Package 'artfishr'

Description

Produces a plot for the accuracy

Usage

accuracy_plot(sample = NULL, population, method = "higher")

Arguments

Description

artfish_accuracy

Usage

artfish_accuracy(n, N, method = "higher")

Arguments

Description

artfish_estimates

Usage

artfish_estimates(
  data_effort = NULL,
  data_landing = NULL,
  ref_fishingunits = NULL,
  ref_species,
  year = NULL,
  month = NULL
)

Arguments

Value

the result of Artfish

Description

artfish_estimates_by_fleet_segment

Usage

artfish_estimates_by_fleet_segment(
  data_effort = NULL,
  data_landing = NULL,
  ref_fishingunits = NULL,
  ref_species,
  year = NULL,
  month = NULL
)

Arguments

Value

the result of Artfish

Description

artfish_flouca_by_period

Usage

artfish_flouca_by_period(
  year = NULL,
  month = NULL,
  active_vessels,
  effort,
  effort_source = c("survey", "registry"),
  active_days = NULL,
  landings,
  minor_strata = NULL,
  validate = TRUE
)

Arguments

Value

the result of Artfish for a given year/month

Description

artfish_new

Usage

artfish_new(
  active_vessels = NULL,
  effort = NULL,
  effort_source = c("fisher_interview", "boat_counting"),
  active_days = NULL,
  landings = NULL,
  minor_strata = NULL
)

Arguments

Value

the result of Artfish

Description

artfish_new_by_period

Usage

artfish_new_by_period(
  year = NULL,
  month = NULL,
  active_vessels,
  effort,
  effort_source = c("fisher_interview", "boat_counting"),
  active_days = NULL,
  landings,
  minor_strata = NULL,
  validate = TRUE
)

Arguments

Value

the result of Artfish for a given year/month

Description

Accuracy toolbox shiny module server

Usage

artfish_shiny_accuracy_server(id, lang = NULL)

Arguments

Description

POC shiny module UI

Usage

artfish_shiny_accuracy_ui(id)

Arguments

Description

Server-side shiny logic to run Artfish computation as business service

Usage

artfish_shiny_computation_server(
  id,
  refresh,
  effort,
  effort_source,
  active_vessels,
  active_vessels_strategy,
  active_days,
  landings,
  minor_strata = NULL,
  progress_fn = NULL
)

Arguments

Description

Server-side logic for the ARTFISH Fishing Unit dashboard.

The *"Catch and effort - Detailed by fishing unit"* module provides a dedicated workspace for exploring key output metrics derived from artfish_compute_report, with a focus on one or multiple fishing units. It enables users to analyse the evolution of fishing activity over time, with filtering options for fishing units and time range.

The module is composed of:

• A selector panel to adjust the time range and fishing units to display

• A set of KPIs presenting aggregated metrics for the selected period

• Three boxed plots powered by fdishinyr::generic_chart allowing users to analyse ranked top items for:

  • (1) Species composition (by quantity)

  • (2) Fishing unit composition (by quantity)

  • (3) Fishing unit composition (by value)

• Six boxed plots powered by fdishinyr::generic_chart allowing users to analyse key Artfish metrics:

  • (1) Total catch estimation

  • (2) Catch per unit effort (CPUE)

  • (3) Total nominal effort

  • (4) Activity coefficient

  • (5) Number of active vessels

  • (6) Total value estimation

Usage

artfish_shiny_fishing_unit_server(
  id,
  lang = NULL,
  estimate,
  effort_source,
  minor_strata = NULL,
  opts = list()
)

Arguments

Description

User interface for the ARTFISH Fishing Unit Dashboard

Usage

artfish_shiny_fishing_unit_ui(id)

Arguments

Description

Server-side logic for the ARTFISH Overview dashboard.

The *"Total catch and effort - Statistics at country level"* module provides a high-level visualization of key output metrics derived from artfish_compute_report. It acts as a dashboard to track the overall evolution of fishing activity over time, with the ability to filter by fishing units and time range.

The module is composed of:

• A selector panel to adjust the time range and fishing units to display

• A set of KPIs presenting aggregated country-level metrics for the selected period

• Seven boxed plots powered by fdishinyr::generic_chart allowing users to analyse:

  • (1) Total catch estimation

  • (2) Species composition (by quantity)

  • (3) Total nominal effort

  • (4) Number of active vessels

  • (5) Total value estimation

  • (6) Species composition (by value)

  • (7) Catch per unit effort (CPUE)

Usage

artfish_shiny_overview_server(
  id,
  lang = NULL,
  estimate,
  effort_source = NULL,
  minor_strata = NULL,
  opts = list()
)

Arguments

Description

User interface for the ARTFISH Overview Dashboard

Usage

artfish_shiny_overview_ui(id)

Arguments

Description

Server-side logic for the ARTFISH Report dashboard.

