CalFishTrack
Central Valley Enhanced
Acoustic Tagging Project
Spring Pulse Flow Chinook salmon Survival Study
1. Project Status
Study is complete, all tags are no longer active as of 2023-08-31. All times in Pacific Standard Time.
Study began on 2023-04-18 09:05:00, see tagging details below:| Release | First_release_time | Last_release_time | Number_fish_released | Release_location | Release_rkm | Mean_length | Mean_weight |
|---|---|---|---|---|---|---|---|
| Before Pulse | 2023-04-18 09:05:00 | 2023-04-19 10:05:00 | 251 | RBDD_Rel | 461.579 | 84.1 | 7.5 |
| First Pulse | 2023-04-24 10:10:00 | 2023-04-25 09:45:00 | 250 | RBDD_Rel | 461.579 | 84.8 | 6.9 |
| Second Pulse - Week 1 | 2023-05-02 09:07:00 | 2023-05-03 09:07:00 | 250 | RBDD_Rel | 461.579 | 86.2 | 7.5 |
| Second Pulse - Week 2 | 2023-05-09 09:05:00 | 2023-05-10 08:25:00 | 250 | RBDD_Rel | 461.579 | 86.6 | 7.2 |
| Second Pulse - Week 3 | 2023-05-16 08:26:00 | 2023-05-17 08:50:00 | 250 | RBDD_Rel | 461.579 | 88.5 | 7.5 |
2. Real-time Fish Detections
library(leaflet)
library(maps)
library(htmlwidgets)
library(leaflet.extras)
library(dplyr)
library(dbplyr)
library(DBI)
library(odbc)
library(data.table)
# Create connection with cloud database
con <- dbConnect(odbc(),
Driver = "SQL Server",
Server = "calfishtrack-server.database.windows.net",
Database = "realtime_detections",
UID = "realtime_user",
PWD = "Pass@123",
Port = 1433)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
## THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
cat("No detections yet")
# Use dbplyr to load realtime_locs and qryHexCodes sql table
gen_locs <- tbl(con, "realtime_locs") %>% collect()
# gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F) %>% filter(is.na(stop))
leaflet(data = gen_locs[is.na(gen_locs$stop),]) %>%
# setView(-72.14600, 43.82977, zoom = 8) %>%
addProviderTiles("Esri.WorldStreetMap", group = "Map") %>%
addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
# Marker data are from the sites data frame. We need the ~ symbols
# to indicate the columns of the data frame.
addMarkers(~longitude, ~latitude, label = ~general_location, group = "Receiver Sites", popup = ~location) %>%
# addAwesomeMarkers(~lon_dd, ~lat_dd, label = ~locality, group = "Sites", icon=icons) %>%
addScaleBar(position = "bottomleft") %>%
addLayersControl(
baseGroups = c("Street Map", "Satellite", "Relief"),
overlayGroups = c("Receiver Sites"),
options = layersControlOptions(collapsed = FALSE)) %>%
addSearchFeatures(targetGroups = c("Receiver Sites"))
} else {
# Use dbplyr to load realtime_locs and qryHexCodes sql table
gen_locs <- tbl(con, "realtime_locs") %>% collect()
# gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))
beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F) %>%
mutate(day = as.Date(day)) %>%
# Subset to only look at data for the correct beacon for that day
filter(TagCode == beacon) %>%
# Only keep beacon by day for days since fish were released
filter(day >= as.Date(min(study_tagcodes$release_time)) & day <= endtime) %>%
dplyr::left_join(., gen_locs[,c("location", "general_location","rkm")], by = "location")
arrivals_per_day <- detects_study %>%
group_by(general_location, TagCode) %>%
summarise(DateTime_PST = min(DateTime_PST, na.rm = T)) %>%
arrange(TagCode, general_location) %>%
mutate(day = as.Date(DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8")) %>%
group_by(day, general_location) %>%
summarise(New_arrivals = length(TagCode)) %>%
na.omit() %>%
mutate(day = as.Date(day)) %>%
dplyr::left_join(unique(beacon_by_day[,c("general_location", "day", "rkm")]), .,
by = c("general_location", "day")) %>%
arrange(general_location, day) %>%
mutate(day = as.factor(day)) %>%
filter(general_location != "Bench_test") %>% # Remove bench test
filter(!(is.na(general_location))) # Remove NA locations
## Remove sites that were not operation the whole time
#### FOR THE SEASONAL SURVIVAL PAGE, KEEP ALL SITES SINCE PEOPLE WANT TO SEE DETECTIONS OF LATER FISH AT NEWLY
#### DEPLOYED SPOTS
gen_locs_days_in_oper <- arrivals_per_day %>%
group_by(general_location) %>%
summarise(days_in_oper = length(day))
#gen_locs_days_in_oper <- gen_locs_days_in_oper[gen_locs_days_in_oper$days_in_oper ==
# max(gen_locs_days_in_oper$days_in_oper),]
arrivals_per_day_in_oper <- arrivals_per_day %>%
filter(general_location %in% gen_locs_days_in_oper$general_location)
fish_per_site <- arrivals_per_day_in_oper %>%
group_by(general_location) %>%
summarise(fish_count = sum(New_arrivals, na.rm=T))
gen_locs_mean_coords <- gen_locs %>%
filter(is.na(stop) & general_location %in% fish_per_site$general_location) %>%
group_by(general_location) %>%
summarise(latitude = mean(latitude), # estimate mean lat and lons for each genloc
longitude = mean(longitude))
fish_per_site <- merge(fish_per_site, gen_locs_mean_coords)
if(!is.na(release_stats$Release_lat[1])){
leaflet(data = fish_per_site) %>%
addProviderTiles("Esri.WorldStreetMap", group = "Street Map") %>%
addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
# Marker data are from the sites data frame. We need the ~ symbols
# to indicate the columns of the data frame.
addPulseMarkers(lng = fish_per_site$longitude, lat = fish_per_site$latitude, label = ~fish_count,
labelOptions = labelOptions(noHide = T, textsize = "15px"), group = "Receiver Sites",
popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
addCircleMarkers(data = release_stats, ~Release_lon, ~Release_lat, label = ~Number_fish_released, stroke = F, color = "blue", fillOpacity = 1,
group = "Release Sites", popup = ~Release_location, labelOptions = labelOptions(noHide = T, textsize = "15px")) %>%
addScaleBar(position = "bottomleft") %>%
addLegend("bottomright", labels = c("Receivers", "Release locations"), colors = c("red","blue")) %>%
addLayersControl(baseGroups = c("Street Map", "Satellite", "Relief"), options = layersControlOptions(collapsed = FALSE))
