:25 UTC. Find more info
about the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/intrinsic_resistant.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.rds)
(0.1 MB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
(10.9 MB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
(3.1 MB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
(2.5 MB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet)
(0.3 MB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
(16 MB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
(28.6 MB)
**Example content**
Example rows when filtering on *Enterobacter cloacae*:
| microorganism | antibiotic |
|:--------------------:|:---------------------------:|
| Enterobacter cloacae | Acetylmidecamycin |
| Enterobacter cloacae | Acetylspiramycin |
| Enterobacter cloacae | Amoxicillin |
| Enterobacter cloacae | Amoxicillin/clavulanic acid |
| Enterobacter cloacae | Ampicillin |
| Enterobacter cloacae | Ampicillin/sulbactam |
| Enterobacter cloacae | Avoparcin |
| Enterobacter cloacae | Azithromycin |
| Enterobacter cloacae | Benzylpenicillin |
| Enterobacter cloacae | Bleomycin |
| Enterobacter cloacae | Cadazolid |
| Enterobacter cloacae | Cefadroxil |
| Enterobacter cloacae | Cefalexin |
| Enterobacter cloacae | Cefalotin |
| Enterobacter cloacae | Cefazolin |
| Enterobacter cloacae | Cefoxitin |
| Enterobacter cloacae | Clarithromycin |
| Enterobacter cloacae | Clindamycin |
| Enterobacter cloacae | Cycloserine |
| Enterobacter cloacae | Dalbavancin |
| Enterobacter cloacae | Dirithromycin |
| Enterobacter cloacae | Erythromycin |
| Enterobacter cloacae | Flurithromycin |
| Enterobacter cloacae | Fusidic acid |
| Enterobacter cloacae | Gamithromycin |
| Enterobacter cloacae | Josamycin |
| Enterobacter cloacae | Kitasamycin |
| Enterobacter cloacae | Lincomycin |
| Enterobacter cloacae | Linezolid |
| Enterobacter cloacae | Meleumycin |
| Enterobacter cloacae | Midecamycin |
| Enterobacter cloacae | Miocamycin |
| Enterobacter cloacae | Nafithromycin |
| Enterobacter cloacae | Norvancomycin |
| Enterobacter cloacae | Oleandomycin |
| Enterobacter cloacae | Oritavancin |
| Enterobacter cloacae | Ostreogrycin |
| Enterobacter cloacae | Pirlimycin |
| Enterobacter cloacae | Primycin |
| Enterobacter cloacae | Pristinamycin |
| Enterobacter cloacae | Quinupristin/dalfopristin |
| Enterobacter cloacae | Ramoplanin |
| Enterobacter cloacae | Rifampicin |
| Enterobacter cloacae | Rokitamycin |
| Enterobacter cloacae | Roxithromycin |
| Enterobacter cloacae | Solithromycin |
| Enterobacter cloacae | Spiramycin |
| Enterobacter cloacae | Tedizolid |
| Enterobacter cloacae | Teicoplanin |
| Enterobacter cloacae | Telavancin |
| Enterobacter cloacae | Telithromycin |
| Enterobacter cloacae | Thiacetazone |
| Enterobacter cloacae | Tildipirosin |
| Enterobacter cloacae | Tilmicosin |
| Enterobacter cloacae | Troleandomycin |
| Enterobacter cloacae | Tulathromycin |
| Enterobacter cloacae | Tylosin |
| Enterobacter cloacae | Tylvalosin |
| Enterobacter cloacae | Vancomycin |
| Enterobacter cloacae | Virginiamycine |
| Enterobacter cloacae | Zorbamycin |
------------------------------------------------------------------------
## `dosage`: Dosage Guidelines from EUCAST
A data set with 759 rows and 9 columns, containing the following column
names:
*ab*, *name*, *type*, *dose*, *dose_times*, *administration*, *notes*,
*original_txt*, and *eucast_version*.
This data set is in R available as `dosage`, after you load the `AMR`
package.
It was last updated on 20 April 2025 10:55:31 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/dosage.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.rds)
(4 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.txt)
(66 kB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.xlsx)
(37 kB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.feather)
(28 kB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.parquet)
(9 kB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.sav)
(97 kB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.dta)
(0.2 MB)
**Example content**
| ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv | | 25-30 mg/kg x 1 iv | 15 |
| AMX | Amoxicillin | high_dosage | 2 g | 6 | iv | | 2 g x 6 iv | 15 |
| AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv | | 1 g x 3-4 iv | 15 |
| AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral | | 0.75-1 g x 3 oral | 15 |
| AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
| AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
------------------------------------------------------------------------
## `example_isolates`: Example Data for Practice
A data set with 2 000 rows and 46 columns, containing the following
column names:
*date*, *patient*, *age*, *gender*, *ward*, *mo*, *PEN*, *OXA*, *FLC*,
*AMX*, *AMC*, *AMP*, *TZP*, *CZO*, *FEP*, *CXM*, *FOX*, *CTX*, *CAZ*,
*CRO*, *GEN*, *TOB*, *AMK*, *KAN*, *TMP*, *SXT*, *NIT*, *FOS*, *LNZ*,
*CIP*, *MFX*, *VAN*, *TEC*, *TCY*, *TGC*, *DOX*, *ERY*, *CLI*, *AZM*,
*IPM*, *MEM*, *MTR*, *CHL*, *COL*, *MUP*, and *RIF*.
