R/clean.columns.R
In rBatt/trawlData: Read and Clean Data from Bottom Trawl Surveys
Defines functions
clean.columns
makeHaul
clean.columns.ai
clean.columns.ebs
clean.columns.gmex
clean.columns.goa
clean.columns.neus
clean.columns.newf
clean.columns.ngulf
clean.columns.sa
clean.columns.sgulf
clean.columns.shelf
clean.columns.wcann
clean.columns.wctri
Documented in
clean.columns
#' Clean Columns
#'
#' Clean by ensuring the presence of standard columns.
#'
#' @details
#' Add missing columns if they don't exist. These are standard values like \code{wtcpue}, \code{spp}, \code{datetime}, and \code{reg}. I will make a full list in future versions, when the decisions are finalized.
#'
#' @template X_reg
#'
#' @template clean_seeAlso_template
#'
#' @import data.table
#' @export clean.columns
clean.columns <- function(X, reg=c("ai", "ebs", "gmex", "goa", "neus", "newf", "ngulf", "sa", "sgulf", "shelf", "wcann", "wctri")){
reg <- match.arg(reg)
clean.columns0 <- function(x){
switch(x,
ai = clean.columns.ai(X),
ebs = clean.columns.ebs(X),
gmex = clean.columns.gmex(X),
goa = clean.columns.goa(X),
neus = clean.columns.neus(X),
newf = clean.columns.newf(X),
ngulf = clean.columns.ngulf(X),
sa = clean.columns.sa(X),
sgulf = clean.columns.sgulf(X),
shelf = clean.columns.shelf(X),
wcann = clean.columns.wcann(X),
wctri = clean.columns.wctri(X)
)
}
clean.columns0(reg)
}
makeHaul <- function(X){
haul.pt <- function(x){
formatC(x, width=3, flag=0)
}
X[,haulid:=paste(haul.pt(vessel), haul.pt(cruise), haul.pt(haul), sep='-')]
invisible(NULL)
}
# ======
# = AI =
# ======
clean.columns.ai <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=getDate(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="ai"]
invisible(NULL)
}
# =======
# = EBS =
# =======
clean.columns.ebs <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=getDate(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="ebs"]
invisible(NULL)
}
# ========
# = GMEX =
# ========
clean.columns.gmex <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=3, flag=0), formatC(cruise, width=3, flag=0), formatC(P_STA_NO, width=5, flag=0, format='d'), sep='-')]
# date, time, datetime
X[,datetime:=getDate(paste(date,time), truncated=3)]
X[,year:=data.table::year(datetime)]
# season
# just naming from the survey name
# X[grepl("spring", survey.name, ignore.case=T), season:="spring"]
# X[grepl("summer", survey.name, ignore.case=T), season:="summer"]
# X[grepl("fall", survey.name, ignore.case=T), season:="fall"]
# X[grepl("winter", survey.name, ignore.case=T), season:="winter"]
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=rowMeans(cbind(lat.deg.start + lat.min.start/60, lat.deg.end + lat.min.end/60), na.rm=TRUE)] # mean of start and end positions, but allow one to be NA (missing)
X[,lon:=-rowMeans(cbind(lon.deg.start + lon.min.start/60, lon.deg.end + lon.min.end/60), na.rm=TRUE)] # need negative sign since western hemisphere
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(lon)+0.5, floor(depth/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,towarea:=(towspeed*1.85200*1E3*towduration/60*gearsize*0.3048)]
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight*1E4/effort]
X[,cntcpue:=cnt*1E4/effort]
# region name
X[,reg:="gmex"]
invisible(NULL)
}
# =======
# = GOA =
# =======
clean.columns.goa <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=as.POSIXct(datetime, format="%m/%d/%Y %H:%M", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="goa"]
invisible(NULL)
}
# ========
# = NEUS =
# ========
clean.columns.neus <- function(X){
# haulid
X[,haulid:=paste(formatC(cruise, width=6, flag=0), formatC(station, width=3, flag=0), formatC(stratum, width=4, flag=0), sep='-')]
