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Data Processing with Tivy

library(Tivy)

Data Processing Workflow

This vignette demonstrates the basic workflow for processingfisheries data with Tivy.

Step 1: Process Individual Datasets

# Process hauls datahauls<-process_hauls(data_hauls = raw_hauls,correct_coordinates =TRUE,verbose =TRUE)# Process trips datatrips<-process_fishing_trips(data_fishing_trips = raw_trips,verbose =TRUE)# Process length datalengths<-process_length(data_length = raw_lengths,verbose =TRUE)

Step 2: Data Validation

# Check data qualityhaul_quality<-validate_haul_data(hauls)trip_quality<-validate_fishing_trip_data(trips)length_quality<-validate_length_data(lengths)# Print quality scoresprint(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 firstlength_trips<-merge(x = lengths,y = trips,by ="fishing_trip_code",all =TRUE)# Then merge with hauls datacomplete_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 variablesenhanced_data<-add_variables(data = complete_data,JuvLim =12,distance_type ="haversine",unit ="nm")

Coordinate Processing

Convert various coordinate formats:

# Example coordinates in different formatscoords<-c("15°30'25\"S",# Complete DMS"75°45'W",# DM format"16 15 30 S"# Space-separated)# Convert to decimal degreesdecimal<-dms_to_decimal(coordinates = coords,hemisphere ="S",correct_errors =TRUE)

Error Handling

# Handle missing columns gracefullytryCatch({  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 patternspecies_col<-find_column(patterns =c("especie","species","sp"),column_names =names(your_data))# Find numeric length columnslength_cols<-find_columns_by_pattern(data = your_data,pattern ="^[0-9]+(\\.[0-9]+)?$")

Tips

  1. Consistent naming: Use consistent column namesacross files
  2. Data validation: Always validate data quality afterprocessing
  3. Error correction: Enable coordinate errorcorrection for better results
  4. Pattern matching: The package is flexible withcolumn name variations

For function-specific details, see the individual functiondocumentation.

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