Package 'Tivy'

Title: Toolkit for Investigation and Visualization of Young Anchovies
Description: Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones.
Authors: Hans Ttito [aut, cre] (ORCID: <https://orcid.org/0000-0003-3732-9419>)
Maintainer: Hans Ttito <[email protected]>
License: MIT + file LICENSE
Version: 0.1.1
Built: 2026-05-15 05:40:22 UTC
Source: https://github.com/hansttito/tivy

Help Index

Add variables for juveniles, sample length, distance to coast, and distance category

Description

Adds new variables to a dataset, including the proportion of juveniles, the total number of individuals in the sample, the distance to the coast, and the distance category.

Usage

add_variables(
  data,
  JuvLim = 12,
  distance_type = "haversine",
  window = 0.5,
  unit = "nm",
  coastline = NULL,
  suppress_warnings = TRUE
)

Arguments

data

Data frame that must contain latitude (lat_initial) and longitude (lon_initial) coordinates, as well as columns with individual length.
JuvLim

Length threshold to consider juveniles.
distance_type

Type of distance calculation to the coast.
window

Window parameter to smooth the coastline.
unit

Distance unit used in the calculation ("nm", "km", etc.).
coastline

Data frame with coastline coordinates. Must have columns named Long and Lat. If NULL, uses internal dataset peru_coastline.
suppress_warnings

Logical. If TRUE, warnings are suppressed.

Value

Data frame with new variables: juv (proportion of juveniles), sample (total individuals), dc (distance to coast), and dc_cat (categorical distance).

Examples

## Not run: 
data_hauls <- process_hauls(data_hauls = calas_bitacora)
data_fishing_trips <- process_fishing_trips(data_fishing_trips = faenas_bitacora)
hauls_length <- process_length(data_length = tallas_bitacora)

data_length_trips <- merge(
  x = data_fishing_trips, 
  y = hauls_length, 
  by = 'fishing_trip_code'
)
data_total <- merge_length_fishing_trips_hauls(
  data_hauls = data_hauls, 
  data_length_fishing_trips = data_length_trips
)

results <- add_variables(data = data_total)

## End(Not run)

Apply catch weighting to data frame

Description

Applies catch weighting to length frequency columns in a data frame. Supports parallel processing for large datasets.

Usage

apply_catch_weighting(
  data,
  length_cols,
  catch_col,
  a,
  b,
  parallel = FALSE,
  num_cores = NULL,
  block_size = 10000,
  silence_warnings = TRUE
)

Arguments

data

Data frame containing length columns and catch data.
length_cols

Character vector of length column names.
catch_col

Name of the catch column.
a

Coefficient of the length-weight relationship.
b

Exponent of the length-weight relationship.
parallel

Logical. Use parallel processing.
num_cores

Number of cores for parallel processing. If NULL, auto-detect.
block_size

Block size for parallel processing.
silence_warnings

Logical. Suppress warnings.

Value

Data frame with original columns plus weighted length columns (prefixed with "weighted_").

Examples

## Not run: 
length_cols <- c("8", "8.5", "9", "9.5", "10", "10.5", "11", "11.5")
weighted_data <- apply_catch_weighting(
  data = fishery_data,
  length_cols = length_cols,
  catch_col = "total_catch",
  a = 0.0001,
  b = 2.984
)

## End(Not run)

Fishing hauls

Description

Dataset containing information about fishing hauls in the Peruvian sea. Hauls are specific locations where fishing activities are conducted.

Usage

calas_bitacora

Format

A data.frame with sample data of hauls made by vessels in the Peruvian sea.

Source

Randomly generated data.

Examples

data(calas_bitacora)

Calculate vectorized distances to coastline

Description

Internal function to calculate distances between multiple points and a coastline. Supports different distance calculation methods and spatial filtering.

Usage

calculate_distances_vectorized(
  lon_point,
  lat_point,
  coast_lon,
  coast_lat,
  distance_type,
  window,
  unit,
  resolution = 0.25
)

Arguments

lon_point

Vector of point longitudes.
lat_point

Vector of point latitudes.
coast_lon

Vector of coastline longitudes.
coast_lat

Vector of coastline latitudes.
distance_type

Distance calculation method.
window

Spatial filter window in degrees.
unit

Distance unit.
resolution

Grid resolution (only used if distance_type = "grid").

Value

List with distance vectors and corresponding indices.

Calculate fish weight from length

Description

Estimates individual fish weight from length using the length-weight relationship: W = a * L^b

Usage

calculate_fish_weight(length, a, b)

Arguments

length

Numeric vector of fish lengths.
a

Coefficient of the length-weight relationship.
b

Exponent of the length-weight relationship.

