R/SABMPAsurveydatareview.R
In brent0/SCReports: Generate Snow Crab Reports
#This script creates the required tables for the SABMPA post activity report
#---- set up-----
MPA.SAB.data = function (current_year) {
######NEED TO MODIFY THE REST SIMILAR TO MPA.sab.data############
write.dir = file.path(bio.datadirectory, "bio.snowcrab", "reports", current_year, "SAB")
message(paste("Creating SAB report data tables for ", current_year, sep = ""))
message("")
if(!dir.exists(write.dir))dir.create(write.dir, recursive= T)
message(paste("All relevant tables can be found at: ", write.dir, sep = ""))
message("")
# ----get catch data for all tows inside the MPA ----
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
# ----get catch data for all tows inside the MPA ----
require(ROracle)
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
#select data from the 14 tows in the SABMPA
sabdat=dbGetQuery(con, (" SELECT trip.trip_id, trip.trip,
trip.board_date, st.set_no, st.station, pr.latitude, pr.longitude,
st.est_catch, ca.speccd_id, ca.est_num_caught, ca.est_discard_wt
FROM istrips trip, isgears gr, isfishsets st, iscatches ca, issetprofile pr
WHERE trip.tripcd_id = 7061
AND trip.trip_id = gr.trip_Id
AND pr.PNTCD_ID=2
AND (trip.trip_id = st.trip_Id
AND gr.gear_id = st.gear_id)
AND (st.fishset_id = pr.fishset_Id
AND st.set_no = pr.set_no)
AND (st.fishset_id = ca.fishset_id
AND st.haulccd_id = '1')
and CAST(st.station AS int) in (16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801, 36, 51, 204, 206, 507)
order by board_date, station, speccd_id"))
sab<-sabdat
if(length(unique(sab$STATION)) != 19){
warning("Not all SAB Stations entered!!")
}
str(sab)
#separate date
sab$BOARD_DATE <- as.Date(sab$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
sab = sab %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
sab$year.assesment = sab$year
sab$year.assesment[which(sab$month < 5)] = sab$year.assesment[which(sab$month < 5)]-1
sab$STATION = as.numeric(sab$STATION)
#number of distinct species by year
sab %>%
group_by(year) %>%
summarise(unique = n_distinct(SPECCD_ID))
#change speccd_id to name and group certain species
data1=sab
data1$SPECCD_ID[data1$SPECCD_ID == 8601] <- 8600
data1$SPECCD_ID[data1$SPECCD_ID == 10] <- "Atlantic Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 11] <- "Haddock"
data1$SPECCD_ID[data1$SPECCD_ID == 12] <- "White Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 13] <- "Red Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 14] <- "Silver Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 15] <- "Cusk"
data1$SPECCD_ID[data1$SPECCD_ID == 16] <- "Pollock"
data1$SPECCD_ID[data1$SPECCD_ID == 17] <- "Tom Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 23] <- "Redfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 30] <- "Halibut"
data1$SPECCD_ID[data1$SPECCD_ID == 31] <- "Turbot"
data1$SPECCD_ID[data1$SPECCD_ID == 40] <- "American Plaice"
data1$SPECCD_ID[data1$SPECCD_ID == 41] <- "Witch Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 42] <- "Yellowtail Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 43] <- "Winter Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 44] <- "Gulfstream Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 50] <- "Striped Atlantic Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 51] <- "Spotted Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 52] <- "Northern Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 60] <- "Herring"
data1$SPECCD_ID[data1$SPECCD_ID == 61] <- "American Shad"
data1$SPECCD_ID[data1$SPECCD_ID == 62] <- "Alewife"
data1$SPECCD_ID[data1$SPECCD_ID == 64] <- "Capelin"
data1$SPECCD_ID[data1$SPECCD_ID == 70] <- "Mackerel"
data1$SPECCD_ID[data1$SPECCD_ID == 112] <- "Longfin Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 114] <- "Fourbeard Rockling"
data1$SPECCD_ID[data1$SPECCD_ID == 118] <- "Greenland Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 142] <- "Four-spot Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 143] <- "Windowpane Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 201] <- "Thorny Skate"
data1$SPECCD_ID[data1$SPECCD_ID == 202] <- "Smooth Skate"
data1$SPECCD_ID[data1$SPECCD_ID == 211] <- "Skates NS"
data1$SPECCD_ID[data1$SPECCD_ID == 241] <- "Northern Hagfish"
data1$SPECCD_ID[data1$SPECCD_ID == 300] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 301] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 303] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 304] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 306] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 311] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 317] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 320] <- "Sea Raven"
data1$SPECCD_ID[data1$SPECCD_ID == 323] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 350] <- "Atlantic Sea Poacher"
data1$SPECCD_ID[data1$SPECCD_ID == 351] <- "Alligatorfish"
data1$SPECCD_ID[data1$SPECCD_ID == 400] <- "Monkfish"
data1$SPECCD_ID[data1$SPECCD_ID == 410] <- "Marlinspike Grenadier"
data1$SPECCD_ID[data1$SPECCD_ID == 500] <- "Snailfish Sp."