# date, time, datetime
X[,datetime:=as.POSIXct(datetime, tz="GMT")]
# season
# neus already has its own season definition
# X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# actually missing, but told that
# the effort is almost constant, so
# __ is = __cpue
X[,cntcpue:=cnt]
X[,wtcpue:=weight]
# region name
X[,reg:="neus"]
invisible(NULL)
}
# ========
# = NEWF =
# ========
clean.columns.newf <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=2, flag=0), formatC(trip, width=3, flag=0), formatC(set, width=3, flag=0, format='d'), sep='-')]
# # date, time, datetime
X[,datetime:=as.POSIXct(paste(year, month, day, sep="-"), tz="GMT")]
X[season=="fall" & !is.na(season) & as.numeric(month)<4, year:=year-1] # this is survey year, not calendar year
# season
X[is.na(season), season:=getSeason(datetime)]
# lon/ lat
X[,lat:=(lat.start+lat.end)/2]
X[,lon:=(lon.start+lon.end)/2]
X[lat.end==0 | is.na(lat.end),lat:=lat.start]
X[lon.end==0 | is.na(lon.end),lon:=lon.start]
# depth
# not needed
# stratum
# not needed
# stratum area
# missing
# towarea/ effort
# Malin's method to
# getting "effort" (distance)
# in cases where no distance was
# recorded
# fix.area <- function(x){
# bad.d <- X[,towduration==x & !is.na(towduration)]
# bad <- X[,bad.d & (area==0 | is.na(area))]
# val <- X[, mean(area[bad.d&area>0&!is.na(area)&year%in%year[bad]])]
# X[bad, area:=val]
# invisible(NULL)
# }
#
# X[,sum(towduration==15 & !is.na(towduration) & (area==0 | is.na(area)))]
# fix.area(15)
#
# b25 <- X[,towduration==25 & !is.na(towduration) & (area==0 | is.na(area))]
# fix.area(25)
#
# b25 <- X[,towduration==30 & !is.na(towduration) & (area==0 | is.na(area))]
# fix.area(30)
# my method for getting missing towarea
# and/or distance
# is regression based, and covers
# all of the towdurations
dist.hat <- X[,j={
dt <- copy(.SD)
setkey(dt, haulid)
dt <- unique(dt[towdistance!=0 & is.finite(towdistance) & towduration <=200 & is.finite(towduration)]) #
setorder(dt, year)
mod <- dt[,lm(towdistance~towduration*I(as.factor(year))-1)]
if(any(!unique(year)%in%dt[,unique(year)])){
message("crap; missing years")
}
dist.hat <- predict(mod, .SD[,list(towduration, year=as.factor(year))])
dist.hat
}]
need.replaced <- X[,(towdistance==0 | is.na(towdistance)) & (towduration>0 & !is.na(towduration)) & dist.hat>0 & !is.na(dist.hat)] #
X[need.replaced,towdistance:=dist.hat[need.replaced]]
X[,towarea:=towdistance/10 * 1852 * 55.25 * 0.3048]
X[,effort:=towarea/mean(towarea[!is.na(towarea)&towarea!=0])]
# __cpue
X[,wtcpue:=(weight/100)/effort]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="newf"]
invisible(NULL)
}
# =========
# = NGULF =
# =========
clean.columns.ngulf <- function(X){
message("Function not ready yet")
}
# ======
# = SA =
# ======
clean.columns.sa <- function(X){
# haulid
# not needed
# date, time, datetime
X[,datetime:=getDate(paste(date, time))]
X[,year:=data.table::year(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=(lat.start+lat.end)/2]
X[,lon:=(lon.start+lon.end)/2]
# depth
X[,depth:=rowMeans(cbind(depth.start,depth.end),na.rm=T)]
# stratum
# not needed
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
# not needed
# __cpue
X[,cntcpue:=cnt/effort]
X[,wtcpue:=weight/effort]
# region name
X[,reg:="sa"]
invisible(NULL)
}
# =========
# = SGULF =
# =========
clean.columns.sgulf <- function(X){
# haulid
X[,haulid:=paste(vessel, cruise, set, sep="-")]
# date, time, datetime
X[,datetime:=as.POSIXct(paste(paste(year, month, day, sep="-"), time), format="%Y-%m-%d %H:%M", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# ...