Details

The length-weight relationship follows the allometric equation W = a * L^b, where W is weight, L is length, and a and b are species-specific parameters.

Value

Numeric vector of estimated weights.

References

Froese, R. (2006). Cube law, condition factor and weight–length relationships: history, meta-analysis and recommendations. Journal of Applied Ichthyology, 22(4), 241-253.

Calculate juvenile percentage

Description

Calculates the percentage of individuals considered juveniles based on a length threshold.

Usage

calculate_juvenile_percentage(
  frequency,
  length,
  juvenile_limit = 12,
  silence_warnings = FALSE
)

Arguments

frequency

Numeric vector of length frequencies.
length

Numeric vector of corresponding lengths.
juvenile_limit

Length threshold for juvenile classification.
silence_warnings

Logical. Suppress warnings.

Details

Juvenile percentage calculations are essential for fisheries management decisions, particularly in determining fishing quotas and closure periods.

Value

Percentage of juveniles in the sample.

References

IMARPE (2020). Protocolo Elaboración de la Tabla de Decisión para la determinación del Límite Máximo Total Permisible por temporada de pesca en la pesquería del stock norte-centro de la anchoveta peruana. IMP-DGIRP/AFDPERP, Edición: 05, Revisión 00, 40p.

Calculate juvenile statistics for a group

Description

Helper function that calculates juvenile percentages in number and weight. Used internally by summarize_juveniles_by_group.

Usage

calculate_juvenile_statistics(
  frequencies,
  length_values,
  juvenile_limit = 12,
  a = 0.0012,
  b = 3.1242
)

Arguments

frequencies

Numeric vector of frequencies by length.
length_values

Numeric vector of corresponding lengths.
juvenile_limit

Length threshold for juveniles.
a

Coefficient of length-weight relationship.
b

Exponent of length-weight relationship.

Value

Data frame with juvenile statistics.

Examples

frequencies <- c(10, 15, 25, 30, 20, 10)
lengths <- c(8, 9, 10, 11, 12, 13)
stats <- calculate_juvenile_statistics(frequencies, lengths)

Vectorized distance to coast

Description

Estimates the distance between a set of points (lon, lat) and a coastline defined by coordinates. Can be executed sequentially or in parallel, and also return the indices of the nearest coastal points.

Usage

coast_distance(
  lon,
  lat,
  coastline = NULL,
  return_indices = FALSE,
  distance_type = "haversine",
  unit = "nm",
  window = 1,
  parallel = FALSE,
  cores = 4
)

Arguments

lon

Numeric vector with the longitudes of the points of interest.
lat

Numeric vector with the latitudes of the points of interest.
coastline

Data frame with coastline coordinates. Must have columns named Long and Lat. If NULL, uses internal dataset peru_coastline.
return_indices

Logical. If TRUE, also returns the indices of the nearest coastline points.
distance_type

Type of geographic distance to use: "haversine", "euclidean", "grid".
unit

Unit of measurement for distance: "nm" (nautical miles), "km", etc.
window

Search window in degrees around the point to limit calculations and improve efficiency.
parallel

Logical. If TRUE, performs the calculation in parallel using multiple cores.
cores

Number of cores to use for parallel processing.

Value

If return_indices = FALSE, returns a numeric vector with distances to the coast for each point. If return_indices = TRUE, returns a list with distance and index components.

Examples

## Not run: 
data_hauls <- process_hauls(data_hauls = calas_bitacora)
distances <- coast_distance(
  lon = data_hauls$lon_final,
  lat = data_hauls$lat_final,
  distance_type = "haversine",
  unit = "nm",
  parallel = TRUE,
  cores = 2
)

## End(Not run)

Convert numbers to weight

Description

Converts numerical length frequencies to weight estimates using length-weight relationship.

Usage

convert_numbers_to_weight(data, length_cols, a, b)

Arguments

data

Data frame with length frequency columns.
length_cols

Vector of length column names or numeric values.
a

Coefficient of the length-weight relationship.
b

Exponent of the length-weight relationship.

Value

Data frame with original columns plus weight columns (prefixed with "weight_").

Examples

## Not run: 
weight_data <- convert_numbers_to_weight(
  data = frequency_data,
  length_cols = c("8", "8.5", "9", "9.5", "10"),
  a = 0.0012,
  b = 3.1242
)

## End(Not run)

Convert dates to standard format

Description

Converts a vector of dates in various formats to a standard date format. The function tries to parse each date using predefined formats and returns the first valid date found for each entry. If a date cannot be interpreted, it is assigned as NA.

