R_Scripts/SABL PacFIN MetaData.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures.
# =============== Import the PacFIN Sablefish metadata =============================
setwd("C:/SIDT/Sable_Comm")
library(JRWToolBox)
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
# Toolbox functions
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/PacFIN.Data.Extraction/master/R/PacFIN.BDS.Extraction.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/GitHub_File_Download.R")
GitHub_File_Download("John-R-Wallace-NOAA/PacFIN.Data.Extraction/master/R/PacFIN.BDS.Extraction.R")
PacFIN.Login <- UID <- "wallacej"
PacFIN.PW <- PWD <- "*********"
DSN <- "PacFIN"
import.sql("Select * from pacfin.bds_sp where rownum < 11", dsn="PacFIN", uid="wallacej", pwd=PacFIN.PW) # Test
PacFIN_SABL_Age <- PacFIN.BDS.Extraction("'SABL'")
save(PacFIN_SABL_Age, file = "C:\\SIDT\\SABL_Comm_2012_2023\\PacFIN_SABL_Age.RData")
base::load("C:\\SIDT\\SABL_Comm_2012_2023\\PacFIN_SABL_Age.RData")
headTail(PacFIN_SABL_Age,2,2,2,70)
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# PacFIN_SABL_Age$Length_cm <- PacFIN_SABL_Age$FISH_LENGTH/10
# PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm < 10 & !is.na(PacFIN_SABL_Age$Length_cm)] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm < 10 & !is.na(PacFIN_SABL_Age$Length_cm)] * 10
PacFIN_SABL_Age$Length_cm <- PacFIN_SABL_Age$FISH_LENGTH
PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$FISH_LENGTH_UNITS == 'MM'] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$FISH_LENGTH_UNITS == 'MM']/10
range(PacFIN_SABL_Age$Length_cm, na.rm = TRUE)
plot(PacFIN_SABL_Age$Length_cm)
# PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm > 100 & !is.na(PacFIN_SABL_Age$Length_cm)] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm > 100 & !is.na(PacFIN_SABL_Age$Length_cm)] / 10
# range(PacFIN_SABL_Age$Length_cm, na.rm = TRUE)
# plot(PacFIN_SABL_Age$Length_cm)
range(PacFIN_SABL_Age$FISH_WEIGHT, na.rm = TRUE)
Table(PacFIN_SABL_Age$FISH_WEIGHT_UNITS)
G P
45042 10679 1
PacFIN_SABL_Age$Weight_kg <- PacFIN_SABL_Age$FISH_WEIGHT
PacFIN_SABL_Age$Weight_kg[PacFIN_SABL_Age$FISH_WEIGHT_UNITS == 'G'] <- PacFIN_SABL_Age$Weight_kg[PacFIN_SABL_Age$FISH_WEIGHT_UNITS == 'G']/1000
range(PacFIN_SABL_Age$Weight_kg, na.rm = TRUE)
change(PacFIN_SABL_Age)
browsePlot('plot(Weight_kg, Length_cm)')
browsePlot('
par(mfrow = c(2,1))
plot(AGE_IN_YEARS, Length_cm)
plot(AGE_IN_YEARS, Weight_kg)
')
PacFIN_SABL_Age$Sex <- PacFIN_SABL_Age$SEX
PacFIN_SABL_Age$Year <- PacFIN_SABL_Age$SAMPLE_YEAR
PacFIN_SABL_Age$Depth <- PacFIN_SABL_Age$DEPTH_AVERAGE_FATHOMS
PacFIN_SABL_Age$Month <- PacFIN_SABL_Age$SAMPLE_MONTH
save(PacFIN_SABL_Age, file = 'PacFIN_SABL_Age.RData')
# ==================================== Match the PacFIN Metadata onto the combined WA, OR, CA commercial Model_Spectra_Meta ========================================================================================
setwd("C:/SIDT/Sable_Comm")
library(JRWToolBox)
load("C:\\SIDT\\Sable_Comm\\PacFIN_SABL_Age.RData")
PacFIN_SABL_Age.NN <- PacFIN_SABL_Age[, c("BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "AGENCY_CODE", "PACFIN_SPECIES_CODE", "FISH_SEQUENCE_NUMBER", "FINAL_FISH_AGE_IN_YEARS", "Year", "Month", "SAMPLE_DAY", "Length_cm", "Weight_kg", "Sex", "Depth")]
# !!!!! Find the column names that are missing !!!!!
Columns <- c("BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "AGENCY_CODE", "PACFIN_SPECIES_CODE", "FISH_SEQUENCE_NUMBER", "FINAL_FISH_AGE_IN_YEARS", "Year", "Month", "SAMPLE_DAY", "Length_cm", "Weight_kg", "Sex", "Depth")
Columns[!Columns %in% names(PacFIN_SABL_Age)]
# --- Load Model_Spectra_Meta data ---
load("C:\\SIDT\\Sable_Comm\\Sable_CA_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_CA <- Model_Spectra_Meta
load("C:\\SIDT\\Sable_Comm\\Sable_OR_Comm_2020__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_OR <- Model_Spectra_Meta
load("C:\\SIDT\\Sable_Comm\\Sable_WA_Comm_2020__2023_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_WA <- Model_Spectra_Meta
Table(Model_Spectra_Meta_CA$Sex)
Table(Model_Spectra_Meta_OR$Sex)
Table(Model_Spectra_Meta_WA$Sex)
Model_Spectra_Meta_CA$Sex <- NA
dim(Model_Spectra_Meta_CA)
dim(Model_Spectra_Meta_OR)
dim(Model_Spectra_Meta_WA)
# Use the least common column set
Columns <- names(Model_Spectra_Meta_CA)
Columns[!Columns %in% names(Model_Spectra_Meta_OR)]
Columns[!Columns %in% names(Model_Spectra_Meta_WA)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CA[, Columns], Model_Spectra_Meta_OR[, Columns], Model_Spectra_Meta_WA[, Columns])
Model_Spectra_Meta$Sex <- recode.simple(Model_Spectra_Meta$Sex, cbind(c(1, 2, 3, 9, NA), c("M", "F", "U", "U", "U")))
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$sample_year)
# ---------------------------------------------------------------------------------
Table(!is.na(Model_Spectra_Meta$structure_weight_dg))
TRUE
11034
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
save(Model_Spectra_Meta, file = 'SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData')
#--------------------------------------------------------------------
Model_Spectra_Meta$Sex_SR <- Model_Spectra_Meta$Sex
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
sum(table(Model_Spectra_Meta$TMA))
100 * sum(table(Model_Spectra_Meta$TMA))/nrow(Model_Spectra_Meta)
Model_Spectra_Meta$age_structure_id[1:5]
[1] "CA1870028-SABL-1-1-O" "CA1870232-SABL-1-1-O" "CA1870028-SABL-1-10-O" "CA1870232-SABL-2-1-O" "CA1870028-SABL-2-1-O"
tail(Model_Spectra_Meta$age_structure_id)
[1] "WA23001-SABL-98-O" "WA23078-SABL-18-O" "WA23001-SABL-99-O" "WA23078-SABL-19-O" "WA23001-SABL-100-O" "WA23078-SABL-20-O"
Model_Spectra_Meta$Agency_ID <- substr(Model_Spectra_Meta$age_structure_id,1,2)
Model_Spectra_Meta$Agency_ID[Model_Spectra_Meta$Agency_ID %in% '10'] <- "WA"
Table(Model_Spectra_Meta$Agency_ID)
# !!!!!!!!!!!!!!!!!!! At some point in 2021, Patrick's 'age_structure_id' switched from a 2 digit year to a 4 digit year !!!!!!!!!!!!!!!
