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landings_extraction.R
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landings_extraction.R
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# comm landings extraction
#```{r comland, echo = T, eval = F}
#Comland.r
#Version now controlled by git - originally part of comcatch.r
#Grab commercial landings data from US and Foreign countries (NAFO)
#Need to fix menhaden data
#SML
#Requires the following files:
# data.dir.2\\Comland_skates_hakes.R
# data.dir\\Menhaden.csv
# data.dir.3\\SS_NAFO_21A.csv
# data.dir.3\\species.txt
#User parameters
if(Sys.info()['sysname']=="Windows"){
data.dir <- "L:\\EcoAP\\Data\\Commercial"
data.dir.2 <- "L:\\Rworkspace\\RCom"
data.dir.3 <- "L:\\EcoAP\\Data\\NAFO"
out.dir <- "L:\\EcoAP\\Data\\Commercial"
memory.limit(4000)
channel <- odbcDriverConnect()
}
if(Sys.info()['sysname']=="Linux"){
data.dir <- "/home/slucey/slucey/EcoAP/Data/Commercial"
data.dir.2 <- "/home/slucey/slucey/Rworkspace/RCom"
data.dir.3 <- "/home/slucey/slucey/EcoAP/Data/NAFO"
out.dir <- "/home/slucey/slucey/EcoAP/Data/Commercial"
uid <- 'slucey'
cat("Oracle Password: ")
pwd <- scan(stdin(), character(), n = 1)
}
landed <- 'y' #use landed weight for scallops and clams instead of live weight
foreign <- 'y' #Mark foreign landings and keep seperate
adjust.ppi <- 'y' #Adjust value for inflation
use.existing <- 'n' #use raw data from a previous run - saves time
sum.by <- 'EPU' #Variable to sum landings by [EPU, stat.area]
#Final year of query
endyear <- 2016
#If adjusting for inflation
refyear <- 2016
refmonth <- 1
#-------------------------------------------------------------------------------
#Required packages
library(RODBC); library(data.table); library(rgdal)
#-------------------------------------------------------------------------------
#User created functions
#Convert NA's to zeros
na.zero <- function(x){
for(i in 1:length(x[1, ])){
if(length(which(is.na(x[, i]))) > 0){
x[which(is.na(x[, i])), i] <- 0}
}
return(x)
}
#-------------------------------------------------------------------------------
#Connect to database
if(Sys.info()['sysname'] == "Windows")channel <- odbcDriverConnect()
if(Sys.info()['sysname'] == "Linux") channel <- odbcConnect('sole', uid, pwd)
if(use.existing == 'n'){
#Landings
tables <- c(paste0('WOLANDS', 64:81),
paste0('WODETS', 82:93),
paste0('CFDETS', 1994:endyear, 'AA'))
#Generate one table
comland <- c()
for(i in 1:length(tables)){
landings.qry <- paste("select year, month, negear, toncl1, nespp3, nespp4, area,
spplivlb, spplndlb, sppvalue, utilcd
from", tables[i])
comland.yr <- as.data.table(sqlQuery(channel, landings.qry))
setkey(comland.yr,
YEAR,
MONTH,
NEGEAR,
TONCL1,
NESPP3,
NESPP4,
AREA,
UTILCD)
if(landed == 'y') comland.yr[NESPP3 %in% 743:800, SPPLIVLB := SPPLNDLB]
#Sum landings and value
#landings
comland.yr[, V1 := sum(SPPLIVLB), by = key(comland.yr)]
#value
#Fix null values
comland.yr[is.na(SPPVALUE), SPPVALUE := 0]
comland.yr[, V2 := sum(SPPVALUE), by = key(comland.yr)]
#Remove extra rows/columns
comland.yr <- unique(comland.yr, by = key(comland.yr))
comland.yr[, c('SPPLIVLB', 'SPPLNDLB', 'SPPVALUE') := NULL]
#Rename summed columns
setnames(comland.yr, c('V1', 'V2'), c('SPPLIVLB', 'SPPVALUE'))
comland <- rbindlist(list(comland, comland.yr))
}
if(landed == 'n') save(comland, file = file.path(out.dir, "comland_raw_US.RData"))
#Last run 8/31/16
if(landed == 'y') save(comland, file = file.path(out.dir, "comland_raw_US_meatwt.RData"))
#Last run 1/25/18
}
if(use.existing == 'y'){
if(landed == 'n') load(file = file.path(out.dir, "comland_raw_US.RData"))
if(landed == 'y') load(file = file.path(out.dir, "comland_raw_US_meatwt.RData"))
}
#-------------------------------------------------------------------------------
#Convert from lbs to metric tons
comland[, SPPLIVMT := SPPLIVLB * 0.00045359237]
comland[, SPPLIVLB := NULL]
#fix years
comland[YEAR < 100, YEAR := YEAR + 1900L]
if(adjust.ppi == 'y'){
#Adjust SPPVALUE for inflation
temp <- tempfile()
download.file("http://download.bls.gov/pub/time.series/wp/wp.data.3.ProcessedFoods", temp)
inflate <- as.data.table(read.delim(temp))
unlink(temp)
inflate[, series_id := gsub(" ", "", inflate[, series_id])]
deflate <- inflate[series_id == "WPU0223", ]
deflate[, MONTH := as.numeric(substr(period, 2, 3))]
setnames(deflate, c('year', 'value'), c('YEAR', 'PPI'))
deflate <- deflate[, list(YEAR, MONTH, PPI)]
#Set yearly deflator to 0 instead of 13 to match unknown month designation
deflate[MONTH == 13, MONTH := 0]
deflate.base <- deflate[YEAR == refyear & MONTH == refmonth, PPI]
