R_Scripts/Read in Chilipepper Scans.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures.
setwd("C:/SIDT/Chilipepper")
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)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/headTail.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/replaceString.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")
source("C:\\SIDT\\Train_NN_Model\\Read_OPUS_Spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Import_Species_Metadata_from_NWFSC_Warehouse.R")
# source("C:\\SIDT\\Chilipepper\\Import_Species_Metadata_from_NWFSC_Warehouse.R")
Chilipepper_Combo_Metadata <- Import_Species_Metadata_from_NWFSC_Warehouse("chilipepper")
headTail(Chilipepper_Combo_Metadata)
save(Chilipepper_Combo_Metadata, file = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData") # R object name needs to have "Metadata" in it
# For testing Read_OPUS_Spectra(): Spectra_Set = "PWHT_Acoustic2023"; Spectra_Path = "PWHT_Acoustic2023_Scans"; fineFreqAdj = 0;
# Meta_Path <- "C:/SIDT/PWHT_Acoustic_2023/Acoustic_2023_PWHT_NIR0069_Scanning_Session_Report_For_NWC.xlsx";
# plot <- TRUE; Meta_Add <- TRUE; spectraInterp = 'stats_splinefun_lowess'; excelSheet <- 3; opusReader = 'philippbaumann_opusreader2';
# Predicted_Ages_Path = "Predicted_Ages"; (htmlPlotFolder <- paste0(Predicted_Ages_Path, '/', Spectra_Set, '_Spectra_Sample_of_', 20))
#
for (i in (2010:2016)) {
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = paste0("CLPR_SWFSC_", i), Spectra_Path = paste0(i, "_scans"), htmlPlotFolder = paste0("Figures_", i), Static_Figure = paste0("CLPR_SWFSC_", i, ".png"),
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_Otolith_Weights_SWFSC_with_Scans.xlsx", excelSheet = i - 2009,
Extra_Meta_Path = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData", Debug = TRUE)
if(i == 2010)
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$shortName %in% "CLPR_100165282_Combo_Survey", ]
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
save(Model_Spectra_Meta, file = paste0("CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA_TEST.RData"))
}
for (i in c(2018, 2019, 2021:2024)) {
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = paste0("CLPR_NWFSC_", i), Spectra_Path = paste0(i, "_scans"), htmlPlotFolder = paste0("Figures_", i), Static_Figure = paste0("CLPR_SWFSC_", i, ".png"),
Meta_Path = paste0("C:/SIDT/Chilipepper/CLPR_COMBO_", i, "_Scanning_Session_Report_For_NWC.xlsx"),
Extra_Meta_Path = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData", Debug = FALSE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = paste0("CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
}
# --- CA Commercial 2019, 2020- The 2018 oties were missing... ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_2019_2020", Spectra_Path = "2019_2020_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_2019_2020",
shortNameSegments = 6, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_2019_2020.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_CACOMM_2019_2020_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_2019_2020_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- CA Commercial 1985 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Cols <- c("project", "specimen_id", "sample_year", "Sex", "Length_cm", "Weight_kg", "TMA", "Month", "structure_weight_g")
C_1985_sheet_1 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC_OA.xlsx", sheet = 1, detectDates = TRUE)
C_1985_sheet_1$Length_cm <- C_1985_sheet_1$Length_mm/10
C_1985_sheet_1$Weight_kg <- NA
C_1985_sheet_1$Month <- NA
C_1985_sheet_2 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC_OA.xlsx", sheet = 2, detectDates = TRUE)
C_1985_sheet_2$Length_cm <- C_1985_sheet_2$Length_mm/10
C_1985_sheet_2$specimen_id <- paste(C_1985_sheet_2$specimen_id, replaceString(format(C_1985_sheet_2$clust_no, width = 3), " ", "0"), replaceString(format(C_1985_sheet_2$fish_no, width = 3), " ", "0"), sep= "_")
C_1985_sheet_2$Month <- NA
openxlsx::write.xlsx(rbind(C_1985_sheet_1[, Cols], C_1985_sheet_2[, Cols]), file = "C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_1985", Spectra_Path = "1985_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_1985",
shortNameSegments = 2, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_1985.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
# match.f(data.frame(FN = substring(fileNames, 13)), data.frame(MD = metadata$specimen_id), "FN", "MD", "MD")
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_1985_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- CA Commercial 1986 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Cols <- c("project", "specimen_id", "sample_year", "Sex", "Length_cm", "Weight_kg", "TMA", "Month", "structure_weight_g")
C_1986_sheet_1 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC_OA.xlsx", sheet = 1, detectDates = TRUE)
C_1986_sheet_1$Length_cm <- C_1986_sheet_1$Length_mm/10
C_1986_sheet_1$Weight_kg <- NA
C_1986_sheet_1$Month <- NA
openxlsx::write.xlsx(C_1986_sheet_1[, Cols], file = "C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_1986", Spectra_Path = "1986_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_1986",
shortNameSegments = 2, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_1986.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
# match.f(data.frame(FN = substring(fileNames, 13)), data.frame(MD = metadata$specimen_id), "FN", "MD", "MD")
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_1986_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- Triennial 2004 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" ** lower case 'p' ** needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_Triennial_2004", Spectra_Path = "2004_Triennial_Scans", htmlPlotFolder = "Figures_CLPR_Triennial_2004",
