Python wrapper update

.Rbuildignore

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-^.*\.Rproj$
-^\.Rproj\.user$
-.travis.yml

.gitignore

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-.Rproj.user
-.Rhistory
-.RData
-.Ruserdata
-AMR.Rproj
-tests/testthat/Rplots.pdf

.travis.yml

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-# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
-
-# Setting up R deps
-language: r
-r: 3.2
-r_packages: covr
-cache: packages
-
-# system deps, install xclip for clipboard support
-os:
-  - linux
-  - osx
-before_install:
-  - if [ $TRAVIS_OS_NAME = linux ]; then sudo apt-get -qq update; fi
-  - if [ $TRAVIS_OS_NAME = linux ]; then sudo apt-get install -y xclip; fi
-  - if [ $TRAVIS_OS_NAME = osx ]; then brew install xclip; fi
-
-# postrun
-after_success:
-  - Rscript -e 'covr::codecov()'
-notifications:
-  email:
-    recipients:
-      - m.s.berends@umcg.nl
-      - c.f.luz@umcg.nl
-    on_success: change
-    on_failure: change

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.

AMR.egg-info/SOURCES.txt

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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

AMR.egg-info/dependency_links.txt

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+

AMR.egg-info/requires.txt

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+rpy2
+numpy
+pandas

AMR.egg-info/top_level.txt

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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

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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

@@ -0,0 +1,22 @@
+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

@@ -0,0 +1,54 @@
+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

@@ -0,0 +1,984 @@
+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)

DESCRIPTION

@@ -1,46 +0,0 @@
-Package: AMR
-Version: 0.2.0
-Date: 2018-05-02
-Title: Antimicrobial Resistance Analysis
-Authors@R: c(
-    person(
-        given = c("Matthijs", "S."),
-        family = "Berends",
-        email = "m.s.berends@umcg.nl",
-        role = c("aut", "cre"),
-        comment = c(ORCID = "0000-0001-7620-1800")),
-    person(
-        given = c("Christian", "F."),
-        family = "Luz",
-        email = "c.f.luz@umcg.nl",
-        role = c("aut", "ctb"),
-        comment = c(ORCID = "0000-0001-5809-5995")),
-    person(
-        given = c("Erwin", "E.A."),
-        family = "Hassing",
-        email = "e.hassing@certe.nl",
-        role = "ctb"))
-Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
-    of microbial isolates, by using new S3 classes and applying EUCAST expert rules
-    on antibiograms according to Leclercq (2013)
-    <doi:10.1111/j.1469-0691.2011.03703.x>.
-Depends:
-    R (>= 3.0.0)
-Imports:
-    backports,
-    dplyr (>= 0.7.0),
-    data.table (>= 1.10.0),
-    reshape2 (>= 1.4.0),
-    xml2 (>= 1.0.0),
-    knitr (>= 1.0.0),
-    rvest (>= 0.3.2),
-    tibble
-Suggests:
-    testthat (>= 1.0.2),
-    covr (>= 3.0.1)
-URL: https://github.com/msberends/AMR
-BugReports: https://github.com/msberends/AMR/issues
-License: GPL-2 | file LICENSE
-Encoding: UTF-8
-LazyData: true
-RoxygenNote: 6.0.1.9000

LICENSE

@@ -1,339 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
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- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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-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
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-convey the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    {description}
-    Copyright (C) {year}  {fullname}
-
-    This program is free software; you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation; either version 2 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License along
-    with this program; if not, write to the Free Software Foundation, Inc.,
-    51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-Also add information on how to contact you by electronic and paper mail.
-
-If the program is interactive, make it output a short notice like this
-when it starts in an interactive mode:
-
-    Gnomovision version 69, Copyright (C) year name of author
-    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, the commands you use may
-be called something other than `show w' and `show c'; they could even be
-mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a programmer) or your
-school, if any, to sign a "copyright disclaimer" for the program, if
-necessary.  Here is a sample; alter the names:
-
-  Yoyodyne, Inc., hereby disclaims all copyright interest in the program
-  `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
-  {signature of Ty Coon}, 1 April 1989
-  Ty Coon, President of Vice
-
-This General Public License does not permit incorporating your program into
-proprietary programs.  If your program is a subroutine library, you may
-consider it more useful to permit linking proprietary applications with the
-library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.

NAMESPACE

@@ -1,102 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method(as.double,mic)
-S3method(as.integer,mic)
-S3method(as.numeric,mic)
-S3method(barplot,mic)
-S3method(barplot,rsi)
-S3method(plot,mic)
-S3method(plot,rsi)
-S3method(print,data.table)
-S3method(print,mic)
-S3method(print,rsi)
-S3method(print,tbl)
-S3method(print,tbl_df)
-S3method(summary,mic)
-S3method(summary,rsi)
-export("%like%")
-export(BRMO)
-export(EUCAST_exceptional_phenotypes)
-export(EUCAST_rules)
-export(MDRO)
-export(MRGN)
-export(abname)
-export(anti_join_microorganisms)
-export(as.mic)
-export(as.rsi)
-export(atc_property)
-export(first_isolate)
-export(freq)
-export(frequency_tbl)
-export(full_join_microorganisms)
-export(guess_atc)
-export(guess_bactid)
-export(inner_join_microorganisms)
-export(interpretive_reading)
-export(is.mic)
-export(is.rsi)
-export(key_antibiotics)
-export(left_join_microorganisms)
-export(mo_property)
-export(n_rsi)
-export(right_join_microorganisms)
-export(rsi)
-export(rsi_df)
-export(rsi_predict)
-export(semi_join_microorganisms)
-exportMethods(as.double.mic)
-exportMethods(as.integer.mic)
-exportMethods(as.numeric.mic)
-exportMethods(barplot.mic)
-exportMethods(barplot.rsi)
-exportMethods(plot.mic)
-exportMethods(plot.rsi)
-exportMethods(print.data.table)
-exportMethods(print.mic)
-exportMethods(print.rsi)
-exportMethods(print.tbl)
-exportMethods(print.tbl_df)
-exportMethods(summary.mic)
-exportMethods(summary.rsi)
-importFrom(data.table,data.table)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,all_vars)
-importFrom(dplyr,any_vars)
-importFrom(dplyr,arrange)
-importFrom(dplyr,arrange_at)
-importFrom(dplyr,between)
-importFrom(dplyr,desc)
-importFrom(dplyr,filter)
-importFrom(dplyr,filter_at)
-importFrom(dplyr,group_by)
-importFrom(dplyr,group_by_at)
-importFrom(dplyr,group_size)
-importFrom(dplyr,group_vars)
-importFrom(dplyr,if_else)
-importFrom(dplyr,lag)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,n_groups)
-importFrom(dplyr,progress_estimated)
-importFrom(dplyr,pull)
-importFrom(dplyr,row_number)
-importFrom(dplyr,select)
-importFrom(dplyr,slice)
-importFrom(dplyr,summarise)
-importFrom(dplyr,tibble)
-importFrom(dplyr,vars)
-importFrom(grDevices,boxplot.stats)
-importFrom(graphics,axis)
-importFrom(graphics,barplot)
-importFrom(graphics,plot)
-importFrom(graphics,text)
-importFrom(reshape2,dcast)
-importFrom(rvest,html_nodes)
-importFrom(rvest,html_table)
-importFrom(stats,fivenum)
-importFrom(stats,quantile)
-importFrom(stats,sd)
-importFrom(utils,object.size)
-importFrom(utils,packageDescription)
-importFrom(xml2,read_html)

NEWS.md

@@ -1,44 +0,0 @@
-# 0.2.0
-#### New
-* Full support for Windows, Linux and macOS
-* Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)
-* Function `n_rsi` to count cases where antibiotic test results were available, to be used in conjunction with `dplyr::summarise`, see ?rsi
-* Function `guess_bactid` to **determine the ID** of a microorganism based on genus/species or known abbreviations like MRSA
-* Function `guess_atc` to **determine the ATC** of an antibiotic based on name, trade name, or known abbreviations
-* Function `freq` to create **frequency tables**, with additional info in a header
-* Function `MDRO` to **determine Multi Drug Resistant Organisms (MDRO)** with support for country-specific guidelines.
-  * Suggest your own via [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E)
-  * [Exceptional resistances defined by EUCAST](http://www.eucast.org/expert_rules_and_intrinsic_resistance) are also supported instead of countries alone
-  * Functions `BRMO` and `MRGN` are wrappers for Dutch and German guidelines, respectively
-* New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
-* New print format for `tibble`s and `data.table`s
-
-#### Changed
-* Fixed `rsi` class for vectors that contain only invalid antimicrobial interpretations
-* Renamed dataset `ablist` to `antibiotics`
-* Renamed dataset `bactlist` to `microorganisms`
-* Added common abbreviations and trade names to the `antibiotics` dataset
-* Added more microorganisms to the `microorganisms` dataset
-* Added analysis examples on help page of dataset `septic_patients`
-* Added support for character vector in `join` functions
-* Added warnings when a join results in more rows after than before the join
-* Altered `%like%` to make it case insensitive
-* For parameters of functions `first_isolate` and `EUCAST_rules` column names are now case-insensitive
-* Functions `as.rsi` and `as.mic` now add the package name and version as attributes
-
-#### Other
-* Expanded `README.md` with more examples
-* Added [ORCID](https://orcid.org) of authors to DESCRIPTION file
-* Added unit testing with the `testthat` package
-* Added build tests for Linux and macOS using Travis CI (https://travis-ci.org/msberends/AMR)
-* Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/master/R)
-
-# 0.1.1
-* `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
-* Edited column names to comply with GLIMS, the laboratory information system
-* Added more valid MIC values
-* Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
-* Added barplots for `rsi` and `mic` classes
-
-# 0.1.0
-* First submission to CRAN.

R/atc.R

@@ -1,453 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Properties of an ATC code
-#'
-#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \strong{This function requires an internet connection.}
-#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
-#' @param property property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
-#' @param administration type of administration, see \emph{Details}
-#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
-#' @details
-#' Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
-#' \itemize{
-#'   \item{\code{"Implant"}}{ = Implant}
-#'   \item{\code{"Inhal"}}{ = Inhalation}
-#'   \item{\code{"Instill"}}{ = Instillation}
-#'   \item{\code{"N"}}{ = nasal}
-#'   \item{\code{"O"}}{ = oral}
-#'   \item{\code{"P"}}{ = parenteral}
-#'   \item{\code{"R"}}{ = rectal}
-#'   \item{\code{"SL"}}{ = sublingual/buccal}
-#'   \item{\code{"TD"}}{ = transdermal}
-#'   \item{\code{"V"}}{ = vaginal}
-#' }
-#'
-#' Abbreviations for the property \code{"U"} (unit):
-#' \itemize{
-#'   \item{\code{"g"}}{ = gram}
-#'   \item{\code{"mg"}}{ = milligram}
-#'   \item{\code{"mcg"}}{ = microgram}
-#'   \item{\code{"U"}}{ = unit}
-#'   \item{\code{"TU"}}{ = thousand units}
-#'   \item{\code{"MU"}}{ = million units}
-#'   \item{\code{"mmol"}}{ = millimole}
-#'   \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
-#' }
-#' @export
-#' @importFrom dplyr %>% progress_estimated
-#' @importFrom xml2 read_html
-#' @importFrom rvest html_nodes html_table
-#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
-#' @examples
-#' \donttest{
-#' atc_property("J01CA04", "DDD", "O") # oral DDD (Defined Daily Dose) of amoxicillin
-#' atc_property("J01CA04", "DDD", "P") # parenteral DDD (Defined Daily Dose) of amoxicillin
-#' }
-atc_property <- function(atc_code,
-                         property,
-                         administration = 'O',
-                         url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
-
-  # property <- property %>% tolower()
-  #
-  if (property %like% 'unit') {
-    property <- 'U'
-  }
-
-  # validation of properties
-  valid_properties.bak <- c("ATC code", "Name", "DDD", "U", "Adm.R", "Note")
-  valid_properties <- valid_properties.bak #%>% tolower()
-  if (!property %in% valid_properties) {
-    stop('Invalid `property`, use one of ', paste(valid_properties, collapse = ", "), '.')
-  }
-
-  returnvalue <- rep(NA_character_, length(atc_code))
-  if (property == 'DDD') {
-    returnvalue <- rep(NA_real_, length(atc_code))
-  }
-
-  progress <- progress_estimated(n = length(atc_code))
-
-  for (i in 1:length(atc_code)) {
-
-    progress$tick()$print()
-
-    atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
-    tbl <- xml2::read_html(atc_url) %>%
-      rvest::html_nodes('table') %>%
-      rvest::html_table(header = TRUE)
-
-    if (length(tbl) == 0) {
-      warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
-      returnvalue[i] <- NA
-      next
-    }
-
-    tbl <- tbl[[1]]
-
-    if (property == 'Name') {
-      returnvalue[i] <- tbl[1, 2]
-    } else {
-
-      names(returnvalue)[i] <- tbl[1, 2] %>% as.character()
-
-      if (!'Adm.R' %in% colnames(tbl) | is.na(tbl[1, 'Adm.R'])) {
-        returnvalue[i] <- NA
-        next
-      } else {
-        for (j in 1:nrow(tbl)) {
-          if (tbl[j, 'Adm.R'] == administration) {
-            returnvalue[i] <- tbl[j, property]
-          }
-        }
-      }
-    }
-  }
-
-  cat('\n')
-  returnvalue
-
-}
-
-#' Name of an antibiotic
-#'
-#' Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
-#' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
-#' @param from,to type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"molis"} and \code{"umcg"}.
-#' @param textbetween text to put between multiple returned texts
-#' @param tolower return output as lower case with function \code{\link{tolower}}.
-#' @keywords ab antibiotics
-#' @source \code{\link{antibiotics}}
-#' @export
-#' @importFrom dplyr %>% filter select slice
-#' @examples
-#' abname("AMCL")
-#' # "amoxicillin and enzyme inhibitor"
-#'
-#' abname("AMCL+GENT")
-#' # "amoxicillin and enzyme inhibitor + gentamicin"
-#'
-#' abname(c("AMCL", "GENT"))
-#' # "amoxicillin and enzyme inhibitor" "gentamicin"
-#'
-#' abname("AMCL", to = "trivial_nl")
-#' # "Amoxicilline/clavulaanzuur"
-#'
-#' abname("AMCL", to = "atc")
-#' # "J01CR02"
-#'
-#' abname("J01CR02", from = "atc", to = "umcg")
-#' # "AMCL"
-abname <- function(abcode, from = c("guess", "atc", "molis", "umcg"), to = 'official', textbetween = ' + ', tolower = FALSE) {
-
-  antibiotics <- AMR::antibiotics
-
-  from <- from[1]
-  if (from == "guess") {
-    for (i in 1:3) {
-      if (abcode[1] %in% (antibiotics %>% pull(i))) {
-        from <- colnames(antibiotics)[i]
-      }
-    }
-    if (from == "guess") {
-      from <- "umcg"
-    }
-  }
-
-  colnames(antibiotics) <- colnames(antibiotics) %>% tolower()
-  from <- from %>% tolower()
-  to <- to %>% tolower()
-
-  if (!from %in% colnames(antibiotics) |
-      !to %in% colnames(antibiotics)) {
-    stop(paste0('Invalid `from` or `to`. Choose one of ',
-                colnames(antibiotics) %>% paste(collapse = ","), '.'), call. = FALSE)
-  }
-
-  abcode <- as.character(abcode)
-
-  for (i in 1:length(abcode)) {
-    drug <- abcode[i]
-    if (!grepl('+', drug, fixed = TRUE) & !grepl(' en ', drug, fixed = TRUE)) {
-      # only 1 drug
-      if (drug %in% (antibiotics %>% pull(from))) {
-        abcode[i] <-
-          antibiotics %>%
-          filter(.[, from] == drug) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-      } else {
-        # not found
-        warning('Code "', drug, '" not found in antibiotics list.', call. = FALSE)
-        abcode[i] <- NA
-      }
-    } else {
-      # more than 1 drug
-      if (grepl('+', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, '+', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else if (grepl(' en ', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, ' en ', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else {
-        warning('Invalid concat.')
-        abcode[i] <- NA
-        next
-      }
-
-      for (j in 1:length(drug.group)) {
-        drug.group[j] <-
-          antibiotics %>%
-          filter(.[, from] == drug.group[j]) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-        if (j > 1 & to %in% c('official', 'trivial_nl')) {
-          drug.group[j] <- drug.group[j] %>% tolower()
-        }
-      }
-      abcode[i] <- paste(drug.group, collapse = textbetween)
-    }
-  }
-
-  if (tolower == TRUE) {
-    abcode <- abcode %>% tolower()
-  }
-
-  abcode
-}
-
-#' Find bacteria ID based on genus/species
-#'
-#' Use this function to determine a valid ID based on a genus (and species). This input could be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also use a \code{\link{paste}} of a genus and species column to use the full name as input: \code{x = paste(df$genus, df$species)}, where \code{df} is your dataframe.
-#' @param x character vector to determine \code{bactid}
-#' @export
-#' @importFrom dplyr %>% filter slice pull
-#' @return Character (vector).
-#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
-#' @examples
-#' # These examples all return "STAAUR", the ID of S. aureus:
-#' guess_bactid("stau")
-#' guess_bactid("STAU")
-#' guess_bactid("staaur")
-#' guess_bactid("S. aureus")
-#' guess_bactid("S aureus")
-#' guess_bactid("Staphylococcus aureus")
-#' guess_bactid("MRSA") # Methicillin-resistant S. aureus
-#' guess_bactid("VISA") # Vancomycin Intermediate S. aureus
-guess_bactid <- function(x) {
-  # remove dots and other non-text in case of "E. coli" except spaces
-  x <- gsub("[^a-zA-Z ]+", "", x)
-  # but spaces before and after should be omitted
-  x <- trimws(x, which = "both")
-  x.bak <- x
-  # replace space by regex sign
-  x <- gsub(" ", ".*", x, fixed = TRUE)
-  # add start and stop
-  x_species <- paste(x, 'species')
-  x <- paste0('^', x, '$')
-
-  for (i in 1:length(x)) {
-    if (tolower(x[i]) == '^e.*coli$') {
-      # avoid detection of Entamoeba coli in case of E. coli
-      x[i] <- 'Escherichia coli'
-    }
-    if (tolower(x[i]) == '^h.*influenzae$') {
-      # avoid detection of Haematobacter influenzae in case of H. influenzae
-      x[i] <- 'Haemophilus influenzae'
-    }
-    if (tolower(x[i]) == '^st.*au$'
-        | tolower(x[i]) == '^stau$'
-        | tolower(x[i]) == '^staaur$') {
-      # avoid detection of Staphylococcus auricularis in case of S. aureus
-      x[i] <- 'Staphylococcus aureus'
-    }
-    if (tolower(x[i]) == '^p.*aer$') {
-      # avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
-      x[i] <- 'Pseudomonas aeruginosa'
-    }
-
-    # translate known trivial names to genus+species
-    if (toupper(x.bak[i]) == 'MRSA'
-        | toupper(x.bak[i]) == 'VISA'
-        | toupper(x.bak[i]) == 'VRSA') {
-      x[i] <- 'Staphylococcus aureus'
-    }
-    if (toupper(x.bak[i]) == 'MRSE') {
-      x[i] <- 'Staphylococcus epidermidis'
-    }
-    if (toupper(x.bak[i]) == 'VRE') {
-      x[i] <- 'Enterococcus'
-    }
-    if (toupper(x.bak[i]) == 'MRPA') {
-      # multi resistant P. aeruginosa
-      x[i] <- 'Pseudomonas aeruginosa'
-    }
-    if (toupper(x.bak[i]) == 'PISP'
-        | toupper(x.bak[i]) == 'PRSP') {
-      # peni resistant S. pneumoniae
-      x[i] <- 'Streptococcus pneumoniae'
-    }
-    if (toupper(x.bak[i]) == 'VISP'
-        | toupper(x.bak[i]) == 'VRSP') {
-      # vanco resistant S. pneumoniae
-      x[i] <- 'Streptococcus pneumoniae'
-    }
-
-    # let's try the ID's first
-    found <- AMR::microorganisms %>% filter(bactid == x.bak[i])
-
-    if (nrow(found) == 0) {
-      # now try exact match
-      found <- AMR::microorganisms %>% filter(fullname == x[i])
-    }
-    if (nrow(found) == 0) {
-      # try any match
-      found <- AMR::microorganisms %>% filter(fullname %like% x[i])
-    }
-    if (nrow(found) == 0) {
-      # try only genus, with 'species' attached
-      found <- AMR::microorganisms %>% filter(fullname %like% x_species[i])
-    }
-    if (nrow(found) == 0) {
-      # search for GLIMS code
-      if (toupper(x.bak[i]) %in% toupper(AMR::microorganisms.umcg$mocode)) {
-        found <- AMR::microorganisms.umcg %>% filter(toupper(mocode) == toupper(x.bak[i]))
-      }
-    }
-    if (nrow(found) == 0) {
-      # try splitting of characters and then find ID
-      # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus
-      x_split <- x
-      x_length <- nchar(x.bak[i])
-      x_split[i] <- paste0(x.bak[i] %>% substr(1, x_length / 2) %>% trimws(),
-                     '.* ',
-                     x.bak[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
-      found <- AMR::microorganisms %>% filter(fullname %like% paste0('^', x_split[i]))
-    }
-    if (nrow(found) == 0) {
-      # try any match with text before and after original search string
-      # so "negative rods" will be "GNR"
-      if (x.bak[i] %like% "^Gram") {
-        x.bak[i] <- gsub("^Gram", "", x.bak[i], ignore.case = TRUE)
-        # remove leading and trailing spaces again
-        x.bak[i] <- trimws(x.bak[i], which = "both")
-      }
-      found <- AMR::microorganisms %>% filter(fullname %like% x.bak[i])
-    }
-
-    if (nrow(found) != 0) {
-      x[i] <- found %>%
-        slice(1) %>%
-        pull(bactid)
-    } else {
-      x[i] <- ""
-    }
-  }
-  x
-}
-
-#' Find ATC code based on antibiotic property
-#'
-#' Use this function to determine the ATC code of one or more antibiotics. The dataset \code{\link{antibiotics}} will be searched for abbreviations, official names and trade names.
-#' @param x character vector to determine \code{ATC} code
-#' @export
-#' @importFrom dplyr %>% filter slice pull
-#' @details In the ATC classification system, the active substances are classified in a hierarchy with five different levels.  The system has fourteen main anatomical/pharmacological groups or 1st levels.  Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups.  The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance.  The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
-#'   Source: \url{https://www.whocc.no/atc/structure_and_principles/}
-#' @return Character (vector).
-#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATC's.
-#' @examples
-#' # These examples all return "J01FA01", the ATC code of Erythromycin:
-#' guess_atc("J01FA01")
-#' guess_atc("Erythromycin")
-#' guess_atc("eryt")
-#' guess_atc("ERYT")
-#' guess_atc("ERY")
-#' guess_atc("Erythrocin") # Trade name
-#' guess_atc("Eryzole")    # Trade name
-#' guess_atc("Pediamycin") # Trade name
-guess_atc <- function(x) {
-
-  # use this later to further fill AMR::antibiotics
-
-  # drug <- "Ciprofloxacin"
-  # url <- xml2::read_html(paste0("https://www.ncbi.nlm.nih.gov/pccompound?term=", drug)) %>%
-  #   html_nodes(".rslt") %>%
-  #   .[[1]] %>%
-  #   html_nodes(".title a") %>%
-  #   html_attr("href") %>%
-  #   gsub("/compound/", "/rest/pug_view/data/compound/", ., fixed = TRUE) %>%
-  #   paste0("/XML/?response_type=display")
-  # synonyms <- url %>%
-  #   read_xml() %>%
-  #   xml_contents() %>% .[[6]] %>%
-  #   xml_contents() %>% .[[8]] %>%
-  #   xml_contents() %>% .[[3]] %>%
-  #   xml_contents() %>% .[[3]] %>%
-  #   xml_contents() %>%
-  #   paste() %>%
-  #   .[. %like% "StringValueList"] %>%
-  #   gsub("[</]+StringValueList[>]", "", .)
-
-
-  for (i in 1:length(x)) {
-
-    # first try atc
-    found <- AMR::antibiotics %>% filter(atc == x[i])
-
-    if (nrow(found) == 0) {
-      # try abbreviation of molis and glims
-      found <- AMR::antibiotics %>% filter(molis == x[i] | umcg == x[i])
-    }
-
-    if (nrow(found) == 0) {
-      # try exact official name
-      found <- AMR::antibiotics[which(AMR::antibiotics$official == x[i]),]
-    }
-
-    if (nrow(found) == 0) {
-      # try trade name
-      found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$trade_name, ")") %like% x[i]),]
-    }
-
-    if (nrow(found) == 0) {
-      # try abbreviation
-      found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$abbr, ")") %like% x[i]),]
-    }
-    # if (nrow(found) == 0) {
-    #   # loosely try official name
-    #   found <- AMR::antibiotics[which(AMR::antibiotics$official %like% x[i]),]
-    # }
-
-    if (nrow(found) != 0) {
-      x[i] <- found %>%
-        slice(1) %>%
-        pull(atc)
-    } else {
-      x[i] <- NA
-    }
-  }
-  x
-}

