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MRIP_read_data_26yr.r
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MRIP_read_data_26yr.r
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##Read in MRIP data for slot limit analysis
## 1. Samples measured fish to assign a length to each unmeasured fish
## 2. non compliance is only calculated over years of same regs (2012:2015)
## 3. Includes additional measured lengths (CTVAS, NYHB) in the pool of
## unmeasured fish
## 3a. VAS- includes all fish >16 in, and same pr of non-compliant
## fish as in MRIP
## 3b. NYHB- includes all harvested fish as that is the same pr of
## non-compliant fish as MRIP
## 4. Output is an organized catch table for use for catch length data or
## bag size analysis
## 5. MRIP data for 2004-2015 are extracted
## 6. this setups up a CM slot limit calculation, such that all mm lengths are
## round(length/10) and IN lengths are randomly rounded, then converted to round## cm
## 7. imputede lengths are dropped from pooled data, but used in org data
## NOTE: This code DOES NOT include the MRIP weighting factors!!! This assumes
## that size distribution and catch rates are similar across interview sites.
###############################################################################
#########################
### PREPARE
setwd("C:/Users/jacob/OneDrive - University of Connecticut/UCONN/Projects/Tautog_ASMFC/Slot-MEffects/database/raw/ps_2015_csv")
library(reshape2)
options(scipen=999)
# Specify criteria
years <- c(2012:2015)
yr.min <- 2012
waves <- c(2, 4, 5, 6) #specify state and wave
sp1 <- 8839010101 # MRIP SP_CODE
sp2 <- "TAUTOG" # MRIP common name
set.seed(1126) # Code does some sampling,
## so this ensures consitent results. Can obviously be turned off if desired.
#####################
### READ IN DATA ###
#####################
#############CT CT CT CT
# Trip data CTt
states <- c(9) # MRIP state codes #ny 36, ct 9
trips <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("trip_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp <- tmp[tmp$ST %in% states, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA", "AREA_X", "ID_CODE", "CNTRBTRS", "LEADER",
"PARTY", "PRT_CODE")]
trips <- rbind(trips, tmp)
}
}
## Import catch data CT
catch <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("catch_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp <- tmp[tmp$ST %in% states & tmp$common == sp2, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA_X", "ID_CODE", "SP_CODE", "common", "CLAIM",
"RELEASE", "HARVEST") ]
catch <- rbind(catch, tmp)
}
}
## Import length data CT
lengths <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("size_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp$targ <- 0
tmp$targ[tmp$SP_CODE == sp1] <- 1
tmp <- tmp[tmp$ST %in% states & tmp$targ == 1, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA_X", "ID_CODE", "LNGTH", "lngth_imp")]
lengths <- rbind(lengths, tmp)
}
}
##NY NY NY YN
area <- "C"
states <- c(36) ##NY
waves <- c(5, 6) #specify state and wave
##Trip data
tripsNY <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("trip_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp <- tmp[tmp$ST %in% states & tmp$AREA == area, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA", "AREA_X", "ID_CODE", "CNTRBTRS", "LEADER",
"PARTY", "PRT_CODE")]
tripsNY <- rbind(tripsNY, tmp)
}
}
tapply(tripsNY$ID_CODE, list(tripsNY$YEAR), length) # Count total number of interviews conducted per year
##Import catch data NY
catchNY <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("catch_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp <- tmp[tmp$ST %in% states & tmp$common == sp2, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA_X", "ID_CODE", "SP_CODE", "common", "CLAIM",
"RELEASE", "HARVEST") ]
catchNY <- rbind(catchNY, tmp)
}
}
## Import length data
lengthsNY <- data.frame(NULL)
for(y in years) {
for (w in waves) {
fname <- paste("size_", y, w, ".csv", sep="")
tmp <- read.csv(fname, head=TRUE)
tmp$targ <- 0
tmp$targ[tmp$SP_CODE == sp1] <- 1
tmp <- tmp[tmp$ST %in% states & tmp$targ == 1, c("ST", "YEAR", "WAVE",
"MODE_FX", "AREA_X", "ID_CODE", "LNGTH", "lngth_imp")]
lengthsNY <- rbind(lengthsNY, tmp)
}
}
########### MERGE NY and CT
trips <- rbind(trips, tripsNY)
tripsNY <- NULL
ALLcatch <- rbind(catch, catchNY)
catchNY <- NULL
catch <- ALLcatch ##renames ALLcatch, but preserves it for future use
lengths <- rbind(lengths, lengthsNY)
