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| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Project: Reproduce the GOA.SPLIT_FRACTIONS tables using the gapindex | ||
| ## R package | ||
| ## Author: Zack Oyafuso ([email protected]) | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
|
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||
| ## Restart R Session before running | ||
| rm(list = ls()) | ||
|
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| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Setup | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Import Libraries | ||
| library(gapindex) | ||
|
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||
| ## Connect to Oracle | ||
| sql_channel <- gapindex::get_connected() | ||
|
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| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Pull Data from the gapindex R Package | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Pull GOA Management Groups from the GOA schema | ||
| goa_mgmt_spp <- RODBC::sqlQuery(channel = sql_channel, | ||
| query = "SELECT MANAGEMENT_GROUP, SPECIES_CODE | ||
| FROM GOA.GOA_MANAGEMENT_GROUPS") | ||
| names(x = goa_mgmt_spp)[1] <- "GROUP" | ||
|
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||
| ## Pull data from gapindex based on the species groupings from | ||
| ## GOA.GOA_MANAGEMENT_GROUPS for only the past few survey years. | ||
| gp_data <- gapindex::get_data(year_set = c(2019, 2021, 2023), | ||
| survey_set = "GOA", | ||
| spp_codes = goa_mgmt_spp, | ||
| sql_channel = sql_channel, | ||
| pull_lengths = F) | ||
|
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| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Post-stratify hauls based on the NMFS Areas | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Calculate midpoint of the observed start and end longitudes for each haul | ||
| gp_data$haul$MID_LONGITUDE <- | ||
| (gp_data$haul$START_LONGITUDE + gp_data$haul$END_LONGITUDE) / 2 | ||
|
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||
| ## Following ... reclassify hauls | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 40 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 50 | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 142 & | ||
| gp_data$haul$MID_LONGITUDE < -140] <- 141 | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 142 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 152 | ||
|
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| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 143] <- 152 ## Note: new stratum | ||
|
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| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 240 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 250 | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 241 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 250 | ||
|
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||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 340 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 350 | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 341 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 351 | ||
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| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 440 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 450 | ||
| gp_data$haul$STRATUM[gp_data$haul$STRATUM == 540 & | ||
| gp_data$haul$MID_LONGITUDE >= -140] <- 550 | ||
|
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||
| ## This is a hard-copied version of GOA.SPLIT_something | ||
| updated_stratum_area <- | ||
| data.frame( | ||
| SURVEY_DEFINITION_ID = 47, SURVEY = "GOA", DESIGN_YEAR = 1984, | ||
| STRATUM = c(40, 41, 50, 140, 141, 152, 150, 151, 240, 241, 250, 251, | ||
| 340, 341, 350, 351, 440, 450, 540, 550), | ||
| AREA_NAME = NA, DESCRIPTION = NA, | ||
| AREA_KM2 = c(4980.005501, 6714.745, 11514.5325, 7346.035, 9993.915778, | ||
| 12043.32322, 4196.599, 6888.172, 2286.139849, 1503.635678, | ||
| 1882.079151, 4551.089322, 751.2781795, 1296.716466, | ||
| 2700.424821, 996.6395344, 1252.954196, 1249.897804, | ||
| 1609.551032, 1484.372968) ) | ||
|