Usage

convert_to_date(date_vector, output_type = c("date", "datetime"))

Arguments

date_vector

A character vector containing dates in various formats.
output_type

Type of object to return: "date" for Date, "datetime" for POSIXct.

Value

A vector of Date or POSIXct objects, or NA if the date cannot be converted.

Examples

dates <- c("2025-04-10", "10/04/2025", "April 10, 2025")
converted_dates <- convert_to_date(dates)
print(converted_dates)

Create fishery dashboard

Description

Generates a comprehensive dashboard for fishery analysis including juvenile analysis, catch trends, spatial distribution, and summary statistics. This function consolidates all dashboard functionality.

Usage

create_fishery_dashboard(
  data,
  date_col = NULL,
  length_cols = NULL,
  a = 1e-04,
  b = 2.984,
  latitude_col = NULL,
  longitude_col = NULL,
  catch_col = NULL,
  juvenile_col = NULL,
  map_xlim = c(-85, -70),
  map_ylim = c(-20, 0),
  color_palette = NULL,
  date_breaks = NULL,
  sort_comparison = FALSE,
  comparison_title = NULL,
  catch_title = NULL,
  map_title = NULL,
  trend_title = NULL
)

Arguments

data

Data frame with complete fishery data.
date_col

Date column name. If NULL, auto-detect.
length_cols

Length frequency column names. If NULL, auto-detect.
a

Length-weight coefficient.
b

Length-weight exponent.
latitude_col

Latitude column name. If NULL, auto-detect.
longitude_col

Longitude column name. If NULL, auto-detect.
catch_col

Catch column name. If NULL, auto-detect.
juvenile_col

Juvenile percentage column name. If NULL, auto-detect.
map_xlim

Map longitude limits.
map_ylim

Map latitude limits.
color_palette

Custom color palette.
date_breaks

Date axis breaks.
sort_comparison

Sort comparison plot.
comparison_title

Comparison plot title.
catch_title

Catch plot title.
map_title

Map plot title.
trend_title

Trend plot title.

Value

List with individual plots and combined dashboard (if patchwork available).

Examples

## Not run: 
dashboard <- create_fishery_dashboard(
  data = complete_fishery_data,
  date_col = "date",
  length_cols = paste0("length_", seq(8, 15, 0.5)),
  catch_col = "total_catch",
  latitude_col = "latitude",
  longitude_col = "longitude"
)

dashboard$comparison
dashboard$catch_trends
dashboard$spatial_map
dashboard$trends
dashboard$dashboard

## End(Not run)

Convert latitude or longitude to decimal degrees

Description

Converts coordinates expressed in degrees, minutes and seconds (DMS) or degrees and minutes (DM) format to decimal degrees. By default, coordinates are assumed to be in the southern hemisphere (negative latitudes). The function can automatically correct common errors such as minutes or seconds greater than 60.

Usage

dms_to_decimal(coordinates, hemisphere = "S", correct_errors = TRUE)

Arguments

coordinates

Character vector. Each element should be in formats such as: "D M S", "D M", "17 26 S"
hemisphere

Character indicating hemisphere when not specified in the coordinate. One of "N", "S", "E", "W" or "O". "S" and "W"/"O" generate negative values.
correct_errors

Logical. If TRUE, automatically corrects out-of-range values.

Value

Numeric vector with coordinates converted to decimal degrees.

Examples

dms_to_decimal(c("73 15 0"), hemisphere = "W")

Extract length values from column names

Description

Helper function to extract numerical length values from column names. Handles different naming patterns like "length_8.5", "weighted_9", "8", etc. Uses multiple extraction strategies to ensure robust parsing of column names.

Usage

extract_numeric_values(
  column_names,
  use_fallback = TRUE,
  fallback_type = "sequential",
  verbose = FALSE
)

Arguments

column_names

Character vector of column names.
use_fallback

Logical. If TRUE, uses fallback strategy when numeric values cannot be extracted.
fallback_type

Character. Type of fallback to use when use_fallback = TRUE. Options: "sequential", "ones", "zeros".
verbose

Logical. Print information about extraction strategy used.

Details

The function uses the following extraction strategies in order:

  1. Specific prefixes: "length_", "weighted_", "pond_" followed by numbers

  2. Purely numeric column names

  3. General pattern: extracts first number found in each name

  4. Fallback: uses specified fallback strategy if previous methods fail

Value

Numeric vector of length values extracted from column names.