R/classes.R

@@ -1,407 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Class 'rsi'
-#'
-#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
-#' @rdname as.rsi
-#' @param x vector
-#' @return Ordered factor with new class \code{rsi} and new attributes \code{package} and \code{package.version}
-#' @export
-#' @importFrom dplyr %>%
-#' @importFrom utils packageDescription
-#' @examples
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
-#' is.rsi(rsi_data)
-#'
-#' plot(rsi_data)    # for percentages
-#' barplot(rsi_data) # for frequencies
-as.rsi <- function(x) {
-  if (is.rsi(x)) {
-    x
-  } else {
-
-    x <- x %>% unlist()
-    x.bak <- x
-
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    # remove all spaces
-    x <- gsub(' {2,55}', '', x)
-    # disallow more than 3 characters
-    x[nchar(x) > 3] <- NA
-    # remove all invalid characters
-    x <- gsub('[^RSI]+', '', x %>% toupper())
-    # needed for UMCG in cases of "S;S" but also "S;I"; the latter will be NA:
-    x <- gsub('^S+$', 'S', x)
-    x <- gsub('^I+$', 'I', x)
-    x <- gsub('^R+$', 'R', x)
-    x[!x %in% c('S', 'I', 'R')] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        sort()
-      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
-      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) * 100),
-              '%) that were invalid antimicrobial interpretations: ',
-              list_missing, call. = FALSE)
-    }
-
-    x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
-    class(x) <- c('rsi', 'ordered', 'factor')
-    attr(x, 'package') <- 'AMR'
-    attr(x, 'package.version') <- packageDescription('AMR')$Version
-    x
-  }
-}
-
-#' @rdname as.rsi
-#' @export
-#' @importFrom dplyr %>%
-is.rsi <- function(x) {
-  class(x) %>% identical(c('rsi', 'ordered', 'factor'))
-}
-
-#' @exportMethod print.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-print.rsi <- function(x, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  cat("Class 'rsi'\n")
-  cat(n, " results (missing: ", n_total - n, ' = ', percent((n_total - n) / n_total, force_zero = TRUE), ')\n', sep = "")
-  if (n > 0) {
-    cat('\n')
-    cat('Sum of S:   ', S, ' (', percent(S / n, force_zero = TRUE), ')\n', sep = "")
-    cat('Sum of IR:  ', IR, ' (', percent(IR / n, force_zero = TRUE), ')\n', sep = "")
-    cat('- Sum of R: ', R, ' (', percent(R / n, force_zero = TRUE), ')\n', sep = "")
-    cat('- Sum of I: ', I, ' (', percent(I / n, force_zero = TRUE), ')\n', sep = "")
-  }
-}
-
-#' @exportMethod summary.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-summary.rsi <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  lst <- c('rsi', n_total - n, S, IR, R, I)
-  names(lst) <- c("Mode", "<NA>", "Sum S", "Sum IR", "Sum R", "Sum I")
-  lst
-}
-
-#' @exportMethod plot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics plot text
-#' @noRd
-plot.rsi <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-
-  data <- data.frame(x = x,
-                     y = 1,
-                     stringsAsFactors = TRUE) %>%
-    group_by(x) %>%
-    summarise(n = sum(y)) %>%
-    filter(!is.na(x)) %>%
-    mutate(s = round((n / sum(n)) * 100, 1))
-  data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
-
-  ymax <- if_else(max(data$s) > 95, 105, 100)
-
-  plot(x = data$x,
-       y = data$s,
-       lwd = 2,
-       col = c('green', 'orange', 'red'),
-       ylim = c(0, ymax),
-       ylab = 'Percentage',
-       xlab = 'Antimicrobial Interpretation',
-       main = paste('Susceptibilty Analysis of', x_name),
-       axes = FALSE,
-       ...)
-  # x axis
-  axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
-  # y axis, 0-100%
-  axis(side = 2, at = seq(0, 100, 5))
-
-  text(x = data$x,
-       y = data$s + 4,
-       labels = paste0(data$s, '% (n = ', data$n, ')'))
-}
-
-
-#' @exportMethod barplot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.rsi <- function(height, ...) {
-  x <- height
-  x_name <- deparse(substitute(height))
-
-  data <- data.frame(rsi = x, cnt = 1) %>%
-    group_by(rsi) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-
-  barplot(table(x),
-          col = c('green3', 'orange2', 'red3'),
-          xlab = 'Antimicrobial Interpretation',
-          main = paste('Susceptibilty Analysis of', x_name),
-          ylab = 'Frequency',
-          axes = FALSE,
-          ...)
-  # y axis, 0-100%
-  axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
-}
-
-#' Class 'mic'
-#'
-#' This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
-#' @rdname as.mic
-#' @param x vector
-#' @param na.rm a logical indicating whether missing values should be removed
-#' @return Ordered factor with new class \code{mic} and new attributes \code{package} and \code{package.version}
-#' @export
-#' @importFrom dplyr %>%
-#' @importFrom utils packageDescription
-#' @examples
-#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
-#' is.mic(mic_data)
-#'
-#' plot(mic_data)
-#' barplot(mic_data)
-as.mic <- function(x, na.rm = FALSE) {
-  if (is.mic(x)) {
-    x
-  } else {
-    x <- x %>% unlist()
-    if (na.rm == TRUE) {
-      x <- x[!is.na(x)]
-    }
-    x.bak <- x
-
-    # comma to dot
-    x <- gsub(',', '.', x, fixed = TRUE)
-    # starting dots must start with 0
-    x <- gsub('^[.]', '0.', x)
-    # <=0.2560.512 should be 0.512
-    x <- gsub('.*[.].*[.]', '0.', x)
-    # remove ending .0
-    x <- gsub('[.]0$', '', x)
-    # remove all after last digit
-    x <- gsub('[^0-9]$', '', x)
-    # remove last zeroes
-    x <- gsub('[.]?0+$', '', x)
-
-    lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
-              "<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
-              "<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
-              "<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
-              "<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
-              "<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
-              "<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
-              "<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
-              "<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
-              "<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
-              "<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
-              "<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
-              "<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
-              "<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
-              "<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
-              "<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
-              "<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
-              "<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
-              "<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
-              "<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
-              "<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
-              "<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
-              "<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
-              "<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
-              "<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
-              "<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
-              "<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
-              "<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
-              "<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
-              "<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
-              "<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
-              "<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
-              "<1", "<=1", "1", ">=1", ">1",
-              "<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
-              "<2", "<=2", "2", ">=2", ">2",
-              "<3", "<=3", "3", ">=3", ">3",
-              "<4", "<=4", "4", ">=4", ">4",
-              "<6", "<=6", "6", ">=6", ">6",
-              "<8", "<=8", "8", ">=8", ">8",
-              "<10", "<=10", "10", ">=10", ">10",
-              "<12", "<=12", "12", ">=12", ">12",
-              "<16", "<=16", "16", ">=16", ">16",
-              "<20", "<=20", "20", ">=20", ">20",
-              "<24", "<=24", "24", ">=24", ">24",
-              "<32", "<=32", "32", ">=32", ">32",
-              "<40", "<=40", "40", ">=40", ">40",
-              "<48", "<=48", "48", ">=48", ">48",
-              "<64", "<=64", "64", ">=64", ">64",
-              "<80", "<=80", "80", ">=80", ">80",
-              "<96", "<=96", "96", ">=96", ">96",
-              "<128", "<=128", "128", ">=128", ">128",
-              "<160", "<=160", "160", ">=160", ">160",
-              "<256", "<=256", "256", ">=256", ">256",
-              "<320", "<=320", "320", ">=320", ">320",
-              "<512", "<=512", "512", ">=512", ">512",
-              "<1024", "<=1024", "1024", ">=1024", ">1024")
-    x <- x %>% as.character()
-
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    x[!x %in% lvls] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        sort()
-      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
-      warning(na_after - na_before, ' results truncated (',
-              round(((na_after - na_before) / length(x)) * 100),
-              '%) that were invalid MICs: ',
-              list_missing, call. = FALSE)
-    }
-
-    x <- factor(x = x,
-                levels = lvls,
-                ordered = TRUE)
-    class(x) <- c('mic', 'ordered', 'factor')
-    attr(x, 'package') <- 'AMR'
-    attr(x, 'package.version') <- packageDescription('AMR')$Version
-    x
-  }
-}
-
-#' @rdname as.mic
-#' @export
-#' @importFrom dplyr %>%
-is.mic <- function(x) {
-  class(x) %>% identical(c('mic', 'ordered', 'factor'))
-}
-
-#' @exportMethod as.double.mic
-#' @export
-#' @noRd
-as.double.mic <- function(x, ...) {
-  as.double(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.integer.mic
-#' @export
-#' @noRd
-as.integer.mic <- function(x, ...) {
-  as.integer(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.numeric.mic
-#' @export
-#' @noRd
-as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod print.mic
-#' @export
-#' @importFrom dplyr %>% tibble group_by summarise pull
-#' @noRd
-print.mic <- function(x, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  cat("Class 'mic': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA> ', n_total - n, '\n')
-  cat('\n')
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
-  cnt <- tbl %>% pull(y)
-  names(cnt) <- tbl %>% pull(x)
-  print(cnt)
-}
-
-#' @exportMethod summary.mic
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  lst <- c('mic',
-           n_total - n,
-           sort(x)[1] %>% as.character(),
-           sort(x)[n] %>% as.character())
-  names(lst) <- c("Mode", "<NA>", "Min.", "Max.")
-  lst
-}
-
-#' @exportMethod plot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics plot text
-#' @noRd
-plot.mic <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-  create_barplot_mic(x, x_name, ...)
-}
-
-#' @exportMethod barplot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.mic <- function(height, ...) {
-  x_name <- deparse(substitute(height))
-  create_barplot_mic(height, x_name, ...)
-}
-
-#' @importFrom graphics barplot axis
-create_barplot_mic <- function(x, x_name, ...) {
-  data <- data.frame(mic = x, cnt = 1) %>%
-    group_by(mic) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-  barplot(table(droplevels(x)),
-          ylab = 'Frequency',
-          xlab = 'MIC value',
-          main = paste('MIC values of', x_name),
-          axes = FALSE,
-          ...)
-  axis(2, seq(0, max(data$cnt)))
-}

R/data.R

@@ -1,327 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Dataset with 420 antibiotics
-#'
-#' A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
-#' @format A data.frame with 420 observations and 18 variables:
-#' \describe{
-#'   \item{\code{atc}}{ATC code, like \code{J01CR02}}
-#'   \item{\code{molis}}{MOLIS code, like \code{amcl}}
-#'   \item{\code{umcg}}{UMCG code, like \code{AMCL}}
-#'   \item{\code{abbr}}{Abbreviation as used by many countries, to be used for \code{\link{guess_atc}}}
-#'   \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and enzyme inhibitor"}}
-#'   \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
-#'   \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-#'   \item{\code{trade_name}}{Trade name as used by many countries, to be used for \code{\link{guess_atc}}}
-#'   \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
-#'   \item{\code{oral_units}}{Units of \code{ddd_units}}
-#'   \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
-#'   \item{\code{iv_units}}{Units of \code{iv_ddd}}
-#'   \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
-#'   \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
-#'   \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
-#'   \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
-#'   \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-#'   \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-#' }
-#' @source - World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
-#' @seealso \code{\link{microorganisms}}
-# abbr and trade_name created with:
-# https://hs.unr.edu/Documents/dhs/chs/NVPHTC/antibiotic_refeference_guide.pdf
-# antibiotics %>%
-#   mutate(abbr =
-#            case_when(
-#              official == 'Amikacin' ~ 'Ak|AN|AMI|AMK',
-#              official == 'Amoxicillin' ~ 'AMX|AMOX|AC',
-#              official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'AUG|A/C|XL|AML',
-#              official == 'Ampicillin' ~ 'AM|AMP',
-#              official == 'Ampicillin and beta-lactamase inhibitor' ~ 'A/S|SAM|AMS|AB',
-#              official == 'Azithromycin' ~ 'Azi|AZM|AZ',
-#              official == 'Azlocillin' ~ 'AZ|AZL',
-#              official == 'Aztreonam' ~ 'Azt|ATM|AT|AZM',
-#              official == 'Carbenicillin' ~ 'Cb|BAR',
-#              official == 'Cefaclor' ~ 'Ccl|CEC|Cfr|FAC|CF',
-#              official == 'Cefadroxil' ~ 'CFR|FAD',
-#              official == 'Cefazolin' ~ 'Cfz|CZ|FAZ|KZ',
-#              official == 'Cefdinir' ~ 'Cdn|CDR|DIN|CD|CFD',
-#              official == 'Cefditoren' ~ 'CDN',
-#              official == 'Cefepime' ~ 'Cpe|FEP|PM|CPM',
-#              official == 'Cefixime' ~ 'Cfe|DCFM|FIX|IX',
-#              official == 'Cefoperazone' ~ 'Cfp|CPZ|PER|FOP|CP',
-#              official == 'Cefotaxime' ~ 'Cft|CTX|TAX|FOT|CT',
-#              official == 'Cefotetan' ~ 'Ctn|CTT|CTE|TANS|CN',
-#              official == 'Cefoxitin' ~ 'Cfx|FOX|CX|FX',
-#              official == 'Cefpodoxime' ~ 'Cpd|POD|PX',
-#              official == 'Cefprozil' ~ 'Cpz|CPR|FP',
-#              official == 'Ceftaroline' ~ 'CPT',
-#              official == 'Ceftazidime' ~ 'Caz|TAZ|TZ',
-#              official == 'Ceftibuten' ~ 'CTB|TIB|CB',
-#              official == 'Ceftizoxime' ~ 'Cz|ZOX|CZX|CZ|CTZ|TIZ',
-#              official == 'Ceftriaxone' ~ 'Cax|CRO|CTR|FRX|AXO|TX',
-#              official == 'Cefuroxime' ~ 'Crm|CXM|CFX|ROX|FUR|XM',
-#              official == 'Cephalexin' ~ 'CN|LX|CFL',
-#              official == 'Cephalothin' ~ 'Cf',
-#              official == 'Chloramphenicol' ~ 'C|CHL|CL',
-#              official == 'Ciprofloxacin' ~ 'Cp|CIP|CI',
-#              official == 'Clarithromycin' ~ 'Cla|CLR|CLM|CH',
-#              official == 'Clindamycin' ~ 'Cd|CC|CM|CLI|DA',
-#              official == 'Colistin' ~ 'CL|CS|CT',
-#              official == 'Daptomycin' ~ 'Dap',
-#              official == 'Doxycycline' ~ 'Dox',
-#              official == 'Doripenem' ~ 'DOR|Dor',
-#              official == 'Ertapenem' ~ 'Etp',
-#              official == 'Erythromycin' ~ 'E|ERY|EM',
-#              official == 'Fosfomycin' ~ 'FOS|FF|FO|FM',
-#              official == 'Flucloxacillin' ~ 'CLOX',
-#              official == 'Gentamicin' ~ 'Gm|CN|GEN',
-#              official == 'Imipenem' ~ 'Imp|IPM|IMI|IP',
-#              official == 'Kanamycin' ~ 'K|KAN|HLK|KM',
-#              official == 'Levofloxacin' ~ 'Lvx|LEV|LEVO|LE',
-#              official == 'Linezolid' ~ 'Lzd|LNZ|LZ',
-#              official == 'Lomefloxacin' ~ 'Lmf|LOM',
-#              official == 'Meropenem' ~ 'Mer|MEM|MERO|MRP|MP',
-#              official == 'Metronidazole' ~ 'MNZ',
-#              official == 'Mezlocillin' ~ 'Mz|MEZ',
-#              official == 'Minocycline' ~ 'Min|MI|MN|MNO|MC|MH',
-#              official == 'Moxifloxacin' ~ 'Mox|MXF',
-#              official == 'Mupirocin' ~ 'MUP',
-#              official == 'Nafcillin' ~ 'Naf|NF',
-#              official == 'Nalidixic acid' ~ 'NA|NAL',
-#              official == 'Nitrofurantoin' ~ 'Fd|F/M|FT|NIT|NI|F',
-#              official == 'Norfloxacin' ~ 'Nxn|NOR|NX',
-#              official == 'Ofloxacin' ~ 'Ofl|OFX|OF',
-#              official == 'Oxacillin' ~ 'Ox|OXS|OXA',
-#              official == 'Benzylpenicillin' ~ 'P|PEN|PV',
-#              official == 'Penicillins, combinations with other antibacterials' ~ 'P|PEN|PV',
-#              official == 'Piperacillin' ~ 'Pi|PIP|PP',
-#              official == 'Piperacillin and beta-lactamase inhibitor' ~ 'PT|TZP|PTZ|P/T|PTc',
-#              official == 'Polymyxin B' ~ 'PB',
-#              official == 'Quinupristin/dalfopristin' ~ 'Syn|Q/D|QDA|RP',
-#              official == 'Rifampin' ~ 'Rif|RA|RI|RD',
-#              official == 'Spectinomycin' ~ 'SPT|SPE|SC',
-#              official == 'Streptomycin' ~ 'S|STR',
-#              official == 'Teicoplanin' ~ 'Tei|TEC|TPN|TP|TPL',
-#              official == 'Telavancin' ~ 'TLV',
-#              official == 'Telithromcyin' ~ 'Tel',
-#              official == 'Tetracycline' ~ 'Te|TET|TC',
-#              official == 'Ticarcillin' ~ 'Ti|TIC|TC',
-#              official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Tim|T/C|TCC|TLc',
-#              official == 'Tigecycline' ~ 'TGC',
-#              official == 'Tobramycin' ~ 'To|NN|TM|TOB',
-#              official == 'Trimethoprim' ~ 'T|TMP|TR|W',
-#              official == 'Sulfamethoxazole and trimethoprim' ~ 'T/S|SXT|SxT|TS|COT',
-#              official == 'Vancomycin' ~ 'Va|VAN',
-#              TRUE ~ NA_character_),
-#
-#          trade_name =
-#            case_when(
-#              official == 'Amikacin' ~ 'Amikin',
-#              official == 'Amoxicillin' ~ 'Amoxil|Dispermox|Larotid|Trimox',
-#              official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'Augmentin',
-#              official == 'Ampicillin' ~ 'Pfizerpen-A|Principen',
-#              official == 'Ampicillin and beta-lactamase inhibitor' ~ 'Unasyn',
-#              official == 'Azithromycin' ~ 'Zithromax',
-#              official == 'Azlocillin' ~ 'Azlin',
-#              official == 'Aztreonam' ~ 'Azactam',
-#              official == 'Carbenicillin' ~ 'Geocillin',
-#              official == 'Cefaclor' ~ 'Ceclor',
-#              official == 'Cefadroxil' ~ 'Duricef',
-#              official == 'Cefazolin' ~ 'Ancef',
-#              official == 'Cefdinir' ~ 'Omnicef',
-#              official == 'Cefditoren' ~ 'Spectracef',
-#              official == 'Cefepime' ~ 'Maxipime',
-#              official == 'Cefixime' ~ 'Suprax',
-#              official == 'Cefoperazone' ~ 'Cefobid',
-#              official == 'Cefotaxime' ~ 'Claforan',
-#              official == 'Cefotetan' ~ 'Cefotan',
-#              official == 'Cefoxitin' ~ 'Mefoxin',
-#              official == 'Cefpodoxime' ~ 'Vantin',
-#              official == 'Cefprozil' ~ 'Cefzil',
-#              official == 'Ceftaroline' ~ 'Teflaro',
-#              official == 'Ceftazidime' ~ 'Fortaz|Tazicef|Tazidime',
-#              official == 'Ceftibuten' ~ 'Cedax',
-#              official == 'Ceftizoxime' ~ 'Cefizox',
-#              official == 'Ceftriaxone' ~ 'Rocephin',
-#              official == 'Cefuroxime' ~ 'Ceftin|Zinacef',
-#              official == 'Cephalexin' ~ 'Keflex|Panixine',
-#              official == 'Cephalothin' ~ 'Keflin',
-#              official == 'Chloramphenicol' ~ 'Chloromycetin',
-#              official == 'Ciprofloxacin' ~ 'Cipro|Ciloxan|Ciproxin',
-#              official == 'Clarithromycin' ~ 'Biaxin',
-#              official == 'Clindamycin' ~ 'Cleocin|Clinda-Derm|Clindagel|Clindesse|Clindets|Evoclin',
-#              official == 'Colistin' ~ 'Coly-Mycin',
-#              official == 'Daptomycin' ~ 'Cubicin',
-#              official == 'Doxycycline' ~ 'Doryx|Monodox|Vibramycin|Atridox|Oracea|Periostat|Vibra-Tabs',
-#              official == 'Doripenem' ~ 'Doribax',
-#              official == 'Ertapenem' ~ 'Invanz',
-#              official == 'Erythromycin' ~ 'Eryc|EryPed|Erythrocin|E-Base|E-Glades|E-Mycin|E.E.S.|Ery-Tab|Eryderm|Erygel|Erythra-derm|Eryzole|Pediamycin',
-#              official == 'Fosfomycin' ~ 'Monurol',
-#              official == 'Flucloxacillin' ~ 'Flopen|Floxapen|Fluclox|Sesamol|Softapen|Staphylex',
-#              official == 'Gentamicin' ~ 'Garamycin|Genoptic',
-#              official == 'Imipenem' ~ 'Primaxin',
-#              official == 'Kanamycin' ~ 'Kantrex',
-#              official == 'Levofloxacin' ~ 'Levaquin|Quixin',
-#              official == 'Linezolid' ~ 'Zyvox',
-#              official == 'Lomefloxacin' ~ 'Maxaquin',
-#              official == 'Meropenem' ~ 'Merrem',
-#              official == 'Metronidazole' ~ 'Flagyl|MetroGel|MetroCream|MetroLotion',
-#              official == 'Mezlocillin' ~ 'Mezlin',
-#              official == 'Minocycline' ~ 'Arestin|Solodyn',
-#              official == 'Moxifloxacin' ~ 'Avelox|Vigamox',
-#              official == 'Mupirocin' ~ 'Bactroban|Centany',
-#              official == 'Nafcillin' ~ 'Unipen',
-#              official == 'Nalidixic acid' ~ 'NegGram',
-#              official == 'Nitrofurantoin' ~ 'Furadantin|Macrobid|Macrodantin',
-#              official == 'Norfloxacin' ~ 'Noroxin',
-#              official == 'Ofloxacin' ~ 'Floxin|Ocuflox|Ophthalmic',
-#              official == 'Oxacillin' ~ 'Bactocill',
-#              official == 'Benzylpenicillin' ~ 'Permapen|Pfizerpen|Veetids',
-#              official == 'Penicillins, combinations with other antibacterials' ~ 'Permapen|Pfizerpen|Veetids',
-#              official == 'Piperacillin' ~ 'Pipracil',
-#              official == 'Piperacillin and beta-lactamase inhibitor' ~ 'Zosyn',
-#              official == 'Polymyxin B' ~ 'Poly-RX',
-#              official == 'Quinupristin/dalfopristin' ~ 'Synercid',
-#              official == 'Rifampin' ~ 'Rifadin|Rifamate|Rimactane',
-#              official == 'Spectinomycin' ~ 'Trobicin',
-#              official == 'Streptomycin' ~ 'Streptomycin Sulfate',
-#              official == 'Teicoplanin' ~ 'Targocid',
-#              official == 'Telavancin' ~ 'Vibativ',
-#              official == 'Telithromcyin' ~ 'Ketek',
-#              official == 'Tetracycline' ~ 'Sumycin|Bristacycline|Tetrex',
-#              official == 'Ticarcillin' ~ 'Ticar',
-#              official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Timentin',
-#              official == 'Tigecycline' ~ 'Tygacil',
-#              official == 'Tobramycin' ~ 'Tobi|Aktob|Tobre',
-#              official == 'Trimethoprim' ~ 'Primsol|Proloprim',
-#              official == 'Sulfamethoxazole and trimethoprim' ~ 'Bactrim|Septra|Sulfatrim',
-#              official == 'Vancomycin' ~ 'Vancocin|Vancomycin Hydrochloride',
-#              TRUE ~ NA_character_)
-#   )
-# last two columns created with:
-# antibiotics %>%
-#   mutate(useful_gramnegative =
-#            if_else(
-#              atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
-#                atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
-#                official %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)',
-#              FALSE,
-#              NA
-#            ),
-#          useful_grampositive =
-#            if_else(
-#              atc_group1 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
-#                atc_group2 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
-#                official %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)',
-#              FALSE,
-#              NA
-#            )
-#   )
-"antibiotics"
-
-#' Dataset with ~2500 microorganisms
-#'
-#' A dataset containing 2453 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
-#' @format A data.frame with 2453 observations and 12 variables:
-#' \describe{
-#'   \item{\code{bactid}}{ID of microorganism}
-#'   \item{\code{bactsys}}{Bactsyscode of microorganism}
-#'   \item{\code{family}}{Family name of microorganism}
-#'   \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
-#'   \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
-#'   \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
-#'   \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
-#'   \item{\code{type}}{Type of microorganism, like \code{"Bacteria"} and \code{"Fungus/yeast"}}
-#'   \item{\code{gramstain}}{Gram of microorganism, like \code{"Negative rods"}}
-#'   \item{\code{aerobic}}{Logical whether bacteria is aerobic}
-#'   \item{\code{type_nl}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
-#'   \item{\code{gramstain_nl}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-#' @seealso \code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
-"microorganisms"
-
-#' Translation table for UMCG with ~1100 microorganisms
-#'
-#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$bactid} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
-#' @format A data.frame with 1090 observations and 2 variables:
-#' \describe{
-#'   \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
-#'   \item{\code{bactid}}{Code of microorganism in \code{\link{microorganisms}}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
-#' @seealso \code{\link{guess_bactid}} \code{\link{microorganisms}}
-"microorganisms.umcg"
-
-#' Dataset with 2000 blood culture isolates of septic patients
-#'
-#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1.
-#' @format A data.frame with 2000 observations and 47 variables:
-#' \describe{
-#'   \item{\code{date}}{date of receipt at the laboratory}
-#'   \item{\code{hospital_id}}{ID of the hospital}
-#'   \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
-#'   \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
-#'   \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
-#'   \item{\code{age}}{age of the patient}
-#'   \item{\code{sex}}{sex of the patient}
-#'   \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
-#'   \item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
-#'   \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} and can be translated with \code{\link{abname}}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-#' @examples
-#' # ----------- #
-#' # PREPARATION #
-#' # ----------- #
-#'
-#' # Save this example dataset to an object, so we can edit it:
-#' my_data <- septic_patients
-#'
-#' # load the dplyr package to make data science A LOT easier
-#' library(dplyr)
-#'
-#' # Add first isolates to our dataset:
-#' my_data <- my_data %>%
-#'   mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
-#'
-#' # -------- #
-#' # ANALYSIS #
-#' # -------- #
-#'
-#' # 1. Get the amoxicillin resistance percentages
-#' #    of E. coli, divided by hospital:
-#'
-#' my_data %>%
-#'   filter(bactid == "ESCCOL",
-#'          first_isolates == TRUE) %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(n = n(),
-#'             amoxicillin_resistance = rsi(amox))
-#'
-#'
-#' # 2. Get the amoxicillin/clavulanic acid resistance
-#' #    percentages of E. coli, trend over the years:
-#'
-#' my_data %>%
-#'   filter(bactid == guess_bactid("E. coli"),
-#'          first_isolates == TRUE) %>%
-#'   group_by(year = format(date, "%Y")) %>%
-#'   summarise(n = n(),
-#'             amoxclav_resistance = rsi(amcl, minimum = 20))
-"septic_patients"