} else {
leaflet(data = fish_per_site) %>%
addProviderTiles("Esri.NatGeoWorldMap", group = "Street Map") %>%
addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
# Marker data are from the sites data frame. We need the ~ symbols
# to indicate the columns of the data frame.
addPulseMarkers(lng = fish_per_site$longitude, lat = fish_per_site$latitude, label = ~fish_count,
labelOptions = labelOptions(noHide = T, textsize = "15px"), group = "Receiver Sites",
popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
addScaleBar(position = "bottomleft") %>%
addLayersControl(baseGroups = c("Street Map", "Satellite", "Relief"),
options = layersControlOptions(collapsed = FALSE))
}
}2.1 Map of unique fish detections at operational realtime detection locations
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) > 0){
detects_study <- detects_study[order(detects_study$TagCode, detects_study$DateTime_PST),]
## Now estimate the time in hours between the previous and next detection, for each detection.
detects_study$prev_genloc <- shift(detects_study$general_location, fill = NA, type = "lag")
#detects_study$prev_genloc <- shift(detects_study$General_Location, fill = NA, type = "lag")
## Now make NA the time diff values when it's between 2 different tagcodes or genlocs
detects_study[which(detects_study$TagCode != shift(detects_study$TagCode, fill = NA, type = "lag")), "prev_genloc"] <- NA
detects_study[which(detects_study$general_location != detects_study$prev_genloc), "prev_genloc"] <- NA
detects_study$mov_score <- 0
detects_study[is.na(detects_study$prev_genloc), "mov_score"] <- 1
detects_study$mov_counter <- cumsum(detects_study$mov_score)
detects_summary <- aggregate(list(first_detect = detects_study$DateTime_PST), by = list(TagCode = detects_study$TagCode, length = detects_study$length, release_time = detects_study$release_time, mov_counter = detects_study$mov_counter ,general_location = detects_study$general_location, river_km = detects_study$river_km, release_rkm = detects_study$release_rkm), min)
detects_summary <- detects_summary[is.na(detects_summary$first_detect) == F,]
releases <- aggregate(list(first_detect = detects_summary$release_time), by = list(TagCode = detects_summary$TagCode, length = detects_summary$length, release_time = detects_summary$release_time, release_rkm = detects_summary$release_rkm), min)
releases$river_km <- releases$release_rkm
releases$mov_counter <- NA
releases$general_location <- NA
detects_summary <- rbindlist(list(detects_summary, releases), use.names = T)
detects_summary <- detects_summary[order(detects_summary$TagCode, detects_summary$first_detect),]
starttime <- as.Date(min(detects_study$release_time), "Etc/GMT+8")
## Endtime should be either now, or end of predicted tag life, whichever comes first
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d"))+1, max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
#par(mar=c(6, 5, 2, 5) + 0.1)
plot_ly(detects_summary, width = 900, height = 600, dynamicTicks = TRUE) %>%
add_lines(x = ~first_detect, y = ~river_km, color = ~TagCode) %>%
add_markers(x = ~first_detect, y = ~river_km, color = ~TagCode, showlegend = F) %>%
layout(showlegend = T,
xaxis = list(title = "<b> Date <b>", mirror=T,ticks="outside",showline=T, range=c(starttime,endtime)),
yaxis = list(title = "<b> Kilometers upstream of the Golden Gate <b>", mirror=T,ticks="outside",showline=T, range=c(max(detects_study$Rel_rkm)+10, min(gen_locs[is.na(gen_locs$stop),"rkm"])-10)),
legend = list(title=list(text='<b> Tag Code </b>')),
margin=list(l = 50,r = 100,b = 50,t = 50)
)
}else{
plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Kilometers upstream of the Golden Gate")
text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}2.2 Waterfall Detection Plot
_______________________________________________________________________________________________________
library(tidyr)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
detects_3 <- detects_study %>% filter(general_location == "Blw_Salt_RT")
if(nrow(detects_3) == 0){
plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
detects_3 <- detects_3 %>%
dplyr::left_join(., detects_3 %>%
group_by(TagCode) %>%
summarise(first_detect = min(DateTime_PST))) %>%
mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))
starttime <- as.Date(min(detects_3$release_time), "Etc/GMT+8")
# Endtime should be either now, or end of predicted tag life, whichever comes first
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
rels <- unique(study_tagcodes$Release)
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_3$Release))])
tagcount1 <- detects_3 %>%
group_by(Day, Release) %>%
summarise(unique_tags = length(unique(TagCode))) %>%
spread(Release, unique_tags)
daterange1 <- merge(daterange, tagcount1, all.x=T)
daterange1[is.na(daterange1)] <- 0
if(length(rels_no_detects)>0){
for(i in rels_no_detects){
daterange1 <- cbind(daterange1, x=NA)
names(daterange1)[names(daterange1) == "x"] <- paste(i)
}
}
# Download flow data
flow_day <- readNWISuv(siteNumbers = "11377100", parameterCd="00060", startDate = starttime,
endDate = endtime+1) %>%
mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
group_by(Day) %>%
summarise(parameter_value = mean(X_00060_00000))
## reorder columns in alphabetical order so its coloring in barplots is consistent
daterange2 <- daterange1[,order(colnames(daterange1))] %>%
dplyr::left_join(., flow_day, by = "Day")
rownames(daterange2) <- daterange2$Day
daterange2$Date <- daterange2$Day
daterange2$Day <- NULL
daterange2_flow <- daterange2 %>% select(Date, parameter_value)
daterange3 <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))],
id.vars = "Date", variable.name = ".")
daterange3$. <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))
par(mar=c(6, 5, 2, 5) + 0.1)
ay <- list(
overlaying = "y",
nticks = 5,
color = "#947FFF",
side = "right",
title = "Flow (cfs) at Bend Bridge",
automargin = TRUE
)
plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
add_bars(x = ~Date, y = ~value, color = ~.) %>%
add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
x=0.01, xanchor="left",
y=1.056, yanchor="top", # Same y as legend below
legendtitle=TRUE, showarrow=FALSE ) %>%
add_lines(x=~daterange2_flow$Date,
y=~daterange2_flow$parameter_value,
line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE,
inherit=FALSE) %>%
layout(yaxis2 = ay,showlegend = T,
barmode = "stack",
xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T),
yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
legend = list(orientation = "h",x = 0.34, y = 1.066),
margin=list(l = 50,r = 100,b = 50,t = 50))
}2.3 Detections at Salt Creek versus Sacramento River flows at Bend Bridge for duration of tag life
_______________________________________________________________________________________________________
library(tidyr)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
detects_4 <- detects_study %>% filter(general_location == "MeridianBr")
if(nrow(detects_4) == 0){
plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
detects_4 <- detects_4 %>%
dplyr::left_join(., detects_4 %>%
group_by(TagCode) %>%
summarise(first_detect = min(DateTime_PST))) %>%
mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))
starttime <- as.Date(min(detects_4$release_time), "Etc/GMT+8")
# Endtime should be either now, or end of predicted tag life, whichever comes first
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
rels <- unique(study_tagcodes$Release)
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_4$Release))])
tagcount1 <- detects_4 %>%
group_by(Day, Release) %>%
summarise(unique_tags = length(unique(TagCode))) %>%
spread(Release, unique_tags)
daterange1 <- merge(daterange, tagcount1, all.x=T)
daterange1[is.na(daterange1)] <- 0
if(length(rels_no_detects)>0){
for(i in rels_no_detects){
daterange1 <- cbind(daterange1, x=NA)
names(daterange1)[names(daterange1) == "x"] <- paste(i)
}
}
# Download flow data
flow_day <- readNWISuv(siteNumbers = "11390500", parameterCd="00060", startDate = starttime,
endDate = endtime+1) %>%
mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
group_by(Day) %>%
summarise(parameter_value = mean(X_00060_00000))
## reorder columns in alphabetical order so its coloring in barplots is consistent
daterange2 <- daterange1[,order(colnames(daterange1))] %>%
dplyr::left_join(., flow_day, by = "Day")
rownames(daterange2) <- daterange2$Day
daterange2$Date <- daterange2$Day
daterange2$Day <- NULL
daterange2_flow <- daterange2 %>% select(Date, parameter_value)
daterange3 <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))],
id.vars = "Date", variable.name = ".")