Examples

extract_numeric_values(c("length_8.5", "weighted_10", "pond_12"))
extract_numeric_values(c("8", "10.5", "12"))
extract_numeric_values(c("size_8", "data_10.5", "value_12"))
extract_numeric_values(c("length_8", "no_numbers"), use_fallback = FALSE)
extract_numeric_values(c("bad_name1", "bad_name2"), fallback_type = "ones")

Extract data from PDF announcements

Description

Processes PDF files containing official fishing announcements and extracts relevant information such as dates, coordinates, and nautical miles. Handles both local files and URLs.

Usage

extract_pdf_data(
  pdf_sources = NULL,
  temp_dir = NULL,
  verbose = TRUE,
  max_retries = 3
)

Arguments

pdf_sources

Character vector of PDF file paths or URLs.
temp_dir

Temporary directory for downloaded files. If NULL, uses tempdir().
verbose

Show processing messages.
max_retries

Maximum download retries for URLs.

Value

Data frame with extracted announcement information including coordinates, dates, and nautical mile distances.

Examples

## Not run: 
pdf_files <- c("announcement1.pdf", "announcement2.pdf")
results <- extract_pdf_data(pdf_sources = pdf_files)

pdf_urls <- c(
  "https://example.com/announcement1.pdf",
  "https://example.com/announcement2.pdf"
)
results <- extract_pdf_data(pdf_sources = pdf_urls)

## End(Not run)

Fishing trips

Description

Dataset containing information about fishing trips conducted along the Peruvian littoral.

Usage

faenas_bitacora

Format

A data.frame with sample data of fishing trips made by vessels in the Peruvian sea.

Source

Randomly generated data.

Examples

data(faenas_bitacora)

Fetch fishing announcements from external sources

Description

Retrieves fishing announcements from official websites within a specified date range. This function is specifically designed for PRODUCE (Peru) but can be adapted for other sources.

Usage

fetch_fishing_announcements(
  start_date,
  end_date,
  download = FALSE,
  download_dir = "downloads",
  batch_size = 10,
  verbose = TRUE,
  source_url = NULL,
  max_records = 5000
)

Arguments

start_date

Start date in "dd/mm/yyyy" format.
end_date

End date in "dd/mm/yyyy" format.
download

Logical. Download PDF files.
download_dir

Directory for downloaded files.
batch_size

Records per request.
verbose

Print detailed information.
source_url

Base URL for the announcement source. Defaults to the PRODUCE page: https://consultasenlinea.produce.gob.pe/ConsultasEnLinea/consultas.web/comunicados/suspensionPreventiva
max_records

Maximum records to retrieve.

Value

Data frame with announcement information and download links.

Examples

## Not run: 
announcements <- fetch_fishing_announcements(
  start_date = "01/01/2023",
  end_date = "31/12/2023"
)

announcements <- fetch_fishing_announcements(
  start_date = "01/01/2023",
  end_date = "31/01/2023",
  download = TRUE,
  download_dir = "announcements"
)

## End(Not run)

Find column by pattern matching

Description

Searches for a column in a data frame using multiple pattern options. If multiple columns match, warns the user and returns the first match.

Usage

find_column(patterns, column_names, verbose = FALSE)

Arguments

patterns

Character vector of regex patterns to search for.
column_names

Character vector of column names to search in.
verbose

Logical. If TRUE, prints detailed matching information.

Value

Integer index of the first matching column, or NULL if no match found.

Examples

cols <- c("codigo_faena", "numero_cala", "especie", "especies_capturadas")
species_patterns <- c("especie", "species", "sp")
col_index <- find_column(species_patterns, cols)

Find columns by pattern

Description

Identifies columns in a data frame that match a specific pattern. Useful for finding length columns, weight columns, etc.

Usage

find_columns_by_pattern(data, pattern = "weighted_", sort = TRUE)

Arguments

data

Data frame to search.
pattern

Regular expression pattern to match.
sort

Logical. Sort results numerically.

Value

Character vector of matching column names.

Examples

# Create dummy data
data <- data.frame(
  weighted_8.5 = c(1, 2, 3),
  weighted_10 = c(4, 5, 6),
  `8` = c(7, 8, 9),
  other = c(10, 11, 12)
)

# Find weighted columns
weighted_cols <- find_columns_by_pattern(data, pattern = "weighted_")

# Find numeric-only named columns (e.g., "8")
length_cols <- find_columns_by_pattern(data, pattern = "")

Format extracted announcement data

Description

Formats and filters data extracted from announcements, converting dates to proper formats and allowing filtering by date range.

