Run an rgrind puzzle against your own solution

Tests a user-submitted function against a challenge's test cases, printing styled pass/fail feedback to the console. On a full pass, shows an explanation of the idiomatic solution and updates your local solving streak. On any failure, shows details for the failed tests and a hint.

Usage

run_challenge(challenge_id, user_fun)

Arguments

challenge_id: Character. The id of the challenge to run. See list_challenges() for all available ids.
user_fun: Function. Your own solution to test — written and defined in your own R session, then passed in directly.

Value

Invisibly, a list with passed (number of test cases passed) and total (total number of test cases).

Examples

old_opt <- options(rgrind.storage_dir = tempdir())
my_solution <- function(x) sum(x[x %% 2 == 0], na.rm = TRUE)
run_challenge("sum_evens", my_solution)
#> 
#> ── Sum of Even Numbers ─────────────────────────────────────────────────────────
#> Base R Optimisation • Easy
#> 
#> ────────────────────────────────────────────────────────────────────────────────
#> ✔ All 7 tests passed!
#> 🔥 Current streak: 1 day
#> 
#> 
#> ── Explanation 
#> Idiomatic solution: sum(x[x %% 2 == 0], na.rm = TRUE) This avoids a for-loop
#> entirely by using R's vectorised modulo operator to build a logical mask, then
#> subsetting. This is roughly 50-100x faster than a for-loop for large vectors
#> because R's C-level vectorised operations avoid per-element interpreter
#> overhead.
#> 
options(old_opt)