In sean-rohan-NOAA/akfishcondition: Groundfish morphometric condition indicator
Introduction
This document contains code and instructions for retrieving and updating AFSC/RACE/GAP data in the akfishcondition package. It is intended to be run locally from a clone of the akfishcondition repository and with a connection to AFSC's database. Run code blocks for sections 1-3 in order without clearing the environment or restarting R/R Studio.
The data in the package are used to calculate the summer groundfish morphometric condition indicators that are used in Ecosystem Status Reports (ESRs) and Ecosystem and Socioeconomic Profiles (ESPs) for the eastern Bering Sea continental shelf, northern Bering Sea, Aleutian Islands, and Gulf of Alaska regions. Data and indicators in the akfishcondition package are intended to be updated annually for each stock assessment cycle.
library(akfishcondition)
# library(VAST)
channel <- akfishcondition:::get_connected(schema = "AFSC")
akfishcondition::get_condition_data(channel = channel)
akfishcondition:::make_data_summary(dat_csv = here::here("data", "nbs_all_species.csv"), region = "NBS")
akfishcondition:::make_data_summary(dat_csv = here::here("data", "ebs_all_species.csv"), region = "EBS")
akfishcondition:::make_data_summary(dat_csv = here::here("data", "ai_all_species.csv"), region = "AI")
akfishcondition:::make_data_summary(dat_csv = here::here("data", "goa_all_species.csv"), region = "GOA")
ESR_SETTINGS <- list(ESR_SPECIES = data.frame(common_name = c(
"walleye pollock", "walleye pollock (100-250 mm)", "walleye pollock (>250 mm)", "Pacific cod",
"Pacific cod (juvenile)", "Pacific cod (adult)", "Atka mackerel", "arrowtooth flounder",
"flathead sole", "yellowfin sole", "northern rock sole", "southern rock sole", "Alaska plaice",
"Pacific ocean perch", "dusky rockfish", "northern rockfish", "Dover sole", "rex sole", "shortraker rockfish", "rougheye rockfish", "blackspotted rockfish", "sharpchin rockfish"),
species_code = c(21740, 21741, 21742, 21720, 21721, 21722, 21921, 10110, 10130,
10210, 10261, 10262, 10285, 30060, 30152, 30420, 10180, 10200, 30576, 30051, 30052, 30560),
AI = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE),
GOA = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE),
EBS = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE),
NBS = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)),
VAST_SETTINGS = data.frame(species_code = c(30420, 30060, 10262, 21720, 21740, 10110, 30152,
30420, 30060, 10262, 21720, 21740, 21921, 10110, 21741, 21742,
10130, 10210, 10285, 21740, 21720, 10110, 10261, 21741, 21742,
21740, 21720, 10210, 10285, 21741, 21742
),
region = c("GOA", "GOA", "GOA", "GOA", "GOA", "GOA", "GOA",
"AI", "AI", "AI", "AI", "AI", "AI", "AI", "AI", "AI",
"EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS",
"NBS", "NBS", "NBS", "NBS", "NBS", "NBS"),
ObsModel_1 = c(2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2),
ObsModel_2 = c(1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1),
ObsModel_3 = c(3, 3, 3, 3, 4, 4, 4,
3, 3, 3, 3, 4, 3, 4, 4, 4,
3, 3, 3, 4, 3, 4, 3, 4, 4,
4, 4, 3, 4, 4, 4),
ObsModel_4 = c(3, 3, 3, 3, 4, 4, 4,
3, 3, 3, 3, 4, 3, 4, 4, 4,
3, 3, 3, 4, 3, 4, 3, 4, 4,
4, 4, 3, 4, 4, 4),
fl_min = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 100, 251,
0, 0, 0, 0, 0, 0, 0, 100, 251,
0, 0, 0, 0, 100, 251),
fl_max = c(1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7,
1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 250, 1e7,
1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 250, 1e7,
1e7, 1e7, 1e7, 1e7, 250, 1e7),
n_knots = c(400, 400, 400, 400, 400, 400, 400,
400, 600, 400, 400, 400, 400, 400, 400, 400,
400, 600, 400, 400, 750, 400, 400, 400, 400,
200, 200, 200, 200, 200, 200))
)
ESP_SETTINGS <- list(ESP_SPECIES =
data.frame(
common_name = c("Pacific cod (juvenile)", "Pacific cod (adult)"),