data1$SPECCD_ID[data1$SPECCD_ID == 501] <- "Lumpfish"
data1$SPECCD_ID[data1$SPECCD_ID == 502] <- "Spiny Lumpsucker"
data1$SPECCD_ID[data1$SPECCD_ID == 565] <- "Barracudina NS"
data1$SPECCD_ID[data1$SPECCD_ID == 590] <- "Sand Lances NS"
data1$SPECCD_ID[data1$SPECCD_ID == 619] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 622] <- "Snakeblenny"
data1$SPECCD_ID[data1$SPECCD_ID == 623] <- "Daubed Shanny"
data1$SPECCD_ID[data1$SPECCD_ID == 625] <- "Radiated Shanny"
data1$SPECCD_ID[data1$SPECCD_ID == 626] <- "Fourline Snakeblenny"
data1$SPECCD_ID[data1$SPECCD_ID == 627] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 630] <- "Wrymouth"
data1$SPECCD_ID[data1$SPECCD_ID == 640] <- "Ocean Pout"
data1$SPECCD_ID[data1$SPECCD_ID == 642] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 644] <- "Blennies,Shannies,Gunnels"
data1$SPECCD_ID[data1$SPECCD_ID == 647] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 712] <- "Barracudina NS"
data1$SPECCD_ID[data1$SPECCD_ID == 720] <- "Atlantic Saury"
data1$SPECCD_ID[data1$SPECCD_ID == 880] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1100] <- "Eggs Unid."
data1$SPECCD_ID[data1$SPECCD_ID == 1224] <- "Skate Eggs NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1510] <- "Whelk Eggs NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1821] <- "Sea Squirts"
data1$SPECCD_ID[data1$SPECCD_ID == 1823] <- "Sea Potato"
data1$SPECCD_ID[data1$SPECCD_ID == 2000] <- "Crustacea C."
data1$SPECCD_ID[data1$SPECCD_ID == 2100] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2211] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2212] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2313] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2316] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2411] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2413] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2415] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2416] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2511] <- "Jonah Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2513] <- "Atlantic Rock Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2521] <- "Lesser Toad Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2523] <- "Northern Stone Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2526] <- "Snow Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2527] <- "Toad Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2559] <- "Hermit Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2565] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2990] <- "Barnacles"
data1$SPECCD_ID[data1$SPECCD_ID == 3100] <- "Bristle Worm"
data1$SPECCD_ID[data1$SPECCD_ID == 3200] <- "Sea Mouse"
data1$SPECCD_ID[data1$SPECCD_ID == 4200] <- "Snails and Slugs"
data1$SPECCD_ID[data1$SPECCD_ID == 4210] <- "Whelk NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4310] <- "Clam NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4312] <- "Northern Propeller Clam"
data1$SPECCD_ID[data1$SPECCD_ID == 4321] <- "Sea Scallop"
data1$SPECCD_ID[data1$SPECCD_ID == 4322] <- "Icelandic Scallop"
data1$SPECCD_ID[data1$SPECCD_ID == 4340] <- "Cockles NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4400] <- "Sea Slug"
data1$SPECCD_ID[data1$SPECCD_ID == 4430] <- "Canoe Shells"
data1$SPECCD_ID[data1$SPECCD_ID == 4511] <- "Shortfin Squid"
data1$SPECCD_ID[data1$SPECCD_ID == 4514] <- "Squid NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4521] <- "Octopus"
data1$SPECCD_ID[data1$SPECCD_ID == 4522] <- "Bobtail Squid"
data1$SPECCD_ID[data1$SPECCD_ID == 5100] <- "Sea Spider"
data1$SPECCD_ID[data1$SPECCD_ID == 6100] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6113] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6115] <- "Mud Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6117] <- "Horse Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6119] <- "Blood Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6121] <- "Purple Sunstar"
data1$SPECCD_ID[data1$SPECCD_ID == 6123] <- "Spiny Sunstar"
data1$SPECCD_ID[data1$SPECCD_ID == 6125] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6128] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6200] <- "Brittle Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6300] <- "Basket Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6400] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6411] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6413] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6500] <- "Sand Dollar"
