Introduction

The AMR package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the AMR Python package.

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). You can set up such an environment by running:

# 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, after which the venv is ready to work with.

Install AMR

1. Since the Python package is available on the official Python Package Index, you can just run:

   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:

   # Ubuntu / Debian
   sudo apt install r-base
   # Fedora:
   sudo dnf install R
   # CentOS/RHEL
   sudo yum install R

   For macOS (using Homebrew):

   brew install r

   For Windows, visit the CRAN download page to download and install R.

Examples of Usage

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)

import AMR
import pandas as pd

df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result)

[0.59555556]

result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
print(result2a)

result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
print(result2b)

AMR.microorganisms

AMR.antimicrobials

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.