species_code = c(21721, 21722),
AI = c(TRUE, TRUE),
GOA = c(TRUE, TRUE),
EBS = c(TRUE, TRUE),
NBS = c(FALSE, FALSE)),
VAST_SETTINGS = data.frame(species_code = c(21721, 21722,
21721, 21722,
21721, 21722),
region = c("GOA", "GOA",
"AI", "AI",
"EBS", "EBS"),
ObsModel_1 = c(2, 2,
2, 2,
2, 2),
ObsModel_2 = c(1, 1,
1, 1,
1, 1),
ObsModel_3 = c(3, 3,
4, 4,
3, 3),
ObsModel_4 = c(3, 3,
4, 4,
3, 3),
n_knots = c(400, 400,
400, 600,
750, 750),
fl_min = c(0, 504,
0, 581,
0, 581),
fl_max = c(503, 1e7,
580, 1e7,
580, 1e7))
)
1. Update stratum-biomass weighted condition indicators
# Aleutian Islands
# ai_sbw <- akfishcondition:::run_sbw_condition(
# region = "AI",
# min_n = 10)
ai_sbw <- akfishcondition:::run_sbw_condition_multimodel(
region = "AI",
covariates_to_use = c('sex', 'stratum'),
min_n = 10)
# Gulf of Alaska
# goa_sbw <- akfishcondition:::run_sbw_condition(
# region = "GOA",
# min_n = 10)
goa_sbw <- akfishcondition:::run_sbw_condition_multimodel(
region = "GOA",
covariates_to_use = c('sex', 'stratum'),
min_n = 10)
# Eastern Bering Sea
# ebs_sbw <- akfishcondition:::run_sbw_condition(
# region = "EBS",
# min_n = 10)
ebs_sbw <- akfishcondition:::run_sbw_condition_multimodel(
region = "EBS",
covariates_to_use = c('sex', 'stratum'),
min_n = 10)
# Northern Bering Sea
# nbs_sbw <- akfishcondition:::run_sbw_condition(
# region = "NBS",
# min_n = 10)
nbs_sbw <- akfishcondition:::run_sbw_condition_multimodel(
region = "NBS",
covariates_to_use = c('sex', 'stratum'),
min_n = 10)
2. Save raw data to inst/extdata
AI_raw <- ai_sbw$input_data
GOA_raw <- goa_sbw$input_data
EBS_raw <- ebs_sbw$input_data
NBS_raw <- nbs_sbw$input_data
save(AI_raw,
GOA_raw,
EBS_raw,
NBS_raw,
file = here::here("inst", "extdata", "raw_lw_bio.rda"))
3. Run VAST indicator
3.1 Run VAST: Aleutian Islands
# ai_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS,
# region == "AI")
#
# for(ii in 1:nrow(ai_spp)) {
# akfishcondition::run_vast_condition(x = ai_spp[ii,],
# response = "count")
# gc()
# }
3.2 Run VAST: Gulf of Alaska
# goa_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS,
# region == "GOA")
#
# for(ii in 1:nrow(goa_spp)) {
#
# akfishcondition::run_vast_condition(x = goa_spp[ii,],
# response = "count")
# gc()
# }
3.3 Run VAST: EBS Shelf
# ebs_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS,
# region == "EBS")
#
# for(ii in 1:nrow(ebs_spp)) {
# akfishcondition::run_vast_condition(x = ebs_spp[ii,],
# response = "count")
# gc()
# }
3.4 Run VAST: NBS
# nbs_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS,
# region == "NBS")
#
# for(ii in 1:nrow(nbs_spp)) {
# akfishcondition::run_vast_condition(x = nbs_spp[ii,],
# response = "count")
# gc()
# }
3.5 Run VAST: ESP Pacific cod
# pcod_spp <- dplyr::filter(ESP_SETTINGS$VAST_SETTINGS,
# region == "EBS")
#
# for(ii in 1:nrow(pcod_spp)) {
# akfishcondition::run_vast_condition(x = pcod_spp[ii,],
# response = "count")
# gc()
# }
4. Load VAST indicators
# akfishcondition:::make_vast_table(region = "GOA", write_table = TRUE)
# akfishcondition:::make_vast_table(region = "EBS", write_table = TRUE)
# akfishcondition:::make_vast_table(region = "NBS", write_table = TRUE)
# akfishcondition:::make_vast_table(region = "AI", write_table = TRUE)
#
# # goa_vast_df <-bundle_vast_condition(region = "GOA", years = c(seq(1984, 1999, 3), seq(2001, 2022, 2)))
# ebs_vast_df <- akfishcondition::bundle_vast_condition(region = "EBS", years = c(1999:2019, 2021:2022))
# nbs_vast_df <- akfishcondition::bundle_vast_condition(region = "NBS", years = c(2010, 2017, 2019, 2021, 2022))
# ai_vast_df <- akfishcondition::bundle_vast_condition(region = "AI",
# years = c(1986, seq(1991, 2000, 3),
# seq(2002, 2006, 2),
# seq(2010, 2018, 2), 2022))
5. Update version in DESCRIPTION file
Update sysdata.rda with raw data for the condition indicator and write raw data to inst/extdata.
