Type:	Package
Title:	Toolkit for Investigation and Visualization of Young Anchovies
Version:	0.1.1
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.
URL:	https://github.com/HansTtito/Tivy
BugReports:	https://github.com/HansTtito/Tivy/issues
Depends:	R (≥ 4.0.0)
Imports:	dplyr (≥ 1.0.0), tidyr (≥ 1.0.0), lubridate (≥ 1.7.0), stringr (≥ 1.4.0), stringi (≥ 1.4.0), ggplot2 (≥ 3.3.0), leaflet (≥ 2.0.0), RColorBrewer (≥ 1.1.0), rlang (≥ 0.4.0), httr (≥ 1.4.0), rvest (≥ 1.0.0), jsonlite (≥ 1.6.0), pdftools (≥ 3.0.0), stats, utils, future (≥ 1.21.0), future.apply (≥ 1.7.0), patchwork (≥ 1.1.0), scales (≥ 1.1.0)
Suggests:	rnaturalearth (≥ 0.1.0), rnaturalearthdata, testthat (≥ 3.0.0), knitr, rmarkdown
VignetteBuilder:	knitr
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-07-23 04:27:04 UTC; hkev2
Author:	Hans Ttito [aut, cre]
Maintainer:	Hans Ttito <kvttitos@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-23 08:00:08 UTC

Tivy: 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.

Author(s)

Maintainer: Hans Ttito kvttitos@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/HansTtito/Tivy

• Report bugs at https://github.com/HansTtito/Tivy/issues

Helper function to add faceting to juvenile plots

Description

Helper function to add faceting to juvenile plots

Usage

add_juvenile_faceting(p, facet_var, use_facet_wrap, facet_cols, fill_var)

Helper function to add geoms to juvenile plots

Description

Helper function to add geoms to juvenile plots

Usage

add_juvenile_geoms(
  p,
  plot_type,
  bar_position,
  fill_var,
  use_facet_wrap,
  group_by_type
)

Helper function to add reference line to juvenile plots

Description

Helper function to add reference line to juvenile plots

Usage

add_juvenile_reference_line(p, reference_line, data_long, x_var)

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

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

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)

Helper function to apply theme to juvenile plots

Description

Helper function to apply theme to juvenile plots

Usage

apply_juvenile_theme(p, theme_style, legend_position, rotate_x_labels)

Build request parameters

Description

Creates parameter set for API request.

Usage

build_request_params(
  start_index,
  batch_size,
  token,
  tipo = 2,
  start_date = "",
  end_date = ""
)

Arguments

Value

List of parameters.

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 coast longitude

Description

Internal function to calculate approximate longitude of coast at a given latitude.

Usage

calculate_coast_longitude(coastline, latitude)

Arguments

Value

Approximate coast longitude.

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

Value

List with distance vectors and corresponding indices.

Calculate Euclidean distance

Description

Internal function to calculate Euclidean distance between coordinates, adapted for geographic coordinate systems.

Usage

calculate_euclidean_distance(lon1, lat1, lon2, lat2, unit)

Arguments

Details

The Euclidean distance calculates the straight-line distance between two points, adapted for geographic coordinates using spherical projection approximations. This implementation accounts for Earth's curvature by applying latitude corrections.

Value

List with minimum distance and corresponding index.

Note

This implementation adapts the standard Euclidean distance metric for geographic coordinate systems by incorporating Earth's radius and latitude corrections.

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

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 grid distance

Description

Internal function to calculate distance with fixed grid resolution, using coordinate discretization followed by Haversine distance calculation.

Usage

calculate_grid_distance(lon1, lat1, lon2, lat2, resolution = 0.25, unit = "km")

Arguments

Details

This method rounds coordinates to the nearest grid point based on the specified resolution, then calculates the Haversine distance between the origin and each grid-aligned destination point. This approach is useful for applications requiring consistent spatial discretization.

Value

List with minimum distance and index.

Note

This implementation combines coordinate discretization with the Haversine formula for applications requiring grid-based spatial analysis.

Calculate distance using Haversine formula

Description

Internal function to calculate distances using the Haversine formula with WGS84 ellipsoid. Implements the algorithm described in Sinnott (1984) for computing great circle distances.

Usage

calculate_haversine_distance_wgs84(lon1, lat1, lon2, lat2, unit)

Arguments

Details

The Haversine formula calculates the shortest distance between two points on a sphere given their latitude and longitude.

Value

List with minimum distance and corresponding index.

References

Sinnott, R.W. (1984). Virtues of the Haversine. Sky and Telescope, 68(2), 159.

Veness, C. (2002-2022). Calculate distance, bearing and more between Latitude/Longitude points. Movable Type Scripts. https://www.movable-type.co.uk/scripts/latlong.html

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

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

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)

Calculate Manhattan distance between coordinates

Description

Internal function to calculate Manhattan distance (L1 norm) between coordinates, adapted for geographic coordinate systems.

