Selecting a row with alternatives in R

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Summary

Issue: Duplicated lab results for subjects with both hb and hb_urg entries in the dataset.
Goal: Retain only hb_urg for subjects with both results, while keeping single entries as-is.

Root Cause

  • Duplicate entries for subjects with both hb and hb_urg lab results.
  • Lack of filtering logic to prioritize hb_urg over hb when both exist.

Why This Happens in Real Systems

  • Data collection inconsistencies: Multiple systems or methods may record similar data with slight variations (e.g., hb vs hb_urg).
  • Missing deduplication steps: Data pipelines often lack rules to handle overlapping or alternative entries.

Real-World Impact

  • Data redundancy: Increased storage and processing overhead.
  • Analysis errors: Duplicates can skew statistical results or machine learning models.
  • Operational inefficiency: Manual cleanup required before analysis.

Example or Code (if necessary and relevant)

library(dplyr)

dd <- read.table(text = "id lab value
                      1 hb 13
                      1 hb_urg 14
                      2 hb 12
                      2 hb_urg 13
                      3 hb 13
                      4 hb 13
                      5 hb 12", header = TRUE)

result %
  group_by(id) %>%
  arrange(desc(lab == "hb_urg")) %>%  # Prioritize hb_urg
  distinct(id, .keep_all = TRUE) %>%
  ungroup()

print(result)

How Senior Engineers Fix It

  • Use data wrangling libraries (e.g., dplyr in R) for efficient filtering and deduplication.
  • Implement prioritization rules to handle alternative entries programmatically.
  • Automate data validation to catch duplicates early in the pipeline.

Why Juniors Miss It

  • Lack of domain knowledge: Unaware of the significance of alternative lab codes.
  • Overlooking grouping logic: Fail to group by id before applying filtering rules.
  • Manual cleanup attempts: Rely on ad-hoc methods instead of scalable code solutions.

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