inst/IP/ClimateChangeModelling/ModellindDataFrameSizeData.R

In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes

require(sdmTMB)
require(bio.lobster)
require(bio.utilities)
require(lubridate)
require(devtools)
require(dplyr)
require(ggplot2)
require(INLA)
options(stringAsFactors=F)
require(PBSmapping)
require(SpatialHub)
require(sf)
la()
fd=file.path(project.datadirectory('bio.lobster'),'analysis','ClimateModelling')
dir.create(fd,showWarnings=F)
setwd(fd)

aT = compileAbundPresAbs(redo=F,size=T)
aT$Rec = aT$P.68+aT$P.73+aT$P.78
survey <- aT %>%   
  st_as_sf(coords=c('LONGITUDE',"LATITUDE"),crs=4326) %>% st_transform(32620)

st_geometry(survey) = st_geometry(survey)/1000
st_crs(survey) = 32620
survey$W = ceiling(yday(survey$DATE)/366*25)

survey = subset(survey, YEAR %in% 2000:2022)
ba = readRDS('~/git/bio.lobster.data/mapping_data/bathymetrySF.rds')
ba = ba %>% st_as_sf() 
st_geometry(ba) = st_geometry(ba)/1000
st_crs(ba) = 32620

				 ss = st_nearest_feature(survey,ba)
       	 ds = st_distance(survey,ba[ss,],by_element=T)
       	 st_geometry(ba) = NULL
       	 survey$z = ba$z[ss]
       	 survey$z_dist = as.numeric(ds)
       	 survey = subset(survey,z_dist<1)

s = file.path(project.datadirectory('bio.lobster'),'Temperature Data','GLORYS','SummaryFiles')
k = dir(s,full.names=T)
k = k[grep('ShelfBoF',k)]
k = k[-grep('2020_',k)]
k = k[-grep('199',k)]

ol = list()
ol = list()
m=0
for(i in 1:length(k)){
			h = readRDS(k[i])
			h$Date = as.Date(h$Date)
			y = unique(year(h$Date))
			#h$W  = ceiling(yday(h$Date)/366*25)
			#h = aggregate(bottomT~W+X+Y,data=h,FUN=median)
			h = h %>% st_as_sf(coords=c('X','Y'),crs=4326) %>% st_transform(32620)
			st_geometry(h) = st_geometry(h)/1000
			st_crs(h) = 32620

			x1 = subset(survey,YEAR == y)
			x1$Date = as.Date(x1$DATE)
			uW = unique(x1$Date)
					for(j in 1:length(uW)){
									m=m+1
									nn = subset(x1,Date==uW[j])
						hh = subset(h,Date==uW[j])
						ou = st_nearest_feature(nn,hh)
       	 ds = st_distance(nn,hh[ou,],by_element=T)
       	 st_geometry(hh) = NULL
       	 nn$GlT = hh$bottomT[ou]
       	 nn$GlD = as.numeric(ds)
			ol[[m]] = nn       	
			}
		}

		
crs_utm20 <- 32620
da = dplyr::bind_rows(ol)	
da = st_as_sf(da)
da = subset(da,GlD<6) #remove ~5% outliers 

saveRDS(da,file=file.path(project.datadirectory('bio.lobster'),'data','BaseDataForClimateModelSize.rds'))