Scripts_with_Legacy_Names/PacFIN Catch Extraction, Sablefish Example.R
In John-R-Wallace-NOAA/PacFIN.Data.Extraction: What the package does (short line)
# Download into your working directory and view this script in R with:
rgit::gitAFile('John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/Scripts_with_Legacy_Names/PacFIN Catch Extraction, Sablefish Example.R', type = "script",
File = 'PacFIN Catch Extraction, Sablefish Example.R', show = TRUE)
# If you have copied and updated gitEdit() with your favorite editor, then download and insert this script into your editor with:
gitEdit('PacFIN Catch Extraction, Sablefish Example.R', 'John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/Scripts_with_Legacy_Names/')
# -------- PacFIN login and password --------
UID <- "wallacej"
PWD <- PacFIN.PW
# -------- Check species info --------
sp <- import.sql("Select * from pacfin.sp", dsn="PacFIN", uid= UID, pwd = PWD)
sp[grep("SABLEFISH", sp$CNAME), 1:7] # Same as below
sp[grep("SBL", sp$SPID), 1:7]
SPID CNAME COMPLEX COMPLEX2 COMPLEX3 MGRP SNAME
309 SABL SABLEFISH ROND .... .... GRND ANOPLOPOMA FIMBRIA
# Example of third category from BOCACCIO+CHILIPEPPER
# 232 RCK1 BOCACCIO+CHILIPEPPER RCKFSH ROCK .... NSLF GRND SEBASTES SPP.
# The 'Agency market category listing' on the PadFIN website gives some more state information on the codes:
SABLEFISH SABL C 190 SABLEFISH
SABLEFISH SABL O 477 SABLEFISH
SABLEFISH SABL W 221 SABLEFISH ANOPLOPOMA FIMBRIA
SABLEFISH SABL W 321 SABLEFISH (REDUCTION) ANOPLOPOMA FIMBRIA
SABLEFISH SABL W 421 SABLEFISH (ANIMAL FOOD) ANOPLOPOMA FIMBRIA
# -------- Data from the Comprehensive_FT table --------
# Gear table
(gr <- import.sql("Select * from pacfin.gr", dsn="PacFIN", uid=UID, pwd=PWD))
# Area table
ar <- import.sql("Select * from pacfin.ar", dsn="PacFIN", uid=UID, pwd=PWD)
AR_COUNCIL_P <- renum(ar[ar$COUNCIL %in% 'P',])
# Only the created 'INPFC_PSMFC_AREA_GROUP %in% PSMFC' gets exculsively the more finer areas of PSMFC.
# (Old but still correct: This will now allow a mapping between ARID to INPFC_PSMFC_AREA_GROUP which could be renamed to ARID for a foo sc table)
# Last 15 rows of the AR_COUNCIL_P table are PSMFC
AR_COUNCIL_P$INPFC_PSMFC_AREA_GROUP <- "INPFC"
# "UNK-PSMFC" will be matched with "UP", "3C-S" will be matched with "3S", and "MNTREY BAY" will be matched with "1D" in the matching below and both will be labeled with as PSMFC (line 72 below)
AR_COUNCIL_P$INPFC_PSMFC_AREA_GROUP[AR_COUNCIL_P$NAME %in% c("UNK-PSMFC","1A", "1B", "MNTREY BAY", "1E", "1C", "2A", "2B", "2C", "2E", "2F", "2D", "3A", "3B", "3C-S")] <- "PSMFC"
AR_COUNCIL_P
# COUNCIL_CODE = 'P'; with research catch included
# For species with a nominal category use, e.g.: < PACFIN_SPECIES_CODE = any ('PTRL', 'PTR1') >
SABL.CompFT.05.May.2019 <- JRWToolBox::import.sql(
"Select COUNCIL_CODE, AGENCY_CODE, DAHL_GROUNDFISH_CODE, INPFC_AREA_TYPE_CODE, LANDING_YEAR, LANDING_DATE, FTID, PARTICIPATION_GROUP_CODE, PACFIN_CATCH_AREA_CODE, ORIG_PACFIN_CATCH_AREA_CODE, PACFIN_PORT_CODE, FLEET_CODE, VESSEL_ID,
PACFIN_GEAR_CODE, IS_IFQ_LANDING, REMOVAL_TYPE_CODE, CONDITION_CODE, DISPOSITION_CODE, EXVESSEL_REVENUE, PACFIN_SPECIES_CODE, NOMINAL_TO_ACTUAL_PACFIN_SPECIES_CODE,
IS_SPECIES_COMP_USED, GRADE_CODE, GRADE_NAME, PACFIN_GROUP_GEAR_CODE, ROUND_WEIGHT_LBS, LANDED_WEIGHT_MTONS
from pacfin_marts.Comprehensive_FT
where PACFIN_SPECIES_CODE = any ('SABL')
and COUNCIL_CODE = 'P'
and AGENCY_CODE in ('W','O','C')", dsn="PacFIN", uid=UID, pwd=PWD)
# Grab nameConvertVdrfdToCompFT from GitHub and convert to the old style short names
rgit::gitAFile('John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/R/nameConvertVdrfdToCompFT.R')
names(SABL.CompFT.05.May.2019) <- JRWToolBox::recode.simple(names(SABL.CompFT.05.May.2019), nameConvertVdrfdToCompFT)