data1$SPECCD_ID[data1$SPECCD_ID == 6511] <- "Sand Dollar"
data1$SPECCD_ID[data1$SPECCD_ID == 6600] <- "Sea Cucumbers"
data1$SPECCD_ID[data1$SPECCD_ID == 8300] <- "Anemones NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8313] <- "Anemones NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8318] <- "Sea Pen"
data1$SPECCD_ID[data1$SPECCD_ID == 8332] <- "Coral NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8500] <- "Jellyfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8520] <- "Jellyfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8600] <- "Sponges NS"
data1$SPECCD_ID[data1$SPECCD_ID == 9300] <- "Seaweeds NS"
data1$SPECCD_ID[data1$SPECCD_ID == 385] <- "Deepwater Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 4221] <- "Northern Moonsnail"
data1$SPECCD_ID[data1$SPECCD_ID == 4355] <- "Artic Surf Clam"
data1$SPECCD_ID[data1$SPECCD_ID == 2214] <- "Bristled longbeak shrimp"
data1$SPECCD_ID[data1$SPECCD_ID == 6611] <- "Northern Sea Cucumbers"
data1$SPECCD_ID[data1$SPECCD_ID == 1827] <- "Sea Peach"
data1$SPECCD_ID[data1$SPECCD_ID == 2312] <- "Lebbeus Polaris"
data1$SPECCD_ID[data1$SPECCD_ID == 2414] <- "Sclerocrangon Boreas"
data1$SPECCD_ID[data1$SPECCD_ID == 302] <- "Arctic Staghorn Sculpin"
data1$SPECCD_ID[data1$SPECCD_ID == 313] <- "Two horn Sculpin"
data1$SPECCD_ID[data1$SPECCD_ID == 620] <- "Laval's Eelpout"
data1$SPECCD_ID[data1$SPECCD_ID == 6213] <- "Ophiura Sarsi"
data1$SPECCD_ID[data1$SPECCD_ID == 6717] <- "Caudina Arenata"
data1$SPECCD_ID[data1$SPECCD_ID == 8316] <- "Hormathia SP"
data1$SPECCD_ID[data1$SPECCD_ID == 8317] <- "Bolocera SP"
datacheck<-data1 %>% filter(SPECCD_ID == "Sea Urchin", year == 2006)
# separate date for new file
data1$BOARD_DATE <- as.Date(data1$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
data1 = data1 %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
summary(data1)
# table of total species caught per year in the 10 tows
table1 <- data1 %>%
group_by(year.assesment, SPECCD_ID) %>%
summarise(numcaught = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table1
write.table(table1,file=file.path(write.dir,"SABMPAspectableallyears.csv"), sep=",")
# table of total species caught per year in the 14 tows inside the MPA
table2 <- data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>%
group_by(year.assesment, SPECCD_ID) %>%
summarise(numcaught = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectableallyears_inside.csv"), sep=",")
table1a<-data1 %>% # individuals by spec by year
group_by(SPECCD_ID, year.assesment) %>%
summarise(N = sum(EST_NUM_CAUGHT)) %>%
spread(year.assesment, N)
table2a<-data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>% # individuals by spec by year
group_by(SPECCD_ID, year.assesment) %>%
summarise(N = sum(EST_NUM_CAUGHT)) %>%
spread(year.assesment, N)
ggplot(table1) +
geom_point(aes(x = year.assesment, y = numcaught)) +
facet_zoom(ylim = c(0, 800))
ggsave(width = 8, height = 5, file.path(write.dir,paste("SABMPAspec_", current_year,".jpg", sep = "")))
ggplot(table2) +
geom_point(aes(x = year.assesment, y = numcaught))
ggsave(width = 8, height = 5, file.path(write.dir,paste("SABMPAspec_inside", current_year,".jpg", sep = "")))
specplot <- ggplot(table1 %>% filter(numcaught > 99)) +
geom_point(aes(x = year.assesment, y = numcaught, col = SPECCD_ID), size = 4, shape = "triangle") +
geom_line(aes(x = year.assesment, y = numcaught, col = SPECCD_ID)) +
xlab("Year") +
ylab("Number Captured") +
theme_bw(12) +
scale_x_continuous(breaks = c(2004, 2008, 2012, 2016, 2020)) +
ggtitle(paste("SAB MPA Captured Species (2004-", current_year," * no survey in 2020)", sep = "")) +
facet_zoom(ylim = c(0, 800))
ggsave(width = 8, height = 5, file.path(write.dir,"MPASABspecallyears.jpg"))
specplot <- ggplot(table2 %>% filter(numcaught > 99)) +
geom_point(aes(x = year.assesment, y = numcaught, col = SPECCD_ID), size = 4, shape = "triangle") +
geom_line(aes(x = year.assesment, y = numcaught, col = SPECCD_ID)) +
xlab("Year") +
ylab("Number Captured") +
theme_bw(12) +
scale_x_continuous(breaks = c(2004, 2008, 2012, 2016, 2020)) +
ggtitle(paste("SAB MPA Captured Species (2004-", current_year," * no survey in 2020)", sep = ""))
ggsave(width = 8, height = 5, file.path(write.dir,"SABMPAspecallyears_inside.jpg"))
message("Species plot written to: ", file.path(write.dir,"SABMPAspecallyears_inside.pdf"), sep = "")
message("Add this as a figure")
message("Also add the map (Figure 1) from previous years.")
