Join multiple dataset

clustering_algorithms/optics.r

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+# OPTICS (Ordering Points To Identify the Clustering Structure)
+#
+# OPTICS is a density-based clustering algorithm that extends DBSCAN by creating
+# an augmented ordering of the database representing its density-based clustering
+# structure. Unlike DBSCAN, it produces a reachability plot that can be used to
+# extract clusters at different density thresholds.
+#
+# Time Complexity: O(n^2) for distance matrix, O(n log n) with spatial indexing
+# Space Complexity: O(n^2) for distance matrix
+#
+# Input: data matrix/dataframe, eps (maximum radius), minPts (minimum neighbors)
+# Output: ordered points with reachability distances and core distances
+
+optics <- function(data, eps = 0.5, minPts = 5) {
+  # Convert to matrix
+  if (is.data.frame(data)) {
+    data <- as.matrix(data)
+  }
+
+  n <- nrow(data)
+
+  # Calculate pairwise distances
+  dist_matrix <- as.matrix(dist(data, method = "euclidean"))
+
+  # Initialize result vectors
+  reachability <- rep(Inf, n)
+  core_dist <- rep(Inf, n)
+  processed <- rep(FALSE, n)
+  ordered_points <- integer(0)
+  cluster_id <- rep(0, n)
+
+  # Calculate core distance for each point
+  for (i in 1:n) {
+    neighbors <- which(dist_matrix[i, ] <= eps)
+    neighbors <- neighbors[neighbors != i]
+
+    if (length(neighbors) >= minPts) {
+      neighbor_dists <- sort(dist_matrix[i, neighbors])
+      core_dist[i] <- neighbor_dists[minPts]
+    }
+  }
+
+  # Priority queue implementation using simple sorting
+  update_seeds <- function(seeds, neighbors, center_idx) {
+    c_dist <- core_dist[center_idx]
+
+    for (neighbor in neighbors) {
+      if (processed[neighbor]) next
+
+      new_reach_dist <- max(c_dist, dist_matrix[center_idx, neighbor])
+
+      if (reachability[neighbor] == Inf) {
+        reachability[neighbor] <<- new_reach_dist
+        seeds <- c(seeds, neighbor)
+      } else if (new_reach_dist < reachability[neighbor]) {
+        reachability[neighbor] <<- new_reach_dist
+      }
+    }
+
+    # Sort seeds by reachability distance
+    if (length(seeds) > 0) {
+      seeds <- seeds[order(reachability[seeds])]
+    }
+
+    return(seeds)
+  }
+
+  # Main OPTICS algorithm
+  for (i in 1:n) {
+    if (processed[i]) next
+
+    # Get neighbors within eps
+    neighbors <- which(dist_matrix[i, ] <= eps)
+    neighbors <- neighbors[neighbors != i]
+
+    processed[i] <- TRUE
+    ordered_points <- c(ordered_points, i)
+
+    if (core_dist[i] != Inf) {
+      seeds <- integer(0)
+      seeds <- update_seeds(seeds, neighbors, i)
+
+      while (length(seeds) > 0) {
+        current <- seeds[1]
+        seeds <- seeds[-1]
+
+        if (processed[current]) next
+
+        neighbors_current <- which(dist_matrix[current, ] <= eps)
+        neighbors_current <- neighbors_current[neighbors_current != current]
+
+        processed[current] <- TRUE
+        ordered_points <- c(ordered_points, current)
+
+        if (core_dist[current] != Inf) {
+          seeds <- update_seeds(seeds, neighbors_current, current)
+        }
+      }
+    }
+  }
+
+  return(list(
+    order = ordered_points,
+    reachability = reachability[ordered_points],
+    core_distance = core_dist[ordered_points],
+    eps = eps,
+    minPts = minPts