# Match INPFC_PSMFC_AREA_GROUP and compare to INPFC_ARID and ARID.
# SABL.vdv.28.Mar.2019 <- match.f(SABL.vdv.28.Mar.2019, AR_COUNCIL_P, "ARID", "ARID", c("COUNCIL", "INPFC_ARID", "INPFC_PSMFC_AREA_GROUP"))
# tmp <- match.f(SABL.CompFT.05.May.2019, AR_COUNCIL_P, "ARID", "ARID", "INPFC_ARID")
SABL.CompFT.05.May.2019 <- match.f(SABL.CompFT.05.May.2019, AR_COUNCIL_P, "ARID", "ARID", "INPFC_PSMFC_AREA_GROUP")
SABL.CompFT.05.May.2019[1:4, ]
Table(SABL.CompFT.05.May.2019$INPFC_ARID, SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP)
INPFC PSMFC
CL 43465 481694
CP 0 89012
EK 94094 128532
MT 0 189418
OC 10495 0
UI 24550 0
VN 128196 179739
Table(SABL.CompFT.05.May.2019$INPFC_ARID, SABL.CompFT.05.May.2019$ARID)
1A 1B 1C 2A 2B 2C 2D 2E 2F 3A 3B 3S CL EK OC TL UP VN
CL 0 0 0 0 132728 70760 1 60447 74623 143135 0 0 43455 0 0 10 0 0
CP 89012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
EK 0 0 128532 94086 0 0 0 0 0 0 0 0 0 8 0 0 0 0
MT 0 189418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
OC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10495 0 0 0
UI 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24550 0
VN 0 0 0 0 0 0 0 0 0 0 93263 86476 0 0 0 0 0 128196
Table(SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP, SABL.CompFT.05.May.2019$ARID)
1A 1B 1C 2A 2B 2C 2D 2E 2F 3A 3B 3S CL EK OC TL UP VN
INPFC 0 0 0 0 0 0 0 0 0 0 0 0 43455 8 10495 10 24560 128215
PSMFC 89021 189442 128532 94087 132729 70760 1 60447 74623 143135 93263 86476 0 0 0 0 0 0
# Create W_O_C_Port_Groups
SABL.CompFT.05.May.2019$W_O_C_Port_Groups <- SABL.CompFT.05.May.2019$AGID
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'W'] <- "AWA"
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'O'] <- "AOR"
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'C'] <- "ACA"
# Create PERIOD (months) from TDATE
SABL.CompFT.05.May.2019$PERIOD <- Months.POSIXt(SABL.CompFT.05.May.2019$TDATE)
# Look at the data
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$W_O_C_Port_Groups)
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$YEAR)
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$YEAR, SABL.CompFT.05.May.2019$AGID)
, , = C
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 6779 8094 6290 6150 6878 7388 6825 6802 8121 9223 11008 12675 8394 7429 18781 20462 18849 11231 12749 13225 10841 10707 11257 9238 10014 9652 8628 9208 10980 11008 8589 6663 7385
2014 2015 2016 2017 2018 2019
SABL 6882 7504 7456 7037 5960 1821
, , = O
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 3735 7002 7844 5276 5911 5398 12140 15916 21703 24002 32499 38188 52498 35355 39661 39883 41462 29279 30364 22116 22069 15462 19903 14094 13388 16248 15519 19493 25196 11732 13426 11254 9203
2014 2015 2016 2017 2018 2019
SABL 6538 10779 10605 11002 9564 862
, , = W
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 2169 3871 3563 5221 5141 5427 6747 7927 6815 7251 10671 16860 14005 12377 12539 13076 13068 8074 9225 8408 8715 6909 8504 7326 8032 6933 6082 5270 5535 4978 5265 4729 3656
2014 2015 2016 2017 2018 2019
SABL 3410 3567 3865 4694 4402 200
save(SABL.CompFT.05.May.2019, file = 'SABL.CompFT.05.May.2019.RData')
# Create research and tribal data frame
change(SABL.CompFT.05.May.2019)
SABL.Research.Tribal.Catch.05.May.2019 <- aggregate(list(Catch.mt = CATCH.LBS/2204.62), List(YEAR, FLEET, AGID), sum, na.rm=T)
save(SABL.Research.Tribal.Catch.05.May.2019, file = 'SABL.Research.Tribal.Catch.05.May.2019.RData')
r(SABL.Research.Tribal.Catch.05.May.2019, 2)
YEAR FLEET AGID Catch.mt
1 1994 LE C 1960.49
2 1995 LE C 2345.01
...
222 2019 OA W 45.56
223 2020 OA W 24.14
224 1990 R W 0.28
225 1997 R W 0.21
226 1999 R W 0.45
227 2000 R W 13.92
228 2001 R W 0.32
229 2006 R W 0.72
230 2007 R W 0.36
231 2008 R W 0.59
232 2014 R W 0.02
233 2016 R W 0.27
234 2019 R W 1.30
235 1981 TI W 0.00
236 1983 TI W 0.02
237 1984 TI W 0.09
...
# Here is how 'Fleet' compares to 'Removal type'
# Fleet type: limited entry 'LE', open access 'OA', tribal indian 'TI', research 'R', unknown 'XX'
# Removal type: Commercial (Non-EFP) 'C', Commercial(Direct Sales) 'D', Exempted fishing permit(EFP) 'E', Other 'O', Personal use 'P', Research 'R', Unknown 'U'
Table(SABL.CompFT.05.May.2019$FLEET, SABL.CompFT.05.May.2019$REMOVAL_TYPE)