daterange3$. <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))
par(mar=c(6, 5, 2, 5) + 0.1)
ay <- list(
overlaying = "y",
nticks = 5,
color = "#947FFF",
side = "right",
title = "Flow (cfs) at Wilkins Slough",
automargin = TRUE
)
plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
add_bars(x = ~Date, y = ~value, color = ~.) %>%
add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
x=0.01, xanchor="left",
y=1.056, yanchor="top", # Same y as legend below
legendtitle=TRUE, showarrow=FALSE ) %>%
add_lines(x=~daterange2_flow$Date,
y=~daterange2_flow$parameter_value,
line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE,
inherit=FALSE) %>%
layout(yaxis2 = ay,showlegend = T,
barmode = "stack",
xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T),
yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
legend = list(orientation = "h",x = 0.34, y = 1.066),
margin=list(l = 50,r = 100,b = 50,t = 50))
}2.4 Detections at Meridian Bridge versus Sacramento River flows at Wilkins Slough for duration of tag life
_______________________________________________________________________________________________________
library(tidyr)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
detects_5 <- detects_study %>% filter(general_location == "TowerBridge")
if(nrow(detects_5) == 0){
plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
detects_5 <- detects_5 %>%
dplyr::left_join(., detects_5 %>%
group_by(TagCode) %>%
summarise(first_detect = min(DateTime_PST))) %>%
mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))
starttime <- as.Date(min(detects_5$release_time), "Etc/GMT+8")
# Endtime should be either now, or end of predicted tag life, whichever comes first
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
rels <- unique(study_tagcodes$Release)
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_5$Release))])
tagcount1 <- detects_5 %>%
group_by(Day, Release) %>%
summarise(unique_tags = length(unique(TagCode))) %>%
spread(Release, unique_tags)
daterange1 <- merge(daterange, tagcount1, all.x=T)
daterange1[is.na(daterange1)] <- 0
if(length(rels_no_detects)>0){
for(i in rels_no_detects){
daterange1 <- cbind(daterange1, x=NA)
names(daterange1)[names(daterange1) == "x"] <- paste(i)
}
}
# Download flow data
flow_day <- readNWISuv(siteNumbers = "11425500", parameterCd="00060", startDate = starttime,
endDate = endtime+1) %>%
mutate(Day = as.Date(format(dateTime, "%Y-%m-%d"))) %>%
group_by(Day) %>%
summarise(parameter_value = mean(X_00060_00000))
## reorder columns in alphabetical order so its coloring in barplots is consistent
daterange2 <- daterange1[,order(colnames(daterange1))] %>%
dplyr::left_join(., flow_day, by = "Day")
rownames(daterange2) <- daterange2$Day
daterange2$Date <- daterange2$Day
daterange2$Day <- NULL
daterange2_flow <- daterange2 %>% select(Date, parameter_value)
daterange3 <- melt(daterange2[,!(names(daterange2) %in% c("parameter_value"))],
id.vars = "Date", variable.name = ".")
daterange3$. <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))
par(mar=c(6, 5, 2, 5) + 0.1)
ay <- list(
overlaying = "y",
nticks = 5,
color = "#947FFF",
side = "right",
title = "Flow (cfs) at Verona",
automargin = TRUE
)
plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
add_bars(x = ~Date, y = ~value, color = ~.) %>%
add_annotations(text="Release (click on legend items to isolate)", xref="paper", yref="paper",
x=0.01, xanchor="left",
y=1.056, yanchor="top", # Same y as legend below
legendtitle=TRUE, showarrow=FALSE ) %>%
add_lines(x=~daterange2_flow$Date,
y=~daterange2_flow$parameter_value,
line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE,
inherit=FALSE) %>%
layout(yaxis2 = ay,showlegend = T,
barmode = "stack",
xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T),
yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
legend = list(orientation = "h",x = 0.34, y = 1.066),
margin=list(l = 50,r = 100,b = 50,t = 50))
}2.5 Detections at Tower Bridge (downtown Sacramento) versus Sacramento River flows at Verona for duration of tag life
_______________________________________________________________________________________________________
library(tidyr)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
detects_6 <- detects_study %>% filter(general_location == "Benicia_west" | general_location == "Benicia_east")
if(nrow(detects_6) == 0){
plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
} else {
detects_6 <- detects_6 %>%
dplyr::left_join(., detects_6 %>%
group_by(TagCode) %>%
summarise(first_detect = min(DateTime_PST))) %>%
mutate(Day = as.Date(as.Date(first_detect, "Etc/GMT+8")))
starttime <- as.Date(min(detects_6$release_time), "Etc/GMT+8")
# Endtime should be either now, or end of predicted tag life, whichever comes first
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
rels <- unique(study_tagcodes$Release)
rel_num <- length(rels)
rels_no_detects <- as.character(rels[!(rels %in% unique(detects_6$Release))])
tagcount1 <- detects_6 %>%
group_by(Day, Release) %>%
summarise(unique_tags = length(unique(TagCode))) %>%
spread(Release, unique_tags)
daterange1 <- merge(daterange, tagcount1, all.x=T)
daterange1[is.na(daterange1)] <- 0
if(length(rels_no_detects)>0){
for(i in rels_no_detects){
daterange1 <- cbind(daterange1, x=NA)
names(daterange1)[names(daterange1) == "x"] <- paste(i)
}
}
## reorder columns in alphabetical order so its coloring in barplots is consistent
daterange1 <- daterange1[,order(colnames(daterange1))]
daterange2 <- daterange1
rownames(daterange2) <- daterange2$Day
daterange2$Day <- NULL
par(mar=c(6, 5, 2, 5) + 0.1)
daterange2$Date <- as.Date(row.names(daterange2))
daterange3 <- melt(daterange2, id.vars = "Date", variable.name = ".", )
daterange3$. <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))
plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
add_bars(x = ~Date, y = ~value, color = ~.) %>%
add_annotations( text="Release (click on legend items to isolate)", xref="paper", yref="paper",
x=0.01, xanchor="left",
y=1.056, yanchor="top", # Same y as legend below
legendtitle=TRUE, showarrow=FALSE ) %>%
layout(showlegend = T,
barmode = "stack",
xaxis = list(title = "Date", mirror=T,ticks="outside",showline=T),
yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks="outside",showline=T),
legend = list(orientation = "h",x = 0.34, y = 1.066),
margin=list(l = 50,r = 100,b = 50,t = 50))
}2.6 Detections at Benicia Bridge for duration of tag life
3. Survival and Routing Probability
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
detects_tower <- detects_study %>% filter(general_location == "TowerBridge")
if(nrow(detects_tower) == 0){
WR.surv <- data.frame("Release"=NA, "Survival (%)"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA,
"95% upper C.I."=NA, "Detection efficiency (%)"=NA)
colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.",
"95% upper C.I.", "Detection efficiency (%)")
print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
survival model). If Yolo Bypass Weirs are overtopping during migration, fish may have taken
that route, and therefore this is a minimum estimate of survival") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"),
full_width = F, position = "left"))
} else {
study_count <- nrow(study_tagcodes)
# Only do survival to Sac for now
surv <- detects_study %>% filter(river_km > 168 & river_km < 175)
# calculate mean and SD travel time
travel <- aggregate(list(first_detect = surv$DateTime_PST), by = list(Release = surv$Release, TagCode = surv$TagCode, RelDT = surv$RelDT), min)
travel$days <- as.numeric(difftime(travel$first_detect, travel$RelDT, units = "days"))
travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days),n = nrow(travel)))
# Create inp for survival estimation
inp <- as.data.frame(reshape2::dcast(surv, TagCode ~ river_km, fun.aggregate = length))
# Sort columns by river km in descending order
gen_loc_sites <- ncol(inp)-1 # Count number of genlocs
if(gen_loc_sites < 2){
WR.surv <- data.frame("Release"=NA, "Survival (%)"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA,