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# not needed
# __cpue
# not needed
# region name
X[,reg:="sgulf"]
invisible(NULL)
}
# =========
# = SHELF =
# =========
clean.columns.shelf <- function(X){
# haulid
X[,haulid:=paste(MISSION, formatC(SETNO, width=3, flag=0))]
# date, time, datetime
X[,datetime:=as.POSIXct(paste(date, time), tz="GMT")]
X[,year:=as.numeric(format.Date(datetime, format="%Y"))]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=lat.start]
X[,lon:=lon.start]
# depth
invisible(X[is.na(depth), depth:=rowMeans(cbind(depth.min+depth.max),na.rm=TRUE)])
invisible(X[is.na(depth)&is.na(depth.min)&!is.na(depth.max), depth:=depth.max])
invisible(X[is.na(depth)&is.na(depth.max)&!is.na(depth.min), depth:=depth.min])
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# not needed
# __cpue
# not needed
# region name
X[,reg:="shelf"]
invisible(NULL)
}
# ==========
# = WC ANN =
# ==========
clean.columns.wcann <- function(X){
# haulid
# not needed
# date, time, datetime
X[,datetime:=getDate(datetime)]
X[,year:=data.table::year(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(lon/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight/effort]
X[,cnt:=(weight/Individual.Average.Weight..kg.)]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="wcann"]
invisible(NULL)
}
# ==========
# = WC TRI =
# ==========
clean.columns.wctri <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=3, flag=0), formatC(cruise, width=3, flag=0), formatC(haul, width=3, flag=0), sep='-')]
# date, time, datetime
X[,year:=as.numeric(substr(cruise, 1, 4))]
X[,datetime:=as.POSIXct(datetime, format="%m/%d/%Y %H:%M:%S", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=rowMeans(cbind(lat.start,lat.end),na.rm=TRUE)]
X[,lon:=rowMeans(cbind(lon.start,lon.end),na.rm=TRUE)]
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(depth/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,c("towduration", "towdistance", "gearsize"):=lapply(list(towduration, towdistance, gearsize), fill.mean), by=c("haulid")]
X[,c("gearsize"):=lapply(list(gearsize), fill.mean), by=c("year", "geartype", "vessel")]
X[,towarea:=towdistance*1E3*gearsize/1E4]
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight/effort]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="wctri"]
invisible(NULL)
}
rBatt/trawlData documentation built on May 26, 2019, 7:45 p.m.
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Defines functions clean.columns makeHaul clean.columns.ai clean.columns.ebs clean.columns.gmex clean.columns.goa clean.columns.neus clean.columns.newf clean.columns.ngulf clean.columns.sa clean.columns.sgulf clean.columns.shelf clean.columns.wcann clean.columns.wctri
Documented in clean.columns
#' Clean Columns
#'
#' Clean by ensuring the presence of standard columns.
#'
#' @details
#' Add missing columns if they don't exist. These are standard values like \code{wtcpue}, \code{spp}, \code{datetime}, and \code{reg}. I will make a full list in future versions, when the decisions are finalized.
#'
#' @template X_reg
#'
#' @template clean_seeAlso_template
#'
#' @import data.table
#' @export clean.columns
clean.columns <- function(X, reg=c("ai", "ebs", "gmex", "goa", "neus", "newf", "ngulf", "sa", "sgulf", "shelf", "wcann", "wctri")){
reg <- match.arg(reg)
clean.columns0 <- function(x){
switch(x,
ai = clean.columns.ai(X),
ebs = clean.columns.ebs(X),
gmex = clean.columns.gmex(X),
goa = clean.columns.goa(X),
neus = clean.columns.neus(X),
newf = clean.columns.newf(X),
ngulf = clean.columns.ngulf(X),
sa = clean.columns.sa(X),
sgulf = clean.columns.sgulf(X),
shelf = clean.columns.shelf(X),
wcann = clean.columns.wcann(X),
wctri = clean.columns.wctri(X)
)
}
clean.columns0(reg)
}
makeHaul <- function(X){
haul.pt <- function(x){
formatC(x, width=3, flag=0)
}
X[,haulid:=paste(haul.pt(vessel), haul.pt(cruise), haul.pt(haul), sep='-')]
invisible(NULL)
}
# ======
# = AI =
# ======
clean.columns.ai <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=getDate(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="ai"]
invisible(NULL)
}
# =======
# = EBS =
# =======
clean.columns.ebs <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=getDate(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="ebs"]
invisible(NULL)
}
# ========
# = GMEX =
# ========
clean.columns.gmex <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=3, flag=0), formatC(cruise, width=3, flag=0), formatC(P_STA_NO, width=5, flag=0, format='d'), sep='-')]
# date, time, datetime
X[,datetime:=getDate(paste(date,time), truncated=3)]
X[,year:=data.table::year(datetime)]