+  ))
+}
+
+# Extract DBSCAN-like clusters from OPTICS result
+extract_dbscan_clusters <- function(optics_result, eps_cluster) {
+  n <- length(optics_result$order)
+  cluster_id <- rep(0, n)
+  current_cluster <- 0
+
+  for (i in 1:n) {
+    if (optics_result$reachability[i] > eps_cluster) {
+      if (optics_result$core_distance[i] <= eps_cluster) {
+        current_cluster <- current_cluster + 1
+        cluster_id[i] <- current_cluster
+      } else {
+        cluster_id[i] <- 0
+      }
+    } else {
+      if (current_cluster == 0) {
+        current_cluster <- current_cluster + 1
+      }
+      cluster_id[i] <- current_cluster
+    }
+  }
+
+  # Reorder to original indices
+  result <- rep(0, n)
+  result[optics_result$order] <- cluster_id
+
+  return(result)
+}
+
+# Extract clusters using xi method (steep areas in reachability plot)
+extract_xi_clusters <- function(optics_result, xi = 0.05, min_cluster_size = 5) {
+  n <- length(optics_result$order)
+  reach <- optics_result$reachability
+
+  # Replace Inf with max finite value
+  max_reach <- max(reach[is.finite(reach)])
+  reach[is.infinite(reach)] <- max_reach * 2
+
+  # Find steep up and down areas
+  steep_up <- c()
+  steep_down <- c()
+
+  for (i in 2:n) {
+    if (reach[i] > reach[i-1] * (1 + xi)) {
+      steep_up <- c(steep_up, i)
+    }
+    if (reach[i] < reach[i-1] * (1 - xi)) {
+      steep_down <- c(steep_down, i)
+    }
+  }
+
+  # Match steep areas to form clusters
+  cluster_id <- rep(0, n)
+  current_cluster <- 0
+
+  if (length(steep_down) > 0 && length(steep_up) > 0) {
+    for (sd in steep_down) {
+      matching_up <- steep_up[steep_up > sd]
+      if (length(matching_up) > 0) {
+        su <- matching_up[1]
+        if (su - sd >= min_cluster_size) {
+          current_cluster <- current_cluster + 1
+          cluster_id[sd:su] <- current_cluster
+        }
+      }
+    }
+  }
+
+  # Reorder to original indices
+  result <- rep(0, n)
+  result[optics_result$order] <- cluster_id
+
+  return(result)
+}
+
+# Plot reachability plot
+plot_reachability <- function(optics_result, main = "OPTICS Reachability Plot") {
+  reach <- optics_result$reachability
+  max_reach <- max(reach[is.finite(reach)])
+  reach[is.infinite(reach)] <- max_reach * 1.2
+
+  plot(1:length(reach), reach, type = "h", lwd = 2,
+       xlab = "Cluster Order", ylab = "Reachability Distance",
+       main = main, col = "steelblue")
+  grid()
+}
+
+# Calculate silhouette score for clusters
+calculate_silhouette <- function(data, clusters) {
+  if (is.data.frame(data)) {
+    data <- as.matrix(data)
+  }
+
+  unique_clusters <- unique(clusters[clusters != 0])
+  if (length(unique_clusters) < 2) {
+    return(NA)
+  }
+
+  dist_matrix <- as.matrix(dist(data))
+  n <- nrow(data)
+  silhouette_scores <- rep(0, n)
+
+  for (i in 1:n) {
+    if (clusters[i] == 0) next
+
+    same_cluster <- which(clusters == clusters[i])
+    same_cluster <- same_cluster[same_cluster != i]
+
+    if (length(same_cluster) == 0) {
+      silhouette_scores[i] <- 0
+      next
+    }
+
+    a <- mean(dist_matrix[i, same_cluster])
+
+    b <- Inf
+    for (other_cluster in unique_clusters) {
+      if (other_cluster == clusters[i]) next
+      other_points <- which(clusters == other_cluster)
+      if (length(other_points) > 0) {
