fix 2005 landings conversion factor

indicator_processing/fishery_dependent/INDICATOR_total_landings.R

@@ -5,17 +5,13 @@ rm(list = ls())
 
 # input data for Puerto Rico ---------------------------
 setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_processing/fishery_dependent/")
-dat <- read.csv("C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/Jun2022/PR_landings_83_20_wSC.csv")
+dat <- read.csv("C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/Jun2022/PR_landings_83_20_wSC_2005cor.csv")
 
 table(dat$YEAR_LANDED)
 
 # check multiplier adjustments -------------------------
 
-dat$xADJ <- dat$POUNDS_LANDED * 1/dat$CORRECTION_FACTOR
-hist(dat$ADJUSTED_POUNDS - dat$xADJ)
-max(abs(dat$ADJUSTED_POUNDS - dat$xADJ), na.rm = T)
-table(round(dat$ADJUSTED_POUNDS) - round(dat$xADJ))
-#dat$ADJUSTED_POUNDS <- dat$xADJ
+summary(dat$xADJ == dat$POUNDS_LANDED * 1/dat$CORRECTION_FACTOR)
 
 # define start and end years ---------------------------
 styear <- 1990
@@ -35,20 +31,25 @@ sort(table(d$ITIS_COMMON_NAME[grep("LOBSTER", d$ITIS_COMMON_NAME)]))
 sort(table(d$ITIS_COMMON_NAME[grep("CONCH", d$ITIS_COMMON_NAME)]))
 
 d$sppgrp <- "other"
-#d$sppgrp[grep("LOBSTER", d$ITIS_COMMON_NAME)] <- "lobster"
-d$sppgrp[grep("LOBSTERS,SPINY", d$ITIS_COMMON_NAME)] <- "lobster"
+d$sppgrp[grep("LOBSTER", d$ITIS_COMMON_NAME)] <- "lobster"
+#d$sppgrp[grep("LOBSTERS,SPINY", d$ITIS_COMMON_NAME)] <- "lobster"  # to compare directly to only spiny lobster
 d$sppgrp[grep("CONCH", d$ITIS_COMMON_NAME)] <- "conch"
 
 table(d$ITIS_COMMON_NAME, d$sppgrp)
 
+# check for rule of 3 ------------------------
+
+table(d$sppgrp, d$YEAR_LANDED)
+table(table(d$sppgrp, d$YEAR_LANDED) < 3)
+
 # sum landings by  year ----------------------
 
 totland_pr <- tapply(d$ADJUSTED_POUNDS, list(d$YEAR_LANDED, d$sppgrp), sum, na.rm = T) / 10^3
 dim(totland_pr)
 totland_pr
-matplot(totland_pr, type = "l", lty = 1, lwd = 2)
+matplot(1990:2020, totland_pr, type = "l", lty = 1, lwd = 2)
 
-ls()
+rm(list = ls()[-match(c("totland_pr", "styear", "enyear"), ls())])
 
 # calculate for STT --------------------------------------
 
@@ -76,36 +77,89 @@ d$sppgrp[grep("CONCH", d$SPECIES_NM)] <- "conch"
 
 table(d$SPECIES_NM, d$sppgrp)
 
+# check for rule of 3 ------------------------
+
+table(d$TRIP_YEAR, d$sppgrp)
+table(table(d$TRIP_YEAR, d$sppgrp) <= 3)
+
 # sum landings by  year ----------------------
 
 totland_st <- tapply(d$POUNDS_LANDED, list(d$TRIP_YEAR, d$sppgrp), sum, na.rm = T) / 10^3
+totland_st[3, 1] <- NA  # fix confidentiality issue
 dim(totland_st)
 totland_st
 totland_st[is.na(totland_st)] <- 0
 matplot(totland_st, type = "l")
 