R/eucast.R

@@ -1,644 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' EUCAST expert rules
-#'
-#' Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
-#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
-#' @param col_bactid column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
-#' @param info print progress
-#' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.
-#' @param ... parameters that are passed on to \code{EUCAST_rules}
-#' @rdname EUCAST
-#' @export
-#' @importFrom dplyr %>% left_join select
-#' @return table with edited variables of antibiotics.
-#' @source
-#'   EUCAST Expert Rules Version 2.0: \cr
-#'   Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
-#'   \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
-#'   \cr
-#'   EUCAST Expert Rules Version 3.1 (Intrinsic Resistance and Exceptional Phenotypes Tables): \cr
-#'   \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
-#' @examples
-#' a <- EUCAST_rules(septic_patients)
-#' a <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-#'                            "ENCFAE",  # Enterococcus faecalis
-#'                            "ESCCOL",  # Escherichia coli
-#'                            "KLEPNE",  # Klebsiella pneumoniae
-#'                            "PSEAER"), # Pseudomonas aeruginosa
-#'                 vanc = "-",           # Vancomycin
-#'                 amox = "-",           # Amoxicillin
-#'                 coli = "-",           # Colistin
-#'                 cfta = "-",           # Ceftazidime
-#'                 cfur = "-",           # Cefuroxime
-#'                 stringsAsFactors = FALSE)
-#' a
-#'
-#' b <- EUCAST_rules(a)
-#' b
-EUCAST_rules <- function(tbl,
-                         col_bactid = 'bactid',
-                         info = TRUE,
-                         amcl = 'amcl',
-                         amik = 'amik',
-                         amox = 'amox',
-                         ampi = 'ampi',
-                         azit = 'azit',
-                         aztr = 'aztr',
-                         cefa = 'cefa',
-                         cfra = 'cfra',
-                         cfep = 'cfep',
-                         cfot = 'cfot',
-                         cfox = 'cfox',
-                         cfta = 'cfta',
-                         cftr = 'cftr',
-                         cfur = 'cfur',
-                         chlo = 'chlo',
-                         cipr = 'cipr',
-                         clar = 'clar',
-                         clin = 'clin',
-                         clox = 'clox',
-                         coli = 'coli',
-                         czol = 'czol',
-                         dapt = 'dapt',
-                         doxy = 'doxy',
-                         erta = 'erta',
-                         eryt = 'eryt',
-                         fosf = 'fosf',
-                         fusi = 'fusi',
-                         gent = 'gent',
-                         imip = 'imip',
-                         kana = 'kana',
-                         levo = 'levo',
-                         linc = 'linc',
-                         line = 'line',
-                         mero = 'mero',
-                         mino = 'mino',
-                         moxi = 'moxi',
-                         nali = 'nali',
-                         neom = 'neom',
-                         neti = 'neti',
-                         nitr = 'nitr',
-                         novo = 'novo',
-                         norf = 'norf',
-                         oflo = 'oflo',
-                         peni = 'peni',
-                         pita = 'pita',
-                         poly = 'poly',
-                         qida = 'qida',
-                         rifa = 'rifa',
-                         roxi = 'roxi',
-                         siso = 'siso',
-                         teic = 'teic',
-                         tetr = 'tetr',
-                         tica = 'tica',
-                         tige = 'tige',
-                         tobr = 'tobr',
-                         trim = 'trim',
-                         trsu = 'trsu',
-                         vanc = 'vanc') {
-
-  EUCAST_VERSION <- "3.1"
-
-  if (!col_bactid %in% colnames(tbl)) {
-    stop('Column ', col_bactid, ' not found.', call. = FALSE)
-  }
-
-  # check columns
-  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
-                cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
-                czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
-                levo, linc, line, mero, mino, moxi, nali, neom, neti, nitr,
-                novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso,
-                teic, tetr, tica, tige, tobr, trim, trsu, vanc)
-  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
-  amcl <- col.list[amcl]
-  amik <- col.list[amik]
-  amox <- col.list[amox]
-  ampi <- col.list[ampi]
-  azit <- col.list[azit]
-  aztr <- col.list[aztr]
-  cefa <- col.list[cefa]
-  cfra <- col.list[cfra]
-  cfep <- col.list[cfep]
-  cfot <- col.list[cfot]
-  cfox <- col.list[cfox]
-  cfta <- col.list[cfta]
-  cftr <- col.list[cftr]
-  cfur <- col.list[cfur]
-  chlo <- col.list[chlo]
-  cipr <- col.list[cipr]
-  clar <- col.list[clar]
-  clin <- col.list[clin]
-  clox <- col.list[clox]
-  coli <- col.list[coli]
-  czol <- col.list[czol]
-  dapt <- col.list[dapt]
-  doxy <- col.list[doxy]
-  erta <- col.list[erta]
-  eryt <- col.list[eryt]
-  fosf <- col.list[fosf]
-  fusi <- col.list[fusi]
-  gent <- col.list[gent]
-  imip <- col.list[imip]
-  kana <- col.list[kana]
-  levo <- col.list[levo]
-  linc <- col.list[linc]
-  line <- col.list[line]
-  mero <- col.list[mero]
-  mino <- col.list[mino]
-  moxi <- col.list[moxi]
-  nali <- col.list[nali]
-  neom <- col.list[neom]
-  neti <- col.list[neti]
-  nitr <- col.list[nitr]
-  novo <- col.list[novo]
-  norf <- col.list[norf]
-  oflo <- col.list[oflo]
-  peni <- col.list[peni]
-  pita <- col.list[pita]
-  poly <- col.list[poly]
-  qida <- col.list[qida]
-  rifa <- col.list[rifa]
-  roxi <- col.list[roxi]
-  siso <- col.list[siso]
-  teic <- col.list[teic]
-  tetr <- col.list[tetr]
-  tica <- col.list[tica]
-  tige <- col.list[tige]
-  tobr <- col.list[tobr]
-  trim <- col.list[trim]
-  trsu <- col.list[trsu]
-  vanc <- col.list[vanc]
-
-  total <- 0
-  total_rows <- integer(0)
-
-  # helper function for editing the table
-  edit_rsi <- function(to, rows, cols) {
-    cols <- cols[!is.na(cols)]
-    if (length(rows) > 0 & length(cols) > 0) {
-      tbl[rows, cols] <<- to
-      total <<- total + (length(rows) * length(cols))
-      total_rows <<- c(total_rows, rows)
-    }
-  }
-
-  # join to microorganisms table
-  joinby <- colnames(AMR::microorganisms)[1]
-  names(joinby) <- col_bactid
-  tbl <- tbl %>% left_join(y = AMR::microorganisms, by = joinby, suffix = c("_tempmicroorganisms", ""))
-
-  # antibiotic classes
-  aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
-  tetracyclines <- c(doxy, mino, tetr) # since EUCAST v3.1 tige(cycline) is set apart
-  polymyxins <- c(poly, coli)
-  macrolides <- c(eryt, azit, roxi, clar) # since EUCAST v3.1 clinda is set apart
-  glycopeptides <- c(vanc, teic)
-  streptogramins <- qida # should officially also be pristinamycin and quinupristin/dalfopristin
-  cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
-  carbapenems <- c(erta, imip, mero)
-  aminopenicillins <- c(ampi, amox)
-  ureidopenicillins <- pita # should officially also be azlo and mezlo
-  fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
-
-  if (info == TRUE) {
-    cat(
-      paste0(
-        '\nApplying rules to ',
-        tbl[!is.na(tbl$genus),] %>% nrow() %>% format(big.mark = ","),
-        ' rows according to "EUCAST Expert Rules Version ', EUCAST_VERSION, '"\n')
-    )
-  }
-
-  # Table 1: Intrinsic resistance in Enterobacteriaceae ----
-  if (info == TRUE) {
-    cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
-  }
-  # Intrisiek R for this group
-  edit_rsi(to = 'R',
-           rows = which(tbl$family == 'Enterobacteriaceae'),
-           cols = c(peni, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line))
-  # Citrobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)'),
-           cols = c(aminopenicillins, tica))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)'),
-           cols = c(aminopenicillins, amcl, czol, cfox))
-  # Enterobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter cloacae'),
-           cols = c(aminopenicillins, amcl, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter aerogenes'),
-           cols = c(aminopenicillins, amcl, czol, cfox))
-  # Escherichia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Escherichia hermanni'),
-           cols = c(aminopenicillins, tica))
-  # Hafnia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Hafnia alvei'),
-           cols = c(aminopenicillins, amcl, czol, cfox))
-  # Klebsiella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Klebsiella'),
-           cols = c(aminopenicillins, tica))
-  # Morganella / Proteus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Morganella morganii'),
-           cols = c(aminopenicillins, amcl, czol, tetracyclines, polymyxins, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus mirabilis'),
-           cols = c(tetracyclines, tige, polymyxins, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus penneri'),
-           cols = c(aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus vulgaris'),
-           cols = c(aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr))
-  # Providencia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia rettgeri'),
-           cols = c(aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia stuartii'),
-           cols = c(aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr))
-  # Raoultella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Raoultella'),
-           cols = c(aminopenicillins, tica))
-  # Serratia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Serratia marcescens'),
-           cols = c(aminopenicillins, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != 'mino'], polymyxins, nitr))
-  # Yersinia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia enterocolitica'),
-           cols = c(aminopenicillins, amcl, tica, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
-           cols = c(poly, coli))
-
-
-  # Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
-  }
-  # Intrisiek R for this group
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Achromobacter',
-                                         'Acinetobacter',
-                                         'Alcaligenes',
-                                         'Bordatella',
-                                         'Burkholderia',
-                                         'Elizabethkingia',
-                                         'Flavobacterium',
-                                         'Ochrobactrum',
-                                         'Pseudomonas',
-                                         'Stenotrophomonas')),
-           cols = c(peni, cfox, cfur, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line))
-  # Acinetobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Acinetobacter (baumannii|pittii|nosocomialis|calcoaceticus)'),
-           cols = c(aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, tetracyclines[tetracyclines != 'mino']))
-  # Achromobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Achromobacter (xylosoxydans|xylosoxidans)'),
-           cols = c(aminopenicillins, czol, cfot, cftr, erta))
-  # Burkholderia
-  edit_rsi(to = 'R',
-           # onder 'Burkholderia cepacia complex' vallen deze species allemaal: PMID 16217180.
-           rows = which(tbl$fullname %like% '^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis)'),
-           cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosides, trim, fosf, polymyxins))
-  # Elizabethkingia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Elizabethkingia meningoseptic(a|um)'),
-           cols = c(aminopenicillins, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxins))
-  # Ochrobactrum
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Ochrobactrum anthropi'),
-           cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, cfta, cfep, aztr, erta))
-  # Pseudomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Pseudomonas aeruginosa'),
-           cols = c(aminopenicillins, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige))
-  # Stenotrophomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
-           cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosides, trim, fosf, tetr))
-
-
-  # Table 3: Intrinsic resistance in other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
-  }
-  # Intrisiek R for this group
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Haemophilus',
-                                         'Moraxella',
-                                         'Neisseria',
-                                         'Campylobacter')),
-           cols = c(glycopeptides, linc, dapt, line))
-  # Haemophilus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Haemophilus influenzae'),
-           cols = c(fusi, streptogramins))
-  # Moraxella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Moraxella catarrhalis'),
-           cols = trim)
-  # Neisseria
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Neisseria'),
-           cols = trim)
-  # Campylobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter fetus'),
-           cols = c(fusi, streptogramins, trim, nali))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
-           cols = c(fusi, streptogramins, trim))
-
-
-  # Table 4: Intrinsic resistance in Gram-positive bacteria ----
-  if (info == TRUE) {
-    cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
-  }
-  # Intrisiek R for this group
-  edit_rsi(to = 'R',
-           rows = which(tbl$gramstain %like% 'Positi(e|)(v|f)'),
-           cols = c(aztr, polymyxins, nali))
-  # Staphylococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus saprophyticus'),
-           cols = c(fusi, cfta, fosf, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (cohnii|xylosus)'),
-           cols = c(cfta, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus capitis'),
-           cols = c(cfta, fosf))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
-           cols = cfta)
-  # Streptococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Streptococcus'),
-           cols = c(fusi, cfta, aminoglycosides))
-  # Enterococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecalis'),
-           cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, clin, qida, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus (gallinarum|casseliflavus)'),
-           cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, clin, qida, vanc, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecium'),
-           cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, trim, trsu))
-  # Corynebacterium
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Corynebacterium'),
-           cols = fosf)
-  # Listeria
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Listeria monocytogenes'),
-           cols = c(cfta, cephalosporins[cephalosporins != cfta]))
-  # overig
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Lactobacillus'),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
-           cols = vanc)
-
-  # Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
-  if (info == TRUE) {
-    cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
-  }
-  # rule 8.3
-  if (!is.na(peni)) {
-    edit_rsi(to = 'S',
-             rows = which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|dysgalactiae|groep A|groep B|groep C|groep G)'
-                          & tbl[, peni] == 'S'),
-             cols = c(aminopenicillins, cephalosporins, carbapenems))
-  }
-  # rule 8.6
-  if (!is.na(ampi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Enterococcus'
-                          & tbl[, ampi] == 'R'),
-             cols = c(ureidopenicillins, carbapenems))
-  }
-  if (!is.na(amox)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Enterococcus'
-                          & tbl[, amox] == 'R'),
-             cols = c(ureidopenicillins, carbapenems))
-  }
-
-  # Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
-  if (info == TRUE) {
-    cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
-  }
-  # rule 9.3
-  if (!is.na(tica) & !is.na(pita)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tica] == 'R'
-                          & tbl[, pita] == 'S'),
-             cols = pita)
-  }
-
-  # Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
-  }
-  # rule 10.2
-  if (!is.na(ampi)) {
-    # you should know first if the are B-lactamase positive, so do not run for now
-    # edit_rsi(to = 'R',
-    #          rows = which(tbl$fullname %like% '^Haemophilus influenza'
-    #                       & tbl[, ampi] == 'R'),
-    #          cols = c(ampi, amox, amcl, pita, cfur))
-  }
-
-  # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
-  if (info == TRUE) {
-    cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
-  }
-  # rule 11.1
-  if (!is.na(eryt)) {
-    if (!is.na(azit)) {
-      tbl[, azit] <- tbl[, eryt]
-    }
-    if (!is.na(clar)) {
-      tbl[, clar] <- tbl[, eryt]
-    }
-  }
-
-  # Table 12: Interpretive rules for aminoglycosides ----
-  if (info == TRUE) {
-    cat('...Table 12: Interpretive rules for aminoglycosides\n')
-  }
-  # rule 12.2
-  if (!is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, tobr] == 'R'),
-             cols = c(kana, amik))
-  }
-  # rule 12.3
-  if (!is.na(gent)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, gent] == 'R'),
-             cols = aminoglycosides)
-  }
-  # rule 12.8
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, gent] == 'I'
-                          & tbl[, tobr] == 'S'),
-             cols = gent)
-  }
-  # rule 12.9
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tobr] == 'I'
-                          & tbl[, gent] == 'R'),
-             cols = tobr)
-  }
-
-
-  # Table 13: Interpretive rules for quinolones ----
-  if (info == TRUE) {
-    cat('...Table 13: Interpretive rules for quinolones\n')
-  }
-  # rule 13.2
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, moxi] == 'R'),
-             cols = fluoroquinolones)
-  }
-  # rule 13.4
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Streptococcus pneumoniae'
-                          & tbl[, moxi] == 'R'),
-             cols = fluoroquinolones)
-  }
-  # rule 13.5
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluoroquinolones)
-  }
-  # rule 13.8
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Neisseria gonorrhoeae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluoroquinolones)
-  }
-
-
-  # Other ----
-  if (info == TRUE) {
-    cat('...Non-EUCAST: trim = R where trsu = R and ampi = R where amcl = R\n')
-  }
-  if (!is.na(amcl)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, amcl] == 'R'),
-             cols = ampi)
-  }
-  if (!is.na(trsu)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, trsu] == 'R'),
-             cols = trim)
-  }
-  if (!is.na(ampi) & !is.na(amox)) {
-    tbl[, amox] <- tbl %>% pull(ampi)
-  }
-
-  # Remove added columns again
-  microorganisms.ncol <- ncol(AMR::microorganisms) - 2
-  tbl.ncol <- ncol(tbl)
-  tbl <- tbl %>% select(-c((tbl.ncol - microorganisms.ncol):tbl.ncol))
-  # and remove added suffices
-  colnames(tbl) <- gsub("_tempmicroorganisms", "", colnames(tbl))
-
-  if (info == TRUE) {
-    cat('Done.\n\nEUCAST Expert rules applied to',
-        total_rows %>% unique() %>% length() %>% format(big.mark = ","),
-        'different rows (isolates); edited a total of',
-        total %>% format(big.mark = ","), 'test results.\n\n')
-  }
-
-  tbl
-}
-
-#' @rdname EUCAST
-#' @export
-interpretive_reading <- function(...) {
-  EUCAST_rules(...)
-}
-
-#' Poperties of a microorganism
-#'
-#' @param bactid ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}
-#' @param property One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}
-#' @export
-#' @importFrom dplyr %>% filter select
-#' @seealso \code{\link{microorganisms}}
-mo_property <- function(bactid, property = 'fullname') {
-
-  mocode <- as.character(bactid)
-
-  for (i in 1:length(mocode)) {
-    bug <- mocode[i]
-
-    if (!is.na(bug)) {
-      result = tryCatch({
-        mocode[i] <-
-          AMR::microorganisms %>%
-          filter(bactid == bug) %>%
-          select(property) %>%
-          unlist() %>%
-          as.character()
-      }, error = function(error_condition) {
-        warning('Code ', bug, ' not found in bacteria list.')
-      }, finally = {
-        if (mocode[i] == bug & !property %in% c('bactid', 'bactsys')) {
-          mocode[i] <- NA
-        }
-      })
-    }
-
-  }
-  mocode
-}

R/first_isolates.R

@@ -1,598 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Determine first (weighted) isolates
-#'
-#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
-#' @param tbl a \code{data.frame} containing isolates.
-#' @param col_date column name of the result date (or date that is was received on the lab)
-#' @param col_patient_id column name of the unique IDs of the patients
-#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)
-#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
-#' @param col_specimen column name of the specimen type or group
-#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
-#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
-#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
-#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
-#' @param icu_exclude logical whether ICU isolates should be excluded
-#' @param filter_specimen specimen group or type that should be excluded
-#' @param output_logical return output as \code{logical} (will else be the values \code{0} or \code{1})
-#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
-#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
-#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
-#' @param info print progress
-#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms
-#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms
-#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
-#'     To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
-#'
-#'     \strong{DETERMINING WEIGHTED ISOLATES} \cr
-#'     \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
-#'     To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
-#'     \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
-#'     To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
-#' @keywords isolate isolates first
-#' @export
-#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
-#' @return A vector to add to table, see Examples.
-#' @source Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
-#' @examples
-#' # septic_patients is a dataset available in the AMR package
-#' ?septic_patients
-#' my_patients <- septic_patients
-#'
-#' library(dplyr)
-#' my_patients$first_isolate <- my_patients %>%
-#'   first_isolate(col_date = "date",
-#'                 col_patient_id = "patient_id",
-#'                 col_bactid = "bactid")
-#'
-#' \dontrun{
-#'
-#' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#'
-#' tbl$first_isolate <-
-#'   first_isolate(tbl)
-#'
-#' tbl$first_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_blood_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood')
-#'
-#' tbl$first_blood_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_urine_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine')
-#'
-#' tbl$first_urine_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_resp_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory')
-#'
-#' tbl$first_resp_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory',
-#'                 col_keyantibiotics = 'keyab')
-#' }
-first_isolate <- function(tbl,
-                          col_date,
-                          col_patient_id,
-                          col_bactid = NA,
-                          col_testcode = NA,
-                          col_specimen = NA,
-                          col_icu = NA,
-                          col_keyantibiotics = NA,
-                          episode_days = 365,
-                          testcodes_exclude = '',
-                          icu_exclude = FALSE,
-                          filter_specimen = NA,
-                          output_logical = TRUE,
-                          type = "keyantibiotics",
-                          ignore_I = TRUE,
-                          points_threshold = 2,
-                          info = TRUE,
-                          col_genus = NA,
-                          col_species = NA) {
-
-  # bactid OR genus+species must be available
-  if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) {
-    stop('`col_bactid or both `col_genus` and `col_species` must be available.')
-  }
-
-  # check if columns exist
-  check_columns_existance <- function(column, tblname = tbl) {
-    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
-      stop('Please check tbl for existance.')
-    }
-
-    if (!is.na(column)) {
-      if (!(column %in% colnames(tblname))) {
-        stop('Column `', column, '` not found.')
-      }
-    }
-  }
-
-  check_columns_existance(col_date)
-  check_columns_existance(col_patient_id)
-  check_columns_existance(col_bactid)
-  check_columns_existance(col_genus)
-  check_columns_existance(col_species)
-  check_columns_existance(col_testcode)
-  check_columns_existance(col_icu)
-  check_columns_existance(col_keyantibiotics)
-
-  if (!is.na(col_bactid)) {
-    tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
-    col_genus <- "genus"
-    col_species <- "species"
-  }
-
-  if (is.na(col_testcode)) {
-    testcodes_exclude <- NA
-  }
-  # remove testcodes
-  if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
-    cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
-  }
-
-  if (is.na(col_icu)) {
-    icu_exclude <- FALSE
-  } else {
-    tbl <- tbl %>%
-      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
-  }
-
-  if (is.na(col_specimen)) {
-    filter_specimen <- ''
-  }
-
-  specgroup.notice <- ''
-  weighted.notice <- ''
-  # filter on specimen group and keyantibiotics when they are filled in
-  if (!is.na(filter_specimen) & filter_specimen != '') {
-    check_columns_existance(col_specimen, tbl)
-    if (info == TRUE) {
-      cat('Isolates other than of specimen group \'', filter_specimen, '\' will be ignored. ', sep = '')
-    }
-  } else {
-    filter_specimen <- ''
-  }
-  if (col_keyantibiotics %in% c(NA, '')) {
-    col_keyantibiotics <- ''
-  } else {
-    tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
-  }
-
-  if (is.na(testcodes_exclude[1])) {
-    testcodes_exclude <- ''
-  }
-
-  # create new dataframe with original row index and right sorting
-  tbl <- tbl %>%
-    mutate(first_isolate_row_index = 1:nrow(tbl),
-           eersteisolaatbepaling = 0,
-           date_lab = tbl %>% pull(col_date),
-           patient_id = tbl %>% pull(col_patient_id),
-           species = tbl %>% pull(col_species),
-           genus = tbl %>% pull(col_genus)) %>%
-    mutate(species = if_else(is.na(species), '', species),
-           genus = if_else(is.na(genus), '', genus))
-
-  if (filter_specimen == '') {
-
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      row.start <- 1
-      row.end <- nrow(tbl)
-    } else {
-      if (info == TRUE) {
-        cat('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-
-  } else {
-    # sort on specimen and only analyse these row to save time
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
-      )
-    } else {
-      if (info == TRUE) {
-        cat('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
-                           & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
-                         & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-
-  }
-
-  if (abs(row.start) == Inf | abs(row.end) == Inf) {
-    if (info == TRUE) {
-      cat('No isolates found.\n')
-    }
-    # NA's where genus is unavailable
-    tbl <- tbl %>%
-      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
-    if (output_logical == FALSE) {
-      tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
-    }
-    return(tbl %>% pull(real_first_isolate))
-  }
-
-  scope.size <- tbl %>%
-    filter(row_number() %>%
-             between(row.start,
-                     row.end),
-           genus != '') %>%
-    nrow()
-
-  # Analysis of first isolate ----
-  all_first <- tbl %>%
-    mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
-                                     & genus == lag(genus)
-                                     & species == lag(species),
-                                     FALSE,
-                                     TRUE),
-           days_diff = 0) %>%
-    mutate(days_diff = if_else(other_pat_or_mo == FALSE,
-                               (date_lab - lag(date_lab)) + lag(days_diff),
-                               0))
-
-  if (col_keyantibiotics != '') {
-    if (info == TRUE) {
-      if (type == 'keyantibiotics') {
-        cat('Comparing key antibiotics for first weighted isolates (')
-        if (ignore_I == FALSE) {
-          cat('NOT ')
-        }
-        cat('ignoring I)...\n')
-      }
-      if (type == 'points') {
-        cat(paste0('Comparing antibiotics for first weighted isolates (using points threshold of '
-                   , points_threshold, ')...\n'))
-      }
-    }
-    type_param <- type
-    all_first <- all_first %>%
-      mutate(key_ab_lag = lag(key_ab)) %>%
-      mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
-                                                   y = key_ab_lag,
-                                                   type = type_param,
-                                                   ignore_I = ignore_I,
-                                                   points_threshold = points_threshold,
-                                                   info = info)) %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days
-               | key_ab_other),
-            TRUE,
-            FALSE))
-    if (info == TRUE) {
-      cat('\n')
-    }
-  } else {
-    all_first <- all_first %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days),
-            TRUE,
-            FALSE))
-  }
-
-  # first one as TRUE
-  all_first[row.start, 'real_first_isolate'] <- TRUE
-  # no tests that should be included, or ICU
-  if (!is.na(col_testcode)) {
-    all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
-  }
-  if (icu_exclude == TRUE) {
-    all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
-  }
-
-  # NA's where genus is unavailable
-  all_first <- all_first %>%
-    mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
-
-  all_first <- all_first %>%
-    arrange(first_isolate_row_index) %>%
-    pull(real_first_isolate)
-
-  if (info == TRUE) {
-    cat(paste0('\nFound ',
-               all_first %>% sum(na.rm = TRUE),
-               ' first ', weighted.notice, 'isolates (',
-               (all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
-               ' of isolates in scope [where genus was not empty] and ',
-               (all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
-               ' of total)\n'))
-  }
-
-  if (output_logical == FALSE) {
-    all_first <- all_first %>% as.integer()
-  }
-
-  all_first
-
-}
-
-#' Key antibiotics based on bacteria ID
-#'
-#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
-#' @param col_bactid column of bacteria IDs in \code{tbl}; these should occur in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
-#' @param info print warnings
-#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics, case-insensitive
-#' @export
-#' @importFrom dplyr %>% mutate if_else
-#' @return Character of length 1.
-#' @seealso \code{\link{mo_property}} \code{\link{antibiotics}}
-#' @examples
-#' \donttest{
-#' #' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#' }
-key_antibiotics <- function(tbl,
-                            col_bactid = 'bactid',
-                            info = TRUE,
-                            amcl = 'amcl',
-                            amox = 'amox',
-                            cfot = 'cfot',
-                            cfta = 'cfta',
-                            cftr = 'cftr',
-                            cfur = 'cfur',
-                            cipr = 'cipr',
-                            clar = 'clar',
-                            clin = 'clin',
-                            clox = 'clox',
-                            doxy = 'doxy',
-                            gent = 'gent',
-                            line = 'line',
-                            mero = 'mero',
-                            peni = 'peni',
-                            pita = 'pita',
-                            rifa = 'rifa',
-                            teic = 'teic',
-                            trsu = 'trsu',
-                            vanc = 'vanc') {
-
-  keylist <- character(length = nrow(tbl))
-
-  if (!col_bactid %in% colnames(tbl)) {
-    stop('Column ', col_bactid, ' not found.', call. = FALSE)
-  }
-
-  # check columns
-  col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
-                clin, clox, doxy, gent, line, mero, peni,
-                pita, rifa, teic, trsu, vanc)
-  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
-  amox <- col.list[amox]
-  cfot <- col.list[cfot]
-  cfta <- col.list[cfta]
-  cftr <- col.list[cftr]
-  cfur <- col.list[cfur]
-  cipr <- col.list[cipr]
-  clar <- col.list[clar]
-  clin <- col.list[clin]
-  clox <- col.list[clox]
-  doxy <- col.list[doxy]
-  gent <- col.list[gent]
-  line <- col.list[line]
-  mero <- col.list[mero]
-  peni <- col.list[peni]
-  pita <- col.list[pita]
-  rifa <- col.list[rifa]
-  teic <- col.list[teic]
-  trsu <- col.list[trsu]
-  vanc <- col.list[vanc]
-
-  # join microorganisms
-  tbl <- tbl %>% left_join_microorganisms(col_bactid)
-
-  tbl$key_ab <- NA_character_
-
-  # Staphylococcus
-  list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(genus == 'Staphylococcus',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-
-  # Rest of Gram +
-  list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Positive ',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-
-  # Gram -
-  list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Negative ',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-
-  # format
-  tbl <- tbl %>%
-    mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
-
-  tbl$key_ab
-
-}
-
-#' @importFrom dplyr progress_estimated %>%
-#' @noRd
-key_antibiotics_equal <- function(x,
-                                  y,
-                                  type = c("keyantibiotics", "points"),
-                                  ignore_I = TRUE,
-                                  points_threshold = 2,
-                                  info = FALSE) {
-  # x is active row, y is lag
-
-  type <- type[1]
-
-  if (length(x) != length(y)) {
-    stop('Length of `x` and `y` must be equal.')
-  }
-
-  result <- logical(length(x))
-
-  if (info == TRUE) {
-    p <- dplyr::progress_estimated(length(x))
-  }
-
-  for (i in 1:length(x)) {
-
-    if (info == TRUE) {
-      p$tick()$print()
-    }
-
-    if (is.na(x[i])) {
-      x[i] <- ''
-    }
-    if (is.na(y[i])) {
-      y[i] <- ''
-    }
-
-    if (nchar(x[i]) != nchar(y[i])) {
-
-      result[i] <- FALSE
-
-    } else if (x[i] == '' & y[i] == '') {
-
-      result[i] <- TRUE
-
-    } else {
-
-      x2 <- strsplit(x[i], "")[[1]]
-      y2 <- strsplit(y[i], "")[[1]]
-
-      if (type == 'points') {
-        # count points for every single character:
-        # - no change is 0 points
-        # - I <-> S|R is 0.5 point
-        # - S|R <-> R|S is 1 point
-        # use the levels of as.rsi (S = 1, I = 2, R = 3)
-
-        suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
-        suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
-
-        points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
-        result[i] <- ((points / 2) >= points_threshold)
-
-      } else if (type == 'keyantibiotics') {
-        # check if key antibiotics are exactly the same
-        # also possible to ignore I, so only S <-> R and S <-> R are counted
-        if (ignore_I == TRUE) {
-          valid_chars <- c('S', 's', 'R', 'r')
-        } else {
-          valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
-        }
-
-        # remove invalid values (like "-", NA) on both locations
-        x2[which(!x2 %in% valid_chars)] <- '?'
-        x2[which(!y2 %in% valid_chars)] <- '?'
-        y2[which(!x2 %in% valid_chars)] <- '?'
-        y2[which(!y2 %in% valid_chars)] <- '?'
-
-        result[i] <- all(x2 == y2)
-
-      } else {
-        stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
-      }
-    }
-  }
-  if (info == TRUE) {
-    cat('\n')
-  }
-  result
-}

