library(gapindex)
## Connect to Oracle
channel <- gapindex::get_connected()
## Pull data.
gapindex_data <- gapindex::get_data(
year_set = c(2007, 2009),
survey_set = "GOA",
spp_codes = 10261,
haul_type = 3,
abundance_haul = "Y",
pull_lengths = T,
channel = channel)
## Fill in zeros and calculate CPUE
cpue <- gapindex::calc_cpue(gapdata = gapindex_data)
## Calculate stratum-level biomass, population abundance, mean CPUE and
## associated variances
biomass_stratum <- gapindex::calc_biomass_stratum(
gapdata = gapindex_data,
cpue = cpue)
## Calculate aggregated biomass and population abundance across subareas,
## management areas, and regions
biomass_subareas <- gapindex::calc_biomass_subarea(
gapdata = gapindex_data,
biomass_stratum = biomass_stratum)
## Calculate size composition by stratum. See ?gapindex::calc_sizecomp_stratum
## for details on arguments
size_comp_stratum <- gapindex::calc_sizecomp_stratum(
gapdata = gapindex_data,
cpue = cpue,
abundance_stratum = biomass_stratum,
spatial_level = "stratum",
fill_NA_method = "AIGOA")
## Calculate aggregated size compositon across subareas, management areas, and
## regions
size_comp_subareas <- gapindex::calc_sizecomp_subarea(
gapdata = gapindex_data,
sizecomp_stratum = size_comp_stratum)
## Calculate age-length key. See ?gapindex::calc_ALK for details on arguments
alk <- gapindex::calc_alk(gapdata = gapindex_data,
unsex = "all",
global = F)
## Calculate age composition by stratum
age_comp_stratum <- gapindex::calc_agecomp_stratum(
gapdata = gapindex_data,
alk = alk,
sizecomp_stratum = size_comp_stratum)
## Calculate aggregated age compositon across regions
age_comp_region <- gapindex::calc_agecomp_region(
gapdata = gapindex_data,
agecomp_stratum = age_comp_stratum)
