R Code Review & Debugging — Up to 200 Lines of Code

I will review up to 200 lines of your R code and deliver a report covering bugs, inefficiencies, vectorisation opportunities, tidyverse vs. base R consistency issues, memory management problems, and reproducibility gaps — with specific line references and recommended fixes. R code written for correctness often contains performance anti-patterns that become critical when applied to larger datasets — for loops where vectorised operations should be used, repeated data loading inside functions, and object copies created unnecessarily by non-reference semantics.

The review covers logical correctness, silent coercion risks (numeric to character, factor to integer), NA handling completeness, ggplot2 theming consistency, function scoping issues, reproducibility (set.seed, sessionInfo), and package dependency management. Each finding is categorised by severity with a specific R code improvement.

This service suits R programmers, academic researchers, and data analysts preparing R scripts for production, publication supplementary material, or handover who want an expert review.