C D E O P R U <NA>
LE 713816 284 10319 0 3398 4 5 118
OA 178355 52 31 28 1158 0 17 3
R 0 0 0 0 0 3756 0 0
TI 54882 0 0 2 330 4 0 0
XX 427757 56 0 41 6 0 0 0
# Fleet breakdown including research and tribal catch
# - Tribal catch is included but not separable in a 'sc' type table.
# - I would not assume this is all the research catch and would ask the Region what they have.
# ------------------------------------------- INPFC sc table -----------------------------------------------------------------------------------------------------------------------
# Take out research catch for a summary catch (sc) like table
# change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R") & SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP %in% 'INPFC',]) <<== !!! WRONG !!! see ARID = INPFC_ARID below
rm(SPID) # Old in PacFIN R working directory
change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R"), ])
PacFIN.SABL.Catch.INPFC.05.May.2019 <- aggregate(list(CATCH.KG = CATCH.LBS/2.20462), list(COUNCIL = COUNCIL, DAHL_SECTOR = DAHL_SECTOR, YEAR = YEAR, PERIOD = PERIOD, SPID = SPID, ARID = INPFC_ARID,
GRID = GRID, GRGROUP = GRGROUP, PCID = W_O_C_Port_Groups), sum, na.rm=T)
PacFIN.SABL.Catch.INPFC.05.May.2019 <- sort.f(PacFIN.SABL.Catch.INPFC.05.May.2019, c('YEAR', 'PERIOD', 'ARID', 'GRID', 'PCID'))
change(PacFIN.SABL.Catch.INPFC.05.May.2019)
SC.vdv.SABL.INPFC.agg <- agg.table(aggregate(list(Catch.mt = CATCH.KG/1000), List(YEAR, PCID), sum), Print = F)
SC.vdv.SABL.INPFC.agg[is.na(SC.vdv.SABL.INPFC.agg)] <- 0
r(SC.vdv.SABL.INPFC.agg, 3)
Table(PacFIN.SABL.Catch.INPFC.05.May.2019$ARID, PacFIN.SABL.Catch.INPFC.05.May.2019$PCID)
ACA AOR AWA
CL 0 2835 1856
CP 3280 0 0
EK 2357 1627 11
MT 4189 22 0
OC 0 0 624
UI 70 196 2
VN 0 589 2281
save(PacFIN.SABL.Catch.INPFC.05.May.2019, file= 'PacFIN.SABL.Catch.INPFC.05.May.2019.RData')
# ------------------------------------------- PSMFC sc table ------------------------------------------------------------------------------------------------------------------------
change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R") & SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP %in% 'PSMFC',])
PacFIN.SABL.Catch.PSMFC.05.May.2019 <- aggregate(list(CATCH.KG = CATCH.LBS/2.20462), list(COUNCIL = COUNCIL, DAHL_SECTOR = DAHL_SECTOR, YEAR = YEAR, PERIOD = PERIOD, SPID = SPID, ARID = ARID,
GRID = GRID, GRGROUP = GRGROUP, PCID = W_O_C_Port_Groups), sum, na.rm=T)
PacFIN.SABL.Catch.PSMFC.05.May.2019 <- sort.f(PacFIN.SABL.Catch.PSMFC.05.May.2019, c('YEAR', 'PERIOD', 'ARID', 'GRID', 'PCID'))
change(PacFIN.SABL.Catch.PSMFC.05.May.2019)
SC.vdv.SABL.PSMFC.agg <- agg.table(aggregate(list(Catch.mt = CATCH.KG/1000), List(YEAR, PCID), sum, na.rm=T), Print = F)
SC.vdv.SABL.PSMFC.agg[is.na(SC.vdv.SABL.PSMFC.agg)] <- 0
r(SC.vdv.SABL.PSMFC.agg, 3)
Table(PacFIN.SABL.Catch.PSMFC.05.May.2019$ARID, PacFIN.SABL.Catch.PSMFC.05.May.2019$PCID)
ACA AOR AWA
1A 3280 0 0
1B 4189 22 0
1C 2356 496 3
2A 14 1607 7
2B 0 2068 46
2C 0 605 162
2D 0 1 0
2E 0 657 0
2F 0 1467 0
3A 0 1807 525
3B 0 583 634
3S 0 423 591
save(PacFIN.SABL.Catch.PSMFC.05.May.2019, file="PacFIN.SABL.Catch.PSMFC.05.May.2019.RData")
#----------------- Comparison of PSMFC sc table to INPFC sc table -----------------------------
names(SC.vdv.SABL.INPFC.agg) <- paste0(names(SC.vdv.SABL.INPFC.agg), ".INPFC")
(SC.vdv.SABL.INPFC.agg <- SC.vdv.SABL.INPFC.agg[,order(names(SC.vdv.SABL.INPFC.agg))]) # Make sure the ordering is correct
names(SC.vdv.SABL.PSMFC.agg) <- paste0(names(SC.vdv.SABL.PSMFC.agg), ".PSMFC")
(SC.vdv.SABL.PSMFC.agg <- SC.vdv.SABL.PSMFC.agg[,order(names(SC.vdv.SABL.PSMFC.agg))])
# Need to take off last year to match below
# SC.vdv.SABL.PSMFC.agg <- SC.vdv.SABL.PSMFC.agg[-nrow(SC.vdv.SABL.PSMFC.agg), ]
N <- nrow(SC.vdv.SABL.INPFC.agg)
Diff.and.Ratio <- cbind(SC.vdv.SABL.INPFC.agg, " " = rep(" ", N), SC.vdv.SABL.PSMFC.agg, " " = rep(" ", N),
SC.vdv.SABL.INPFC.agg - SC.vdv.SABL.PSMFC.agg, " " = rep(" ", N), SC.vdv.SABL.INPFC.agg/SC.vdv.SABL.PSMFC.agg)
names(Diff.and.Ratio) <- c(names(SC.vdv.SABL.INPFC.agg), " ", names(SC.vdv.SABL.PSMFC.agg), " ", "CA.diff" , "OR.diff", "WA.diff", " ", "CA.ratio" , "OR.ratio", "WA.ratio")
Tmp.Diff <- Diff.and.Ratio[, 1:11]
# Tmp.Diff[is.na(Tmp.Diff )] <- 0
Diff.and.Ratio <- cbind(Tmp.Diff, Diff.and.Ratio[,12:15]) # unsupported matrix index in replacement, so need temp file
r(Diff.and.Ratio, 2)
ACA.INPFC AOR.INPFC AWA.INPFC ACA.PSMFC AOR.PSMFC AWA.PSMFC CA.diff OR.diff WA.diff CA.ratio OR.ratio WA.ratio
1981 6718.47 2343.43 2357.10 6716.28 1039.48 559.34 2.19 1303.95 1797.75 1.00 2.25 4.21
1982 9656.05 5089.73 3881.44 9655.97 2138.87 1708.13 0.09 2950.85 2173.31 1.00 2.38 2.27
1983 6694.87 4642.46 3314.55 6694.67 1891.62 1290.63 0.20 2750.84 2023.91 1.00 2.45 2.57
1984 4826.82 4838.08 4350.27 4826.64 2063.28 2224.78 0.18 2774.79 2125.49 1.00 2.34 1.96
1985 5174.07 5272.77 3685.59 5173.90 2431.86 739.99 0.17 2840.91 2945.61 1.00 2.17 4.98
1986 6220.31 4654.72 2275.40 6220.10 2530.73 547.94 0.21 2123.99 1727.46 1.00 1.84 4.15
