CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

American River Hatchery Fall-run Chinook salmon

2022-2023 Season (PROVISIONAL DATA)

Telemetry Study Template for this study can be found here

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1. Project Status

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Study is complete, all tags are no longer active as of 2023-10-29. All times in Pacific Standard Time.

Study began on 2023-05-16 10:00:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Week 1	2023-05-16 10:00:00	2023-05-16 10:00:00	508	Upper Sunrise Boat Ramp	200	91.3	8.5

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2. Real-time Fish Detections

---

library(leaflet)
library(maps)
library(htmlwidgets)
library(leaflet.extras)
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")

   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 {

   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$Release_lat[1])){
     leaflet(data = fish_per_site) %>%
       addProviderTiles("Esri.WorldStreetMap", group = "Map") %>%
       addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
       addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
       # Marker data are from the sites data frame. We need the ~ symbols
       # to indicate the columns of the data frame.
       addPulseMarkers(lng = fish_per_site$longitude, lat = fish_per_site$latitude, label = ~fish_count, 
                       labelOptions = labelOptions(noHide = T, textsize = "15px"), group = "Receiver Sites",
                       popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
       addCircleMarkers(data = release_stats, ~Release_lon, ~Release_lat, label = ~Number_fish_released, stroke = F, color = "blue", fillOpacity = 1, 
                        group = "Release Sites", popup = ~Release_location, labelOptions = labelOptions(noHide = T, textsize = "15px")) %>%
       addScaleBar(position = "bottomleft") %>%
       addLegend("bottomright", labels = c("Receivers", "Release locations"), colors = c("red","blue")) %>%
       addLayersControl(baseGroups = c("Street Map", "Satellite", "Relief"), options = layersControlOptions(collapsed = FALSE))
   } else {
     leaflet(data = fish_per_site) %>%
       addProviderTiles("Esri.WorldStreetMap", group = "Map") %>%
       addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
       addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
       # Marker data are from the sites data frame. We need the ~ symbols
       # to indicate the columns of the data frame.
       addPulseMarkers(lng = fish_per_site$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 == "TowerBridge")

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 = "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))

}

2.3 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_4 <- detects_study %>% filter(general_location == "Benicia_west" | general_location == "Benicia_east")

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)
    }
  }

  ## 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))
}

2.4 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"))
  }
}

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

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	70.3	2.6	65.1	75.1	57.4	4.8	4.7	329
Week 1	70.3	2.6	65.1	75.1	NA	4.8	4.7	329

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) 
    }
  }
}

3.2 Minimum through-Delta survival, and travel time: City of Sacramento to Benicia (using CJS survival model)

Release	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Mean Delta passage (days)	SD of Delta Passage (days)	Count
ALL	45.9	2.8	40.5	51.4	3.9	2.5	150
Week 1	45.9	2.8	40.5	51.4	3.9	2.5	150

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"))
}

3.3 Minimum survival to Benicia Bridge East Span (using CJS survival model), and travel time

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	34	2.1	30	38.2	96.4	9.4	5.1	172
Week 1	34	2.1	30	38.2	NA	9.4	5.1	172

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")
    }
  }
}

4.1 Detections for all releases combined

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
MeridianBr	2023-05-24 12:24:14	2023-05-28 18:49:06	2023-06-02 20:01:13	4	0.78	290.848
TowerBridge	2023-05-16 03:25:01	2023-05-21 15:51:13	2023-06-09 17:47:48	206	40.39	172.000
I80-50_Br	2023-05-16 03:45:35	2023-05-21 00:41:20	2023-06-09 04:56:56	289	56.67	170.748
Old_River_Quimby	2023-10-08 08:53:49	2023-10-08 08:53:49	2023-10-08 08:53:49	1	0.20	141.000
Sac_BlwGeorgiana	2023-05-17 07:09:07	2023-05-22 03:06:50	2023-06-07 07:29:21	210	41.18	119.058
Sac_BlwGeorgiana2	2023-05-17 09:06:11	2023-05-22 13:36:35	2023-07-02 08:29:46	213	41.76	118.398
Benicia_east	2023-05-19 05:02:42	2023-05-25 23:40:30	2023-06-14 15:28:54	167	32.75	52.240
Benicia_west	2023-05-19 05:05:33	2023-05-26 04:26:56	2023-06-13 14:40:33	140	27.45	52.040