# PacFIN_SABL_Age.NN$SAMPLE_NUMBER[1:5]
# PacFIN_SABL_Age.NN$AGENCY_ID <- recode.simple(PacFIN_SABL_Age.NN$AGENCY_CODE, cbind(c('W', 'O', 'C'), c('WA', 'OR', 'CA')))
# Table(PacFIN_SABL_Age.NN$AGENCY_ID, PacFIN_SABL_Age.NN$AGENCY_CODE)
# --- CA ---
PacFIN_SABL_Age.NN$age_structure_id_CA_1 <- paste0('CA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id[1:5]
# [1] "CA67010001-SABL-1-1-O" "CA67010001-SABL-1-2-O" "CA67010001-SABL-1-3-O" "CA67010001-SABL-1-4-O" "CA67010001-SABL-1-5-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_CA_1$Length_cm), Model_Spectra_Meta_CA_1$sample_year, Model_Spectra_Meta_CA_1$Agency_ID)
PacFIN_SABL_Age.NN$age_structure_id_CA_2 <- paste0('CA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 1, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
tail(PacFIN_SABL_Age.NN$age_structure_id_2)
# [1] "CA2025010010-SABL-1-18-O" "CA2025010010-SABL-1-19-O" "CA2025010010-SABL-1-20-O" "CA2025010010-SABL-1-21-O" "CA2025010010-SABL-1-22-O" "CA2025010010-SABL-1-23-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_CA_2$Length_cm), Model_Spectra_Meta_CA_2$sample_year, Model_Spectra_Meta_CA_2$Agency_ID)
# --- WA ---
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'WA'][1:5]
[1] "100397679-SABL-O" "100397685-SABL-O" "100397681-SABL-O" "100397686-SABL-O" "100397697-SABL-O"
tail(Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'WA'], 50)
[1] "WA23001-SABL-98-O" "WA23078-SABL-18-O" "WA23001-SABL-99-O" "WA23078-SABL-19-O" "WA23001-SABL-100-O" "WA23078-SABL-20-O"
PacFIN_SABL_Age.NN$age_structure_id_WA_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 10, 14), substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 2, 5), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-O")
# PacFIN_SABL_Age.NN$age_structure_id_WA_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-O")
PacFIN_SABL_Age.NN$age_structure_id_WA_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'][1:5]
tail(PacFIN_SABL_Age.NN$age_structure_id_WA_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'])
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_WA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_WA_1$Length_cm), Model_Spectra_Meta_WA_1$sample_year, Model_Spectra_Meta_WA_1$Agency_ID)
PacFIN_SABL_Age.NN$age_structure_id_WA_2 <- paste0('WA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 8, 12), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id_WA_2[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'][1:5]
# [1] "WA20674-SABL-1-O" "WA20674-SABL-2-O" "WA20674-SABL-3-O" "WA20674-SABL-4-O" "WA20674-SABL-5-O"
tail(PacFIN_SABL_Age.NN$age_structure_id_WA_2[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'])
# [1] "WA20254-SABL-18-O" "WA20254-SABL-19-O" "WA20254-SABL-20-O" "WA20254-SABL-21-O" "WA20254-SABL-22-O" "WA20254-SABL-23-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_WA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_WA_2$Length_cm), Model_Spectra_Meta_WA_2$sample_year, Model_Spectra_Meta_WA_2$Agency_ID)
Table(!is.na(Model_Spectra_Meta_WA_2$TMA), Model_Spectra_Meta_WA_2$sample_year, Model_Spectra_Meta_WA_2$Agency_ID)
Table(!is.na(Model_Spectra_Meta_WA_2$TMA), round(Model_Spectra_Meta_WA_2$Length_cm), Model_Spectra_Meta_WA_2$Agency_ID)
# --- OR ---
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR'][1:5]
[1] "OR2076798-SABL-20-O" "OR2076637-SABL-1-O" "OR2076637-SABL-2-O" "OR2076637-SABL-3-O" "OR2076637-SABL-4-O"
tail(Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR'])
[1] "OR2494375-SABL-19-O" "OR2495171-SABL-5-O" "OR2494960-SABL-1-O" "OR2496595-SABL-27-O" "OR2494960-SABL-2-O" "OR2495171-SABL-9-O"
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR' & Model_Spectra_Meta$sample_year %in% 2018][1:5]
PacFIN_SABL_Age.NN$age_structure_id_OR_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 1, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id_OR_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'O'][1:5]
tail(PacFIN_SABL_Age.NN$age_structure_id_OR_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'O'])
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_OR_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_OR_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_OR_1$Length_cm), Model_Spectra_Meta_OR_1$sample_year, Model_Spectra_Meta_OR_1$Agency_ID)
# ===================================================
headTail(PacFIN_SABL_Age.NN)
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_CA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_OR_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_OR_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_WA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CA_1[!is.na(Model_Spectra_Meta_CA_1$Length_cm), ], Model_Spectra_Meta_CA_2[!is.na(Model_Spectra_Meta_CA_2$Length_cm), ],
Model_Spectra_Meta_OR_1[!is.na(Model_Spectra_Meta_OR_1$Length_cm), ], Model_Spectra_Meta_WA_2[!is.na(Model_Spectra_Meta_WA_2$Length_cm), ])
Table(round(Model_Spectra_Meta$Length_cm))
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
sum(table(Model_Spectra_Meta$TMA))
1647
100 * sum(table(Model_Spectra_Meta$TMA))/nrow(Model_Spectra_Meta)
18.6% (= 1647/8858)
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$sample_year)
2018 2020 2021 2022 2023 2024
F 155 782 315 2848 748 965
M 40 469 172 1093 408 336
U 2 0 41 290 194 0
Table(Model_Spectra_Meta$Sex_SR, Model_Spectra_Meta$sample_year)
2018 2020 2021 2022 2023 2024
F 0 760 277 2693 940 438
M 0 467 164 1069 298 163
U 197 24 87 469 112 700
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$Sex_SR)
F M U
F 4686 21 1106
M 139 2056 323
U 283 84 160
Table(!is.na(Model_Spectra_Meta$Month))
TRUE
8858
Table(!is.na(Model_Spectra_Meta$Length_cm))
TRUE
8858
Table(!is.na(Model_Spectra_Meta$Weight_kg))
FALSE TRUE
2104 6754
Table(!is.na(Model_Spectra_Meta$Sex))
TRUE
8859
Table(!is.na(Model_Spectra_Meta$Depth))
FALSE TRUE
8167 691
Table(!is.na(Model_Spectra_Meta$structure_weight_dg))
TRUE
8858
# Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$structure_weight_dg), ]
# headTail(Model_Spectra_Meta, 2, 2, 2, 70)
# if(!is.null(Model_Spectra_Meta$Length_cm))
# Model_Spectra_Meta$Length_prop_max <- Model_Spectra_Meta$Length_cm/max(Model_Spectra_Meta$Length_cm, na.rm = TRUE)
# if(!is.null(Model_Spectra_Meta$Weight_kg))
# Model_Spectra_Meta$Weight_prop_max <- (Model_Spectra_Meta$Weight_kg - min(Model_Spectra_Meta$Weight_kg, na.rm = TRUE))/(max(Model_Spectra_Meta$Weight_kg, na.rm = TRUE) - min(Model_Spectra_Meta$Weight_kg, na.rm = TRUE))
# if(!is.null(Model_Spectra_Meta$Sex))
# Model_Spectra_Meta$Sex_prop_max <- as.numeric(recode.simple(Model_Spectra_Meta$Sex, data.frame(c('F','M', 'U'), 0:2)))/2 # ** All variables have to be numeric **
# if(!is.null(Model_Spectra_Meta$Depth_m))
# Model_Spectra_Meta$Depth_prop_max <- (Model_Spectra_Meta$Depth_m - min(Model_Spectra_Meta$Depth_m, na.rm = TRUE))/(max(Model_Spectra_Meta$Depth_m, na.rm = TRUE) - min(Model_Spectra_Meta$Depth_m, na.rm = TRUE))
# if(!is.null(Model_Spectra_Meta$Latitude_dd))
# Model_Spectra_Meta$Latitude_prop_max <- (Model_Spectra_Meta$Latitude_dd - 30.5)/(49.1 - 30.5)
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
# -- Month, One hot encoding --
if(!is.null(Model_Spectra_Meta$Month) & !all(is.na(Model_Spectra_Meta$Month))) {
Month_ <- c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')[Model_Spectra_Meta$Month]
if(length(unique(Model_Spectra_Meta$Month)) > 2) {
Model_Spectra_Meta <- cbind(Model_Spectra_Meta[!is.na(Model_Spectra_Meta$Month), ], as.data.frame(model.matrix(formula(~ -1 + Month_)))) # Indicator columns added: Month_May, Month_Jun, Month_Jul, ...
