Performance: Refactor high cyclomatic complexity functions

[anthropic-code-agent]

Description

Refactor functions with high cyclomatic complexity (exceeding 10) to improve maintainability, testability, and performance. Complex functions are harder to optimize and often contain redundant logic.

Current Implementation Issues

R/MappedData.R

initialize() method (Lines 42-138)

• Contains nested if statements with multiple initialization steps
• High complexity from conditional logic across ~96 lines
• Difficult to test individual initialization steps
• Suggested refactoring: Extract helper methods for each initialization phase

translateErrorAestethics() (Lines 352-420)

• Deeply nested logic with 4+ levels of nesting (lines 353-417)
• Complex conditional branches for error aesthetic translation
• Hard to understand and modify
• Suggested refactoring: Extract nested logic into separate validation and translation methods

R/MappedDataTimeProfile.R

scaleDataForSecondaryAxis() (Lines 138-287)

• Contains 4 major conditional branches (lines 184-245)
• Each branch has nested operations
• Spans ~149 lines with complex scaling logic
• Suggested refactoring: Split into separate methods for different scaling strategies

R/plotYVsX.R

plotYVsX() main function (Lines 179-447)

• Approximately 15 conditional branches
• Spans ~268 lines
• Handles multiple concerns: mapping, LLOQ, comparison lines, labels
• Suggested refactoring: Extract logical sections into helper functions

LLOQ handling section (Lines 255-373)

• Nested conditionals spanning ~118 lines
• Complex logic for lower limit of quantification
• Suggested refactoring: Extract to dedicated handleLLOQ() function

R/plotHistogram.R

plotHistogram() (Lines 25-168)

• Multiple conditional branches for distribution fitting
• Spans ~143 lines
• Handles plotting, binning, and distribution fitting in one function
• Suggested refactoring: Separate distribution fitting logic from plotting

Suggested Implementation Strategy

General Refactoring Approach

1. Extract Method: Break complex functions into smaller, focused helpers
2. Single Responsibility: Each function should do one thing well
3. Guard Clauses: Use early returns to reduce nesting
4. Strategy Pattern: For functions with multiple branches, consider strategy objects
5. Keep cyclomatic complexity under 10: Aim for 5-7 for optimal maintainability

Example Refactoring

Before:

processData <- function(data, option1, option2, option3) {
  if (option1) {
    if (option2) {
      # complex logic
    } else {
      # more complex logic
    }
  } else if (option3) {
    # even more logic
  }
  # ... continues for 100+ lines
}

After:

processData <- function(data, option1, option2, option3) {
  if (option1) return(handleOption1(data, option2))
  if (option3) return(handleOption3(data))
  return(defaultProcessing(data))
}

handleOption1 <- function(data, option2) {
  if (option2) return(processWithOption2(data))
  return(processWithoutOption2(data))
}

Expected Benefits

• Easier to understand and maintain
• Better testability (can test each helper independently)
• Potential for performance optimization in smaller functions
• Compiler/interpreter can better optimize simpler functions
• Reduced cognitive load for developers

Implementation Notes

• Start with most complex functions first
• Ensure extracted functions have clear, descriptive names
• Add unit tests for each extracted helper
• Use R6 private methods for class-specific helpers
• Document the purpose of each extracted function
• Follow OSP R coding standards: https://dev.open-systems-pharmacology.org/r-development-resources/coding_standards_r

Files to Modify

• R/MappedData.R
• R/MappedDataTimeProfile.R
• R/plotYVsX.R
• R/plotHistogram.R

Testing

• Run existing unit tests to ensure no regressions
• Add tests for newly extracted functions
• Verify plots render identically before and after refactoring
• Consider using code coverage tools to ensure all branches tested