1987 4414.62 5238.15 2948.79 4377.32 5234.91 827.37 37.29 3.24 2121.42 1.01 1.00 3.56
1988 3856.73 4082.12 2804.95 3856.70 4081.44 664.32 0.03 0.68 2140.63 1.00 1.00 4.22
1989 4075.16 3948.48 2260.89 4075.00 3948.44 469.33 0.17 0.04 1791.56 1.00 1.00 4.82
1990 3750.67 3704.99 1609.54 3737.60 3704.91 345.91 13.07 0.08 1263.63 1.00 1.00 4.65
1991 3358.30 3905.98 2236.51 3357.48 3905.94 324.99 0.81 0.04 1911.52 1.00 1.00 6.88
1992 3715.22 3856.12 1789.82 3714.08 3853.04 361.26 1.13 3.08 1428.57 1.00 1.00 4.95
1993 2598.15 3835.48 1712.87 2597.46 3835.16 425.15 0.68 0.32 1287.73 1.00 1.00 4.03
1994 2185.81 4004.84 1387.95 2185.79 4000.94 384.71 0.02 3.90 1003.24 1.00 1.00 3.61
1995 2818.97 3134.68 1961.05 2818.95 3133.11 315.87 0.03 1.58 1645.18 1.00 1.00 6.21
1996 3195.90 3174.86 1946.28 3195.87 3174.76 312.24 0.03 0.10 1634.03 1.00 1.00 6.23
1997 2968.12 2924.25 2049.90 2967.86 2921.48 345.59 0.25 2.76 1704.31 1.00 1.00 5.93
1998 1448.50 1744.21 1180.18 1448.49 1742.82 184.66 0.01 1.39 995.51 1.00 1.00 6.39
1999 1970.07 2946.56 1699.05 1970.05 2946.52 277.69 0.02 0.03 1421.36 1.00 1.00 6.12
2000 1895.06 2796.76 1564.18 1895.04 2742.48 195.21 0.02 54.28 1368.97 1.00 1.02 8.01
2001 1557.76 2525.45 1547.11 1557.74 2514.93 278.44 0.01 10.53 1268.67 1.00 1.00 5.56
2002 1313.31 1405.75 1074.16 1313.30 1403.20 129.34 0.01 2.56 944.81 1.00 1.00 8.30
2003 1650.11 2049.55 1619.73 1650.09 2025.12 188.19 0.01 24.44 1431.54 1.00 1.01 8.61
2004 1433.88 2551.63 1768.15 1433.87 2411.56 195.77 0.01 140.07 1572.37 1.00 1.06 9.03
2005 1651.17 2645.11 1907.22 1651.16 2620.68 225.68 0.01 24.42 1681.53 1.00 1.01 8.45
2006 1641.00 2648.80 1905.98 1640.98 2633.86 0.00 0.01 14.94 1905.98 1.00 1.01 Inf
2007 1471.00 2427.47 1343.32 1470.99 2407.48 165.52 0.01 19.99 1177.79 1.00 1.01 8.12
2008 1593.30 2957.24 1319.11 1593.29 2945.80 156.93 0.01 11.44 1162.18 1.00 1.00 8.41
2009 2311.87 3301.16 1584.75 2311.33 3258.63 201.98 0.54 42.52 1382.77 1.00 1.01 7.85
2010 2498.19 2857.50 1475.80 2498.16 2857.48 113.83 0.02 0.03 1361.96 1.00 1.00 12.96
2011 2566.08 2302.09 1550.32 2566.05 2301.68 193.94 0.02 0.41 1356.38 1.00 1.00 7.99
2012 1782.80 2141.25 1364.29 1782.79 2139.99 130.13 0.02 1.27 1234.17 1.00 1.00 10.48
2013 1502.80 1735.84 893.80 1502.79 1734.62 76.93 0.01 1.23 816.87 1.00 1.00 11.62
2014 1874.69 1490.82 1056.05 1874.67 1490.38 75.33 0.02 0.44 980.72 1.00 1.00 14.02
2015 1847.53 2247.41 1054.96 1847.51 2241.08 2.73 0.02 6.33 1052.23 1.00 1.00 386.22
2016 1744.62 2502.33 1153.41 1744.61 2502.31 3.71 0.02 0.02 1149.70 1.00 1.00 311.07
2017 1784.27 2517.15 1236.51 1784.26 2511.86 79.90 0.02 5.29 1156.60 1.00 1.00 15.47
2018 1483.91 2548.42 1146.54 1483.61 2539.83 69.65 0.30 8.59 1076.88 1.00 1.00 16.46
2019 399.27 472.86 81.24 398.73 470.32 0.00 0.54 2.53 81.24 1.00 1.01 Inf
# ARID by YEAR by AGID - shows where the differences in the INPFC and PSMFC areas are.
Table(SABL.CompFT.05.May.2019$ARID, SABL.CompFT.05.May.2019$YEAR, SABL.CompFT.05.May.2019$AGID)
# Research catch by year and removal type - compare with FLEET removal
change(SABL.CompFT.05.May.2019)
r(agg.table(aggregate(list(Catch.mt = CATCH.LBS/2204.62), List(YEAR, REMOVAL_TYPE), sum, na.rm=T), Print = F), 3)
C O P D R U E
1981 11418.893 NA NA NA NA NA NA
1982 18627.051 NA NA NA NA NA NA
1983 14651.436 0.303 NA NA NA NA NA
1984 14015.020 0.024 NA NA NA NA NA
1985 14094.399 37.891 0.009 NA NA NA NA
1986 13129.724 20.584 NA NA NA NA NA
1987 12590.189 10.871 NA 0.386 NA NA NA
1988 10743.709 NA NA NA NA NA NA
1989 10280.754 NA 0.039 3.646 NA NA NA
1990 9065.119 NA NA NA 0.279 NA NA
1991 9497.216 NA 0.020 3.469 NA NA NA
1992 9360.157 NA 0.065 0.852 NA NA NA
1993 8146.361 0.054 0.015 NA NA NA NA
1994 7578.301 NA 0.102 0.139 NA NA NA
1995 7908.746 NA 1.741 4.147 0.139 NA NA
1996 8316.638 NA 0.176 0.140 NA NA NA
1997 7938.843 NA 0.268 3.082 0.927 NA NA
1998 4372.722 NA 0.017 0.109 11.165 NA NA
1999 6614.879 NA 0.595 0.113 26.888 0.035 NA
2000 6254.972 NA 0.900 0.067 25.214 NA NA
2001 5588.369 NA 1.963 2.852 5.970 NA 37.088
2002 3773.798 NA 1.557 0.796 5.285 NA 17.034
2003 5255.628 NA 1.887 4.617 100.551 NA 57.214
2004 5671.034 4.140 2.631 6.026 2.418 NA 69.779
2005 6174.845 NA 5.084 0.432 4.886 0.660 22.417
2006 6176.711 NA 5.631 2.322 3.245 NA 11.058
2007 5207.592 NA 3.960 18.759 3.095 2.395 9.038
2008 5858.866 NA 4.506 5.861 2.405 0.100 0.269
2009 7160.791 NA 4.994 10.220 0.650 NA 21.700
2010 6798.593 2.306 3.832 8.521 0.629 0.027 18.144
2011 6369.150 NA 6.879 0.474 2.406 0.685 41.241
2012 5233.499 NA 7.522 0.147 11.151 NA 47.132
2013 4118.979 NA 7.429 0.135 7.941 0.613 5.250
2014 4411.635 NA 4.961 NA 4.790 NA 4.925
2015 5137.022 NA 5.898 0.023 21.154 NA 6.911
2016 5390.066 NA 3.967 0.123 12.854 NA 6.096
2017 5488.273 NA 5.223 0.064 2.940 NA 44.316
2018 5126.120 NA 4.202 0.008 27.243 0.268 48.218
2019 953.076 NA 0.253 0.012 NA NA 0.015
John-R-Wallace-NOAA/PacFIN.Data.Extraction documentation built on June 4, 2024, 11:53 a.m.