The *"Detailed results of Artfish computation – by stratum"* module provides an ergonomic view of key intermediate results and calculation steps underlying the Artfish algorithm for a given temporal unit.

The initial view displays a selector inviting the user to choose a year. If no year is available, the user must first compute at least one indicator using artfish_compute_report. Successive dropdown lists then allow the user to select a month and a fishing unit before submitting the selection. A complete report is displayed on the right-hand side.

The module is composed of:

• A selector panel to choose the report to display by selecting year, month, and fishing unit

• A button to validate the selection

• A set of KPIs presenting the report status and the type of effort source

• A gauge chart presenting the overall accuracy quality of the indicator

• An ergonomic report view including intermediate steps and final indicators for the estimation of:

  • (1) Effort

  • (2) Landings

  • (3) Estimated catch by species

Usage

artfish_shiny_report_server(
  id,
  lang = NULL,
  estimate,
  effort_source,
  minor_strata
)

Arguments

Description

User interface for the ARTFISH Report Dashboard

Usage

artfish_shiny_report_ui(id)

Arguments

Description

Server-side logic for the ARTFISH Species dashboard.

The *"Catch and effort - Detailed by species"* module provides a species-oriented workspace for exploring key output metrics derived from artfish_compute_report, with a focus on individual species caught.

The initial view displays a selector inviting the user to choose a species. The dropdown lists all species for which entries are available in the database. Users can then analyse the evolution of fishing activity over time, with filtering options for species, fishing units, and time range.

The module is composed of:

• A selector panel to choose the species to display

• A selector panel to adjust the time range and fishing units to display

• Two boxed plots powered by fdishinyr::generic_chart allowing users to analyse, at species level:

  • (1) Fishing unit composition (by quantity)

  • (2) Target species ranking (by quantity)

• A set of KPIs presenting aggregated metrics for the selected period

• Five boxed plots powered by fdishinyr::generic_chart allowing users to analyse key Artfish metrics:

  • (1) Total catch estimation

  • (2) Catch per unit effort (CPUE)

  • (3) Total value estimation

  • (4) Average price

  • (5) Total nominal effort

Usage

artfish_shiny_species_server(
  id,
  lang = NULL,
  estimate,
  effort_source = NULL,
  minor_strata = NULL,
  opts = list()
)

Arguments

Description

User interface for the ARTFISH Species Dashboard

Usage

artfish_shiny_species_ui(id)

Arguments

Description

POC shiny module server

Usage

artfish_shiny_welcome_server(id, lang = NULL)

Arguments

Description

POC shiny module UI

Usage

artfish_shiny_welcome_ui(id)

Arguments

Description

artfishr provides a set of utility tools to implement ArtFish methodology

Author(s)

Emmanuel Blondel [email protected]

See Also

Useful links:

• https://github.com/fdiwg/artfishr

• https://fdiwg.github.io/artfishr/

• Report bugs at https://github.com/fdiwg/artfishr/issues

Description

Artfishr shiny server function

Usage

artfish_shiny_server(input, output, session)

Description

Artfishr shiny ui function

Usage

artfish_shiny_ui(id)

Description

If it is set to zero or or is NA, the nb of active days in the period will be autogenerated

Usage

complete_active_days(active_days)

Arguments

Value

the curated active days table

Description

Compute spatial and temporal accuracy for activity coefficient and cpue. Overall accuracy return the minimal value of the 4 accuracy indicators.

Usage

compute_accuracy(
  activity_coefficient,
  effort_estimate,
  cpue,
  sui,
  minor_strata = NULL
)

Arguments

Value

a tibble object giving the different accuracy by strata

Description

Computes catch estimate

Usage

compute_catch_estimate(effort_estimate, landings, minor_strata = NULL)

Arguments

Value

a tibble giving the estimated catch by strata

Description

Computes catch estimates by species

Usage

compute_catch_estimates_by_species(
  landings,
  catch_estimate,
  minor_strata = NULL
)

Arguments

Value

a tibble

Description

The CPUE (Catch Per Unit of Effort) is computed from the landings, as the ratio between nominal landed catches (catch_nominal_landed) and the effort fishing duration (effort_fishing_duration).

The computation is performed grouped by a strata compound by year, month and fishing_unit. This strata can be extended with additional columns with the minor_strata argument.