This data set is in R available as `example_isolates`, after you load
the `AMR` package.
It was last updated on 24 June 2026 16:36:47 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/example_isolates.html).
**Example content**
| date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
| 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | R | R | | | | | R | | |
------------------------------------------------------------------------
## `example_isolates_unclean`: Example Data for Practice
A data set with 3 000 rows and 8 columns, containing the following
column names:
*patient_id*, *hospital*, *date*, *bacteria*, *AMX*, *AMC*, *CIP*, and
*GEN*.
This data set is in R available as `example_isolates_unclean`, after you
load the `AMR` package.
It was last updated on 27 August 2022 18:49:37 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/example_isolates_unclean.html).
**Example content**
| patient_id | hospital | date | bacteria | AMX | AMC | CIP | GEN |
|:----------:|:--------:|:----------:|:-------------:|:---:|:---:|:---:|:---:|
| J3 | A | 2012-11-21 | E. coli | R | I | S | S |
| R7 | A | 2018-04-03 | K. pneumoniae | R | I | S | S |
| P3 | A | 2014-09-19 | E. coli | R | S | S | S |
| P10 | A | 2015-12-10 | E. coli | S | I | S | S |
| B7 | A | 2015-03-02 | E. coli | S | S | S | S |
| W3 | A | 2018-03-31 | S. aureus | R | S | R | S |
------------------------------------------------------------------------
## `microorganisms.codes`: Common Laboratory Codes
A data set with 6 029 rows and 2 columns, containing the following
column names:
*code* and *mo*.
This data set is in R available as `microorganisms.codes`, after you
load the `AMR` package.
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/microorganisms.codes.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
(27 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
(0.1 MB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.xlsx)
(98 kB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.feather)
(0.1 MB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
(68 kB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
(0.2 MB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.dta)
(0.2 MB)
**Example content**
| code | mo |
|:----:|:------------:|
| 1011 | B_GRAMP |
| 1012 | B_GRAMP |
| 1013 | B_GRAMN |
| 1014 | B_GRAMN |
| 1015 | F_YEAST |
| 103 | B_ESCHR_COLI |
------------------------------------------------------------------------
## `antivirals`: Antiviral Drugs
A data set with 120 rows and 11 columns, containing the following column
names:
*av*, *name*, *atc*, *cid*, *atc_group*, *synonyms*, *oral_ddd*,
*oral_units*, *iv_ddd*, *iv_units*, and *loinc*.
This data set is in R available as `antivirals`, after you load the
`AMR` package.
It was last updated on 20 October 2023 12:51:48 UTC. Find more info
about the contents, (scientific) source, and structure of this [data set
here](https://amr-for-r.org/reference/antimicrobials.html).
**Direct download links:**
- Download as [original R Data Structure (RDS)
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.rds)
(6 kB)
- Download as [tab-separated text
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.txt)
(17 kB)
- Download as [Microsoft Excel
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.xlsx)
(16 kB)
- Download as [Apache Feather
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.feather)
(16 kB)
- Download as [Apache Parquet
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.parquet)
(13 kB)
- Download as [IBM SPSS Statistics data
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.sav)
(32 kB)
- Download as [Stata DTA
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.dta)
(78 kB)
The tab-separated text, Microsoft Excel, SPSS, and Stata files all
contain the trade names and LOINC codes as comma separated values.