comland <- merge(comland, deflate, by = c('YEAR', 'MONTH'), all.x = T)
comland[, SPPVALUE := round((SPPVALUE * deflate.base) / PPI)]
#Remove extra column
comland[, PPI := NULL]
}
#Remove market categories of parts
comland <- comland[!NESPP4 %in% c(119, 123, 125, 127, 812, 819, 828, 829, 1731, 2351,
2690, 2699, 3472, as.numeric(paste(348:359, 8, sep = '')),
3868, as.numeric(paste(469:471, 4, sep = '')),
as.numeric(paste(480:499, 8, sep ='')), 5018, 5039,
5261, 5265), ]
#Generate NESPP3 and MKTCAT in comland data
comland[NESPP4 < 100, MKTCAT := as.numeric(substring(NESPP4, 2, 2))]
comland[NESPP4 > 99 & NESPP4 < 1000, MKTCAT := as.numeric(substring(NESPP4, 3, 3))]
comland[NESPP4 > 999, MKTCAT := as.numeric(substring(NESPP4, 4, 4))]
#drop NESPP4
comland[, NESPP4 := NULL]
#Deal with Hakes and Skates------------------------------------------------------------------
source(file.path(data.dir.2, 'Comland_skates_hakes.R'))
#get little skates and winter skates from skates(ns) - use survey in half years
#Generate Half year variable in comland
comland.skates <- comland[NESPP3 == 365, ]
comland.skates[MONTH %in% 1:6, Half := 1]
comland.skates[MONTH %in% 7:12, Half := 2]
setkey(skate.hake.us,
YEAR,
Half,
AREA)
comland.skates <- merge(comland.skates, skate.hake.us, by = key(skate.hake.us), all.x = T)
comland.skates[, little := little.per * SPPLIVMT]
comland.skates[, little.value := round(little.per * SPPVALUE)]
comland.skates[is.na(little), little := 0]
comland.skates[is.na(little.value), little.value := 0]
comland.skates[, winter := winter.per * SPPLIVMT]
comland.skates[, winter.value := round(winter.per * SPPVALUE)]
comland.skates[is.na(winter), winter := 0]
comland.skates[is.na(winter.value), winter.value := 0]
comland.skates[, other.skate := SPPLIVMT - (little + winter)]
comland.skates[, other.skate.value := SPPVALUE - (little.value + winter.value)]
#Little (366), winter (367), skates(ns) (365)
#put skates in comland format to merge back
little <- comland.skates[, list(YEAR, Half, AREA, MONTH, NEGEAR,
TONCL1, NESPP3, UTILCD, MKTCAT, little,
little.value)]
little[, NESPP3 := 366]
setnames(little, c('little', 'little.value'), c('SPPLIVMT', 'SPPVALUE'))
little <- little[SPPLIVMT > 0, ]
winter <- comland.skates[, list(YEAR, Half, AREA, MONTH, NEGEAR,
TONCL1, NESPP3, UTILCD, MKTCAT, winter,
winter.value)]
winter[, NESPP3 := 367]
setnames(winter, c('winter', 'winter.value'), c('SPPLIVMT', 'SPPVALUE'))
winter <- winter[SPPLIVMT > 0, ]
other <- comland.skates[, list(YEAR, Half, AREA, MONTH, NEGEAR,
TONCL1, NESPP3, UTILCD, MKTCAT, other.skate,
other.skate.value)]
other[, NESPP3 := 365]
setnames(other, c('other.skate', 'other.skate.value'), c('SPPLIVMT', 'SPPVALUE'))
other <- other[SPPLIVMT > 0, ]
#merge all three and reformat for comland
skates.add.back <- rbindlist(list(little, winter, other))
skates.add.back[, Half := NULL]
setcolorder(skates.add.back, names(comland))
comland <- rbindlist(list(comland[NESPP3 != 365, ], skates.add.back))
#get silver hake from mixed hakes - use survey in half years
#Generate Half year variable in comland
comland.hakes <- comland[NESPP3 == 507, ]
comland.hakes[MONTH %in% 1:6, Half := 1]
comland.hakes[MONTH %in% 7:12, Half := 2]
comland.hakes <- merge(comland.hakes, skate.hake.us, by = key(skate.hake.us), all.x = T)
comland.hakes[, silver := silver.per * SPPLIVMT]
comland.hakes[, silver.value := round(silver.per * SPPVALUE)]
comland.hakes[is.na(silver), silver := 0]
comland.hakes[is.na(silver.value), silver.value := 0]
comland.hakes[, off.hake := SPPLIVMT - silver]
comland.hakes[, off.hake.value := SPPVALUE - silver.value]
#Silver hake (509), mix hakes (507)
#put hakes in comland format to merge back
silver <- comland.hakes[, list(YEAR, Half, AREA, MONTH, NEGEAR,
TONCL1, NESPP3, UTILCD, MKTCAT, silver,
silver.value)]
silver[, NESPP3 := 509]
setnames(silver, c('silver', 'silver.value'), c('SPPLIVMT', 'SPPVALUE'))
silver <- silver[SPPLIVMT > 0, ]
offshore <- comland.hakes[, list(YEAR, Half, AREA, MONTH, NEGEAR,
TONCL1, NESPP3, UTILCD, MKTCAT, off.hake,
off.hake.value)]
offshore[, NESPP3 := 507]
setnames(offshore, c('off.hake', 'off.hake.value'), c('SPPLIVMT', 'SPPVALUE'))
offshore <- offshore[SPPLIVMT > 0, ]
#merge both and reformat for comland
hakes.add.back <- rbindlist(list(silver, offshore))
hakes.add.back[, Half := NULL]
setcolorder(hakes.add.back, names(comland))
comland <- rbindlist(list(comland[NESPP3 != 507, ], hakes.add.back))
#Herring---------------------------------------------------------------------------------
#Herring data is housed by the state of Maine.