"95% upper C.I."=NA, "Detection efficiency (%)"=NA)
colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.",
"Detection efficiency (%)")
print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
survival model). If Yolo Bypass Weirs are overtopping during migration, fish may
have taken that route, and therefore this is a minimum estimate of survival") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"),
full_width = F,position = "left"))
} else {
inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)] %>%
dplyr::left_join(study_tagcodes, ., by = "TagCode")
inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)] %>%
replace(is.na(.), 0) %>%
replace(., . > 0, 1)
inp <- cbind(inp, inp2)
groups <- as.character(sort(unique(inp$Release)))
surv$Release <- factor(surv$Release, levels = groups)
inp[,groups] <- 0
for (i in groups) {
inp[as.character(inp$Release) == i, i] <- 1
}
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
if(length(groups) > 1){
# make sure factor levels have a release that has detections first. if first release in factor order
# has zero detectins, model goes haywire
inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1,
rel = factor(inp$Release, levels = names(sort(table(surv$Release),decreasing = T))),
stringsAsFactors = F)
WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl,
model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
WR.mark.rel <- mark(WR.process, WR.ddl,
model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)),
silent = T, output = F)
WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),
c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv$Detection_efficiency <- NA
WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
WR.surv <- cbind(c("ALL", names(sort(table(surv$Release),decreasing = T))), WR.surv)
}
if(length(intersect(colnames(inp),groups)) < 2){
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""), " ", 1,sep = "")
write.table(inp$inp_final,"WRinp.inp",row.names = F, col.names = F, quote = F)
WRinp <- convert.inp("WRinp.inp")
WR.process <- process.data(WRinp, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl,
model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
WR.mark.rel <- mark(WR.process, WR.ddl,
model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),
c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv$Detection_efficiency <- NA
WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
WR.surv <- cbind(c("ALL", groups), WR.surv)
}
colnames(WR.surv)[1] <- "Release"
WR.surv <- merge(WR.surv, travel_final, by = "Release", all.x = T)
WR.surv$mean_travel_time <- round(WR.surv$mean_travel_time,1)
WR.surv$sd_travel_time <- round(WR.surv$sd_travel_time,1)
colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.",
"95% upper C.I.", "Detection efficiency (%)", "Mean time to Tower (days)", "SD of time to Tower (days)","Count")
WR.surv <- WR.surv %>% arrange(., Release)
print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS
survival model), and travel time. If Yolo Bypass Weirs are overtopping during migration, fish may have taken
that route, and therefore this is a minimum estimate of survival") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"),
full_width = F, position = "left"))
}
}| Release | Survival (%) | SE | 95% lower C.I. | 95% upper C.I. | Detection efficiency (%) | Mean time to Tower (days) | SD of time to Tower (days) | Count |
|---|---|---|---|---|---|---|---|---|
| ALL | 41.3 | 1.4 | 38.6 | 44.1 | 88.2 | 6.2 | 4.5 | 510 |
| Before Pulse | 53.8 | 3.2 | 47.5 | 59.9 | NA | 8.5 | 2.8 | 134 |
| First Pulse | 25.1 | 2.8 | 20.0 | 30.9 | NA | 5.5 | 6.0 | 62 |
| Second Pulse - Week 1 | 34.5 | 3.0 | 28.8 | 40.7 | NA | 7.0 | 6.1 | 85 |
| Second Pulse - Week 2 | 48.6 | 3.2 | 42.3 | 55.0 | NA | 4.8 | 3.8 | 119 |
| Second Pulse - Week 3 | 44.8 | 3.2 | 38.6 | 51.1 | NA | 4.6 | 2.7 | 110 |
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
try(Delta <- read.csv("Delta_surv.csv", stringsAsFactors = F))
if(nrow(detects_study[is.na(detects_study$DateTime_PST) == F,]) == 0){
WR.surv1 <- data.frame("Measure"=NA, "Estimate"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
colnames(WR.surv1) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival: City of Sacramento to Benicia (using CJS survival model)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
test4 <- detects_study[detects_study$general_location %in% c("TowerBridge", "I80-50_Br", "Benicia_west", "Benicia_east"),]
if(nrow(test4[test4$general_location =="Benicia_west",]) == 0 | nrow(test4[test4$general_location =="Benicia_east",]) == 0){
WR.surv1 <- data.frame("Measure"=NA, "Estimate"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
colnames(WR.surv1) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival: City of Sacramento to Benicia (using CJS survival model)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
# calculate mean and SD travel time
sac <- test4[test4$general_location %in% c("TowerBridge", "I80-50_Br"),]
ben <- test4[test4$general_location %in% c("Benicia_west", "Benicia_east"),]
travel_sac <- aggregate(list(first_detect_sac = sac$DateTime_PST), by = list(Release = sac$Release, TagCode = sac$TagCode), min)
travel_ben <- aggregate(list(first_detect_ben = ben$DateTime_PST), by = list(Release = ben$Release, TagCode = ben$TagCode), min)
travel <- merge(travel_sac, travel_ben, by = c("Release","TagCode"))
travel$days <- as.numeric(difftime(travel$first_detect_ben, travel$first_detect_sac, units = "days"))
travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days), n = nrow(travel)))
inp <- as.data.frame(reshape2::dcast(test4, TagCode ~ general_location, fun.aggregate = length))
# add together detections at Tower and I80 to ensure good detection entering Delta
if("I80-50_Br" %in% colnames(inp) & "TowerBridge" %in% colnames(inp)){
inp$`I80-50_Br` <- inp$`I80-50_Br` + inp$TowerBridge
} else if("TowerBridge" %in% colnames(inp)){
inp$`I80-50_Br` <- inp$TowerBridge
}
# Sort columns by river km in descending order, this also removes TowerBridge, no longer needed
inp <- inp[,c("TagCode","I80-50_Br", "Benicia_east", "Benicia_west")]
# Count number of genlocs
gen_loc_sites <- ncol(inp)-1
inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
inp <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)
inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
inp2[is.na(inp2)] <- 0
inp2[inp2 > 0] <- 1
inp <- cbind(inp, inp2)
groups <- as.character(sort(unique(inp$Release)))
groups_w_detects <- names(table(detects_study[which(detects_study$river_km < 53),"Release"]))
inp[,groups] <- 0
for(i in groups){
inp[as.character(inp$Release) == i, i] <- 1
}
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
if(length(groups) > 1){
# make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)
if(length(groups_w_detects) > 1){
WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")
WR.ddl <- make.design.data(WR.process)
WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)),
silent = T, output = F)
} else {
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
}
WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[2,c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv.rel <- cbind(Release = groups_w_detects,
round(WR.mark.rel$results$real[seq(from=2,to=length(groups_w_detects)*3,by = 3),
c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
WR.surv <- rbind(WR.surv, WR.surv.rel)
} else {
inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),
silent = T, output = F)
WR.surv <- cbind(Release = c("ALL", groups),round(WR.mark.all$results$real[2,c("estimate", "se", "lcl", "ucl")] * 100,1))
}
WR.surv1 <- WR.surv
colnames(WR.surv1)[1] <- "Release"
WR.surv1 <- merge(WR.surv1, travel_final, by = "Release", all.x = T)
WR.surv1$mean_travel_time <- round(WR.surv1$mean_travel_time,1)
WR.surv1$sd_travel_time <- round(WR.surv1$sd_travel_time,1)
colnames(WR.surv1) <- c("Release", "Survival (%)", "SE", "95% lower C.I.",
"95% upper C.I.", "Mean Delta passage (days)", "SD of Delta Passage (days)","Count")
#colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.")