} else {
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$Month), ]
Model_Spectra_Meta[paste0("Month_", unique(Month_))] <- 1
}
}
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
browsePlot('plot(Model_Spectra_Meta$TMA, Model_Spectra_Meta$structure_weight_dg)')
browsePlot('plot(Model_Spectra_Meta$TMA, Model_Spectra_Meta$Length_cm)')
browsePlot('print(lattice::xyplot(Length_cm ~ TMA, groups = Sex, data = Model_Spectra_Meta))')
save(Model_Spectra_Meta, file = 'SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData')
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta_PF <- Model_Spectra_Meta
Model_Spectra_Meta_PF$Sex_PF <- Model_Spectra_Meta_PF$Sex
Table(Model_Spectra_Meta_PF$Sex_PF)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta <- match.f(Model_Spectra_Meta, Model_Spectra_Meta_PF, 'filenames', 'filenames', 'Sex_PF')
change(Model_Spectra_Meta)
Table(Sex, Sex_PF)
Table(Sex)
Table(Sex_PF)
Model_Spectra_Meta$Sex[Model_Spectra_Meta$Sex %in% 'U'] <- Model_Spectra_Meta$Sex_PF[Model_Spectra_Meta$Sex %in% 'U']
Table(Model_Spectra_Meta$Sex)
F M U <NA>
7593 2873 160 408
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- 'U'
F M U
7593 2873 568
Model_Spectra_Meta$Sex_PF <- NULL
Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Model_Spectra_Meta$Sex_F.1 <- Model_Spectra_Meta$Sex_M.1 <- Model_Spectra_Meta$Sex_U.1 <- NULL
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta,2,2,2,55)
Table(Model_Spectra_Meta$Sex)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
# ------------------------------
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
sum(table(Model_Spectra_Meta$TMA))
2600
Table(Model_Spectra_Meta$sample_year, !is.na(Model_Spectra_Meta$TMA))
FALSE TRUE
2018 96 101
2020 0 1773
2021 450 390
2022 4535 336
2023 2052 0
2024 1301 0
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34 37 38 40 41 43 47 48 52 53 54 <NA>
2018 0 0 4 7 15 34 4 3 14 5 0 2 1 0 1 0 2 1 1 0 0 0 2 0 1 0 0 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 96
2020 0 5 45 67 884 99 121 288 50 33 34 34 33 10 9 5 6 1 5 5 8 5 2 2 1 1 2 2 2 1 1 2 1 1 1 1 0 1 1 0 1 1 1 1 0
2021 1 27 11 18 17 238 27 18 14 4 3 7 0 0 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 450
2022 0 14 113 60 7 10 61 23 4 10 9 4 3 1 2 3 3 2 0 0 1 1 0 0 0 0 0 0 0 0 0 1 2 0 0 1 1 0 0 0 0 0 0 0 4535
2023 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2052
2024 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1301
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 78 88
2017 9 211 50 116 189 66 52 46 45 35 25 13 15 9 8 18 17 14 16 8 6 7 7 6 7 4 6 4 4 3 4 1 2 2 2 5 2 0 3 4 2 4 3 0 1 3 2 8 3 3 2 1 0 1 1 1 2 4 2 2 1 1 1 2 2 1 0 2 0 1 0 2 0 0 0
2018 19 41 312 73 102 173 87 82 58 47 50 18 16 9 12 14 15 14 11 12 13 14 7 5 2 3 2 4 6 8 4 7 5 4 1 5 2 3 5 6 3 0 0 2 4 2 2 1 3 3 1 5 3 1 2 2 4 3 1 1 0 1 4 0 1 0 1 0 0 1 0 0 0 0 0
2019 30 71 13 180 45 41 116 36 29 24 16 19 6 6 11 5 6 3 9 11 12 3 3 4 1 2 2 1 5 2 3 1 2 1 0 2 1 1 3 3 1 1 0 0 0 0 0 2 0 2 2 1 1 0 1 1 0 1 0 2 0 2 2 0 0 0 1 0 1 0 0 0 0 0 0
2021 213 450 130 65 60 300 94 76 80 64 66 60 60 41 29 21 23 9 11 6 10 17 17 17 7 12 9 10 9 3 3 2 4 7 3 6 1 3 3 5 3 2 4 6 2 3 0 2 0 1 2 1 3 2 4 1 2 1 2 1 1 2 0 1 1 0 1 1 3 0 1 2 2 0 1
2022 26 332 368 110 41 29 170 42 36 43 36 33 39 32 22 19 15 6 11 9 11 10 5 6 10 5 6 3 5 0 1 2 2 1 6 2 5 2 3 1 0 2 4 1 2 2 4 0 2 1 0 1 2 2 2 0 4 0 0 1 3 6 1 1 0 2 1 1 0 1 1 0 0 1 0
Table(Model_Spectra_Meta$sample_year, !is.na(Model_Spectra_Meta$TMA))
TRUE
2017 1099
2018 1322
2019 750
2021 2064
2022 1553
# All Commercial plus 2017, 2019, 2022
2600 + 1099 + 750 + 1553 + 1032
# -- Just non-missing TMA for model training --
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Combo <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2017, 2019, 2022), ]
dim(Model_Spectra_Meta_Combo)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta$sample_year <- paste(Model_Spectra_Meta$sample_year, "Comm", sep = "_")
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ]
dim(Model_Spectra_Meta)
Model_Spectra_Meta$age_structure_side <- Model_Spectra_Meta$NWFSC_NIR_Filename <- Model_Spectra_Meta$unscannable_other <- Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Columns <- names(Model_Spectra_Meta)
Columns[!Columns %in% names(Model_Spectra_Meta_Combo)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta[, Columns], Model_Spectra_Meta_Combo[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta, 2,2,2,55)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Combo_Comm_2017__2022_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# -- All the data for prediction --
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Combo <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2017, 2019, 2022), ]
dim(Model_Spectra_Meta_Combo)
headTail(Model_Spectra_Meta_Combo,2,2,2,55)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Comm_Len_Month <- Model_Spectra_Meta
headTail(Model_Spectra_Meta_Comm_Len_Month,2,2,2,55)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
Model_Spectra_Meta$sample_year <- paste(Model_Spectra_Meta$sample_year, "Comm", sep = "_")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta <- match.f(Model_Spectra_Meta, Model_Spectra_Meta_Comm_Len_Month, 'filenames', 'filenames', c('Length_cm', 'Month'))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Month)
Model_Spectra_Meta$age_structure_side <- Model_Spectra_Meta$NWFSC_NIR_Filename <- Model_Spectra_Meta$unscannable_other <- Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Columns <- names(Model_Spectra_Meta)
Columns[!Columns %in% names(Model_Spectra_Meta_Combo)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta[, Columns], Model_Spectra_Meta_Combo[, Columns])
