Gamified Algorithmic and Data Wrangling Challenges for R

An in-console, gamified learning and practice engine for R. Solve algorithmic and data-wrangling challenges directly in the R console, get instant styled feedback with worked explanations, and track your progress locally with a solving streak, attempt history, and an activity heatmap.

rgrind

rgrind brings gamified, LeetCode-style algorithmic and data wrangling challenges directly into your R console. Solve puzzles against real test cases, get instant styled feedback with explanations, and build a daily solving streak, all running locally, with zero setup and zero cost.

Why rgrind?

Popular coding practice platforms (LeetCode, HackerRank, Codewars) barely support R. Existing R learning tools (like DataCamp) rely on passive video courses in a browser. rgrind is different: it is an active, in-console practice tool built specifically for R’s own idioms: vectorisation, the tidyverse, and statistical computing, with nothing to install beyond the package itself.

Installation

You can install the development version of rgrind from GitHub with:

# install.packages("pak")
pak::pak("DevWebWacky/rgrind")

Quick example

library(rgrind)

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.
#> 

Browse available challenges

list_challenges()
#>  [1] "avg_above"            "bootstrap_ci"         "count_missing"       
#>  [4] "count_na"             "first_duplicate"      "max_consecutive_ones"
#>  [7] "pivot_long_scores"    "remove_outliers"      "rolling_sum"         
#> [10] "sum_evens"

rgrind currently ships with 10 challenges across four categories: Base R Optimisation, Tidyverse Wrangling, Vectorisation Efficiency, and Statistical Algorithms, ranging from Easy warm-ups to Medium/Hard puzzles.

Track your progress

Every attempt is logged locally on your own machine, nothing is sent anywhere. Check your stats and keep your streak alive:

rg_stats()
#> 
#> ── Your rgrind Stats ───────────────────────────────────────────────────────────
#> ℹ Challenges solved: 1/10
#> ℹ Total attempts: 1
#> ℹ 🔥 Current streak: 1 day
#> ℹ 🏆 Longest streak: 1 day
rg_heatmap()
#> 
#> ── Last 28 Days
#> · · · · · · ·
#> · · · · · · ·
#> · · · · · · ·
#> · · · · · · ▪
#> 
#> · none ▪ 1 ▓ 2-3 █ 4+

License

MIT © Uwakmfon Paul