message("")
# species caught per year, for current year
table2 <-data1 %>%
filter(year.assesment == current_year) %>%
group_by(SPECCD_ID) %>%
summarise(total = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectable2.csv"), sep=",", row.names = F)
# species caught per year, for current year, inside
table2 <-data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>%
filter(year.assesment == current_year) %>%
group_by(SPECCD_ID) %>%
summarise(total = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectable2_inside.csv"), sep=",", row.names = F)
pos_date = data1[which(data1$year.assesment == current_year),]
pos_date = data.frame(pos_date$BOARD_DATE, pos_date$STATION, pos_date$LATITUDE, pos_date$LONGITUDE)
pos_date = unique(pos_date)
write.table(pos_date,file=file.path(write.dir,"location_date.csv"), sep=",", row.names = F)
message("Towed locations and dates written to: ", file.path(write.dir,"location_date.csv"), sep = "")
message("Format and add this table in Box 3")
message("")
#SABinside = data1[which(data1$STATION %in% c('016', '018', '029', '032', '034', '091', '093', '511', '609')),]
#write.table(SABinside,file=file.path(write.dir,"SAB_inside.csv"), sep=",", row.names = F)
#----stomach sample details----
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
sabstom=dbGetQuery(con, ("SELECT stom.trip, stom.board_date, stom.set_no, sets.station,
stom.speccd_id, stom.fish_no
FROM sncrabsets sets, snstomachdetails stom
WHERE sets.trip = stom.trip
AND sets.trip = stom.trip
And sets.set_no = stom.set_no
AND sets.setcd_id = '11'
AND sets.haulccd_id = '1'
and CAST(sets.station AS int) in (16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801, 36, 51, 204, 206, 507)
order by trip, board_date, station, speccd_id"))
sabstom$STATION = as.numeric(sabstom$STATION)
#separate date
sabstom$BOARD_DATE <- as.Date(sabstom$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
sabstom = sabstom %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
sabstom$year.assesment = sabstom$year
sabstom$year.assesment[which(sabstom$month < 5)] = sabstom$year.assesment[which(sabstom$month < 5)]-1
t1<-distinct(sabstom)# removes duplicates if there are any
# number of stomach samples per year, for current year assesment.
table3 <-t1 %>%
filter(year.assesment == current_year) %>%
group_by(STATION) %>%
count(SPECCD_ID)
if(length(unique(table3$STATION)) != 14){
warning("Not all SAB Stomachs entered!!")
}
write.table(table3,file=file.path(write.dir,"SABstomachbystation_spec_table.csv"), sep=",", row.names = F)
# to get total SABMPA stomachs for current year
table4 <-t1 %>%
filter(year.assesment == current_year) %>%
count(year.assesment)
table4
write.table(table4,file=file.path(write.dir,"SABtotaltable.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the SABMPA
table5in <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)) %>%
count(STATION)
write.table(table5in,file=file.path(write.dir,"SABstom_in_bytow_table.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the SABMPA
write.table(table5in,file=file.path(write.dir,"SABstom_in_bytow_table.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the GullyMPA
table5in2 <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)) %>%
count(year.assesment)
write.table(table5in2,file=file.path(write.dir,"SABstom_in_total_table.csv"), sep=",", row.names = F)
message(paste("Total number of stomachs taken inside SAB MPA: ", table5in2$n, sep = ""))
message("Include this number somewhere in Box 4.")
message("")
#to get total number of stomach samples taken adjacent to the GullyMPA
table5out <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(36, 51, 204, 206, 507))
table5out
write.table(table5out,file=file.path(write.dir,"SABstom_out_bytow_table.csv"), sep=",", row.names = F)
table5out2 <-t1 %>%
filter(STATION %in% c(36, 51, 204, 206, 507), year.assesment == current_year) %>%
count(year.assesment)
table5out2
write.table(table5out2,file=file.path(write.dir,"SABstom_out_total_table.csv"), sep=",", row.names = F)
message(paste("Total number of stomachs taken outside SAB MPA: ", table5out2$n, sep = ""))
message("Include this number somewhere in Box 4.")
message("")
message("Add any other relevant information to the report!!!!")
}
brent0/SCReports documentation built on Feb. 28, 2025, 12:12 a.m.