+# calculate for STX --------------------------------------
+
+rm(list = ls()[-match(c("totland_st", "totland_pr", "styear", "enyear"), ls())])
+
+dat <- read.csv("C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/STX_072011_present_LANDINGS_trip_2021-03-11.csv")
+head(dat)
+
+# take a look at data fields ----------------------------
+
+table(dat$LANDING_AREA_NAME)
+table(dat$TRIP_YEAR)
+d <- dat[which(dat$TRIP_YEAR >= styear & dat$TRIP_YEAR <= enyear), ]
+d$TRIP_YEAR <- factor(d$TRIP_YEAR, levels = c(styear: enyear))
+table(d$TRIP_YEAR)
+
+# check field codes -----------------
+
+table(d$TRIP_YEAR, useNA = "always")
+
+# look at top landings -----------------------
+
+sort(tapply(d$POUNDS_LANDED, d$SPECIES_NM, sum, na.rm = T))
+sort(table(d$SPECIES_NM[grep("LOBSTER", d$SPECIES_NM)]))
+sort(table(d$SPECIES_NM[grep("CONCH", d$SPECIES_NM)]))
+
+d$sppgrp <- "other"
+d$sppgrp[grep("LOBSTER", d$SPECIES_NM)] <- "lobster"
+d$sppgrp[grep("CONCH", d$SPECIES_NM)] <- "conch"
+
+table(d$SPECIES_NM, d$sppgrp)
+
+# check for rule of 3 ------------------------
+
+table(d$TRIP_YEAR, d$sppgrp)
+table(table(d$TRIP_YEAR, d$sppgrp) <= 3)
+
+# sum landings by  year ----------------------
+
+totland_sx <- tapply(d$POUNDS_LANDED, list(d$TRIP_YEAR, d$sppgrp), sum, na.rm = T) / 10^3
+dim(totland_sx)
+totland_sx
+#totland_sx[is.na(totland_sx)] <- 0
+matplot(totland_sx, type = "l")
+
 # summarize and plot ---------------------
 
 ls()[grep("totland", ls())]
 
 datdata <- styear:enyear
-inddata <- data.frame(cbind(totland_pr[, 2], totland_st[, 2], 
-                            totland_pr[, 1], totland_st[, 1],
-                            totland_pr[, 3], totland_st[, 3]))
+inddata <- data.frame(cbind(totland_pr[, 2], totland_st[, 2], totland_sx[, 2], 
+                            totland_pr[, 1], totland_st[, 1], totland_sx[, 1],
+                            totland_pr[, 3], totland_st[, 3], totland_sx[, 3]))
 labs <- c("Lobster landings", "thousands of pounds", "Puerto Rico", 
           "Lobster landings", "thousands of pounds", "St. Thomas and St. John", 
+          "Lobster landings", "thousands of pounds", "St. Croix", 
           "Conch landings", "thousands of pounds", "Puerto Rico",
           "Conch landings", "thousands of pounds", "St. Thomas and St. John",
+          "Conch landings", "thousands of pounds", "St. Croix", 
           "Landings of all other species", "thousands of pounds", "Puerto Rico", 
-          "Landings of all other species",  "thousands of pounds", "St. Thomas and St. John")
+          "Landings of all other species",  "thousands of pounds", "St. Thomas and St. John", 
+          "Landings of all other species",  "thousands of pounds", "St. Croix")
 
 indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
 s <- list(labels = indnames, indicators = inddata, datelist = datdata) #, ulim = ulidata, llim = llidata)
 class(s) <- "indicatordata"
 
 setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_plots/")
 
-plotIndicatorTimeSeries(s, coltoplot = 1:6, plotrownum = 2, sublabel = T, sameYscale = F, 
+plotIndicatorTimeSeries(s, coltoplot = 1:9, plotrownum = 3, plotcolnum = 3, sublabel = T, sameYscale = F, 
                         widadj = 0.8, hgtadj = 0.7, trendAnalysis = F)   # outtype = "png")
 
 #inddata <- s

indicator_processing/fishery_dependent/PREPROCESS_build_reference_file.R

@@ -166,7 +166,76 @@ dim(dat2)
 #write.table(dat2, file = "C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/Jun2022/PR_landings_83_20_wSC.csv", 
 #            sep = ",", col.names = T, row.names = F)
 
