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Games & Puzzles

Bernardo Lares

2026-04-23

Introduction

Beyond data analysis and machine learning, lares includes a collection of fun game solvers and helpers! Challenge yourself with Wordle, Scrabble, Sudoku, mazes…

library(lares)

Wordle

Play or solve Wordle puzzles with validation, hints, and simulations.

Basic Wordle Validation

# Check your guess against a word
wordle_check("OPENS", "ABBEY")
#> O P E N S

wordle_check("BABES", "ABBEY")
#> B A B E S

wordle_check("ABBEY", "ABBEY")
#> A B B E Y

Get Wordle Hints

Use scrabble_words() to find possible words:

# After OPENS: O is not in word, P/E/N/S are not in positions 2/3/4/5
hints <- scrabble_words(
  tiles = "abcdefghijklmrtuvwxyz", # Available letters
  exclude_here = list("2" = "p", "3" = "e", "4" = "n", "5" = "s"),
  force_exclude = c("o"),
  force_n = 5, # 5-letter words
  language = "en"
)
#> 274869 > 145893 > 8857 > 8857 > 1604 > 1604 > 1604 > 1604 > 1577 > 1577 > 1577 > 1577 
#> 

head(hints, 10)
#> # A tibble: 10 × 3
#>    word  scores length
#>    <chr>  <int>  <int>
#>  1 jacky     23      5
#>  2 jumby     23      5
#>  3 mujik     22      5
#>  4 zymic     22      5
#>  5 jambu     21      5
#>  6 jaxie     21      5
#>  7 avyze     20      5
#>  8 bwazi     20      5
#>  9 furzy     20      5
#> 10 jakey     20      5

Wordle Simulation

Simulate solving a Wordle puzzle:

# Simulate solving with different starting words
simulation <- wordle_simulation(
  input = "SAINT",
  word = "ABBEY",
  seed = 123
)
#> S A I N T reduced from 14,855 to 946

#> C L A M P reduced from 946 to 195

#> H O O K A reduced from 195 to 72

#> A R G U E reduced from 72 to 6

#> A D D E D reduced from 6 to 1

#> A B B E Y reduced from 1 to 1

print(simulation)
#> Seed Word: SAINT
#> Objective Word: ABBEY
#> Iterations: 1
#>   Mean to succeed: 6
#>   Max to succeed: 6 [seed = 1]

Wordle Dictionary

Access word lists for different languages:

# Get English 5-letter words
en_words <- wordle_dictionary("en")
head(en_words, 20)

# Spanish words
es_words <- wordle_dictionary("es")

Scrabble

Maximize your Scrabble score with word finders and calculators!

Find Highest-Scoring Words

# Find best words from your tiles
scrabble_words(
  tiles = "aeiourtn",
  force_max = 8, # Max 8 letters
  language = "en",
  scores = "en"
)
#> 115977 > 115977 > 115977 > 115977 > 293 > 293 > 293 > 293 
#> 

#> # A tibble: 293 × 3
#>    word    scores length
#>    <chr>    <int>  <int>
#>  1 outearn      9      7
#>  2 rainout      9      7
#>  3 routine      9      7
#>  4 ruinate      9      7
#>  5 taurine      9      7
#>  6 uranite      9      7
#>  7 urinate      9      7
#>  8 aunter       8      6
#>  9 auntie       8      6
#> 10 nature       8      6
#> # ℹ 283 more rows

With Board Constraints

# Must contain specific letters
scrabble_words(
  tiles = "bernardo",
  force_str = "arn",
  force_max = 7,
  language = "en"
)
#> 115977 > 115977 > 115977 > 75329 > 245 > 245 > 245 > 6 > 6 
#> 

#> # A tibble: 6 × 3
#>   word   scores length
#>   <chr>   <int>  <int>
#> 1 barned     11      6
#> 2 barn        8      4
#> 3 darner      8      6
#> 4 dearn       7      5
#> 5 darn        6      4
#> 6 earn        5      4

Calculate Word Scores

# Get point values for each letter
en_scores <- scrabble_points("en")
print(en_scores)
#>    tiles scores
#> 1      a      1
#> 2      b      4
#> 3      c      4
#> 4      d      2
#> 5      e      1
#> 6      f      4
#> 7      g      3
#> 8      h      3
#> 9      i      1
#> 10     j     10
#> 11     k      5
#> 12     l      2
#> 13     m      4
#> 14     n      2
#> 15     o      1
#> 16     p      4
#> 17     q     10
#> 18     r      1
#> 19     s      1
#> 20     t      1
#> 21     u      2
#> 22     v      5
#> 23     w      4
#> 24     x      8
#> 25     y      3
#> 26     z     10

# Calculate scores for words
words <- c("QUEEN", "QUIZ", "HELLO")
scrabble_score(words, en_scores)
#>    word scores length
#> 1 QUEEN      0      5
#> 2  QUIZ      0      4
#> 3 HELLO      0      5

Multi-Language Support

# Spanish Scrabble
scrabble_words(
  tiles = "españa",
  language = "es",
  scores = "es"
)

# French Scrabble
scrabble_words(
  tiles = "bonjour",
  language = "fr",
  scores = "fr"
)

Sudoku

Solve Sudoku puzzles automatically!