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta,2,2,2,55) # Look for one hot encoding sex and structure_weight_dg for the NN model
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$structure_weight_g)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Month)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Sex)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Combo_Comm_2017__2022_Model_Spectra_Meta_ALL_GOOD_DATA_TMA_NAs.RData")
# =================================================
# FINAL_FISH_AGE_IN_YEARS is 14, but AGE_IN_YEARS by McBride in CA is 98
PacFIN_SABL_Age[PacFIN_SABL_Age$BDS_ID %in% 2469163, ]
BDS_ID SAMPLE_ID SAMPLE_NUMBER SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY SAMPLE_TYPE SAMPLE_METHOD_CODE SAMPLE_AGENCY AGENCY_CODE DATA_TYPE AGENCY_CONDITION_CODE PACFIN_CONDITION_CODE
44149 2469163 63753579 1989220130744 1989 3 31 R NA C C N
PACFIN_PORT_CODE PACFIN_PORT_NAME FTID PACFIN_GEAR_CODE PACFIN_GEAR_NAME VESSEL_ID PSMFC_CATCH_AREA_CODE DEPTH_AVERAGE_FATHOMS DEPTH_MAXIMUM_FATHOMS DEPTH_MINIMUM_FATHOMS
44149 ERK EUREKA GFT GFSH-TRAWL NA NA NA
MARKET_CATEGORY CLUSTER_SEQUENCE_NUMBER CLUSTER_WEIGHT_LBS ADJUSTED_CLUSTER_WEIGHT_LBS FRAME_CLUSTER_WEIGHT_LBS PACFIN_SPECIES_CODE FISH_SEQUENCE_NUMBER OBSERVED_FREQUENCY
44149 190 1 80 NA NA SABL 16 1
FISH_LENGTH_TYPE_CODE FISH_LENGTH FORK_LENGTH FORK_LENGTH_IS_ESTIMATED FISH_WEIGHT SEX_CODE AGENCY_FISH_MATURITY_CODE FISH_MATURITY_CODE WEIGHT_OF_MALES_LBS WEIGHT_OF_FEMALES_LBS
44149 F 488 NA NA NA M 9 U NA NA
NUMBER_OF_MALES NUMBER_OF_FEMALES WEIGHT_OF_LANDING_LBS EXPANDED_SAMPLE_WEIGHT SPECIES_WEIGHT_LBS FINAL_FISH_AGE_CODE FINAL_FISH_AGE_IN_YEARS AGE_SEQUENCE_NUMBER AGE_METHOD_CODE
44149 NA NA 1558 NA NA NA 14 2 BB
AGE_READABILITY PERSON_WHO_AGED DATE_AGE_RECORDED AGE_IN_YEARS AGE_STRUCTURE_CODE AGENCY_AGE_STRUCTURE_CODE PACFIN_LOAD_DATE AGENCY_GRADE_CODE PACFIN_GRADE_CODE
44149 NA McBride <NA> 98 2025-04-03 04:01:27 S S
FISH_LENGTH_UNITS FISH_WEIGHT_UNITS FISH_LENGTH_IS_ESTIMATED FISH_WEIGHT_UNITS_NAME all_cluster_sum_kg DAHL_GROUNDFISH_CODE Length_cm Weight_kg Sex Year Depth Month
44149 MM FALSE 36.28739 NA 48.8 NA M 1989 NA 3
No_NA <- PacFIN_SABL_Age[!is.na(PacFIN_SABL_Age$AGE_IN_YEARS) & !is.na(PacFIN_SABL_Age$FINAL_FISH_AGE_IN_YEARS), ]
renum(No_NA[No_NA$AGE_IN_YEARS > 65| No_NA$FINAL_FISH_AGE_IN_YEARS > 65,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "FISH_SEQUENCE_NUMBER",
"AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS")])
AGENCY_CODE SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY BDS_ID SAMPLE_ID SAMPLE_NUMBER CLUSTER_SEQUENCE_NUMBER FISH_SEQUENCE_NUMBER AGE_SEQUENCE_NUMBER PERSON_WHO_AGED AGE_IN_YEARS FINAL_FISH_AGE_IN_YEARS
1 C 1989 9 25 4553212 63738574 1989201330766 2 1 1 Pearson 32 98
2 C 1989 9 25 4553213 63738574 1989201330766 2 1 2 McBride 98 98
3 C 1989 3 31 2469163 63753579 1989220130744 1 16 2 McBride 98 14
4 W 1992 1 22 3090557 63725717 20924301010009 1 10 1 SWFC/JO 68 68
5 O 1996 6 4 4641375 63491683 OR960031 1 9 1 76 76
6 O 1997 7 7 515848 63480810 OR970104 1 6 1 92 92
7 O 1997 8 31 4005968 63492217 OR970644 1 6 1 87 87
8 C 2000 4 14 1549261 63784801 2000400230305 2 10 1 PJM - CAP 67 67
9 W 2006 5 22 3911216 63714208 20064301010114 1 80 1 NWFC/PM 102 102
10 O 2007 11 9 3511361 63484683 OR072118 2 21 1 66 66
11 O 2013 7 14 2496972 63472358 OR130227 1 45 1 71 71
12 W 2013 4 15 1481477 63725518 20134301010022 1 21 1 NMFS IS 66 66
13 O 2014 2 12 3699983 62906135 OR142262 2 12 1 Lance Sullivan 70 70
14 O 2017 8 31 3844013 62694790 OR1762861 1 2 1 Nikki Paige 70 70
15 O 2017 9 20 429333 62695622 OR1763382 2 24 1 James Hale 75 75
16 O 2017 10 27 3469521 62697029 OR1764090 1 1 1 Patrick McDonald 68 68
17 O 2018 10 1 46946 62639196 OR1869074 1 18 1 Patrick McDonald 82 82
18 O 2018 11 20 45733 62639116 OR1869956 2 15 1 Patrick McDonald 73 73
19 O 2018 12 10 47203 62638513 OR1870085 1 3 1 Patrick McDonald 69 69
renum(PacFIN_SABL_Age[PacFIN_SABL_Age$SAMPLE_NUMBER == 1989220130744 & PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER == 1 & PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER == 16,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER",
"FISH_SEQUENCE_NUMBER", "AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS")])
AGENCY_CODE SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY BDS_ID SAMPLE_ID SAMPLE_NUMBER CLUSTER_SEQUENCE_NUMBER FISH_SEQUENCE_NUMBER AGE_SEQUENCE_NUMBER PERSON_WHO_AGED AGE_IN_YEARS FINAL_FISH_AGE_IN_YEARS
1 C 1989 3 31 2469162 63753579 1989220130744 1 16 1 Pearson 14 14
2 C 1989 3 31 2469163 63753579 1989220130744 1 16 2 McBride 98 14
PacFIN_SABL_Age$age_structure_id_CA_1 <- paste0('CA', substr(PacFIN_SABL_Age$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age$age_structure_id_CA_2 <- paste0('CA', substr(PacFIN_SABL_Age$SAMPLE_NUMBER, 1, 4), substring(PacFIN_SABL_Age$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER, "-O")
renum(PacFIN_SABL_Age[PacFIN_SABL_Age$SAMPLE_NUMBER == 1989220130744 & PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER == 1 & PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER == 16,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER",
"FISH_SEQUENCE_NUMBER", "AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS", "age_structure_id_CA_1", "age_structure_id_CA_2")])
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# =============== Import the PacFIN Sablefish metadata =============================
setwd("C:/SIDT/Sable_Comm")
library(JRWToolBox)
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