shortNameSegments = 2:4, shortNameSuffix = 'Triennial', Static_Figure = "CLPR_Triennial_2004.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_2004_Triennial_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Vessel_short <- Model_Spectra_Meta$Vessel
Model_Spectra_Meta$Vessel_short[Model_Spectra_Meta$Vessel %in% "Morning Star"] <- 1
Model_Spectra_Meta$Vessel_short[!Model_Spectra_Meta$Vessel %in% "Morning Star"] <- 2
Model_Spectra_Meta$Vessel_short <- as.numeric(Model_Spectra_Meta$Vessel_short)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Vessel_short', Debug= TRUE)
source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Vessel', Debug = TRUE)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Tray', Debug = TRUE)
Model_Spectra_Meta$Scan_Pos <- 'Low'
Model_Spectra_Meta$Scan_Pos[Model_Spectra_Meta$X4000 > 1.05] <- 'High'
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Scan_Pos', Debug = TRUE)
Model_Spectra_Meta$Month <- Months.POSIXt(Model_Spectra_Meta$Date)
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$Scan_Pos %in% 'Low', ]
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA_LOW.RData")
# --- OR Commercial 2022, 2023, 2024 ---
# !!!!!! Change age-best to TMA and capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_ORCOMM_2022__2024", Spectra_Path = "2022__2024_ORCOMM_Scans", htmlPlotFolder = "Figures_CLPR_ORCOMM_2022__2024",
shortNameSegments = 6, shortNameSuffix = 'OR_Comm', Static_Figure = "CLPR_ORCOMM_2022__2024.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_ORCOMM_2022__2024_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$sample_year), ]
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
save(Model_Spectra_Meta, file = paste0("CLPR_ORCOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")) # Was "CLPR_ORCOMM_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData"
# --- CA Commercial 2022, 2023, 2024 ---
# !!!!!! Change age-best to TMA and capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_2022__2024", Spectra_Path = "2022__2024_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_2022__2024",
shortNameSegments = 6, shortNameSuffix = 'OR_Comm', Static_Figure = "CLPR_CACOMM_2022__2024.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_CACOMM_2022__2024_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$sample_year), ]
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- Chilipepper_2023_2024_CA_Rec_SWFSC ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" ** lower case 'p' ** needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
CA_Rec <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", sheet = 2, detectDates = TRUE)
CA_Rec_otie_wgt <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/MASTER_2024_2023_recreation_chili_CDFW.xlsx", sheet = 1, detectDates = TRUE)
CA_Rec <- match.f(CA_Rec, CA_Rec_otie_wgt , 'specimen_id', 'specimen_id', 'structure_weight_g')
CA_Rec$Wgt_g <- NULL
openxlsx::write.xlsx(CA_Rec, file = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx")
CA_Rec <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", detectDates = TRUE)
CA_Rec$Sex <- recode.simple(CA_Rec$Sex, cbind(c(1,2,9), c('M', 'F', 'U')))
CA_Rec$Month <- Months.POSIXt(CA_Rec$Date)
CA_Rec$Length_cm <- CA_Rec$Length_cm/10 # was mm
openxlsx::write.xlsx(CA_Rec, file = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CA_Rec_2023_2024", Spectra_Path = "2023_2024_CA_Rec_Scans", htmlPlotFolder = "Figures_CLPR_CA_Rec_2023_2024",
shortNameSegments = 2:4, shortNameSuffix = 'Triennial', Static_Figure = "CLPR_CA_Rec_2023_2024.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_CA_Rec_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# =============================================== Combine years ==============================================================================
library(JRWToolBox)
setwd("C:/SIDT/Chilipepper")
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)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# Remove "CLPR_100165282_Combo_Survey otie
load("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2010_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$shortName %in% "CLPR_100165282_Combo_Survey", ]
headTail(Model_Spectra_Meta, 3, 3, 3, 40)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2010_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# Create a vector with the column names wanted
load("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2014_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta$NOTES <- NULL
Columns <- names(Model_Spectra_Meta)
matrix(Columns, ncol = 1)
Columns <- Columns[c(1:511, 517, 518, 513, 520:526, 519, 527:533, 538)]
matrix(Columns, ncol = 1) # Length is now 530
Columns <- c(Columns[1:529], c("Month_May", "Month_Jun", "Month_Jul", "Month_Aug", "Month_Sep", "Month_Oct"), Columns[530])
Columns[c(1, 507:536)]
[1] "filenames" "X3960" "X3952" "project" "sample_year""Sex" "Length_cm" "TMA" "structure_weight_g"
[10] "specimen_id" "shortName" "structure_weight_dg" "Length_prop_max" "Sex_F" "Sex_M" "Sex_U"
[1] "filenames" "X3960" "X3952" "project" "sample_year" "TMA" "specimen_id" "shortName"
[9] "structure_weight_g" "Length_cm" "Weight_kg" "Sex" "Depth_m" "Latitude_dd" "Month" "Days_into_Year"
[17] "structure_weight_dg" "Length_prop_max" "Weight_prop_max" "Sex_F" "Sex_M" "Sex_U" "Depth_prop_max" "Latitude_prop_max"
[25] "Month_May" "Month_Jun" "Month_Jul" "Month_Aug" "Month_Sep" "Month_Oct" "Days_into_Year_prop_max"
for(i in c(2010, 2014:2016, 2019, 2021:2024)[2]) {
print(i)
base::load(paste0("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
Table(Model_Spectra_Meta$sample_year)
}
Model_Spectra_Meta_All <- NULL
for(i in c(2010, 2014:2016, 2018:2019, 2021:2024)[2:10]) {
print(i)
base::load(paste0("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
if(i == 2018)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), 50)), colorGroup = 'TMA')
