R/mk9_extinction.R
In sean-rohan/trawllight: Derive apparent optical properties from trawl-mounted light sensors
Defines functions
mk9_extinction
Documented in
mk9_extinction
#' Write time-corrected data to csv
#'
#' Use estimated offsets to time-correct MK9 data.
#'
#' @param channel Oracle connection as an RODBC connection object.
#' @param survey RACE survey code as a character vector.
#' @param vessel RACE vessel code a numeric vector.
#' @param cruise RACE cruise code as a numeric vector.
#' @param make.plots Should cast plots be written to pdf?
#' @export
mk9_extinction <- function(channel, survey, vessel, cruise, make.plots = TRUE){
channel <- trawllight:::get_connected(channel = channel)
## establish region names and light data location
survey.names <- as.data.frame(cbind(cap_name = c("AI","BS","GOA","SLOPE","NBS"),
dir_name = c("ai","ebs","goa","slope","nbs")))
yy <- as.character(substr(cruise,3,4))
sur <- survey.names$dir_name[survey.names$cap_name == survey]
light.loc <- here::here("data", "mk9", survey, cruise, vessel)
## get data
hauls <- trawllight:::mk9_get_haul_list(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
casts <- read.csv(file = paste(light.loc, "/CastTimes.csv", sep = ""), header = T)
perf <- read.csv(file = paste(light.loc, "/MK9.performance.csv", sep = ""), header = T)
sgt <- trawllight:::mk9_get_sgt_data(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
mbt <- trawllight:::mk9_get_mbt_data(survey = survey,
vessel = vessel,
cruise = cruise,
channel = channel)
## getting values from regression to correct light meter times
coef <- trawllight:::mk9_get_regr(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
# get trawl-mounted MK9 good performance casts
good.perf <- perf[perf$datum_code == 0, c("vessel","cruise","haul")]
## get Light (MK9) data
print("reading trawl-mounted MK9 file...")
files.in.dir <- list.files(path = light.loc, pattern = ".csv")
file.idx1 <- which(substr(files.in.dir, 1, 4) == "trwl")
## read in trawl-mounted light meter data (there could be more than one file)
for(file in file.idx1) {
assign(paste("trwl", file, sep = ""),
read.csv(paste(light.loc, "/", files.in.dir[file], sep= "" ), header = F, skip = 1))
}
if(cruise <= 201801){
trwl.dat <- data.frame(V1 = numeric(),
V2 = numeric(),
V3 = numeric(),
V4 = numeric(),
V5 = numeric(),
stringsAsFactors = FALSE)
}else{
trwl.dat <- data.frame(V1 = numeric(),
V2 = numeric(),
V3 = numeric(),
V4 = numeric(),
V5 = numeric(),
V6 = numeric(),
stringsAsFactors = FALSE)
}
## put multiple files together
obj <- objects(pattern = "trwl")
for(i in 1:length(obj)){
trwl.dat <- rbind(trwl.dat, get(obj[i]))
}
## make MK9 date and time comparable with sgt and mbt date format
trwl.dat$date_time <- as.POSIXct(paste(trwl.dat$V1, trwl.dat$V2), format = "%m/%d/%Y %H:%M:%S")
light <- trwl.dat
# empty data set to receive raw MK9 data
## 2004 and 2005 MK9 data have a different format than subsequent years; here's code to deal with it
if(ncol(light) == 6){
light <- light[,3:6]
colnames(light) <- c("ldepth","ltemp","llight","ldate_time")
## Setup data.frame to combine records using the loop below
light.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), cdepth = numeric(), ctime = character(),
ldepth = numeric(), ltemp = numeric(), llight = numeric(), stringsAsFactors = FALSE)
df.names <- names(light.df)
} else if(ncol(light) == 7){
light <- light[,3:7]
colnames(light) <- c("ldepth","ltemp","llight","lcond","ldate_time")
## Setup data.frame to combine records using the loop below
light.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), cdepth = numeric(), ctime = character(),
ldepth = numeric(), ltemp = numeric(), llight = numeric(), lcond = numeric(), stringsAsFactors = FALSE)
df.names <- names(light.df)
} else {
stop(paste0("extinction: Unexpected number of columns, (", ncol(light), ") in light data csv. Expected 6 or 7."))