# Toolbox functions
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/PacFIN.Data.Extraction/master/R/PacFIN.BDS.Extraction.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/GitHub_File_Download.R")
GitHub_File_Download("John-R-Wallace-NOAA/PacFIN.Data.Extraction/master/R/PacFIN.BDS.Extraction.R")
PacFIN.Login <- UID <- "wallacej"
PacFIN.PW <- PWD <- "*********"
DSN <- "PacFIN"
import.sql("Select * from pacfin.bds_sp where rownum < 11", dsn="PacFIN", uid="wallacej", pwd=PacFIN.PW) # Test
PacFIN_SABL_Age <- PacFIN.BDS.Extraction("'SABL'")
save(PacFIN_SABL_Age, file = "C:\\SIDT\\SABL_Comm_2012_2023\\PacFIN_SABL_Age.RData")
base::load("C:\\SIDT\\SABL_Comm_2012_2023\\PacFIN_SABL_Age.RData")
headTail(PacFIN_SABL_Age,2,2,2,70)
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# PacFIN_SABL_Age$Length_cm <- PacFIN_SABL_Age$FISH_LENGTH/10
# PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm < 10 & !is.na(PacFIN_SABL_Age$Length_cm)] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm < 10 & !is.na(PacFIN_SABL_Age$Length_cm)] * 10
PacFIN_SABL_Age$Length_cm <- PacFIN_SABL_Age$FISH_LENGTH
PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$FISH_LENGTH_UNITS == 'MM'] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$FISH_LENGTH_UNITS == 'MM']/10
range(PacFIN_SABL_Age$Length_cm, na.rm = TRUE)
plot(PacFIN_SABL_Age$Length_cm)
# PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm > 100 & !is.na(PacFIN_SABL_Age$Length_cm)] <- PacFIN_SABL_Age$Length_cm[PacFIN_SABL_Age$Length_cm > 100 & !is.na(PacFIN_SABL_Age$Length_cm)] / 10
# range(PacFIN_SABL_Age$Length_cm, na.rm = TRUE)
# plot(PacFIN_SABL_Age$Length_cm)
range(PacFIN_SABL_Age$FISH_WEIGHT, na.rm = TRUE)
Table(PacFIN_SABL_Age$FISH_WEIGHT_UNITS)
G P
45042 10679 1
PacFIN_SABL_Age$Weight_kg <- PacFIN_SABL_Age$FISH_WEIGHT
PacFIN_SABL_Age$Weight_kg[PacFIN_SABL_Age$FISH_WEIGHT_UNITS == 'G'] <- PacFIN_SABL_Age$Weight_kg[PacFIN_SABL_Age$FISH_WEIGHT_UNITS == 'G']/1000
range(PacFIN_SABL_Age$Weight_kg, na.rm = TRUE)
change(PacFIN_SABL_Age)
browsePlot('plot(Weight_kg, Length_cm)')
browsePlot('
par(mfrow = c(2,1))
plot(AGE_IN_YEARS, Length_cm)
plot(AGE_IN_YEARS, Weight_kg)
')
PacFIN_SABL_Age$Sex <- PacFIN_SABL_Age$SEX
PacFIN_SABL_Age$Year <- PacFIN_SABL_Age$SAMPLE_YEAR
PacFIN_SABL_Age$Depth <- PacFIN_SABL_Age$DEPTH_AVERAGE_FATHOMS
PacFIN_SABL_Age$Month <- PacFIN_SABL_Age$SAMPLE_MONTH
save(PacFIN_SABL_Age, file = 'PacFIN_SABL_Age.RData')
# ==================================== Match the PacFIN Metadata onto the combined WA, OR, CA commercial Model_Spectra_Meta ========================================================================================
setwd("C:/SIDT/Sable_Comm")
library(JRWToolBox)
load("C:\\SIDT\\Sable_Comm\\PacFIN_SABL_Age.RData")
PacFIN_SABL_Age.NN <- PacFIN_SABL_Age[, c("BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "AGENCY_CODE", "PACFIN_SPECIES_CODE", "FISH_SEQUENCE_NUMBER", "FINAL_FISH_AGE_IN_YEARS", "Year", "Month", "SAMPLE_DAY", "Length_cm", "Weight_kg", "Sex", "Depth")]
# !!!!! Find the column names that are missing !!!!!
Columns <- c("BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "AGENCY_CODE", "PACFIN_SPECIES_CODE", "FISH_SEQUENCE_NUMBER", "FINAL_FISH_AGE_IN_YEARS", "Year", "Month", "SAMPLE_DAY", "Length_cm", "Weight_kg", "Sex", "Depth")
Columns[!Columns %in% names(PacFIN_SABL_Age)]
# --- Load Model_Spectra_Meta data ---
load("C:\\SIDT\\Sable_Comm\\Sable_CA_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_CA <- Model_Spectra_Meta
load("C:\\SIDT\\Sable_Comm\\Sable_OR_Comm_2020__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_OR <- Model_Spectra_Meta
load("C:\\SIDT\\Sable_Comm\\Sable_WA_Comm_2020__2023_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_WA <- Model_Spectra_Meta
Table(Model_Spectra_Meta_CA$Sex)
Table(Model_Spectra_Meta_OR$Sex)
Table(Model_Spectra_Meta_WA$Sex)
Model_Spectra_Meta_CA$Sex <- NA
dim(Model_Spectra_Meta_CA)
dim(Model_Spectra_Meta_OR)
dim(Model_Spectra_Meta_WA)
# Use the least common column set
Columns <- names(Model_Spectra_Meta_CA)
Columns[!Columns %in% names(Model_Spectra_Meta_OR)]
Columns[!Columns %in% names(Model_Spectra_Meta_WA)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CA[, Columns], Model_Spectra_Meta_OR[, Columns], Model_Spectra_Meta_WA[, Columns])
Model_Spectra_Meta$Sex <- recode.simple(Model_Spectra_Meta$Sex, cbind(c(1, 2, 3, 9, NA), c("M", "F", "U", "U", "U")))
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$sample_year)
# ---------------------------------------------------------------------------------
Table(!is.na(Model_Spectra_Meta$structure_weight_dg))
TRUE
11034
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
save(Model_Spectra_Meta, file = 'SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData')
#--------------------------------------------------------------------
Model_Spectra_Meta$Sex_SR <- Model_Spectra_Meta$Sex
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
sum(table(Model_Spectra_Meta$TMA))
100 * sum(table(Model_Spectra_Meta$TMA))/nrow(Model_Spectra_Meta)
Model_Spectra_Meta$age_structure_id[1:5]
[1] "CA1870028-SABL-1-1-O" "CA1870232-SABL-1-1-O" "CA1870028-SABL-1-10-O" "CA1870232-SABL-2-1-O" "CA1870028-SABL-2-1-O"
tail(Model_Spectra_Meta$age_structure_id)
[1] "WA23001-SABL-98-O" "WA23078-SABL-18-O" "WA23001-SABL-99-O" "WA23078-SABL-19-O" "WA23001-SABL-100-O" "WA23078-SABL-20-O"
Model_Spectra_Meta$Agency_ID <- substr(Model_Spectra_Meta$age_structure_id,1,2)
Model_Spectra_Meta$Agency_ID[Model_Spectra_Meta$Agency_ID %in% '10'] <- "WA"
Table(Model_Spectra_Meta$Agency_ID)
# !!!!!!!!!!!!!!!!!!! At some point in 2021, Patrick's 'age_structure_id' switched from a 2 digit year to a 4 digit year !!!!!!!!!!!!!!!