Model_Spectra_Meta_All <- rbind(Model_Spectra_Meta_All, Model_Spectra_Meta[, Columns])
}
Model_Spectra_Meta <- Model_Spectra_Meta_All
headTail(Model_Spectra_Meta, 3,3,3,40)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
headTail(Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ], 3,3,3,40)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010_2018_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# For the preliminary model with only 2010 and 2014
Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2010, 2014), ]
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010_2014_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ---- Add CA comm scans - *** need "Columns" vector from above *** ----
load("CLPR_SWFSC_2010_2018_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010_2018_2024 <- Model_Spectra_Meta
# -------- Update for sparse TMA results --------
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% c("2023_OR_Comm", "2024_OR_Comm"), ]
Table(Model_Spectra_Meta_SWFSC_1985__2024$sample_year)
# --------------------------------
load("CLPR_CACOMM_2019_2020_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# Model_Spectra_Meta$TMA <- NA
# Model_Spectra_Meta$Length_cm <- NA
# Model_Spectra_Meta$Weight_kg <- NA
# Model_Spectra_Meta$Sex <- NA
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
# Model_Spectra_Meta$Month <- NA
Model_Spectra_Meta$Days_into_Year <- NA
# Model_Spectra_Meta$Length_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Comm")
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
headTail(Model_Spectra_Meta,3,3,3,62)
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010_2018_2024, Model_Spectra_Meta[, Columns]) # Columns[c(1, 507:536)]
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ========== Add OR comm scans - *** need "Columns" vector from above *** =================
# load("C:\\SIDT\\CLPR_Combo_2010__2024\\CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
load("CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010__2024 <- Model_Spectra_Meta
# -------- Update for sparse TMA results --------
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% c("2023_OR_Comm", "2024_OR_Comm"), ]
Table(Model_Spectra_Meta_SWFSC_1985__2024$sample_year, Model_Spectra_Meta_SWFSC_1985__2024$TMA)
# --------------------------------
load("CLPR_ORCOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_OR_Comm")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# --- OA ----
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010__2024, Model_Spectra_Meta[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ----------- Update for TMA with 1985+ data ---------------
# Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, Columns])
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns[!Columns %in% names(Model_Spectra_Meta)]
# ====================== Add CACOMM with sparse TMA results ================================
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("CLPR_CACOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Comm")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update for TMA with 1985+ data ---------------
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ======== Add 1985, 1986 CA comm scans - *** need "Columns" vector from above *** ===========
load("CLPR_SWFSC_2010__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010__2024 <- Model_Spectra_Meta
load("C:\\SIDT\\Chilipepper\\CLPR_CACOMM_1985_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_CLPR_CACOMM_1985 <- Model_Spectra_Meta
load("C:\\SIDT\\Chilipepper\\CLPR_CACOMM_1986_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CLPR_CACOMM_1985, Model_Spectra_Meta)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_OR_Comm")
Table(Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% 'NA_OR_Comm', ]
Table(Model_Spectra_Meta$sample_year)
headTail(Model_Spectra_Meta,3,3,3,62)
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010__2024, Model_Spectra_Meta[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ====================== Add 2004 Triennial - All scans ================================
library(JRWToolBox)
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_NO_TRI.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,3,3,3,45)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_Triennial")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update for TMA with 1985+ data ---------------
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns <- names(Model_Spectra_Meta_SWFSC_1985__2024)
Columns[!Columns %in% names(Model_Spectra_Meta)]
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ====================== CLPR_CA_Rec_2023_2024 ================================
library(JRWToolBox)
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("C:/SIDT/Chilipepper/CLPR_CA_Rec_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,3,3,3,45)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Rec")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update the 1985+ data ---------------
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns <- names(Model_Spectra_Meta_SWFSC_1985__2024)
Columns[!Columns %in% names(Model_Spectra_Meta)]
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ==================================================================================================================
setwd("C:/SIDT/Chilipepper")
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)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# ***** CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData moved to CLPR_Combo_1985__2024 ****
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
dir.create("Figs_Global", showWarnings = FALSE)
source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
(ylimGlobal <- c(0, max(Model_Spectra_Meta[, 2:grep("X3952", names(Model_Spectra_Meta))])))
