Project Impact

I designed, developed, and published a production-ready R package that solves critical challenges in environmental data collection and analysis.

The PurpleAirAPI package has been officially accepted to CRAN, demonstrating its adherence to rigorous software development standards and potential for widespread adoption in the R community.

Key Achievements

  • Successfully published to CRAN after meeting strict quality requirements
  • Implemented smart chunking algorithms that handle large-scale data retrieval efficiently
  • Developed comprehensive error handling that provides actionable feedback
  • Created extensive documentation and examples to facilitate adoption
  • Built with scalability in mind, supporting concurrent multi-sensor data collection

Technical Innovation

The PurpleAirAPI package introduces several innovative features that set it apart:

Advanced Data Management

  • Intelligent Request Handling: Automatically chunks large time periods into optimal API requests
  • Concurrent Processing: Downloads data from multiple sensors simultaneously
  • Automatic Rate Limiting: Prevents API throttling while maximizing throughput

Robust Architecture

  • Error Recovery: Graceful handling of network issues and API limitations
  • Data Validation: Comprehensive checks for data integrity and completeness
  • Progress Monitoring: Real-time feedback for long-running operations

User Experience

  • Flexible Query System: Supports all PurpleAir data fields with intuitive parameter selection
  • Smart Defaults: Optimized settings for common use cases
  • Clear Feedback: Informative progress updates and error messages

Implementation Details

Core Functionality

# Retrieve sensor metadata with custom fields
sensors <- getPurpleairSensors(
  apiReadKey = "YOUR_API_KEY",
  fields = c("latitude", "longitude", "date_created", "model")
)

# Download historical data with automatic chunking
history <- getSensorHistory(
  sensorIndex = sensor_ids,
  apiReadKey = "YOUR_API_KEY",
  startDate = "2024-01-01",
  endDate = "2024-01-31",
  average = 60,  # hourly averages
  fields = c("pm2.5_alt", "temperature", "humidity")
)

Technical Architecture

  • API Integration: RESTful communication using httr with custom headers
  • Data Processing: Efficient JSON parsing and dataframe operations
  • Error Handling: Comprehensive try-catch blocks with informative messages
  • Testing: Extensive unit tests covering edge cases and error conditions

Development Lifecycle

  1. Requirements Analysis

    • Identified gaps in existing solutions
    • Gathered user requirements from research community
    • Defined technical specifications

  2. Design & Implementation

    • Developed modular architecture
    • Implemented core functionality
    • Created comprehensive test suite

  3. Quality Assurance

    • Conducted extensive testing
    • Performed code reviews
    • Optimized performance

  4. Documentation & Distribution

    • Wrote detailed documentation
    • Created usage examples
    • Prepared CRAN submission

Real-World Applications

The package enables various environmental monitoring applications:

  • Research Projects: Facilitates large-scale air quality studies
  • Data Analysis: Streamlines environmental data processing
  • Monitoring Systems: Enables automated data collection
  • Policy Analysis: Supports evidence-based decision making

Technical Stack

  • Core Language: R 4.0+
  • Key Dependencies:
    • httr: API communication
    • jsonlite: Data parsing
    • testthat: Testing framework
    • roxygen2: Documentation generation

Resources

Installation

# From CRAN
install.packages("PurpleAirAPI")

# Development version
devtools::install_github("heba-razzak/PurpleAirAPI")

I welcome collaboration and feedback from the community. Feel free to reach out for questions, suggestions, or potential collaboration opportunities.

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