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# Download into your working directory and view this script in R with:
rgit::gitAFile('John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/Scripts_with_Legacy_Names/PacFIN Catch Extraction, Sablefish Example.R', type = "script",
File = 'PacFIN Catch Extraction, Sablefish Example.R', show = TRUE)
# If you have copied and updated gitEdit() with your favorite editor, then download and insert this script into your editor with:
gitEdit('PacFIN Catch Extraction, Sablefish Example.R', 'John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/Scripts_with_Legacy_Names/')
# -------- PacFIN login and password --------
UID <- "wallacej"
PWD <- PacFIN.PW
# -------- Check species info --------
sp <- import.sql("Select * from pacfin.sp", dsn="PacFIN", uid= UID, pwd = PWD)
sp[grep("SABLEFISH", sp$CNAME), 1:7] # Same as below
sp[grep("SBL", sp$SPID), 1:7]
SPID CNAME COMPLEX COMPLEX2 COMPLEX3 MGRP SNAME
309 SABL SABLEFISH ROND .... .... GRND ANOPLOPOMA FIMBRIA
# Example of third category from BOCACCIO+CHILIPEPPER
# 232 RCK1 BOCACCIO+CHILIPEPPER RCKFSH ROCK .... NSLF GRND SEBASTES SPP.
# The 'Agency market category listing' on the PadFIN website gives some more state information on the codes:
SABLEFISH SABL C 190 SABLEFISH
SABLEFISH SABL O 477 SABLEFISH
SABLEFISH SABL W 221 SABLEFISH ANOPLOPOMA FIMBRIA
SABLEFISH SABL W 321 SABLEFISH (REDUCTION) ANOPLOPOMA FIMBRIA
SABLEFISH SABL W 421 SABLEFISH (ANIMAL FOOD) ANOPLOPOMA FIMBRIA
# -------- Data from the Comprehensive_FT table --------
# Gear table
(gr <- import.sql("Select * from pacfin.gr", dsn="PacFIN", uid=UID, pwd=PWD))
# Area table
ar <- import.sql("Select * from pacfin.ar", dsn="PacFIN", uid=UID, pwd=PWD)
AR_COUNCIL_P <- renum(ar[ar$COUNCIL %in% 'P',])
# Only the created 'INPFC_PSMFC_AREA_GROUP %in% PSMFC' gets exculsively the more finer areas of PSMFC.
# (Old but still correct: This will now allow a mapping between ARID to INPFC_PSMFC_AREA_GROUP which could be renamed to ARID for a foo sc table)
# Last 15 rows of the AR_COUNCIL_P table are PSMFC
AR_COUNCIL_P$INPFC_PSMFC_AREA_GROUP <- "INPFC"
# "UNK-PSMFC" will be matched with "UP", "3C-S" will be matched with "3S", and "MNTREY BAY" will be matched with "1D" in the matching below and both will be labeled with as PSMFC (line 72 below)
AR_COUNCIL_P$INPFC_PSMFC_AREA_GROUP[AR_COUNCIL_P$NAME %in% c("UNK-PSMFC","1A", "1B", "MNTREY BAY", "1E", "1C", "2A", "2B", "2C", "2E", "2F", "2D", "3A", "3B", "3C-S")] <- "PSMFC"
AR_COUNCIL_P
# COUNCIL_CODE = 'P'; with research catch included
# For species with a nominal category use, e.g.: < PACFIN_SPECIES_CODE = any ('PTRL', 'PTR1') >
SABL.CompFT.05.May.2019 <- JRWToolBox::import.sql(
"Select COUNCIL_CODE, AGENCY_CODE, DAHL_GROUNDFISH_CODE, INPFC_AREA_TYPE_CODE, LANDING_YEAR, LANDING_DATE, FTID, PARTICIPATION_GROUP_CODE, PACFIN_CATCH_AREA_CODE, ORIG_PACFIN_CATCH_AREA_CODE, PACFIN_PORT_CODE, FLEET_CODE, VESSEL_ID,
PACFIN_GEAR_CODE, IS_IFQ_LANDING, REMOVAL_TYPE_CODE, CONDITION_CODE, DISPOSITION_CODE, EXVESSEL_REVENUE, PACFIN_SPECIES_CODE, NOMINAL_TO_ACTUAL_PACFIN_SPECIES_CODE,
IS_SPECIES_COMP_USED, GRADE_CODE, GRADE_NAME, PACFIN_GROUP_GEAR_CODE, ROUND_WEIGHT_LBS, LANDED_WEIGHT_MTONS
from pacfin_marts.Comprehensive_FT
where PACFIN_SPECIES_CODE = any ('SABL')
and COUNCIL_CODE = 'P'
and AGENCY_CODE in ('W','O','C')", dsn="PacFIN", uid=UID, pwd=PWD)
# Grab nameConvertVdrfdToCompFT from GitHub and convert to the old style short names
rgit::gitAFile('John-R-Wallace-NOAA/PacFIN-Data-Extraction/master/R/nameConvertVdrfdToCompFT.R')
names(SABL.CompFT.05.May.2019) <- JRWToolBox::recode.simple(names(SABL.CompFT.05.May.2019), nameConvertVdrfdToCompFT)