herr.qry <- "select year, month, stock_area, negear, gearname, keptmt, discmt
from maine_herring_catch"
herr.catch <- as.data.table(sqlQuery(channel, herr.qry))
setkey(herr.catch, YEAR, MONTH, STOCK_AREA, NEGEAR)
herring <- herr.catch[, list(sum(KEPTMT), sum(DISCMT)), by = key(herr.catch)]
setnames(herring, c('STOCK_AREA', 'V1', 'V2'),
c('AREA', 'SPPLIVMT', 'DISCMT'))
#Using averages from comland to fill in categories
herring[, MKTCAT := 5]
herring[, TONCL1 := 2]
herring[, UTILCD := 0]
#compute price/utilization from CF tables
herring.comland <- comland[NESPP3 == 168, ]
#Price from comland
herring.price <- herring.comland[, (sum(SPPVALUE) / sum(SPPLIVMT)), by = c('YEAR', 'MONTH')]
setnames(herring.price, 'V1', 'price')
herring <- merge(herring, herring.price, by = c('YEAR', 'MONTH'), all.x = T)
#Use 1964 prices for < 1964
herring[YEAR < 1964, price := mean(herring[YEAR == 1964, price])]
#Calculate SPPVALUE from price
herring[, SPPVALUE := round(price * SPPLIVMT)]
#Utilization from comland
herring.util <- herring.comland[, sum(SPPLIVMT), by = c('YEAR', 'MONTH', 'UTILCD')]
setnames(herring.util, 'V1', 'SPPLIVMT')
herring.util[, SPPLIVMT.ALL := sum(SPPLIVMT), by = c('YEAR', 'MONTH')]
herring.util[, Prop := SPPLIVMT/SPPLIVMT.ALL]
setorder(herring.util, YEAR, MONTH, Prop)
herring.util[, cum.prop := cumsum(Prop), by = c('YEAR', 'MONTH')]
#Apply proportions to Maine data set
#Not pulled all the time - current through 2017
herring[, Total := sum(SPPLIVMT), by = c('YEAR', 'MONTH')]
herring[, Prop := SPPLIVMT / Total]
setorder(herring, YEAR, MONTH, Prop)
herring[, cum.prop := cumsum(Prop), by = c('YEAR', 'MONTH')]
for(iyear in unique(herring.util[, YEAR])){
for(imonth in unique(herring.util[YEAR == iyear, MONTH])){
cum.prop.low <- 0
for(iutil in herring.util[YEAR == iyear & MONTH == imonth, UTILCD]){
cum.prop.high <- herring.util[YEAR == iyear & MONTH == imonth &
UTILCD == iutil, cum.prop]
herring[YEAR == iyear & MONTH == imonth & cum.prop <= cum.prop.high &
cum.prop > cum.prop.low, UTILCD := iutil]
cum.prop.low <- cum.prop.high
}
}
}
#fix column headings
herring[, c('Total', 'Prop', 'cum.prop', 'price', 'DISCMT') := NULL]
herring[, NESPP3 := 168]
setcolorder(herring, names(comland))
#remove herring from data pull and add in Maine numbers
comland <- rbindlist(list(comland[NESPP3 != 168, ], herring))
#Menhaden------------------------------------------------------------------------------------
##fix menhaden records - data from Tom Miller/ Andre Bouchheister
#menhaden <- as.data.table(read.csv(paste(data.dir, "Menhaden.csv", sep = '')))
#menhaden.mab <- menhaden[, MA.Total + CB.Total, by = Year]
##file metric is 1000s of lbs - convert to mt
#menhaden.mab[, SPPLIVMT := (V1 * 1000) * 0.00045359237]
#menhaden.mab[, V1 := NULL]
#
#menhaden.gom <- menhaden[, list(Year, NE.Total)]
#menhaden.gom[, SPPLIVMT := (NE.Total * 1000) * 0.00045359237]
#menhaden.gom[, NE.Total := NULL]
#save(comland, file = paste(out.dir, "Comland_unkA.RData", sep = ''))
#Deal with unknowns-------------------------------------------------------------------------
comland[NEGEAR == 999, NEGEAR := 0]
comland[is.na(TONCL1), TONCL1 := 0]
comland[is.na(AREA), AREA := as.factor(0)]
comland[AREA == 999, AREA := as.factor(0)]
comland[is.na(MKTCAT), MKTCAT := 0]
comland[is.na(UTILCD), UTILCD := 0]
#1 - drop unknown species/landings
comland <- comland[NESPP3 != 0 & SPPLIVMT != 0, ]
#Sumarry tables
#missing area
#known.area <- comland[AREA != 0, sum(SPPLIVMT), by = NESPP3]
#unknown.area <- comland[AREA == 0, sum(SPPLIVMT), by = NESPP3]
#setnames(known.area, "V1", "AREA.MT.known")
#setnames(unknown.area, "V1", "AREA.MT.unknown")
#missing.table <- merge(known.area, unknown.area, by = 'NESPP3', all = T)
#
#missing.table[is.na(AREA.MT.known), AREA.MT.known := 0]
#missing.table[is.na(AREA.MT.unknown), AREA.MT.unknown := 0]
#missing.table[, AREA.Ratio := AREA.MT.unknown / AREA.MT.known]
#
##missing month
#known.month <- comland[MONTH != 0, sum(SPPLIVMT), by = NESPP3]
#unknown.month <- comland[MONTH == 0, sum(SPPLIVMT), by = NESPP3]
#setnames(known.month, "V1", "MONTH.MT.known")
#setnames(unknown.month, "V1", "MONTH.MT.unknown")
#missing.table <- merge(missing.table, known.month, by = 'NESPP3', all = T)
#missing.table <- merge(missing.table, unknown.month, by = 'NESPP3', all = T)