EBS_INDICATOR <- list(
FULL_REGION = as.data.frame(
ebs_sbw$full_sbw) |>
dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS]),
STRATUM = as.data.frame(
dplyr::filter(
ebs_sbw$stratum_sbw,
common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS])
),
LAST_UPDATE = Sys.Date()
)
NBS_INDICATOR <- list(
FULL_REGION = as.data.frame(
nbs_sbw$full_sbw) |>
dplyr::filter(
common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$NBS]),
STRATUM = as.data.frame(
dplyr::filter(
nbs_sbw$stratum_sbw,
common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS])
),
LAST_UPDATE = Sys.Date()
)
GOA_INDICATOR <- list(
FULL_REGION = as.data.frame(
goa_sbw$full_sbw) |>
dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$GOA]),
STRATUM = as.data.frame(
dplyr::filter(
goa_sbw$stratum_sbw,
common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$GOA])
),
LAST_UPDATE = Sys.Date()
)
AI_INDICATOR <- list(
FULL_REGION = as.data.frame(
ai_sbw$full_sbw) |>
dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$AI]),
STRATUM = as.data.frame(
dplyr::filter(
ai_sbw$stratum_sbw,
common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$AI])
),
LAST_UPDATE = Sys.Date()
)
PCOD_ESP <- list(
FULL_REGION_EBS = as.data.frame(
dplyr::filter(ebs_sbw$full_sbw,
common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
FULL_REGION_GOA = as.data.frame(
dplyr::filter(goa_sbw$full_sbw,
common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
FULL_REGION_AI = as.data.frame(
dplyr::filter(ai_sbw$full_sbw,
common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
FULL_REGION_NBS = as.data.frame(
dplyr::filter(
nbs_sbw$full_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
STRATUM_EBS = as.data.frame(
dplyr::filter(
ebs_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
STRATUM_GOA = as.data.frame(
dplyr::filter(
goa_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
STRATUM_AI = as.data.frame(
dplyr::filter(
ai_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name
)),
stratum_NBS = NA,
LAST_UPDATE = Sys.Date()
)
usethis::use_data(EBS_INDICATOR, overwrite = TRUE)
usethis::use_data(AI_INDICATOR, overwrite = TRUE)
usethis::use_data(GOA_INDICATOR, overwrite = TRUE)
usethis::use_data(NBS_INDICATOR, overwrite = TRUE)
usethis::use_data(PCOD_ESP, overwrite = TRUE)
save(
EBS_INDICATOR,
NBS_INDICATOR,
GOA_INDICATOR,
AI_INDICATOR,
PCOD_ESP,
ESR_SETTINGS,
ESP_SETTINGS,
file = here::here("R", "sysdata.rda")
)
6. Update documentation, install, and restart
Update version number in DESCRIPTION and update documentation using:
devtools::document(roclets = c('rd', 'collate', 'namespace'))
After updating, reinstall the akfishcondition package.
knitr::knit_exit()
Rcmd.exe INSTALL --no-multiarch --with-keep.source akfishcondition
7. Check that data appears correctly
Check that the updated condition data is included in the package.
print(akfishcondition::AI_INDICATOR$LAST_UPDATE)
print(akfishcondition::GOA_INDICATOR$LAST_UPDATE)
print(akfishcondition::EBS_INDICATOR$LAST_UPDATE)
print(akfishcondition::NBS_INDICATOR$LAST_UPDATE)
sean-rohan-NOAA/akfishcondition documentation built on June 9, 2025, 3:54 p.m.