Simple Sudoku

# Easy puzzle (0 represents empty cells)
trivial <- matrix(c(
  0, 9, 0, 7, 0, 0, 8, 6, 0,
  0, 3, 1, 0, 0, 5, 0, 2, 0,
  8, 0, 6, 0, 0, 0, 0, 0, 0,
  0, 0, 7, 0, 5, 0, 0, 0, 6,
  0, 0, 0, 3, 0, 7, 0, 0, 0,
  5, 0, 0, 0, 1, 0, 7, 0, 0,
  0, 0, 0, 0, 0, 0, 1, 0, 9,
  0, 2, 0, 6, 0, 0, 3, 5, 0,
  0, 5, 4, 0, 0, 8, 0, 7, 0
), nrow = 9, byrow = TRUE)

solution <- sudoku_solver(trivial)
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#>  [1,]    2    9    5    7    4    3    8    6    1
#>  [2,]    4    3    1    8    6    5    9    2    7
#>  [3,]    8    7    6    1    9    2    5    4    3
#>  [4,]    3    8    7    4    5    9    2    1    6
#>  [5,]    6    1    2    3    8    7    4    9    5
#>  [6,]    5    4    9    2    1    6    7    3    8
#>  [7,]    7    6    3    5    2    4    1    8    9
#>  [8,]    9    2    8    6    7    1    3    5    4
#>  [9,]    1    5    4    9    3    8    6    7    2
print(solution)
#> [1] TRUE

Complex Sudoku

# Harder puzzle
difficult <- matrix(c(
  5, 3, 0, 0, 7, 0, 0, 0, 0,
  6, 0, 0, 1, 9, 5, 0, 0, 0,
  0, 9, 8, 0, 0, 0, 0, 6, 0,
  8, 0, 0, 0, 6, 0, 0, 0, 3,
  4, 0, 0, 8, 0, 3, 0, 0, 1,
  7, 0, 0, 0, 2, 0, 0, 0, 6,
  0, 6, 0, 0, 0, 0, 2, 8, 0,
  0, 0, 0, 4, 1, 9, 0, 0, 5,
  0, 0, 0, 0, 8, 0, 0, 7, 9
), nrow = 9, byrow = TRUE)

sudoku_solver(difficult)

Maze Solver

Solve mazes using depth-first search algorithms!

Basic Maze

# Create a simple maze (0 = path, 1 = wall)
simple_maze <- matrix(c(
  0, 1, 0, 0, 0,
  0, 1, 0, 1, 0,
  0, 0, 0, 1, 0,
  1, 1, 0, 0, 0,
  0, 0, 0, 1, 0
), nrow = 5, byrow = TRUE)

solution <- maze_solve(
  simple_maze,
  start = c(1, 1),
  end = c(5, 5)
)
#> Setup: Inertia (FALSE) | Aim (TRUE) | Random (FALSE)
#>   Total steps:  6 
#>   Total turns:  4 
#> 
#>    1  2 3  4 5
#> 1  ↓ []       
#> 2  ↘ []   []  
#> 3     ↘   []  
#> 4 [] [] →  ↘  
#> 5         [] X
print(solution)
#> Setup: Inertia (FALSE) | Aim (TRUE) | Random (FALSE)
#>   Total steps:  6 
#>   Total turns:  4 
#> 
#>    1  2 3  4 5
#> 1  ↓ []       
#> 2  ↘ []   []  
#> 3     ↘   []  
#> 4 [] [] →  ↘  
#> 5         [] X

Micromouse Competition Maze

# Classic Micromouse-style maze
micromouse <- matrix(c(
  1, 1, 1, 1, 1, 1, 1, 1,
  1, 0, 0, 0, 0, 0, 0, 1,
  1, 0, 1, 1, 1, 1, 0, 1,
  1, 0, 1, 0, 0, 0, 0, 1,
  1, 0, 1, 0, 1, 1, 0, 1,
  1, 0, 0, 0, 0, 1, 0, 1,
  1, 0, 1, 1, 0, 0, 0, 1,
  1, 1, 1, 1, 1, 1, 1, 1
), nrow = 8, byrow = TRUE)

maze_solve(
  micromouse,
  start = c(2, 2),
  end = c(7, 7),
  diagonal = FALSE
)

Advanced Options

# With diagonal movement and aiming toward goal
maze_solve(
  micromouse,
  start = c(2, 2),
  end = c(7, 7),
  diagonal = TRUE, # Allow diagonal moves
  aim = TRUE, # Prefer directions toward goal
  inertia = TRUE, # Prefer continuing in same direction
  timeout = 5 # Max 5 seconds
)

Combining Games & Data Science

Use game functions for:

Text Analysis:

# Find anagrams in your dataset
words <- c("listen", "silent", "hello")
scrabble_words(tiles = "listen", language = "en")

Algorithm Teaching:

# Demonstrate recursion with maze solving
maze_solve(simple_maze, start = c(1, 1), end = c(5, 5))

Pattern Recognition:

# Wordle simulations for optimal starting words
seeds <- 1:100
results <- lapply(seeds, function(s) {
  wordle_simulation("SAINT", "ABBEY", seed = s, quiet = TRUE)
})

Further Reading

Package Resources

Game References

Next Steps

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.