#
#missing.table[is.na(MONTH.MT.known), MONTH.MT.known := 0]
#missing.table[is.na(MONTH.MT.unknown), MONTH.MT.unknown := 0]
#missing.table[, MONTH.Ratio := MONTH.MT.unknown / MONTH.MT.known]
#
##missing gear
#known.gear <- comland[NEGEAR != 0, sum(SPPLIVMT), by = NESPP3]
#unknown.gear <- comland[NEGEAR == 0, sum(SPPLIVMT), by = NESPP3]
#setnames(known.gear, "V1", "GEAR.MT.known")
#setnames(unknown.gear, "V1", "GEAR.MT.unknown")
#missing.table <- merge(missing.table, known.gear, by = 'NESPP3', all = T)
#missing.table <- merge(missing.table, unknown.gear, by = 'NESPP3', all = T)
#
#missing.table[is.na(GEAR.MT.known), GEAR.MT.known := 0]
#missing.table[is.na(GEAR.MT.unknown), GEAR.MT.unknown := 0]
#missing.table[, GEAR.Ratio := GEAR.MT.unknown / GEAR.MT.known]
#
##missing tonnage class
#known.tc <- comland[TONCL1 != 0, sum(SPPLIVMT), by = NESPP3]
#unknown.tc <- comland[TONCL1 == 0, sum(SPPLIVMT), by = NESPP3]
#setnames(known.tc, "V1", "TC.MT.known")
#setnames(unknown.tc, "V1", "TC.MT.unknown")
#missing.table <- merge(missing.table, known.tc, by = 'NESPP3', all = T)
#missing.table <- merge(missing.table, unknown.tc, by = 'NESPP3', all = T)
#
#missing.table[is.na(TC.MT.known), TC.MT.known := 0]
#missing.table[is.na(TC.MT.unknown), TC.MT.unknown := 0]
#missing.table[, TC.Ratio := TC.MT.unknown / TC.MT.known]
#
#write.csv(missing.table, paste(out.dir, "\\Missing_table.csv", sep = ''), row.names = F)
#
#2 - aggregate by quarter year, half year, major gear, and small/large TC
comland[MONTH %in% 1:3, QY := 1]
comland[MONTH %in% 4:6, QY := 2]
comland[MONTH %in% 7:9, QY := 3]
comland[MONTH %in% 10:12, QY := 4]
comland[MONTH == 0, QY := 0]
comland[MONTH %in% 1:6, HY := 1]
comland[MONTH %in% 7:12, HY := 2]
comland[MONTH == 0, HY := 0]
otter <- 50:59
dredge.sc <- 131:132
pot <- c(189:190, 200:219, 300, 301)
longline <- c(10, 40)
seine <- c(70:79, 120:129, 360)
gillnet <- c(100:119, 500, 510, 520)
midwater <- c(170, 370)
dredge.o <- c(281, 282, 380:400)
comland[NEGEAR %in% otter, GEAR := 'otter']
comland[NEGEAR %in% dredge.sc, GEAR := 'dredge.sc']
comland[NEGEAR %in% pot, GEAR := 'pot']
comland[NEGEAR %in% longline, GEAR := 'longline']
comland[NEGEAR %in% seine, GEAR := 'seine']
comland[NEGEAR %in% gillnet, GEAR := 'gillnet']
comland[NEGEAR %in% midwater, GEAR := 'midwater']
comland[NEGEAR %in% dredge.o, GEAR := 'dredge.o']
comland[NEGEAR == 0, GEAR := 'unknown']
comland[is.na(GEAR), GEAR := 'other']
comland[, GEAR := as.factor(GEAR)]
comland[TONCL1 %in% 1:3, SIZE := 'small']
comland[TONCL1 > 3, SIZE := 'large']
comland[TONCL1 == 0, SIZE := 'unknown']
comland[, SIZE := as.factor(SIZE)]
setkey(comland,
YEAR,
QY,
HY,
GEAR,
SIZE,
AREA,
NESPP3,
UTILCD)
comland.agg <- comland[, list(sum(SPPLIVMT), sum(SPPVALUE)), by = key(comland)]
setnames(comland.agg, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
#3 - Use proportions of known catch to assign unknown catch
#3.A QY/HY------------------------------------------------------------------------------
unk.month <- comland.agg[QY == 0, ]
k.month <- comland.agg[QY != 0, ]
#3.A.1 - All match
match.key <- c('YEAR', 'NESPP3', 'GEAR', 'SIZE', 'AREA')
unk.month.all <- unk.month[GEAR != 'unknown']
unk.month.all <- unk.month.all[SIZE != 'unknown', ]
unk.month.all <- unk.month.all[AREA != 0, ]
k.month.all <- k.month[GEAR != 'unknown', ]
k.month.all <- k.month.all[SIZE != 'unknown', ]
k.month.all <- k.month.all[AREA != 0, ]
setkeyv(unk.month.all, match.key)
setkeyv(k.month.all, match.key)
month.all <- k.month.all[unk.month.all]
#No match - need to match with larger aggregation
no.match <- month.all[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop SIZE
setkey(no.match, YEAR, NESPP3, AREA, GEAR)
setkeyv(k.month.all, key(no.match))
month.all.2 <- k.month.all[no.match]
no.match.2 <- month.all.2[is.na(SPPLIVMT), ]
no.match.2[, c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.SIZE', 'i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop GEAR
setkey(no.match.2, YEAR, NESPP3, AREA)
setkeyv(k.month.all, key(no.match.2))
month.all.3 <- k.month.all[no.match.2]
no.match.3 <- month.all.3[is.na(SPPLIVMT), ]
no.match.3[, c('GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.3, c('i.GEAR', 'i.SIZE', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop AREA