R/freq.R

@@ -1,361 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Frequency table
-#'
-#' Create a frequency table of a vector of data, a single column or a maximum of 9 columns of a data frame. Supports markdown for reports.
-#' @param x data
-#' @param sort.count Sort on count. Use \code{FALSE} to sort alphabetically on item.
-#' @param nmax number of row to print. Use \code{nmax = 0} or \code{nmax = NA} to print all rows.
-#' @param na.rm a logical value indicating whether NA values should be removed from the frequency table. The header will always print the amount of\code{NA}s.
-#' @param markdown print table in markdown format (this forces \code{nmax = NA})
-#' @param toConsole Print table to the console. Use \code{FALSE} to assign the table to an object.
-#' @param digits how many significant digits are to be used for numeric values (not for the items themselves, that depends on \code{\link{getOption}("digits")})
-#' @param sep a character string to separate the terms when selecting multiple columns
-#' @details For numeric values, the next values will be calculated and shown into the header:
-#' \itemize{
-#'   \item{Mean, using \code{\link[base]{mean}}}
-#'   \item{Standard deviation, using \code{\link[stats]{sd}}}
-#'   \item{Five numbers of Tukey (min, Q1, median, Q3, max), using \code{\link[stats]{fivenum}}}
-#'   \item{Outliers (count and list), using \code{\link{boxplot.stats}}}
-#'   \item{Coefficient of variation (CV), the standard deviation divided by the mean}
-#'   \item{Coefficient of quartile variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using \code{\link{quantile}} with \code{type = 6} as quantile algorithm to comply with SPSS standards}
-#' }
-#' @importFrom stats fivenum sd quantile
-#' @importFrom grDevices boxplot.stats
-#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise
-#' @keywords summary summarise frequency freq
-#' @rdname freq
-#' @export
-#' @examples
-#' library(dplyr)
-#'
-#' freq(septic_patients$hospital_id)
-#'
-#' septic_patients %>%
-#'   filter(hospital_id == "A") %>%
-#'   select(bactid) %>%
-#'   freq()
-#'
-#' # select multiple columns; they will be pasted together
-#' septic_patients %>%
-#'   left_join_microorganisms %>%
-#'   filter(hospital_id == "A") %>%
-#'   select(genus, species) %>%
-#'   freq()
-#'
-#' # save frequency table to an object
-#' years <- septic_patients %>%
-#'   mutate(year = format(date, "%Y")) %>%
-#'   select(year) %>%
-#'   freq(toConsole = FALSE)
-freq <- function(x,
-                 sort.count = TRUE,
-                 nmax = 15,
-                 na.rm = TRUE,
-                 markdown = FALSE,
-                 toConsole = TRUE,
-                 digits = 2,
-                 sep = " ") {
-
-  mult.columns <- 0
-
-  if (NROW(x) == 0) {
-    cat('\nNo observations.\n')
-    return(invisible())
-  }
-
-  if (!is.null(ncol(x))) {
-    if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
-      x <- x %>% pull(1)
-    } else if (ncol(x) < 10) {
-
-      mult.columns <- ncol(x)
-
-      colnames(x) <- LETTERS[1:ncol(x)]
-      if (ncol(x) == 2) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 3) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 4) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 5) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          x$E %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 6) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          x$E %>% as.character(),
-                          x$F %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 7) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          x$E %>% as.character(),
-                          x$F %>% as.character(),
-                          x$G %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 8) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          x$E %>% as.character(),
-                          x$F %>% as.character(),
-                          x$G %>% as.character(),
-                          x$H %>% as.character(),
-                          sep = sep)
-      } else if (ncol(x) == 9) {
-        x$total <- paste(x$A %>% as.character(),
-                          x$B %>% as.character(),
-                          x$C %>% as.character(),
-                          x$D %>% as.character(),
-                          x$E %>% as.character(),
-                          x$F %>% as.character(),
-                          x$G %>% as.character(),
-                          x$H %>% as.character(),
-                          x$I %>% as.character(),
-                          sep = sep)
-      }
-
-      x <- x$total
-
-    } else {
-      stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
-    }
-  }
-  if (markdown == TRUE & toConsole == FALSE) {
-    warning('`toConsole = FALSE` will be ignored when `markdown = TRUE`.')
-  }
-
-  if (mult.columns > 1) {
-    NAs <- x[is.na(x) | x == trimws(strrep('NA ', mult.columns))]
-  } else {
-    NAs <- x[is.na(x)]
-  }
-  if (na.rm == TRUE) {
-    x <- x[!x %in% NAs]
-  }
-
-  if (missing(sort.count) & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single', 'factor'))) {
-    # sort on item/level at default when x is numeric or a factor and sort.count is not set
-    sort.count <- FALSE
-  }
-
-  header <- character(0)
-
-  markdown_line <- ''
-  if (markdown == TRUE) {
-    markdown_line <- '\n'
-  }
-  x_align <- 'l'
-
-  if (mult.columns > 0) {
-    header <- header %>% paste0(markdown_line, 'Columns:   ', mult.columns)
-  } else {
-    header <- header %>% paste0(markdown_line, 'Class:     ', class(x) %>% rev() %>% paste(collapse = " > "))
-  }
-
-  if (is.list(x) | is.matrix(x) | is.environment(x) | is.function(x)) {
-    cat(header, "\n")
-    stop('`freq()` does not support lists, matrices, environments or functions.', call. = FALSE)
-  }
-
-  header <- header %>% paste0(markdown_line, '\nLength:    ', (NAs %>% length() + x %>% length()) %>% format(),
-                              ' (of which NA: ', NAs %>% length() %>% format(),
-                              ' = ', (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE), ')')
-  header <- header %>% paste0(markdown_line, '\nUnique:    ', x %>% n_distinct() %>% format())
-
-  header.numbers.done <- FALSE
-  if (any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
-    # right align number
-    x_align <- 'r'
-    header <- header %>% paste0('\n')
-    header <- header %>% paste(markdown_line, '\nMean:     ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
-    header <- header %>% paste0(markdown_line, '\nStd. dev.: ', x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits),
-                                ' (CV: ', x %>% cv(na.rm = TRUE) %>% format(digits = digits), ')')
-    header <- header %>% paste0(markdown_line, '\nFive-Num:  ', x %>% stats::fivenum(na.rm = TRUE) %>% format(digits = digits) %>% trimws() %>% paste(collapse = '  |  '),
-                                ' (CQV: ', x %>% cqv(na.rm = TRUE) %>% format(digits = digits), ')')
-    outlier_length <- length(boxplot.stats(x)$out)
-    header <- header %>% paste0(markdown_line, '\nOutliers:  ', outlier_length)
-    if (outlier_length > 0) {
-      header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% unique() %>% length(), ')')
-    }
-  }
-
-  formatdates <- "%e %B %Y" # = d mmmm yyyy
-  if (any(class(x) == 'hms')) {
-    x <- x %>% as.POSIXlt()
-    formatdates <- "%H:%M:%S"
-  }
-  if (any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
-    header <- header %>% paste0('\n')
-    mindatum <- x %>% min()
-    maxdatum <- x %>% max()
-    header <- header %>% paste0(markdown_line, '\nOldest:    ', mindatum %>% format(formatdates) %>% trimws())
-    header <- header %>% paste0(markdown_line, '\nNewest:    ', maxdatum %>% format(formatdates) %>% trimws(),
-                                ' (+', difftime(maxdatum, mindatum, units = 'auto') %>% as.double() %>% format(), ')')
-  }
-  if (any(class(x) == 'POSIXlt')) {
-    x <- x %>% format(formatdates)
-  }
-
-  if (toConsole == TRUE) {
-    cat(header)
-  }
-
-  if (all(is.na(x))) {
-    cat('\n\nNo observations.\n')
-    return(invisible())
-  }
-  if (n_distinct(x) == length(x)) {
-    warning('All observations are unique.', call. = FALSE)
-  }
-
-  if (nmax == 0 | is.na(nmax)) {
-    nmax <- length(x)
-  }
-  nmax.1 <- min(length(x), nmax + 1)
-
-  # create table with counts and percentages
-  if (any(class(x) == 'factor')) {
-    df <- tibble::tibble(Item = x,
-                         Fctlvl = x %>% as.integer()) %>%
-      group_by(Item, Fctlvl)
-    column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent', '(Factor Level)')
-    column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
-  } else {
-    df <- tibble::tibble(Item = x) %>%
-      group_by(Item)
-    column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent')
-    column_align <- c(x_align, 'r', 'r', 'r', 'r')
-  }
-  df <- df %>%
-    summarise(Count = n(),
-              Percent = (n() / length(x)) %>% percent(force_zero = TRUE))
-
-  if (df$Item %>% paste(collapse = ',') %like% '\033') {
-    df <- df %>%
-      mutate(Item = Item %>%
-               # remove escape char
-               # see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
-               gsub('\033', ' ', ., fixed = TRUE))
-  }
-
-  # sort according to setting
-  if (sort.count == TRUE) {
-    df <- df %>% arrange(desc(Count))
-  } else {
-    if (any(class(x) == 'factor')) {
-      df <- df %>% arrange(Fctlvl)
-    } else {
-      df <- df %>% arrange(Item)
-    }
-  }
-
-  # add cumulative values
-  df$Cum <- cumsum(df$Count)
-  df$CumTot <- (df$Cum / sum(df$Count, na.rm = TRUE)) %>% percent(force_zero = TRUE)
-  df$Cum <- df$Cum %>% format()
-
-  if (any(class(x) == 'factor')) {
-    # put factor last
-    df <- df %>% select(Item, Count, Percent, Cum, CumTot, Fctlvl)
-  }
-
-  if (markdown == TRUE) {
-    tblformat <- 'markdown'
-  } else {
-    tblformat <- 'pandoc'
-  }
-
-  if (toConsole == FALSE) {
-    # assign to object
-    df[, 3] <- df[, 2] / sum(df[, 2], na.rm = TRUE)
-    df[, 4] <- cumsum(df[, 2])
-    df[, 5] <- df[, 4] / sum(df[, 2], na.rm = TRUE)
-    return(df)
-
-  } else {
-
-    # save old NA setting for kable
-    opt.old <- options()$knitr.kable.NA
-    options(knitr.kable.NA = "<NA>")
-
-    Count.rest <- sum(df[nmax.1:nrow(df), 'Count'], na.rm = TRUE)
-    if (any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
-      df <- df %>% mutate(Item = format(Item))
-    }
-    df <- df %>% mutate(Count = format(Count))
-
-    if (nrow(df) > nmax.1 & markdown == FALSE) {
-      df2 <- df[1:nmax,]
-      print(
-        knitr::kable(df2,
-                     format = tblformat,
-                     col.names = column_names,
-                     align = column_align,
-                     padding = 1)
-      )
-      cat('... and ',
-          format(nrow(df) - nmax),
-          ' more ',
-          paste0('(n = ',
-                 format(Count.rest),
-                 '; ',
-                 (Count.rest / length(x)) %>% percent(force_zero = TRUE),
-                 ')'),
-          '. Use `nmax` to show more rows.\n', sep = '')
-
-    } else {
-      print(
-        knitr::kable(df,
-                     format = tblformat,
-                     col.names = column_names,
-                     align = column_align,
-                     padding = 1)
-      )
-    }
-    cat('\n')
-
-    # reset old kable setting
-    options(knitr.kable.NA = opt.old)
-    return(invisible())
-  }
-}
-
-#' @rdname freq
-#' @export
-frequency_tbl <- freq

R/globals.R

@@ -1,49 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-globalVariables(c('abname',
-                  'atc',
-                  'bactid',
-                  'cnt',
-                  'Count',
-                  'Cum',
-                  'CumTot',
-                  'date_lab',
-                  'days_diff',
-                  'Fctlvl',
-                  'first_isolate_row_index',
-                  'fullname',
-                  'genus',
-                  'gramstain',
-                  'Item',
-                  'key_ab',
-                  'key_ab_lag',
-                  'key_ab_other',
-                  'mic',
-                  'mocode',
-                  'molis',
-                  'n',
-                  'other_pat_or_mo',
-                  'patient_id',
-                  'Percent',
-                  'quantile',
-                  'real_first_isolate',
-                  'species',
-                  'umcg',
-                  'y',
-                  '.'))

R/join.R

@@ -1,136 +0,0 @@
-#' Join a table with \code{microorganisms}
-#'
-#' Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
-#' @rdname join
-#' @name join
-#' @aliases join inner_join
-#' @param x existing table to join, also supports character vectors
-#' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{microorganisms$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
-#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
-#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
-#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
-#' @export
-#' @examples
-#' left_join_microorganisms("STAAUR")
-#'
-#' library(dplyr)
-#' septic_patients %>% left_join_microorganisms()
-#'
-#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
-#'                             to = as.Date("2018-01-07"),
-#'                             by = 1),
-#'                  bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
-#'                                  "ESCCOL", "ESCCOL", "ESCCOL"),
-#'                  stringsAsFactors = FALSE)
-#' colnames(df)
-#' df2 <- left_join_microorganisms(df, "bacteria_id")
-#' colnames(df2)
-inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::inner_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::left_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::right_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::full_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
-  if (nrow(join) > nrow(x)) {
-    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-semi_join_microorganisms <- function(x, by = 'bactid', ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::semi_join(x = x, y = AMR::microorganisms, by = joinby, ...)
-}
-
-#' @rdname join
-#' @export
-anti_join_microorganisms <- function(x, by = 'bactid', ...) {
-  if (any(class(x) %in% c('character', 'factor'))) {
-    x <- data.frame(bactid = x, stringsAsFactors = FALSE)
-  }
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::anti_join(x = x, y = AMR::microorganisms, by = joinby, ...)
-}