# PacFIN_SABL_Age.NN$SAMPLE_NUMBER[1:5]
# PacFIN_SABL_Age.NN$AGENCY_ID <- recode.simple(PacFIN_SABL_Age.NN$AGENCY_CODE, cbind(c('W', 'O', 'C'), c('WA', 'OR', 'CA')))
# Table(PacFIN_SABL_Age.NN$AGENCY_ID, PacFIN_SABL_Age.NN$AGENCY_CODE)
# --- CA ---
PacFIN_SABL_Age.NN$age_structure_id_CA_1 <- paste0('CA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id[1:5]
# [1] "CA67010001-SABL-1-1-O" "CA67010001-SABL-1-2-O" "CA67010001-SABL-1-3-O" "CA67010001-SABL-1-4-O" "CA67010001-SABL-1-5-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_CA_1$Length_cm), Model_Spectra_Meta_CA_1$sample_year, Model_Spectra_Meta_CA_1$Agency_ID)
PacFIN_SABL_Age.NN$age_structure_id_CA_2 <- paste0('CA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 1, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
tail(PacFIN_SABL_Age.NN$age_structure_id_2)
# [1] "CA2025010010-SABL-1-18-O" "CA2025010010-SABL-1-19-O" "CA2025010010-SABL-1-20-O" "CA2025010010-SABL-1-21-O" "CA2025010010-SABL-1-22-O" "CA2025010010-SABL-1-23-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_CA_2$Length_cm), Model_Spectra_Meta_CA_2$sample_year, Model_Spectra_Meta_CA_2$Agency_ID)
# --- WA ---
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'WA'][1:5]
[1] "100397679-SABL-O" "100397685-SABL-O" "100397681-SABL-O" "100397686-SABL-O" "100397697-SABL-O"
tail(Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'WA'], 50)
[1] "WA23001-SABL-98-O" "WA23078-SABL-18-O" "WA23001-SABL-99-O" "WA23078-SABL-19-O" "WA23001-SABL-100-O" "WA23078-SABL-20-O"
PacFIN_SABL_Age.NN$age_structure_id_WA_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 10, 14), substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 2, 5), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-O")
# PacFIN_SABL_Age.NN$age_structure_id_WA_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 9), "-O")
PacFIN_SABL_Age.NN$age_structure_id_WA_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'][1:5]
tail(PacFIN_SABL_Age.NN$age_structure_id_WA_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'])
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_WA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_WA_1$Length_cm), Model_Spectra_Meta_WA_1$sample_year, Model_Spectra_Meta_WA_1$Agency_ID)
PacFIN_SABL_Age.NN$age_structure_id_WA_2 <- paste0('WA', substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 8, 12), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id_WA_2[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'][1:5]
# [1] "WA20674-SABL-1-O" "WA20674-SABL-2-O" "WA20674-SABL-3-O" "WA20674-SABL-4-O" "WA20674-SABL-5-O"
tail(PacFIN_SABL_Age.NN$age_structure_id_WA_2[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'W'])
# [1] "WA20254-SABL-18-O" "WA20254-SABL-19-O" "WA20254-SABL-20-O" "WA20254-SABL-21-O" "WA20254-SABL-22-O" "WA20254-SABL-23-O"
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_WA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_WA_2$Length_cm), Model_Spectra_Meta_WA_2$sample_year, Model_Spectra_Meta_WA_2$Agency_ID)
Table(!is.na(Model_Spectra_Meta_WA_2$TMA), Model_Spectra_Meta_WA_2$sample_year, Model_Spectra_Meta_WA_2$Agency_ID)
Table(!is.na(Model_Spectra_Meta_WA_2$TMA), round(Model_Spectra_Meta_WA_2$Length_cm), Model_Spectra_Meta_WA_2$Agency_ID)
# --- OR ---
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR'][1:5]
[1] "OR2076798-SABL-20-O" "OR2076637-SABL-1-O" "OR2076637-SABL-2-O" "OR2076637-SABL-3-O" "OR2076637-SABL-4-O"
tail(Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR'])
[1] "OR2494375-SABL-19-O" "OR2495171-SABL-5-O" "OR2494960-SABL-1-O" "OR2496595-SABL-27-O" "OR2494960-SABL-2-O" "OR2495171-SABL-9-O"
Model_Spectra_Meta$age_structure_id[Model_Spectra_Meta$Agency_ID %in% 'OR' & Model_Spectra_Meta$sample_year %in% 2018][1:5]
PacFIN_SABL_Age.NN$age_structure_id_OR_1 <- paste0(substr(PacFIN_SABL_Age.NN$SAMPLE_NUMBER, 1, 9), "-", PacFIN_SABL_Age.NN$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age.NN$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age.NN$age_structure_id_OR_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'O'][1:5]
tail(PacFIN_SABL_Age.NN$age_structure_id_OR_1[PacFIN_SABL_Age.NN$AGENCY_CODE %in% 'O'])
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_OR_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_OR_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Table(!is.na(Model_Spectra_Meta_OR_1$Length_cm), Model_Spectra_Meta_OR_1$sample_year, Model_Spectra_Meta_OR_1$Agency_ID)
# ===================================================
headTail(PacFIN_SABL_Age.NN)
Model_Spectra_Meta.xx <- Model_Spectra_Meta
Model_Spectra_Meta.xx$Length_cm <- Model_Spectra_Meta.xx$Weight_kg <- Model_Spectra_Meta.xx$Sex <- Model_Spectra_Meta.xx$Depth <- NULL
Model_Spectra_Meta_CA_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_CA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_CA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_OR_1 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_OR_1', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta_WA_2 <- match.f(Model_Spectra_Meta.xx, PacFIN_SABL_Age.NN, 'age_structure_id', 'age_structure_id_WA_2', c("Month", "Length_cm", "Weight_kg", "Sex", "Depth"))
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CA_1[!is.na(Model_Spectra_Meta_CA_1$Length_cm), ], Model_Spectra_Meta_CA_2[!is.na(Model_Spectra_Meta_CA_2$Length_cm), ],
Model_Spectra_Meta_OR_1[!is.na(Model_Spectra_Meta_OR_1$Length_cm), ], Model_Spectra_Meta_WA_2[!is.na(Model_Spectra_Meta_WA_2$Length_cm), ])
Table(round(Model_Spectra_Meta$Length_cm))
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
sum(table(Model_Spectra_Meta$TMA))
1647
100 * sum(table(Model_Spectra_Meta$TMA))/nrow(Model_Spectra_Meta)
18.6% (= 1647/8858)
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$sample_year)
2018 2020 2021 2022 2023 2024
F 155 782 315 2848 748 965
M 40 469 172 1093 408 336
U 2 0 41 290 194 0
Table(Model_Spectra_Meta$Sex_SR, Model_Spectra_Meta$sample_year)
2018 2020 2021 2022 2023 2024
F 0 760 277 2693 940 438
M 0 467 164 1069 298 163
U 197 24 87 469 112 700
Table(Model_Spectra_Meta$Sex, Model_Spectra_Meta$Sex_SR)
F M U
F 4686 21 1106
M 139 2056 323
U 283 84 160
Table(!is.na(Model_Spectra_Meta$Month))
TRUE
8858
Table(!is.na(Model_Spectra_Meta$Length_cm))
TRUE
8858
Table(!is.na(Model_Spectra_Meta$Weight_kg))
FALSE TRUE
2104 6754
Table(!is.na(Model_Spectra_Meta$Sex))
TRUE
8859
Table(!is.na(Model_Spectra_Meta$Depth))
FALSE TRUE
8167 691
Table(!is.na(Model_Spectra_Meta$structure_weight_dg))
TRUE
8858
# Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$structure_weight_dg), ]
# headTail(Model_Spectra_Meta, 2, 2, 2, 70)
# if(!is.null(Model_Spectra_Meta$Length_cm))
# Model_Spectra_Meta$Length_prop_max <- Model_Spectra_Meta$Length_cm/max(Model_Spectra_Meta$Length_cm, na.rm = TRUE)
# if(!is.null(Model_Spectra_Meta$Weight_kg))
# Model_Spectra_Meta$Weight_prop_max <- (Model_Spectra_Meta$Weight_kg - min(Model_Spectra_Meta$Weight_kg, na.rm = TRUE))/(max(Model_Spectra_Meta$Weight_kg, na.rm = TRUE) - min(Model_Spectra_Meta$Weight_kg, na.rm = TRUE))
# if(!is.null(Model_Spectra_Meta$Sex))
# Model_Spectra_Meta$Sex_prop_max <- as.numeric(recode.simple(Model_Spectra_Meta$Sex, data.frame(c('F','M', 'U'), 0:2)))/2 # ** All variables have to be numeric **
# if(!is.null(Model_Spectra_Meta$Depth_m))
# Model_Spectra_Meta$Depth_prop_max <- (Model_Spectra_Meta$Depth_m - min(Model_Spectra_Meta$Depth_m, na.rm = TRUE))/(max(Model_Spectra_Meta$Depth_m, na.rm = TRUE) - min(Model_Spectra_Meta$Depth_m, na.rm = TRUE))
# if(!is.null(Model_Spectra_Meta$Latitude_dd))
# Model_Spectra_Meta$Latitude_prop_max <- (Model_Spectra_Meta$Latitude_dd - 30.5)/(49.1 - 30.5)
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
# -- Month, One hot encoding --
if(!is.null(Model_Spectra_Meta$Month) & !all(is.na(Model_Spectra_Meta$Month))) {
Month_ <- c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')[Model_Spectra_Meta$Month]
if(length(unique(Model_Spectra_Meta$Month)) > 2) {
Model_Spectra_Meta <- cbind(Model_Spectra_Meta[!is.na(Model_Spectra_Meta$Month), ], as.data.frame(model.matrix(formula(~ -1 + Month_)))) # Indicator columns added: Month_May, Month_Jun, Month_Jul, ...