Usage

format_extracted_data(
  data,
  min_date = NULL,
  max_date = NULL,
  convert_coordinates = TRUE
)

Arguments

data

Data frame with structure from extract_pdf_data.
min_date

Minimum date for filtering (YYYY-MM-DD format or Date/POSIXct object).
max_date

Maximum date for filtering (YYYY-MM-DD format or Date/POSIXct object).
convert_coordinates

Logical. Convert DMS coordinates to decimal.

Value

Data frame with formatted and filtered announcement data.

Examples

## Not run: 
formatted_data <- format_extracted_data(raw_data)

filtered_data <- format_extracted_data(
  data = raw_data,
  min_date = "2024-11-01",
  max_date = "2024-12-31"
)

## End(Not run)

Get length range from frequencies

Description

Finds the minimum or maximum length with positive frequency.

Usage

get_length_range(frequency, length, type = "min")

Arguments

frequency

Numeric vector of length frequencies.
length

Numeric vector of corresponding lengths.
type

Either "min" or "max" to specify which range to return.

Value

Minimum or maximum length value with frequency > 0.

Examples

freq <- c(0, 0, 1, 2, 3, 4, 2, 1, 0)
lengths <- c(5, 6, 7, 8, 9, 10, 11, 12, 13)
min_length <- get_length_range(freq, lengths, type = "min")
max_length <- get_length_range(freq, lengths, type = "max")

Points on land

Description

Classifies a set of geographic coordinates (longitude and latitude) as "land" or "sea" according to their relative position to a coastline. A point is considered to be on land if its longitude is greater than that of its nearest point on the coastline.

Usage

land_points(
  x_point,
  y_point,
  coastline = NULL,
  parallel = FALSE,
  cores = 4,
  distance_type = "haversine",
  window = 0.5,
  unit = "nm"
)

Arguments

x_point

Numeric vector of longitudes (in decimal degrees).
y_point

Numeric vector of latitudes (in decimal degrees).
coastline

Data frame with coastline coordinates. Must have columns named Long and Lat. If NULL, uses internal dataset peru_coastline.
parallel

Logical. If TRUE, performs the calculation in parallel using multiple cores.
cores

Number of cores to use for parallel processing.
distance_type

Type of geodesic distance to use in the calculation.
window

Geographic window in degrees to reduce the number of coastline points to consider.
unit

Unit of measurement for distance: "km" or "nm".

Value

Text vector of the same length as x_point, indicating whether each point is on "land" or "sea". NA values are maintained as NA.

Examples

## Not run: 
data_hauls <- process_hauls(data_hauls = calas_bitacora)
result <- land_points(
  x_point = data_hauls$lon_final,
  y_point = data_hauls$lat_final
)
table(result)

## End(Not run)

Merge fishing trips, length and hauls data

Description

Joins data from fishing trips, length and hauls, combining catches by species, length ranges (minimum and maximum) and spatial-temporal information of each haul.

Usage

merge_length_fishing_trips_hauls(data_hauls, data_length_fishing_trips)

Arguments

data_hauls

Data frame processed with process_hauls().
data_length_fishing_trips

Data frame with length data by fishing trip and haul.

Value

Data frame with consolidated data from fishing trips, length and hauls.

Examples

## Not run: 
data_hauls <- process_hauls(data_hauls = calas_bitacora)
data_fishing_trips <- process_fishing_trips(data_fishing_trips = faenas_bitacora)
data_length <- process_length(data_length = tallas_bitacora)

data_length_fishing_trips <- merge(
   x = data_length,
   y = data_fishing_trips,
   by = "fishing_trip_code",
   all = TRUE
)

data_total <- merge_length_fishing_trips_hauls(
   data_hauls = data_hauls,
   data_length_fishing_trips = data_length_fishing_trips
)

## End(Not run)

Lines parallel to the Peruvian coast

Description

Dataset containing lines parallel to the Peruvian coast at different distances. These lines are useful for spatial analyses related to fishery management.

Usage

peru_coast_parallels

Format

A list of data.frames, each representing a line parallel to the Peruvian coast.

Each data frame contains:

lon

Longitude corresponding to the isoparalittoral area

lat

Latitude corresponding to the isoparalittoral area

dc

Distance to coast category (10-200)

Source

Indicate the data source

Examples

data(peru_coast_parallels)

Peruvian coastline

Description

Dataset containing the coastline of Peru represented as a spatial object.

