Add Breadth-First Search (BFS) algorithm implementation #[HACKTOBERFEST 2025]

DIRECTORY.md

@@ -35,6 +35,7 @@
   * [Lasso](https://github.com/TheAlgorithms/R/blob/HEAD/data_preprocessing/lasso.r)
 
 ## Graph Algorithms
+  * [Breadth First Search](https://github.com/TheAlgorithms/R/blob/HEAD/graph_algorithms/breadth_first_search.r)
   * [Depth First Search](https://github.com/TheAlgorithms/R/blob/HEAD/graph_algorithms/depth_first_search.r)
 
 ## Mathematics

graph_algorithms/breadth_first_search.r

@@ -0,0 +1,217 @@
+# Breadth-First Search (BFS) Algorithm
+#
+# BFS is a graph traversal algorithm that explores all vertices at the current depth
+# before moving to vertices at the next depth level. It uses a queue data structure.
+#
+# Time Complexity: O(V + E) where V is vertices and E is edges
+# Space Complexity: O(V) for the visited array and queue
+#
+# Input: An adjacency list representation of a graph and a starting vertex
+# Output: The order in which vertices are visited during BFS traversal
+
+# BFS function using queue (implemented with vector)
+breadth_first_search <- function(graph, start_vertex) {
+  # Get all vertices in the graph
+  all_vertices <- unique(c(names(graph), unlist(graph)))
+
+  # Initialize visited array and queue
+  visited <- rep(FALSE, max(all_vertices))
+  names(visited) <- 1:max(all_vertices)
+  queue <- c(start_vertex)
+  result <- c()
+
+  # Mark starting vertex as visited
+  visited[start_vertex] <- TRUE
+
+  while (length(queue) > 0) {
+    # Dequeue vertex from front of queue
+    vertex <- queue[1]
+    queue <- queue[-1]
+    result <- c(result, vertex)
+
+    # Add all unvisited neighbors to queue
+    if (as.character(vertex) %in% names(graph)) {
+      for (neighbor in graph[[as.character(vertex)]]) {
+        if (!visited[neighbor]) {
+          visited[neighbor] <- TRUE
+          queue <- c(queue, neighbor)
+        }
+      }
+    }
+  }
+
+  return(result)
+}
+
+# BFS to find shortest path between two vertices
+bfs_shortest_path <- function(graph, start_vertex, end_vertex) {
+  # Get all vertices in the graph
+  all_vertices <- unique(c(names(graph), unlist(graph)))
+
+  # Initialize visited array, queue, and parent tracking
+  visited <- rep(FALSE, max(all_vertices))
+  names(visited) <- 1:max(all_vertices)
+  parent <- rep(-1, max(all_vertices))
+  names(parent) <- 1:max(all_vertices)
+  queue <- c(start_vertex)
+
+  # Mark starting vertex as visited
+  visited[start_vertex] <- TRUE
+
+  while (length(queue) > 0) {
+    # Dequeue vertex from front of queue
+    vertex <- queue[1]
+    queue <- queue[-1]
+
+    # If we reached the target vertex, reconstruct path
+    if (vertex == end_vertex) {
+      path <- c()
+      current <- end_vertex
+
+      # Backtrack using parent array
+      while (current != -1) {
+        path <- c(current, path)
+        current <- parent[current]
+      }
+
+      return(list(
+        path = path,
+        distance = length(path) - 1
+      ))
+    }
+
+    # Add all unvisited neighbors to queue
+    if (as.character(vertex) %in% names(graph)) {
+      for (neighbor in graph[[as.character(vertex)]]) {
+        if (!visited[neighbor]) {
+          visited[neighbor] <- TRUE
+          parent[neighbor] <- vertex
+          queue <- c(queue, neighbor)
+        }
+      }
+    }
+  }
+
+  # No path found
+  return(list(
+    path = NULL,
+    distance = -1
+  ))
+}
+
+# BFS to find all vertices at a specific distance