(numColorGlobal <- ifelse(any(is.na(Model_Spectra_Meta$TMA)), length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE)) + 1, length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE))))
# (numColorGlobal <- length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE)))
for( i in sort(unique(Model_Spectra_Meta$sample_year))[4]) {
cat(paste0("\n\n", i, ": "))
MSM <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% i, ]
plotly.Spec(MSM, N_Samp = nrow(MSM), colorGroup = 'TMA', numColors = numColorGlobal, ylim = ylimGlobal, main = i, scanUniqueName = 'filenames', Debug = FALSE,
paletteFunc = function(n, alpha) hcl.colors(n, "Zissou 1", alpha = alpha, rev = TRUE))
dir.create(paste0("Figs_Global/", i), showWarnings = FALSE)
saveHtmlFolder(paste0("Figs_Global/", i), view = !interactive())
}
# ========================================= All Year Groups =========================================================================
setwd("C:/SIDT/Chilipepper")
library(JRWToolBox)
library(ggplot2)
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
(ylimGlobal <- c(0, max(Model_Spectra_Meta[, 2:grep("X3952", names(Model_Spectra_Meta))])))
set.seed(c(707, 747)[2])
plotly.Spec(Model_Spectra_Meta, N_Samp = 750, colorGroup = 'sample_year', ylim = ylimGlobal, main = "All Year Groups", scanUniqueName = 'filenames', Debug = TRUE)
names(Spec)[4] <- "Sample_Year"
# # https://r-graph-gallery.com/color-palette-finder
# scale_colour_paletteer_d("vapoRwave::vapoRwave")
# # scale_fill_paletteer_d("vapoRwave::vapoRwave")
#
# # vapoRwave
# Colors <- colorRampPalette(c("#20DE8BFF", "#CCDE8BFF", "#FFDE8BFF", "#FFA88BFF", "#FF6A8BFF", "#FF6AD5FF", "#C874AAFF", "#C774E7FF", "#AD8CFFFF", "#966BFFFF", "#90CFFFFF"))(16)
#
# # GravityFalls
# # Colors <- colorRampPalette(c("#417BA1FF", "#FF1493FF", "#FFFF2EFF", "#345634FF", "#8B0000FF", "#FF6700FF", "#93C0D5FF", "#8B4513FF", "#9248A7FF", "#1C8859FF", "#474747FF", "#8FBC8FFF", "#D2B48CFF", "#000000FF"))(16)
#
# print(ggplotly(ggplot(Spec, aes(x = Waveband, y = Absorbance, z = Scan)) + geom_line(aes(colour = Sample_Year), linewidth = 0.2) + labs(colour = "Sample_Year") +
# ylim(ylimGlobal[1], ylimGlobal[2]) + scale_color_manual(values = Colors) + ggtitle("All Year Groups (750 random samples)")))
# Just stuck with rainbos() - sigh.....
color.palette <- list(function(n) hcl.colors(n, "Zissou 1", rev = FALSE), rainbow, heat.colors, terrain.colors, topo.colors, cm.colors)[[2]]
print(ggplotly(ggplot(Spec, aes(x = Waveband, y = Absorbance, z = Scan)) + geom_line(aes(colour = Sample_Year), linewidth = 0.2) + labs(colour = "Sample_Year") +
ylim(ylimGlobal[1], ylimGlobal[2]) + scale_color_manual(values = color.palette(length(unique(Spec$Sample_Year)))) + ggtitle("All Year Groups (750 random samples)")))
saveHtmlFolder(paste0("All Year Groups"), view = !interactive())
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.
R Package Documentation
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setwd("C:/SIDT/Chilipepper")
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)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/headTail.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/replaceString.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")
source("C:\\SIDT\\Train_NN_Model\\Read_OPUS_Spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Import_Species_Metadata_from_NWFSC_Warehouse.R")
# source("C:\\SIDT\\Chilipepper\\Import_Species_Metadata_from_NWFSC_Warehouse.R")
Chilipepper_Combo_Metadata <- Import_Species_Metadata_from_NWFSC_Warehouse("chilipepper")
headTail(Chilipepper_Combo_Metadata)
save(Chilipepper_Combo_Metadata, file = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData") # R object name needs to have "Metadata" in it
# For testing Read_OPUS_Spectra(): Spectra_Set = "PWHT_Acoustic2023"; Spectra_Path = "PWHT_Acoustic2023_Scans"; fineFreqAdj = 0;
# Meta_Path <- "C:/SIDT/PWHT_Acoustic_2023/Acoustic_2023_PWHT_NIR0069_Scanning_Session_Report_For_NWC.xlsx";
# plot <- TRUE; Meta_Add <- TRUE; spectraInterp = 'stats_splinefun_lowess'; excelSheet <- 3; opusReader = 'philippbaumann_opusreader2';
# Predicted_Ages_Path = "Predicted_Ages"; (htmlPlotFolder <- paste0(Predicted_Ages_Path, '/', Spectra_Set, '_Spectra_Sample_of_', 20))
#
for (i in (2010:2016)) {
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = paste0("CLPR_SWFSC_", i), Spectra_Path = paste0(i, "_scans"), htmlPlotFolder = paste0("Figures_", i), Static_Figure = paste0("CLPR_SWFSC_", i, ".png"),
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_Otolith_Weights_SWFSC_with_Scans.xlsx", excelSheet = i - 2009,
Extra_Meta_Path = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData", Debug = TRUE)
if(i == 2010)
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$shortName %in% "CLPR_100165282_Combo_Survey", ]
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
save(Model_Spectra_Meta, file = paste0("CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA_TEST.RData"))
}
for (i in c(2018, 2019, 2021:2024)) {
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = paste0("CLPR_NWFSC_", i), Spectra_Path = paste0(i, "_scans"), htmlPlotFolder = paste0("Figures_", i), Static_Figure = paste0("CLPR_SWFSC_", i, ".png"),
Meta_Path = paste0("C:/SIDT/Chilipepper/CLPR_COMBO_", i, "_Scanning_Session_Report_For_NWC.xlsx"),
Extra_Meta_Path = "C:/SIDT/Get Otie Info from Data Warehouse/Chilipepper_Combo_Metadata.RData", Debug = FALSE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = paste0("CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
}