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#This script creates the required tables for the SABMPA post activity report
#---- set up-----
MPA.SAB.data = function (current_year) {
######NEED TO MODIFY THE REST SIMILAR TO MPA.sab.data############
write.dir = file.path(bio.datadirectory, "bio.snowcrab", "reports", current_year, "SAB")
message(paste("Creating SAB report data tables for ", current_year, sep = ""))
message("")
if(!dir.exists(write.dir))dir.create(write.dir, recursive= T)
message(paste("All relevant tables can be found at: ", write.dir, sep = ""))
message("")
# ----get catch data for all tows inside the MPA ----
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
# ----get catch data for all tows inside the MPA ----
require(ROracle)
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
#select data from the 14 tows in the SABMPA
sabdat=dbGetQuery(con, (" SELECT trip.trip_id, trip.trip,
trip.board_date, st.set_no, st.station, pr.latitude, pr.longitude,
st.est_catch, ca.speccd_id, ca.est_num_caught, ca.est_discard_wt
FROM istrips trip, isgears gr, isfishsets st, iscatches ca, issetprofile pr
WHERE trip.tripcd_id = 7061
AND trip.trip_id = gr.trip_Id
AND pr.PNTCD_ID=2
AND (trip.trip_id = st.trip_Id
AND gr.gear_id = st.gear_id)
AND (st.fishset_id = pr.fishset_Id
AND st.set_no = pr.set_no)
AND (st.fishset_id = ca.fishset_id
AND st.haulccd_id = '1')
and CAST(st.station AS int) in (16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801, 36, 51, 204, 206, 507)
order by board_date, station, speccd_id"))
sab<-sabdat
if(length(unique(sab$STATION)) != 19){
warning("Not all SAB Stations entered!!")
}
str(sab)
#separate date
sab$BOARD_DATE <- as.Date(sab$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
sab = sab %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
sab$year.assesment = sab$year
sab$year.assesment[which(sab$month < 5)] = sab$year.assesment[which(sab$month < 5)]-1
sab$STATION = as.numeric(sab$STATION)
#number of distinct species by year
sab %>%
group_by(year) %>%
summarise(unique = n_distinct(SPECCD_ID))
#change speccd_id to name and group certain species
data1=sab
data1$SPECCD_ID[data1$SPECCD_ID == 8601] <- 8600
data1$SPECCD_ID[data1$SPECCD_ID == 10] <- "Atlantic Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 11] <- "Haddock"
data1$SPECCD_ID[data1$SPECCD_ID == 12] <- "White Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 13] <- "Red Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 14] <- "Silver Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 15] <- "Cusk"
data1$SPECCD_ID[data1$SPECCD_ID == 16] <- "Pollock"
data1$SPECCD_ID[data1$SPECCD_ID == 17] <- "Tom Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 23] <- "Redfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 30] <- "Halibut"
data1$SPECCD_ID[data1$SPECCD_ID == 31] <- "Turbot"
data1$SPECCD_ID[data1$SPECCD_ID == 40] <- "American Plaice"
data1$SPECCD_ID[data1$SPECCD_ID == 41] <- "Witch Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 42] <- "Yellowtail Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 43] <- "Winter Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 44] <- "Gulfstream Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 50] <- "Striped Atlantic Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 51] <- "Spotted Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 52] <- "Northern Wolffish"
data1$SPECCD_ID[data1$SPECCD_ID == 60] <- "Herring"
data1$SPECCD_ID[data1$SPECCD_ID == 61] <- "American Shad"
data1$SPECCD_ID[data1$SPECCD_ID == 62] <- "Alewife"
data1$SPECCD_ID[data1$SPECCD_ID == 64] <- "Capelin"
data1$SPECCD_ID[data1$SPECCD_ID == 70] <- "Mackerel"
data1$SPECCD_ID[data1$SPECCD_ID == 112] <- "Longfin Hake"
data1$SPECCD_ID[data1$SPECCD_ID == 114] <- "Fourbeard Rockling"
data1$SPECCD_ID[data1$SPECCD_ID == 118] <- "Greenland Cod"
data1$SPECCD_ID[data1$SPECCD_ID == 142] <- "Four-spot Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 143] <- "Windowpane Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 201] <- "Thorny Skate"
data1$SPECCD_ID[data1$SPECCD_ID == 202] <- "Smooth Skate"
data1$SPECCD_ID[data1$SPECCD_ID == 211] <- "Skates NS"
data1$SPECCD_ID[data1$SPECCD_ID == 241] <- "Northern Hagfish"
data1$SPECCD_ID[data1$SPECCD_ID == 300] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 301] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 303] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 304] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 306] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 311] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 317] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 320] <- "Sea Raven"
data1$SPECCD_ID[data1$SPECCD_ID == 323] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 350] <- "Atlantic Sea Poacher"
data1$SPECCD_ID[data1$SPECCD_ID == 351] <- "Alligatorfish"
data1$SPECCD_ID[data1$SPECCD_ID == 400] <- "Monkfish"
data1$SPECCD_ID[data1$SPECCD_ID == 410] <- "Marlinspike Grenadier"
data1$SPECCD_ID[data1$SPECCD_ID == 500] <- "Snailfish Sp."