} else {
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$Month), ]
Model_Spectra_Meta[paste0("Month_", unique(Month_))] <- 1
}
}
headTail(Model_Spectra_Meta, 2, 2, 2, 70)
browsePlot('plot(Model_Spectra_Meta$TMA, Model_Spectra_Meta$structure_weight_dg)')
browsePlot('plot(Model_Spectra_Meta$TMA, Model_Spectra_Meta$Length_cm)')
browsePlot('print(lattice::xyplot(Length_cm ~ TMA, groups = Sex, data = Model_Spectra_Meta))')
save(Model_Spectra_Meta, file = 'SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData')
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta_PF <- Model_Spectra_Meta
Model_Spectra_Meta_PF$Sex_PF <- Model_Spectra_Meta_PF$Sex
Table(Model_Spectra_Meta_PF$Sex_PF)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta <- match.f(Model_Spectra_Meta, Model_Spectra_Meta_PF, 'filenames', 'filenames', 'Sex_PF')
change(Model_Spectra_Meta)
Table(Sex, Sex_PF)
Table(Sex)
Table(Sex_PF)
Model_Spectra_Meta$Sex[Model_Spectra_Meta$Sex %in% 'U'] <- Model_Spectra_Meta$Sex_PF[Model_Spectra_Meta$Sex %in% 'U']
Table(Model_Spectra_Meta$Sex)
F M U <NA>
7593 2873 160 408
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- 'U'
F M U
7593 2873 568
Model_Spectra_Meta$Sex_PF <- NULL
Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Model_Spectra_Meta$Sex_F.1 <- Model_Spectra_Meta$Sex_M.1 <- Model_Spectra_Meta$Sex_U.1 <- NULL
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta,2,2,2,55)
Table(Model_Spectra_Meta$Sex)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
# ------------------------------
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
sum(table(Model_Spectra_Meta$TMA))
2600
Table(Model_Spectra_Meta$sample_year, !is.na(Model_Spectra_Meta$TMA))
FALSE TRUE
2018 96 101
2020 0 1773
2021 450 390
2022 4535 336
2023 2052 0
2024 1301 0
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 30 31 32 33 34 37 38 40 41 43 47 48 52 53 54 <NA>
2018 0 0 4 7 15 34 4 3 14 5 0 2 1 0 1 0 2 1 1 0 0 0 2 0 1 0 0 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 96
2020 0 5 45 67 884 99 121 288 50 33 34 34 33 10 9 5 6 1 5 5 8 5 2 2 1 1 2 2 2 1 1 2 1 1 1 1 0 1 1 0 1 1 1 1 0
2021 1 27 11 18 17 238 27 18 14 4 3 7 0 0 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 450
2022 0 14 113 60 7 10 61 23 4 10 9 4 3 1 2 3 3 2 0 0 1 1 0 0 0 0 0 0 0 0 0 1 2 0 0 1 1 0 0 0 0 0 0 0 4535
2023 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2052
2024 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1301
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 78 88
2017 9 211 50 116 189 66 52 46 45 35 25 13 15 9 8 18 17 14 16 8 6 7 7 6 7 4 6 4 4 3 4 1 2 2 2 5 2 0 3 4 2 4 3 0 1 3 2 8 3 3 2 1 0 1 1 1 2 4 2 2 1 1 1 2 2 1 0 2 0 1 0 2 0 0 0
2018 19 41 312 73 102 173 87 82 58 47 50 18 16 9 12 14 15 14 11 12 13 14 7 5 2 3 2 4 6 8 4 7 5 4 1 5 2 3 5 6 3 0 0 2 4 2 2 1 3 3 1 5 3 1 2 2 4 3 1 1 0 1 4 0 1 0 1 0 0 1 0 0 0 0 0
2019 30 71 13 180 45 41 116 36 29 24 16 19 6 6 11 5 6 3 9 11 12 3 3 4 1 2 2 1 5 2 3 1 2 1 0 2 1 1 3 3 1 1 0 0 0 0 0 2 0 2 2 1 1 0 1 1 0 1 0 2 0 2 2 0 0 0 1 0 1 0 0 0 0 0 0
2021 213 450 130 65 60 300 94 76 80 64 66 60 60 41 29 21 23 9 11 6 10 17 17 17 7 12 9 10 9 3 3 2 4 7 3 6 1 3 3 5 3 2 4 6 2 3 0 2 0 1 2 1 3 2 4 1 2 1 2 1 1 2 0 1 1 0 1 1 3 0 1 2 2 0 1
2022 26 332 368 110 41 29 170 42 36 43 36 33 39 32 22 19 15 6 11 9 11 10 5 6 10 5 6 3 5 0 1 2 2 1 6 2 5 2 3 1 0 2 4 1 2 2 4 0 2 1 0 1 2 2 2 0 4 0 0 1 3 6 1 1 0 2 1 1 0 1 1 0 0 1 0
Table(Model_Spectra_Meta$sample_year, !is.na(Model_Spectra_Meta$TMA))
TRUE
2017 1099
2018 1322
2019 750
2021 2064
2022 1553
# All Commercial plus 2017, 2019, 2022
2600 + 1099 + 750 + 1553 + 1032
# -- Just non-missing TMA for model training --
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Combo <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2017, 2019, 2022), ]
dim(Model_Spectra_Meta_Combo)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta$sample_year <- paste(Model_Spectra_Meta$sample_year, "Comm", sep = "_")
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ]
dim(Model_Spectra_Meta)
Model_Spectra_Meta$age_structure_side <- Model_Spectra_Meta$NWFSC_NIR_Filename <- Model_Spectra_Meta$unscannable_other <- Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Columns <- names(Model_Spectra_Meta)
Columns[!Columns %in% names(Model_Spectra_Meta_Combo)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta[, Columns], Model_Spectra_Meta_Combo[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta, 2,2,2,55)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Combo_Comm_2017__2022_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# -- All the data for prediction --
load("C:\\SIDT\\Sablefish Combo Multi Year\\Sable_Combo_All_Oties_2017_18_19_21_22\\Sable_Combo_Multi_17_22_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Combo <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2017, 2019, 2022), ]
dim(Model_Spectra_Meta_Combo)
headTail(Model_Spectra_Meta_Combo,2,2,2,55)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_Comm_Len_Month <- Model_Spectra_Meta
headTail(Model_Spectra_Meta_Comm_Len_Month,2,2,2,55)
load("C:\\SIDT\\Sable_Comm\\SABL_Comm_2018__2024_Model_Spectra_Meta_ALL_GOOD_DATA_SR_Sex.RData")
Model_Spectra_Meta$sample_year <- paste(Model_Spectra_Meta$sample_year, "Comm", sep = "_")
headTail(Model_Spectra_Meta,2,2,2,55)
Model_Spectra_Meta <- match.f(Model_Spectra_Meta, Model_Spectra_Meta_Comm_Len_Month, 'filenames', 'filenames', c('Length_cm', 'Month'))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Month)
Model_Spectra_Meta$age_structure_side <- Model_Spectra_Meta$NWFSC_NIR_Filename <- Model_Spectra_Meta$unscannable_other <- Model_Spectra_Meta$Sex_F <- Model_Spectra_Meta$Sex_M <- Model_Spectra_Meta$Sex_U <- NULL
Columns <- names(Model_Spectra_Meta)
Columns[!Columns %in% names(Model_Spectra_Meta_Combo)]
Model_Spectra_Meta <- rbind(Model_Spectra_Meta[, Columns], Model_Spectra_Meta_Combo[, Columns])
# -- Sex, One hot encoding --
if(!is.null(Model_Spectra_Meta$Sex)) {
Model_Spectra_Meta$Sex <- as.character(Model_Spectra_Meta$Sex)
