Upload Processing_CleanedUp_Commented.R

Processing_CleanedUp_Commented.R

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+# ============================================================================
+# GoogleTrendArchive DATA PROCESSING PIPELINE
+# ============================================================================
+# This script processes raw daily CSV files from Google's Trending Now system
+# into a single consolidated dataset with calculated trend durations.
+#
+# Input: Daily CSV files organized by location in separate folders
+# see daily_compressed.zip
+# Output: Single CSV with deduplicated trends and calculated durations
+# ============================================================================
+
+library(tidyverse)
+library(lubridate)
+library(data.table)
+
+daily_base_dir <- ""#data directory with the (sub)folders
+output_file <- "googletrendarchive_preprocessed.csv"
+
+# ============================================================================
+# HELPER FUNCTIONS
+# ============================================================================
+
+# Parse Google's bucketed search volume format (e.g., "50K+", "2M+", "500+")
+parse_search_volume <- function(volume_str) {
+  if (is.na(volume_str) || volume_str == "") return(NA_real_)
+  
+  clean <- str_remove(volume_str, "\\+")
+  
+  if (str_detect(clean, "K$")) {
+    return(as.numeric(str_remove(clean, "K")) * 1000)
+  } else if (str_detect(clean, "M$")) {
+    return(as.numeric(str_remove(clean, "M")) * 1000000)
+  } else {
+    return(as.numeric(clean))
+  }
+}
+
+# Load all CSV files for a single location
+load_location_data <- function(location_folder) {
+  location <- basename(location_folder)
+  
+  files <- list.files(location_folder, 
+                      pattern = "trending_.*_1d_.*\\.csv$", 
+                      full.names = TRUE, 
+                      recursive = TRUE)
+  
+  if (length(files) == 0) {
+    cat("  ", location, ": No files found\n")
+    return(NULL)
+  }
+  
+  cat("  Loading", location, ":", length(files), "files\n")
+  
+  # Load and combine all files for this location
+  data <- map_dfr(files, function(file) {
+    tryCatch({
+      df <- read_csv(file, show_col_types = FALSE, col_types = cols(.default = "c"))
+      
+      if (nrow(df) == 0) return(NULL)
+      
+      # Extract collection date from filename (format: YYYYMMDD)
+      filename <- basename(file)
+      date_match <- str_match(filename, "(\\d{8})")
+      collection_date <- if (!is.na(date_match[1])) {
+        ymd(date_match[2])
+      } else {
+        NA_Date_
+      }
+      
+      # Add metadata
+      df %>%
+        mutate(
+          location = location,
+          collection_date = collection_date
+        )
+    }, error = function(e) {
+      warning("Error loading ", file, ": ", e$message)
+      return(NULL)
+    })
+  })
+  
+  return(data)
+}
+
+# ============================================================================
+# STEP 1: LOAD ALL RAW DATA
+# ============================================================================
+
+cat("=== STEP 1: LOADING RAW DATA ===\n\n")
+
+# Find all location folders
+folders <- list.dirs(daily_base_dir, full.names = TRUE, recursive = FALSE)
+folders <- folders[basename(folders) != "weekly" & basename(folders) != "reconstructed"]
+
+cat("Found", length(folders), "locations\n\n")
+
+# Load data from all locations
+all_data <- map_dfr(folders, load_location_data)
+
+cat("\n✓ Loaded", format(nrow(all_data), big.mark = ","), "raw trend records\n")
+cat("  Date range:", min(all_data$collection_date, na.rm = TRUE), 
+    "to", max(all_data$collection_date, na.rm = TRUE), "\n\n")
+
+# ============================================================================
+# STEP 2: PARSE AND STANDARDIZE FIELDS
+# ============================================================================
+
+cat("=== STEP 2: PARSING FIELDS ===\n\n")
+
+# Standardize column names
+colnames(all_data) <- tolower(colnames(all_data))
+colnames(all_data) <- str_replace_all(colnames(all_data), " ", "_")
+
+cat("Parsing search volumes...\n")
+all_data <- all_data %>%
+  mutate(search_volume_lower = map_dbl(search_volume, parse_search_volume))
+
+cat("Parsing timestamps...\n")
+all_data_parsed <- all_data %>%
+  mutate(
+    # Remove timezone suffix and parse
+    started_clean = str_remove(started, " UTC[+-]?\\d+$"),
+    ended_clean = str_remove(ended, " UTC[+-]?\\d+$"),