Since landings give details data on landed species, nominal landed catches are sum grouped by the strata and by fishing trip to get the total nominal landed catches by fishing trip. Both catch_nominal_landed and effort_fishing_duration are then sum by strata and the CPUE is computed as the ratio of these sum.

Note: Additional checks are performed to remove data with NAs.

Usage

compute_cpue(landings, minor_strata = NULL)

Arguments

Value

a tibble object giving the CPUE by strata

Description

The activity coefficient is computed from the effort. Depending on the source of effort data, the source information used is different: - In the case of 'fisher_interview' the coefficient of activity is computed as the ratio between the effort_fishing_duration and the effort_fishing_reference_period. - In the case of 'boat_counting' the coefficient of activity is computed as the ratio between the fleet_engagement_number and the fleet_engagement_max

The computation is performed grouped by a strata compound at minimum by the year, month and fishing_unit. This strata can be extended by adding one or more columns with the minor_strata argument.

Note: Additional check are performed to remove data with NAs, and ensure data consistency

Usage

compute_effort_activity_coefficient(
  effort,
  effort_source = c("fisher_interview", "boat_counting"),
  minor_strata = NULL
)

Arguments

Value

a tibble object giving activity coefficient by strata

Description

Computes nominal effort estimate

Usage

compute_effort_estimate(
  effort,
  effort_source = c("fisher_interview", "boat_counting", "household_interview"),
  landings,
  active_days = NULL,
  active_vessels = NULL,
  active_vessels_strategy = c("latest", "closest"),
  census_typology = NULL,
  minor_strata = NULL,
  progress_fn = NULL
)

Description

The effort estimate is computed as the product of the fleet_engagement_number (derived from the active_vessels), the effort_fishable_duration (derivated from the active_days, and the effort_activity_coefficient computed with the compute_effort_activity_coefficient function.

The computation of the nominal effort estimate is done grouped by strata compound of year, month and fishing_unit. This strata can be extended with additional columns with the minor_strata argument.

Active vessels:

The active_vessels can be either time-dependent,ie given by year/month, year only or be atemporal, mainly depending on how and at which frequency the vessels census/survey is operated in the country.

An additional argument called active_vessels_strategy controls how to select the active_vessels data in time, when this data is time-dependent. The latest strategy will select the latest data acquired in time, while the closest will select the closest data acquired in time, which could be data acquired after the effort data considered.

As example, a vessel census is performed each five years, and data is available for 2007 and 2012. Effort data for 2011 needs to be computed. Which active_vessels data should be used? In case of a latest strategy, in 2011, the latest data available is 2007, so this one will be used. In the case of a closest strategy, in 2011, we used the closest vessel data (ie 2012), assuming it betters characterizes the fleet engagement for the year considered.

Active days:

In case of 'fisher_interview' (as source of the effort data), the active_days data should be ignored, as the days of the month will be used (and generated automatically by the function)

Usage

compute_effort_estimate_with_FAO_Artfish(
  effort,
  effort_source = c("fisher_interview", "boat_counting"),
  landings,
  active_days = NULL,
  active_vessels,
  active_vessels_strategy = c("latest", "closest"),
  minor_strata = NULL,
  progress_fn = NULL
)

Arguments

Description

TBD

Usage

compute_effort_estimate_with_IRD_Pechart(
  effort,
  effort_source = c("household_interview"),
  landings,
  active_days,
  census_typology,
  minor_stratum = NULL,
  progress_fn = NULL
)

Author(s)

Johanna Herfaut

Description

compute_report

Usage

compute_report(
  effort,
  effort_source = c("fisher_interview", "boat_counting", "household_interview"),
  active_vessels = NULL,
  active_vessels_strategy = NULL,
  active_days,
  landings,
  minor_strata = NULL,
  validate = FALSE,
  progress_fn = NULL
)

Arguments

Value

the result of Artfish

Description

Compute the sufficient uniformity index for effort and catch corresponding to the uniformity of sampling over the sampled days.

Usage

compute_sui(effort, landings, minor_strata = NULL)

Arguments

Value

a tibble object giving the different accuracy by strata

Description

Generates an empty data frame following one of the artfishr format specifications.

Usage

create_artfish_template(
  format = c("artfish_A_active_vessels", "artfish_B1_effort", "artfish_B2_effort",
    "artfish_C_active_days", "artfish_D_landings"),
  include_meta = FALSE,
  save_as = NULL
)

Arguments

Value

A zero-row data frame matching the schema definition.

See Also

[decode_artfish_schema()]

Description

Family of functions to create empty templates for artfishr dataset types.