Model_Spectra_Meta$Sex[is.na(Model_Spectra_Meta$Sex)] <- "U"
Sex_ <- Model_Spectra_Meta$Sex
Model_Spectra_Meta <- cbind(Model_Spectra_Meta, as.data.frame(model.matrix(formula(~ -1 + Sex_)))) # Three indicator columns added: Sex_F, Sex_M, Sex_U
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
}
headTail(Model_Spectra_Meta,2,2,2,55) # Look for one hot encoding sex and structure_weight_dg for the NN model
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$structure_weight_g)
Table(Model_Spectra_Meta$sample_year, round(Model_Spectra_Meta$Length_cm))
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Month)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$Sex)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Sable_Comm\\SABL_Combo_Comm_2017__2022_Model_Spectra_Meta_ALL_GOOD_DATA_TMA_NAs.RData")
# =================================================
# FINAL_FISH_AGE_IN_YEARS is 14, but AGE_IN_YEARS by McBride in CA is 98
PacFIN_SABL_Age[PacFIN_SABL_Age$BDS_ID %in% 2469163, ]
BDS_ID SAMPLE_ID SAMPLE_NUMBER SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY SAMPLE_TYPE SAMPLE_METHOD_CODE SAMPLE_AGENCY AGENCY_CODE DATA_TYPE AGENCY_CONDITION_CODE PACFIN_CONDITION_CODE
44149 2469163 63753579 1989220130744 1989 3 31 R NA C C N
PACFIN_PORT_CODE PACFIN_PORT_NAME FTID PACFIN_GEAR_CODE PACFIN_GEAR_NAME VESSEL_ID PSMFC_CATCH_AREA_CODE DEPTH_AVERAGE_FATHOMS DEPTH_MAXIMUM_FATHOMS DEPTH_MINIMUM_FATHOMS
44149 ERK EUREKA GFT GFSH-TRAWL NA NA NA
MARKET_CATEGORY CLUSTER_SEQUENCE_NUMBER CLUSTER_WEIGHT_LBS ADJUSTED_CLUSTER_WEIGHT_LBS FRAME_CLUSTER_WEIGHT_LBS PACFIN_SPECIES_CODE FISH_SEQUENCE_NUMBER OBSERVED_FREQUENCY
44149 190 1 80 NA NA SABL 16 1
FISH_LENGTH_TYPE_CODE FISH_LENGTH FORK_LENGTH FORK_LENGTH_IS_ESTIMATED FISH_WEIGHT SEX_CODE AGENCY_FISH_MATURITY_CODE FISH_MATURITY_CODE WEIGHT_OF_MALES_LBS WEIGHT_OF_FEMALES_LBS
44149 F 488 NA NA NA M 9 U NA NA
NUMBER_OF_MALES NUMBER_OF_FEMALES WEIGHT_OF_LANDING_LBS EXPANDED_SAMPLE_WEIGHT SPECIES_WEIGHT_LBS FINAL_FISH_AGE_CODE FINAL_FISH_AGE_IN_YEARS AGE_SEQUENCE_NUMBER AGE_METHOD_CODE
44149 NA NA 1558 NA NA NA 14 2 BB
AGE_READABILITY PERSON_WHO_AGED DATE_AGE_RECORDED AGE_IN_YEARS AGE_STRUCTURE_CODE AGENCY_AGE_STRUCTURE_CODE PACFIN_LOAD_DATE AGENCY_GRADE_CODE PACFIN_GRADE_CODE
44149 NA McBride <NA> 98 2025-04-03 04:01:27 S S
FISH_LENGTH_UNITS FISH_WEIGHT_UNITS FISH_LENGTH_IS_ESTIMATED FISH_WEIGHT_UNITS_NAME all_cluster_sum_kg DAHL_GROUNDFISH_CODE Length_cm Weight_kg Sex Year Depth Month
44149 MM FALSE 36.28739 NA 48.8 NA M 1989 NA 3
No_NA <- PacFIN_SABL_Age[!is.na(PacFIN_SABL_Age$AGE_IN_YEARS) & !is.na(PacFIN_SABL_Age$FINAL_FISH_AGE_IN_YEARS), ]
renum(No_NA[No_NA$AGE_IN_YEARS > 65| No_NA$FINAL_FISH_AGE_IN_YEARS > 65,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER", "FISH_SEQUENCE_NUMBER",
"AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS")])
AGENCY_CODE SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY BDS_ID SAMPLE_ID SAMPLE_NUMBER CLUSTER_SEQUENCE_NUMBER FISH_SEQUENCE_NUMBER AGE_SEQUENCE_NUMBER PERSON_WHO_AGED AGE_IN_YEARS FINAL_FISH_AGE_IN_YEARS
1 C 1989 9 25 4553212 63738574 1989201330766 2 1 1 Pearson 32 98
2 C 1989 9 25 4553213 63738574 1989201330766 2 1 2 McBride 98 98
3 C 1989 3 31 2469163 63753579 1989220130744 1 16 2 McBride 98 14
4 W 1992 1 22 3090557 63725717 20924301010009 1 10 1 SWFC/JO 68 68
5 O 1996 6 4 4641375 63491683 OR960031 1 9 1 76 76
6 O 1997 7 7 515848 63480810 OR970104 1 6 1 92 92
7 O 1997 8 31 4005968 63492217 OR970644 1 6 1 87 87
8 C 2000 4 14 1549261 63784801 2000400230305 2 10 1 PJM - CAP 67 67
9 W 2006 5 22 3911216 63714208 20064301010114 1 80 1 NWFC/PM 102 102
10 O 2007 11 9 3511361 63484683 OR072118 2 21 1 66 66
11 O 2013 7 14 2496972 63472358 OR130227 1 45 1 71 71
12 W 2013 4 15 1481477 63725518 20134301010022 1 21 1 NMFS IS 66 66
13 O 2014 2 12 3699983 62906135 OR142262 2 12 1 Lance Sullivan 70 70
14 O 2017 8 31 3844013 62694790 OR1762861 1 2 1 Nikki Paige 70 70
15 O 2017 9 20 429333 62695622 OR1763382 2 24 1 James Hale 75 75
16 O 2017 10 27 3469521 62697029 OR1764090 1 1 1 Patrick McDonald 68 68
17 O 2018 10 1 46946 62639196 OR1869074 1 18 1 Patrick McDonald 82 82
18 O 2018 11 20 45733 62639116 OR1869956 2 15 1 Patrick McDonald 73 73
19 O 2018 12 10 47203 62638513 OR1870085 1 3 1 Patrick McDonald 69 69
renum(PacFIN_SABL_Age[PacFIN_SABL_Age$SAMPLE_NUMBER == 1989220130744 & PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER == 1 & PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER == 16,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER",
"FISH_SEQUENCE_NUMBER", "AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS")])
AGENCY_CODE SAMPLE_YEAR SAMPLE_MONTH SAMPLE_DAY BDS_ID SAMPLE_ID SAMPLE_NUMBER CLUSTER_SEQUENCE_NUMBER FISH_SEQUENCE_NUMBER AGE_SEQUENCE_NUMBER PERSON_WHO_AGED AGE_IN_YEARS FINAL_FISH_AGE_IN_YEARS
1 C 1989 3 31 2469162 63753579 1989220130744 1 16 1 Pearson 14 14
2 C 1989 3 31 2469163 63753579 1989220130744 1 16 2 McBride 98 14
PacFIN_SABL_Age$age_structure_id_CA_1 <- paste0('CA', substr(PacFIN_SABL_Age$SAMPLE_NUMBER, 3, 4), substring(PacFIN_SABL_Age$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER, "-O")
PacFIN_SABL_Age$age_structure_id_CA_2 <- paste0('CA', substr(PacFIN_SABL_Age$SAMPLE_NUMBER, 1, 4), substring(PacFIN_SABL_Age$SAMPLE_NUMBER, 9), "-", PacFIN_SABL_Age$PACFIN_SPECIES_CODE, "-" , PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER, "-", PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER, "-O")
renum(PacFIN_SABL_Age[PacFIN_SABL_Age$SAMPLE_NUMBER == 1989220130744 & PacFIN_SABL_Age$CLUSTER_SEQUENCE_NUMBER == 1 & PacFIN_SABL_Age$FISH_SEQUENCE_NUMBER == 16,
c("AGENCY_CODE", "SAMPLE_YEAR", "SAMPLE_MONTH", "SAMPLE_DAY", "BDS_ID", "SAMPLE_ID", "SAMPLE_NUMBER", "CLUSTER_SEQUENCE_NUMBER",
"FISH_SEQUENCE_NUMBER", "AGE_SEQUENCE_NUMBER", "PERSON_WHO_AGED", "AGE_IN_YEARS", "FINAL_FISH_AGE_IN_YEARS", "age_structure_id_CA_1", "age_structure_id_CA_2")])
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