print(kable(WR.surv1, row.names = F, "html", caption = "3.2 Minimum through-Delta survival, and travel time: City of Sacramento to Benicia (using CJS survival model)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
if(exists("Delta")==T & is.numeric(WR.surv1[1,2])){
reltimes <- aggregate(list(RelDT = study_tagcodes$release_time), by = list(Release = study_tagcodes$Release), FUN = mean)
reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$release_time)))
# Assign whether the results are tentative or final
quality <- "tentative"
if(endtime < as.Date(format(Sys.time(), "%Y-%m-%d"))){
quality <- "final"}
WR.surv <- merge(WR.surv, reltimes, by = "Release", all.x = T)
WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = "1970-01-01")
Delta$RelDT <- as.POSIXct(Delta$RelDT)
# remove old benicia record for this studyID
Delta <- Delta[!Delta$StudyID %in% unique(detects_study$Study_ID),]
Delta <- rbind(Delta, data.frame(WR.surv, StudyID = unique(detects_study$Study_ID), data_quality = quality))
write.csv(Delta, "Delta_surv.csv", row.names = F, quote = F)
}
}
}| Release | Survival (%) | SE | 95% lower C.I. | 95% upper C.I. | Mean Delta passage (days) | SD of Delta Passage (days) | Count |
|---|---|---|---|---|---|---|---|
| ALL | 69.1 | 2.0 | 64.9 | 72.9 | 3.4 | 1.3 | 352 |
| Before Pulse | 74.2 | 3.8 | 66.1 | 80.9 | 3.5 | 1.2 | 100 |
| First Pulse | 67.8 | 5.9 | 55.4 | 78.2 | 3.5 | 2.6 | 42 |
| Second Pulse - Week 1 | 75.3 | 4.7 | 65.0 | 83.3 | 3.9 | 1.1 | 64 |
| Second Pulse - Week 2 | 76.2 | 3.9 | 67.7 | 83.0 | 3.1 | 0.7 | 91 |
| Second Pulse - Week 3 | 52.0 | 4.6 | 42.9 | 60.9 | 2.8 | 0.9 | 55 |
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
try(benicia <- read.csv("benicia_surv.csv", stringsAsFactors = F))
detects_benicia <- detects_study[detects_study$general_location %in% c("Benicia_west", "Benicia_east"),]
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
if(nrow(detects_benicia) == 0){
if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
WR.surv <- data.frame("Release"="ALL", "estimate"=0, "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
} else {
WR.surv <- data.frame("Release"=NA, "estimate"="NO DETECTIONS YET", "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
}
WR.surv1 <- WR.surv
colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else if(length(table(detects_benicia$general_location)) == 1){
if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
WR.surv <- data.frame("Release"="ALL", "estimate"=round(length(unique(detects_benicia$TagCode))/length(unique(detects_study$TagCode))*100,1),
"se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
} else {
WR.surv <- data.frame("Release" = NA, "estimate" = "NOT ENOUGH DETECTIONS", "se" = NA, "lcl" = NA, "ucl" = NA, "Detection_efficiency" = NA)
}
WR.surv1 <- WR.surv
colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
# Only do survival to Benicia here
test3 <- detects_study[which(detects_study$river_km < 53),]
# calculate mean and SD travel time
travel <- aggregate(list(first_detect = test3$DateTime_PST), by = list(Release = test3$Release, TagCode = test3$TagCode, RelDT = test3$RelDT), min)
travel$days <- as.numeric(difftime(travel$first_detect, travel$RelDT, units = "days"))
travel_final <- aggregate(list(mean_travel_time = travel$days), by = list(Release = travel$Release), mean)
travel_final <- merge(travel_final, aggregate(list(sd_travel_time = travel$days), by = list(Release = travel$Release), sd))
travel_final <- merge(travel_final, aggregate(list(n = travel$days), by = list(Release = travel$Release), length))
travel_final <- rbind(travel_final, data.frame(Release = "ALL", mean_travel_time = mean(travel$days), sd_travel_time = sd(travel$days), n = nrow(travel)))
# Create inp for survival estimation
inp <- as.data.frame(reshape2::dcast(test3, TagCode ~ river_km, fun.aggregate = length))
# Sort columns by river km in descending order
# Count number of genlocs
gen_loc_sites <- ncol(inp)-1
inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
inp <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)
inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
inp2[is.na(inp2)] <- 0
inp2[inp2 > 0] <- 1
inp <- cbind(inp, inp2)
groups <- as.character(sort(unique(inp$Release)))
groups_w_detects <- names(table(test3$Release))
inp[,groups] <- 0
for(i in groups){
inp[as.character(inp$Release) == i, i] <- 1
}
inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
if(length(groups) > 1){
# make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)
if(length(groups_w_detects) > 1){
WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")
WR.ddl <- make.design.data(WR.process)
WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)
} else {
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
}
WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv.rel <- cbind(Release = groups_w_detects, round(WR.mark.rel$results$real[seq(from=1,to=length(groups_w_detects)*2,by = 2),
c("estimate", "se", "lcl", "ucl")] * 100,1))
WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
WR.surv <- rbind(WR.surv, WR.surv.rel)
} else {
inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)
WR.process <- process.data(inp.df, model="CJS", begin.time=1)
WR.ddl <- make.design.data(WR.process)
WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
WR.surv <- cbind(Release = c("ALL", groups),round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
}
WR.surv$Detection_efficiency <- NA
WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
WR.surv1 <- WR.surv
colnames(WR.surv1)[1] <- "Release"
WR.surv1 <- merge(WR.surv1, travel_final, by = "Release", all.x = T)
WR.surv1$mean_travel_time <- round(WR.surv1$mean_travel_time,1)
WR.surv1$sd_travel_time <- round(WR.surv1$sd_travel_time,1)
colnames(WR.surv1) <- c("Release", "Survival (%)", "SE", "95% lower C.I.",
"95% upper C.I.", "Detection efficiency (%)", "Mean time to Benicia (days)", "SD of time to Benicia (days)", "Count")
#colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
print(kable(WR.surv1, row.names = F, "html", caption = "3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model), and travel time") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
}| Release | Survival (%) | SE | 95% lower C.I. | 95% upper C.I. | Detection efficiency (%) | Mean time to Benicia (days) | SD of time to Benicia (days) | Count |
|---|---|---|---|---|---|---|---|---|
| ALL | 29.4 | 1.3 | 26.9 | 31.9 | 99.1 | 9.2 | 3.7 | 367 |
| Before Pulse | 40.6 | 3.1 | 34.7 | 46.8 | NA | 11.8 | 1.8 | 102 |
| First Pulse | 17.6 | 2.4 | 13.4 | 22.8 | NA | 8.1 | 3.4 | 44 |
| Second Pulse - Week 1 | 26.4 | 2.8 | 21.3 | 32.2 | NA | 10.3 | 4.8 | 66 |
| Second Pulse - Week 2 | 37.2 | 3.1 | 31.4 | 43.4 | NA | 7.4 | 2.6 | 93 |
| Second Pulse - Week 3 | 24.8 | 2.7 | 19.9 | 30.6 | NA | 7.2 | 3.5 | 62 |
if(exists("benicia")==T & is.numeric(WR.surv1[1,2])){
# Find mean release time per release group, and ALL
reltimes <- aggregate(list(RelDT = study_tagcodes$release_time), by = list(Release = study_tagcodes$Release), FUN = mean)
reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$release_time)))
# Assign whether the results are tentative or final
quality <- "tentative"
if(endtime < as.Date(format(Sys.time(), "%Y-%m-%d"))){
quality <- "final"
}
WR.surv <- merge(WR.surv, reltimes, by = "Release", all.x = T)
WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = "1970-01-01")
benicia$RelDT <- as.POSIXct(benicia$RelDT)
# remove old benicia record for this studyID
benicia <- benicia[!benicia$StudyID == unique(detects_study$Study_ID),]
benicia <- rbind(benicia, data.frame(WR.surv, StudyID = unique(detects_study$Study_ID), data_quality = quality))
write.csv(benicia, "benicia_surv.csv", row.names = F, quote = F)
}4. Detections statistics at all realtime receivers
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
if(nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
"No detections yet"
} else {
arrivals <- detects_study %>%
group_by(general_location, TagCode) %>%
summarise(DateTime_PST = min(DateTime_PST)) %>%
arrange(TagCode)
tag_stats <- arrivals %>%
group_by(general_location) %>%
summarise(First_arrival = min(DateTime_PST),
Mean_arrival = mean(DateTime_PST),
Last_arrival = max(DateTime_PST),
Fish_count = length(unique(TagCode))) %>%
mutate(Percent_arrived = round(Fish_count/nrow(study_tagcodes) * 100,2)) %>%
dplyr::left_join(., unique(detects_study[,c("general_location", "river_km")])) %>%
arrange(desc(river_km)) %>%
mutate(First_arrival = format(First_arrival, tz = "Etc/GMT+8"),
Mean_arrival = format(Mean_arrival, tz = "Etc/GMT+8"),
Last_arrival = format(Last_arrival, tz = "Etc/GMT+8")) %>%
na.omit()
print(kable(tag_stats, row.names = F,
caption = "4.1 Detections for all releases combined",
"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
count <- 0
for(j in sort(unique(study_tagcodes$Release))){
if(nrow(detects_study[detects_study$Release == j,]) > 0){
count <- count + 1
arrivals1 <- detects_study %>%
filter(Release == j) %>%
group_by(general_location, TagCode) %>%
summarise(DateTime_PST = min(DateTime_PST)) %>%
arrange(TagCode)
rel_count <- nrow(study_tagcodes[study_tagcodes$Release == j,])
tag_stats1 <- arrivals1 %>%
group_by(general_location) %>%
summarise(First_arrival = min(DateTime_PST),
Mean_arrival = mean(DateTime_PST),
Last_arrival = max(DateTime_PST),
Fish_count = length(unique(TagCode))) %>%
mutate(Percent_arrived = round(Fish_count/rel_count * 100,2)) %>%
dplyr::left_join(., unique(detects_study[,c("general_location", "river_km")])) %>%
arrange(desc(river_km)) %>%
mutate(First_arrival = format(First_arrival, tz = "Etc/GMT+8"),
Mean_arrival = format(Mean_arrival, tz = "Etc/GMT+8"),
Last_arrival = format(Last_arrival, tz = "Etc/GMT+8")) %>%
na.omit()
final_stats <- kable(tag_stats1, row.names = F,
caption = paste("4.2.", count, " Detections for ", j, " release groups", sep = ""),
"html")
print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
cat("\n\n\\pagebreak\n")
print(paste("No detections for",j,"release group yet", sep=" "), quote = F)
cat("\n\n\\pagebreak\n")
}
}
}| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-04-18 10:26:00 | 2023-05-01 11:11:38 | 2023-05-18 05:01:20 | 1017 | 81.29 | 457.000 |
| MeridianBr | 2023-04-20 16:57:28 | 2023-05-07 06:29:09 | 2023-06-01 19:55:48 | 610 | 48.76 | 290.848 |
| TowerBridge | 2023-04-22 11:39:42 | 2023-05-08 16:56:40 | 2023-06-10 08:38:08 | 456 | 36.45 | 172.000 |
| I80-50_Br | 2023-04-22 12:03:41 | 2023-05-08 15:06:55 | 2023-06-10 08:58:06 | 456 | 36.45 | 170.748 |