# --- CA Commercial 2019, 2020- The 2018 oties were missing... ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_2019_2020", Spectra_Path = "2019_2020_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_2019_2020",
shortNameSegments = 6, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_2019_2020.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_CACOMM_2019_2020_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_2019_2020_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- CA Commercial 1985 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Cols <- c("project", "specimen_id", "sample_year", "Sex", "Length_cm", "Weight_kg", "TMA", "Month", "structure_weight_g")
C_1985_sheet_1 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC_OA.xlsx", sheet = 1, detectDates = TRUE)
C_1985_sheet_1$Length_cm <- C_1985_sheet_1$Length_mm/10
C_1985_sheet_1$Weight_kg <- NA
C_1985_sheet_1$Month <- NA
C_1985_sheet_2 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC_OA.xlsx", sheet = 2, detectDates = TRUE)
C_1985_sheet_2$Length_cm <- C_1985_sheet_2$Length_mm/10
C_1985_sheet_2$specimen_id <- paste(C_1985_sheet_2$specimen_id, replaceString(format(C_1985_sheet_2$clust_no, width = 3), " ", "0"), replaceString(format(C_1985_sheet_2$fish_no, width = 3), " ", "0"), sep= "_")
C_1985_sheet_2$Month <- NA
openxlsx::write.xlsx(rbind(C_1985_sheet_1[, Cols], C_1985_sheet_2[, Cols]), file = "C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_1985", Spectra_Path = "1985_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_1985",
shortNameSegments = 2, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_1985.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_1985_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
# match.f(data.frame(FN = substring(fileNames, 13)), data.frame(MD = metadata$specimen_id), "FN", "MD", "MD")
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_1985_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- CA Commercial 1986 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Cols <- c("project", "specimen_id", "sample_year", "Sex", "Length_cm", "Weight_kg", "TMA", "Month", "structure_weight_g")
C_1986_sheet_1 <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC_OA.xlsx", sheet = 1, detectDates = TRUE)
C_1986_sheet_1$Length_cm <- C_1986_sheet_1$Length_mm/10
C_1986_sheet_1$Weight_kg <- NA
C_1986_sheet_1$Month <- NA
openxlsx::write.xlsx(C_1986_sheet_1[, Cols], file = "C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_1986", Spectra_Path = "1986_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_1986",
shortNameSegments = 2, shortNameSuffix = 'CA_Comm', Static_Figure = "CLPR_CACOMM_1986.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_1986_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
# match.f(data.frame(FN = substring(fileNames, 13)), data.frame(MD = metadata$specimen_id), "FN", "MD", "MD")
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_1986_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- Triennial 2004 ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" ** lower case 'p' ** needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_Triennial_2004", Spectra_Path = "2004_Triennial_Scans", htmlPlotFolder = "Figures_CLPR_Triennial_2004",
shortNameSegments = 2:4, shortNameSuffix = 'Triennial', Static_Figure = "CLPR_Triennial_2004.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_2004_Triennial_Otolith_Weights_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Vessel_short <- Model_Spectra_Meta$Vessel
Model_Spectra_Meta$Vessel_short[Model_Spectra_Meta$Vessel %in% "Morning Star"] <- 1
Model_Spectra_Meta$Vessel_short[!Model_Spectra_Meta$Vessel %in% "Morning Star"] <- 2
Model_Spectra_Meta$Vessel_short <- as.numeric(Model_Spectra_Meta$Vessel_short)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Vessel_short', Debug= TRUE)
source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Vessel', Debug = TRUE)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Tray', Debug = TRUE)
Model_Spectra_Meta$Scan_Pos <- 'Low'
Model_Spectra_Meta$Scan_Pos[Model_Spectra_Meta$X4000 > 1.05] <- 'High'
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), Max_N_Spectra)), colorGroup = 'Scan_Pos', Debug = TRUE)
Model_Spectra_Meta$Month <- Months.POSIXt(Model_Spectra_Meta$Date)
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$Scan_Pos %in% 'Low', ]
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA_LOW.RData")
# --- OR Commercial 2022, 2023, 2024 ---
# !!!!!! Change age-best to TMA and capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_ORCOMM_2022__2024", Spectra_Path = "2022__2024_ORCOMM_Scans", htmlPlotFolder = "Figures_CLPR_ORCOMM_2022__2024",
shortNameSegments = 6, shortNameSuffix = 'OR_Comm', Static_Figure = "CLPR_ORCOMM_2022__2024.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_ORCOMM_2022__2024_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$sample_year), ]
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
save(Model_Spectra_Meta, file = paste0("CLPR_ORCOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")) # Was "CLPR_ORCOMM_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData"
# --- CA Commercial 2022, 2023, 2024 ---
# !!!!!! Change age-best to TMA and capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CACOMM_2022__2024", Spectra_Path = "2022__2024_CACOMM_Scans", htmlPlotFolder = "Figures_CLPR_CACOMM_2022__2024",
shortNameSegments = 6, shortNameSuffix = 'OR_Comm', Static_Figure = "CLPR_CACOMM_2022__2024.png",
Meta_Path = "C:/SIDT/Chilipepper/CLPR_CACOMM_2022__2024_Scanning_Session_Report_For_NWC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$sample_year), ]
Table(Model_Spectra_Meta$TMA, Model_Spectra_Meta$sample_year)
save(Model_Spectra_Meta, file = paste0("CLPR_CACOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
# --- Chilipepper_2023_2024_CA_Rec_SWFSC ---
# !!!!!! Capitalize the first letter of "length_cm", weight_kg", and "sex" in the Excel file !!!!!!
# !!!!!! Also "project" ** lower case 'p' ** needs to be in the first column, and there needs to be "TMA", "specimen_id", "sample_year", and "structure_weight_g" columns. !!!!!!