# season
# just naming from the survey name
# X[grepl("spring", survey.name, ignore.case=T), season:="spring"]
# X[grepl("summer", survey.name, ignore.case=T), season:="summer"]
# X[grepl("fall", survey.name, ignore.case=T), season:="fall"]
# X[grepl("winter", survey.name, ignore.case=T), season:="winter"]
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=rowMeans(cbind(lat.deg.start + lat.min.start/60, lat.deg.end + lat.min.end/60), na.rm=TRUE)] # mean of start and end positions, but allow one to be NA (missing)
X[,lon:=-rowMeans(cbind(lon.deg.start + lon.min.start/60, lon.deg.end + lon.min.end/60), na.rm=TRUE)] # need negative sign since western hemisphere
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(lon)+0.5, floor(depth/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,towarea:=(towspeed*1.85200*1E3*towduration/60*gearsize*0.3048)]
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight*1E4/effort]
X[,cntcpue:=cnt*1E4/effort]
# region name
X[,reg:="gmex"]
invisible(NULL)
}
# =======
# = GOA =
# =======
clean.columns.goa <- function(X){
# haulid
makeHaul(X)
# date, time, datetime
X[,datetime:=as.POSIXct(datetime, format="%m/%d/%Y %H:%M", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# not needed
# region name
X[,reg:="goa"]
invisible(NULL)
}
# ========
# = NEUS =
# ========
clean.columns.neus <- function(X){
# haulid
X[,haulid:=paste(formatC(cruise, width=6, flag=0), formatC(station, width=3, flag=0), formatC(stratum, width=4, flag=0), sep='-')]
# date, time, datetime
X[,datetime:=as.POSIXct(datetime, tz="GMT")]
# season
# neus already has its own season definition
# X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# missing
# __cpue
# actually missing, but told that
# the effort is almost constant, so
# __ is = __cpue
X[,cntcpue:=cnt]
X[,wtcpue:=weight]
# region name
X[,reg:="neus"]
invisible(NULL)
}
# ========
# = NEWF =
# ========
clean.columns.newf <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=2, flag=0), formatC(trip, width=3, flag=0), formatC(set, width=3, flag=0, format='d'), sep='-')]
# # date, time, datetime
X[,datetime:=as.POSIXct(paste(year, month, day, sep="-"), tz="GMT")]
X[season=="fall" & !is.na(season) & as.numeric(month)<4, year:=year-1] # this is survey year, not calendar year
# season
X[is.na(season), season:=getSeason(datetime)]
# lon/ lat
X[,lat:=(lat.start+lat.end)/2]
X[,lon:=(lon.start+lon.end)/2]
X[lat.end==0 | is.na(lat.end),lat:=lat.start]
X[lon.end==0 | is.na(lon.end),lon:=lon.start]
# depth
# not needed
# stratum
# not needed
# stratum area
# missing
# towarea/ effort
# Malin's method to
# getting "effort" (distance)
# in cases where no distance was
# recorded
# fix.area <- function(x){
# bad.d <- X[,towduration==x & !is.na(towduration)]
# bad <- X[,bad.d & (area==0 | is.na(area))]
# val <- X[, mean(area[bad.d&area>0&!is.na(area)&year%in%year[bad]])]
# X[bad, area:=val]
# invisible(NULL)
# }
#
# X[,sum(towduration==15 & !is.na(towduration) & (area==0 | is.na(area)))]
# fix.area(15)
#
# b25 <- X[,towduration==25 & !is.na(towduration) & (area==0 | is.na(area))]
# fix.area(25)
#
# b25 <- X[,towduration==30 & !is.na(towduration) & (area==0 | is.na(area))]
# fix.area(30)
# my method for getting missing towarea
# and/or distance
# is regression based, and covers
# all of the towdurations
dist.hat <- X[,j={
dt <- copy(.SD)
setkey(dt, haulid)
dt <- unique(dt[towdistance!=0 & is.finite(towdistance) & towduration <=200 & is.finite(towduration)]) #
setorder(dt, year)
mod <- dt[,lm(towdistance~towduration*I(as.factor(year))-1)]
if(any(!unique(year)%in%dt[,unique(year)])){
message("crap; missing years")
}
dist.hat <- predict(mod, .SD[,list(towduration, year=as.factor(year))])
dist.hat
}]
need.replaced <- X[,(towdistance==0 | is.na(towdistance)) & (towduration>0 & !is.na(towduration)) & dist.hat>0 & !is.na(dist.hat)] #
X[need.replaced,towdistance:=dist.hat[need.replaced]]
X[,towarea:=towdistance/10 * 1852 * 55.25 * 0.3048]
X[,effort:=towarea/mean(towarea[!is.na(towarea)&towarea!=0])]
# __cpue
X[,wtcpue:=(weight/100)/effort]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="newf"]
invisible(NULL)
}
# =========
# = NGULF =
# =========
clean.columns.ngulf <- function(X){
message("Function not ready yet")
}
# ======
# = SA =
# ======
clean.columns.sa <- function(X){
# haulid
# not needed
# date, time, datetime
X[,datetime:=getDate(paste(date, time))]
X[,year:=data.table::year(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=(lat.start+lat.end)/2]
X[,lon:=(lon.start+lon.end)/2]
# depth
X[,depth:=rowMeans(cbind(depth.start,depth.end),na.rm=T)]
# stratum
# not needed
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
# not needed
# __cpue
X[,cntcpue:=cnt/effort]
X[,wtcpue:=weight/effort]
# region name
X[,reg:="sa"]
invisible(NULL)
}
# =========
# = SGULF =
# =========
clean.columns.sgulf <- function(X){
# haulid
X[,haulid:=paste(vessel, cruise, set, sep="-")]
# date, time, datetime
X[,datetime:=as.POSIXct(paste(paste(year, month, day, sep="-"), time), format="%Y-%m-%d %H:%M", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# ...