setkey(no.match.3, YEAR, NESPP3)
setkeyv(k.month.all, key(no.match.3))
month.all.4 <- k.month.all[no.match.3]
no.match.4 <- month.all.4[is.na(SPPLIVMT), ]
no.match.4[, c('AREA', 'GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.4, c('i.AREA', 'i.GEAR', 'i.SIZE', 'i.QY', 'i.HY', 'i.UTILCD',
'i.SPPLIVMT', 'i.SPPVALUE'),
c('AREA', 'GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.4[, c('QY', 'HY') := 1]
#Merge all together and proportion catch to known months
month.all <- month.all [!is.na(SPPLIVMT), ]
month.all.2 <- month.all.2[!is.na(SPPLIVMT), ]
month.all.2[, SIZE := i.SIZE]
month.all.2[, i.SIZE := NULL]
setcolorder(month.all.2, names(month.all))
month.all.3 <- month.all.3[!is.na(SPPLIVMT), ]
month.all.3[, GEAR := i.GEAR]
month.all.3[, SIZE := i.SIZE]
month.all.3[, i.GEAR := NULL]
month.all.3[, i.SIZE := NULL]
setcolorder(month.all.3, names(month.all))
month.all.4 <- month.all.4[!is.na(SPPLIVMT), ]
month.all.4[, AREA := i.AREA]
month.all.4[, GEAR := i.GEAR]
month.all.4[, SIZE := i.SIZE]
month.all.4[, i.AREA := NULL]
month.all.4[, i.GEAR := NULL]
month.all.4[, i.SIZE := NULL]
setcolorder(month.all.4, names(month.all))
month.all <- rbindlist(list(month.all, month.all.2, month.all.3, month.all.4))
month.all[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.all[, unk := i.SPPLIVMT * prop]
month.all[, unk2 := i.SPPVALUE * prop]
month.all[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY',
'i.QY', 'i.UTILCD', 'prop') := NULL]
setnames(month.all, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
setcolorder(no.match.4, names(month.all))
month.solved <- rbindlist(list(month.all, no.match.4))
rm(list = c(ls(pattern = 'month.all'), ls(pattern = 'no.match')))
#3.A.2 - GEAR/SIZE
match.key <- c('YEAR', 'NESPP3', 'GEAR', 'SIZE')
unk.month.g.s <- unk.month[GEAR != 'unknown']
unk.month.g.s <- unk.month.g.s[SIZE != 'unknown', ]
unk.month.g.s <- unk.month.g.s[AREA == 0, ]
unk.month.g.s <- unk.month.g.s[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.g.s, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.g.s <- k.month[GEAR != 'unknown', ]
k.month.g.s <- k.month.g.s[SIZE != 'unknown', ]
k.month.g.s <- k.month.g.s[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.g.s, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.g.s, match.key)
setkeyv(k.month.g.s, match.key)
month.g.s <- k.month.g.s[unk.month.g.s]
#No match - need to match with larger aggregation
no.match <- month.g.s[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop SIZE
setkey(no.match, YEAR, NESPP3, GEAR)
setkeyv(k.month.g.s, key(no.match))
month.g.s.2 <- k.month.g.s[no.match]
no.match.2 <- month.g.s.2[is.na(SPPLIVMT), ]
no.match.2[, c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.SIZE', 'i.QY', 'i.HY', 'i.UTILCD',
'i.SPPLIVMT', 'i.SPPVALUE'),
c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop GEAR
setkey(no.match.2, YEAR, NESPP3)
setkeyv(k.month.g.s, key(no.match.2))
month.g.s.3 <- k.month.g.s[no.match.2]
no.match.3 <- month.g.s.3[is.na(SPPLIVMT), ]
no.match.3[, c('GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.3, c('i.GEAR', 'i.SIZE', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('GEAR', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.3[, c('QY', 'HY') := 1]
no.match.3[, AREA := 0]
#Merge all together and proportion catch to known months
month.g.s <- month.g.s [!is.na(SPPLIVMT), ]
month.g.s.2 <- month.g.s.2[!is.na(SPPLIVMT), ]
if(nrow(month.g.s.2) > 0){
month.g.s.2[, SIZE := i.SIZE]
month.g.s.2[, i.SIZE := NULL]
setcolorder(month.g.s.2, names(month.g.s))
month.g.s <- rbindlist(list(month.g.s, month.g.s.2))
}
month.g.s.3 <- month.g.s.3[!is.na(SPPLIVMT), ]
if(nrow(month.g.s.3) > 0){
month.g.s.3[, GEAR := i.GEAR]
month.g.s.3[, SIZE := i.SIZE]
month.g.s.3[, i.GEAR := NULL]
month.g.s.3[, i.SIZE := NULL]
setcolorder(month.g.s.3, names(month.g.s))
month.g.s <- rbindlist(list(month.g.s, month.g.s.3))
}
month.g.s[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.g.s[, unk := i.SPPLIVMT * prop]
month.g.s[, unk2 := i.SPPVALUE * prop]
month.g.s[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY',
'i.QY', 'i.UTILCD', 'prop') := NULL]
setnames(month.g.s, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.g.s[, AREA := 0]
setcolorder(month.g.s, names(month.solved))
