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(v2.1.1.9128) fix for bug-drug combinations

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dr. M.S. (Matthijs) Berends 2025-01-27 11:51:40 +01:00
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2
DESCRIPTION
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@ -1,5 +1,5 @@
Package: AMR
Version: 2.1.1.9127
Version: 2.1.1.9128
Date: 2025-01-27
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)

2
NEWS.md
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# AMR 2.1.1.9127
# AMR 2.1.1.9128
*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*

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PythonPackage/AMR/AMR.egg-info/PKG-INFO
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Metadata-Version: 2.2
Name: AMR
Version: 2.1.1.9127
Version: 2.1.1.9128
Summary: A Python wrapper for the AMR R package
Home-page: https://github.com/msberends/AMR
Author: Matthijs Berends

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PythonPackage/AMR/dist/AMR-2.1.1.9127-py3-none-any.whl → PythonPackage/AMR/dist/AMR-2.1.1.9128-py3-none-any.whl vendored
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PythonPackage/AMR/dist/amr-2.1.1.9127.tar.gz vendored
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PythonPackage/AMR/dist/amr-2.1.1.9128.tar.gz vendored Normal file
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PythonPackage/AMR/setup.py
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@ -2,7 +2,7 @@ from setuptools import setup, find_packages
setup(
name='AMR',
version='2.1.1.9127',
version='2.1.1.9128',
packages=find_packages(),
install_requires=[
'rpy2',

8
R/bug_drug_combinations.R
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@ -172,16 +172,16 @@ bug_drug_combinations <- function(x,
res
}
if (include_n_rows == FALSE) {
out <- out[, colnames(out)[colnames(out) != "total_rows"], drop = FALSE]
}
if (data_has_groups) {
out <- apply_group(x, "run_it", groups)
} else {
out <- run_it(x)
}
if (include_n_rows == FALSE) {
out <- out[, colnames(out)[colnames(out) != "total_rows"], drop = FALSE]
}
out <- out %pm>% pm_arrange(mo, ab)
out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
rownames(out) <- NULL

85
data-raw/gpt_training_text_v2.1.1.9127.txt → data-raw/gpt_training_text_v2.1.1.9128.txt
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This files contains all context you must know about the AMR package for R.
First and foremost, you are trained on version 2.1.1.9127. Remember this whenever someone asks which AMR package version you’re at.
First and foremost, you are trained on version 2.1.1.9128. Remember this whenever someone asks which AMR package version you’re at.
--------------------------------
THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@ -10044,48 +10044,6 @@ print(df)
* **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".
## 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`, `antibiotics`, `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.antibiotics
```
| 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 |
## Calculating AMR
```python
@ -10136,6 +10094,47 @@ print(result2b)
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`, `antibiotics`, `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.antibiotics
```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
| AMA | 4649.0 | 4-aminosalicylic acid| Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| ... | ... | ... | ... | ... | ... | ... | ... |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
# Conclusion
With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.