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||
| ## Update the stratum information in the gp_data list | ||
| gp_data$strata <- | ||
| rbind(subset(x = gp_data$strata, | ||
| subset = !(STRATUM %in% updated_stratum_area$STRATUM)), | ||
| updated_stratum_area) | ||
|
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||
| ## Update the stratum groupings in the gp_data list to include the new stratum | ||
| ## 152. Note I'm only modifying the stratum grouping for AREA_ID 959 because | ||
| ## it is the only AREA_ID that is affected. | ||
| gp_data$stratum_groups <- rbind( | ||
| gp_data$stratum_groups, | ||
| data.frame(SURVEY_DEFINITION_ID = 47, | ||
| SURVEY = "GOA", | ||
| AREA_ID = c(959), | ||
| DESIGN_YEAR = 1984, | ||
| STRATUM = 152)) | ||
|
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||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Calculate CPUE, stratum biomass, then aggregate biomass across subareas | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| gp_cpue <- | ||
| gapindex::calc_cpue(racebase_tables = gp_data) | ||
| gp_stratum_biomass <- | ||
| gapindex::calc_biomass_stratum(racebase_tables = gp_data, | ||
| cpue = gp_cpue) | ||
| gp_subarea_biomass <- | ||
| gapindex::calc_biomass_subarea(racebase_tables = gp_data, | ||
| biomass_strata = gp_stratum_biomass) | ||
|
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||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Reformat gp_subarea_biomass to something that looks like | ||
| ## GOA.GOA_SPLIT_FRACTION | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| goa_split_fractions <- | ||
| gp_subarea_biomass[gp_subarea_biomass$AREA_ID %in% c(949, 959), | ||
| c("YEAR", "SPECIES_CODE", "AREA_ID", "BIOMASS_MT")] | ||
| goa_split_fractions <- | ||
| reshape(data = goa_split_fractions, | ||
| idvar = c("SPECIES_CODE", "YEAR"), | ||
| timevar = "AREA_ID", | ||
| direction = "wide") | ||
| names(x = goa_split_fractions) <- c("YEAR", "MANAGEMENT_GROUP", | ||
| "WEST_BIOMASS", "EAST_BIOMASS") | ||
| goa_split_fractions$WEST_BIOMASS <- | ||
| round(x = goa_split_fractions$WEST_BIOMASS, digits = 1) | ||
| goa_split_fractions$EAST_BIOMASS <- | ||
| round(x = goa_split_fractions$EAST_BIOMASS, digits = 1) | ||
|
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||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| ## Compare output from gapindex to what is currently in the GOA schema | ||
| ##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
|
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||
| ## Pull current split fractions table | ||
| historical_split <- | ||
| RODBC::sqlQuery(channel = sql_channel, | ||
| query = "SELECT YEAR, MANAGEMENT_GROUP, | ||
| WEST_BIOMASS, EAST_BIOMASS | ||
| FROM GOA.SPLIT_FRACTIONS | ||
| WHERE YEAR >= 2019") | ||
|
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||
| ## Merge the current split fraction table with the gapindex-produced table | ||
| test_split <- | ||
| merge(x = historical_split, | ||
| y = goa_split_fractions, | ||
| by = c("YEAR", "MANAGEMENT_GROUP"), | ||
| all.x = TRUE, | ||
| suffixes = c("_HIST", "_GP")) | ||
|
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| ## Calculate absolute differences between the west and east biomass estimates | ||
| test_split$WEST_BIOMASS_DIFF <- | ||
| test_split$WEST_BIOMASS_GP - test_split$WEST_BIOMASS_HIST | ||
| test_split$EAST_BIOMASS_DIFF <- | ||
| test_split$EAST_BIOMASS_GP - test_split$EAST_BIOMASS_HIST | ||
|
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| write.csv(x = test_split[order(test_split$MANAGEMENT_GROUP), | ||
| c("YEAR", "MANAGEMENT_GROUP", | ||
| "WEST_BIOMASS_HIST", "WEST_BIOMASS_GP", | ||
| "WEST_BIOMASS_DIFF", "EAST_BIOMASS_HIST", | ||
| "EAST_BIOMASS_GP", "EAST_BIOMASS_DIFF")], | ||
| file = "code/goa_split_fractions/goa_split_fractions.csv", | ||
| row.names = F) | ||
|
|
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| "YEAR","MANAGEMENT_GROUP","WEST_BIOMASS_HIST","WEST_BIOMASS_GP","WEST_BIOMASS_DIFF","EAST_BIOMASS_HIST","EAST_BIOMASS_GP","EAST_BIOMASS_DIFF" | ||
| 2019,"arrowtooth flounder",68988.4,68988.2,-0.19999999999709,142295.9,142296,0.100000000005821 | ||