setcolorder(no.match.3, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.g.s, no.match.3))
rm(list = c(ls(pattern = 'month.g.s'), ls(pattern = 'no.match')))
#3.A.3 - AREA/GEAR
match.key <- c('YEAR', 'NESPP3', 'GEAR', 'AREA')
unk.month.a.g <- unk.month[GEAR != 'unknown']
unk.month.a.g <- unk.month.a.g[SIZE == 'unknown', ]
unk.month.a.g <- unk.month.a.g[AREA != 0, ]
unk.month.a.g <- unk.month.a.g[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.a.g, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.a.g <- k.month[GEAR != 'unknown', ]
k.month.a.g <- k.month.a.g[AREA != 0, ]
k.month.a.g <- k.month.a.g[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.a.g, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.a.g, match.key)
setkeyv(k.month.a.g, match.key)
month.a.g <- k.month.a.g[unk.month.a.g]
#No match - need to match with larger aggregation
no.match <- month.a.g[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop GEAR
setkey(no.match, YEAR, NESPP3, AREA)
setkeyv(k.month.a.g, key(no.match))
month.a.g.2 <- k.month.a.g[no.match]
no.match.2 <- month.a.g.2[is.na(SPPLIVMT), ]
no.match.2[, c('GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.GEAR', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop AREA
setkey(no.match.2, YEAR, NESPP3)
setkeyv(k.month.a.g, key(no.match.2))
month.a.g.3 <- k.month.a.g[no.match.2]
no.match.3 <- month.a.g.3[is.na(SPPLIVMT), ]
no.match.3[, c('AREA', 'GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.3, c('i.AREA', 'i.GEAR', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('AREA', 'GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.3[, c('QY', 'HY') := 1]
no.match.3[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
#Merge all together and proportion catch to known months
month.a.g <- month.a.g [!is.na(SPPLIVMT), ]
month.a.g.2 <- month.a.g.2[!is.na(SPPLIVMT), ]
if(nrow(month.a.g.2) > 0){
month.a.g.2[, GEAR := i.GEAR]
month.a.g.2[, i.GEAR := NULL]
setcolorder(month.a.g.2, names(month.a.g))
month.a.g <- rbindlist(list(month.a.g, month.a.g.2))
}
month.a.g.3 <- month.a.g.3[!is.na(SPPLIVMT), ]
if(nrow(month.a.g.3) > 0){
month.a.g.3[, AREA := i.AREA]
month.a.g.3[, GEAR := i.GEAR]
month.a.g.3[, i.AREA := NULL]
month.a.g.3[, i.GEAR := NULL]
setcolorder(month.a.g.3, names(month.a.g))
month.a.g <- rbindlist(list(month.a.g, month.a.g.3))
}
month.a.g[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.a.g[, unk := i.SPPLIVMT * prop]
month.a.g[, unk2 := i.SPPVALUE * prop]
month.a.g[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY',
'i.QY', 'i.UTILCD', 'prop') := NULL]
setnames(month.a.g, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.a.g[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
setcolorder(month.a.g, names(month.solved))
setcolorder(no.match.3, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.a.g, no.match.3))
rm(list = c(ls(pattern = 'month.a.g'), ls(pattern = 'no.match')))
#3.A.4 - AREA/TC
match.key <- c('YEAR', 'NESPP3', 'SIZE', 'AREA')
unk.month.a.s <- unk.month[GEAR == 'unknown']
unk.month.a.s <- unk.month.a.s[SIZE != 'unknown', ]
unk.month.a.s <- unk.month.a.s[AREA != 0, ]
unk.month.a.s <- unk.month.a.s[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.a.s, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.a.s <- k.month[SIZE != 'unknown', ]
k.month.a.s <- k.month.a.s[AREA != 0, ]
k.month.a.s <- k.month.a.s[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.a.s, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.a.s, match.key)
setkeyv(k.month.a.s, match.key)
month.a.s <- k.month.a.s[unk.month.a.s]
#No match - need to match with larger aggregation
no.match <- month.a.s[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop SIZE
setkey(no.match, YEAR, NESPP3, AREA)
setkeyv(k.month.a.s, key(no.match))
month.a.s.2 <- k.month.a.s[no.match]
no.match.2 <- month.a.s.2[is.na(SPPLIVMT), ]
no.match.2[, c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.SIZE', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop AREA
setkey(no.match.2, YEAR, NESPP3)
setkeyv(k.month.a.s, key(no.match.2))
month.a.s.3 <- k.month.a.s[no.match.2]
no.match.3 <- month.a.s.3[is.na(SPPLIVMT), ]