**Example content**
| av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g | | | 29113-8, 30273-7, 30287-7, … |
| ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g | |
| ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg | | | |
| AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g | | | |
| AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g | | | 29114-6, 30296-8, 30297-6, … |
| ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g | | | |
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</dd><dt><a href="WISCA.html">Estimating Empirical Coverage with WISCA</a></dt>
<dd>
</dd></dl></div>
</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 GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in the Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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# Articles
### All vignettes
- [AMR for Python](https://amr-for-r.org/articles/AMR_for_Python.md):
- [AMR with
tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.md):
- [Conduct AMR data analysis](https://amr-for-r.org/articles/AMR.md):
- [Download data sets for download / own
use](https://amr-for-r.org/articles/datasets.md):
- [Apply EUCAST rules](https://amr-for-r.org/articles/EUCAST.md):
- [Conduct principal component analysis (PCA) for
AMR](https://amr-for-r.org/articles/PCA.md):
- [Work with WHONET data](https://amr-for-r.org/articles/WHONET.md):
- [Estimating Empirical Coverage with
WISCA](https://amr-for-r.org/articles/WISCA.md):
authors.html
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<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>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</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>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Matthew Saab</strong>. Contributor. <a href="https://orcid.org/0009-0008-6626-7919" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Rogier P. Schade</strong>. Contributor. <a href="https://orcid.org/0000-0002-9487-4467" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
<li>
<p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
</p>
</li>
</ul></div>
<div class="section level2">
<h2 id="citation">Citation</h2>
<p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
<p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
“AMR: An R Package for Working with Antimicrobial Resistance Data.”
<em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
<a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
</p>
<pre>@Article{,
title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
journal = {Journal of Statistical Software},
year = {2022},
volume = {104},
number = {3},
pages = {1--31},
doi = {10.18637/jss.v104.i03},
}</pre>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
</nav></aside></div>
<footer><div class="pkgdown-footer-left">
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in the Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
</div>
<div class="pkgdown-footer-right">
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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authors.md
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# Authors and Citation
## Authors
- **Matthijs S. Berends**. Author, maintainer.
[](https://orcid.org/0000-0001-7620-1800)
- **Dennis Souverein**. Author, contributor.
[](https://orcid.org/0000-0003-0455-0336)
- **Erwin E. A. Hassing**. Author, contributor.
- **Aislinn Cook**. Contributor.
[](https://orcid.org/0000-0002-9189-7815)
- **Andrew P. Norgan**. Contributor.
[](https://orcid.org/0000-0002-2955-2066)
- **Anita Williams**. Contributor.
[](https://orcid.org/0000-0002-5295-8451)
- **Annick Lenglet**. Contributor.
[](https://orcid.org/0000-0003-2013-8405)
- **Anthony Underwood**. Contributor.
[](https://orcid.org/0000-0002-8547-4277)
- **Anton Mymrikov**. Contributor.
- **Bart C. Meijer**. Contributor.
- **Christian F. Luz**. Contributor.
[](https://orcid.org/0000-0001-5809-5995)
- **Dmytro Mykhailenko**. Contributor.
- **Eric H. L. C. M. Hazenberg**. Contributor.
- **Gwen Knight**. Contributor.
[](https://orcid.org/0000-0002-7263-9896)
- **Jane Hawkey**. Contributor.
[](https://orcid.org/0000-0001-9661-5293)
- **Jason Stull**. Contributor.
[](https://orcid.org/0000-0002-9028-8153)
- **Javier Sanchez**. Contributor.
[](https://orcid.org/0000-0003-2605-8094)
- **Jonas Salm**. Contributor.
- **Judith M. Fonville**. Contributor.
- **Kathryn Holt**. Contributor.
[](https://orcid.org/0000-0003-3949-2471)
- **Larisse Bolton**. Contributor.
[](https://orcid.org/0000-0001-7879-2173)
- **Matthew Saab**. Contributor.
[](https://orcid.org/0009-0008-6626-7919)
- **Natacha Couto**. Contributor.
[](https://orcid.org/0000-0002-9152-5464)
- **Peter Dutey-Magni**. Contributor.
[](https://orcid.org/0000-0002-8942-9836)
- **Rogier P. Schade**. Contributor.
[](https://orcid.org/0000-0002-9487-4467)
- **Sofia Ny**. Contributor. [](https://orcid.org/0000-0002-2017-1363)
- **Alex W. Friedrich**. Thesis advisor.
[](https://orcid.org/0000-0003-4881-038X)
- **Bhanu N. M. Sinha**. Thesis advisor.
[](https://orcid.org/0000-0003-1634-0010)
- **Casper J. Albers**. Thesis advisor.
[](https://orcid.org/0000-0002-9213-6743)
- **Corinna Glasner**. Thesis advisor.
[](https://orcid.org/0000-0003-1241-1328)
## Citation
Source:
[`inst/CITATION`](https://github.com/msberends/AMR/blob/main/inst/CITATION)
Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C
(2022). “AMR: An R Package for Working with Antimicrobial Resistance
Data.” *Journal of Statistical Software*, **104**(3), 1–31.
[doi:10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03).
@Article{,
title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
journal = {Journal of Statistical Software},
year = {2022},
volume = {104},
number = {3},
pages = {1--31},
doi = {10.18637/jss.v104.i03},
}
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deps/bootstrap-5.3.8/font.css
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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;
}
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