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Data Processing with Tivy

library(Tivy)

Data Processing Workflow

This vignette demonstrates the basic workflow for processing fisheries data with Tivy.

Step 1: Process Individual Datasets

# Process hauls data
hauls <- process_hauls(
  data_hauls = raw_hauls,
  correct_coordinates = TRUE,
  verbose = TRUE
)

# Process trips data  
trips <- process_fishing_trips(
  data_fishing_trips = raw_trips,
  verbose = TRUE
)

# Process length data
lengths <- process_length(
  data_length = raw_lengths,
  verbose = TRUE
)

Step 2: Data Validation

# Check data quality
haul_quality <- validate_haul_data(hauls)
trip_quality <- validate_fishing_trip_data(trips)
length_quality <- validate_length_data(lengths)

# Print quality scores
print(paste("Hauls quality:", haul_quality$quality_score, "%"))
print(paste("Trips quality:", trip_quality$quality_score, "%"))
print(paste("Lengths quality:", length_quality$quality_score, "%"))

Step 3: Merge Datasets

# Merge length and trip data first
length_trips <- merge(
  x = lengths,
  y = trips,
  by = "fishing_trip_code",
  all = TRUE
)

# Then merge with hauls data
complete_data <- merge_length_fishing_trips_hauls(
  data_hauls = hauls,
  data_length_fishing_trips = length_trips
)

Step 4: Add Derived Variables

# Add juvenile analysis and distance variables
enhanced_data <- add_variables(
  data = complete_data,
  JuvLim = 12,
  distance_type = "haversine",
  unit = "nm"
)

Coordinate Processing

Convert various coordinate formats:

# Example coordinates in different formats
coords <- c(
  "15°30'25\"S",     # Complete DMS
  "75°45'W",         # DM format  
  "16 15 30 S"       # Space-separated
)

# Convert to decimal degrees
decimal <- dms_to_decimal(
  coordinates = coords,
  hemisphere = "S",
  correct_errors = TRUE
)

Error Handling

# Handle missing columns gracefully
tryCatch({
  processed <- process_hauls(incomplete_data)
}, error = function(e) {
  message("Processing failed: ", e$message)
})

Column Detection

The package automatically detects columns using pattern matching:

# Find columns by pattern
species_col <- find_column(
  patterns = c("especie", "species", "sp"),
  column_names = names(your_data)
)

# Find numeric length columns
length_cols <- find_columns_by_pattern(
  data = your_data,
  pattern = "^[0-9]+(\\.[0-9]+)?$"
)

Tips

  1. Consistent naming: Use consistent column names across files
  2. Data validation: Always validate data quality after processing
  3. Error correction: Enable coordinate error correction for better results
  4. Pattern matching: The package is flexible with column name variations

For function-specific details, see the individual function documentation.

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.