| 2021,"arrowtooth flounder",53981.6,53981.8,0.200000000004366,95332.3,95332.2,-0.100000000005821 | ||
| 2023,"arrowtooth flounder",92762.8,92762.8,0,191769.6,191769.6,0 | ||
| 2019,"deep water flatfish",16948.8,16948.8,0,13929.1,13929.1,0 | ||
| 2021,"deep water flatfish",11099.8,11100.8,1,16151.1,16150.9,-0.200000000000728 | ||
| 2023,"deep water flatfish",13823.5,13823.6,0.100000000000364,17234.1,17234.2,0.100000000002183 | ||
| 2019,"dusky rockfish",5776.1,5776,-0.100000000000364,2227.8,2227.8,0 | ||
| 2021,"dusky rockfish",659.5,659.6,0.100000000000023,1016.3,1016.3,0 | ||
| 2023,"dusky rockfish",762.6,762.6,0,2335.9,2336,0.0999999999999091 | ||
| 2019,"flathead sole",14118,14117.8,-0.200000000000728,11139.7,11139.7,0 | ||
| 2021,"flathead sole",12631.5,12631.4,-0.100000000000364,15680.2,15680.2,0 | ||
| 2023,"flathead sole",15742.1,15742.1,0,8314.4,8314.3,-0.100000000000364 | ||
| 2019,"harlequin rockfish",342.7,342.7,0,209.5,209.5,0 | ||
| 2021,"harlequin rockfish",50.2,50.2,0,72.7,72.6,-0.100000000000009 | ||
| 2023,"harlequin rockfish",0,0,0,81.7,81.8,0.0999999999999943 | ||
| 2019,"OROXM006",70.6,70.6,0,464.7,464.7,0 | ||
| 2021,"OROXM006",0,0,0,2257.1,2257.2,0.0999999999999091 | ||
| 2023,"OROXM006",1,1,0,4740.1,4740,-0.100000000000364 | ||
| 2019,"OROXM007",547.3,547.3,0,2964.2,2964.2,0 | ||
| 2021,"OROXM007",392.9,392.9,0,1381.4,1381.6,0.199999999999818 | ||
| 2023,"OROXM007",603.9,603.9,0,2066.2,2066.2,0 | ||
| 2019,"Pacific cod",2978,2978,0,6688.1,6688.1,0 | ||
| 2021,"Pacific cod",8837.3,8837.3,0,15218.2,15218,-0.200000000000728 | ||
| 2023,"Pacific cod",13318.6,13318.7,0.100000000000364,12710.1,12710.1,0 | ||
| 2019,"Pacific ocean perch",47901.8,47901.8,0,200557.5,200557.3,-0.200000000011642 | ||
| 2021,"Pacific ocean perch",30463.6,30463.5,-0.0999999999985448,127524.4,127524.4,0 | ||
| 2023,"Pacific ocean perch",39406,39406,0,834319.7,834319.8,0.100000000093132 | ||
| 2019,"redbanded rockfish",1077,1077,0,2587.6,2587.7,0.0999999999999091 | ||
| 2021,"redbanded rockfish",283.2,283.2,0,4818.2,4818,-0.199999999999818 | ||
| 2023,"redbanded rockfish",654.5,654.5,0,4345.1,4344.8,-0.300000000000182 | ||
| 2019,"redstripe rockfish",1631.9,1631.8,-0.100000000000136,10058,10058,0 | ||
| 2021,"redstripe rockfish",76.7,76.7,0,2346.5,2346.5,0 | ||
| 2023,"redstripe rockfish",61.3,61.3,0,6671.9,6672,0.100000000000364 | ||
| 2019,"rex sole",8308.7,8308.7,0,15737.9,15737.9,0 | ||
| 2021,"rex sole",8703.5,8703.3,-0.200000000000728,17086,17086,0 | ||
| 2023,"rex sole",6539.4,6539.4,0,10917.4,10917.3,-0.100000000000364 | ||
| 2023,"rougheye blackspotted complex",4128.6,4128.8,0.199999999999818,17018.8,17019.6,0.799999999999272 | ||
| 2019,"shallow water flatfish",5167.8,5167.7,-0.100000000000364,11641.5,11641.4,-0.100000000000364 | ||
| 2021,"shallow water flatfish",12915.9,12916.1,0.200000000000728,12347.4,12347.3,-0.100000000000364 | ||
| 2023,"shallow water flatfish",16311,16311,0,17227.4,17227.6,0.19999999999709 | ||
| 2019,"sharpchin rockfish",2870.8,2870.7,-0.100000000000364,5669.8,5669.7,-0.100000000000364 | ||
| 2021,"sharpchin rockfish",1394,1394,0,6332.1,6332.2,0.0999999999994543 | ||
| 2023,"sharpchin rockfish",306.8,306.9,0.0999999999999659,5319.2,5319.2,0 | ||
| 2023,"shortraker rockfish",8812.1,8812.1,0,13239.4,13239.4,0 | ||
| 2019,"shortraker/rougheye",19044.1,NA,NA,22088.9,NA,NA | ||
| 2021,"shortraker/rougheye",8459,NA,NA,8885.1,NA,NA | ||
| 2019,"shortspine thornyhead",13414,13413.9,-0.100000000000364,10905.3,10905.3,0 | ||
| 2021,"shortspine thornyhead",11383.3,11383.5,0.200000000000728,12355.6,12355.6,0 | ||
| 2023,"shortspine thornyhead",11976.3,11976.2,-0.0999999999985448,12641.2,12641.1,-0.100000000000364 | ||
| 2019,"silvergray rockfish",5181.5,5181.6,0.100000000000364,23577,23577,0 | ||
| 2021,"silvergray rockfish",1480.4,1480.4,0,42528.1,42528,-0.0999999999985448 | ||
| 2023,"silvergray rockfish",12039.1,12039.1,0,30745.8,30745.9,0.100000000002183 | ||
| 2019,"walleye pollock",10952.4,10952.5,0.100000000000364,49695.1,49695.1,0 | ||
| 2021,"walleye pollock",19466.2,19466.3,0.0999999999985448,34273.5,34273.5,0 | ||
| 2023,"walleye pollock",19655.4,19655.5,0.0999999999985448,30767,30767,0 |