R

Avoid common R mistakes — vectorization traps, NA propagation, factor surprises, and indexing gotchas.

Vectorization

• Loops are slow — use apply(), lapply(), sapply(), or purrr::map()
• Vectorized functions operate on whole vectors — sum(x) not for (i in x) total <- total + i
• ifelse() is vectorized — if is not, use ifelse() for vector conditions
• Column operations faster than row — R is column-major

Indexing Gotchas

• R is 1-indexed — first element is x[1], not x[0]
• x[0] returns empty vector — not error, silent bug
• Negative index excludes — x[-1] removes first element
• [[ extracts single element — [ returns subset (list stays list)
• df[, 1] drops to vector — use df[, 1, drop = FALSE] to keep data frame

NA Handling

• NA propagates — 1 + NA is NA, NA == NA is NA
• Use is.na() to check — not x == NA
• Most functions need na.rm = TRUE — mean(x) returns NA if any NA present
• na.omit() removes rows with any NA — may lose data unexpectedly
• complete.cases() returns logical vector — rows without NA

Factor Traps

• Old R converted strings to factors by default — use stringsAsFactors = FALSE or modern R
• levels() shows categories — but factor values are integers internally
• Adding new value not in levels gives NA — use factor(x, levels = c(old, new))
• as.numeric(factor) gives level indices — use as.numeric(as.character(factor)) for values
• Dropping unused levels: droplevels() — or factor() again

Recycling

• Shorter vector recycled to match longer — c(1,2,3) + c(10,20) gives 11, 22, 13
• No error if lengths aren't multiples — just warning, easy to miss
• Single values recycle intentionally — x + 1 adds 1 to all elements

Data Frames vs Tibbles

• Tibble never converts strings to factors — safer defaults
• Tibble never drops dimensions — df[, 1] stays tibble
• Tibble prints better — shows type, doesn't flood console
• as_tibble() to convert — from tibble or dplyr package

Assignment

• <- is idiomatic R — = works but avoided in style guides
• <<- assigns to parent environment — global assignment, usually a mistake
• -> right assignment exists — rarely used, confusing

Scope

• Functions look up in parent environment — can accidentally use global variable
• Local variable shadows global — same name hides outer variable
• local() creates isolated scope — variables don't leak out

Common Mistakes

• T and F can be overwritten — use TRUE and FALSE always
• 1:length(x) fails on empty x — gives c(1, 0), use seq_along(x)
• sample(5) vs sample(c(5)) — different! first gives 1:5 permutation
• String splitting: strsplit() returns list — even for single string