Performance: Vectorize loops in MappedDataTimeProfile.R

[anthropic-code-agent]

Description

Optimize loops in R/MappedDataTimeProfile.R by replacing them with vectorized operations for improved performance.

Current Implementation Issues

Loop in scaleDataForSecondaryAxis() (Lines 252-263)

• Iterates through scaling-relevant mappings one by one
• Can be replaced with vectorized operations

Loop in dataForPlot() (Lines 323-345)

• Iterates through aesthetics checking for negative values
• Inefficient for datasets with many aesthetics
• Repeatedly accesses data frame columns in loop

Loop in checkForCallAesthetics() (Lines 365-389)

• Iterates through scaling-relevant mappings sequentially
• Similar pattern to scaleDataForSecondaryAxis()

Nested loop in setyLimits() (Lines 410-429)

• Iterates through aesthetics within axis loop
• Double iteration pattern that can be optimized

Suggested Implementation

1. scaleDataForSecondaryAxis(): Use lapply() or vapply() with vectorized scaling operations
2. dataForPlot(): Use vectorized column checking with any() and all() functions across aesthetics
3. checkForCallAesthetics(): Refactor to use list operations instead of explicit loops
4. setyLimits(): Flatten nested structure or use expand.grid() approach

Expected Benefits

• Faster time profile plot generation
• Better performance with large time-series datasets
• Reduced memory allocations during plotting

Implementation Notes

• Ensure secondary axis scaling remains accurate
• Verify log-scale handling works correctly after optimization
• Add unit tests for edge cases (negative values, empty data)
• Follow OSP R coding standards: https://dev.open-systems-pharmacology.org/r-development-resources/coding_standards_r

Files to Modify

• R/MappedDataTimeProfile.R

Testing

• Run existing time profile tests
• Test with various secondary axis configurations
• Verify scaling calculations remain accurate
• Test with large time-series datasets