lengthsNY <- NULL
## Drop imputed lengths so code is only sampling true
## Maybe it would be better to inlcude imputed lengths and only sample for
## lengths without true or imputed value???
## For tautog, had to include imputed lengths since some states/regions had no
## observed lengths at all!
######################################################################
### SAMPLE FROM MEASURED FISH TO ASSIGN LENGTHS TO UNMEASURED FISH ###
######################################################################
# Number of fish removed per trip
catch$tot_harv <- round(catch$CLAIM + catch$HARVEST, 0)
catch <- catch[catch$tot_harv > 0, ]
# Number of true lengths per trip
n_msrd <- aggregate(lengths$LNGTH, list(lengths$ID_CODE), length)
names(n_msrd) <- c("ID_CODE", "N_msrd")
# How many unmeasured fish
catch <- merge(catch, n_msrd, by = "ID_CODE", all = TRUE)
catch$N_msrd[is.na(catch$N_msrd)] <- 0
catch$miss_len <- catch$tot_harv - catch$N_msrd
miss <- catch[catch$miss_len > 0, ]
## Not sure why, but some values are less than 0 -->
## more fish measured than caught!
miss <- na.omit(miss)
n.samples <- sum(miss$miss_len)
##TO INCLUDE ADDITIONAL MEASURED LENGTHS OUTSIDE OF MRIP#####
har.gam <- read.table("C:/Users/jacob/OneDrive - University of Connecticut/UCONN/Projects/Tautog_ASMFC/Slot-MEffects/database/stage2/harvest_gamma.txt")
pool <- round(rgamma(n.samples, shape = har.gam[33, 1], rate = har.gam[33, 2]))
# Sample from pool for each missing length and assign it to an ID_CODE
new_len <- data.frame(NULL)
for(i in 1:nrow(miss)) {
tmp <- data.frame(ID_CODE = rep(miss$ID_CODE[i], miss$miss_len[i]),
LNGTH = sample(pool, miss$miss_len[i], TRUE))
new_len <- rbind(new_len, tmp)
}
new_len$lngth_imp <- 1 # To identify which are true lengths and which were sampled
## Combine observed lengths with sampled lengths
trip_info <- miss[,c("ST", "YEAR", "WAVE", "MODE_FX", "AREA_X", "ID_CODE")]
new_len <- merge(trip_info, new_len, by = "ID_CODE", all = TRUE)
lengths$LNGTH <- round(lengths$LNGTH/10)
all_len <- rbind(lengths, new_len)
all_len$Inch <- round(all_len$LNGTH / 2.54, 2)
unique(all_len$LNGTH)
# At this point, there should be a length (observed or sampled) for each harvested fish
######################################################
### DISTRIBUTE GROUP CATCHES TO INDIVIDUAL ANGLERS ###
######################################################
tmp.group <- subset(trips, trips$CNTRBTRS > 1)
tmp.group <- tmp.group[, c("ID_CODE", "CNTRBTRS")]
catch <- merge(catch, tmp.group, by = "ID_CODE", all = TRUE)
catch <- catch[!is.na(catch$tot_harv), ]
catch$CNTRBTRS[is.na(catch$CNTRBTRS)] <- 1
setwd("C:/Users/jacob/OneDrive - University of Connecticut/UCONN/Projects/Tautog_ASMFC/Slot-MEffects/HarvestSlots")