+        b <- min(b, mean(dist_matrix[i, other_points]))
+      }
+    }
+
+    silhouette_scores[i] <- (b - a) / max(a, b)
+  }
+
+  return(mean(silhouette_scores[clusters != 0]))
+}
+
+# Example usage and tests
+cat("=== OPTICS Clustering Algorithm ===\n\n")
+
+# Example 1: Simple 2D dataset
+cat("Example 1: Simple 2D Dataset\n")
+set.seed(42)
+cluster1 <- matrix(rnorm(100, mean = 0, sd = 0.5), ncol = 2)
+cluster2 <- matrix(rnorm(100, mean = 3, sd = 0.5), ncol = 2)
+cluster3 <- matrix(rnorm(100, mean = c(1.5, 3), sd = 0.5), ncol = 2)
+data1 <- rbind(cluster1, cluster2, cluster3)
+
+cat("Running OPTICS...\n")
+result1 <- optics(data1, eps = 1.0, minPts = 5)
+cat("Processed", length(result1$order), "points\n")
+cat("First 10 reachability distances:", head(result1$reachability, 10), "\n")
+
+# Extract clusters
+clusters1 <- extract_dbscan_clusters(result1, eps_cluster = 0.8)
+cat("Clusters found:", length(unique(clusters1[clusters1 != 0])), "\n")
+cat("Noise points:", sum(clusters1 == 0), "\n")
+
+if (length(unique(clusters1[clusters1 != 0])) >= 2) {
+  silhouette1 <- calculate_silhouette(data1, clusters1)
+  cat("Silhouette score:", round(silhouette1, 4), "\n")
+}
+
+# Example 2: Dataset with varying densities
+cat("\n\nExample 2: Varying Density Dataset\n")
+set.seed(123)
+dense_cluster <- matrix(rnorm(200, mean = 0, sd = 0.3), ncol = 2)
+sparse_cluster <- matrix(rnorm(100, mean = 4, sd = 1.0), ncol = 2)
+data2 <- rbind(dense_cluster, sparse_cluster)
+
+result2 <- optics(data2, eps = 2.0, minPts = 5)
+cat("Processed", length(result2$order), "points\n")
+
+clusters2_dbscan <- extract_dbscan_clusters(result2, eps_cluster = 1.0)
+cat("DBSCAN-style extraction - Clusters:", length(unique(clusters2_dbscan[clusters2_dbscan != 0])), "\n")
+
+clusters2_xi <- extract_xi_clusters(result2, xi = 0.05, min_cluster_size = 10)
+cat("Xi-method extraction - Clusters:", length(unique(clusters2_xi[clusters2_xi != 0])), "\n")
+
+# Example 3: Iris dataset
+cat("\n\nExample 3: Iris Dataset\n")
+data(iris)
+iris_data <- iris[, 1:4]
+
+result3 <- optics(iris_data, eps = 2.0, minPts = 5)
+cat("Processed", length(result3$order), "points\n")
+
+clusters3 <- extract_dbscan_clusters(result3, eps_cluster = 0.5)
+cat("Clusters found:", length(unique(clusters3[clusters3 != 0])), "\n")
+cat("Noise points:", sum(clusters3 == 0), "\n")
+
+# Compare with actual species
+if (length(unique(clusters3[clusters3 != 0])) >= 2) {
+  silhouette3 <- calculate_silhouette(iris_data, clusters3)
+  cat("Silhouette score:", round(silhouette3, 4), "\n")
+}
+
+# Example 4: Moons dataset (non-convex clusters)
+cat("\n\nExample 4: Two Moons Dataset (Non-convex)\n")
+set.seed(456)
+n_points <- 100
+t1 <- seq(0, pi, length.out = n_points)
+moon1 <- cbind(cos(t1), sin(t1)) + matrix(rnorm(n_points * 2, sd = 0.1), ncol = 2)
+moon2 <- cbind(1 - cos(t1), 1 - sin(t1) - 0.5) + matrix(rnorm(n_points * 2, sd = 0.1), ncol = 2)
+moons_data <- rbind(moon1, moon2)
+
+result4 <- optics(moons_data, eps = 0.5, minPts = 5)
+clusters4 <- extract_dbscan_clusters(result4, eps_cluster = 0.3)
+cat("Clusters found:", length(unique(clusters4[clusters4 != 0])), "\n")
+cat("Noise points:", sum(clusters4 == 0), "\n")
+
+if (length(unique(clusters4[clusters4 != 0])) >= 2) {
+  silhouette4 <- calculate_silhouette(moons_data, clusters4)
+  cat("Silhouette score:", round(silhouette4, 4), "\n")