no.match.3[, c('AREA', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.3, c('i.AREA', 'i.SIZE', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('AREA', 'SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.3[, c('QY', 'HY') := 1]
no.match.3[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
#Merge all together and proportion catch to known months
month.a.s <- month.a.s [!is.na(SPPLIVMT), ]
month.a.s.2 <- month.a.s.2[!is.na(SPPLIVMT), ]
if(nrow(month.a.s.2) > 0){
month.a.s.2[, SIZE := i.SIZE]
month.a.s.2[, i.SIZE := NULL]
setcolorder(month.a.s.2, names(month.a.s))
month.a.s <- rbindlist(list(month.a.s, month.a.s.2))
}
month.a.s.3 <- month.a.s.3[!is.na(SPPLIVMT), ]
if(nrow(month.a.s.3) > 0){
month.a.s.3[, AREA := i.AREA]
month.a.s.3[, SIZE := i.SIZE]
month.a.s.3[, i.AREA := NULL]
month.a.s.3[, i.SIZE := NULL]
setcolorder(month.a.s.3, names(month.a.s))
month.a.s <- rbindlist(list(month.a.s, month.a.s.3))
}
month.a.s[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.a.s[, unk := i.SPPLIVMT * prop]
month.a.s[, unk2 := i.SPPVALUE * prop]
month.a.s[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY',
'i.QY', 'i.UTILCD', 'prop') := NULL]
setnames(month.a.s, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.a.s[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
setcolorder(month.a.s, names(month.solved))
setcolorder(no.match.3, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.a.s, no.match.3))
rm(list = c(ls(pattern = 'month.a.s'), ls(pattern = 'no.match')))
#3.A.5 - SIZE
match.key <- c('YEAR', 'NESPP3', 'SIZE')
unk.month.si <- unk.month[GEAR == 'unknown']
unk.month.si <- unk.month.si[SIZE != 'unknown', ]
unk.month.si <- unk.month.si[AREA == 0, ]
unk.month.si <- unk.month.si[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.si, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.si <- k.month[SIZE != 'unknown', ]
k.month.si <- k.month.si[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.si, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.si, match.key)
setkeyv(k.month.si, match.key)
month.si <- k.month.si[unk.month.si]
#No match - need to match with larger aggregation
no.match <- month.si[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop SIZE
setkey(no.match, YEAR, NESPP3)
setkeyv(k.month.si, key(no.match))
month.si.2 <- k.month.si[no.match]
no.match.2 <- month.si.2[is.na(SPPLIVMT), ]
no.match.2[, c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.SIZE', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('SIZE', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.2[, c('QY', 'HY') := 1]
no.match.2[, AREA := 0]
no.match.2[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
#Merge all together and proportion catch to known months
month.si <- month.si [!is.na(SPPLIVMT), ]
month.si.2 <- month.si.2[!is.na(SPPLIVMT), ]
if(nrow(month.si.2) > 0){
month.si.2[, SIZE := i.SIZE]
month.si.2[, i.SIZE := NULL]
setcolorder(month.si.2, names(month.si))
month.si <- rbindlist(list(month.si, month.si.2))
}
month.si[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.si[, unk := i.SPPLIVMT * prop]
month.si[, unk2 := i.SPPVALUE * prop]
month.si[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY', 'i.QY',
'i.UTILCD', 'prop') := NULL]
setnames(month.si, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.si[, AREA := 0]
month.si[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
setcolorder(month.si, names(month.solved))
setcolorder(no.match.2, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.si, no.match.2))
rm(list = c(ls(pattern = 'month.si'), ls(pattern = 'no.match')))
#3.A.6 - GEAR
match.key <- c('YEAR', 'NESPP3', 'GEAR')
unk.month.g <- unk.month[GEAR != 'unknown']
unk.month.g <- unk.month.g[SIZE == 'unknown', ]
unk.month.g <- unk.month.g[AREA == 0, ]
unk.month.g <- unk.month.g[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.g, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.g <- k.month[GEAR != 'unknown', ]
k.month.g <- k.month.g[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.g, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.g, match.key)
setkeyv(k.month.g, match.key)
month.g <- k.month.g[unk.month.g]
#No match - need to match with larger aggregation
no.match <- month.g[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop GEAR
setkey(no.match, YEAR, NESPP3)
setkeyv(k.month.g, key(no.match))
month.g.2 <- k.month.g[no.match]