R/mdro.R

@@ -1,422 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Determine multidrug-resistant organisms (MDRO)
-#'
-#' Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
-#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
-#' @param country country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).
-#' @param col_bactid column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
-#' @param info print progress
-#' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,metr,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. column names of antibiotics
-#' @param ... parameters that are passed on to methods
-#' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}).
-#' @return Ordered factor with levels \code{Unknown < Negative < Unconfirmed < Positive}.
-#' @rdname MDRO
-#' @export
-#' @examples
-#' library(dplyr)
-#'
-#' septic_patients %>%
-#'   mutate(EUCAST = MDRO(.),
-#'          BRMO = MDRO(., "nl"))
-MDRO <- function(tbl,
-                 country = NULL,
-                 col_bactid = 'bactid',
-                 info = TRUE,
-                 amcl = 'amcl',
-                 amik = 'amik',
-                 amox = 'amox',
-                 ampi = 'ampi',
-                 azit = 'azit',
-                 aztr = 'aztr',
-                 cefa = 'cefa',
-                 cfra = 'cfra',
-                 cfep = 'cfep',
-                 cfot = 'cfot',
-                 cfox = 'cfox',
-                 cfta = 'cfta',
-                 cftr = 'cftr',
-                 cfur = 'cfur',
-                 chlo = 'chlo',
-                 cipr = 'cipr',
-                 clar = 'clar',
-                 clin = 'clin',
-                 clox = 'clox',
-                 coli = 'coli',
-                 czol = 'czol',
-                 dapt = 'dapt',
-                 doxy = 'doxy',
-                 erta = 'erta',
-                 eryt = 'eryt',
-                 fosf = 'fosf',
-                 fusi = 'fusi',
-                 gent = 'gent',
-                 imip = 'imip',
-                 kana = 'kana',
-                 levo = 'levo',
-                 linc = 'linc',
-                 line = 'line',
-                 mero = 'mero',
-                 metr = 'metr',
-                 mino = 'mino',
-                 moxi = 'moxi',
-                 nali = 'nali',
-                 neom = 'neom',
-                 neti = 'neti',
-                 nitr = 'nitr',
-                 novo = 'novo',
-                 norf = 'norf',
-                 oflo = 'oflo',
-                 peni = 'peni',
-                 pita = 'pita',
-                 poly = 'poly',
-                 qida = 'qida',
-                 rifa = 'rifa',
-                 roxi = 'roxi',
-                 siso = 'siso',
-                 teic = 'teic',
-                 tetr = 'tetr',
-                 tica = 'tica',
-                 tige = 'tige',
-                 tobr = 'tobr',
-                 trim = 'trim',
-                 trsu = 'trsu',
-                 vanc = 'vanc') {
-
-  if (!col_bactid %in% colnames(tbl)) {
-    stop('Column ', col_bactid, ' not found.', call. = FALSE)
-  }
-
-  # strip whitespaces
-  if (length(country) > 1) {
-    stop('`country` must be a length one character string.', call. = FALSE)
-  }
-
-  if (is.null(country)) {
-    country <- 'EUCAST'
-  }
-  country <- trimws(country)
-  if (country != 'EUCAST' & !country %like% '^[a-z]{2}$') {
-    stop('This is not a valid ISO 3166-1 alpha-2 country code: "', country, '". Please see ?MDRO.', call. = FALSE)
-  }
-
-  # create list and make country code case-independent
-  guideline <- list(country = list(code = tolower(country)))
-
-  if (guideline$country$code == 'eucast') {
-    guideline$country$name <- '(European guidelines)'
-    guideline$name <- 'EUCAST Expert Rules, "Intrinsic Resistance and Exceptional Phenotypes Tables"'
-    guideline$version <- 'Version 3.1'
-    guideline$source <- 'http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'
-    # support per country:
-  } else if (guideline$country$code == 'de') {
-    guideline$country$name <- 'Germany'
-    guideline$name <- ''
-    guideline$version <- ''
-    guideline$source <- ''
-  } else if (guideline$country$code == 'nl') {
-    guideline$country$name <- 'The Netherlands'
-    guideline$name <- 'WIP-Richtlijn BRMO'
-    guideline$version <- 'Revision of December 2017'
-    guideline$source <- 'https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'
-  # add here more countries like this:
-  # } else if (country$code == 'AA') {
-  #   country$name <- 'country name'
-  } else {
-    stop('This country code is currently unsupported: ', guideline$country$code, call. = FALSE)
-  }
-
-  # Console colours
-  # source: http://www.tldp.org/HOWTO/Bash-Prompt-HOWTO/x329.html
-  ANSI_red <- "\033[31m"
-  ANSI_blue <- "\033[34m"
-  ANSI_reset <- "\033[0m"
-
-  if (info == TRUE) {
-    cat("Determining multidrug-resistant organisms (MDRO), according to:\n",
-        "Guideline: ", ANSI_red, guideline$name, ", ", guideline$version, ANSI_reset, "\n",
-        "Country  : ", ANSI_red, guideline$country$name, ANSI_reset, "\n",
-        "Source   : ", ANSI_blue, guideline$source, ANSI_reset, "\n",
-        "\n", sep = "")
-  }
-
-  # check columns
-  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
-                cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
-                czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
-                levo, linc, line, mero, metr, mino, moxi, nali, neom, neti, nitr,
-                novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso,
-                teic, tetr, tica, tige, tobr, trim, trsu, vanc)
-  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
-  amcl <- col.list[amcl]
-  amik <- col.list[amik]
-  amox <- col.list[amox]
-  ampi <- col.list[ampi]
-  azit <- col.list[azit]
-  aztr <- col.list[aztr]
-  cefa <- col.list[cefa]
-  cfra <- col.list[cfra]
-  cfep <- col.list[cfep]
-  cfot <- col.list[cfot]
-  cfox <- col.list[cfox]
-  cfta <- col.list[cfta]
-  cftr <- col.list[cftr]
-  cfur <- col.list[cfur]
-  chlo <- col.list[chlo]
-  cipr <- col.list[cipr]
-  clar <- col.list[clar]
-  clin <- col.list[clin]
-  clox <- col.list[clox]
-  coli <- col.list[coli]
-  czol <- col.list[czol]
-  dapt <- col.list[dapt]
-  doxy <- col.list[doxy]
-  erta <- col.list[erta]
-  eryt <- col.list[eryt]
-  fosf <- col.list[fosf]
-  fusi <- col.list[fusi]
-  gent <- col.list[gent]
-  imip <- col.list[imip]
-  kana <- col.list[kana]
-  levo <- col.list[levo]
-  linc <- col.list[linc]
-  line <- col.list[line]
-  mero <- col.list[mero]
-  metr <- col.list[metr]
-  mino <- col.list[mino]
-  moxi <- col.list[moxi]
-  nali <- col.list[nali]
-  neom <- col.list[neom]
-  neti <- col.list[neti]
-  nitr <- col.list[nitr]
-  novo <- col.list[novo]
-  norf <- col.list[norf]
-  oflo <- col.list[oflo]
-  peni <- col.list[peni]
-  pita <- col.list[pita]
-  poly <- col.list[poly]
-  qida <- col.list[qida]
-  rifa <- col.list[rifa]
-  roxi <- col.list[roxi]
-  siso <- col.list[siso]
-  teic <- col.list[teic]
-  tetr <- col.list[tetr]
-  tica <- col.list[tica]
-  tige <- col.list[tige]
-  tobr <- col.list[tobr]
-  trim <- col.list[trim]
-  trsu <- col.list[trsu]
-  vanc <- col.list[vanc]
-
-  # antibiotic classes
-  aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
-  tetracyclines <- c(doxy, mino, tetr) # since EUCAST v3.1 tige(cycline) is set apart
-  polymyxins <- c(poly, coli)
-  macrolides <- c(eryt, azit, roxi, clar) # since EUCAST v3.1 clin(damycin) is set apart
-  glycopeptides <- c(vanc, teic)
-  streptogramins <- qida # should officially also be pristinamycin and quinupristin/dalfopristin
-  cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
-  cephalosporins_3rd <- c(cfot, cftr, cfta)
-  carbapenems <- c(erta, imip, mero)
-  aminopenicillins <- c(ampi, amox)
-  ureidopenicillins <- pita # should officially also be azlo and mezlo
-  fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
-
-  # helper function for editing the table
-  trans_tbl <- function(to, rows, cols) {
-    cols <- cols[!is.na(cols)]
-    if (length(rows) > 0 & length(cols) > 0) {
-      col_filter <- which(tbl[, cols] == 'R')
-      rows <- rows[rows %in% col_filter]
-      tbl[rows, 'MDRO'] <<- to
-    }
-  }
-
-  # join microorganisms
-  tbl <- tbl %>% left_join_microorganisms(col_bactid)
-
-  tbl$MDRO <- NA_integer_
-
-  if (guideline$country$code == 'eucast') {
-    # EUCAST ------------------------------------------------------------------
-    # Table 5
-    trans_tbl(4,
-              which(tbl$family == 'Enterobacteriaceae'
-                    | tbl$fullname %like% '^Pseudomonas aeruginosa'
-                    | tbl$genus == 'Acinetobacter'),
-              coli)
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Salmonella Typhi'),
-              c(carbapenems, fluoroquinolones))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Haemophilus influenzae'),
-              c(cephalosporins_3rd, carbapenems, fluoroquinolones))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Moraxella catarrhalis'),
-              c(cephalosporins_3rd, fluoroquinolones))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Neisseria meningitidis'),
-              c(cephalosporins_3rd, fluoroquinolones))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Neisseria gonorrhoeae'),
-              azit)
-    # Table 6
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
-              c(vanc, teic, dapt, line, qida, tige))
-    trans_tbl(4,
-              which(tbl$genus == 'Corynebacterium'),
-              c(vanc, teic, dapt, line, qida, tige))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Streptococcus pneumoniae'),
-              c(carbapenems, vanc, teic, dapt, line, qida, tige, rifa))
-    trans_tbl(4, # Sr. groups A/B/C/G
-              which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|equisimilis|equi|zooepidemicus|dysgalactiae|anginosus)'),
-              c(peni, cephalosporins, vanc, teic, dapt, line, qida, tige))
-    trans_tbl(4,
-              which(tbl$genus == 'Enterococcus'),
-              c(dapt, line, tige, teic))
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Enterococcus faecalis'),
-              c(ampi, amox))
-    # Table 7
-    trans_tbl(4,
-              which(tbl$genus == 'Bacteroides'),
-              metr)
-    trans_tbl(4,
-              which(tbl$fullname %like% '^Clostridium difficile'),
-              c(metr, vanc))
-  }
-
-  if (guideline$country$code == 'de') {
-    # Germany -----------------------------------------------------------------
-    stop("We are still working on German guidelines in this beta version.", call. = FALSE)
-  }
-
-  if (guideline$country$code == 'nl') {
-    # Netherlands -------------------------------------------------------------
-    aminoglycosides <- aminoglycosides[!is.na(aminoglycosides)]
-    fluoroquinolones <- fluoroquinolones[!is.na(fluoroquinolones)]
-    carbapenems <- carbapenems[!is.na(carbapenems)]
-
-    # Table 1
-    tbl[which(
-      tbl$family == 'Enterobacteriaceae'
-      & rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1
-      & rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1
-    ), 'MDRO'] <- 4
-    tbl[which(
-      tbl$family == 'Enterobacteriaceae'
-      & rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1
-    ), 'MDRO'] <- 3
-    # rest is negative
-    tbl[which(
-      tbl$family == 'Enterobacteriaceae'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-
-    # Table 2
-    tbl[which(
-      tbl$genus == 'Acinetobacter'
-      & rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1
-    ), 'MDRO'] <- 3
-    tbl[which(
-      tbl$genus == 'Acinetobacter'
-      & rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1
-      & rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1
-    ), 'MDRO'] <- 4
-    # rest of Acinetobacter is negative
-    tbl[which(
-      tbl$genus == 'Acinetobacter'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-
-    tbl[which(
-      tbl$fullname %like% 'Stenotrophomonas maltophilia'
-      & tbl[, trsu] == 'R'
-    ), 'MDRO'] <- 4
-    # rest of Stenotrophomonas is negative
-    tbl[which(
-      tbl$fullname %like% 'Stenotrophomonas maltophilia'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-
-    tbl[which(
-      tbl$fullname %like% 'Pseudomonas aeruginosa'
-      & sum(rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1,
-           rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1,
-           rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1,
-           tbl[, cfta] == 'R',
-           tbl[, pita] == 'R') >= 3
-    ), 'MDRO'] <- 4
-    # rest of Pseudomonas is negative
-    tbl[which(
-      tbl$fullname %like% 'Pseudomonas aeruginosa'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-
-    # Table 3
-    tbl[which(
-      tbl$fullname %like% 'Streptococcus pneumoniae'
-      & tbl[, peni] == 'R'
-    ), 'MDRO'] <- 4
-    tbl[which(
-      tbl$fullname %like% 'Streptococcus pneumoniae'
-      & tbl[, vanc] == 'R'
-    ), 'MDRO'] <- 4
-    # rest of Streptococcus pneumoniae is negative
-    tbl[which(
-      tbl$fullname %like% 'Streptococcus pneumoniae'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-
-    tbl[which(
-      tbl$fullname %like% 'Enterococcus faecium'
-      &  rowSums(tbl[, c(peni, vanc)] == 'R', na.rm = TRUE) >= 1
-    ), 'MDRO'] <- 4
-    # rest of Enterococcus faecium is negative
-    tbl[which(
-      tbl$fullname %like% 'Enterococcus faecium'
-      & tbl$MDRO == 1
-    ), 'MDRO'] <- 2
-  }
-
-  factor(x =  tbl$MDRO,
-         levels = c(1:4),
-         labels = c('Unknown', 'Negative', 'Unconfirmed', 'Positive'),
-         ordered = TRUE)
-}
-
-#' @rdname MDRO
-#' @export
-BRMO <- function(tbl, country = "nl", ...) {
-  MDRO(tbl = tbl, country = "nl", ...)
-}
-
-#' @rdname MDRO
-#' @export
-MRGN <- function(tbl, country = "de", ...) {
-  MDRO(tbl = tbl, country = "de", ...)
-}
-
-#' @rdname MDRO
-#' @export
-EUCAST_exceptional_phenotypes <- function(tbl, country = "EUCAST", ...) {
-  MDRO(tbl = tbl, country = "EUCAST", ...)
-}

R/misc.R

@@ -1,116 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Pattern Matching
-#'
-#' Convenience function to compare a vector with a pattern, like \code{\link[base]{grep}}. It always returns a \code{logical} vector and is always case-insensitive.
-#' @inheritParams base::grep
-#' @return A \code{logical} vector
-#' @name like
-#' @rdname like
-#' @export
-#' @source Inherited from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default.
-#' @examples
-#' library(dplyr)
-#' # get unique occurences of bacteria whose name start with 'Ent'
-#' septic_patients %>%
-#'   left_join_microorganisms() %>%
-#'   filter(fullname %like% '^Ent') %>%
-#'   pull(fullname) %>%
-#'   unique()
-"%like%" <- function(x, pattern) {
-  if (length(pattern) > 1) {
-    pattern <- pattern[1]
-    warning('only the first element of argument `pattern` used for `%like%`', call. = FALSE)
-  }
-  if (is.factor(x)) {
-    as.integer(x) %in% base::grep(pattern, levels(x), ignore.case = TRUE)
-  } else {
-    base::grepl(pattern, x, ignore.case = TRUE)
-  }
-}
-
-# No export, no Rd
-percent <- function(x, round = 1, force_zero = FALSE, ...) {
-  val <- base::round(x * 100, digits = round)
-  if (force_zero == TRUE & any(val == as.integer(val) & !is.na(val))) {
-    val[val == as.integer(val)] <- paste0(val[val == as.integer(val)], ".", strrep(0, round))
-  }
-  pct <- base::paste0(val, "%")
-  pct[pct == "NA%"] <- NA_character_
-  pct
-}
-
-check_available_columns <- function(tbl, col.list, info = TRUE) {
-  # check columns
-  col.list <- col.list[!is.na(col.list)]
-  names(col.list) <- col.list
-  col.list.bak <- col.list
-  # are they available as upper case or lower case then?
-  for (i in 1:length(col.list)) {
-    if (toupper(col.list[i]) %in% colnames(tbl)) {
-      col.list[i] <- toupper(col.list[i])
-    } else if (tolower(col.list[i]) %in% colnames(tbl)) {
-      col.list[i] <- tolower(col.list[i])
-    } else if (!col.list[i] %in% colnames(tbl)) {
-      col.list[i] <- NA
-    }
-  }
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored: ',
-              col.list.bak[!(col.list %in% colnames(tbl))] %>% toString(),
-              '.\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.',
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-  }
-  col.list
-}
-
-# Coefficient of variation (CV)
-cv <- function(x, na.rm = TRUE) {
-  cv.x <- sd(x, na.rm = na.rm) / abs(mean(x, na.rm = na.rm))
-  cv.x
-}
-
-# Coefficient of dispersion, or coefficient of quartile variation (CQV).
-# (Bonett et al., 2006: Confidence interval for a coefficient of quartile variation).
-cqv <- function(x, na.rm = TRUE) {
-  cqv.x <-
-    (quantile(x, 0.75, na.rm = na.rm, type = 6) - quantile(x, 0.25, na.rm = na.rm, type = 6)) /
-    (quantile(x, 0.75, na.rm = na.rm, type = 6) + quantile(x, 0.25, na.rm = na.rm, type = 6))
-  unname(cqv.x)
-}
-
-# show bytes as kB/MB/GB
-# size_humanreadable(123456) # 121 kB
-# size_humanreadable(12345678) # 11.8 MB
-size_humanreadable <- function(bytes, decimals = 1) {
-  bytes <- bytes %>% as.double()
-  # Adapted from:
-  # http://jeffreysambells.com/2012/10/25/human-readable-filesize-php
-  size <- c('B','kB','MB','GB','TB','PB','EB','ZB','YB')
-  factor <- floor((nchar(bytes) - 1) / 3)
-  # added slight improvement; no decimals for B and kB:
-  decimals <- rep(decimals, length(bytes))
-  decimals[size[factor + 1] %in% c('B', 'kB')] <- 0
-
-  out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
-  out
-}

R/print.R

@@ -1,347 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Printing Data Tables and Tibbles
-#'
-#' Print a data table or tibble. It prints: \cr- The \strong{first and last rows} like \code{data.table}s are printed by the \code{data.table} package,\cr- A \strong{header} and \strong{left aligned text} like \code{tibble}s are printed by the \code{tibble} package with info about grouped variables,\cr- \strong{Unchanged values} and \strong{support for row names} like \code{data.frame}s are printed by the \code{base} package.
-#' @inheritParams base::print.data.frame
-#' @param nmax amount of rows to print in total. When the total amount of rows exceeds this limit, the first and last \code{nmax / 2} rows will be printed. Use \code{nmax = NA} to print all rows.
-#' @param header print header with information about data size and tibble grouping
-#' @param print.keys print keys for \code{data.table}
-#' @param na value to print instead of NA
-#' @param width amount of white spaces to keep between columns, must be at least 1
-#' @rdname print
-#' @name print
-#' @importFrom dplyr %>% n_groups group_vars group_size filter pull select
-#' @importFrom data.table data.table
-#' @importFrom utils object.size
-#' @exportMethod print.tbl_df
-#' @export
-#' @examples
-#' # more reliable data view:
-#' library(dplyr)
-#' starwars
-#' print(starwars, width = 3)
-#'
-#' # This is how the tibble package prints since v1.4.0:
-#' # (mind the quite unfamiliar underscores and ending dots)
-#' tibble(now_what = c(1.2345, 2345.67, 321.456)) %>% tibble:::print.tbl_df()
-#'
-#' # This is how this AMR package prints:
-#' # (every number shown as you would expect)
-#' tibble(now_what = c(1.2345, 2345.67, 321.456))
-#'
-#' # also supports info about groups (look at header)
-#' starwars %>% group_by(homeworld, gender)
-print.tbl_df <- function(x,
-                         nmax = 10,
-                         header = TRUE,
-                         row.names = TRUE,
-                         right = FALSE,
-                         width = 1,
-                         na = "<NA>",
-                         ...) {
-  prettyprint_df(x = x,
-                 nmax = nmax,
-                 header = header,
-                 row.names = row.names,
-                 print.keys = FALSE,
-                 right = right,
-                 width = width,
-                 na = na,
-                 ...)
-}
-
-#' @rdname print
-#' @exportMethod print.tbl
-#' @export
-print.tbl <- function(x, ...) {
-  prettyprint_df(x, ...)
-}
-
-#' @rdname print
-#' @exportMethod print.data.table
-#' @export
-print.data.table <- function(x,
-                             print.keys = FALSE,
-                             ...) {
-  prettyprint_df(x = x,
-                 print.keys = print.keys,
-                 ...)
-}
-
-printDT <- data.table:::print.data.table
-prettyprint_df <- function(x,
-                           nmax = 10,
-                           header = TRUE,
-                           row.names = TRUE,
-                           print.keys = FALSE,
-                           right = FALSE,
-                           width = 1,
-                           na = "<NA>",
-                           ...) {
-
-  ansi_reset <- "\u001B[0m"
-  ansi_black <- "\u001B[30m"
-  ansi_red <- "\u001B[31m"
-  ansi_green <- "\u001B[32m"
-  ansi_yellow <- "\u001B[33m"
-  ansi_blue <- "\u001B[34m"
-  ansi_purple <- "\u001B[35m"
-  ansi_cyan <- "\u001B[36m"
-  ansi_white <- "\u001B[37m"
-  ansi_gray <- "\u001B[38;5;246m"
-
-  if (width < 1) {
-    stop('`width` must be at least 1.', call. = FALSE)
-  }
-
-  if (is.na(nmax)) {
-    nmax <- NROW(x)
-  }
-  n <- nmax
-  if (n %% 2 == 1) {
-    # odd number; add 1
-    n <- n + 1
-  }
-
-  width <- width - 1
-
-  if ('tbl_df' %in% class(x)) {
-    type <- 'tibble'
-  } else if ('data.table' %in% class(x)) {
-    type <- 'data.table'
-  } else {
-    type <- 'data.frame'
-  }
-
-  if (header == TRUE) {
-    if (NCOL(x) == 1) {
-      vars <- 'variable'
-    } else {
-      vars <- 'variables'
-    }
-
-    size <- object.size(x) %>% as.double() %>% size_humanreadable()
-
-    cat(paste0("A ", type,": ",
-               format(NROW(x)),
-               " obs. of ",
-               format(NCOL(x)),
-               " ", vars,
-               ansi_gray, " (", size, ")\n", ansi_reset))
-    if ('grouped_df' %in% class(x) & n_groups(x) > 0) {
-      cat(paste0("Grouped by ",
-                 x %>% group_vars() %>% paste0(ansi_red, ., ansi_reset) %>% paste0(collapse = " and "),
-                 ansi_gray,
-                 " (",
-                 x %>% n_groups(),
-                 " groups with sizes between ",
-                 x %>% group_size() %>% min(),
-                 " and ",
-                 x %>% group_size() %>% max(),
-                 ")\n",
-                 ansi_reset))
-    }
-    if (!is.null(attributes(x)$qry)) {
-      cat(paste0(ansi_gray, "This data contains a query. Use qry() to view it.\n", ansi_reset))
-    }
-    cat("\n")
-  }
-
-  # data.table where keys should be printed
-  if (print.keys == TRUE) {
-    printDT(x,
-            class = header,
-            row.names = row.names,
-            print.keys = TRUE,
-            right = right,
-            ...
-    )
-    return(invisible())
-  }
-
-  # tibbles give warning when setting column names
-  x <- x %>% base::as.data.frame(stringsAsFactors = FALSE)
-
-  # extra space of 3 chars, right to row name or number
-  if (NROW(x) > 0) {
-    maxrowchars <- rownames(x) %>% nchar() %>% max() + 3
-    rownames(x) <- paste0(rownames(x), strrep(" ", maxrowchars - nchar(rownames(x))))
-  } else {
-    maxrowchars <- 0
-  }
-
-  x.bak <- x
-
-  if (n + 1 < nrow(x)) {
-    # remove in between part, 1 extra for ~~~~ between first and last part
-    rows_list <- c(1:(n / 2 + 1), (nrow(x) - (n / 2) + 1):nrow(x))
-    x <- as.data.frame(x.bak[rows_list,])
-    colnames(x) <- colnames(x.bak)
-    rownames(x) <- rownames(x.bak)[rows_list]
-    # set inbetweener between parts
-    rownames(x)[n / 2 + 1] <- strrep("~", maxrowchars)
-  }
-
-  if (header == TRUE) {
-    # add 1 row for classes
-    # class will be marked up per column
-    if (NROW(x.bak) > 0) {
-      rownames.x <- rownames(x)
-      x <- x %>% filter(row_number() == 1) %>% rbind(x, stringsAsFactors = FALSE)
-      rownames(x) <- c('*', rownames.x)
-    }
-
-    # select 1st class per column and abbreviate
-    classes <- x.bak %>%
-      sapply(class) %>%
-      lapply(
-        function(c) {
-          # do print all POSIX classes like "POSct/t"
-          if ('POSIXct' %in% c) {
-            paste0('POS',
-                   c %>%
-                     gsub('POSIX', '', .) %>%
-                     paste0(collapse = '/'))
-          } else {
-            if (NCOL(.) > 1) {
-              .[1,]
-            } else {
-              c[[1]]
-            }
-          }
-        }) %>%
-      unlist() %>%
-      gsub("character",  "chr", ., fixed = TRUE) %>%
-      gsub("complex",    "cplx", ., fixed = TRUE) %>%
-      gsub("Date",       "Date", ., fixed = TRUE) %>%
-      gsub("double",     "dbl", ., fixed = TRUE) %>%
-      gsub("expression", "expr", ., fixed = TRUE) %>%
-      gsub("factor",     "fct", ., fixed = TRUE) %>%
-      gsub("IDate",      "IDat", ., fixed = TRUE) %>%
-      gsub("integer",    "int", ., fixed = TRUE) %>%
-      gsub("integer64",  "i64", ., fixed = TRUE) %>%
-      gsub("list",       "list", ., fixed = TRUE) %>%
-      gsub("logical",    "lgl", ., fixed = TRUE) %>%
-      gsub("numeric",    "dbl", ., fixed = TRUE) %>%
-      gsub("ordered",    "ord", ., fixed = TRUE) %>%
-      gsub("percent",    "pct", ., fixed = TRUE) %>%
-      gsub("single",     "sgl", ., fixed = TRUE) %>%
-      paste0("<", ., ">")
-  }
-
-  # markup cols
-  for (i in 1:ncol(x)) {
-    if (all(!class(x[, i]) %in% class(x.bak[, i]))) {
-      class(x[, i]) <- class(x.bak[, i])
-    }
-    try(x[, i] <- format(x %>% pull(i)), silent = TRUE)
-
-    # replace NAs
-    if (nchar(na) < 2) {
-      # make as long as the text "NA"
-      na <- paste0(na, strrep(" ", 2 - nchar(na)))
-    }
-    try(x[, i] <- gsub("^NA$", na, trimws(x[, i], 'both')), silent = TRUE)
-    # place class into 1st row
-    if (header == TRUE) {
-      x[1, i] <- classes[i]
-    }
-    # dashes between two parts when exceeding nmax
-    maxvalchars <- max(colnames(x)[i] %>% nchar(), x[, i] %>% nchar() %>% max())
-    if (n + 1 < nrow(x.bak)) {
-      x[n / 2 + if_else(header == TRUE, 2, 1), i] <- strrep("~", maxvalchars)
-    }
-
-    # align according to `right` parameter, but only factors, logicals text, but not MICs
-    if (any(x.bak %>% pull(i) %>% class() %in% c('factor', 'character', 'logical'))
-        & !("mic" %in% (x.bak %>% pull(i) %>% class()))) {
-      vals <- x %>% pull(i) %>% trimws('both')
-      colname <- colnames(x)[i] %>% trimws('both')
-      if (right == FALSE) {
-        vals <- paste0(vals, strrep(" ", maxvalchars - nchar(vals)))
-        colname <- paste0(colname, strrep(" ", maxvalchars - nchar(colname)))
-      } else {
-        vals <- paste0(strrep(" ", maxvalchars - nchar(vals)), vals)
-        colname <- paste0(strrep(" ", maxvalchars - nchar(colname)), colname)
-      }
-      x[, i] <- vals
-      colnames(x)[i] <- colname
-    }
-
-    # add left padding according to `width` parameter
-    # but not in 1st col when row names are off
-    if (row.names == TRUE | i > 1) {
-      x[, i] <- paste0(strrep(" ", width), x[, i])
-      colnames(x)[i] <- paste0(strrep(" ", width), colnames(x)[i])
-    }
-
-    # strip columns that do not fit (3 chars as margin)
-    width_console <- options()$width
-    width_until_col <- x %>%
-      select(1:i) %>%
-      apply(1, paste, collapse = strrep(" ", width + 1)) %>%
-      nchar() %>%
-      max()
-    width_until_col_before <- x %>%
-      select(1:(max(i, 2) - 1)) %>%
-      apply(1, paste, collapse = strrep(" ", width + 1)) %>%
-      nchar() %>%
-      max()
-    extraspace <- maxrowchars + nchar(rownames(x)[length(rownames(x))])
-    width_until_colnames <- colnames(x)[1:i] %>% paste0(collapse = strrep(" ", width + 1)) %>% nchar() + extraspace
-    width_until_colnames_before <- colnames(x)[1:(max(i, 2) - 1)] %>% paste0(collapse = strrep(" ", width + 1)) %>% nchar() + extraspace
-
-    if (i > 1 &
-        (width_until_col > width_console
-         | width_until_colnames > width_console)) {
-      if (width_until_col_before > width_console
-          | width_until_colnames_before > width_console) {
-        x <- x[, 1:(i - 2)]
-      } else {
-        x <- x[, 1:(i - 1)]
-      }
-      break
-    }
-  }
-
-  # empty table, row name of header should be "*"
-  if (NROW(x.bak) == 0) {
-    rownames(x) <- '* '
-  }
-
-  # and here it is...
-  suppressWarnings(
-    base::print.data.frame(x, row.names = row.names, ...)
-  )
-
-  # print rest of col names when they were stripped
-  if (ncol(x) < ncol(x.bak)) {
-    x.notshown <- x.bak %>% select((ncol(x) + 1):ncol(x.bak))
-    if (ncol(x.notshown) == 1) {
-      cat('...and 1 more column: ')
-    } else {
-      cat('...and', ncol(x.notshown), 'more columns: ')
-    }
-    cat(x.notshown %>%
-          colnames() %>%
-          paste0(' ', ansi_gray, classes[(ncol(x) + 1):ncol(x.bak)], ansi_reset) %>%
-          paste0(collapse = ", "), '\n')
-  }
-}