Usage

peru_coastline

Format

A data.frame with the following fields:

Long

Longitude of the Peruvian coastline

Lat

Latitude of the Peruvian coastline

Source

Marine Institute of Peru (IMARPE)

Examples

data(peru_coastline)

Isoparalittoral areas

Description

Dataset containing isoparalittoral areas of the Peruvian coast. These areas represent zones with similar characteristics along the littoral.

Usage

peru_isoparalitoral_areas

Format

A data.frame with the following attributes:

lon

Longitude corresponding to the isoparalittoral area

lat

Latitude corresponding to the isoparalittoral area

area

Isoparalittoral Area code

grad

Latitude category every 0.5 degrees (3 - 19.5)

dc

Distance to coast category (10-200)

Source

Marine Institute of Peru (IMARPE)

Examples

data(peru_isoparalitoral_areas)

Plot fishing zones

Description

Creates visualizations of fishing zones using either ggplot2 (static) or leaflet (interactive). This function consolidates all zone plotting functionality.

Usage

plot_fishing_zones(
  data,
  coastline = NULL,
  parallels = NULL,
  type = "static",
  title = NULL,
  colors = NULL,
  show_legend = FALSE,
  legend_title = NULL,
  zone_labels = NULL,
  add_grid = FALSE,
  base_layers = FALSE,
  minimap = FALSE
)

Arguments

data

Data frame with fishing zone coordinates and metadata.
coastline

Data frame with coastline coordinates (columns "Long" and "Lat"). If NULL, uses internal dataset.
parallels

List of data frames with coast-parallel lines.
type

Plot type: "static" for ggplot2 or "interactive" for leaflet.
title

Plot title.
colors

Vector of colors for zones. If NULL, auto-generated.
show_legend

Logical. Show legend/layer control.
legend_title

Legend title.
zone_labels

Vector of custom labels for zones.
add_grid

Logical. Add coordinate grid (static only).
base_layers

Logical. Include multiple base map layers (interactive only).
minimap

Logical. Add minimap (interactive only).

Value

ggplot object (static) or leaflet object (interactive).

Examples

## Not run: 
plot_fishing_zones(
  data = zone_data,
  coastline = coastline_data,
  type = "static",
  title = "Fishing Zones",
  show_legend = TRUE
)

plot_fishing_zones(
  data = zone_data,
  coastline = coastline_data,
  type = "interactive",
  base_layers = TRUE,
  minimap = TRUE
)

## End(Not run)

Plot juvenile analysis

Description

Creates comprehensive visualizations for juvenile fish analysis including bar charts, line plots, and comparative analyses. This function consolidates all juvenile plotting functionality.

Usage

plot_juvenile_analysis(
  data,
  x_var,
  fill_var = NULL,
  length_cols = NULL,
  a = 0.0012,
  b = 3.1242,
  x_date_breaks = NULL,
  plot_type = "bars",
  title = NULL,
  subtitle = NULL,
  sort_by = "x",
  color_palette = NULL,
  facet_var = NULL,
  facet_cols = 2,
  bar_position = "dodge",
  y_limits = c(0, 100),
  use_facet_wrap = TRUE,
  group_by_type = TRUE,
  reference_line = NULL,
  theme_style = "light",
  legend_position = "bottom",
  rotate_x_labels = TRUE,
  na_to_zero = FALSE
)

Arguments

data

Data frame with juvenile analysis data.
x_var

Column name for x-axis variable.
fill_var

Column name for fill/color variable.
length_cols

Vector of length frequency column names.
a

Length-weight relationship coefficient.
b

Length-weight relationship exponent.
x_date_breaks

Date breaks for x-axis (e.g., "1 day", "1 month").
plot_type

Plot type: "bars", "lines", "points", "mixed".
title

Plot title.
subtitle

Plot subtitle.
sort_by

Sorting method: "x", "number", "weight".
color_palette

Custom color palette.
facet_var

Variable for faceting.
facet_cols

Number of facet columns.
bar_position

Bar position: "dodge", "stack", "fill".
y_limits

Y-axis limits.
use_facet_wrap

Use facet wrap for juvenile type.
group_by_type

Group by juvenile type when not faceting.
reference_line

Reference line value (e.g., legal limit).
theme_style

Theme style: "classic", "minimal", "light", "dark".
legend_position

Legend position.
rotate_x_labels

Rotate x-axis labels.
na_to_zero

Convert NA values to zeros.

Value

ggplot object.