}
if(vessel == 162 & cruise %in% c(202201, 202202)) {
light$ldepth <- trawllight:::mk9_lowpass_filter(x = light$ldepth,
vessel = vessel,
cruise = cruise)
}
light <- trawllight:::mk9_find_offset(light = light,
mbt = mbt,
try.offsets = c(seq(-8,8,0.5),0.25,-0.25),
results.file = paste0(light.loc, "/offset_step1_log.txt"))
## identify offsets file and read in (there should be just one)
file.idx2 <- which(substr(files.in.dir, 1, 7) == "offsets")
assign("offsets", read.csv(paste(light.loc, "/", files.in.dir[file.idx2], sep = ""), header = TRUE))
# eliminate hauls where an offset time could not be calculated
offset.names <- names(offsets)
## substitute smoothed offset times for NAs and for offsets where R-sqr < 0.97
mixed.offsets <- ifelse(is.na(offsets$offset),round(offsets$smooth_offset,0),
ifelse(offsets$max.rsqr < 0.97,round(offsets$smooth_offset,0),
offsets$offset))
## substitute the mixed_offsets vector for the original offsets column
offsets <- cbind(offsets[,c("vessel","cruise","haul")], mixed.offsets, offsets[,c("max.rsqr","a","b","eq100m","smooth_offset")])
names(offsets) <- offset.names
## eliminate any remaining records where offset was NA; these will be cases where the whole record was NA
## presumably because of no irradiance data
offsets <- offsets[!is.na(offsets$offset),]
## Depending on data vintage start time can be a 1 or a 15
start_time <- sgt[sgt$time_flag == 1 | sgt$time_flag == 15, c("vessel","cruise","haul","date_time")]
names(start_time) <- c("vessel","cruise","haul","starttime")
## haul back time event remains 6
hb.time <- sgt[sgt$time_flag == 6, c("vessel","cruise","haul","date_time")]
## merge sgt and hauls to get hauls aligned
hb.time <- merge(hauls, hb.time)
## linear regression of duration from haul back to doors up + 50% more
wire.time <- ceiling(2*60*(coef$coefficients[1] + (coef$coefficients[2]*hb.time$wire_length)))
############# potential for different length haul lists needs to be handled: use one master haul list #####################3
## add predicted haul end to haulback time
haul.end <- hb.time$date_time + wire.time
end_time <- cbind(hb.time[, c("vessel","cruise","haul")], haul.end)
## append start and end times to haul list
haullist <- merge(offsets, start_time)
haullist <- merge(haullist, end_time)
haullist <- merge(haullist, good.perf)
# extinct.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), extinction = numeric(), longitude = numeric(),
# latitude = numeric(), stringsAsFactors = FALSE)
# e.names <- names(extinct.df)
## assign haul numbers, time offsets, and depth corrections to MK9 data to produce corrected data set
## also compute extinction coefficients for down and upcasts and assign them to global positioning coordinates
for(h in sort(haullist$haul)) {
## subset data by haul
haultime <- haullist[haullist$haul == h, c("starttime","haul.end","a","b","offset")]
## get smaller subset of data between start and end times
light.haul <- with(light, subset(light, light$ldate_time >= haultime$starttime & light$ldate_time <= haultime$haul.end))
if(length(haultime[,1]) == 0 | length(light.haul[,1]) == 0) next
## correct
L <- length(light.haul[,1])
vch <- as.data.frame(cbind(rep(vessel, L), rep(cruise, L), rep(h, L)))
c.depth <- ((haultime$b * light.haul$ldepth) + haultime$a)
c.time <- (light.haul$ldate_time + haultime$offset)
if(ncol(light) == 4){
c.light.dat <- cbind(vch[1], vch[2], vch[3], c.depth, c.time, light.haul$ldepth, light.haul$ltemp, light.haul$llight)
names(c.light.dat) <- df.names
light.df <- rbind(c.light.dat, light.df)
names(light.df) <- df.names