| Sac_BlwGeorgiana | 2023-04-24 15:54:43 | 2023-05-10 03:31:59 | 2023-06-10 20:22:23 | 313 | 25.02 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-04-24 16:08:25 | 2023-05-10 06:08:12 | 2023-06-10 20:35:34 | 320 | 25.58 | 118.398 |
| Benicia_east | 2023-04-26 15:16:35 | 2023-05-11 06:51:37 | 2023-06-15 03:19:25 | 364 | 29.10 | 52.240 |
| Benicia_west | 2023-04-27 09:50:56 | 2023-05-10 22:08:02 | 2023-06-15 03:25:47 | 340 | 27.18 | 52.040 |
| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-04-18 10:26:00 | 2023-04-19 07:51:27 | 2023-04-25 14:30:12 | 226 | 90.04 | 457.000 |
| MeridianBr | 2023-04-20 16:57:28 | 2023-04-25 14:45:33 | 2023-05-05 18:23:22 | 145 | 57.77 | 290.848 |
| TowerBridge | 2023-04-22 11:39:42 | 2023-04-27 08:02:27 | 2023-05-22 18:12:20 | 122 | 48.61 | 172.000 |
| I80-50_Br | 2023-04-22 12:03:41 | 2023-04-27 05:57:30 | 2023-05-02 02:16:20 | 127 | 50.60 | 170.748 |
| Sac_BlwGeorgiana | 2023-04-24 15:54:43 | 2023-04-28 01:28:24 | 2023-05-23 17:52:55 | 82 | 32.67 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-04-24 16:08:25 | 2023-04-28 01:40:31 | 2023-05-23 18:05:04 | 82 | 32.67 | 118.398 |
| Benicia_east | 2023-04-26 15:16:35 | 2023-04-30 16:03:51 | 2023-05-11 13:22:23 | 102 | 40.64 | 52.240 |
| Benicia_west | 2023-04-27 09:50:56 | 2023-04-30 16:21:28 | 2023-05-11 13:29:19 | 100 | 39.84 | 52.040 |
| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-04-24 11:16:12 | 2023-04-25 01:45:38 | 2023-04-29 12:30:57 | 246 | 98.4 | 457.000 |
| MeridianBr | 2023-04-26 04:01:50 | 2023-04-28 12:10:07 | 2023-05-28 23:43:45 | 85 | 34.0 | 290.848 |
| TowerBridge | 2023-04-27 19:30:09 | 2023-04-30 09:34:28 | 2023-06-05 00:54:25 | 59 | 23.6 | 172.000 |
| I80-50_Br | 2023-04-27 19:51:43 | 2023-04-30 09:13:00 | 2023-06-05 01:18:05 | 57 | 22.8 | 170.748 |
| Sac_BlwGeorgiana | 2023-04-28 11:01:03 | 2023-05-01 15:11:19 | 2023-06-05 15:27:55 | 36 | 14.4 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-04-28 11:11:56 | 2023-05-01 14:08:32 | 2023-06-05 15:38:02 | 37 | 14.8 | 118.398 |
| Benicia_east | 2023-04-30 13:50:55 | 2023-05-02 20:55:28 | 2023-05-22 06:52:18 | 44 | 17.6 | 52.240 |
| Benicia_west | 2023-04-30 13:58:14 | 2023-05-02 21:07:04 | 2023-05-22 06:55:06 | 44 | 17.6 | 52.040 |
| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-05-02 10:00:47 | 2023-05-02 22:25:51 | 2023-05-03 14:18:35 | 228 | 91.2 | 457.000 |
| MeridianBr | 2023-05-03 22:52:07 | 2023-05-07 20:30:46 | 2023-05-31 03:53:52 | 109 | 43.6 | 290.848 |
| TowerBridge | 2023-05-05 14:51:21 | 2023-05-09 17:26:35 | 2023-05-31 18:30:26 | 74 | 29.6 | 172.000 |
| I80-50_Br | 2023-05-05 15:12:05 | 2023-05-09 16:55:47 | 2023-05-31 19:06:51 | 76 | 30.4 | 170.748 |
| Sac_BlwGeorgiana | 2023-05-06 06:47:16 | 2023-05-11 13:33:48 | 2023-06-01 07:53:48 | 49 | 19.6 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-05-06 06:58:30 | 2023-05-11 14:53:56 | 2023-06-01 08:01:07 | 52 | 20.8 | 118.398 |
| Benicia_east | 2023-05-09 09:22:04 | 2023-05-13 05:12:46 | 2023-06-02 17:16:24 | 64 | 25.6 | 52.240 |
| Benicia_west | 2023-05-09 07:03:39 | 2023-05-13 08:38:44 | 2023-06-02 17:23:34 | 58 | 23.2 | 52.040 |
| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-05-09 10:06:23 | 2023-05-10 02:06:06 | 2023-05-10 12:31:40 | 135 | 54.0 | 457.000 |
| MeridianBr | 2023-05-10 19:28:48 | 2023-05-12 19:06:31 | 2023-06-01 19:55:48 | 140 | 56.0 | 290.848 |
| TowerBridge | 2023-05-12 05:57:01 | 2023-05-14 16:14:33 | 2023-06-06 05:25:36 | 113 | 45.2 | 172.000 |
| I80-50_Br | 2023-05-12 06:17:59 | 2023-05-14 16:55:33 | 2023-06-06 05:42:59 | 100 | 40.0 | 170.748 |
| Sac_BlwGeorgiana | 2023-05-12 22:50:27 | 2023-05-15 04:25:49 | 2023-06-06 17:39:31 | 75 | 30.0 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-05-12 23:09:15 | 2023-05-15 04:11:08 | 2023-06-06 17:50:27 | 76 | 30.4 | 118.398 |
| Benicia_east | 2023-05-15 05:12:28 | 2023-05-17 02:32:26 | 2023-05-27 12:18:23 | 92 | 36.8 | 52.240 |
| Benicia_west | 2023-05-15 05:15:45 | 2023-05-17 07:10:13 | 2023-06-02 07:17:26 | 88 | 35.2 | 52.040 |
| general_location | First_arrival | Mean_arrival | Last_arrival | Fish_count | Percent_arrived | river_km |
|---|---|---|---|---|---|---|
| Blw_Salt_RT | 2023-05-16 09:16:54 | 2023-05-16 22:43:59 | 2023-05-18 05:01:20 | 182 | 72.8 | 457.000 |
| MeridianBr | 2023-05-17 19:32:07 | 2023-05-19 11:10:02 | 2023-05-26 13:34:00 | 131 | 52.4 | 290.848 |
| TowerBridge | 2023-05-19 07:55:20 | 2023-05-21 12:20:54 | 2023-06-10 08:38:08 | 88 | 35.2 | 172.000 |
| I80-50_Br | 2023-05-19 08:10:06 | 2023-05-21 09:39:33 | 2023-06-10 08:58:06 | 96 | 38.4 | 170.748 |
| Sac_BlwGeorgiana | 2023-05-19 23:33:24 | 2023-05-22 02:56:55 | 2023-06-10 20:22:23 | 71 | 28.4 | 119.058 |
| Sac_BlwGeorgiana2 | 2023-05-19 23:46:41 | 2023-05-22 05:50:21 | 2023-06-10 20:35:34 | 73 | 29.2 | 118.398 |
| Benicia_east | 2023-05-21 09:23:09 | 2023-05-24 02:14:06 | 2023-06-15 03:19:25 | 62 | 24.8 | 52.240 |
| Benicia_west | 2023-05-21 09:27:27 | 2023-05-24 10:19:20 | 2023-06-15 03:25:47 | 50 | 20.0 | 52.040 |
library(dplyr)
library(dbplyr)
library(DBI)
library(odbc)
library(data.table)
# Create connection with cloud database
con <- dbConnect(odbc(),
Driver = "SQL Server",
Server = "calfishtrack-server.database.windows.net",