# Match INPFC_PSMFC_AREA_GROUP and compare to INPFC_ARID and ARID.
# SABL.vdv.28.Mar.2019 <- match.f(SABL.vdv.28.Mar.2019, AR_COUNCIL_P, "ARID", "ARID", c("COUNCIL", "INPFC_ARID", "INPFC_PSMFC_AREA_GROUP"))
# tmp <- match.f(SABL.CompFT.05.May.2019, AR_COUNCIL_P, "ARID", "ARID", "INPFC_ARID")
SABL.CompFT.05.May.2019 <- match.f(SABL.CompFT.05.May.2019, AR_COUNCIL_P, "ARID", "ARID", "INPFC_PSMFC_AREA_GROUP")
SABL.CompFT.05.May.2019[1:4, ]
Table(SABL.CompFT.05.May.2019$INPFC_ARID, SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP)
INPFC PSMFC
CL 43465 481694
CP 0 89012
EK 94094 128532
MT 0 189418
OC 10495 0
UI 24550 0
VN 128196 179739
Table(SABL.CompFT.05.May.2019$INPFC_ARID, SABL.CompFT.05.May.2019$ARID)
1A 1B 1C 2A 2B 2C 2D 2E 2F 3A 3B 3S CL EK OC TL UP VN
CL 0 0 0 0 132728 70760 1 60447 74623 143135 0 0 43455 0 0 10 0 0
CP 89012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
EK 0 0 128532 94086 0 0 0 0 0 0 0 0 0 8 0 0 0 0
MT 0 189418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
OC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10495 0 0 0
UI 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24550 0
VN 0 0 0 0 0 0 0 0 0 0 93263 86476 0 0 0 0 0 128196
Table(SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP, SABL.CompFT.05.May.2019$ARID)
1A 1B 1C 2A 2B 2C 2D 2E 2F 3A 3B 3S CL EK OC TL UP VN
INPFC 0 0 0 0 0 0 0 0 0 0 0 0 43455 8 10495 10 24560 128215
PSMFC 89021 189442 128532 94087 132729 70760 1 60447 74623 143135 93263 86476 0 0 0 0 0 0
# Create W_O_C_Port_Groups
SABL.CompFT.05.May.2019$W_O_C_Port_Groups <- SABL.CompFT.05.May.2019$AGID
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'W'] <- "AWA"
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'O'] <- "AOR"
SABL.CompFT.05.May.2019$W_O_C_Port_Groups[SABL.CompFT.05.May.2019$AGID %in% 'C'] <- "ACA"
# Create PERIOD (months) from TDATE
SABL.CompFT.05.May.2019$PERIOD <- Months.POSIXt(SABL.CompFT.05.May.2019$TDATE)
# Look at the data
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$W_O_C_Port_Groups)
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$YEAR)
Table(SABL.CompFT.05.May.2019$SPID, SABL.CompFT.05.May.2019$YEAR, SABL.CompFT.05.May.2019$AGID)
, , = C
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 6779 8094 6290 6150 6878 7388 6825 6802 8121 9223 11008 12675 8394 7429 18781 20462 18849 11231 12749 13225 10841 10707 11257 9238 10014 9652 8628 9208 10980 11008 8589 6663 7385
2014 2015 2016 2017 2018 2019
SABL 6882 7504 7456 7037 5960 1821
, , = O
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 3735 7002 7844 5276 5911 5398 12140 15916 21703 24002 32499 38188 52498 35355 39661 39883 41462 29279 30364 22116 22069 15462 19903 14094 13388 16248 15519 19493 25196 11732 13426 11254 9203
2014 2015 2016 2017 2018 2019
SABL 6538 10779 10605 11002 9564 862
, , = W
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
SABL 2169 3871 3563 5221 5141 5427 6747 7927 6815 7251 10671 16860 14005 12377 12539 13076 13068 8074 9225 8408 8715 6909 8504 7326 8032 6933 6082 5270 5535 4978 5265 4729 3656
2014 2015 2016 2017 2018 2019
SABL 3410 3567 3865 4694 4402 200
save(SABL.CompFT.05.May.2019, file = 'SABL.CompFT.05.May.2019.RData')
# Create research and tribal data frame
change(SABL.CompFT.05.May.2019)
SABL.Research.Tribal.Catch.05.May.2019 <- aggregate(list(Catch.mt = CATCH.LBS/2204.62), List(YEAR, FLEET, AGID), sum, na.rm=T)
save(SABL.Research.Tribal.Catch.05.May.2019, file = 'SABL.Research.Tribal.Catch.05.May.2019.RData')
r(SABL.Research.Tribal.Catch.05.May.2019, 2)
YEAR FLEET AGID Catch.mt
1 1994 LE C 1960.49
2 1995 LE C 2345.01
...
222 2019 OA W 45.56
223 2020 OA W 24.14
224 1990 R W 0.28
225 1997 R W 0.21
226 1999 R W 0.45
227 2000 R W 13.92
228 2001 R W 0.32
229 2006 R W 0.72
230 2007 R W 0.36
231 2008 R W 0.59
232 2014 R W 0.02
233 2016 R W 0.27
234 2019 R W 1.30
235 1981 TI W 0.00
236 1983 TI W 0.02
237 1984 TI W 0.09
...
# Here is how 'Fleet' compares to 'Removal type'
# Fleet type: limited entry 'LE', open access 'OA', tribal indian 'TI', research 'R', unknown 'XX'
# Removal type: Commercial (Non-EFP) 'C', Commercial(Direct Sales) 'D', Exempted fishing permit(EFP) 'E', Other 'O', Personal use 'P', Research 'R', Unknown 'U'
Table(SABL.CompFT.05.May.2019$FLEET, SABL.CompFT.05.May.2019$REMOVAL_TYPE)