-d <- dat2
+# fix error with 2005 correction factor ----------------
+
+rm(list = ls())
+dat <- read.csv("C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/Jun2022/PR_landings_83_20_wSC.csv")
+
+table(dat$YEAR_LANDED)
+
+# check multiplier adjustments -------------------------
+
+dat$xADJ <- dat$POUNDS_LANDED * 1/dat$CORRECTION_FACTOR
+hist(dat$ADJUSTED_POUNDS - dat$xADJ)
+max(abs(dat$ADJUSTED_POUNDS - dat$xADJ), na.rm = T)
+table(round(dat$ADJUSTED_POUNDS) - round(dat$xADJ))
+#dat$ADJUSTED_POUNDS <- dat$xADJ
+
+# fix 2005 correction factor ---------------------------
+
+d5 <- dat[which(dat$YEAR_LANDED == 2005), ]
+table(d5$CORRECTION_FACTOR, d5$MUNICIPALITY)
+plot(d5$CORRECTION_FACTOR ~ d5$MUNICIPALITY)
+tapply(d5$CORRECTION_FACTOR, d5$MUNICIPALITY, sd)
+
+d4 <- dat[which(dat$YEAR_LANDED == 2004), ]
+d6 <- dat[which(dat$YEAR_LANDED == 2006), ]
+
+tapply(d4$CORRECTION_FACTOR, d4$MUNICIPALITY, sd, na.rm = T)
+tapply(d6$CORRECTION_FACTOR, d6$MUNICIPALITY, sd, na.rm = T)
+
+c4 <- tapply(d4$CORRECTION_FACTOR, d4$MUNICIPALITY, mean, na.rm = T)
+c6 <- tapply(d6$CORRECTION_FACTOR, d6$MUNICIPALITY, mean, na.rm = T)
+summary(names(c4) == names(c6))
+
+c5 <- (c4 + c6) / 2
+c5[10]
+c6[10]
+table(c4)
+table(c6)
+table(c5)
+
+table(d5$CORRECTION_FACTOR)
+lis265 <- names(which(c5 == 0.265))
+lis465 <- names(which(c5 == 0.465))
+lis475 <- names(which(c5 == 0.475))
+lis835 <- names(which(c5 == 0.835))
+
+dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005 & dat$MUNICIPALITY %in% lis265)] <- 0.265
+dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005 & dat$MUNICIPALITY %in% lis465)] <- 0.465
+dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005 & dat$MUNICIPALITY %in% lis475)] <- 0.475
+dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005 & dat$MUNICIPALITY %in% lis835)] <- 0.835
+
+table(dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005)])
+dat[which(dat$CORRECTION_FACTOR > 0.9 & dat$YEAR_LANDED == 2005), ]
+c6[10]
+dat$CORRECTION_FACTOR[which(dat$CORRECTION_FACTOR > 0.9 & dat$YEAR_LANDED == 2005)] <- c6[10]
+table(dat$CORRECTION_FACTOR[which(dat$YEAR_LANDED == 2005)])
+
+
+# check  
+dat$xADJ <- dat$POUNDS_LANDED * 1/dat$CORRECTION_FACTOR
+hist(dat$ADJUSTED_POUNDS - dat$xADJ)
+max(abs(dat$ADJUSTED_POUNDS - dat$xADJ), na.rm = T)
+table(round(dat$ADJUSTED_POUNDS) - round(dat$xADJ))
+dat$ADJUSTED_POUNDS <- dat$xADJ
+
+#write.table(dat, file = "C:/Users/mandy.karnauskas/Desktop/CONFIDENTIAL/CaribbeanData/Jun2022/PR_landings_83_20_wSC_2005cor.csv", 
+#            sep = ",", col.names = T, row.names = F)
+
+#################################################################
+
+d <- dat
 
 # take a look at data fields ----------------------------
 table(d$VESSEL, useNA = "always")