+bfs_vertices_at_distance <- function(graph, start_vertex, target_distance) {
+  # Get all vertices in the graph
+  all_vertices <- unique(c(names(graph), unlist(graph)))
+
+  # Initialize visited array, queue with distances
+  visited <- rep(FALSE, max(all_vertices))
+  names(visited) <- 1:max(all_vertices)
+  queue <- list(list(vertex = start_vertex, distance = 0))
+  vertices_at_distance <- c()
+
+  # Mark starting vertex as visited
+  visited[start_vertex] <- TRUE
+
+  while (length(queue) > 0) {
+    # Dequeue vertex with distance from front of queue
+    current <- queue[[1]]
+    queue <- queue[-1]
+    vertex <- current$vertex
+    distance <- current$distance
+
+    # If current distance matches target, add to result
+    if (distance == target_distance) {
+      vertices_at_distance <- c(vertices_at_distance, vertex)
+    }
+
+    # Don't explore further if we've reached target distance
+    if (distance < target_distance) {
+      # Add all unvisited neighbors to queue
+      if (as.character(vertex) %in% names(graph)) {
+        for (neighbor in graph[[as.character(vertex)]]) {
+          if (!visited[neighbor]) {
+            visited[neighbor] <- TRUE
+            queue <- c(queue, list(list(vertex = neighbor, distance = distance + 1)))
+          }
+        }
+      }
+    }
+  }
+
+  return(vertices_at_distance)
+}
+
+# Example usage and testing
+cat("=== Breadth-First Search (BFS) Algorithm ===\n")
+
+# Create a sample graph as adjacency list
+# Graph structure:
+#     1
+#    / \
+#   2   3
+#  / \   \
+# 4   5   6
+graph <- list(
+  "1" = c(2, 3),
+  "2" = c(4, 5),
+  "3" = c(6),
+  "4" = c(),
+  "5" = c(),
+  "6" = c()
+)
+
+cat("Graph structure (adjacency list):\n")
+for (vertex in names(graph)) {
+  cat("Vertex", vertex, "-> [", paste(graph[[vertex]], collapse = ", "), "]\n")
+}
+
+# Test BFS traversal
+cat("\nBFS starting from vertex 1:\n")
+result <- breadth_first_search(graph, 1)
+cat("Traversal order:", paste(result, collapse = " -> "), "\n")
+
+# Test BFS from different starting vertex
+cat("\nBFS starting from vertex 2:\n")
+result_from_2 <- breadth_first_search(graph, 2)
+cat("Traversal order:", paste(result_from_2, collapse = " -> "), "\n")
+
+# Test shortest path finding
+cat("\n=== Shortest Path Finding ===\n")
+path_result <- bfs_shortest_path(graph, 1, 5)
+if (!is.null(path_result$path)) {
+  cat("Shortest path from 1 to 5:", paste(path_result$path, collapse = " -> "), "\n")
+  cat("Distance:", path_result$distance, "\n")
+} else {
+  cat("No path found from 1 to 5\n")
+}
+
+# Test vertices at specific distance
+cat("\n=== Vertices at Specific Distance ===\n")
+vertices_dist_2 <- bfs_vertices_at_distance(graph, 1, 2)
+cat("Vertices at distance 2 from vertex 1:", paste(vertices_dist_2, collapse = ", "), "\n")
+
+# Example with a more complex connected graph
+cat("\n=== Example with More Complex Graph ===\n")
+complex_graph <- list(
+  "1" = c(2, 3),
+  "2" = c(1, 4, 5),
+  "3" = c(1, 6),
+  "4" = c(2, 7),
+  "5" = c(2, 8),
+  "6" = c(3, 9),
+  "7" = c(4),
+  "8" = c(5),
+  "9" = c(6)
+)
+
+cat("Complex graph BFS starting from vertex 1:\n")
+complex_result <- breadth_first_search(complex_graph, 1)
+cat("Traversal order:", paste(complex_result, collapse = " -> "), "\n")
+
+# Test shortest path in complex graph
+path_complex <- bfs_shortest_path(complex_graph, 1, 9)
+if (!is.null(path_complex$path)) {
+  cat("Shortest path from 1 to 9:", paste(path_complex$path, collapse = " -> "), "\n")
+  cat("Distance:", path_complex$distance, "\n")
+}