Database = "realtime_detections",
UID = "realtime_user",
PWD = "Pass@123",
Port = 1433)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
# THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if(nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
"No detections yet"
} else {
arrivals <- detects_study %>%
group_by(general_location, TagCode) %>%
summarise(DateTime_PST = min(DateTime_PST)) %>%
mutate(day = as.Date(DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8"))
# Use dbplyr to load realtime_locs and qryHexCodes sql table
gen_locs <- tbl(con, "realtime_locs") %>% collect()
# gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F) %>%
mutate(day = as.Date(day)) %>%
filter(TagCode == beacon) %>% # Now subset to only look at data for the correct beacon for that day
filter(day >= as.Date(min(study_tagcodes$release_time)) &
day <= endtime) %>% # Now only keep beacon by day for days since fish were released
dplyr::left_join(., gen_locs[,c("location", "general_location","rkm")], by = "location")
arrivals_per_day <- arrivals %>%
group_by(day, general_location) %>%
summarise(New_arrivals = length(TagCode)) %>%
arrange(general_location) %>% na.omit() %>%
mutate(day = as.Date(day)) %>%
dplyr::left_join(unique(beacon_by_day[,c("general_location", "day", "rkm")]),
., by = c("general_location", "day")) %>%
arrange(general_location, day) %>%
mutate(day = factor(day)) %>%
filter(general_location != "Bench_test") %>% # Remove bench test and other NA locations
filter(!(is.na(general_location))) %>%
arrange(desc(rkm)) %>% # Change order of data to plot decreasing river_km
mutate(general_location = factor(general_location, unique(general_location)))
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))
crosstab <- xtabs(formula = arrivals_per_day$New_arrivals ~ arrivals_per_day$day + arrivals_per_day$general_location,
addNA =T)
crosstab[is.na(crosstab)] <- ""
crosstab[crosstab==0] <- NA
crosstab <- as.data.frame.matrix(crosstab)
kable(crosstab, align = "c", caption = "4.3 Fish arrivals per day (\"NA\" means receivers were non-operational)") %>%
kable_styling(c("striped", "condensed"), font_size = 11, full_width = F, position = "left", fixed_thead = TRUE) %>%
column_spec(column = 1:ncol(crosstab),width_min = "50px",border_left = T, border_right = T) %>%
column_spec(1, bold = T, width_min = "75px")%>%
scroll_box(height = "700px")
}| Blw_Salt_RT | MeridianBr | TowerBridge | I80-50_Br | MiddleRiver | Clifton_Court_US_Radial_Gates | Holland_Cut_Quimby | CVP_Tank | CVP_Trash_Rack_1 | Clifton_Court_Intake_Canal | Old_River_Quimby | Sac_BlwGeorgiana | Sac_BlwGeorgiana2 | Benicia_east | Benicia_west | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-04-18 | 93 | ||||||||||||||
| 2023-04-19 | 115 | ||||||||||||||
| 2023-04-20 | 10 | 1 | |||||||||||||
| 2023-04-21 | 1 | 2 | |||||||||||||
| 2023-04-22 | 9 | 1 | 1 | ||||||||||||
| 2023-04-23 | 1 | 9 | 3 | 3 | |||||||||||
| 2023-04-24 | 115 | 35 | 8 | 8 | 1 | 1 | |||||||||
| 2023-04-25 | 129 | 30 | 22 | 21 | 11 | 10 | |||||||||
| 2023-04-26 | 6 | 43 | 28 | 25 | 12 | 13 | 1 | ||||||||
| 2023-04-27 | 1 | 62 | 25 | 30 | 27 | 27 | 2 | 3 | |||||||
| 2023-04-28 | 19 | 46 | 48 | 25 | 25 | 13 | 13 | ||||||||
| 2023-04-29 | 1 | 8 | 30 | 32 | 28 | 29 | 19 | 19 | |||||||
| 2023-04-30 | 5 | 10 | 10 | 7 | 7 | 32 | 31 | ||||||||
| 2023-05-01 | 1 | 1 | 1 | 1 | 1 | 44 | 43 | ||||||||
| 2023-05-02 | 115 | 2 | 1 | 1 | 1 | 1 | 18 | 18 | |||||||
| 2023-05-03 | 113 | 3 | 2 | 1 | 5 | 5 | |||||||||
| 2023-05-04 | 21 | 1 | 1 | 7 | 7 | ||||||||||
| 2023-05-05 | 46 | 8 | 7 | 2 | 2 | ||||||||||
| 2023-05-06 | 9 | 22 | 22 | 7 | 7 | ||||||||||
| 2023-05-07 | 9 | 15 | 16 | 11 | 13 | ||||||||||
| 2023-05-08 | 4 | 9 | 8 | 11 | 11 | ||||||||||
| 2023-05-09 | 47 | 1 | 5 | 6 | 7 | 7 | 9 | 9 | |||||||
| 2023-05-10 | 88 | 17 | 4 | 3 | 2 | 2 | 15 | 14 | |||||||
| 2023-05-11 | 53 | 2 | 17 | 14 | |||||||||||
| 2023-05-12 | 46 | 37 | 32 | 1 | 1 | 4 | 4 | ||||||||
| 2023-05-13 | 12 | 41 | 36 | 30 | 31 | 6 | 4 | ||||||||
| 2023-05-14 | 8 | 17 | 14 | 28 | 28 | 3 | 4 | ||||||||
| 2023-05-15 | 1 | 5 | 7 | 7 | 7 | 28 | 27 | ||||||||
| 2023-05-16 | 87 | 3 | 6 | 5 | 6 | 6 | 37 | 36 | |||||||
| 2023-05-17 | 94 | 15 | 1 | 2 | 1 | 1 | 14 | 11 | |||||||
| 2023-05-18 | 1 | 53 | 3 | 3 | 3 | 3 | 8 | 7 | |||||||
| 2023-05-19 | 43 | 21 | 19 | 3 | 3 | 4 | 4 | ||||||||
| 2023-05-20 | 9 | 42 | 42 | 29 | 27 | 4 | 4 | ||||||||
| 2023-05-21 | 8 | 11 | 24 | 27 | 27 | 4 | 3 | ||||||||
| 2023-05-22 | 2 | 8 | 7 | 3 | 5 | 21 | 17 | ||||||||
| 2023-05-23 | 1 | 2 | 3 | 4 | 5 | 27 | 20 | ||||||||
| 2023-05-24 | 5 | 2 | 2 | 2 | 4 | 7 | 7 | ||||||||
| 2023-05-25 | 5 | 5 | 5 | 1 | 1 | 4 | 4 | ||||||||
| 2023-05-26 | 1 | 1 | 1 | 3 | 3 | ||||||||||
| 2023-05-27 | 2 | 1 | 1 | 1 | 2 | 1 | |||||||||
| 2023-05-28 | 6 | 3 | 1 | 1 | 1 | 1 | |||||||||
| 2023-05-29 | 1 | 2 | 2 | 2 | 1 | ||||||||||
| 2023-05-30 | 2 | 1 | 3 | 3 | 1 | 1 | |||||||||
| 2023-05-31 | 1 | 4 | 3 | 2 | 2 | 2 | 3 | ||||||||
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| 2023-06-05 | 1 | 1 | 1 | 1 | |||||||||||
| 2023-06-06 | 1 | 1 | 1 | 1 | |||||||||||
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rm(list = ls())
cleanup(ask = F)
For questions or comments, please contact cyril.michel@noaa.gov