C D E O P R U <NA>
LE 713816 284 10319 0 3398 4 5 118
OA 178355 52 31 28 1158 0 17 3
R 0 0 0 0 0 3756 0 0
TI 54882 0 0 2 330 4 0 0
XX 427757 56 0 41 6 0 0 0
# Fleet breakdown including research and tribal catch
# - Tribal catch is included but not separable in a 'sc' type table.
# - I would not assume this is all the research catch and would ask the Region what they have.
# ------------------------------------------- INPFC sc table -----------------------------------------------------------------------------------------------------------------------
# Take out research catch for a summary catch (sc) like table
# change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R") & SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP %in% 'INPFC',]) <<== !!! WRONG !!! see ARID = INPFC_ARID below
rm(SPID) # Old in PacFIN R working directory
change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R"), ])
PacFIN.SABL.Catch.INPFC.05.May.2019 <- aggregate(list(CATCH.KG = CATCH.LBS/2.20462), list(COUNCIL = COUNCIL, DAHL_SECTOR = DAHL_SECTOR, YEAR = YEAR, PERIOD = PERIOD, SPID = SPID, ARID = INPFC_ARID,
GRID = GRID, GRGROUP = GRGROUP, PCID = W_O_C_Port_Groups), sum, na.rm=T)
PacFIN.SABL.Catch.INPFC.05.May.2019 <- sort.f(PacFIN.SABL.Catch.INPFC.05.May.2019, c('YEAR', 'PERIOD', 'ARID', 'GRID', 'PCID'))
change(PacFIN.SABL.Catch.INPFC.05.May.2019)
SC.vdv.SABL.INPFC.agg <- agg.table(aggregate(list(Catch.mt = CATCH.KG/1000), List(YEAR, PCID), sum), Print = F)
SC.vdv.SABL.INPFC.agg[is.na(SC.vdv.SABL.INPFC.agg)] <- 0
r(SC.vdv.SABL.INPFC.agg, 3)
Table(PacFIN.SABL.Catch.INPFC.05.May.2019$ARID, PacFIN.SABL.Catch.INPFC.05.May.2019$PCID)
ACA AOR AWA
CL 0 2835 1856
CP 3280 0 0
EK 2357 1627 11
MT 4189 22 0
OC 0 0 624
UI 70 196 2
VN 0 589 2281
save(PacFIN.SABL.Catch.INPFC.05.May.2019, file= 'PacFIN.SABL.Catch.INPFC.05.May.2019.RData')
# ------------------------------------------- PSMFC sc table ------------------------------------------------------------------------------------------------------------------------
change(SABL.CompFT.05.May.2019[!(SABL.CompFT.05.May.2019$REMOVAL_TYPE %in% "R") & SABL.CompFT.05.May.2019$INPFC_PSMFC_AREA_GROUP %in% 'PSMFC',])
PacFIN.SABL.Catch.PSMFC.05.May.2019 <- aggregate(list(CATCH.KG = CATCH.LBS/2.20462), list(COUNCIL = COUNCIL, DAHL_SECTOR = DAHL_SECTOR, YEAR = YEAR, PERIOD = PERIOD, SPID = SPID, ARID = ARID,
GRID = GRID, GRGROUP = GRGROUP, PCID = W_O_C_Port_Groups), sum, na.rm=T)
PacFIN.SABL.Catch.PSMFC.05.May.2019 <- sort.f(PacFIN.SABL.Catch.PSMFC.05.May.2019, c('YEAR', 'PERIOD', 'ARID', 'GRID', 'PCID'))
change(PacFIN.SABL.Catch.PSMFC.05.May.2019)
SC.vdv.SABL.PSMFC.agg <- agg.table(aggregate(list(Catch.mt = CATCH.KG/1000), List(YEAR, PCID), sum, na.rm=T), Print = F)
SC.vdv.SABL.PSMFC.agg[is.na(SC.vdv.SABL.PSMFC.agg)] <- 0
r(SC.vdv.SABL.PSMFC.agg, 3)
Table(PacFIN.SABL.Catch.PSMFC.05.May.2019$ARID, PacFIN.SABL.Catch.PSMFC.05.May.2019$PCID)
ACA AOR AWA
1A 3280 0 0
1B 4189 22 0
1C 2356 496 3
2A 14 1607 7
2B 0 2068 46
2C 0 605 162
2D 0 1 0
2E 0 657 0
2F 0 1467 0
3A 0 1807 525
3B 0 583 634
3S 0 423 591
save(PacFIN.SABL.Catch.PSMFC.05.May.2019, file="PacFIN.SABL.Catch.PSMFC.05.May.2019.RData")
#----------------- Comparison of PSMFC sc table to INPFC sc table -----------------------------
names(SC.vdv.SABL.INPFC.agg) <- paste0(names(SC.vdv.SABL.INPFC.agg), ".INPFC")
(SC.vdv.SABL.INPFC.agg <- SC.vdv.SABL.INPFC.agg[,order(names(SC.vdv.SABL.INPFC.agg))]) # Make sure the ordering is correct
names(SC.vdv.SABL.PSMFC.agg) <- paste0(names(SC.vdv.SABL.PSMFC.agg), ".PSMFC")
(SC.vdv.SABL.PSMFC.agg <- SC.vdv.SABL.PSMFC.agg[,order(names(SC.vdv.SABL.PSMFC.agg))])
# Need to take off last year to match below
# SC.vdv.SABL.PSMFC.agg <- SC.vdv.SABL.PSMFC.agg[-nrow(SC.vdv.SABL.PSMFC.agg), ]
N <- nrow(SC.vdv.SABL.INPFC.agg)
Diff.and.Ratio <- cbind(SC.vdv.SABL.INPFC.agg, " " = rep(" ", N), SC.vdv.SABL.PSMFC.agg, " " = rep(" ", N),
SC.vdv.SABL.INPFC.agg - SC.vdv.SABL.PSMFC.agg, " " = rep(" ", N), SC.vdv.SABL.INPFC.agg/SC.vdv.SABL.PSMFC.agg)
names(Diff.and.Ratio) <- c(names(SC.vdv.SABL.INPFC.agg), " ", names(SC.vdv.SABL.PSMFC.agg), " ", "CA.diff" , "OR.diff", "WA.diff", " ", "CA.ratio" , "OR.ratio", "WA.ratio")
Tmp.Diff <- Diff.and.Ratio[, 1:11]
# Tmp.Diff[is.na(Tmp.Diff )] <- 0
Diff.and.Ratio <- cbind(Tmp.Diff, Diff.and.Ratio[,12:15]) # unsupported matrix index in replacement, so need temp file
r(Diff.and.Ratio, 2)
ACA.INPFC AOR.INPFC AWA.INPFC ACA.PSMFC AOR.PSMFC AWA.PSMFC CA.diff OR.diff WA.diff CA.ratio OR.ratio WA.ratio
1981 6718.47 2343.43 2357.10 6716.28 1039.48 559.34 2.19 1303.95 1797.75 1.00 2.25 4.21
1982 9656.05 5089.73 3881.44 9655.97 2138.87 1708.13 0.09 2950.85 2173.31 1.00 2.38 2.27
1983 6694.87 4642.46 3314.55 6694.67 1891.62 1290.63 0.20 2750.84 2023.91 1.00 2.45 2.57
1984 4826.82 4838.08 4350.27 4826.64 2063.28 2224.78 0.18 2774.79 2125.49 1.00 2.34 1.96
1985 5174.07 5272.77 3685.59 5173.90 2431.86 739.99 0.17 2840.91 2945.61 1.00 2.17 4.98