CA_Rec <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", sheet = 2, detectDates = TRUE)
CA_Rec_otie_wgt <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/MASTER_2024_2023_recreation_chili_CDFW.xlsx", sheet = 1, detectDates = TRUE)
CA_Rec <- match.f(CA_Rec, CA_Rec_otie_wgt , 'specimen_id', 'specimen_id', 'structure_weight_g')
CA_Rec$Wgt_g <- NULL
openxlsx::write.xlsx(CA_Rec, file = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx")
CA_Rec <- openxlsx::read.xlsx("C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", detectDates = TRUE)
CA_Rec$Sex <- recode.simple(CA_Rec$Sex, cbind(c(1,2,9), c('M', 'F', 'U')))
CA_Rec$Month <- Months.POSIXt(CA_Rec$Date)
CA_Rec$Length_cm <- CA_Rec$Length_cm/10 # was mm
openxlsx::write.xlsx(CA_Rec, file = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx")
Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set = "CLPR_CA_Rec_2023_2024", Spectra_Path = "2023_2024_CA_Rec_Scans", htmlPlotFolder = "Figures_CLPR_CA_Rec_2023_2024",
shortNameSegments = 2:4, shortNameSuffix = 'Triennial', Static_Figure = "CLPR_CA_Rec_2023_2024.png", excelSheet = 1,
Meta_Path = "C:/SIDT/Chilipepper/Chilipepper_2023_2024_CA_Rec_SWFSC.xlsx", Debug = TRUE)
headTail(Model_Spectra_Meta, 3, 3, 3, 55)
Table(Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/Chilipepper/CLPR_CA_Rec_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# =============================================== Combine years ==============================================================================
library(JRWToolBox)
setwd("C:/SIDT/Chilipepper")
sourceFunctionURL <- function (URL, type = c("function", "script")[1]) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
if (!any(installed.packages()[, 1] %in% "httr")) install.packages("httr")
File.ASCII <- tempfile()
if(type == "function")
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(gsub(' ', '%20', URL))
if(type == "function") {
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
if(type == "script") {
fileName <- strsplit(URL, "/")[[1]]
fileName <- rev(fileName)[1]
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# Remove "CLPR_100165282_Combo_Survey otie
load("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2010_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$shortName %in% "CLPR_100165282_Combo_Survey", ]
headTail(Model_Spectra_Meta, 3, 3, 3, 40)
save(Model_Spectra_Meta, file = "C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2010_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# Create a vector with the column names wanted
load("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_2014_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta$NOTES <- NULL
Columns <- names(Model_Spectra_Meta)
matrix(Columns, ncol = 1)
Columns <- Columns[c(1:511, 517, 518, 513, 520:526, 519, 527:533, 538)]
matrix(Columns, ncol = 1) # Length is now 530
Columns <- c(Columns[1:529], c("Month_May", "Month_Jun", "Month_Jul", "Month_Aug", "Month_Sep", "Month_Oct"), Columns[530])
Columns[c(1, 507:536)]
[1] "filenames" "X3960" "X3952" "project" "sample_year""Sex" "Length_cm" "TMA" "structure_weight_g"
[10] "specimen_id" "shortName" "structure_weight_dg" "Length_prop_max" "Sex_F" "Sex_M" "Sex_U"
[1] "filenames" "X3960" "X3952" "project" "sample_year" "TMA" "specimen_id" "shortName"
[9] "structure_weight_g" "Length_cm" "Weight_kg" "Sex" "Depth_m" "Latitude_dd" "Month" "Days_into_Year"
[17] "structure_weight_dg" "Length_prop_max" "Weight_prop_max" "Sex_F" "Sex_M" "Sex_U" "Depth_prop_max" "Latitude_prop_max"
[25] "Month_May" "Month_Jun" "Month_Jul" "Month_Aug" "Month_Sep" "Month_Oct" "Days_into_Year_prop_max"
for(i in c(2010, 2014:2016, 2019, 2021:2024)[2]) {
print(i)
base::load(paste0("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
Table(Model_Spectra_Meta$sample_year)
}
Model_Spectra_Meta_All <- NULL
for(i in c(2010, 2014:2016, 2018:2019, 2021:2024)[2:10]) {
print(i)
base::load(paste0("C:\\SIDT\\Chilipepper\\CLPR_SWFSC_", i, "_Model_Spectra_Meta_ALL_GOOD_DATA.RData"))
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
if(i == 2018)
plotly.Spec(Model_Spectra_Meta, N_Samp = min(c(nrow(Model_Spectra_Meta), 50)), colorGroup = 'TMA')
Model_Spectra_Meta_All <- rbind(Model_Spectra_Meta_All, Model_Spectra_Meta[, Columns])
}
Model_Spectra_Meta <- Model_Spectra_Meta_All
headTail(Model_Spectra_Meta, 3,3,3,40)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
headTail(Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ], 3,3,3,40)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010_2018_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# For the preliminary model with only 2010 and 2014
Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% c(2010, 2014), ]
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010_2014_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ---- Add CA comm scans - *** need "Columns" vector from above *** ----
load("CLPR_SWFSC_2010_2018_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010_2018_2024 <- Model_Spectra_Meta
# -------- Update for sparse TMA results --------
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% c("2023_OR_Comm", "2024_OR_Comm"), ]
Table(Model_Spectra_Meta_SWFSC_1985__2024$sample_year)