no.match.2 <- month.g.2[is.na(SPPLIVMT), ]
no.match.2[, c('GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.GEAR', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('GEAR', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.2[, c('QY', 'HY') := 1]
no.match.2[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
no.match.2[, AREA := 0]
#Merge all together and proportion catch to known months
month.g <- month.g [!is.na(SPPLIVMT), ]
month.g.2 <- month.g.2[!is.na(SPPLIVMT), ]
if(nrow(month.g.2) > 0){
month.g.2[, GEAR := i.GEAR]
month.g.2[, i.GEAR := NULL]
setcolorder(month.g.2, names(month.g))
month.g <- rbindlist(list(month.g, month.g.2))
}
month.g[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.g[, unk := i.SPPLIVMT * prop]
month.g[, unk2 := i.SPPVALUE * prop]
month.g[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY', 'i.QY',
'i.UTILCD', 'prop') := NULL]
setnames(month.g, c('unk', 'unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.g[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
month.g[, AREA := 0]
setcolorder(month.g, names(month.solved))
setcolorder(no.match.2, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.g, no.match.2))
rm(list = c(ls(pattern = 'month.g'), ls(pattern = 'no.match')))
#3.A.7 - AREA
match.key <- c('YEAR', 'NESPP3', 'AREA')
unk.month.a <- unk.month[GEAR == 'unknown']
unk.month.a <- unk.month.a[SIZE == 'unknown', ]
unk.month.a <- unk.month.a[AREA != 0, ]
unk.month.a <- unk.month.a[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.a, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.a <- k.month[AREA != 0, ]
k.month.a <- k.month.a[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.a, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.a, match.key)
setkeyv(k.month.a, match.key)
month.a <- k.month.a[unk.month.a]
#No match - need to match with larger aggregation
no.match <- month.a[is.na(SPPLIVMT), ]
no.match[, c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match, c('i.QY', 'i.HY', 'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Drop AREA
setkey(no.match, YEAR, NESPP3)
setkeyv(k.month.a, key(no.match))
month.a.2 <- k.month.a[no.match]
no.match.2 <- month.a.2[is.na(SPPLIVMT), ]
no.match.2[, c('AREA', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE') := NULL]
setnames(no.match.2, c('i.AREA', 'i.QY', 'i.HY',
'i.UTILCD', 'i.SPPLIVMT', 'i.SPPVALUE'),
c('AREA', 'QY', 'HY', 'UTILCD', 'SPPLIVMT', 'SPPVALUE'))
#Still no match - assign to first QY/HY
no.match.2[, c('QY', 'HY') := 1]
no.match.2[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
no.match.2[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
#Merge all together and proportion catch to known months
month.a <- month.a [!is.na(SPPLIVMT), ]
month.a.2 <- month.a.2[!is.na(SPPLIVMT), ]
if(nrow(month.a.2) > 0){
month.a.2[, AREA := i.AREA]
month.a.2[, i.AREA := NULL]
setcolorder(month.a.2, names(month.a))
month.a <- rbindlist(list(month.a, month.a.2))
}
month.a[, prop := SPPLIVMT / sum(SPPLIVMT), by = match.key]
month.a[, unk := i.SPPLIVMT * prop]
month.a[, unk2 := i.SPPVALUE * prop]
month.a[, c('SPPLIVMT', 'SPPVALUE', 'i.SPPLIVMT', 'i.SPPVALUE', 'i.HY',
'i.QY', 'i.UTILCD', 'prop') := NULL]
setnames(month.a, c('unk','unk2'), c('SPPLIVMT', 'SPPVALUE'))
month.a[, SIZE := factor('unknown', levels = c('large', 'small', 'unknown'))]
month.a[, GEAR := factor('unknown', levels = levels(k.month[, GEAR]))]
setcolorder(month.a, names(month.solved))
setcolorder(no.match.2, names(month.solved))
month.solved <- rbindlist(list(month.solved, month.a, no.match.2))
rm(list = c(ls(pattern = 'month.a'), ls(pattern = 'no.match')))
#3.A.8 - Species only - no other match
match.key <- c('YEAR', 'NESPP3')
unk.month.sp <- unk.month[GEAR == 'unknown']
unk.month.sp <- unk.month.sp[SIZE == 'unknown', ]
unk.month.sp <- unk.month.sp[AREA == 0, ]
unk.month.sp <- unk.month.sp[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(unk.month.sp, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
k.month.sp <- k.month[, list(sum(SPPLIVMT), sum(SPPVALUE)),
by = c(match.key, 'QY', 'HY', 'UTILCD')]
setnames(k.month.sp, c('V1', 'V2'), c('SPPLIVMT', 'SPPVALUE'))
setkeyv(unk.month.sp, match.key)
setkeyv(k.month.sp, match.key)
month.sp <- k.month.sp[unk.month.sp]
#No match - assign to first QY/HY