} else if(ncol(light) == 5){
c.light.dat <- cbind(vch[1], vch[2], vch[3], c.depth, c.time, light.haul$ldepth, light.haul$ltemp, light.haul$llight,
light.haul$lcond)
names(c.light.dat) <- df.names
light.df <- rbind(c.light.dat, light.df)
names(light.df) <- df.names
}
# get single cast event times
cast <- casts[casts$haul == h, ]
# convert character date times to POSIX
dn.st <- as.POSIXct(cast$downcast_start)
dn.nd <- as.POSIXct(cast$downcast_end)
up.st <- as.POSIXct(cast$upcast_start)
up.nd <- as.POSIXct(cast$upcast_end)
# extract MK9 casts
down <- light.df[light.df$haul == h & light.df$ctime >= dn.st & light.df$ctime <= dn.nd,c("llight","cdepth")]
up <- light.df[light.df$haul == h & light.df$ctime >= up.st & light.df$ctime <= up.nd,c("llight","cdepth")]
# get global position of OB and FB events
## note that using other events here run risk of tapping multiple coordinate formats stored in RACE_DATA
ob.pos <- sgt[sgt$haul == h & sgt$time_flag == 3, c("longitude","latitude")]
fb.pos <- sgt[sgt$haul == h & sgt$time_flag == 7, c("longitude","latitude")]
# if(length(down[,1]) == 0){
# dn.cast.extinct = NA
# } else{
# # get light intercept from light regressed on depth
# down.coef <- lm(llight ~ cdepth, data = down)
# down.0 <- down.coef$coefficients[1]
# # there could be multiple records at max depth so take average
# ob.depth <- max(down$cdepth)
# ob.light <- mean(down[down$cdepth == ob.depth, "llight"])
# # take natural log of the change in light between surface and bottom over bottom depth
# dn.cast.extinct <- -(log(ob.light/down.0)/ob.depth)
# }
#
# if(length(up[,1]) == 0){
# up.cast.extinct = NA
# }else{
# up.coef <- lm(llight ~ cdepth, data = up)
# up.0 <- up.coef$coefficients[1]
# fb.depth <- max(up$cdepth)
# fb.light <- mean(up[up$cdepth == fb.depth, "llight"])
# up.cast.extinct <- -(log(fb.light/up.0)/fb.depth)
# }
#
# r1 <- cbind(unique(vch[1]), unique(vch[2]), unique(vch[3]), extinction = dn.cast.extinct, ob.pos)
# r2 <- cbind(unique(vch[1]), unique(vch[2]), unique(vch[3]), extinction = up.cast.extinct, fb.pos)
# ## for rbind to work need to keep all of the field names the same throughout concatenation process
# r1r2 <- rbind(r1,r2); names(r1r2) <- e.names
#
# extinct.df <- rbind(extinct.df, r1r2); names(extinct.df) <- e.names
}
# names(extinct.df) <- e.names
names(light.df) <- df.names
## place corrected haul-specific light meter data into the vessel's data directory
write.csv(light.df, paste(light.loc, "/corr_MK9hauls.csv", sep= ""), row.names = F)
if(make.plots) {
dat_to_plot <- light.df |>
dplyr::select(vessel, cruise, haul, cdepth, ctime, ltemp, llight)
dat_to_plot$ctime <- as.POSIXct(dat_to_plot$ctime)
unique_hauls <- dat_to_plot |>
dplyr::select(vessel, cruise, haul) |>
unique()
print(paste0("Writing haul plots to ", light.loc, "/plots_cast_dat.pdf"))
pdf(file = paste0(light.loc, "/plots_cast_dat.pdf"), onefile = TRUE)
for(ii in 1:nrow(unique_hauls)) {
haul_sub <- dat_to_plot |>
dplyr::filter(vessel == unique_hauls$vessel[ii],
cruise == unique_hauls$cruise[ii],
haul == unique_hauls$haul[ii])
print(
ggplot2::ggplot() +
ggplot2::geom_line(data = haul_sub |>
tidyr::pivot_longer(cols = c("ltemp", "llight", "cdepth")),
ggplot2::aes(x = ctime,
y = value,
color = name)) +
ggplot2::facet_wrap(~name, scales = "free_y",
nrow = 3) +
ggplot2::ggtitle(paste0("Vessel: ", unique_hauls$vessel[ii], ", Cruise: ", unique_hauls$cruise[ii], ", Haul: ", unique_hauls$haul[ii])) +
ggplot2::theme_bw()
)
}
dev.off()
}
}
sean-rohan/trawllight documentation built on Jan. 13, 2023, 10:43 p.m.