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Introduction
This document contains code and instructions for retrieving and updating AFSC/RACE/GAP data in the akfishcondition package. It is intended to be run locally from a clone of the akfishcondition repository and with a connection to AFSC's database. Run code blocks for sections 1-3 in order without clearing the environment or restarting R/R Studio.
The data in the package are used to calculate the summer groundfish morphometric condition indicators that are used in Ecosystem Status Reports (ESRs) and Ecosystem and Socioeconomic Profiles (ESPs) for the eastern Bering Sea continental shelf, northern Bering Sea, Aleutian Islands, and Gulf of Alaska regions. Data and indicators in the akfishcondition package are intended to be updated annually for each stock assessment cycle.
library(akfishcondition) # library(VAST) channel <- akfishcondition:::get_connected(schema = "AFSC") akfishcondition::get_condition_data(channel = channel) akfishcondition:::make_data_summary(dat_csv = here::here("data", "nbs_all_species.csv"), region = "NBS") akfishcondition:::make_data_summary(dat_csv = here::here("data", "ebs_all_species.csv"), region = "EBS") akfishcondition:::make_data_summary(dat_csv = here::here("data", "ai_all_species.csv"), region = "AI") akfishcondition:::make_data_summary(dat_csv = here::here("data", "goa_all_species.csv"), region = "GOA") ESR_SETTINGS <- list(ESR_SPECIES = data.frame(common_name = c( "walleye pollock", "walleye pollock (100-250 mm)", "walleye pollock (>250 mm)", "Pacific cod", "Pacific cod (juvenile)", "Pacific cod (adult)", "Atka mackerel", "arrowtooth flounder", "flathead sole", "yellowfin sole", "northern rock sole", "southern rock sole", "Alaska plaice", "Pacific ocean perch", "dusky rockfish", "northern rockfish", "Dover sole", "rex sole", "shortraker rockfish", "rougheye rockfish", "blackspotted rockfish", "sharpchin rockfish"), species_code = c(21740, 21741, 21742, 21720, 21721, 21722, 21921, 10110, 10130, 10210, 10261, 10262, 10285, 30060, 30152, 30420, 10180, 10200, 30576, 30051, 30052, 30560), AI = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), GOA = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), EBS = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), NBS = c(FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)), VAST_SETTINGS = data.frame(species_code = c(30420, 30060, 10262, 21720, 21740, 10110, 30152, 30420, 30060, 10262, 21720, 21740, 21921, 10110, 21741, 21742, 10130, 10210, 10285, 21740, 21720, 10110, 10261, 21741, 21742, 21740, 21720, 10210, 10285, 21741, 21742 ), region = c("GOA", "GOA", "GOA", "GOA", "GOA", "GOA", "GOA", "AI", "AI", "AI", "AI", "AI", "AI", "AI", "AI", "AI", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "EBS", "NBS", "NBS", "NBS", "NBS", "NBS", "NBS"), ObsModel_1 = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), ObsModel_2 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), ObsModel_3 = c(3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 3, 4, 4, 4, 3, 3, 3, 4, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4), ObsModel_4 = c(3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 3, 4, 4, 4, 3, 3, 3, 4, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4), fl_min = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 100, 251, 0, 0, 0, 0, 0, 0, 0, 100, 251, 0, 0, 0, 0, 100, 251), fl_max = c(1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 250, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 1e7, 250, 1e7, 1e7, 1e7, 1e7, 1e7, 250, 1e7), n_knots = c(400, 400, 400, 400, 400, 400, 400, 400, 600, 400, 400, 400, 400, 400, 400, 400, 400, 600, 400, 400, 750, 400, 400, 400, 400, 200, 200, 200, 200, 200, 200)) ) ESP_SETTINGS <- list(ESP_SPECIES = data.frame( common_name = c("Pacific cod (juvenile)", "Pacific cod (adult)"), species_code = c(21721, 21722), AI = c(TRUE, TRUE), GOA = c(TRUE, TRUE), EBS = c(TRUE, TRUE), NBS = c(FALSE, FALSE)), VAST_SETTINGS = data.frame(species_code = c(21721, 21722, 21721, 21722, 21721, 21722), region = c("GOA", "GOA", "AI", "AI", "EBS", "EBS"), ObsModel_1 = c(2, 2, 2, 2, 2, 2), ObsModel_2 = c(1, 1, 1, 1, 1, 1), ObsModel_3 = c(3, 3, 4, 4, 3, 3), ObsModel_4 = c(3, 3, 4, 4, 3, 3), n_knots = c(400, 400, 400, 600, 750, 750), fl_min = c(0, 504, 0, 581, 0, 581), fl_max = c(503, 1e7, 580, 1e7, 580, 1e7)) )