data1$SPECCD_ID[data1$SPECCD_ID == 501] <- "Lumpfish"
data1$SPECCD_ID[data1$SPECCD_ID == 502] <- "Spiny Lumpsucker"
data1$SPECCD_ID[data1$SPECCD_ID == 565] <- "Barracudina NS"
data1$SPECCD_ID[data1$SPECCD_ID == 590] <- "Sand Lances NS"
data1$SPECCD_ID[data1$SPECCD_ID == 619] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 622] <- "Snakeblenny"
data1$SPECCD_ID[data1$SPECCD_ID == 623] <- "Daubed Shanny"
data1$SPECCD_ID[data1$SPECCD_ID == 625] <- "Radiated Shanny"
data1$SPECCD_ID[data1$SPECCD_ID == 626] <- "Fourline Snakeblenny"
data1$SPECCD_ID[data1$SPECCD_ID == 627] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 630] <- "Wrymouth"
data1$SPECCD_ID[data1$SPECCD_ID == 640] <- "Ocean Pout"
data1$SPECCD_ID[data1$SPECCD_ID == 642] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 644] <- "Blennies,Shannies,Gunnels"
data1$SPECCD_ID[data1$SPECCD_ID == 647] <- "Eelpout NS"
data1$SPECCD_ID[data1$SPECCD_ID == 712] <- "Barracudina NS"
data1$SPECCD_ID[data1$SPECCD_ID == 720] <- "Atlantic Saury"
data1$SPECCD_ID[data1$SPECCD_ID == 880] <- "Sculpin NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1100] <- "Eggs Unid."
data1$SPECCD_ID[data1$SPECCD_ID == 1224] <- "Skate Eggs NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1510] <- "Whelk Eggs NS"
data1$SPECCD_ID[data1$SPECCD_ID == 1821] <- "Sea Squirts"
data1$SPECCD_ID[data1$SPECCD_ID == 1823] <- "Sea Potato"
data1$SPECCD_ID[data1$SPECCD_ID == 2000] <- "Crustacea C."
data1$SPECCD_ID[data1$SPECCD_ID == 2100] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2211] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2212] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2313] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2316] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2411] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2413] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2415] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2416] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2511] <- "Jonah Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2513] <- "Atlantic Rock Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2521] <- "Lesser Toad Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2523] <- "Northern Stone Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2526] <- "Snow Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2527] <- "Toad Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2559] <- "Hermit Crab"
data1$SPECCD_ID[data1$SPECCD_ID == 2565] <- "Shrimp NS"
data1$SPECCD_ID[data1$SPECCD_ID == 2990] <- "Barnacles"
data1$SPECCD_ID[data1$SPECCD_ID == 3100] <- "Bristle Worm"
data1$SPECCD_ID[data1$SPECCD_ID == 3200] <- "Sea Mouse"
data1$SPECCD_ID[data1$SPECCD_ID == 4200] <- "Snails and Slugs"
data1$SPECCD_ID[data1$SPECCD_ID == 4210] <- "Whelk NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4310] <- "Clam NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4312] <- "Northern Propeller Clam"
data1$SPECCD_ID[data1$SPECCD_ID == 4321] <- "Sea Scallop"
data1$SPECCD_ID[data1$SPECCD_ID == 4322] <- "Icelandic Scallop"
data1$SPECCD_ID[data1$SPECCD_ID == 4340] <- "Cockles NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4400] <- "Sea Slug"
data1$SPECCD_ID[data1$SPECCD_ID == 4430] <- "Canoe Shells"
data1$SPECCD_ID[data1$SPECCD_ID == 4511] <- "Shortfin Squid"
data1$SPECCD_ID[data1$SPECCD_ID == 4514] <- "Squid NS"
data1$SPECCD_ID[data1$SPECCD_ID == 4521] <- "Octopus"
data1$SPECCD_ID[data1$SPECCD_ID == 4522] <- "Bobtail Squid"
data1$SPECCD_ID[data1$SPECCD_ID == 5100] <- "Sea Spider"
data1$SPECCD_ID[data1$SPECCD_ID == 6100] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6113] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6115] <- "Mud Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6117] <- "Horse Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6119] <- "Blood Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6121] <- "Purple Sunstar"
data1$SPECCD_ID[data1$SPECCD_ID == 6123] <- "Spiny Sunstar"
data1$SPECCD_ID[data1$SPECCD_ID == 6125] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6128] <- "Star NS"
data1$SPECCD_ID[data1$SPECCD_ID == 6200] <- "Brittle Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6300] <- "Basket Star"
data1$SPECCD_ID[data1$SPECCD_ID == 6400] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6411] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6413] <- "Sea Urchin"
data1$SPECCD_ID[data1$SPECCD_ID == 6500] <- "Sand Dollar"