Examples

## Not run: 
plot_juvenile_analysis(
  data = fishery_data,
  x_var = "date",
  length_cols = paste0("length_", seq(8, 15, 0.5))
)

plot_juvenile_analysis(
  data = fishery_data,
  x_var = "date",
  fill_var = "vessel",
  length_cols = length_columns,
  plot_type = "mixed",
  reference_line = 10,
  title = "Juvenile Analysis by Vessel and Date"
)

## End(Not run)

Generate interactive plot of polygons with leaflet

Description

Creates an interactive map using leaflet, showing polygons with popup information.

Usage

plot_zones_interactive(
  polygons,
  coastline,
  title,
  colors,
  show_legend = TRUE,
  labels = NULL,
  base_layers = TRUE,
  minimap = TRUE
)

Arguments

polygons

List of polygons. Each must have fields such as coords, announcement, dates and coordinates.
coastline

Data frame with the coastline (columns Long and Lat).
title

Title to display at the top of the map.
colors

Vector of colors. If NULL, they are automatically assigned with RColorBrewer::brewer.pal.
show_legend

Logical. If TRUE, the layers control (legend) is displayed.
labels

Optional vector of names to display in the legend and map labels.
base_layers

Logical. If TRUE, includes base layers such as satellite and ocean maps.
minimap

Logical. If TRUE, displays a minimap in the lower right corner.

Value

A leaflet object with the interactive map.

Generate static plot of polygons on a map

Description

Creates a static plot using ggplot2 that displays geographic polygons on a coastline base. Colors are assigned by groups (announcements/labels), not individual polygons.

Usage

plot_zones_static(
  polygons,
  coastline,
  title,
  colors,
  show_legend = TRUE,
  name_legend = NULL,
  labels = NULL,
  add_grid = TRUE,
  theme = ggplot2::theme_minimal()
)

Arguments

polygons

List of polygons. Each polygon must contain a coords matrix with longitude and latitude columns.
coastline

Data frame with the coastline, with columns Long and Lat.
title

Main title of the plot.
colors

Vector of colors to fill the polygons. If NULL, colors are automatically assigned by groups.
show_legend

Logical. If TRUE, the legend is displayed.
name_legend

Name of the legend (optional). If NULL, legend has no title.
labels

Vector of labels for the polygons (optional).
add_grid

Logical. If TRUE, adds a geographic grid to the plot.
theme

ggplot2 theme to use.

Value

A ggplot object ready to be plotted.

Prepare polygons from coordinate data

Description

Helper function to process data and prepare polygons using preexisting parallel lines.

Usage

prepare_polygons(
  data,
  coastline,
  coast_parallels = NULL,
  column_names = c("lat", "lon", "dc")
)

Arguments

data

Data frame with coordinates.
coastline

Data frame with coastline.
coast_parallels

List of data frames with lines parallel to the coast.
column_names

Vector with column names in coast_parallels data frames.

Value

List of polygons for visualization.

Process fishing trip data from PRODUCE sitrapesca files

Description

Processes fishing trip data from PRODUCE logbooks. Automatically detects required columns and creates standardized output with proper date conversion.

Usage

process_fishing_trips(data_fishing_trips, verbose = FALSE)

Arguments

data_fishing_trips

Data frame with raw fishing trip data.
verbose

Logical. Print column mapping information.

Value

Data frame with 6 standardized columns including trip code, vessel information, and trip dates.

Examples

fishing_trips <- process_fishing_trips(data_fishing_trips = faenas_bitacora)
fishing_trips <- process_fishing_trips(data_fishing_trips = faenas_bitacora, verbose = TRUE)

Process fishing haul data from PRODUCE sitrapesca files

Description

Processes fishing haul data from PRODUCE logbooks. Automatically detects required columns and creates standardized output with coordinates converted to decimal degrees.

Usage

process_hauls(data_hauls, correct_coordinates = TRUE, verbose = FALSE)

Arguments

data_hauls

Data frame with raw haul data.
correct_coordinates

Logical. Correct coordinate errors during conversion.
verbose

Logical. Print column mapping information.

Value

Data frame with 16 standardized columns including fishing trip code, haul number, dates, coordinates, species, and catch data.

Examples

processed_hauls <- process_hauls(data_hauls = calas_bitacora)
processed_hauls <- process_hauls(data_hauls = calas_bitacora, verbose = TRUE)

Process length data from hauls

Description

Processes length data from PRODUCE logbooks. Automatically detects required columns and transforms from long to wide format.

Usage

process_length(data_length, verbose = FALSE)

Arguments

data_length

Data frame with raw length data.
verbose

Logical. Print column mapping information.