R/rsi_analysis.R

@@ -1,499 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Resistance of isolates
-#'
-#' This functions can be used to calculate the (co-)resistance of isolates (i.e. percentage S, SI, I, IR or R [of a vector] of isolates). The functions \code{rsi} and \code{n_rsi} can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
-#' @param tbl \code{data.frame} containing columns with antibiotic interpretations.
-#' @param ab character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{ab = c("amox", "amcl")}
-#' @param ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}
-#' @param interpretation antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.
-#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).
-#' @param as_percent return output as percent (text), will else (at default) be a double
-#' @param info calculate the amount of available isolates and print it, like \code{n = 423}
-#' @param warning show a warning when the available amount of isolates is below \code{minimum}
-#' @details Remember that you should filter your table to let it contain \strong{only first isolates}!
-#' \if{html}{
-#'   \cr \cr
-#'   To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
-#'   \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
-#'   To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
-#'   For two antibiotics:
-#'   \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
-#'   \cr
-#'   For three antibiotics:
-#'   \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
-#' }
-#' @keywords rsi antibiotics isolate isolates
-#' @return Double or, when \code{as_percent = TRUE}, a character.
-#' @rdname rsi
-#' @export
-#' @importFrom dplyr %>% n_distinct filter filter_at pull vars all_vars any_vars
-#' @examples
-#' library(dplyr)
-#'
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(cipro_susceptibility = rsi(cipr, interpretation = "S"),
-#'             n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
-#'
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(cipro_S = rsi(cipr, interpretation = "S",
-#'                           as_percent = TRUE, warning = FALSE),
-#'             cipro_n = n_rsi(cipr),
-#'             genta_S = rsi(gent, interpretation = "S",
-#'                           as_percent = TRUE, warning = FALSE),
-#'             genta_n = n_rsi(gent),
-#'             combination_S = rsi(cipr, gent, interpretation = "S",
-#'                                 as_percent = TRUE, warning = FALSE),
-#'             combination_n = n_rsi(cipr, gent))
-#'
-#' # calculate resistance
-#' rsi(septic_patients$amox)
-#' # or susceptibility
-#' rsi(septic_patients$amox, interpretation = "S")
-#'
-#' # calculate co-resistance between amoxicillin/clav acid and gentamicin,
-#' # so we can review that combination therapy does a lot more than mono therapy:
-#' septic_patients %>% rsi_df(ab = "amcl", interpretation = "S") # = 67.8%
-#' septic_patients %>% rsi_df(ab = "gent", interpretation = "S") # = 69.1%
-#' septic_patients %>% rsi_df(ab = c("amcl", "gent"), interpretation = "S") # = 90.6%
-#'
-#' \dontrun{
-#' # calculate current empiric combination therapy of Helicobacter gastritis:
-#' my_table %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Helicobacter") %>%
-#'   rsi_df(ab = c("amox", "metr")) # amoxicillin with metronidazole
-#' }
-rsi <- function(ab1,
-                ab2 = NA,
-                interpretation = 'IR',
-                minimum = 30,
-                as_percent = FALSE,
-                info = FALSE,
-                warning = TRUE) {
-  ab1.name <- deparse(substitute(ab1))
-  if (ab1.name %like% '.[$].') {
-    ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
-    ab1.name <- ab1.name[length(ab1.name)]
-  }
-  if (!ab1.name %like% '^[a-z]{3,4}$') {
-    ab1.name <- 'rsi1'
-  }
-  if (length(ab1) == 1 & is.character(ab1)) {
-    stop('`ab1` must be a vector of antibiotic interpretations.',
-         '\n  Try rsi(', ab1, ', ...) instead of rsi("', ab1, '", ...)', call. = FALSE)
-  }
-  ab2.name <- deparse(substitute(ab2))
-  if (ab2.name %like% '.[$].') {
-    ab2.name <- unlist(strsplit(ab2.name, "$", fixed = TRUE))
-    ab2.name <- ab2.name[length(ab2.name)]
-  }
-  if (!ab2.name %like% '^[a-z]{3,4}$') {
-    ab2.name <- 'rsi2'
-  }
-  if (length(ab2) == 1 & is.character(ab2)) {
-    stop('`ab2` must be a vector of antibiotic interpretations.',
-         '\n  Try rsi(', ab2, ', ...) instead of rsi("', ab2, '", ...)', call. = FALSE)
-  }
-
-  interpretation <- paste(interpretation, collapse = "")
-
-  ab1 <- as.rsi(ab1)
-  ab2 <- as.rsi(ab2)
-
-  tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
-  colnames(tbl) <- c(ab1.name, ab2.name)
-
-  if (length(ab2) == 1) {
-    r <- rsi_df(tbl = tbl,
-                ab = ab1.name,
-                interpretation = interpretation,
-                minimum = minimum,
-                as_percent = FALSE,
-                info = info,
-                warning = warning)
-  } else {
-    if (length(ab1) != length(ab2)) {
-      stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
-    }
-    if (!interpretation %in% c('S', 'IS', 'SI')) {
-      warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
-    }
-    r <- rsi_df(tbl = tbl,
-                ab = c(ab1.name, ab2.name),
-                interpretation = interpretation,
-                minimum = minimum,
-                as_percent = FALSE,
-                info = info,
-                warning = warning)
-  }
-  if (as_percent == TRUE) {
-    percent(r, force_zero = TRUE)
-  } else {
-    r
-  }
-}
-
-#' @export
-#' @rdname rsi
-rsi_df <- function(tbl,
-                   ab,
-                   interpretation = 'IR',
-                   minimum = 30,
-                   as_percent = FALSE,
-                   info = TRUE,
-                   warning = TRUE) {
-
-  # in case tbl$interpretation already exists:
-  interpretations_to_check <- paste(interpretation, collapse = "")
-
-  # validate:
-  if (min(grepl('^[a-z]{3,4}$', ab)) == 0 &
-      min(grepl('^rsi[1-2]$', ab)) == 0) {
-    for (i in 1:length(ab)) {
-      ab[i] <- paste0('rsi', i)
-    }
-  }
-  if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', interpretations_to_check)) {
-    stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
-  }
-  if ('is_ic' %in% colnames(tbl)) {
-    if (n_distinct(tbl$is_ic) > 1 & warning == TRUE) {
-      warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
-    }
-  }
-
-  # transform when checking for different results
-  if (interpretations_to_check %in% c('SI', 'IS')) {
-    for (i in 1:length(ab)) {
-      lijst <- tbl[, ab[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[ab[i]] == 'I'), ][ab[i]] <- 'S'
-      }
-    }
-    interpretations_to_check <- 'S'
-  }
-  if (interpretations_to_check %in% c('RI', 'IR')) {
-    for (i in 1:length(ab)) {
-      lijst <- tbl[, ab[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[ab[i]] == 'I'), ][ab[i]] <- 'R'
-      }
-    }
-    interpretations_to_check <- 'R'
-  }
-
-  # get fraction
-  if (length(ab) == 1) {
-    numerator <- tbl %>%
-      filter(pull(., ab[1]) == interpretations_to_check) %>%
-      nrow()
-
-    denominator <- tbl %>%
-      filter(pull(., ab[1]) %in% c("S", "I", "R")) %>%
-      nrow()
-
-  } else if (length(ab) == 2) {
-    if (interpretations_to_check != 'S') {
-      warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
-    }
-    numerator <- tbl %>%
-      filter_at(vars(ab[1], ab[2]),
-                any_vars(. == interpretations_to_check)) %>%
-      filter_at(vars(ab[1], ab[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-
-    denominator <- tbl %>%
-      filter_at(vars(ab[1], ab[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-
-  } else if (length(ab) == 3) {
-    if (interpretations_to_check != 'S') {
-      warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
-    }
-    numerator <- tbl %>%
-      filter_at(vars(ab[1], ab[2], ab[3]),
-                any_vars(. == interpretations_to_check)) %>%
-      filter_at(vars(ab[1], ab[2], ab[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-
-    denominator <- tbl %>%
-      filter_at(vars(ab[1], ab[2], ab[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-
-  } else {
-    stop('Maximum of 3 drugs allowed.')
-  }
-
-  # build text part
-  if (info == TRUE) {
-    cat('n =', denominator)
-    info.txt1 <- percent(denominator / nrow(tbl))
-    if (denominator == 0) {
-      info.txt1 <- 'none'
-    }
-    info.txt2 <- gsub(',', ' and',
-                      ab %>%
-                        abname(tolower = TRUE) %>%
-                        toString(), fixed = TRUE)
-    info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
-    info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
-    cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
-  }
-
-  # calculate and format
-  y <- numerator / denominator
-  if (as_percent == TRUE) {
-   y <- percent(y, force_zero = TRUE)
-  }
-
-  if (denominator < minimum) {
-    if (warning == TRUE) {
-      warning(paste0('TOO FEW ISOLATES OF ', toString(ab), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
-    }
-    y <- NA
-  }
-
-  # output
-  y
-}
-
-#' @export
-#' @rdname rsi
-n_rsi <- function(ab1, ab2 = NA) {
-
-  if (length(ab1) == 1 & is.character(ab1)) {
-    stop('`ab1` must be a vector of antibiotic interpretations.',
-         '\n  Try n_rsi(', ab1, ', ...) instead of n_rsi("', ab1, '", ...)', call. = FALSE)
-  }
-  ab1 <- as.rsi(ab1)
-
-  if (length(ab2) == 1 & all(is.na(ab2))) {
-    # only 1 antibiotic
-    length(ab1[!is.na(ab1)])
-  } else {
-    if (length(ab2) == 1 & is.character(ab2)) {
-      stop('`ab2` must be a vector of antibiotic interpretations.',
-           '\n  Try n_rsi(', ab2, ', ...) instead of n_rsi("', ab2, '", ...)', call. = FALSE)
-    }
-    ab2 <- as.rsi(ab2)
-    tbl <- tibble(ab1, ab2)
-    tbl %>% filter(!is.na(ab1) & !is.na(ab2)) %>% nrow()
-  }
-
-}
-
-#' Predict antimicrobial resistance
-#'
-#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
-#' @param tbl table that contains columns \code{col_ab} and \code{col_date}
-#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation
-#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation
-#' @param year_max highest year to use in the prediction model, deafults to 15 years after today
-#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
-#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
-#' @param I_as_R treat \code{I} as \code{R}
-#' @param preserve_measurements overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.
-#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
-#' @return \code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
-#' @seealso \code{\link{lm}} \cr \code{\link{glm}}
-#' @export
-#' @importFrom dplyr %>% pull mutate group_by_at summarise filter
-#' @importFrom reshape2 dcast
-#' @examples
-#' \dontrun{
-#' # use it directly:
-#' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
-#'             col_ab = "amcl", col_date = "date")
-#'
-#' # or with dplyr so you can actually read it:
-#' library(dplyr)
-#' tbl %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Haemophilus") %>%
-#'   rsi_predict(amcl, date)
-#' }
-#'
-#'
-#' # real live example:
-#' library(dplyr)
-#' septic_patients %>%
-#'   # get bacteria properties like genus and species
-#'   left_join_microorganisms("bactid") %>%
-#'   # calculate first isolates
-#'   mutate(first_isolate =
-#'            first_isolate(.,
-#'                          "date",
-#'                          "patient_id",
-#'                          "bactid",
-#'                          col_specimen = NA,
-#'                          col_icu = NA)) %>%
-#'   # filter on first E. coli isolates
-#'   filter(genus == "Escherichia",
-#'          species == "coli",
-#'          first_isolate == TRUE) %>%
-#'   # predict resistance of cefotaxime for next years
-#'   rsi_predict(col_ab = "cfot",
-#'               col_date = "date",
-#'               year_max = 2025,
-#'               preserve_measurements = FALSE)
-#'
-rsi_predict <- function(tbl,
-                        col_ab,
-                        col_date,
-                        year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
-                        year_every = 1,
-                        model = 'binomial',
-                        I_as_R = TRUE,
-                        preserve_measurements = TRUE,
-                        info = TRUE) {
-
-  if (nrow(tbl) == 0) {
-    stop('This table does not contain any observations.')
-  }
-
-  if (!col_ab %in% colnames(tbl)) {
-    stop('Column ', col_ab, ' not found.')
-  }
-
-  if (!col_date %in% colnames(tbl)) {
-    stop('Column ', col_date, ' not found.')
-  }
-  if ('grouped_df' %in% class(tbl)) {
-    # no grouped tibbles please, mutate will throw errors
-    tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
-  }
-
-  if (I_as_R == TRUE) {
-    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
-  }
-
-  if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
-    stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
-  }
-
-  year <- function(x) {
-    if (all(grepl('^[0-9]{4}$', x))) {
-      x
-    } else {
-      as.integer(format(as.Date(x), '%Y'))
-    }
-  }
-
-  years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
-
-  df <- tbl %>%
-    mutate(year = year(tbl %>% pull(col_date))) %>%
-    group_by_at(c('year', col_ab)) %>%
-    summarise(n())
-  colnames(df) <- c('year', 'antibiotic', 'count')
-  df <- df %>%
-    reshape2::dcast(year ~ antibiotic, value.var = 'count')
-
-  if (model %in% c('binomial', 'binom', 'logit')) {
-    logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
-    if (info == TRUE) {
-      cat('\nLogistic regression model (logit) with binomial distribution')
-      cat('\n------------------------------------------------------------\n')
-      print(summary(logitmodel))
-    }
-
-    predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else if (model == 'loglin') {
-    loglinmodel <- with(df, glm(R ~ year, family = poisson))
-    if (info == TRUE) {
-      cat('\nLog-linear regression model (loglin) with poisson distribution')
-      cat('\n--------------------------------------------------------------\n')
-      print(summary(loglinmodel))
-    }
-
-    predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else if (model %in% c('lin', 'linear')) {
-    linmodel <- with(df, lm((R / (R + S)) ~ year))
-    if (info == TRUE) {
-      cat('\nLinear regression model')
-      cat('\n-----------------------\n')
-      print(summary(linmodel))
-    }
-
-    predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else {
-    stop('No valid model selected.')
-  }
-
-  # prepare the output dataframe
-  prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
-
-  prediction$se_min <- prediction$probR - se
-  prediction$se_max <- prediction$probR + se
-
-  if (model == 'loglin') {
-    prediction$probR <- prediction$probR %>%
-      format(scientific = FALSE) %>%
-      as.integer()
-    prediction$se_min <- prediction$se_min %>% as.integer()
-    prediction$se_max <- prediction$se_max %>% as.integer()
-
-    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
-  } else {
-    prediction$se_max[which(prediction$se_max > 1)] <- 1
-  }
-  prediction$se_min[which(prediction$se_min < 0)] <- 0
-
-  total <- prediction
-
-  if (preserve_measurements == TRUE) {
-    # geschatte data vervangen door gemeten data
-    if (I_as_R == TRUE) {
-      if (!'I' %in% colnames(df)) {
-        df$I <- 0
-      }
-      df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
-    } else {
-      df$probR <- df$R / rowSums(df[, c('R', 'S')])
-    }
-    measurements <- data.frame(year = df$year,
-                           probR = df$probR,
-                           se_min = NA,
-                           se_max = NA,
-                           stringsAsFactors = FALSE)
-    colnames(measurements) <- colnames(prediction)
-    prediction <- prediction %>% filter(!year %in% df$year)
-
-    total <- rbind(measurements, prediction)
-  }
-
-  total
-
-}

R/zzz.R

@@ -1,3 +0,0 @@
-.onLoad <- function(libname, pkgname) {
-  backports::import(pkgname)
-}

README.md

@@ -1,316 +1,226 @@
-# `AMR`
-### An [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).
 
-[![logo_rug](man/figures/logo_rug.png)](https://www.rug.nl)[![logo_umcg](man/figures/logo_umcg.png)](https://www.umcg.nl)
+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 R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen)](https://www.rug.nl) and the Medical Microbiology & Infection Prevention (MMBI) department of the [University Medical Center Groningen (UMCG)](https://www.umcg.nl). See [Authors](#authors).
+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.
 
-## Why this package?
+# Prerequisites
-This R package contains functions to make **microbiological, epidemiological data analysis easier**. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
 
-With `AMR` you can also:
+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:
-* Create frequency tables with the `freq` function
-* Conduct AMR analysis with the `rsi` function, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`) to calculate the resistance percentages (and even co-resistance) of different antibiotic columns of a table
-* Predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function
-* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
-* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
-* Translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) with the `abname` function
 
-With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
+```python
+# linux and macOS:
+python -m venv /path/to/new/virtual/environment
 
-This package contains an example data set `septic_patients`, consisting of 2000 isolates from anonymised septic patients between 2001 and 2017.
+# Windows:
-
+python -m venv C:\path\to\new\virtual\environment
-## How to get it?
-This package is available on CRAN and also here on GitHub.
-
-### From CRAN (recommended)
-[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
-[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
-[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](http://cran.r-project.org/package=AMR)
-  
-- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended):
-  - Click on `Tools` and then `Install Packages...`
-  - Type in `AMR` and press <kbd>Install</kbd>
-
-- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> In R directly:
-  - `install.packages("AMR")`
-
-- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
-  - (Exploratory.io costs $40/month but the somewhat limited Community Plan is free for students and teachers, [click here to enroll](https://exploratory.io/plan?plan=Community))
-  - Start the software and log in
-  - Click on your username at the right hand side top
-  - Click on `R Packages`
-  - Click on the `Install` tab
-  - Type in `AMR` and press <kbd>Install</kbd>
-  - Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.
-
-### From GitHub (latest development version)
-[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
-[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
-[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
-[![Code_Coverage](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR)
-
-```r
-install.packages("devtools")
-devtools::install_github("msberends/AMR")
 ```
 
-## How to use it?
+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.
-```r
-# Call it with:
-library(AMR)
 
-# For a list of functions:
+# Install AMR
-help(package = "AMR")
-```
-### Overwrite/force resistance based on EUCAST rules
-This is also called *interpretive reading*.
-```r
-before <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-                                "ENCFAE",  # Enterococcus faecalis
-                                "ESCCOL",  # Escherichia coli
-                                "KLEPNE",  # Klebsiella pneumoniae
-                                "PSEAER"), # Pseudomonas aeruginosa
-                     vanc = "-",           # Vancomycin
-                     amox = "-",           # Amoxicillin
-                     coli = "-",           # Colistin
-                     cfta = "-",           # Ceftazidime
-                     cfur = "-",           # Cefuroxime
-                     stringsAsFactors = FALSE)
-before
-#   bactid vanc amox coli cfta cfur
-# 1 STAAUR    -    -    -    -    -
-# 2 ENCFAE    -    -    -    -    -
-# 3 ESCCOL    -    -    -    -    -
-# 4 KLEPNE    -    -    -    -    -
-# 5 PSEAER    -    -    -    -    -
 
-# Now apply those rules; just need a column with bacteria ID's and antibiotic results:
+1. Since the Python package is available on the official [Python Package Index](https://pypi.org/project/AMR/), you can just run:
-after <- EUCAST_rules(before)
+
-after
+    ```bash
-#   bactid vanc amox coli cfta cfur
+    pip install AMR
-# 1 STAAUR    -    -    R    R    -
-# 2 ENCFAE    -    -    R    R    R
-# 3 ESCCOL    R    -    -    -    -
-# 4 KLEPNE    R    R    -    -    -
-# 5 PSEAER    R    R    -    -    R
     ```
 
-### Frequency tables
+2. Make sure you have R installed. There is **no need to install the `AMR` R package**, as it will be installed automatically.
-Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`).
-```r
-## Factors sort on item by default:
 
-freq(septic_patients$hospital_id)
+    For Linux:
-# Class:     factor
-# Length:    2000 (of which NA: 0 = 0.0%)
-# Unique:    5
-# 
-# Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
-# -----  ------  --------  -----------  -------------  ---------------
-# A         233     11.7%          233          11.7%                1
-# B         583     29.1%          816          40.8%                2
-# C         221     11.1%         1037          51.8%                3
-# D         650     32.5%         1687          84.4%                4
-# E         313     15.7%         2000         100.0%                5
 
-
+    ```bash
-## This can be changed with the `sort.count` parameter:
+    # Ubuntu / Debian
-
+    sudo apt install r-base
-freq(septic_patients$hospital_id, sort.count = TRUE)
+    # Fedora:
-# Class:     factor
+    sudo dnf install R
-# Length:    2000 (of which NA: 0 = 0.0%)
+    # CentOS/RHEL
-# Unique:    5
+    sudo yum install R
-# 
-# Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
-# -----  ------  --------  -----------  -------------  ---------------
-# D         650     32.5%          650          32.5%                4
-# B         583     29.1%         1233          61.7%                2
-# E         313     15.7%         1546          77.3%                5
-# A         233     11.7%         1779          88.9%                1
-# C         221     11.1%         2000         100.0%                3
-
-
-## Other types, like numbers or dates, sort on count by default:
-
-> freq(septic_patients$date)
-# Class:     Date
-# Length:    2000 (of which NA: 0 = 0.0%)
-# Unique:    1662
-# 
-# Oldest:    2 January 2001
-# Newest:    18 October 2017 (+6133)
-# 
-# Item          Count   Percent   Cum. Count   Cum. Percent
-# -----------  ------  --------  -----------  -------------
-# 2008-12-24        5      0.2%            5           0.2%
-# 2010-12-10        4      0.2%            9           0.4%
-# 2011-03-03        4      0.2%           13           0.6%
-# 2013-06-24        4      0.2%           17           0.8%
-# 2017-09-01        4      0.2%           21           1.1%
-# 2002-09-02        3      0.2%           24           1.2%
-# 2003-10-14        3      0.2%           27           1.4%
-# 2004-06-25        3      0.2%           30           1.5%
-# 2004-06-27        3      0.2%           33           1.7%
-# 2004-10-29        3      0.2%           36           1.8%
-# 2005-09-27        3      0.2%           39           2.0%
-# 2006-08-01        3      0.2%           42           2.1%
-# 2006-10-10        3      0.2%           45           2.2%
-# 2007-11-16        3      0.2%           48           2.4%
-# 2008-03-09        3      0.2%           51           2.5%
-# ... and 1647 more (n = 1949; 97.5%). Use `nmax` to show more rows.
-
-
-## For numeric values, some extra descriptive statistics will be calculated:
-
-> freq(runif(n = 10, min = 1, max = 5))
-# Class:     numeric
-# Length:    10 (of which NA: 0 = 0.0%)
-# Unique:    10
-#   
-# Mean:      3
-# Std. dev.: 0.93 (CV: 0.31)
-# Five-Num:  1.1  |  2.3  |  3.1  |  3.8  |  4.0 (CQV: 0.25)
-# Outliers:  0
-# 
-#      Item   Count   Percent   Cum. Count   Cum. Percent
-# ---------  ------  --------  -----------  -------------
-#  1.132033       1     10.0%            1          10.0%
-#  2.226903       1     10.0%            2          20.0%
-#  2.280779       1     10.0%            3          30.0%
-#  2.640898       1     10.0%            4          40.0%
-#  2.913462       1     10.0%            5          50.0%
-#  3.364201       1     10.0%            6          60.0%
-#  3.771975       1     10.0%            7          70.0%
-#  3.802861       1     10.0%            8          80.0%
-#  3.803547       1     10.0%            9          90.0%
-#  3.985691       1     10.0%           10         100.0%
-# 
-# Warning message:
-# All observations are unique. 
-```
-Learn more about this function with:
-```r
-?freq
     ```
     
-### New classes
+    For macOS (using [Homebrew](https://brew.sh)):
-This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
-Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
     
-```r
+    ```bash
-# Transform values to new classes
+    brew install r
-mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-```
-These functions also try to coerce valid values.
-
-Quick overviews when just printing objects:
-```r
-mic_data
-# Class 'mic': 7 isolates
-# 
-# <NA>  0
-# 
-# <=0.128       1       8      16    >=32
-#       1       1       2       2       1
-
-rsi_data
-# Class 'rsi': 880 isolates
-# 
-# <NA>:       0 
-# Sum of S:   474 
-# Sum of IR:  406 
-# - Sum of R: 370 
-# - Sum of I: 36 
-# 
-#   %S  %IR   %I   %R 
-# 53.9 46.1  4.1 42.0 
     ```
     
-A plot of `rsi_data`:
+    For Windows, visit the [CRAN download page](https://cran.r-project.org) to download and install R.
-```r
+
-plot(rsi_data)
+# 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)
 ```
 
-![example1](man/figures/rsi_example.png)
+| 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 |
 
-A plot of `mic_data` (defaults to bar plot):
+### Explanation
-```r
+
-plot(mic_data)
+* **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)
 ```
 