data1$SPECCD_ID[data1$SPECCD_ID == 6511] <- "Sand Dollar"
data1$SPECCD_ID[data1$SPECCD_ID == 6600] <- "Sea Cucumbers"
data1$SPECCD_ID[data1$SPECCD_ID == 8300] <- "Anemones NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8313] <- "Anemones NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8318] <- "Sea Pen"
data1$SPECCD_ID[data1$SPECCD_ID == 8332] <- "Coral NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8500] <- "Jellyfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8520] <- "Jellyfish NS"
data1$SPECCD_ID[data1$SPECCD_ID == 8600] <- "Sponges NS"
data1$SPECCD_ID[data1$SPECCD_ID == 9300] <- "Seaweeds NS"
data1$SPECCD_ID[data1$SPECCD_ID == 385] <- "Deepwater Flounder"
data1$SPECCD_ID[data1$SPECCD_ID == 4221] <- "Northern Moonsnail"
data1$SPECCD_ID[data1$SPECCD_ID == 4355] <- "Artic Surf Clam"
data1$SPECCD_ID[data1$SPECCD_ID == 2214] <- "Bristled longbeak shrimp"
data1$SPECCD_ID[data1$SPECCD_ID == 6611] <- "Northern Sea Cucumbers"
data1$SPECCD_ID[data1$SPECCD_ID == 1827] <- "Sea Peach"
data1$SPECCD_ID[data1$SPECCD_ID == 2312] <- "Lebbeus Polaris"
data1$SPECCD_ID[data1$SPECCD_ID == 2414] <- "Sclerocrangon Boreas"
data1$SPECCD_ID[data1$SPECCD_ID == 302] <- "Arctic Staghorn Sculpin"
data1$SPECCD_ID[data1$SPECCD_ID == 313] <- "Two horn Sculpin"
data1$SPECCD_ID[data1$SPECCD_ID == 620] <- "Laval's Eelpout"
data1$SPECCD_ID[data1$SPECCD_ID == 6213] <- "Ophiura Sarsi"
data1$SPECCD_ID[data1$SPECCD_ID == 6717] <- "Caudina Arenata"
data1$SPECCD_ID[data1$SPECCD_ID == 8316] <- "Hormathia SP"
data1$SPECCD_ID[data1$SPECCD_ID == 8317] <- "Bolocera SP"
datacheck<-data1 %>% filter(SPECCD_ID == "Sea Urchin", year == 2006)
# separate date for new file
data1$BOARD_DATE <- as.Date(data1$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
data1 = data1 %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
summary(data1)
# table of total species caught per year in the 10 tows
table1 <- data1 %>%
group_by(year.assesment, SPECCD_ID) %>%
summarise(numcaught = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table1
write.table(table1,file=file.path(write.dir,"SABMPAspectableallyears.csv"), sep=",")
# table of total species caught per year in the 14 tows inside the MPA
table2 <- data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>%
group_by(year.assesment, SPECCD_ID) %>%
summarise(numcaught = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectableallyears_inside.csv"), sep=",")
table1a<-data1 %>% # individuals by spec by year
group_by(SPECCD_ID, year.assesment) %>%
summarise(N = sum(EST_NUM_CAUGHT)) %>%
spread(year.assesment, N)
table2a<-data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>% # individuals by spec by year
group_by(SPECCD_ID, year.assesment) %>%
summarise(N = sum(EST_NUM_CAUGHT)) %>%
spread(year.assesment, N)
ggplot(table1) +
geom_point(aes(x = year.assesment, y = numcaught)) +
facet_zoom(ylim = c(0, 800))
ggsave(width = 8, height = 5, file.path(write.dir,paste("SABMPAspec_", current_year,".jpg", sep = "")))
ggplot(table2) +
geom_point(aes(x = year.assesment, y = numcaught))
ggsave(width = 8, height = 5, file.path(write.dir,paste("SABMPAspec_inside", current_year,".jpg", sep = "")))
specplot <- ggplot(table1 %>% filter(numcaught > 99)) +
geom_point(aes(x = year.assesment, y = numcaught, col = SPECCD_ID), size = 4, shape = "triangle") +
geom_line(aes(x = year.assesment, y = numcaught, col = SPECCD_ID)) +
xlab("Year") +
ylab("Number Captured") +
theme_bw(12) +
scale_x_continuous(breaks = c(2004, 2008, 2012, 2016, 2020)) +
ggtitle(paste("SAB MPA Captured Species (2004-", current_year," * no survey in 2020)", sep = "")) +
facet_zoom(ylim = c(0, 800))
ggsave(width = 8, height = 5, file.path(write.dir,"MPASABspecallyears.jpg"))
specplot <- ggplot(table2 %>% filter(numcaught > 99)) +
geom_point(aes(x = year.assesment, y = numcaught, col = SPECCD_ID), size = 4, shape = "triangle") +
geom_line(aes(x = year.assesment, y = numcaught, col = SPECCD_ID)) +
xlab("Year") +
ylab("Number Captured") +
theme_bw(12) +
scale_x_continuous(breaks = c(2004, 2008, 2012, 2016, 2020)) +
ggtitle(paste("SAB MPA Captured Species (2004-", current_year," * no survey in 2020)", sep = ""))
ggsave(width = 8, height = 5, file.path(write.dir,"SABMPAspecallyears_inside.jpg"))
message("Species plot written to: ", file.path(write.dir,"SABMPAspecallyears_inside.pdf"), sep = "")
message("Add this as a figure")
message("Also add the map (Figure 1) from previous years.")
