Situations where R strips attributes for S3 classes, and how to work around or avoid them?

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Summary

R’s S3 system can silently strip attributes (including class) when using certain functions like ifelse(). This is a common issue when creating custom S3 classes, especially for specialized data types like tiny p-values. Key takeaway: Understand which functions strip attributes and use defensive programming to prevent silent failures.

Root Cause

  • S3 objects rely on attributes (e.g., class, log10p) for their behavior.
  • Functions like ifelse() coerce inputs to vectors, stripping attributes in the process.
  • R’s generic functions may not always preserve attributes unless explicitly programmed to do so.

Why This Happens in Real Systems

  • Performance optimizations: Some functions prioritize speed over attribute preservation.
  • Lack of awareness: Users may not realize functions like ifelse() strip attributes.
  • Design choice: S3 is lightweight but leaves attribute handling to the developer.

Real-World Impact

  • Data corruption: Loss of class/attributes leads to incorrect behavior.
  • Debugging difficulty: Silent failures make issues hard to trace.
  • Package usability: Custom classes break unexpectedly for users.

Example or Code (if necessary and relevant)

make_tinypval <- function(txt_pvals) {
  structure(txt_pvals, log10p = log10(as.numeric(txt_pvals)), class = "tinypval")
}

A <- make_tinypval(c("4e-40", "3.6e-150", "1.9e-250"))
B <- make_tinypval(c("1.5e-90", "2.1e-230", "9.5e-60"))
C <- ifelse(c(F, F, T), A, B)
attributes(C)  # NULL

How Senior Engineers Fix It

  • Use attribute-preserving alternatives: Replace ifelse() with dplyr::if_else() or data.table::fifelse().
  • Implement custom generics: Define methods for risky functions to preserve attributes.
  • Defensive programming: Add checks to fail gracefully if attributes are stripped.
  • Documentation: Warn users about incompatible functions and provide workarounds.

Why Juniors Miss It

  • Assumption of attribute preservation: Beginners assume R always keeps attributes.
  • Lack of S3 experience: Unfamiliarity with how S3 objects are handled internally.
  • Overlooking edge cases: Focus on functionality without testing edge cases like ifelse().

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