Value

Data frame with length by haul in wide format with individual columns for each length class.

Examples

length_data <- process_length(data_length = tallas_bitacora)
length_data <- process_length(data_length = tallas_bitacora, verbose = TRUE)

Summarize juveniles by group

Description

Calculates juvenile percentages by specified groups, both in number and weight. Uses modern dplyr approach for efficient processing. Can auto-detect length columns if not specified.

Usage

summarize_juveniles_by_group(
  data,
  group_cols,
  length_cols = NULL,
  juvenile_limit = 12,
  a = 0.0012,
  b = 3.1242,
  remove_empty = TRUE,
  verbose = FALSE
)

Arguments

data

Data frame with length frequency data.
group_cols

Vector of column names for grouping.
length_cols

Vector of length column names or indices. If NULL, auto-detection is attempted.
juvenile_limit

Length threshold for juveniles.
a

Coefficient of length-weight relationship.
b

Exponent of length-weight relationship.
remove_empty

Logical. Remove groups with no data.
verbose

Logical. Print information about detected columns.

Value

Data frame with juvenile statistics by group.

Examples

## Not run: 
juvenile_summary <- summarize_juveniles_by_group(
  data = fishery_data,
  group_cols = "date",
  juvenile_limit = 12
)

## End(Not run)

Length data sampled from hauls made by vessels in the Peruvian sea.

Description

Dataset containing information about lengths of marine species captured along the Peruvian littoral.

Usage

tallas_bitacora

Format

A data.frame with sample data of lengths sampled from hauls made by vessels in the Peruvian sea.

Source

Indicate the data source

Examples

data(tallas_bitacora)

Validate processed fishing trip data quality

Description

Validates data quality metrics for processed fishing trip data.

Usage

validate_fishing_trip_data(processed_trips)

Arguments

processed_trips

Data frame returned by process_fishing_trips().

Value

List with data quality metrics including completeness scores and issue counts.

Validate processed haul data quality

Description

Validates data quality metrics for processed haul data.

Usage

validate_haul_data(processed_hauls)

Arguments

processed_hauls

Data frame returned by process_hauls().

Value

List with data quality metrics including completeness scores and issue counts.

Validate processed length data quality

Description

Validates data quality metrics for processed length data.

Usage

validate_length_data(processed_length)

Arguments

processed_length

Data frame returned by process_length().

Value

List with data quality metrics including completeness scores and distribution statistics.

Validate data for polygon creation

Description

Validates that a data frame contains the necessary columns for creating fishing zone polygons. Checks for either coordinate-based or distance-based polygon definition data.

Usage

validate_polygon_data(data)

Arguments

data

Data frame to validate. Must contain polygon definition columns.

Details

The function requires either:

  • Coordinate-based: StartLatitude, EndLatitude, StartLongitude, EndLongitude

  • Distance-based: StartLatitude, EndLatitude, StartNauticalMiles, EndNauticalMiles

Value

Returns TRUE invisibly if validation passes, otherwise throws an error.

Examples

# Coordinate-based polygon data
coord_data <- data.frame(
  StartLatitude = "15 30 S",
  EndLatitude = "15 45 S",
  StartLongitude = "75 30 W",
  EndLongitude = "75 45 W"
)
validate_polygon_data(coord_data)

# Distance-based polygon data
distance_data <- data.frame(
  StartLatitude = "15 30 S",
  EndLatitude = "15 45 S",
  StartNauticalMiles = 5,
  EndNauticalMiles = 15
)
validate_polygon_data(distance_data)

Weight length frequencies by total catch

Description

Scales observed length frequencies based on total recorded catch using length-weight relationship.

Usage

weight_by_catch(frequency, catch, length, a, b, silence_warnings = FALSE)

Arguments

frequency

Numeric vector of observed length frequencies.
catch

Total catch amount (in kg or tons).
length

Numeric vector of lengths corresponding to frequencies.
a

Coefficient of the length-weight relationship.
b

Exponent of the length-weight relationship.
silence_warnings

Logical. Suppress warning messages.

Details

Catch weighting is used to estimate the size composition of catches when only sub-samples are measured for length frequency analysis.

Value

Numeric vector of weighted frequencies.

References

IMARPE (2020). Protocolo Elaboración de la Tabla de Decisión para la determinación del Límite Máximo Total Permisible por temporada de pesca en la pesquería del stock norte-centro de la anchoveta peruana. IMP-DGIRP/AFDPERP, Edición: 05, Revisión 00, 40p.