message("")
# species caught per year, for current year
table2 <-data1 %>%
filter(year.assesment == current_year) %>%
group_by(SPECCD_ID) %>%
summarise(total = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectable2.csv"), sep=",", row.names = F)
# species caught per year, for current year, inside
table2 <-data1[which(data1$STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)),] %>%
filter(year.assesment == current_year) %>%
group_by(SPECCD_ID) %>%
summarise(total = sum(EST_NUM_CAUGHT), wt = sum(EST_DISCARD_WT))
table2
write.table(table2,file=file.path(write.dir,"SABMPAspectable2_inside.csv"), sep=",", row.names = F)
pos_date = data1[which(data1$year.assesment == current_year),]
pos_date = data.frame(pos_date$BOARD_DATE, pos_date$STATION, pos_date$LATITUDE, pos_date$LONGITUDE)
pos_date = unique(pos_date)
write.table(pos_date,file=file.path(write.dir,"location_date.csv"), sep=",", row.names = F)
message("Towed locations and dates written to: ", file.path(write.dir,"location_date.csv"), sep = "")
message("Format and add this table in Box 3")
message("")
#SABinside = data1[which(data1$STATION %in% c('016', '018', '029', '032', '034', '091', '093', '511', '609')),]
#write.table(SABinside,file=file.path(write.dir,"SAB_inside.csv"), sep=",", row.names = F)
#----stomach sample details----
con= dbConnect(DBI::dbDriver("Oracle"), oracle.username, oracle.password, oracle.server)
sabstom=dbGetQuery(con, ("SELECT stom.trip, stom.board_date, stom.set_no, sets.station,
stom.speccd_id, stom.fish_no
FROM sncrabsets sets, snstomachdetails stom
WHERE sets.trip = stom.trip
AND sets.trip = stom.trip
And sets.set_no = stom.set_no
AND sets.setcd_id = '11'
AND sets.haulccd_id = '1'
and CAST(sets.station AS int) in (16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801, 36, 51, 204, 206, 507)
order by trip, board_date, station, speccd_id"))
sabstom$STATION = as.numeric(sabstom$STATION)
#separate date
sabstom$BOARD_DATE <- as.Date(sabstom$BOARD_DATE, format = "%Y-%m-%d hh:mm:ss")
sabstom = sabstom %>%
mutate(BOARD_DATE = ymd(BOARD_DATE)) %>%
mutate_at(vars(BOARD_DATE), funs(year, month, day))
sabstom$year.assesment = sabstom$year
sabstom$year.assesment[which(sabstom$month < 5)] = sabstom$year.assesment[which(sabstom$month < 5)]-1
t1<-distinct(sabstom)# removes duplicates if there are any
# number of stomach samples per year, for current year assesment.
table3 <-t1 %>%
filter(year.assesment == current_year) %>%
group_by(STATION) %>%
count(SPECCD_ID)
if(length(unique(table3$STATION)) != 14){
warning("Not all SAB Stomachs entered!!")
}
write.table(table3,file=file.path(write.dir,"SABstomachbystation_spec_table.csv"), sep=",", row.names = F)
# to get total SABMPA stomachs for current year
table4 <-t1 %>%
filter(year.assesment == current_year) %>%
count(year.assesment)
table4
write.table(table4,file=file.path(write.dir,"SABtotaltable.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the SABMPA
table5in <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)) %>%
count(STATION)
write.table(table5in,file=file.path(write.dir,"SABstom_in_bytow_table.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the SABMPA
write.table(table5in,file=file.path(write.dir,"SABstom_in_bytow_table.csv"), sep=",", row.names = F)
#to get total number of stomach samples inside the GullyMPA
table5in2 <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(16, 18, 29, 32, 34, 91, 93, 511, 609, 17, 31, 205, 611, 801)) %>%
count(year.assesment)
write.table(table5in2,file=file.path(write.dir,"SABstom_in_total_table.csv"), sep=",", row.names = F)
message(paste("Total number of stomachs taken inside SAB MPA: ", table5in2$n, sep = ""))
message("Include this number somewhere in Box 4.")
message("")
#to get total number of stomach samples taken adjacent to the GullyMPA
table5out <-t1 %>%
filter(year.assesment == current_year, STATION %in% c(36, 51, 204, 206, 507))
table5out
write.table(table5out,file=file.path(write.dir,"SABstom_out_bytow_table.csv"), sep=",", row.names = F)
table5out2 <-t1 %>%
filter(STATION %in% c(36, 51, 204, 206, 507), year.assesment == current_year) %>%
count(year.assesment)
table5out2
write.table(table5out2,file=file.path(write.dir,"SABstom_out_total_table.csv"), sep=",", row.names = F)
message(paste("Total number of stomachs taken outside SAB MPA: ", table5out2$n, sep = ""))
message("Include this number somewhere in Box 4.")
message("")
message("Add any other relevant information to the report!!!!")
}
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