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LASER Modeling in R (2)

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Jonathan Bloedow edited this page Nov 20, 2024 · 1 revision

Writing Python for Native R Speakers

When talking about how developers who prefer Python can make their code more accessible to R users, we usually talk about packages like reticulate which let you run one language from another, or we talk about porting code. I'd like to approach this from another direction and ask if it's possible to write Python in a way that it's still Python but is highly accessible to R fans. This assumes that the differences in the language isn't just syntax but also to some degree mental models, design patterns, workflows and expectations.

I include a couple of complete interactions with ChatGPT:

Designing a Python disease modeling tool for an audience primarily familiar with R requires thoughtful consideration to bridge the gap between the two programming environments. This is intended to start a conversation, not set down final answers. Here are some design considerations to make your Python code accessible and intuitive for R users.

1. High-Level APIs with Defaults

  • R Expectation: R users are accustomed to high-level, user-friendly functions with sensible default parameters.
  • Python Implementation:
    • Provide intuitive wrapper functions for your core functionalities, mimicking the simplicity of R’s function calls.
    • Use meaningful default arguments that align with common use cases to minimize the need for parameter tweaking.
def run_simulation(
    population_size=1000, 
    initial_infected=10, 
    timesteps=100, 
    verbose=False
):
    """Run a disease simulation with default settings."""
    # Function body here

2. Familiar Function Names and Argument Order

  • R Expectation: R users are used to expressive and descriptive function names and arguments, often in camelCase or snake_case.
  • Python Implementation:
    • Adopt snake_case for function names and arguments, as it aligns with both Python and modern R conventions.
    • Provide argument names that are descriptive and meaningful.
# R users will expect:
simulate_disease(population_size = 1000, timesteps = 100)

3. Data Structures

  • R Expectation: R users work with data frames, matrices, and vectors.
  • Python Implementation:
    • Make extensive use of pandas DataFrames for tabular data and NumPy arrays for numeric computations.
    • Ensure outputs resemble data frames wherever possible, and document how to manipulate these structures.
# Example of a function returning a DataFrame
import pandas as pd

def get_population_summary(population):
    return pd.DataFrame({
        "Age Group": ["<5", "5+"],
        "Susceptible": [100, 200],
        "Infected": [10, 20],
        "Recovered": [5, 15]
    })

4. Clear Documentation with Examples

  • R Expectation: Comprehensive documentation is provided with examples and default workflows.
  • Python Implementation:
    • Use docstrings extensively with examples that are beginner-friendly.
    • Include Jupyter notebooks or markdown files demonstrating typical use cases.
def calculate_force_of_infection(infected, susceptible, contact_rate):
    """
    Calculate the force of infection based on SIR dynamics.

    Parameters:
    infected (int): Number of infected individuals.
    susceptible (int): Number of susceptible individuals.
    contact_rate (float): Rate of contact between individuals.

    Returns:
    float: Force of infection.

    Example:
    >>> calculate_force_of_infection(10, 100, 0.05)
    0.5
    """

5. Visualization

  • R Expectation: R excels at visualization (e.g., ggplot2).
  • Python Implementation:
    • Use Matplotlib or Seaborn for graphing and make plotting APIs simple.
    • Provide wrapper functions for common visualizations, similar to ggplot2 defaults.
def plot_infection_trend(data):
    """
    Plot infection trends over time.

    Parameters:
    data (DataFrame): A pandas DataFrame with 'time', 'susceptible', 'infected', and 'recovered' columns.

    Example:
    >>> plot_infection_trend(simulation_data)
    """
    import matplotlib.pyplot as plt
    plt.plot(data['time'], data['infected'], label='Infected')
    plt.xlabel('Time')
    plt.ylabel('Number of Infected')
    plt.legend()
    plt.show()

6. Clear Installation and Dependencies

  • R Expectation: R users are accustomed to installing packages with a single install.packages() command.
  • Python Implementation:
    • Provide a setup.py or pyproject.toml with a simple installation process via pip.
    • Minimize dependencies or provide explicit installation instructions for required Python libraries.

7. Use R-Friendly Interoperability Libraries

  • Leverage R and Python integration tools:
    • rpy2: Allow R users to call your Python tool from R seamlessly.
    • Generate outputs in R-compatible formats like .csv or .rds.

8. User Guides for Transitioning

  • Provide a section in your documentation specifically tailored for R users, including:
    • Common R-to-Python equivalencies (e.g., dplyr vs. pandas).
    • FAQs on using Python concepts like lists and DataFrames.