# --------------------------------
load("CLPR_CACOMM_2019_2020_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# Model_Spectra_Meta$TMA <- NA
# Model_Spectra_Meta$Length_cm <- NA
# Model_Spectra_Meta$Weight_kg <- NA
# Model_Spectra_Meta$Sex <- NA
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
# Model_Spectra_Meta$Month <- NA
Model_Spectra_Meta$Days_into_Year <- NA
# Model_Spectra_Meta$Length_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Comm")
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
headTail(Model_Spectra_Meta,3,3,3,62)
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010_2018_2024, Model_Spectra_Meta[, Columns]) # Columns[c(1, 507:536)]
Table(Model_Spectra_Meta$ sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ========== Add OR comm scans - *** need "Columns" vector from above *** =================
# load("C:\\SIDT\\CLPR_Combo_2010__2024\\CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
load("CLPR_SWFSC_2010__2024_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010__2024 <- Model_Spectra_Meta
# -------- Update for sparse TMA results --------
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% c("2023_OR_Comm", "2024_OR_Comm"), ]
Table(Model_Spectra_Meta_SWFSC_1985__2024$sample_year, Model_Spectra_Meta_SWFSC_1985__2024$TMA)
# --------------------------------
load("CLPR_ORCOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_OR_Comm")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# --- OA ----
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010__2024, Model_Spectra_Meta[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_2010__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ----------- Update for TMA with 1985+ data ---------------
# Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, Columns])
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns[!Columns %in% names(Model_Spectra_Meta)]
# ====================== Add CACOMM with sparse TMA results ================================
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("CLPR_CACOMM_2022__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Comm")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update for TMA with 1985+ data ---------------
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ======== Add 1985, 1986 CA comm scans - *** need "Columns" vector from above *** ===========
load("CLPR_SWFSC_2010__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_2010__2024 <- Model_Spectra_Meta
load("C:\\SIDT\\Chilipepper\\CLPR_CACOMM_1985_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta_CLPR_CACOMM_1985 <- Model_Spectra_Meta
load("C:\\SIDT\\Chilipepper\\CLPR_CACOMM_1986_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_CLPR_CACOMM_1985, Model_Spectra_Meta)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_OR_Comm")
Table(Model_Spectra_Meta$sample_year)
Model_Spectra_Meta <- Model_Spectra_Meta[!Model_Spectra_Meta$sample_year %in% 'NA_OR_Comm', ]
Table(Model_Spectra_Meta$sample_year)
headTail(Model_Spectra_Meta,3,3,3,62)
Model_Spectra_Meta <- rbind(Model_Spectra_Meta_SWFSC_2010__2024, Model_Spectra_Meta[, Columns])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ====================== Add 2004 Triennial - All scans ================================
library(JRWToolBox)
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA_NO_TRI.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("C:/SIDT/Chilipepper/CLPR_Triennial_2004_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,3,3,3,45)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_Triennial")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update for TMA with 1985+ data ---------------
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns <- names(Model_Spectra_Meta_SWFSC_1985__2024)
Columns[!Columns %in% names(Model_Spectra_Meta)]
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ====================== CLPR_CA_Rec_2023_2024 ================================
library(JRWToolBox)
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta_SWFSC_1985__2024 <- Model_Spectra_Meta
# --------------------------------
load("C:/SIDT/Chilipepper/CLPR_CA_Rec_2023_2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
headTail(Model_Spectra_Meta,3,3,3,45)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
Model_Spectra_Meta$Depth_m <- NA
Model_Spectra_Meta$Latitude_dd <- NA
Model_Spectra_Meta$Days_into_Year <- NA
Model_Spectra_Meta$Depth_prop_max <- NA
Model_Spectra_Meta$Weight_prop_max <- NA
Model_Spectra_Meta$Latitude_prop_max <- NA
Model_Spectra_Meta$Days_into_Year_prop_max <- NA
if(is.null(Model_Spectra_Meta$Sex_F)) Model_Spectra_Meta$Sex_F <- 0
if(is.null(Model_Spectra_Meta$Sex_M)) Model_Spectra_Meta$Sex_M <- 0
if(is.null(Model_Spectra_Meta$Sex_U)) Model_Spectra_Meta$Sex_U <- 0
if(is.null(Model_Spectra_Meta$Month_May)) Model_Spectra_Meta$Month_May <- 0
if(is.null(Model_Spectra_Meta$Month_Jun)) Model_Spectra_Meta$Month_Jun <- 0
if(is.null(Model_Spectra_Meta$Month_Jul)) Model_Spectra_Meta$Month_Jul <- 0
if(is.null(Model_Spectra_Meta$Month_Aug)) Model_Spectra_Meta$Month_Aug <- 0
if(is.null(Model_Spectra_Meta$Month_Sep)) Model_Spectra_Meta$Month_Sep <- 0
if(is.null(Model_Spectra_Meta$Month_Oct)) Model_Spectra_Meta$Month_Oct <- 0
Model_Spectra_Meta$sample_year <- paste0(Model_Spectra_Meta$sample_year, "_CA_Rec")
headTail(Model_Spectra_Meta,3,3,3,62)