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Defines functions mk9_extinction
Documented in mk9_extinction
#' Write time-corrected data to csv
#'
#' Use estimated offsets to time-correct MK9 data.
#'
#' @param channel Oracle connection as an RODBC connection object.
#' @param survey RACE survey code as a character vector.
#' @param vessel RACE vessel code a numeric vector.
#' @param cruise RACE cruise code as a numeric vector.
#' @param make.plots Should cast plots be written to pdf?
#' @export
mk9_extinction <- function(channel, survey, vessel, cruise, make.plots = TRUE){
channel <- trawllight:::get_connected(channel = channel)
## establish region names and light data location
survey.names <- as.data.frame(cbind(cap_name = c("AI","BS","GOA","SLOPE","NBS"),
dir_name = c("ai","ebs","goa","slope","nbs")))
yy <- as.character(substr(cruise,3,4))
sur <- survey.names$dir_name[survey.names$cap_name == survey]
light.loc <- here::here("data", "mk9", survey, cruise, vessel)
## get data
hauls <- trawllight:::mk9_get_haul_list(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
casts <- read.csv(file = paste(light.loc, "/CastTimes.csv", sep = ""), header = T)
perf <- read.csv(file = paste(light.loc, "/MK9.performance.csv", sep = ""), header = T)
sgt <- trawllight:::mk9_get_sgt_data(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
mbt <- trawllight:::mk9_get_mbt_data(survey = survey,
vessel = vessel,
cruise = cruise,
channel = channel)
## getting values from regression to correct light meter times
coef <- trawllight:::mk9_get_regr(survey = survey ,
vessel = vessel,
cruise = cruise,
channel = channel)
# get trawl-mounted MK9 good performance casts
good.perf <- perf[perf$datum_code == 0, c("vessel","cruise","haul")]
## get Light (MK9) data
print("reading trawl-mounted MK9 file...")
files.in.dir <- list.files(path = light.loc, pattern = ".csv")
file.idx1 <- which(substr(files.in.dir, 1, 4) == "trwl")
## read in trawl-mounted light meter data (there could be more than one file)
for(file in file.idx1) {
assign(paste("trwl", file, sep = ""),
read.csv(paste(light.loc, "/", files.in.dir[file], sep= "" ), header = F, skip = 1))
}
if(cruise <= 201801){
trwl.dat <- data.frame(V1 = numeric(),
V2 = numeric(),
V3 = numeric(),
V4 = numeric(),
V5 = numeric(),
stringsAsFactors = FALSE)
}else{
trwl.dat <- data.frame(V1 = numeric(),
V2 = numeric(),
V3 = numeric(),
V4 = numeric(),
V5 = numeric(),
V6 = numeric(),
stringsAsFactors = FALSE)
}
## put multiple files together
obj <- objects(pattern = "trwl")
for(i in 1:length(obj)){
trwl.dat <- rbind(trwl.dat, get(obj[i]))
}
## make MK9 date and time comparable with sgt and mbt date format
trwl.dat$date_time <- as.POSIXct(paste(trwl.dat$V1, trwl.dat$V2), format = "%m/%d/%Y %H:%M:%S")
light <- trwl.dat
# empty data set to receive raw MK9 data
## 2004 and 2005 MK9 data have a different format than subsequent years; here's code to deal with it
if(ncol(light) == 6){
light <- light[,3:6]
colnames(light) <- c("ldepth","ltemp","llight","ldate_time")
## Setup data.frame to combine records using the loop below
light.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), cdepth = numeric(), ctime = character(),
ldepth = numeric(), ltemp = numeric(), llight = numeric(), stringsAsFactors = FALSE)
df.names <- names(light.df)
} else if(ncol(light) == 7){
light <- light[,3:7]
colnames(light) <- c("ldepth","ltemp","llight","lcond","ldate_time")
## Setup data.frame to combine records using the loop below
light.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), cdepth = numeric(), ctime = character(),
ldepth = numeric(), ltemp = numeric(), llight = numeric(), lcond = numeric(), stringsAsFactors = FALSE)
df.names <- names(light.df)
} else {
stop(paste0("extinction: Unexpected number of columns, (", ncol(light), ") in light data csv. Expected 6 or 7."))