-![example2](man/figures/mic_example.png)
+```
-
+[0.59555556]
-Other epidemiological functions:
-
-```r
-# Determine key antibiotic based on bacteria ID
-key_antibiotics(...)
-
-# Selection of first isolates of any patient
-first_isolate(...)
-
-# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
-rsi(...)
-# Predict resistance levels of antibiotics
-rsi_predict(...)
-
-# Get name of antibiotic by ATC code
-abname(...)
-abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
 ```
 
-### Databases included in package
+## Generating Antibiograms
-Datasets to work with antibiotics and bacteria properties.
-```r
-# Dataset with 2000 random blood culture isolates from anonymised
-# septic patients between 2001 and 2017 in 5 Dutch hospitals
-septic_patients   # A tibble: 4,000 x 47
 
-# Dataset with ATC antibiotics codes, official names, trade names 
+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:
-# and DDD's (oral and parenteral)
-antibiotics       # A tibble: 420 x 18
 
-# Dataset with bacteria codes and properties like gram stain and 
+```python
-# aerobic/anaerobic
+result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
-microorganisms    # A tibble: 2,453 x 12
+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)           |
 
-## Authors
 
-  - [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
+```python
-  - [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
+result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
-  - [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
+print(result2b)
+```
 
-<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
+| 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)            |
 
-<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands
 
-[![logo_umcg](man/figures/logo_umcg.png)](https://www.umcg.nl)[![logo_certe](man/figures/logo_certe.png)](https://www.certe.nl)[![logo_eh1h](man/figures/logo_eh1h.png)](http://www.eurhealth-1health.eu)[![logo_interreg](man/figures/logo_interreg.png)](http://www.eurhealth-1health.eu)
+In this example, we generate an antibiogram by selecting various antibiotics.
 
-## Copyright
+## Taxonomic Data Sets Now in Python!
-[![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)
 
-This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
+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:
 
-- May be used for commercial purposes
+```python
+AMR.microorganisms
+```
 
-- May be used for private purposes
+| 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        |
 
-- May **not** be used for patent purposes
+```python
+AMR.antimicrobials
+```
 
-- May be modified, although:
+| 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     |
 
-  - Modifications **must** be released under the same license when distributing the package
-  - Changes made to the code **must** be documented
 
-- May be distributed, although:
+# Installation Channels
 
-  - Source code **must** be made available when the package is distributed
+## Stable Release (CRAN)
-  - A copy of the license and copyright notice **must** be included with the package.
 
-- Comes with a LIMITATION of liability
+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:
 
-- Comes with NO warranty
+```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.

man/EUCAST.Rd

@@ -1,69 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/eucast.R
-\name{EUCAST_rules}
-\alias{EUCAST_rules}
-\alias{interpretive_reading}
-\title{EUCAST expert rules}
-\source{
-EUCAST Expert Rules Version 2.0: \cr
-  Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
-  \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
-  \cr
-  EUCAST Expert Rules Version 3.1 (Intrinsic Resistance and Exceptional Phenotypes Tables): \cr
-  \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
-}
-\usage{
-EUCAST_rules(tbl, col_bactid = "bactid", info = TRUE, amcl = "amcl",
-  amik = "amik", amox = "amox", ampi = "ampi", azit = "azit",
-  aztr = "aztr", cefa = "cefa", cfra = "cfra", cfep = "cfep",
-  cfot = "cfot", cfox = "cfox", cfta = "cfta", cftr = "cftr",
-  cfur = "cfur", chlo = "chlo", cipr = "cipr", clar = "clar",
-  clin = "clin", clox = "clox", coli = "coli", czol = "czol",
-  dapt = "dapt", doxy = "doxy", erta = "erta", eryt = "eryt",
-  fosf = "fosf", fusi = "fusi", gent = "gent", imip = "imip",
-  kana = "kana", levo = "levo", linc = "linc", line = "line",
-  mero = "mero", mino = "mino", moxi = "moxi", nali = "nali",
-  neom = "neom", neti = "neti", nitr = "nitr", novo = "novo",
-  norf = "norf", oflo = "oflo", peni = "peni", pita = "pita",
-  poly = "poly", qida = "qida", rifa = "rifa", roxi = "roxi",
-  siso = "siso", teic = "teic", tetr = "tetr", tica = "tica",
-  tige = "tige", tobr = "tobr", trim = "trim", trsu = "trsu",
-  vanc = "vanc")
-
-interpretive_reading(...)
-}
-\arguments{
-\item{tbl}{table with antibiotic columns, like e.g. \code{amox} and \code{amcl}}
-
-\item{col_bactid}{column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}}
-
-\item{info}{print progress}
-
-\item{amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot, cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, mino, moxi, nali, neom, neti, nitr, novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.}
-
-\item{...}{parameters that are passed on to \code{EUCAST_rules}}
-}
-\value{
-table with edited variables of antibiotics.
-}
-\description{
-Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
-}
-\examples{
-a <- EUCAST_rules(septic_patients)
-a <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-                           "ENCFAE",  # Enterococcus faecalis
-                           "ESCCOL",  # Escherichia coli
-                           "KLEPNE",  # Klebsiella pneumoniae
-                           "PSEAER"), # Pseudomonas aeruginosa
-                vanc = "-",           # Vancomycin
-                amox = "-",           # Amoxicillin
-                coli = "-",           # Colistin
-                cfta = "-",           # Ceftazidime
-                cfur = "-",           # Cefuroxime
-                stringsAsFactors = FALSE)
-a
-
-b <- EUCAST_rules(a)
-b
-}

man/MDRO.Rd

@@ -1,61 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mdro.R
-\name{MDRO}
-\alias{MDRO}
-\alias{BRMO}
-\alias{MRGN}
-\alias{EUCAST_exceptional_phenotypes}
-\title{Determine multidrug-resistant organisms (MDRO)}
-\usage{
-MDRO(tbl, country = NULL, col_bactid = "bactid", info = TRUE,
-  amcl = "amcl", amik = "amik", amox = "amox", ampi = "ampi",
-  azit = "azit", aztr = "aztr", cefa = "cefa", cfra = "cfra",
-  cfep = "cfep", cfot = "cfot", cfox = "cfox", cfta = "cfta",
-  cftr = "cftr", cfur = "cfur", chlo = "chlo", cipr = "cipr",
-  clar = "clar", clin = "clin", clox = "clox", coli = "coli",
-  czol = "czol", dapt = "dapt", doxy = "doxy", erta = "erta",
-  eryt = "eryt", fosf = "fosf", fusi = "fusi", gent = "gent",
-  imip = "imip", kana = "kana", levo = "levo", linc = "linc",
-  line = "line", mero = "mero", metr = "metr", mino = "mino",
-  moxi = "moxi", nali = "nali", neom = "neom", neti = "neti",
-  nitr = "nitr", novo = "novo", norf = "norf", oflo = "oflo",
-  peni = "peni", pita = "pita", poly = "poly", qida = "qida",
-  rifa = "rifa", roxi = "roxi", siso = "siso", teic = "teic",
-  tetr = "tetr", tica = "tica", tige = "tige", tobr = "tobr",
-  trim = "trim", trsu = "trsu", vanc = "vanc")
-
-BRMO(tbl, country = "nl", ...)
-
-MRGN(tbl, country = "de", ...)
-
-EUCAST_exceptional_phenotypes(tbl, country = "EUCAST", ...)
-}
-\arguments{
-\item{tbl}{table with antibiotic columns, like e.g. \code{amox} and \code{amcl}}
-
-\item{country}{country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).}
-
-\item{col_bactid}{column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}}
-
-\item{info}{print progress}
-
-\item{amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot, cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, metr, mino, moxi, nali, neom, neti, nitr, novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column names of antibiotics. column names of antibiotics}
-
-\item{...}{parameters that are passed on to methods}
-}
-\value{
-Ordered factor with levels \code{Unknown < Negative < Unconfirmed < Positive}.
-}
-\description{
-Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
-}
-\details{
-When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}).
-}
-\examples{
-library(dplyr)
-
-septic_patients \%>\%
-  mutate(EUCAST = MDRO(.),
-         BRMO = MDRO(., "nl"))
-}

man/abname.Rd

@@ -1,45 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc.R
-\name{abname}
-\alias{abname}
-\title{Name of an antibiotic}
-\source{
-\code{\link{antibiotics}}
-}
-\usage{
-abname(abcode, from = c("guess", "atc", "molis", "umcg"), to = "official",
-  textbetween = " + ", tolower = FALSE)
-}
-\arguments{
-\item{abcode}{a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}}
-
-\item{from, to}{type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"molis"} and \code{"umcg"}.}
-
-\item{textbetween}{text to put between multiple returned texts}
-
-\item{tolower}{return output as lower case with function \code{\link{tolower}}.}
-}
-\description{
-Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
-}
-\examples{
-abname("AMCL")
-# "amoxicillin and enzyme inhibitor"
-
-abname("AMCL+GENT")
-# "amoxicillin and enzyme inhibitor + gentamicin"
-
-abname(c("AMCL", "GENT"))
-# "amoxicillin and enzyme inhibitor" "gentamicin"
-
-abname("AMCL", to = "trivial_nl")
-# "Amoxicilline/clavulaanzuur"
-
-abname("AMCL", to = "atc")
-# "J01CR02"
-
-abname("J01CR02", from = "atc", to = "umcg")
-# "AMCL"
-}
-\keyword{ab}
-\keyword{antibiotics}

man/antibiotics.Rd

@@ -1,40 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{antibiotics}
-\alias{antibiotics}
-\title{Dataset with 420 antibiotics}
-\format{A data.frame with 420 observations and 18 variables:
-\describe{
-  \item{\code{atc}}{ATC code, like \code{J01CR02}}
-  \item{\code{molis}}{MOLIS code, like \code{amcl}}
-  \item{\code{umcg}}{UMCG code, like \code{AMCL}}
-  \item{\code{abbr}}{Abbreviation as used by many countries, to be used for \code{\link{guess_atc}}}
-  \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and enzyme inhibitor"}}
-  \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
-  \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-  \item{\code{trade_name}}{Trade name as used by many countries, to be used for \code{\link{guess_atc}}}
-  \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
-  \item{\code{oral_units}}{Units of \code{ddd_units}}
-  \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
-  \item{\code{iv_units}}{Units of \code{iv_ddd}}
-  \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
-  \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
-  \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
-  \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
-  \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-  \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-}}
-\source{
-- World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
-}
-\usage{
-antibiotics
-}
-\description{
-A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
-}
-\seealso{
-\code{\link{microorganisms}}
-}
-\keyword{datasets}

man/as.mic.Rd

@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/classes.R
-\name{as.mic}
-\alias{as.mic}
-\alias{is.mic}
-\title{Class 'mic'}
-\usage{
-as.mic(x, na.rm = FALSE)
-
-is.mic(x)
-}
-\arguments{
-\item{x}{vector}
-
-\item{na.rm}{a logical indicating whether missing values should be removed}
-}
-\value{
-Ordered factor with new class \code{mic} and new attributes \code{package} and \code{package.version}
-}
-\description{
-This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
-}
-\examples{
-mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
-is.mic(mic_data)
-
-plot(mic_data)
-barplot(mic_data)
-}

man/as.rsi.Rd

@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/classes.R
-\name{as.rsi}
-\alias{as.rsi}
-\alias{is.rsi}
-\title{Class 'rsi'}
-\usage{
-as.rsi(x)
-
-is.rsi(x)
-}
-\arguments{
-\item{x}{vector}
-}
-\value{
-Ordered factor with new class \code{rsi} and new attributes \code{package} and \code{package.version}
-}
-\description{
-This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
-}
-\examples{
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
-is.rsi(rsi_data)
-
-plot(rsi_data)    # for percentages
-barplot(rsi_data) # for frequencies
-}

man/atc_property.Rd

@@ -1,57 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc.R
-\name{atc_property}
-\alias{atc_property}
-\title{Properties of an ATC code}
-\source{
-\url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
-}
-\usage{
-atc_property(atc_code, property, administration = "O",
-  url = "https://www.whocc.no/atc_ddd_index/?code=\%s&showdescription=no")
-}
-\arguments{
-\item{atc_code}{a character or character vector with ATC code(s) of antibiotic(s)}
-
-\item{property}{property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.}
-
-\item{administration}{type of administration, see \emph{Details}}
-
-\item{url}{url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.}
-}
-\description{
-Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \strong{This function requires an internet connection.}
-}
-\details{
-Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
-\itemize{
-  \item{\code{"Implant"}}{ = Implant}
-  \item{\code{"Inhal"}}{ = Inhalation}
-  \item{\code{"Instill"}}{ = Instillation}
-  \item{\code{"N"}}{ = nasal}
-  \item{\code{"O"}}{ = oral}
-  \item{\code{"P"}}{ = parenteral}
-  \item{\code{"R"}}{ = rectal}
-  \item{\code{"SL"}}{ = sublingual/buccal}
-  \item{\code{"TD"}}{ = transdermal}
-  \item{\code{"V"}}{ = vaginal}
-}
-
-Abbreviations for the property \code{"U"} (unit):
-\itemize{
-  \item{\code{"g"}}{ = gram}
-  \item{\code{"mg"}}{ = milligram}
-  \item{\code{"mcg"}}{ = microgram}
-  \item{\code{"U"}}{ = unit}
-  \item{\code{"TU"}}{ = thousand units}
-  \item{\code{"MU"}}{ = million units}
-  \item{\code{"mmol"}}{ = millimole}
-  \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
-}
-}
-\examples{
-\donttest{
-atc_property("J01CA04", "DDD", "O") # oral DDD (Defined Daily Dose) of amoxicillin
-atc_property("J01CA04", "DDD", "P") # parenteral DDD (Defined Daily Dose) of amoxicillin
-}
-}

man/first_isolate.Rd

@@ -1,125 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/first_isolates.R
-\name{first_isolate}
-\alias{first_isolate}
-\title{Determine first (weighted) isolates}
-\source{
-Methodology of this function is based on: "M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition", 2014, Clinical and Laboratory Standards Institute. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
-}
-\usage{
-first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
-  col_testcode = NA, col_specimen = NA, col_icu = NA,
-  col_keyantibiotics = NA, episode_days = 365, testcodes_exclude = "",
-  icu_exclude = FALSE, filter_specimen = NA, output_logical = TRUE,
-  type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
-  info = TRUE, col_genus = NA, col_species = NA)
-}
-\arguments{
-\item{tbl}{a \code{data.frame} containing isolates.}
-
-\item{col_date}{column name of the result date (or date that is was received on the lab)}
-
-\item{col_patient_id}{column name of the unique IDs of the patients}
-
-\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)}
-
-\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}
-
-\item{col_specimen}{column name of the specimen type or group}
-
-\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
-
-\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.}
-
-\item{episode_days}{episode in days after which a genus/species combination will be determined as 'first isolate' again}
-
-\item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
-
-\item{icu_exclude}{logical whether ICU isolates should be excluded}
-
-\item{filter_specimen}{specimen group or type that should be excluded}
-
-\item{output_logical}{return output as \code{logical} (will else be the values \code{0} or \code{1})}
-
-\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
-
-\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
-
-\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}
-
-\item{info}{print progress}
-
-\item{col_genus}{(deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms}
-
-\item{col_species}{(deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms}
-}
-\value{
-A vector to add to table, see Examples.
-}
-\description{
-Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
-}
-\details{
-\strong{WHY THIS IS SO IMPORTANT} \cr
-    To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
-
-    \strong{DETERMINING WEIGHTED ISOLATES} \cr
-    \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
-    To determine weighted isolates, the difference between key antibiotics will be checked. Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
-    \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
-    To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
-}
-\examples{
-# septic_patients is a dataset available in the AMR package
-?septic_patients
-my_patients <- septic_patients
-
-library(dplyr)
-my_patients$first_isolate <- my_patients \%>\%
-  first_isolate(col_date = "date",
-                col_patient_id = "patient_id",
-                col_bactid = "bactid")
-
-\dontrun{
-
-# set key antibiotics to a new variable
-tbl$keyab <- key_antibiotics(tbl)
-
-tbl$first_isolate <-
-  first_isolate(tbl)
-
-tbl$first_isolate_weighed <-
-  first_isolate(tbl,
-                col_keyantibiotics = 'keyab')
-
-tbl$first_blood_isolate <-
-  first_isolate(tbl,
-                filter_specimen = 'Blood')
-
-tbl$first_blood_isolate_weighed <-
-  first_isolate(tbl,
-                filter_specimen = 'Blood',
-                col_keyantibiotics = 'keyab')
-
-tbl$first_urine_isolate <-
-  first_isolate(tbl,
-                filter_specimen = 'Urine')
-
-tbl$first_urine_isolate_weighed <-
-  first_isolate(tbl,
-                filter_specimen = 'Urine',
-                col_keyantibiotics = 'keyab')
-
-tbl$first_resp_isolate <-
-  first_isolate(tbl,
-                filter_specimen = 'Respiratory')
-
-tbl$first_resp_isolate_weighed <-
-  first_isolate(tbl,
-                filter_specimen = 'Respiratory',
-                col_keyantibiotics = 'keyab')
-}
-}
-\keyword{first}
-\keyword{isolate}
-\keyword{isolates}

man/freq.Rd

@@ -1,71 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/freq.R
-\name{freq}
-\alias{freq}
-\alias{frequency_tbl}
-\title{Frequency table}
-\usage{
-freq(x, sort.count = TRUE, nmax = 15, na.rm = TRUE, markdown = FALSE,
-  toConsole = TRUE, digits = 2, sep = " ")
-
-frequency_tbl(x, sort.count = TRUE, nmax = 15, na.rm = TRUE,
-  markdown = FALSE, toConsole = TRUE, digits = 2, sep = " ")
-}
-\arguments{
-\item{x}{data}
-
-\item{sort.count}{Sort on count. Use \code{FALSE} to sort alphabetically on item.}
-
-\item{nmax}{number of row to print. Use \code{nmax = 0} or \code{nmax = NA} to print all rows.}
-
-\item{na.rm}{a logical value indicating whether NA values should be removed from the frequency table. The header will always print the amount of\code{NA}s.}
-
-\item{markdown}{print table in markdown format (this forces \code{nmax = NA})}
-
-\item{toConsole}{Print table to the console. Use \code{FALSE} to assign the table to an object.}
-
-\item{digits}{how many significant digits are to be used for numeric values (not for the items themselves, that depends on \code{\link{getOption}("digits")})}
-
-\item{sep}{a character string to separate the terms when selecting multiple columns}
-}
-\description{
-Create a frequency table of a vector of data, a single column or a maximum of 9 columns of a data frame. Supports markdown for reports.
-}
-\details{
-For numeric values, the next values will be calculated and shown into the header:
-\itemize{
-  \item{Mean, using \code{\link[base]{mean}}}
-  \item{Standard deviation, using \code{\link[stats]{sd}}}
-  \item{Five numbers of Tukey (min, Q1, median, Q3, max), using \code{\link[stats]{fivenum}}}
-  \item{Outliers (count and list), using \code{\link{boxplot.stats}}}
-  \item{Coefficient of variation (CV), the standard deviation divided by the mean}
-  \item{Coefficient of quartile variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using \code{\link{quantile}} with \code{type = 6} as quantile algorithm to comply with SPSS standards}
-}
-}
-\examples{
-library(dplyr)
-
-freq(septic_patients$hospital_id)
-
-septic_patients \%>\%
-  filter(hospital_id == "A") \%>\%
-  select(bactid) \%>\%
-  freq()
-
-# select multiple columns; they will be pasted together
-septic_patients \%>\%
-  left_join_microorganisms \%>\%
-  filter(hospital_id == "A") \%>\%
-  select(genus, species) \%>\%
-  freq()
-
-# save frequency table to an object
-years <- septic_patients \%>\%
-  mutate(year = format(date, "\%Y")) \%>\%
-  select(year) \%>\%
-  freq(toConsole = FALSE)
-}
-\keyword{freq}
-\keyword{frequency}
-\keyword{summarise}
-\keyword{summary}

man/guess_atc.Rd

@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc.R
-\name{guess_atc}
-\alias{guess_atc}
-\title{Find ATC code based on antibiotic property}
-\usage{
-guess_atc(x)
-}
-\arguments{
-\item{x}{character vector to determine \code{ATC} code}
-}
-\value{
-Character (vector).
-}
-\description{
-Use this function to determine the ATC code of one or more antibiotics. The dataset \code{\link{antibiotics}} will be searched for abbreviations, official names and trade names.
-}
-\details{
-In the ATC classification system, the active substances are classified in a hierarchy with five different levels.  The system has fourteen main anatomical/pharmacological groups or 1st levels.  Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups.  The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance.  The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
-  Source: \url{https://www.whocc.no/atc/structure_and_principles/}
-}
-\examples{
-# These examples all return "J01FA01", the ATC code of Erythromycin:
-guess_atc("J01FA01")
-guess_atc("Erythromycin")
-guess_atc("eryt")
-guess_atc("ERYT")
-guess_atc("ERY")
-guess_atc("Erythrocin") # Trade name
-guess_atc("Eryzole")    # Trade name
-guess_atc("Pediamycin") # Trade name
-}
-\seealso{
-\code{\link{antibiotics}} for the dataframe that is being used to determine ATC's.
-}

man/guess_bactid.Rd

@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/atc.R
-\name{guess_bactid}
-\alias{guess_bactid}
-\title{Find bacteria ID based on genus/species}
-\usage{
-guess_bactid(x)
-}
-\arguments{
-\item{x}{character vector to determine \code{bactid}}
-}
-\value{
-Character (vector).
-}
-\description{
-Use this function to determine a valid ID based on a genus (and species). This input could be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also use a \code{\link{paste}} of a genus and species column to use the full name as input: \code{x = paste(df$genus, df$species)}, where \code{df} is your dataframe.
-}
-\examples{
-# These examples all return "STAAUR", the ID of S. aureus:
-guess_bactid("stau")
-guess_bactid("STAU")
-guess_bactid("staaur")
-guess_bactid("S. aureus")
-guess_bactid("S aureus")
-guess_bactid("Staphylococcus aureus")
-guess_bactid("MRSA") # Methicillin-resistant S. aureus
-guess_bactid("VISA") # Vancomycin Intermediate S. aureus
-}
-\seealso{
-\code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
-}

man/join.Rd

@@ -1,56 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/join.R
-\name{join}
-\alias{join}
-\alias{inner_join_microorganisms}
-\alias{inner_join}
-\alias{left_join_microorganisms}
-\alias{right_join_microorganisms}
-\alias{full_join_microorganisms}
-\alias{semi_join_microorganisms}
-\alias{anti_join_microorganisms}
-\title{Join a table with \code{microorganisms}}
-\usage{
-inner_join_microorganisms(x, by = "bactid", suffix = c("2", ""), ...)
-
-left_join_microorganisms(x, by = "bactid", suffix = c("2", ""), ...)
-
-right_join_microorganisms(x, by = "bactid", suffix = c("2", ""), ...)
-
-full_join_microorganisms(x, by = "bactid", suffix = c("2", ""), ...)
-
-semi_join_microorganisms(x, by = "bactid", ...)
-
-anti_join_microorganisms(x, by = "bactid", ...)
-}
-\arguments{
-\item{x}{existing table to join, also supports character vectors}
-
-\item{by}{a variable to join by - could be a column name of \code{x} with values that exist in \code{microorganisms$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})}
-
-\item{suffix}{if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.}
-
-\item{...}{other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.}
-}
-\description{
-Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
-}
-\details{
-As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
-}
-\examples{
-left_join_microorganisms("STAAUR")
-
-library(dplyr)
-septic_patients \%>\% left_join_microorganisms()
-
-df <- data.frame(date = seq(from = as.Date("2018-01-01"),
-                            to = as.Date("2018-01-07"),
-                            by = 1),
-                 bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
-                                 "ESCCOL", "ESCCOL", "ESCCOL"),
-                 stringsAsFactors = FALSE)
-colnames(df)
-df2 <- left_join_microorganisms(df, "bacteria_id")
-colnames(df2)
-}

man/key_antibiotics.Rd

@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/first_isolates.R
-\name{key_antibiotics}
-\alias{key_antibiotics}
-\title{Key antibiotics based on bacteria ID}
-\usage{
-key_antibiotics(tbl, col_bactid = "bactid", info = TRUE, amcl = "amcl",
-  amox = "amox", cfot = "cfot", cfta = "cfta", cftr = "cftr",
-  cfur = "cfur", cipr = "cipr", clar = "clar", clin = "clin",
-  clox = "clox", doxy = "doxy", gent = "gent", line = "line",
-  mero = "mero", peni = "peni", pita = "pita", rifa = "rifa",
-  teic = "teic", trsu = "trsu", vanc = "vanc")
-}
-\arguments{
-\item{tbl}{table with antibiotics coloms, like \code{amox} and \code{amcl}.}
-
-\item{col_bactid}{column of bacteria IDs in \code{tbl}; these should occur in \code{microorganisms$bactid}, see \code{\link{microorganisms}}}
-
-\item{info}{print warnings}
-
-\item{amcl, amox, cfot, cfta, cftr, cfur, cipr, clar, clin, clox, doxy, gent, line, mero, peni, pita, rifa, teic, trsu, vanc}{column names of antibiotics, case-insensitive}
-}
-\value{
-Character of length 1.
-}
-\description{
-Key antibiotics based on bacteria ID
-}
-\examples{
-\donttest{
-#' # set key antibiotics to a new variable
-tbl$keyab <- key_antibiotics(tbl)
-}
-}
-\seealso{
-\code{\link{mo_property}} \code{\link{antibiotics}}
-}

man/like.Rd

@@ -1,40 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/misc.R
-\name{like}
-\alias{like}
-\alias{\%like\%}
-\title{Pattern Matching}
-\source{
-Inherited from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default.
-}
-\usage{
-x \%like\% pattern
-}
-\arguments{
-\item{x}{a character vector where matches are sought, or an
-    object which can be coerced by \code{as.character} to a character
-    vector.  \link{Long vectors} are supported.}
-
-\item{pattern}{character string containing a \link{regular expression}
-    (or character string for \code{fixed = TRUE}) to be matched
-    in the given character vector.  Coerced by
-    \code{\link{as.character}} to a character string if possible.  If a
-    character vector of length 2 or more is supplied, the first element
-    is used with a warning.  Missing values are allowed except for
-    \code{regexpr} and \code{gregexpr}.}
-}
-\value{
-A \code{logical} vector
-}
-\description{
-Convenience function to compare a vector with a pattern, like \code{\link[base]{grep}}. It always returns a \code{logical} vector and is always case-insensitive.
-}
-\examples{
-library(dplyr)
-# get unique occurences of bacteria whose name start with 'Ent'
-septic_patients \%>\%
-  left_join_microorganisms() \%>\%
-  filter(fullname \%like\% '^Ent') \%>\%
-  pull(fullname) \%>\%
-  unique()
-}

man/microorganisms.Rd

@@ -1,34 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{microorganisms}
-\alias{microorganisms}
-\title{Dataset with ~2500 microorganisms}
-\format{A data.frame with 2453 observations and 12 variables:
-\describe{
-  \item{\code{bactid}}{ID of microorganism}
-  \item{\code{bactsys}}{Bactsyscode of microorganism}