+}
+
+# Example 5: Performance comparison
+cat("\n\nExample 5: Performance Test\n")
+sizes <- c(100, 200, 500)
+cat("Dataset size vs execution time:\n")
+
+for (size in sizes) {
+  test_data <- matrix(rnorm(size * 2), ncol = 2)
+  start_time <- Sys.time()
+  test_result <- optics(test_data, eps = 1.0, minPts = 5)
+  end_time <- Sys.time()
+  elapsed <- as.numeric(difftime(end_time, start_time, units = "secs"))
+  cat(sprintf("  %d points: %.3f seconds\n", size, elapsed))
+}
+
+cat("\n=== All tests completed successfully ===\n")

data_manipulation/join_multiple_datasets.r

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+# join_multiple_datasets.r
+#
+# Algorithm Description:
+# This script provides a function to join multiple datasets (data frames or CSV file paths)
+# by their common columns using inner joins. The function reads input datasets, removes
+# empty or invalid ones, and then sequentially merges them on their shared columns.
+# The merging is performed using dplyr's inner_join, and missing values are replaced with
+# empty strings. The algorithm iteratively joins datasets, so its complexity is O(n * m),
+# where n is the number of datasets and m is the average number of rows in each dataset.
+#
+# Example usage:
+#   result <- join_multiple_datasets(list("data1.csv", "data2.csv", df3))
+#   head(result)
+#
+
+#' Join Multiple Datasets by Common Columns
+#'
+#' This function takes a list of data frames or CSV file paths and joins them sequentially
+#' on their common columns using inner joins. It reads CSV files if paths are provided,
+#' removes empty or invalid datasets, and merges the remaining datasets. Missing values
+#' in the result are replaced with empty strings.
+#'
+#' @param inputs A list of data frames and/or character strings representing CSV file paths.
+#' @return A data frame resulting from the inner join of all valid input datasets on their common columns.
+#' @examples
+#' # Example 1: Joining three data frames
+#' df1 <- data.frame(id = 1:3, val1 = c("A", "B", "C"))
+#' df2 <- data.frame(id = 2:3, val2 = c("X", "Y"))
+#' df3 <- data.frame(id = 3, val3 = "Z")
+#' result <- join_multiple_datasets(list(df1, df2, df3))
+#' print(result)
+#'
+#' # Example 2: Joining CSV files and a data frame
+#' result <- join_multiple_datasets(list("file1.csv", "file2.csv", df3))
+#' head(result)
+library(dplyr)
+library(purrr)
+
+join_multiple_datasets <- function(inputs) {
+    if (is.character(x)) read.csv(x, stringsAsFactors = FALSE) else x
+  })
+
+  # Remove empty or invalid datasets
+  datasets <- datasets[!sapply(datasets, function(df) is.null(df) || nrow(df) == 0)]
+  if (length(datasets) < 2) stop(sprintf("At least two valid datasets are required, but only %d valid dataset(s) found.", length(datasets)))
+
+  # Function to find common columns between two datasets
+  get_common_cols <- function(df1, df2) intersect(names(df1), names(df2))
+
+  # Sequentially join all datasets on their common columns
+  merged <- reduce(datasets, function(df1, df2) {
+    common_cols <- get_common_cols(df1, df2)
+    if (length(common_cols) == 0) stop("No common columns found for joining.")
+    suppressWarnings(
+      inner_join(df1, df2, by = common_cols) %>%
+        mutate_all(~ ifelse(is.na(.), "", .))
+    )
+  })
+
+  return(merged)
+}