Performance: Optimize repeated regex and string operations

[anthropic-code-agent]

Description

Optimize repeated pattern matching and string operations by caching results, reducing redundant regex calls, and using more efficient string manipulation methods.

Current Implementation Issues

R/MappedData.R - Nested gsub() calls

Lines 153-155: Nested string replacements

• Pattern: gsub("_x", "", gsub("_y", "", ...))
• Creates intermediate strings unnecessarily
• Can be combined into single regex or cached

Line 464: gsub() in loop

• gsub("y", private$direction, ...) called repeatedly in loop
• Same pattern replacement done multiple times
• Should cache result or move outside loop

R/utilities-defaults.R - Repeated pattern replacements

Lines 322-327: Multiple gsub() calls

• gsub() called multiple times with pattern "ospsuite.plots.geom"
• Same pattern used repeatedly
• Could compile regex once or use string constants

R/utilities_export.R - Sequential grepl() calls

Lines 166-171: Multiple pattern checks

• Multiple grepl() calls in sequence for pattern matching
• Each call scans the entire string
• Could combine patterns or use single regex with alternatives

Line 327: gsub() in loop

• Character replacement inside loop
• Same substitution applied repeatedly

R/utilities.R - Redundant string operations

Lines 177-180: Multiple trimws() calls

• trimws(label) called twice
• trimws(unit) called twice
• Should cache trimmed values

Suggested Implementation

1. Cache String Operations

Before:

for (item in items) {
  cleaned <- gsub("pattern", "replacement", item)
  process(cleaned)
}

After:

cleaned_items <- gsub("pattern", "replacement", items)  # Vectorized
for (cleaned in cleaned_items) {
  process(cleaned)
}

2. Combine Multiple Patterns

Before:

if (grepl("pattern1", text)) { }
if (grepl("pattern2", text)) { }

After:

if (grepl("pattern1|pattern2", text)) { }

3. Pre-compile Regex (for R >= 4.1)

Before:

for (text in texts) {
  matched <- grepl("complex.*pattern", text)
}

After:

pattern <- gregexpr("complex.*pattern", texts)  # Vectorized

4. Use stringi for Performance

Consider using stringi package for complex string operations (much faster than base R for large datasets).

Expected Benefits

• Faster string processing (up to 10x for large datasets)
• Reduced redundant regex compilation
• Lower CPU usage
• Better scalability for text-heavy operations

Implementation Notes

• Vectorize string operations when possible
• Cache results of expensive string operations
• For complex patterns, consider pre-compiling (R >= 4.1)
• Profile before and after to measure improvement
• Follow OSP R coding standards: https://dev.open-systems-pharmacology.org/r-development-resources/coding_standards_r

Files to Modify

• R/MappedData.R
• R/utilities-defaults.R
• R/utilities_export.R
• R/utilities.R

Testing

• Run existing unit tests
• Add tests for edge cases in string operations
• Verify all pattern replacements work correctly
• Test with special characters and Unicode
• Consider performance benchmarks for large datasets