}
if(vessel == 162 & cruise %in% c(202201, 202202)) {
light$ldepth <- trawllight:::mk9_lowpass_filter(x = light$ldepth,
vessel = vessel,
cruise = cruise)
}
light <- trawllight:::mk9_find_offset(light = light,
mbt = mbt,
try.offsets = c(seq(-8,8,0.5),0.25,-0.25),
results.file = paste0(light.loc, "/offset_step1_log.txt"))
## identify offsets file and read in (there should be just one)
file.idx2 <- which(substr(files.in.dir, 1, 7) == "offsets")
assign("offsets", read.csv(paste(light.loc, "/", files.in.dir[file.idx2], sep = ""), header = TRUE))
# eliminate hauls where an offset time could not be calculated
offset.names <- names(offsets)
## substitute smoothed offset times for NAs and for offsets where R-sqr < 0.97
mixed.offsets <- ifelse(is.na(offsets$offset),round(offsets$smooth_offset,0),
ifelse(offsets$max.rsqr < 0.97,round(offsets$smooth_offset,0),
offsets$offset))
## substitute the mixed_offsets vector for the original offsets column
offsets <- cbind(offsets[,c("vessel","cruise","haul")], mixed.offsets, offsets[,c("max.rsqr","a","b","eq100m","smooth_offset")])
names(offsets) <- offset.names
## eliminate any remaining records where offset was NA; these will be cases where the whole record was NA
## presumably because of no irradiance data
offsets <- offsets[!is.na(offsets$offset),]
## Depending on data vintage start time can be a 1 or a 15
start_time <- sgt[sgt$time_flag == 1 | sgt$time_flag == 15, c("vessel","cruise","haul","date_time")]
names(start_time) <- c("vessel","cruise","haul","starttime")
## haul back time event remains 6
hb.time <- sgt[sgt$time_flag == 6, c("vessel","cruise","haul","date_time")]
## merge sgt and hauls to get hauls aligned
hb.time <- merge(hauls, hb.time)
## linear regression of duration from haul back to doors up + 50% more
wire.time <- ceiling(2*60*(coef$coefficients[1] + (coef$coefficients[2]*hb.time$wire_length)))
############# potential for different length haul lists needs to be handled: use one master haul list #####################3
## add predicted haul end to haulback time
haul.end <- hb.time$date_time + wire.time
end_time <- cbind(hb.time[, c("vessel","cruise","haul")], haul.end)
## append start and end times to haul list
haullist <- merge(offsets, start_time)
haullist <- merge(haullist, end_time)
haullist <- merge(haullist, good.perf)
# extinct.df <- data.frame(vessel = numeric(), cruise = numeric(), haul = numeric(), extinction = numeric(), longitude = numeric(),
# latitude = numeric(), stringsAsFactors = FALSE)
# e.names <- names(extinct.df)
## assign haul numbers, time offsets, and depth corrections to MK9 data to produce corrected data set
## also compute extinction coefficients for down and upcasts and assign them to global positioning coordinates
for(h in sort(haullist$haul)) {
## subset data by haul
haultime <- haullist[haullist$haul == h, c("starttime","haul.end","a","b","offset")]
## get smaller subset of data between start and end times
light.haul <- with(light, subset(light, light$ldate_time >= haultime$starttime & light$ldate_time <= haultime$haul.end))
if(length(haultime[,1]) == 0 | length(light.haul[,1]) == 0) next
## correct
L <- length(light.haul[,1])
vch <- as.data.frame(cbind(rep(vessel, L), rep(cruise, L), rep(h, L)))
c.depth <- ((haultime$b * light.haul$ldepth) + haultime$a)
c.time <- (light.haul$ldate_time + haultime$offset)
if(ncol(light) == 4){
c.light.dat <- cbind(vch[1], vch[2], vch[3], c.depth, c.time, light.haul$ldepth, light.haul$ltemp, light.haul$llight)
names(c.light.dat) <- df.names
light.df <- rbind(c.light.dat, light.df)
