CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

Stanislaus River wild steelhead

2022-2023 Season (PROVISIONAL DATA)

Telemetry Study Template for this study can be found here

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1. Project Status

---

Study is complete, all tags are no longer active as of 2023-12-13. All times in Pacific Standard Time.

Study began on 2023-03-07 14:45:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Knights_Ferry	2023-03-07 14:45:00	2023-04-19 13:26:39	198	Knights_Ferry	284.5	217.3	124.2

---

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){
   "No detections yet"
} 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)
   icons <- awesomeIcons(iconColor = "lightblue",
                         text = fish_per_site$fish_count)

   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.
      addMarkers(~longitude, ~latitude, label = ~fish_count, group = "Receiver Sites", popup = ~general_location,
                 labelOptions = labelOptions(noHide = T, textsize = "15px")) %>% 
      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 == "Benicia_west" | general_location == "Benicia_east")

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)
    }
  }

  ## 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))
}

2.3 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 = "")))

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)*1.5)))

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 = "3.1 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 = "3.1 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),]

  # 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) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")

  print(kable(WR.surv1, row.names = F, "html", caption = "3.1 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"))
}

3.1 Minimum survival to Benicia Bridge East Span (using CJS survival model)

Release Group	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)
ALL	0.5	0.5	0.1	3.5	100
Knights_Ferry	0.5	0.5	0.1	3.5	NA

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
CVP_Tank	2023-05-23 13:38:11	2023-05-23 13:38:11	2023-05-23 13:38:11	1	0.51	144.531
CVP_Trash_Rack_1	2023-05-23 06:10:34	2023-05-23 06:10:34	2023-05-23 06:10:34	1	0.51	144.500
Benicia_east	2023-05-25 00:51:58	2023-05-25 00:51:58	2023-05-25 00:51:58	1	0.51	52.240
Benicia_west	2023-05-25 01:01:30	2023-05-25 01:01:30	2023-05-25 01:01:30	1	0.51	52.040

4.2.1 Detections for Knights_Ferry release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
CVP_Tank	2023-05-23 13:38:11	2023-05-23 13:38:11	2023-05-23 13:38:11	1	0.51	144.531
CVP_Trash_Rack_1	2023-05-23 06:10:34	2023-05-23 06:10:34	2023-05-23 06:10:34	1	0.51	144.500
Benicia_east	2023-05-25 00:51:58	2023-05-25 00:51:58	2023-05-25 00:51:58	1	0.51	52.240
Benicia_west	2023-05-25 01:01:30	2023-05-25 01:01:30	2023-05-25 01:01:30	1	0.51	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
2023-03-07
2023-03-08
2023-03-09
2023-03-10
2023-03-11
2023-03-12
2023-03-13
2023-03-14
2023-03-15										NA
2023-03-16										NA
2023-03-17										NA
2023-03-18										NA
2023-03-19										NA
2023-03-20
2023-03-21
2023-03-22
2023-03-23
2023-03-24
2023-03-25
2023-03-26
2023-03-27
2023-03-28
2023-03-29
2023-03-30
2023-03-31
2023-04-01
2023-04-02
2023-04-03
2023-04-04
2023-04-05
2023-04-06
2023-04-07
2023-04-08
2023-04-09
2023-04-10
2023-04-11
2023-04-12
2023-04-13
2023-04-14
2023-04-15
2023-04-16
2023-04-17
2023-04-18
2023-04-19
2023-04-20
2023-04-21
2023-04-22
2023-04-23
2023-04-24
2023-04-25
2023-04-26
2023-04-27
2023-04-28
2023-04-29
2023-04-30
2023-05-01
2023-05-02
2023-05-03
2023-05-04
2023-05-05
2023-05-06
2023-05-07
2023-05-08
2023-05-09
2023-05-10
2023-05-11
2023-05-12
2023-05-13
2023-05-14
2023-05-15
2023-05-16
2023-05-17
2023-05-18
2023-05-19
2023-05-20
2023-05-21
2023-05-22
2023-05-23								1	1
2023-05-24
2023-05-25														1	1
2023-05-26
2023-05-27
2023-05-28
2023-05-29
2023-05-30
2023-05-31
2023-06-01
2023-06-02
2023-06-03
2023-06-04
2023-06-05
2023-06-06
2023-06-07
2023-06-08
2023-06-09
2023-06-10
2023-06-11
2023-06-12
2023-06-13
2023-06-14
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
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
2023-09-05	NA
2023-09-06	NA
2023-09-07	NA
2023-09-08	NA
2023-09-09	NA
2023-09-10	NA
2023-09-11	NA
2023-09-12	NA
2023-09-13	NA
2023-09-14
2023-09-15
2023-09-16
2023-09-17
2023-09-18
2023-09-19
2023-09-20
2023-09-21
2023-09-22
2023-09-23
2023-09-24
2023-09-25
2023-09-26
2023-09-27
2023-09-28
2023-09-29
2023-09-30
2023-10-01
2023-10-02
2023-10-03
2023-10-04
2023-10-05
2023-10-06
2023-10-07
2023-10-08
2023-10-09
2023-10-10
2023-10-11
2023-10-12
2023-10-13
2023-10-14
2023-10-15
2023-10-16
2023-10-17
2023-10-18
2023-10-19
2023-10-20
2023-10-21
2023-10-22
2023-10-23
2023-10-24
2023-10-25
2023-10-26
2023-10-27
2023-10-28
2023-10-29
2023-10-30
2023-10-31
2023-11-01
2023-11-02
2023-11-03
2023-11-04
2023-11-05
2023-11-06
2023-11-07
2023-11-08
2023-11-09
2023-11-10
2023-11-11
2023-11-12
2023-11-13
2023-11-14
2023-11-15												NA
2023-11-16												NA	NA
2023-11-17												NA	NA
2023-11-18												NA	NA
2023-11-19												NA	NA
2023-11-20												NA	NA
2023-11-21												NA	NA
2023-11-22												NA	NA
2023-11-23												NA	NA
2023-11-24												NA	NA
2023-11-25												NA	NA
2023-11-26												NA	NA
2023-11-27												NA	NA
2023-11-28												NA	NA
2023-11-29												NA	NA
2023-11-30												NA	NA
2023-12-01												NA	NA
2023-12-02												NA	NA
2023-12-03												NA	NA
2023-12-04												NA	NA
2023-12-05												NA	NA
2023-12-06												NA	NA
2023-12-07												NA	NA
2023-12-08												NA	NA
2023-12-09												NA	NA
2023-12-10												NA	NA
2023-12-11												NA	NA
2023-12-12												NA	NA
2023-12-13												NA	NA

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

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