+    
+    # Parse to POSIXct timestamps (UTC)
+    start_time = parse_date_time(started_clean, 
+                                 orders = c("Bdy IMS p"), 
+                                 tz = "UTC", 
+                                 quiet = TRUE),
+    end_time = parse_date_time(ended_clean, 
+                               orders = c("Bdy IMS p"), 
+                               tz = "UTC", 
+                               quiet = TRUE),
+    
+    # Count queries in trend breakdown (comma-separated)
+    n_queries = str_count(trend_breakdown, ",") + 1
+  ) %>%
+  select(-started_clean, -ended_clean) %>%
+  arrange(trends, location, collection_date, start_time)
+
+cat("✓ Parsing complete\n\n")
+
+# ============================================================================
+# STEP 3: CREATE TREND EPISODES AND CALCULATE DURATIONS
+# ============================================================================
+
+cat("=== STEP 3: EPISODE DEDUPLICATION AND DURATION CALCULATION ===\n\n")
+
+# Convert to data.table for faster processing
+setDT(all_data_parsed)
+
+# Sort by trend, location, and time
+setorder(all_data_parsed, trends, location, collection_date, start_time)
+
+cat("Step 3a: Identifying trend episodes...\n")
+# Identify trend episodes (same trend appearing in multiple daily snapshots)
+all_data_parsed[, `:=`(
+  prev_start = shift(start_time),
+  prev_end = shift(end_time)
+), by = .(trends, location)]
+
+all_data_parsed[, `:=`(
+  start_gap = as.numeric(difftime(start_time, prev_start, units = "hours")),
+  time_gap = as.numeric(difftime(start_time, prev_end, units = "hours"))
+)]
+
+all_data_parsed[, new_episode := is.na(prev_start) | (!is.na(start_gap) & abs(start_gap) > 1)]
+all_data_parsed[is.na(new_episode), new_episode := TRUE]
+all_data_parsed[, episode_id := cumsum(new_episode), by = .(trends, location)]
+
+cat("Step 3b: Aggregating episodes...\n")
+# Collapse multiple occurrences of the same trend into single episodes
+# Use earliest start time and latest end time for each episode
+episodes <- all_data_parsed[, .(
+  start_time = min(start_time, na.rm = TRUE),
+  end_time = max(end_time, na.rm = TRUE),
+  first_collection_date = min(collection_date),
+  last_collection_date = max(collection_date),
+  n_days_observed = uniqueN(collection_date),
+  total_occurrences = .N,
+  search_volume_lower = max(search_volume_lower, na.rm = TRUE),
+  n_queries = first(n_queries),
+  trend_breakdown = first(trend_breakdown),
+  collection_date = first(collection_date)
+), by = .(trends, location, episode_id)]
+
+# Replace Inf values with NA
+episodes[is.infinite(start_time), start_time := as.POSIXct(NA)]
+episodes[is.infinite(end_time), end_time := as.POSIXct(NA)]
+
+cat("Step 3c: Calculating durations with patching...\n")
+# Fix data quality issues and calculate durations
+
+episodes[, `:=`(
+  start_fixed = fifelse(!is.na(start_time) & !is.na(end_time) & end_time < start_time, 
+                        end_time, start_time),
+  end_fixed = fifelse(!is.na(start_time) & !is.na(end_time) & end_time < start_time,
+                      start_time, end_time),
+  times_were_swapped = !is.na(start_time) & !is.na(end_time) & end_time < start_time
+)]
+
+episodes[is.na(end_fixed) & !is.na(start_fixed), 
+         end_estimated := as.POSIXct(paste(last_collection_date, "23:59:59"), tz = "UTC")]
+episodes[is.na(end_estimated), end_estimated := end_fixed]
+
+# Calculate final duration
+episodes[, `:=`(
+  duration_minutes = as.numeric(difftime(end_estimated, start_fixed, units = "mins")),
+  duration_is_estimate = is.na(end_fixed) | times_were_swapped
+)]
+
+episodes[, duration_hours := duration_minutes / 60]
+
+# Add date components for analysis
+episodes[, `:=`(
+  year = year(collection_date),
+  month = month(collection_date),
+  weekday = lubridate::wday(collection_date, label = TRUE)
+)]
+
+cat("Step 3d: Filtering invalid records...\n")
+# Filter out records with missing or invalid data
+all_data_clean <- episodes[
+  !is.na(search_volume_lower) & 
+    !is.na(duration_minutes) & 
+    duration_minutes > 0
+]
+
+# Remove temporary working columns
+all_data_clean[, c("start_fixed", "end_fixed", "end_estimated", 
+                   "prev_start", "prev_end", "start_gap", "time_gap", "new_episode") := NULL]
+
+cat("✓ Episode processing complete\n\n")
+
+
+
+# Write to CSV
+cat("\nWriting to", output_file, "...\n")
+fwrite(all_data_clean, output_file)
+