R-
R Performance
R performance optimization using vectorization, data.table, and profiling tools
Details
Language / Topic
RCategory
Performance
Rules
balanced
- Prefer vectorized operations over `for` loops — use `apply`, `vapply`, or `purrr::map` with explicit `.f` return types.
- Use `data.table` with `:=` in-place assignment and `[, .(col), by = group]` syntax for large dataset aggregations — it is significantly faster than `dplyr` at scale.
- Profile with `profvis::profvis(expr)` before optimizing — visualize the flame graph to identify actual bottlenecks.
- Pre-allocate vectors with `vector("numeric", n)` before filling in loops — growing vectors with `c()` inside a loop causes O(n^2) copying.
- Use `Rcpp` for computational bottlenecks that cannot be vectorized — write the hot path in C++ and call it from R.
- Use `bench::mark()` to compare alternative implementations with proper warm-up — it reports median time and memory allocations.
- Use `parallel::mclapply` or `future.apply::future_lapply` for embarrassingly parallel workloads — avoid forking in Windows environments.