discard_generic_diagnostic: discard_generic_diagnostic: Calculate discards for January...

In noaa-garfo/discaRd: Cochran bycatch estimation

discard_generic_diagnostic: Calculate discards for January fishing year Support tables in this version are function variables. This allows them to be modified and run for diagnostic testing. This function can output a summary of discard information by strata, including number of trips used, variance, CV, discard amount, and KALL. It can also output a table, by subtrip, with all information. This table can be summarized afterward as well. If testing several scenarios, it is recommended to only output the summary rather than the full subtrip table as each output is 1-2GB.

Description

The original function pushed .fst files directly to the Wind server at GARFO. This version does not do that and only produces local results.

Usage

discard_generic_diagnostic(
  con = con_maps,
  species = species,
  FY = fy,
  all_dat = all_dat,
  return_table = T,
  return_summary = F,
  CAMS_GEAR_STRATA = CAMS_GEAR_STRATA,
  STOCK_AREAS = STOCK_AREAS,
  CAMS_DISCARD_MORTALITY_STOCK = CAMS_DISCARD_MORTALITY_STOCK
)

Arguments

con	Oracle connection
species	data frame of species for evaluation. Can be a dataframe of multiple species or single row (preferred for testing)
FY	fishing year for evaluation
all_dat	input data sourced from CAMS_OBS_CATCH
return_table	logical, should a table (very large!) of trip level info be returned?
return_summary	logical, should a summary (very not large) be returned?
CAMS_GEAR_STRATA	support table sourced from Oracle
STOCK_AREAS	support table sourced from Oracle
CAMS_DISCARD_MORTALITY_STOCK	support table sourced from Oracle

Author(s)

Benjamin Galuardi

Examples

		#--------------------------------------------------------------------------#

# you can also use odbc, or connect via server using keyring or other method

library(discaRd)

con <- ROracle::dbConnect(
	drv = ROracle::Oracle(),
	username = uid,
	password = pwd,
	dbname = "NERO.world"
)

# define species of interest

## ----get obs and catch data from oracle ----
# you need to get enough years to cover the current (focal) and previous fishing year. This is for transitions rate determination

start_year = 2017
end_year = year(today())

dat = get_catch_obs(con, start_year, end_year)
gf_dat = dat$gf_dat
non_gf_dat = dat$non_gf_dat
all_dat = dat$all_dat
rm(dat)
gc()

# get calendar year species list ----

species <- tbl(con, sql("
  select *
  from CFG_DISCARD_RUNID
  ")) %>%
	filter(RUN_ID == 'CALENDAR') %>%
	collect() %>%
	group_by(ITIS_TSN) %>%
	slice(1) %>%
	ungroup()

# get one species for testing (black sea bass)
species = species %>%
	filter(NESPP3 == 335)

# GEAR TABLE
CAMS_GEAR_STRATA = tbl(con, sql('  select * from CFG_GEARCODE_STRATA')) %>%
	collect() %>%
	dplyr::rename(GEARCODE = SECGEAR_MAPPED) %>%
	filter(ITIS_TSN == species$ITIS_TSN) %>%
	dplyr::select(-NESPP3, -ITIS_TSN)

# Stock (Estimation) areas table ----
STOCK_AREAS = tbl(con, sql('select * from CFG_STATAREA_STOCK')) %>%
	collect() %>%
	filter(ITIS_TSN == species$ITIS_TSN) %>%
	group_by(AREA_NAME, ITIS_TSN) %>%
	distinct(AREA) %>%
	mutate(AREA = as.character(AREA)
				 , SPECIES_STOCK = AREA_NAME) %>%
	ungroup()

# Discard Mortality table ----
CAMS_DISCARD_MORTALITY_STOCK = tbl(con, sql("select * from CFG_DISCARD_MORTALITY_STOCK"))  %>%
	collect() %>%
	mutate(SPECIES_STOCK = AREA_NAME
				 , GEARCODE = CAMS_GEAR_GROUP
				 , CAMS_GEAR_GROUP = as.character(CAMS_GEAR_GROUP)) %>%
	select(-AREA_NAME) %>%
	filter(ITIS_TSN == species$ITIS_TSN) %>%
	dplyr::select(-ITIS_TSN)

# Now, modify anything you wish to test in the support tables! For example, here is an example for scallop estiamtion areas. CAMS do not match the previous assessment and were worth investigating. CAMS ended up doing a good job in this case, regardless of the area splits.


if(species$ITIS_TSN == '079718'){

	STOCK_AREAS = tbl(con, sql("
				select ITIS_TSN
				, AREA
				, case when area > 599 then 'MA'
				when area like '53%' then 'SNE'
				when area >= 520 and area <599 and area not like '53%'  then 'GB'
				when area < 520 then 'GOM'
				end as AREA_NAME
				, case when area > 599 then 'MA'
				when area like '53%' then 'SNE'
				when area >= 520 and area <599 and area not like '53%'  then 'GB'
				when area < 520 then 'GOM'
				end as SPECIES_STOCK
			  from CFG_STATAREA_STOCK
				where ITIS_TSN = '079718'")) %>%
		collect() %>%
		group_by(AREA_NAME, ITIS_TSN) %>%
		distinct(AREA) %>%
		mutate(AREA = as.character(AREA)
					 , SPECIES_STOCK = AREA_NAME) %>%
		ungroup()

}

noaa-garfo/discaRd documentation built on April 17, 2025, 10:32 p.m.