Usage

calculate_manhattan_distance(lon1, lat1, lon2, lat2, unit)

Arguments

Details

The Manhattan distance calculates the sum of absolute differences between coordinates, adapted for geographic coordinates using spherical projection approximations. This implementation accounts for Earth's curvature by applying latitude corrections to longitude differences.

Value

List with minimum distance and corresponding index.

Note

This implementation adapts the standard Manhattan distance metric for geographic coordinate systems by incorporating Earth's radius and latitude corrections.

Check required packages

Description

Internal function to verify required packages are installed.

Usage

check_required_packages(packages = c("httr", "rvest", "jsonlite"))

Arguments

Value

TRUE if all packages are available, stops execution otherwise.

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

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

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

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 download directory

Description

Creates download directory if it doesn't exist.

Usage

create_download_dir(download_dir, verbose = TRUE)

Arguments

Value

TRUE if directory was created or already exists.

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

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)

Helper function to create base plot for juvenile analysis

Description

Helper function to create base plot for juvenile analysis

Usage

create_juvenile_base_plot(
  data_long,
  x_var,
  fill_var,
  use_facet_wrap,
  group_by_type
)

Helper function to customize axes in juvenile plots

Description

Helper function to customize axes in juvenile plots

Usage

customize_juvenile_axes(p, data_long, x_var, x_date_breaks, y_limits)

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

Value

Numeric vector with coordinates converted to decimal degrees.

Examples

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

Download file

Description

Internal function to download a file from a URL.

Usage

download_file(url, file_name, download_dir, verbose = TRUE)

Arguments

Value

TRUE if download successful, FALSE otherwise.

Extract cookies

Description

Extracts cookies from HTTP response.

Usage

extract_cookies(response, verbose = TRUE)

Arguments

Value

Data frame with cookies.

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

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

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)

Extract points between latitudes

Description

Extracts all points of a parallel line between two latitudes.

Usage

extract_points_between_latitudes(line, lat_min, lat_max, lat_name, lon_name)

Arguments

Value

Matrix of points with longitude and latitude columns.

Extract token

Description

Extracts security token from HTML page.

Usage

extract_token(html_content, verbose = TRUE)

Arguments

Value

Extracted token.

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 announcements batch

Description

Retrieves batch of announcements using token.

Usage

fetch_announcements_batch(
  start_index,
  batch_size,
  token,
  cookies,
  main_url,
  tipo = 2,
  start_date = "",
  end_date = "",
  verbose = TRUE
)

Arguments

Value

Data frame with announcements or NULL if error.

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

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

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

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 = "")

Find parallel line by distance

Description

Internal function to find the parallel line closest to a specified distance.

Usage

find_parallel_line(miles, coast_parallels)

Arguments

Value

List with data frame and actual mile value.

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

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)

Generate download URL

Description

Internal function to construct download URLs for files.

Usage

generate_download_url(
  file_name,
  base_url =
    "https://consultasenlinea.produce.gob.pe/produce/descarga/comunicados/dgsfs/"
)

Arguments

Value

Complete download URL.

Generate robust color palette for any number of groups

Description

Creates a color palette that can handle any number of groups without errors. Uses RColorBrewer when possible, falls back to other methods for large numbers.

Usage

generate_robust_colors(n_groups)

Arguments

Value

Character vector of colors.

Get length range from frequencies

Description

Finds the minimum or maximum length with positive frequency.

Usage

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

Arguments

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")

Get main page

Description

Makes request to main page and returns HTTP response.

Usage

get_main_page(url, verbose = TRUE)

Arguments

Value

HTTP response object.

Interpolate point on line

Description

Internal function to interpolate a point for a specific latitude on a parallel line.

Usage

interpolate_point(line, latitude, lat_name, lon_name)

Arguments

Value

Vector with interpolated longitude and latitude.

Check date range

Description

Internal helper function to check if a date falls within a specified range.

Usage

is_date_in_range(date_str, start_date, end_date)

Arguments

Value

Logical indicating if date is in range.

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

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)

Make API request

Description

Makes POST request to API with provided parameters.

Usage

make_api_request(url, params, cookies, main_url)

Arguments

Value

HTTP response object.

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

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

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

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

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

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

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

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

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 JSON response

Description

Processes JSON response from API and extracts announcements.

Usage

process_json_response(content_text, verbose = TRUE)

Arguments

Value

Data frame with announcements or NULL if error.

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

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)

Process PDF text content

Description

Internal function to extract structured data from PDF text content.

Usage

process_pdf_text(text, file_name, verbose = TRUE)

Arguments

Value

Data frame with extracted information.

Process weighting block

Description

Helper function that processes a block of data for catch weighting. Used internally by apply_catch_weighting.

Usage

process_weighting_block(
  data,
  length_cols,
  catch_col,
  a,
  b,
  silence_warnings = FALSE
)

Arguments

Value

Data frame with weighted columns added.

Safe numeric conversion

Description

Safely converts values to numeric with warning handling.

Usage

safe_numeric_conversion(x, column_name = "column")

Arguments

Value

Numeric vector.

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

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

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

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

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

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

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.