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
# ----------- Update the 1985+ data ---------------
# !!!!!!!!!!!!!!! Find the column names that are missing !!!!!!!!!!!!!
Columns <- names(Model_Spectra_Meta_SWFSC_1985__2024)
Columns[!Columns %in% names(Model_Spectra_Meta)]
Model_Spectra_Meta <- rbind( Model_Spectra_Meta_SWFSC_1985__2024, Model_Spectra_Meta[, names(Model_Spectra_Meta_SWFSC_1985__2024)])
Table(Model_Spectra_Meta$sample_year, Model_Spectra_Meta$TMA)
save(Model_Spectra_Meta, file = "C:/SIDT/CLPR_Combo_1985__2024/CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
# ==================================================================================================================
setwd("C:/SIDT/Chilipepper")
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)
}
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# ***** CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData moved to CLPR_Combo_1985__2024 ****
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
dir.create("Figs_Global", showWarnings = FALSE)
source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
(ylimGlobal <- c(0, max(Model_Spectra_Meta[, 2:grep("X3952", names(Model_Spectra_Meta))])))
(numColorGlobal <- ifelse(any(is.na(Model_Spectra_Meta$TMA)), length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE)) + 1, length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE))))
# (numColorGlobal <- length(0:max(Model_Spectra_Meta$TMA, na.rm = TRUE)))
for( i in sort(unique(Model_Spectra_Meta$sample_year))[4]) {
cat(paste0("\n\n", i, ": "))
MSM <- Model_Spectra_Meta[Model_Spectra_Meta$sample_year %in% i, ]
plotly.Spec(MSM, N_Samp = nrow(MSM), colorGroup = 'TMA', numColors = numColorGlobal, ylim = ylimGlobal, main = i, scanUniqueName = 'filenames', Debug = FALSE,
paletteFunc = function(n, alpha) hcl.colors(n, "Zissou 1", alpha = alpha, rev = TRUE))
dir.create(paste0("Figs_Global/", i), showWarnings = FALSE)
saveHtmlFolder(paste0("Figs_Global/", i), view = !interactive())
}
# ========================================= All Year Groups =========================================================================
setwd("C:/SIDT/Chilipepper")
library(JRWToolBox)
library(ggplot2)
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
# source("C:\\SIDT\\Chilipepper\\plotly.Spec.R")
load("C:\\SIDT\\CLPR_Combo_1985__2024\\CLPR_SWFSC_1985__2024_CA_OR_Comm_Model_Spectra_Meta_ALL_GOOD_DATA.RData")
(ylimGlobal <- c(0, max(Model_Spectra_Meta[, 2:grep("X3952", names(Model_Spectra_Meta))])))
set.seed(c(707, 747)[2])
plotly.Spec(Model_Spectra_Meta, N_Samp = 750, colorGroup = 'sample_year', ylim = ylimGlobal, main = "All Year Groups", scanUniqueName = 'filenames', Debug = TRUE)
names(Spec)[4] <- "Sample_Year"
# # https://r-graph-gallery.com/color-palette-finder
# scale_colour_paletteer_d("vapoRwave::vapoRwave")
# # scale_fill_paletteer_d("vapoRwave::vapoRwave")
#
# # vapoRwave
# Colors <- colorRampPalette(c("#20DE8BFF", "#CCDE8BFF", "#FFDE8BFF", "#FFA88BFF", "#FF6A8BFF", "#FF6AD5FF", "#C874AAFF", "#C774E7FF", "#AD8CFFFF", "#966BFFFF", "#90CFFFFF"))(16)
#
# # GravityFalls
# # Colors <- colorRampPalette(c("#417BA1FF", "#FF1493FF", "#FFFF2EFF", "#345634FF", "#8B0000FF", "#FF6700FF", "#93C0D5FF", "#8B4513FF", "#9248A7FF", "#1C8859FF", "#474747FF", "#8FBC8FFF", "#D2B48CFF", "#000000FF"))(16)
#
# print(ggplotly(ggplot(Spec, aes(x = Waveband, y = Absorbance, z = Scan)) + geom_line(aes(colour = Sample_Year), linewidth = 0.2) + labs(colour = "Sample_Year") +
# ylim(ylimGlobal[1], ylimGlobal[2]) + scale_color_manual(values = Colors) + ggtitle("All Year Groups (750 random samples)")))
# Just stuck with rainbos() - sigh.....
color.palette <- list(function(n) hcl.colors(n, "Zissou 1", rev = FALSE), rainbow, heat.colors, terrain.colors, topo.colors, cm.colors)[[2]]
print(ggplotly(ggplot(Spec, aes(x = Waveband, y = Absorbance, z = Scan)) + geom_line(aes(colour = Sample_Year), linewidth = 0.2) + labs(colour = "Sample_Year") +
ylim(ylimGlobal[1], ylimGlobal[2]) + scale_color_manual(values = color.palette(length(unique(Spec$Sample_Year)))) + ggtitle("All Year Groups (750 random samples)")))
saveHtmlFolder(paste0("All Year Groups"), view = !interactive())
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