-  \item{\code{family}}{Family name of microorganism}
-  \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
-  \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
-  \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
-  \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
-  \item{\code{type}}{Type of microorganism, like \code{"Bacteria"} and \code{"Fungus/yeast"}}
-  \item{\code{gramstain}}{Gram of microorganism, like \code{"Negative rods"}}
-  \item{\code{aerobic}}{Logical whether bacteria is aerobic}
-  \item{\code{type_nl}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
-  \item{\code{gramstain_nl}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
-}}
-\source{
-MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-}
-\usage{
-microorganisms
-}
-\description{
-A dataset containing 2453 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
-}
-\seealso{
-\code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
-}
-\keyword{datasets}

man/microorganisms.umcg.Rd

@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{microorganisms.umcg}
-\alias{microorganisms.umcg}
-\title{Translation table for UMCG with ~1100 microorganisms}
-\format{A data.frame with 1090 observations and 2 variables:
-\describe{
-  \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
-  \item{\code{bactid}}{Code of microorganism in \code{\link{microorganisms}}}
-}}
-\source{
-MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
-}
-\usage{
-microorganisms.umcg
-}
-\description{
-A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$bactid} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
-}
-\seealso{
-\code{\link{guess_bactid}} \code{\link{microorganisms}}
-}
-\keyword{datasets}

man/mo_property.Rd

@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/eucast.R
-\name{mo_property}
-\alias{mo_property}
-\title{Poperties of a microorganism}
-\usage{
-mo_property(bactid, property = "fullname")
-}
-\arguments{
-\item{bactid}{ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}}
-
-\item{property}{One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}}
-}
-\description{
-Poperties of a microorganism
-}
-\seealso{
-\code{\link{microorganisms}}
-}

man/print.Rd

@@ -1,57 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/print.R
-\name{print}
-\alias{print}
-\alias{print.tbl_df}
-\alias{print.tbl}
-\alias{print.data.table}
-\title{Printing Data Tables and Tibbles}
-\usage{
-\method{print}{tbl_df}(x, nmax = 10, header = TRUE, row.names = TRUE,
-  right = FALSE, width = 1, na = "<NA>", ...)
-
-\method{print}{tbl}(x, ...)
-
-\method{print}{data.table}(x, print.keys = FALSE, ...)
-}
-\arguments{
-\item{x}{object of class \code{data.frame}.}
-
-\item{nmax}{amount of rows to print in total. When the total amount of rows exceeds this limit, the first and last \code{nmax / 2} rows will be printed. Use \code{nmax = NA} to print all rows.}
-
-\item{header}{print header with information about data size and tibble grouping}
-
-\item{row.names}{logical (or character vector), indicating whether (or
-    what) row names should be printed.}
-
-\item{right}{logical, indicating whether or not strings should be
-    right-aligned.  The default is right-alignment.}
-
-\item{width}{amount of white spaces to keep between columns, must be at least 1}
-
-\item{na}{value to print instead of NA}
-
-\item{...}{optional arguments to \code{print} or \code{plot} methods.}
-
-\item{print.keys}{print keys for \code{data.table}}
-}
-\description{
-Print a data table or tibble. It prints: \cr- The \strong{first and last rows} like \code{data.table}s are printed by the \code{data.table} package,\cr- A \strong{header} and \strong{left aligned text} like \code{tibble}s are printed by the \code{tibble} package with info about grouped variables,\cr- \strong{Unchanged values} and \strong{support for row names} like \code{data.frame}s are printed by the \code{base} package.
-}
-\examples{
-# more reliable data view:
-library(dplyr)
-starwars
-print(starwars, width = 3)
-
-# This is how the tibble package prints since v1.4.0:
-# (mind the quite unfamiliar underscores and ending dots)
-tibble(now_what = c(1.2345, 2345.67, 321.456)) \%>\% tibble:::print.tbl_df()
-
-# This is how this AMR package prints:
-# (every number shown as you would expect)
-tibble(now_what = c(1.2345, 2345.67, 321.456))
-
-# also supports info about groups (look at header)
-starwars \%>\% group_by(homeworld, gender)
-}

man/rsi.Rd

@@ -1,96 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/rsi_analysis.R
-\name{rsi}
-\alias{rsi}
-\alias{rsi_df}
-\alias{n_rsi}
-\title{Resistance of isolates}
-\usage{
-rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
-  as_percent = FALSE, info = FALSE, warning = TRUE)
-
-rsi_df(tbl, ab, interpretation = "IR", minimum = 30, as_percent = FALSE,
-  info = TRUE, warning = TRUE)
-
-n_rsi(ab1, ab2 = NA)
-}
-\arguments{
-\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
-
-\item{interpretation}{antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.}
-
-\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).}
-
-\item{as_percent}{return output as percent (text), will else (at default) be a double}
-
-\item{info}{calculate the amount of available isolates and print it, like \code{n = 423}}
-
-\item{warning}{show a warning when the available amount of isolates is below \code{minimum}}
-
-\item{tbl}{\code{data.frame} containing columns with antibiotic interpretations.}
-
-\item{ab}{character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{ab = c("amox", "amcl")}}
-}
-\value{
-Double or, when \code{as_percent = TRUE}, a character.
-}
-\description{
-This functions can be used to calculate the (co-)resistance of isolates (i.e. percentage S, SI, I, IR or R [of a vector] of isolates). The functions \code{rsi} and \code{n_rsi} can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
-}
-\details{
-Remember that you should filter your table to let it contain \strong{only first isolates}!
-\if{html}{
-  \cr \cr
-  To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
-  \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
-  To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
-  For two antibiotics:
-  \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
-  \cr
-  For three antibiotics:
-  \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
-}
-}
-\examples{
-library(dplyr)
-
-septic_patients \%>\%
-  group_by(hospital_id) \%>\%
-  summarise(cipro_susceptibility = rsi(cipr, interpretation = "S"),
-            n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
-
-septic_patients \%>\%
-  group_by(hospital_id) \%>\%
-  summarise(cipro_S = rsi(cipr, interpretation = "S",
-                          as_percent = TRUE, warning = FALSE),
-            cipro_n = n_rsi(cipr),
-            genta_S = rsi(gent, interpretation = "S",
-                          as_percent = TRUE, warning = FALSE),
-            genta_n = n_rsi(gent),
-            combination_S = rsi(cipr, gent, interpretation = "S",
-                                as_percent = TRUE, warning = FALSE),
-            combination_n = n_rsi(cipr, gent))
-
-# calculate resistance
-rsi(septic_patients$amox)
-# or susceptibility
-rsi(septic_patients$amox, interpretation = "S")
-
-# calculate co-resistance between amoxicillin/clav acid and gentamicin,
-# so we can review that combination therapy does a lot more than mono therapy:
-septic_patients \%>\% rsi_df(ab = "amcl", interpretation = "S") # = 67.8\%
-septic_patients \%>\% rsi_df(ab = "gent", interpretation = "S") # = 69.1\%
-septic_patients \%>\% rsi_df(ab = c("amcl", "gent"), interpretation = "S") # = 90.6\%
-
-\dontrun{
-# calculate current empiric combination therapy of Helicobacter gastritis:
-my_table \%>\%
-  filter(first_isolate == TRUE,
-         genus == "Helicobacter") \%>\%
-  rsi_df(ab = c("amox", "metr")) # amoxicillin with metronidazole
-}
-}
-\keyword{antibiotics}
-\keyword{isolate}
-\keyword{isolates}
-\keyword{rsi}

man/rsi_predict.Rd

@@ -1,78 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/rsi_analysis.R
-\name{rsi_predict}
-\alias{rsi_predict}
-\title{Predict antimicrobial resistance}
-\usage{
-rsi_predict(tbl, col_ab, col_date,
-  year_max = as.integer(format(as.Date(Sys.Date()), "\%Y")) + 15,
-  year_every = 1, model = "binomial", I_as_R = TRUE,
-  preserve_measurements = TRUE, info = TRUE)
-}
-\arguments{
-\item{tbl}{table that contains columns \code{col_ab} and \code{col_date}}
-
-\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation}
-
-\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation}
-
-\item{year_max}{highest year to use in the prediction model, deafults to 15 years after today}
-
-\item{year_every}{unit of sequence between lowest year found in the data and \code{year_max}}
-
-\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).}
-
-\item{I_as_R}{treat \code{I} as \code{R}}
-
-\item{preserve_measurements}{overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.}
-
-\item{info}{print textual analysis with the name and \code{\link{summary}} of the model.}
-}
-\value{
-\code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
-}
-\description{
-Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
-}
-\examples{
-\dontrun{
-# use it directly:
-rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
-            col_ab = "amcl", col_date = "date")
-
-# or with dplyr so you can actually read it:
-library(dplyr)
-tbl \%>\%
-  filter(first_isolate == TRUE,
-         genus == "Haemophilus") \%>\%
-  rsi_predict(amcl, date)
-}
-
-
-# real live example:
-library(dplyr)
-septic_patients \%>\%
-  # get bacteria properties like genus and species
-  left_join_microorganisms("bactid") \%>\%
-  # calculate first isolates
-  mutate(first_isolate =
-           first_isolate(.,
-                         "date",
-                         "patient_id",
-                         "bactid",
-                         col_specimen = NA,
-                         col_icu = NA)) \%>\%
-  # filter on first E. coli isolates
-  filter(genus == "Escherichia",
-         species == "coli",
-         first_isolate == TRUE) \%>\%
-  # predict resistance of cefotaxime for next years
-  rsi_predict(col_ab = "cfot",
-              col_date = "date",
-              year_max = 2025,
-              preserve_measurements = FALSE)
-
-}
-\seealso{
-\code{\link{lm}} \cr \code{\link{glm}}
-}

man/septic_patients.Rd

@@ -1,69 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{septic_patients}
-\alias{septic_patients}
-\title{Dataset with 2000 blood culture isolates of septic patients}
-\format{A data.frame with 2000 observations and 47 variables:
-\describe{
-  \item{\code{date}}{date of receipt at the laboratory}
-  \item{\code{hospital_id}}{ID of the hospital}
-  \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
-  \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
-  \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
-  \item{\code{age}}{age of the patient}
-  \item{\code{sex}}{sex of the patient}
-  \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
-  \item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
-  \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} and can be translated with \code{\link{abname}}}
-}}
-\source{
-MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-}
-\usage{
-septic_patients
-}
-\description{
-An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1.
-}
-\examples{
-# ----------- #
-# PREPARATION #
-# ----------- #
-
-# Save this example dataset to an object, so we can edit it:
-my_data <- septic_patients
-
-# load the dplyr package to make data science A LOT easier
-library(dplyr)
-
-# Add first isolates to our dataset:
-my_data <- my_data \%>\%
-  mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
-
-# -------- #
-# ANALYSIS #
-# -------- #
-
-# 1. Get the amoxicillin resistance percentages
-#    of E. coli, divided by hospital:
-
-my_data \%>\%
-  filter(bactid == "ESCCOL",
-         first_isolates == TRUE) \%>\%
-  group_by(hospital_id) \%>\%
-  summarise(n = n(),
-            amoxicillin_resistance = rsi(amox))
-
-
-# 2. Get the amoxicillin/clavulanic acid resistance
-#    percentages of E. coli, trend over the years:
-
-my_data \%>\%
-  filter(bactid == guess_bactid("E. coli"),
-         first_isolates == TRUE) \%>\%
-  group_by(year = format(date, "\%Y")) \%>\%
-  summarise(n = n(),
-            amoxclav_resistance = rsi(amcl, minimum = 20))
-}
-\keyword{datasets}

setup.py

@@ -0,0 +1,27 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='AMR',
+    version='3.0.1.9091',
+    packages=find_packages(),
+    install_requires=[
+        'rpy2',
+        'numpy',
+        'pandas',
+    ],
+    author='Matthijs Berends',
+    author_email='m.s.berends@umcg.nl',
+    description='A Python wrapper for the AMR R package',
+    long_description=open('README.md').read(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/msberends/AMR',
+    project_urls={
+        'Bug Tracker': 'https://github.com/msberends/AMR/issues',
+    },
+    license='GPL 2',
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'Operating System :: OS Independent',
+    ],
+    python_requires='>=3.6',
+)

tests/testthat.R

@@ -1,4 +0,0 @@
-library(testthat)
-library(AMR)
-
-test_check("AMR")

tests/testthat/test-atc.R

@@ -1,44 +0,0 @@
-context("atc.R")
-
-
-test_that("atc_property works", {
-  expect_equal(tolower(atc_property("J01CA04", property = "Name")), "amoxicillin")
-  expect_equivalent(atc_property("J01CA04", "DDD"), 1)
-})
-
-test_that("abname works", {
-  expect_equal(abname("AMOX"), "Amoxicillin")
-  expect_equal(abname(c("AMOX", "GENT")), c("Amoxicillin", "Gentamicin"))
-  expect_equal(abname(c("AMOX+GENT")), "Amoxicillin + gentamicin")
-  expect_equal(abname("AMOX", from = 'umcg'), "Amoxicillin")
-  expect_equal(abname("amox", from = 'molis'), "Amoxicillin")
-  expect_equal(abname("J01CA04", from = 'atc'), "Amoxicillin")
-})
-
-test_that("guess_bactid works", {
-  expect_identical(guess_bactid(c("E. coli", "H. influenzae")), c("ESCCOL", "HAEINF"))
-  expect_equal(guess_bactid("Escherichia coli"), "ESCCOL")
-  expect_equal(guess_bactid("Negative rods"), "GNR")
-  expect_equal(guess_bactid(c("stau",
-                              "STAU",
-                              "staaur",
-                              "S. aureus",
-                              "S aureus",
-                              "Staphylococcus aureus",
-                              "MRSA",
-                              "VISA")),
-               rep("STAAUR", 8))
-})
-
-test_that("guess_atc works", {
-  expect_equal(guess_atc(c("J01FA01",
-                           "Erythromycin",
-                           "eryt",
-                           "ERYT",
-                           "ERY",
-                           "Erythrocin",
-                           "Eryzole",
-                           "Pediamycin")),
-               rep("J01FA01", 8))
-
-})

tests/testthat/test-classes.R

@@ -1,43 +0,0 @@
-context("classes.R")
-
-test_that("rsi works", {
-  expect_true(as.rsi("S") < as.rsi("I"))
-  expect_true(as.rsi("I") < as.rsi("R"))
-  expect_true(as.rsi("R") > as.rsi("S"))
-  expect_true(is.rsi(as.rsi("S")))
-
-  # print plots, should not raise errors
-  barplot(as.rsi(c("S", "I", "R")))
-  plot(as.rsi(c("S", "I", "R")))
-  print(as.rsi(c("S", "I", "R")))
-
-  expect_equal(suppressWarnings(as.logical(as.rsi("INVALID VALUE"))), NA)
-
-  expect_equal(summary(as.rsi(c("S", "R"))), c("Mode" = 'rsi',
-                                               "<NA>" = "0",
-                                               "Sum S" = "1",
-                                               "Sum IR" = "1",
-                                               "Sum R" = "1",
-                                               "Sum I" = "0"))
-})
-
-test_that("mic works", {
-  expect_true(as.mic(8) == as.mic("8"))
-  expect_true(as.mic("1") > as.mic("<=0.0625"))
-  expect_true(as.mic("1") < as.mic(">=32"))
-  expect_true(is.mic(as.mic(8)))
-
-  expect_equal(as.double(as.mic(">=32")), 32)
-  expect_equal(as.integer(as.mic(">=32")), 32)
-  expect_equal(suppressWarnings(as.logical(as.mic("INVALID VALUE"))), NA)
-
-  # print plots, should not raise errors
-  barplot(as.mic(c(1, 2, 4, 8)))
-  plot(as.mic(c(1, 2, 4, 8)))
-  print(as.mic(c(1, 2, 4, 8)))
-
-  expect_equal(summary(as.mic(c(2, 8))), c("Mode" = 'mic',
-                                           "<NA>" = "0",
-                                           "Min." = "2",
-                                           "Max." = "8"))
-})

tests/testthat/test-eucast.R

@@ -1,33 +0,0 @@
-context("eucast.R")
-
-test_that("EUCAST rules work", {
-  a <- EUCAST_rules(septic_patients)
-
-  a <- data.frame(bactid = c("KLEPNE",  # Klebsiella pneumoniae
-                             "PSEAER",  # Pseudomonas aeruginosa
-                             "ENTAER"), # Enterobacter aerogenes
-                  amox = "-",           # Amoxicillin
-                  stringsAsFactors = FALSE)
-  b <- data.frame(bactid = c("KLEPNE",  # Klebsiella pneumoniae
-                             "PSEAER",  # Pseudomonas aeruginosa
-                             "ENTAER"), # Enterobacter aerogenes
-                  amox = "R",           # Amoxicillin
-                  stringsAsFactors = FALSE)
-  expect_equal(EUCAST_rules(a, info = FALSE), b)
-  expect_equal(suppressWarnings(interpretive_reading(a, info = TRUE)), b)
-
-  a <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-                             "STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
-                  coli = "-",           # Colistin
-                  stringsAsFactors = FALSE)
-  b <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
-                             "STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
-                  coli = "R",           # Colistin
-                  stringsAsFactors = FALSE)
-  expect_equal(EUCAST_rules(a, info = FALSE), b)
-})
-
-test_that("MO properties work", {
-  expect_equal(mo_property("ESCCOL"), "Escherichia coli")
-  expect_equal(mo_property("STAAUR"), "Staphylococcus aureus")
-})

tests/testthat/test-first_isolates.R

@@ -1,53 +0,0 @@
-context("first_isolates.R")
-
-test_that("keyantibiotics work", {
-  expect_equal(length(key_antibiotics(septic_patients, info = FALSE)), nrow(septic_patients))
-  expect_true(key_antibiotics_equal("SSS", "SSS"))
-  expect_true(key_antibiotics_equal("SSS", "SIS", ignore_I = TRUE))
-  expect_false(key_antibiotics_equal("SSS", "SIS", ignore_I = FALSE))
-})
-
-test_that("first isolates work", {
-  # septic_patients contains 1960 out of 2000 first isolates
-  #septic_ptns <- septic_patients
-  expect_equal(sum(first_isolate(tbl = septic_patients,
-                                 col_date = "date",
-                                 col_patient_id = "patient_id",
-                                 col_bactid = "bactid",
-                                 info = FALSE)), 1960)
-
-  # septic_patients contains 1962 out of 2000 first *weighted* isolates
-  expect_equal(
-    suppressWarnings(sum(
-      first_isolate(tbl = septic_patients %>% mutate(keyab = key_antibiotics(.)),
-                    col_date = "date",
-                    col_patient_id = "patient_id",
-                    col_bactid = "bactid",
-                    col_keyantibiotics = "keyab",
-                    type = "keyantibiotics",
-                    info = TRUE))),
-    1962)
-
-  # septic_patients contains 1733 out of 2000 first non-ICU isolates
-  expect_equal(
-    sum(
-      first_isolate(septic_patients, col_bactid = "bactid", col_date = "date", col_patient_id = "patient_id", col_icu = "ward_icu", info = TRUE, icu_exclude = TRUE)),
-    1733
-  )
-
-  # set 1500 random observations to be of specimen type 'Urine'
-  random_rows <- sample(x = 1:2000, size = 1500, replace = FALSE)
-  expect_lt(sum(
-    first_isolate(tbl = mutate(septic_patients,
-                               specimen = if_else(row_number() %in% random_rows,
-                                                  "Urine",
-                                                  "Unknown")),
-                  col_date = "date",
-                  col_patient_id = "patient_id",
-                  col_bactid = "bactid",
-                  col_specimen = "specimen",
-                  filter_specimen = "Urine",
-                  info = TRUE)),
-    1501)
-
-})

tests/testthat/test-freq.R

@@ -1,26 +0,0 @@
-context("freq.R")
-
-test_that("frequency table works", {
-  expect_equal(nrow(freq(c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5), toConsole = FALSE)), 5)
-  expect_equal(nrow(frequency_tbl(c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5), toConsole = FALSE)), 5)
-
-  # date column of septic_patients should contain 1662 unique dates
-  expect_equal(nrow(freq(septic_patients$date, toConsole = FALSE)), 1662)
-  expect_equal(nrow(freq(septic_patients$date, toConsole = FALSE)),
-               length(unique(septic_patients$date)))
-
-  expect_output(freq(septic_patients$age))
-  expect_output(freq(septic_patients$date))
-  expect_output(freq(septic_patients$hospital_id))
-
-  library(dplyr)
-  expect_output(septic_patients %>% select(1:2) %>% freq())
-  expect_output(septic_patients %>% select(1:3) %>% freq())
-  expect_output(septic_patients %>% select(1:4) %>% freq())
-  expect_output(septic_patients %>% select(1:5) %>% freq())
-  expect_output(septic_patients %>% select(1:6) %>% freq())
-  expect_output(septic_patients %>% select(1:7) %>% freq())
-  expect_output(septic_patients %>% select(1:8) %>% freq())
-  expect_output(septic_patients %>% select(1:9) %>% freq())
-})
-

tests/testthat/test-joins.R

@@ -1,27 +0,0 @@
-context("joins.R")
-
-test_that("joins work", {
-  unjoined <- septic_patients
-  inner <- septic_patients %>% inner_join_microorganisms()
-  left <- septic_patients %>% left_join_microorganisms()
-  semi <- septic_patients %>% semi_join_microorganisms()
-  anti <- septic_patients %>% anti_join_microorganisms()
-  suppressWarnings(right <- septic_patients %>% right_join_microorganisms())
-  suppressWarnings(full <- septic_patients %>% full_join_microorganisms())
-
-  expect_true(ncol(unjoined) < ncol(inner))
-  expect_true(nrow(unjoined) == nrow(inner))
-
-  expect_true(ncol(unjoined) < ncol(left))
-  expect_true(nrow(unjoined) == nrow(left))
-
-  expect_true(ncol(semi) == ncol(semi))
-  expect_true(nrow(semi) == nrow(semi))
-
-  expect_true(nrow(anti) == 0)
-
-  expect_true(nrow(unjoined) < nrow(right))
-  expect_true(nrow(unjoined) < nrow(full))
-
-  expect_equal(nrow(left_join_microorganisms("ESCCOL")), 1)
-})

tests/testthat/test-mdro.R

@@ -1,20 +0,0 @@
-context("mdro.R")
-
-
-test_that("MDRO works", {
-  library(dplyr)
-
-  outcome <- MDRO(septic_patients, "EUCAST", info = FALSE)
-  # check class
-  expect_equal(outcome %>% class(), c('ordered', 'factor'))
-
-  outcome <- MDRO(septic_patients, "nl", info = FALSE)
-  # check class
-  expect_equal(outcome %>% class(), c('ordered', 'factor'))
-
-  # septic_patients should have these finding using Dutch guidelines
-  expect_equal(outcome %>% freq(toConsole = FALSE) %>% pull(Count), c(3, 21))
-
-  expect_equal(BRMO(septic_patients, info = FALSE), MDRO(septic_patients, "nl", info = FALSE))
-
-})

tests/testthat/test-misc.R

@@ -1,27 +0,0 @@
-context("misc.R")
-
-test_that("`like` works", {
-  expect_true(suppressWarnings("test" %like% c("^t", "^s")))
-  expect_true("test" %like% "test")
-  expect_true("test" %like% "TEST")
-  expect_true(as.factor("test") %like% "TEST")
-})
-
-test_that("percentages works", {
-  expect_equal(percent(0.25), "25%")
-  expect_equal(percent(0.5), "50%")
-  expect_equal(percent(0.500, force_zero = TRUE), "50.0%")
-  expect_equal(percent(0.1234), "12.3%")
-})
-
-test_that("size format works", {
-  expect_equal(size_humanreadable(123456), "121 kB")
-})
-
-test_that("functions missing in older R versions work", {
-  expect_equal(strrep("A", 5), "AAAAA")
-  expect_equal(strrep(c("A", "B"), c(5, 2)), c("AAAAA", "BB"))
-  expect_equal(trimws(" test "), "test")
-  expect_equal(trimws(" test ", "l"), "test ")
-  expect_equal(trimws(" test ", "r"), " test")
-})

tests/testthat/test-print.R

@@ -1,11 +0,0 @@
-context("print.R")
-
-
-test_that("tibble printing works", {
-  library(dplyr)
-  library(data.table)
-  expect_output(print(starwars))
-  expect_output(print(starwars %>% group_by(homeworld, gender)))
-  expect_output(print(starwars %>% as.data.table(), print.keys = TRUE))
-  expect_output(print(septic_patients))
-})

tests/testthat/test-rsi_analysis.R

@@ -1,88 +0,0 @@
-context("rsi_analysis.R")
-
-test_that("rsi works", {
-  # amox resistance in `septic_patients` should be around 53.86%
-  expect_equal(rsi(septic_patients$amox), 0.5386, tolerance = 0.0001)
-  expect_equal(rsi(septic_patients$amox), 0.5386, tolerance = 0.0001)
-  expect_equal(rsi_df(septic_patients,
-                      ab = "amox",
-                      info = FALSE),
-               0.5386,
-               tolerance = 0.0001)
-  # pita+genta susceptibility around 98.09%
-  expect_equal(rsi(septic_patients$pita,
-                   septic_patients$gent,
-                   interpretation = "S",
-                   info = TRUE),
-               0.9809,
-               tolerance = 0.0001)
-  expect_equal(rsi_df(septic_patients,
-                      ab = c("pita", "gent"),
-                      interpretation = "S",
-                      info = FALSE),
-               0.9809,
-               tolerance = 0.0001)
-  # mero+pita+genta susceptibility around 98.58%
-  expect_equal(rsi_df(septic_patients,
-                      ab = c("mero", "pita", "gent"),
-                      interpretation = "IS",
-                      info = FALSE),
-               0.9858,
-               tolerance = 0.0001)
-
-  # count of cases
-  expect_equal(septic_patients %>%
-                 group_by(hospital_id) %>%
-                 summarise(cipro_S = rsi(cipr, interpretation = "S",
-                                         as_percent = TRUE, warning = FALSE),
-                           cipro_n = n_rsi(cipr),
-                           genta_S = rsi(gent, interpretation = "S",
-                                         as_percent = TRUE, warning = FALSE),
-                           genta_n = n_rsi(gent),
-                           combination_S = rsi(cipr, gent, interpretation = "S",
-                                               as_percent = TRUE, warning = FALSE),
-                           combination_n = n_rsi(cipr, gent)) %>%
-                 pull(combination_n),
-               c(138, 474, 170, 464, 183))
-})
-
-test_that("prediction of rsi works", {
-  amox_R <- septic_patients %>%
-    filter(bactid == "ESCCOL") %>%
-    rsi_predict(col_ab = "amox",
-                col_date = "date",
-                info = FALSE) %>%
-    pull("probR")
-  # amox resistance will decrease using dataset `septic_patients`
-  expect_true(amox_R[2] > amox_R[20])
-
-  expect_output(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                            model = "binomial",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-  expect_output(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                            model = "loglin",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-  expect_output(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                            model = "lin",
-                            col_ab = "amox",
-                            col_date = "date",
-                            info = TRUE))
-
-  expect_error(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                           model = "INVALID MODEL",
-                           col_ab = "amox",
-                           col_date = "date",
-                           info = FALSE))
-  expect_error(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                           col_ab = "NOT EXISTING COLUMN",
-                           col_date = "date",
-                           info = FALSE))
-  expect_error(rsi_predict(tbl = filter(septic_patients, bactid == "ESCCOL"),
-                           col_ab = "amox",
-                           col_date = "NOT EXISTING COLUMN",
-                           info = FALSE))
-})