9. Additive vs. Destructive Workflow

  • R Expectation: R users often work interactively and incrementally modify data (e.g., chaining functions with %>%).
  • Python Implementation:
    • Use method chaining or functional pipelines where appropriate to mimic this style.
    • Make sure functions return new objects instead of modifying inputs directly.
# Example with method chaining
result = (dataframe
          .filter(condition)
          .groupby(column)
          .aggregate(metric))

10. CLI Options

  • Provide a command-line interface (CLI) for simple workflows, which R users might find akin to running R scripts.
    • Use argparse or Click for robust CLI functionality.
    • Allow users to run models with a single command.
$ python -m disease_model.run_simulation --population-size 1000 --timesteps 200

By prioritizing these considerations, your Python disease modeling tool can feel more intuitive and accessible for an audience familiar with R.

When designing a Python tool for R users, the choice between object-oriented (OO) and functional programming (FP) patterns is critical, as it influences usability and alignment with user expectations. Here are some considerations for each approach, along with recommendations for striking a balance:

Object-Oriented Programming (OO)

Advantages:

  1. State Management:

    • OO makes it easy to encapsulate the state of your model (e.g., population, infection rates) within objects.
    • Example: A DiseaseModel class can hold parameters and simulation results, making it intuitive to manage large systems.

  2. Extensibility:

    • OO allows users to extend functionality by subclassing or overriding methods, which may appeal to users with programming experience.

  3. Natural Mapping to Domain Concepts:

    • Epidemiological concepts like "Population," "Agent," and "Transmission Model" naturally fit into classes, helping with clarity and modularity.

Challenges for R Users:

  • R users are generally more accustomed to functional patterns and may find OO abstractions unfamiliar or unnecessarily complex.

Functional Programming (FP)

Advantages:

  1. Alignment with R Patterns:

    • R emphasizes functional programming: functions take inputs, return outputs, and avoid state mutation. This is familiar and appealing to R users.
    • Example: A function like simulate_timestep() taking the current state as input and returning the next state would feel natural to R users.

  2. Transparency:

    • FP makes workflows explicit, with less "hidden" state compared to OO patterns.

  3. Composability:

    • Users can chain or compose functions easily, mimicking the behavior of R’s %>% operator or tidyverse workflows.

Challenges for Python Design:

  • Large-scale systems may become unwieldy without some encapsulation or grouping of related functions and data.

Recommendations: Striking a Balance

  1. Use OO for the Core Framework, FP for the Interface:

    • Implement the core simulation engine using OO patterns to organize state and encapsulate complexity.
    • Provide functional APIs that abstract away the OO details for end-users.

    Example:

    # OO Core
class DiseaseModel:
    def __init__(self, population_size, contact_matrix):
        self.population_size = population_size
        self.contact_matrix = contact_matrix
        self.state = None

    def run_timestep(self):
        # Update state
        pass

# FP Interface
def run_simulation(population_size, contact_matrix, timesteps):
    model = DiseaseModel(population_size, contact_matrix)
    for _ in range(timesteps):
        model.run_timestep()
    return model.state

  2. Explicit State for FP-Style Workflows:

    • For FP, ensure that every function takes explicit state inputs (e.g., a dictionary or DataFrame) and returns new states, avoiding hidden mutations.

    Example:

    def simulate_timestep(state, contact_matrix):
    # Compute next state based on inputs
    next_state = state.copy()  # Avoid mutation
    # Update logic
    return next_state

  3. Provide Helper Functions for R-Like Workflows:

    • Include utilities for chaining or composing workflows that resemble R’s %>% pipe operator or tidyverse workflows.

    Example with pandas:

    def prepare_population(data):
    return data.assign(susceptible=lambda x: x['total_population'] - x['infected'])

def calculate_infections(data, contact_rate):
    return data.assign(new_infections=lambda x: x['susceptible'] * contact_rate)

# R-like chaining in Python
result = (population_data
          .pipe(prepare_population)
          .pipe(calculate_infections, contact_rate=0.05))

  4. Expose a Simple Functional Interface for Users:

    • The primary user-facing API should be functional, focusing on inputs and outputs, while the OO implementation remains under the hood.

    Example:

    def run_model(initial_population, contact_matrix, timesteps):
    # Internally uses an OO implementation
    pass

  5. Document the OO Approach for Advanced Users:

    • While most R users will prefer the functional API, provide clear documentation for the OO design so that advanced users can leverage it for customization.

Design Decision Summary

Pattern Use Cases Audience
Object-Oriented Core framework, extensibility, state management Advanced users or developers
Functional User-facing interface, transparency, and simplicity General audience (R users)

By designing with this hybrid approach, your tool can offer the extensibility and structure of OO while providing the simplicity and accessibility of FP for most R users.

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