1. Update stratum-biomass weighted condition indicators
# Aleutian Islands # ai_sbw <- akfishcondition:::run_sbw_condition( # region = "AI", # min_n = 10) ai_sbw <- akfishcondition:::run_sbw_condition_multimodel( region = "AI", covariates_to_use = c('sex', 'stratum'), min_n = 10) # Gulf of Alaska # goa_sbw <- akfishcondition:::run_sbw_condition( # region = "GOA", # min_n = 10) goa_sbw <- akfishcondition:::run_sbw_condition_multimodel( region = "GOA", covariates_to_use = c('sex', 'stratum'), min_n = 10) # Eastern Bering Sea # ebs_sbw <- akfishcondition:::run_sbw_condition( # region = "EBS", # min_n = 10) ebs_sbw <- akfishcondition:::run_sbw_condition_multimodel( region = "EBS", covariates_to_use = c('sex', 'stratum'), min_n = 10) # Northern Bering Sea # nbs_sbw <- akfishcondition:::run_sbw_condition( # region = "NBS", # min_n = 10) nbs_sbw <- akfishcondition:::run_sbw_condition_multimodel( region = "NBS", covariates_to_use = c('sex', 'stratum'), min_n = 10)
2. Save raw data to inst/extdata
AI_raw <- ai_sbw$input_data GOA_raw <- goa_sbw$input_data EBS_raw <- ebs_sbw$input_data NBS_raw <- nbs_sbw$input_data save(AI_raw, GOA_raw, EBS_raw, NBS_raw, file = here::here("inst", "extdata", "raw_lw_bio.rda"))
3. Run VAST indicator
3.1 Run VAST: Aleutian Islands
# ai_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS, # region == "AI") # # for(ii in 1:nrow(ai_spp)) { # akfishcondition::run_vast_condition(x = ai_spp[ii,], # response = "count") # gc() # }
3.2 Run VAST: Gulf of Alaska
# goa_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS, # region == "GOA") # # for(ii in 1:nrow(goa_spp)) { # # akfishcondition::run_vast_condition(x = goa_spp[ii,], # response = "count") # gc() # }
3.3 Run VAST: EBS Shelf
# ebs_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS, # region == "EBS") # # for(ii in 1:nrow(ebs_spp)) { # akfishcondition::run_vast_condition(x = ebs_spp[ii,], # response = "count") # gc() # }
3.4 Run VAST: NBS
# nbs_spp <- dplyr::filter(ESR_SETTINGS$VAST_SETTINGS, # region == "NBS") # # for(ii in 1:nrow(nbs_spp)) { # akfishcondition::run_vast_condition(x = nbs_spp[ii,], # response = "count") # gc() # }
3.5 Run VAST: ESP Pacific cod
# pcod_spp <- dplyr::filter(ESP_SETTINGS$VAST_SETTINGS, # region == "EBS") # # for(ii in 1:nrow(pcod_spp)) { # akfishcondition::run_vast_condition(x = pcod_spp[ii,], # response = "count") # gc() # }
4. Load VAST indicators
# akfishcondition:::make_vast_table(region = "GOA", write_table = TRUE) # akfishcondition:::make_vast_table(region = "EBS", write_table = TRUE) # akfishcondition:::make_vast_table(region = "NBS", write_table = TRUE) # akfishcondition:::make_vast_table(region = "AI", write_table = TRUE) # # # goa_vast_df <-bundle_vast_condition(region = "GOA", years = c(seq(1984, 1999, 3), seq(2001, 2022, 2))) # ebs_vast_df <- akfishcondition::bundle_vast_condition(region = "EBS", years = c(1999:2019, 2021:2022)) # nbs_vast_df <- akfishcondition::bundle_vast_condition(region = "NBS", years = c(2010, 2017, 2019, 2021, 2022)) # ai_vast_df <- akfishcondition::bundle_vast_condition(region = "AI", # years = c(1986, seq(1991, 2000, 3), # seq(2002, 2006, 2), # seq(2010, 2018, 2), 2022))
5. Update version in DESCRIPTION file
Update sysdata.rda with raw data for the condition indicator and write raw data to inst/extdata.
