R debugging

R Debugging

Systematic workflow for diagnosing and fixing bugs in R code: REPRODUCE -> ISOLATE -> DIAGNOSE -> FIX -> TEST.

Ask upfront: Is this interactive script code or package code? The strategy differs -- package code has formal test infrastructure and namespacing; interactive scripts need ad-hoc reprex and manual verification.

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Step 1 -- Reproduce

Create a minimal, self-contained example that triggers the bug.

# Use reprex for shareable reproduction cases
reprex::reprex({
  library(dplyr)
  tibble(x = c(1, NA, 3)) |>
    mutate(y = x + 1)
})

Checklist:

• Strip to the smallest data and code that still fails
• Pin package versions with sessionInfo() or renv::snapshot()
• Confirm the error is deterministic (run 3x)

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Step 2 -- Isolate

Narrow down where the failure occurs.

Binary search through a pipeline: Comment out the bottom half of a pipe chain. If the error disappears, the bug is in the removed half. Recurse.

Insert browser() at suspect locations:

transform_data <- function(data) {
  data <- data |>
    filter(!is.na(value))
  browser()
  # Execution pauses here -- inspect `data` in the console
  data |>
    summarise(total = sum(value))
}

Inside browser(): n (next), s (step into), c (continue), Q (quit), ls() (list objects), str(obj) (inspect structure).

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Step 3 -- Diagnose

Use the right tool for the situation.

Call Stack Inspection

# After an error occurs:
traceback()

# For rlang-based errors (tidyverse), get a cleaner tree:
rlang::last_trace()

Read the stack bottom-up: the deepest frame is where the error was thrown; work upward to find your code's entry point.

Interactive Breakpoints

Quick reference: debug(fn) / debugonce(fn) to step through a function, trace(fn, tracer = browser, at = 4) to inject a breakpoint at a specific line, options(error = recover) to drop into browser on any error.

Read references/debugging-tools.md for the full breakpoint command reference, browser() commands table, and package-specific debugging patterns.

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Step 4 -- Fix

Apply the fix and verify against the original reprex.

1. Make the minimal change that addresses the root cause
2. Re-run the exact reprex from Step 1 -- error must be gone
3. Check for side effects: run devtools::check() for packages, or re-run the full script for interactive code

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Step 5 -- Test (Regression)

Write a test that would have caught this bug. Future changes must not re-introduce it.

test_that("transform_data handles NA values without error", {
  input <- tibble::tibble(value = c(1, NA, 3))
  result <- transform_data(input)
  expect_equal(result$total, 4)
})

For packages: place in tests/testthat/test-<relevant-file>.R. For scripts: create a lightweight test file or add to an existing test suite.

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Gotchas

Top traps to check first:

• Factor-to-numeric: as.numeric(factor_var) returns codes, not values — use as.numeric(as.character(x))
• NSE scoping: filter(col > 20) looks for column, not variable — use .data[[col]] for strings, {{ var }} for function args
• Vector recycling: x == c("A", "B") does element-wise recycling — use %in% for set membership
• NULL propagation: list$missing_element returns NULL silently — use [["key"]] with is.null() check
• Floating point: 0.1 + 0.2 == 0.3 is FALSE — use dplyr::near() or all.equal()

Read references/debugging-tools.md for the full gotchas table with detailed explanations, performance debugging tools (profvis, bench::mark), and common performance fixes.

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Dispatch

When the root cause is a code quality issue (anti-pattern, poor style, NSE misuse) rather than a logic bug, hand off to the r-code-reviewer agent for a focused review of the problematic code.

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Examples

Happy Path: Function returns wrong result -- isolate with browser()

Prompt: "My calc_growth() function returns NA for some groups. Help me debug it."

# Input — buggy function
calc_growth <- function(data) {
  data |>
    arrange(date) |>
    mutate(growth = (value - lag(value)) / lag(value) * 100, .by = group)
}

test_data <- tibble(
  group = c("A", "A", "B"),
  date  = as.Date(c("2024-01-01", "2024-02-01", "2024-01-01")),
  value = c(100, 120, 50)
)
calc_growth(test_data)
#> Group B has only 1 row -> lag() returns NA -> growth is NA

# Diagnose — insert browser() to inspect
calc_growth <- function(data) {
  data <- data |> arrange(group, date)
  browser()  # inspect: data |> count(group) reveals single-row groups
  data |> mutate(growth = (value - lag(value)) / lag(value) * 100, .by = group)
}

# Fix — guard against single-row groups
calc_growth <- function(data) {
  data |>
    arrange(group, date) |>
    mutate(
      growth = if (n() > 1) (value - lag(value)) / lag(value) * 100 else NA_real_,
      .by = group
    )
}

Edge Case: Vector recycling producing wrong results without error

Prompt: "My filter seems to work but returns wrong rows. No error at all."

# Input — subtle recycling bug
df <- tibble(id = 1:6, status = c("A", "B", "C", "A", "B", "C"))

# BAD: intended to keep A and B, but c("A", "B") recycles to match 6 rows
# R compares element-wise: row1=="A", row2=="B", row3=="A", row4=="B"...
df |> filter(status == c("A", "B"))
#> Returns 2 rows — wrong! Recycling matched positions (1,2), not values

# GOOD: use %in% for set membership
df |> filter(status %in% c("A", "B"))
#> Returns 4 rows — the correct 4 (all A and B rows)

# Defensive: always use %in% for multi-value comparisons
# If you must use ==, guard with stopifnot(length(x) == length(y))

More example prompts:

• "My dplyr pipeline throws 'object not found' for a column I know exists"
• "This function returns NULL instead of a data frame, but only sometimes"
• "My R script runs fine interactively but fails in Rscript --vanilla"
• "I'm getting different results on macOS vs Windows for string operations"
• "This summarise call is taking 30 seconds on 100k rows, help me profile it"