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# not needed
# __cpue
# not needed
# region name
X[,reg:="sgulf"]
invisible(NULL)
}
# =========
# = SHELF =
# =========
clean.columns.shelf <- function(X){
# haulid
X[,haulid:=paste(MISSION, formatC(SETNO, width=3, flag=0))]
# date, time, datetime
X[,datetime:=as.POSIXct(paste(date, time), tz="GMT")]
X[,year:=as.numeric(format.Date(datetime, format="%Y"))]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=lat.start]
X[,lon:=lon.start]
# depth
invisible(X[is.na(depth), depth:=rowMeans(cbind(depth.min+depth.max),na.rm=TRUE)])
invisible(X[is.na(depth)&is.na(depth.min)&!is.na(depth.max), depth:=depth.max])
invisible(X[is.na(depth)&is.na(depth.max)&!is.na(depth.min), depth:=depth.min])
# stratum
# not needed
# stratum area
# not needed
# towarea/ effort
# not needed
# __cpue
# not needed
# region name
X[,reg:="shelf"]
invisible(NULL)
}
# ==========
# = WC ANN =
# ==========
clean.columns.wcann <- function(X){
# haulid
# not needed
# date, time, datetime
X[,datetime:=getDate(datetime)]
X[,year:=data.table::year(datetime)]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
# not needed
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(lon/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight/effort]
X[,cnt:=(weight/Individual.Average.Weight..kg.)]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="wcann"]
invisible(NULL)
}
# ==========
# = WC TRI =
# ==========
clean.columns.wctri <- function(X){
# haulid
X[,haulid:=paste(formatC(vessel, width=3, flag=0), formatC(cruise, width=3, flag=0), formatC(haul, width=3, flag=0), sep='-')]
# date, time, datetime
X[,year:=as.numeric(substr(cruise, 1, 4))]
X[,datetime:=as.POSIXct(datetime, format="%m/%d/%Y %H:%M:%S", tz="GMT")]
# season
X[!is.na(datetime),season:=getSeason(unique(datetime)),by="datetime"]
# lon/ lat
X[,lat:=rowMeans(cbind(lat.start,lat.end),na.rm=TRUE)]
X[,lon:=rowMeans(cbind(lon.start,lon.end),na.rm=TRUE)]
# depth
# not needed
# stratum
X[,stratum:=paste(floor(lat)+0.5, floor(depth/100)*100+50, sep="-")]
# stratum area
X[!is.na(lon)&!is.na(lat),stratumarea:=suppressMessages(calcarea(cbind(lon, lat))), by=stratum]
# towarea/ effort
X[,c("towduration", "towdistance", "gearsize"):=lapply(list(towduration, towdistance, gearsize), fill.mean), by=c("haulid")]
X[,c("gearsize"):=lapply(list(gearsize), fill.mean), by=c("year", "geartype", "vessel")]
X[,towarea:=towdistance*1E3*gearsize/1E4]
X[,effort:=towarea]
# __cpue
X[,wtcpue:=weight/effort]
X[,cntcpue:=cnt/effort]
# region name
X[,reg:="wctri"]
invisible(NULL)
}
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