Usage

create_active_vessels_template(include_meta = FALSE, save_as = NULL)

create_active_days_template(include_meta = FALSE, save_as = NULL)

create_landings_template(include_meta = FALSE, save_as = NULL)

create_effort_template(
  effort_source = c("boat_counting", "fisher_interview"),
  include_meta = FALSE,
  save_as = NULL
)

Details

These functions are convenience wrappers around [create_artfish_template()].

See Also

[create_artfish_template()], [decode_artfish_schema()]

Description

Reads a JSON format specification (as used in inst/extdata/format_specs) and returns an empty data frame with the appropriate column names.

Usage

decode_artfish_schema(json_path, include_meta = FALSE)

Arguments

Value

A data.frame with zero rows and the columns defined in the schema.

Description

ERROR logger

Usage

ERROR(txt, ...)

Arguments

Generates a tibble of active days.

Description

Function that generates a table of active days for all periods. The unique list of fishing units are inherited from available tables (active_vessels, effort, landings). In the same way, the list of minor strata values will be inherited based on the minor_strata columns available in data.

Usage

generate_active_days(
  effort,
  effort_source = c("fisher_interview", "boat_counting", "household_interview"),
  landings,
  active_vessels = NULL,
  minor_strata = NULL
)

Arguments

Value

an object of class tibble give active days

Generates a tibble of active days.

Description

Function that generates a table of active days by year/month. The unique list of fishing units are inherited from available tables (effort, landings + eventually active_vessels). In the same way, the list of minor strata values will be inherited based on the minor_strata columns available in data.

Usage

generate_active_days_by_period(
  year,
  month,
  effort,
  effort_source = c("fisher_interview", "boat_counting", "household_interview"),
  landings,
  active_vessels = NULL,
  minor_strata = NULL
)

Arguments

Value

an object of class tibble give active days

Description

Get FDI terms for Artfish inputs/outputs

Usage

get_fdi_terms()

Description

Get data validators powered by vrule

Usage

get_vrule_validators()

Value

the list of internal data validators as objects of class format_spec

Description

INFO logger

Usage

INFO(txt, ...)

Arguments

Description

Gives the common column names between two tables. Util to be used in dplyr *_join calls by argument to avoid explicit messages triggered by dplyr when guessing the columns (in case by is not specified) param x a data.frame or tibble param y a data.frame or tibble

Usage

join_guess_by(x, y)

Description

Generic logger

Usage

logger(type = c("INFO", "WARN", "ERROR"), txt, ...)

Arguments

Description

Runs a Artfishr Shiny application

Usage

run_artfish_shiny(...)

Description

Produces a plot for the sample size

Usage

sample_size_plot(
  sample = NULL,
  population,
  sample.mean,
  sample.sd,
  alpha = 0.1
)

Arguments

Description

Select active vessels for a selection of years

Active vessels:

The active_vessels can be either time-dependent,ie given by year/month, year only or be atemporal, mainly depending on how and at which frequency the vessels census/survey is operated in the country.

An additional argument called active_vessels_strategy controls how to select the active_vessels data in time, when this data is time-dependent. The latest strategy will select the latest data acquired in time, while the closest will select the closest data acquired in time, which could be data acquired after the effort data considered.

As example, a vessel census is performed each five years, and data is available for 2007 and 2012. Effort data for 2011 needs to be computed. Which active_vessels data should be used? In case of a latest strategy, in 2011, the latest data available is 2007, so this one will be used. In the case of a closest strategy, in 2011, we used the closest vessel data (ie 2012), assuming it betters characterizes the fleet engagement for the year considered.

Usage

select_active_vessels(
  periods,
  active_vessels,
  active_vessels_strategy = c("latest", "closest")
)

Arguments

Value

a series of active vessels selection for the years considered (added as ref_period)

Description

set_translation_language

Usage

set_translation_language(lang)

Arguments

Description

Set data validators powered by vrule

Usage

set_vrule_validators()

Description

Get translator

Usage

translator()

Value

the translator, object of class Translator

Description

unif_index

Usage

unif_index(days)

Arguments

Description

Family of functions to validate empty input dataset.

Details

These functions are convenience wrappers around [validate_input_datasets()].

See Also

[validate_input_datasets()]

Description

Validate input datasets

Usage

validate_input_datasets(
  active_vessels,
  effort,
  effort_source = c("fisher_interview", "boat_counting"),
  active_days = NULL,
  landings
)

Arguments

Description

WARN logger

Usage

WARN(txt, ...)

Arguments