EBS_INDICATOR <- list( FULL_REGION = as.data.frame( ebs_sbw$full_sbw) |> dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS]), STRATUM = as.data.frame( dplyr::filter( ebs_sbw$stratum_sbw, common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS]) ), LAST_UPDATE = Sys.Date() ) NBS_INDICATOR <- list( FULL_REGION = as.data.frame( nbs_sbw$full_sbw) |> dplyr::filter( common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$NBS]), STRATUM = as.data.frame( dplyr::filter( nbs_sbw$stratum_sbw, common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$EBS]) ), LAST_UPDATE = Sys.Date() ) GOA_INDICATOR <- list( FULL_REGION = as.data.frame( goa_sbw$full_sbw) |> dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$GOA]), STRATUM = as.data.frame( dplyr::filter( goa_sbw$stratum_sbw, common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$GOA]) ), LAST_UPDATE = Sys.Date() ) AI_INDICATOR <- list( FULL_REGION = as.data.frame( ai_sbw$full_sbw) |> dplyr::filter(common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$AI]), STRATUM = as.data.frame( dplyr::filter( ai_sbw$stratum_sbw, common_name %in% ESR_SETTINGS$ESR_SPECIES$common_name[ESR_SETTINGS$ESR_SPECIES$AI]) ), LAST_UPDATE = Sys.Date() ) PCOD_ESP <- list( FULL_REGION_EBS = as.data.frame( dplyr::filter(ebs_sbw$full_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), FULL_REGION_GOA = as.data.frame( dplyr::filter(goa_sbw$full_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), FULL_REGION_AI = as.data.frame( dplyr::filter(ai_sbw$full_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), FULL_REGION_NBS = as.data.frame( dplyr::filter( nbs_sbw$full_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), STRATUM_EBS = as.data.frame( dplyr::filter( ebs_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), STRATUM_GOA = as.data.frame( dplyr::filter( goa_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), STRATUM_AI = as.data.frame( dplyr::filter( ai_sbw$stratum_sbw, common_name %in% ESP_SETTINGS$ESP_SPECIES$common_name )), stratum_NBS = NA, LAST_UPDATE = Sys.Date() ) usethis::use_data(EBS_INDICATOR, overwrite = TRUE) usethis::use_data(AI_INDICATOR, overwrite = TRUE) usethis::use_data(GOA_INDICATOR, overwrite = TRUE) usethis::use_data(NBS_INDICATOR, overwrite = TRUE) usethis::use_data(PCOD_ESP, overwrite = TRUE) save( EBS_INDICATOR, NBS_INDICATOR, GOA_INDICATOR, AI_INDICATOR, PCOD_ESP, ESR_SETTINGS, ESP_SETTINGS, file = here::here("R", "sysdata.rda") )
6. Update documentation, install, and restart
Update version number in DESCRIPTION and update documentation using:
devtools::document(roclets = c('rd', 'collate', 'namespace'))
After updating, reinstall the akfishcondition package.
knitr::knit_exit() Rcmd.exe INSTALL --no-multiarch --with-keep.source akfishcondition
7. Check that data appears correctly
Check that the updated condition data is included in the package.
print(akfishcondition::AI_INDICATOR$LAST_UPDATE) print(akfishcondition::GOA_INDICATOR$LAST_UPDATE) print(akfishcondition::EBS_INDICATOR$LAST_UPDATE) print(akfishcondition::NBS_INDICATOR$LAST_UPDATE)
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