4.2.1 Detections for Week 1 release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
MeridianBr	2023-05-24 12:24:14	2023-05-28 18:49:06	2023-06-02 20:01:13	4	0.78	290.848
TowerBridge	2023-05-16 03:25:01	2023-05-21 15:51:13	2023-06-09 17:47:48	206	40.39	172.000
I80-50_Br	2023-05-16 03:45:35	2023-05-21 00:41:20	2023-06-09 04:56:56	289	56.67	170.748
Old_River_Quimby	2023-10-08 08:53:49	2023-10-08 08:53:49	2023-10-08 08:53:49	1	0.20	141.000
Sac_BlwGeorgiana	2023-05-17 07:09:07	2023-05-22 03:06:50	2023-06-07 07:29:21	210	41.18	119.058
Sac_BlwGeorgiana2	2023-05-17 09:06:11	2023-05-22 13:36:35	2023-07-02 08:29:46	213	41.76	118.398
Benicia_east	2023-05-19 05:02:42	2023-05-25 23:40:30	2023-06-14 15:28:54	167	32.75	52.240
Benicia_west	2023-05-19 05:05:33	2023-05-26 04:26:56	2023-06-13 14:40:33	140	27.45	52.040

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"))

  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")
}

4.3 Fish arrivals per day (“NA” means receivers were non-operational)

	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
2023-05-16			2	2
2023-05-17			51	77								14	16
2023-05-18			26	45								44	42
2023-05-19			23	28								34	32	2	2
2023-05-20			17	21								14	13	13	12
2023-05-21			19	28								21	22	24	17
2023-05-22			12	18								17	17	14	11
2023-05-23			16	27								16	15	19	15
2023-05-24		1	6	5								12	13	13	9
2023-05-25		1	3	5								9	10	16	15
2023-05-26			5	8								2	2	13	9
2023-05-27			1	3								8	8	11	12
2023-05-28			5	2								2	2	7	6
2023-05-29			1	2								2	2	6	6
2023-05-30				2								1	1	5	5
2023-05-31		1	1	1								1	1	3	3
2023-06-01			3	3								2	2	2	2
2023-06-02		1	3	3								3	4	2	2
2023-06-03			5	3								4	4	2	2
2023-06-04			3	3								1	1	3	2
2023-06-05												2	4	2	2
2023-06-06			1	1										5	3
2023-06-07			1									1	1	3	4
2023-06-08				1
2023-06-09			2	1
2023-06-10
2023-06-11
2023-06-12
2023-06-13														1	1
2023-06-14														1
2023-06-15
2023-06-16
2023-06-17
2023-06-18
2023-06-19
2023-06-20
2023-06-21
2023-06-22
2023-06-23
2023-06-24
2023-06-25
2023-06-26
2023-06-27
2023-06-28
2023-06-29
2023-06-30
2023-07-01
2023-07-02													1
2023-07-03
2023-07-04
2023-07-05
2023-07-06
2023-07-07
2023-07-08
2023-07-09
2023-07-10
2023-07-11
2023-07-12
2023-07-13
2023-07-14
2023-07-15
2023-07-16
2023-07-17
2023-07-18
2023-07-19
2023-07-20
2023-07-21
2023-07-22
2023-07-23
2023-07-24
2023-07-25
2023-07-26
2023-07-27
2023-07-28	NA
2023-07-29	NA
2023-07-30	NA
2023-07-31	NA
2023-08-01	NA
2023-08-02	NA
2023-08-03	NA
2023-08-04	NA
2023-08-05	NA
2023-08-06	NA
2023-08-07	NA
2023-08-08	NA
2023-08-09	NA
2023-08-10	NA
2023-08-11	NA
2023-08-12	NA
2023-08-13	NA
2023-08-14	NA
2023-08-15	NA
2023-08-16	NA
2023-08-17	NA
2023-08-18	NA
2023-08-19	NA
2023-08-20	NA
2023-08-21	NA
2023-08-22	NA
2023-08-23	NA
2023-08-24	NA
2023-08-25	NA
2023-08-26	NA
2023-08-27	NA
2023-08-28	NA
2023-08-29	NA
2023-08-30	NA
2023-08-31	NA
2023-09-01	NA
2023-09-02	NA
2023-09-03	NA
2023-09-04	NA

rm(list = ls())
cleanup(ask = F)

For questions or comments, please contact cyril.michel@noaa.gov