names(light.df) <- df.names
} else if(ncol(light) == 5){
c.light.dat <- cbind(vch[1], vch[2], vch[3], c.depth, c.time, light.haul$ldepth, light.haul$ltemp, light.haul$llight,
light.haul$lcond)
names(c.light.dat) <- df.names
light.df <- rbind(c.light.dat, light.df)
names(light.df) <- df.names
}
# get single cast event times
cast <- casts[casts$haul == h, ]
# convert character date times to POSIX
dn.st <- as.POSIXct(cast$downcast_start)
dn.nd <- as.POSIXct(cast$downcast_end)
up.st <- as.POSIXct(cast$upcast_start)
up.nd <- as.POSIXct(cast$upcast_end)
# extract MK9 casts
down <- light.df[light.df$haul == h & light.df$ctime >= dn.st & light.df$ctime <= dn.nd,c("llight","cdepth")]
up <- light.df[light.df$haul == h & light.df$ctime >= up.st & light.df$ctime <= up.nd,c("llight","cdepth")]
# get global position of OB and FB events
## note that using other events here run risk of tapping multiple coordinate formats stored in RACE_DATA
ob.pos <- sgt[sgt$haul == h & sgt$time_flag == 3, c("longitude","latitude")]
fb.pos <- sgt[sgt$haul == h & sgt$time_flag == 7, c("longitude","latitude")]
# if(length(down[,1]) == 0){
# dn.cast.extinct = NA
# } else{
# # get light intercept from light regressed on depth
# down.coef <- lm(llight ~ cdepth, data = down)
# down.0 <- down.coef$coefficients[1]
# # there could be multiple records at max depth so take average
# ob.depth <- max(down$cdepth)
# ob.light <- mean(down[down$cdepth == ob.depth, "llight"])
# # take natural log of the change in light between surface and bottom over bottom depth
# dn.cast.extinct <- -(log(ob.light/down.0)/ob.depth)
# }
#
# if(length(up[,1]) == 0){
# up.cast.extinct = NA
# }else{
# up.coef <- lm(llight ~ cdepth, data = up)
# up.0 <- up.coef$coefficients[1]
# fb.depth <- max(up$cdepth)
# fb.light <- mean(up[up$cdepth == fb.depth, "llight"])
# up.cast.extinct <- -(log(fb.light/up.0)/fb.depth)
# }
#
# r1 <- cbind(unique(vch[1]), unique(vch[2]), unique(vch[3]), extinction = dn.cast.extinct, ob.pos)
# r2 <- cbind(unique(vch[1]), unique(vch[2]), unique(vch[3]), extinction = up.cast.extinct, fb.pos)
# ## for rbind to work need to keep all of the field names the same throughout concatenation process
# r1r2 <- rbind(r1,r2); names(r1r2) <- e.names
#
# extinct.df <- rbind(extinct.df, r1r2); names(extinct.df) <- e.names
}
# names(extinct.df) <- e.names
names(light.df) <- df.names
## place corrected haul-specific light meter data into the vessel's data directory
write.csv(light.df, paste(light.loc, "/corr_MK9hauls.csv", sep= ""), row.names = F)
if(make.plots) {
dat_to_plot <- light.df |>
dplyr::select(vessel, cruise, haul, cdepth, ctime, ltemp, llight)
dat_to_plot$ctime <- as.POSIXct(dat_to_plot$ctime)
unique_hauls <- dat_to_plot |>
dplyr::select(vessel, cruise, haul) |>
unique()
print(paste0("Writing haul plots to ", light.loc, "/plots_cast_dat.pdf"))
pdf(file = paste0(light.loc, "/plots_cast_dat.pdf"), onefile = TRUE)
for(ii in 1:nrow(unique_hauls)) {
haul_sub <- dat_to_plot |>
dplyr::filter(vessel == unique_hauls$vessel[ii],
cruise == unique_hauls$cruise[ii],
haul == unique_hauls$haul[ii])
print(
ggplot2::ggplot() +
ggplot2::geom_line(data = haul_sub |>
tidyr::pivot_longer(cols = c("ltemp", "llight", "cdepth")),
ggplot2::aes(x = ctime,
y = value,
color = name)) +
ggplot2::facet_wrap(~name, scales = "free_y",
nrow = 3) +
ggplot2::ggtitle(paste0("Vessel: ", unique_hauls$vessel[ii], ", Cruise: ", unique_hauls$cruise[ii], ", Haul: ", unique_hauls$haul[ii])) +
ggplot2::theme_bw()
)
}
dev.off()
}
}
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