1986 6220.31 4654.72 2275.40 6220.10 2530.73 547.94 0.21 2123.99 1727.46 1.00 1.84 4.15
1987 4414.62 5238.15 2948.79 4377.32 5234.91 827.37 37.29 3.24 2121.42 1.01 1.00 3.56
1988 3856.73 4082.12 2804.95 3856.70 4081.44 664.32 0.03 0.68 2140.63 1.00 1.00 4.22
1989 4075.16 3948.48 2260.89 4075.00 3948.44 469.33 0.17 0.04 1791.56 1.00 1.00 4.82
1990 3750.67 3704.99 1609.54 3737.60 3704.91 345.91 13.07 0.08 1263.63 1.00 1.00 4.65
1991 3358.30 3905.98 2236.51 3357.48 3905.94 324.99 0.81 0.04 1911.52 1.00 1.00 6.88
1992 3715.22 3856.12 1789.82 3714.08 3853.04 361.26 1.13 3.08 1428.57 1.00 1.00 4.95
1993 2598.15 3835.48 1712.87 2597.46 3835.16 425.15 0.68 0.32 1287.73 1.00 1.00 4.03
1994 2185.81 4004.84 1387.95 2185.79 4000.94 384.71 0.02 3.90 1003.24 1.00 1.00 3.61
1995 2818.97 3134.68 1961.05 2818.95 3133.11 315.87 0.03 1.58 1645.18 1.00 1.00 6.21
1996 3195.90 3174.86 1946.28 3195.87 3174.76 312.24 0.03 0.10 1634.03 1.00 1.00 6.23
1997 2968.12 2924.25 2049.90 2967.86 2921.48 345.59 0.25 2.76 1704.31 1.00 1.00 5.93
1998 1448.50 1744.21 1180.18 1448.49 1742.82 184.66 0.01 1.39 995.51 1.00 1.00 6.39
1999 1970.07 2946.56 1699.05 1970.05 2946.52 277.69 0.02 0.03 1421.36 1.00 1.00 6.12
2000 1895.06 2796.76 1564.18 1895.04 2742.48 195.21 0.02 54.28 1368.97 1.00 1.02 8.01
2001 1557.76 2525.45 1547.11 1557.74 2514.93 278.44 0.01 10.53 1268.67 1.00 1.00 5.56
2002 1313.31 1405.75 1074.16 1313.30 1403.20 129.34 0.01 2.56 944.81 1.00 1.00 8.30
2003 1650.11 2049.55 1619.73 1650.09 2025.12 188.19 0.01 24.44 1431.54 1.00 1.01 8.61
2004 1433.88 2551.63 1768.15 1433.87 2411.56 195.77 0.01 140.07 1572.37 1.00 1.06 9.03
2005 1651.17 2645.11 1907.22 1651.16 2620.68 225.68 0.01 24.42 1681.53 1.00 1.01 8.45
2006 1641.00 2648.80 1905.98 1640.98 2633.86 0.00 0.01 14.94 1905.98 1.00 1.01 Inf
2007 1471.00 2427.47 1343.32 1470.99 2407.48 165.52 0.01 19.99 1177.79 1.00 1.01 8.12
2008 1593.30 2957.24 1319.11 1593.29 2945.80 156.93 0.01 11.44 1162.18 1.00 1.00 8.41
2009 2311.87 3301.16 1584.75 2311.33 3258.63 201.98 0.54 42.52 1382.77 1.00 1.01 7.85
2010 2498.19 2857.50 1475.80 2498.16 2857.48 113.83 0.02 0.03 1361.96 1.00 1.00 12.96
2011 2566.08 2302.09 1550.32 2566.05 2301.68 193.94 0.02 0.41 1356.38 1.00 1.00 7.99
2012 1782.80 2141.25 1364.29 1782.79 2139.99 130.13 0.02 1.27 1234.17 1.00 1.00 10.48
2013 1502.80 1735.84 893.80 1502.79 1734.62 76.93 0.01 1.23 816.87 1.00 1.00 11.62
2014 1874.69 1490.82 1056.05 1874.67 1490.38 75.33 0.02 0.44 980.72 1.00 1.00 14.02
2015 1847.53 2247.41 1054.96 1847.51 2241.08 2.73 0.02 6.33 1052.23 1.00 1.00 386.22
2016 1744.62 2502.33 1153.41 1744.61 2502.31 3.71 0.02 0.02 1149.70 1.00 1.00 311.07
2017 1784.27 2517.15 1236.51 1784.26 2511.86 79.90 0.02 5.29 1156.60 1.00 1.00 15.47
2018 1483.91 2548.42 1146.54 1483.61 2539.83 69.65 0.30 8.59 1076.88 1.00 1.00 16.46
2019 399.27 472.86 81.24 398.73 470.32 0.00 0.54 2.53 81.24 1.00 1.01 Inf
# ARID by YEAR by AGID - shows where the differences in the INPFC and PSMFC areas are.
Table(SABL.CompFT.05.May.2019$ARID, SABL.CompFT.05.May.2019$YEAR, SABL.CompFT.05.May.2019$AGID)
# Research catch by year and removal type - compare with FLEET removal
change(SABL.CompFT.05.May.2019)
r(agg.table(aggregate(list(Catch.mt = CATCH.LBS/2204.62), List(YEAR, REMOVAL_TYPE), sum, na.rm=T), Print = F), 3)
C O P D R U E
1981 11418.893 NA NA NA NA NA NA
1982 18627.051 NA NA NA NA NA NA
1983 14651.436 0.303 NA NA NA NA NA
1984 14015.020 0.024 NA NA NA NA NA
1985 14094.399 37.891 0.009 NA NA NA NA
1986 13129.724 20.584 NA NA NA NA NA
1987 12590.189 10.871 NA 0.386 NA NA NA
1988 10743.709 NA NA NA NA NA NA
1989 10280.754 NA 0.039 3.646 NA NA NA
1990 9065.119 NA NA NA 0.279 NA NA
1991 9497.216 NA 0.020 3.469 NA NA NA
1992 9360.157 NA 0.065 0.852 NA NA NA
1993 8146.361 0.054 0.015 NA NA NA NA
1994 7578.301 NA 0.102 0.139 NA NA NA
1995 7908.746 NA 1.741 4.147 0.139 NA NA
1996 8316.638 NA 0.176 0.140 NA NA NA
1997 7938.843 NA 0.268 3.082 0.927 NA NA
1998 4372.722 NA 0.017 0.109 11.165 NA NA
1999 6614.879 NA 0.595 0.113 26.888 0.035 NA
2000 6254.972 NA 0.900 0.067 25.214 NA NA
2001 5588.369 NA 1.963 2.852 5.970 NA 37.088
2002 3773.798 NA 1.557 0.796 5.285 NA 17.034
2003 5255.628 NA 1.887 4.617 100.551 NA 57.214
2004 5671.034 4.140 2.631 6.026 2.418 NA 69.779
2005 6174.845 NA 5.084 0.432 4.886 0.660 22.417
2006 6176.711 NA 5.631 2.322 3.245 NA 11.058
2007 5207.592 NA 3.960 18.759 3.095 2.395 9.038
2008 5858.866 NA 4.506 5.861 2.405 0.100 0.269
2009 7160.791 NA 4.994 10.220 0.650 NA 21.700
2010 6798.593 2.306 3.832 8.521 0.629 0.027 18.144
2011 6369.150 NA 6.879 0.474 2.406 0.685 41.241
2012 5233.499 NA 7.522 0.147 11.151 NA 47.132
2013 4118.979 NA 7.429 0.135 7.941 0.613 5.250
2014 4411.635 NA 4.961 NA 4.790 NA 4.925
2015 5137.022 NA 5.898 0.023 21.154 NA 6.911
2016 5390.066 NA 3.967 0.123 12.854 NA 6.096
2017 5488.273 NA 5.223 0.064 2.940 NA 44.316
2018 5126.120 NA 4.202 0.008 27.243 0.268 48.218
2019 953.076 NA 0.253 0.012 NA NA 0.015
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