CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

6-year Study San Joaquin River Steelhead - April Releases

2023-2024 Season (PROVISIONAL DATA)

Telemetry Study Template for this study can be found here

---

1. Project Status

---

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

library(knitr)
library(kableExtra)
library(lubridate)
library(data.table)
library(ggplot2)
library(RMark)
library(scales)
library(viridis)
library(forcats)
library(reshape2)
library(png)
library(dataRetrieval)
library(rerddap)
library(plotly)

study <- "SJ_Steelhead_April_2024"

detects_study <- fread("study_detections_archive_2024.csv", stringsAsFactors = F,
                       colClasses = c(DateTime_PST = "character", RelDT = "character")) %>%
   filter(Study_ID == study) %>%
   mutate(DateTime_PST = as.POSIXct(DateTime_PST, format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT+8"),
          release_time = as.POSIXct(RelDT, format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT+8")) %>%
   rename(., weight=Weight, length=Length, release_rkm=Rel_rkm, release_location=Rel_loc, river_km=rkm)


latest <- read.csv("latest_download.csv", stringsAsFactors = F)$x

##################################################################################################################
#### TO RUN THE FOLLOWING CODE CHUNKS FROM HERE ON DOWN USING R ERDDAP, UN-COMMENT THESE NEXT 9 LINES OF CODE ####
##################################################################################################################
# cache_delete_all()
# query = paste('&','Study_ID','="', study, '"', sep = '')
# datafile=URLencode(paste("https://oceanview.pfeg.noaa.gov/erddap/tabledap/","FEDcalFishTrack",".csv?",query,sep = ""))
# options(url.method = "libcurl", download.file.method = "libcurl", timeout = 180)
# detects_study <- data.frame(read.csv(datafile,row.names = NULL, stringsAsFactors = F))
# detects_study <- detects_study[-1,]
# detects_study$DateTime_PST <- as.POSIXct(detects_study$local_time, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
# detects_study$release_time <- as.POSIXct(detects_study$release_time, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
# detects_study$river_km <- as.numeric(detects_study$river_km)
##################################################################################################################

Study is complete, all tags are no longer active as of 2024-09-25. All times in Pacific Standard Time.

if (nrow(detects_study) == 0){
   cat("Study has not yet begun  ")
} else {
   if (min(detects_study$release_time) > Sys.time()){
      cat("Study has not yet begun, below data is a placeholder:  ")
   }
   if (min(detects_study$release_time) < Sys.time()){
      cat(paste("Study began on ", min(detects_study$release_time), ", see tagging details below:", sep = ""))
   }

   ######################################
   #### RELEASE GROUPS ASSIGNED HERE ####
   ######################################
   detects_study$Release <- detects_study$release_location

   study_tagcodes <- as.data.frame(unique(detects_study[,c("TagCode", "release_time", "weight", "length", "release_rkm",
                                                        "release_location", "Release","Rel_latitude","Rel_longitude")]))

   release_stats <- study_tagcodes %>%
      group_by(Release) %>%
      summarise(First_release_time = min(release_time),
                Last_release_time = max(release_time),
                Number_fish_released = length(unique(TagCode)),
                Release_location = head(release_location, 1),
                Release_rkm = head(release_rkm,1),
                Mean_length = mean(length, na.rm=T),
                Mean_weight = mean(weight, na.rm=T),
                Release_lat = head(Rel_latitude,1),
                Release_lon = head(Rel_longitude,1)) %>%
      mutate(Mean_length = round(Mean_length, 1),
             Mean_weight = round(Mean_weight, 1),
             First_release_time = format(First_release_time, tz = "Etc/GMT+8"),
             Last_release_time = format(Last_release_time, tz = "Etc/GMT+8")) %>%
      arrange(First_release_time)

   release_stats_all <- study_tagcodes %>%
     summarise(First_release_time = min(release_time),
               Last_release_time = max(release_time),
               Number_fish_released = length(unique(TagCode)),
               Release_location = NA,
               Release_rkm = mean(release_rkm,na.rm = T),
               Mean_length = mean(length, na.rm=T),
               Mean_weight = mean(weight, na.rm=T),
               Release_lat = head(Rel_latitude,1),
               Release_lon = head(Rel_longitude,1)) %>%
     mutate(Mean_length = round(Mean_length, 1),
            Mean_weight = round(Mean_weight, 1),
            Release_rkm = round(Release_rkm,1),
            First_release_time = format(First_release_time, tz = "Etc/GMT+8"),
            Last_release_time = format(Last_release_time, tz = "Etc/GMT+8"))
   release_stats <- rbind(release_stats, data.frame(Release = "ALL", release_stats_all))

kable(release_stats[,!names(release_stats)%in% c("Release_lon","Release_lat","release_location")], format = "html", row.names = F) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left")
}

Study began on 2024-04-17 08:00:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Dos_Reis_Rel	2024-04-17 08:00:00	2024-04-19 08:32:00	104	Dos_Reis_Rel	151.90	239.9	140.3
Head_of_Old_River_Rel	2024-04-17 08:34:00	2024-04-19 09:16:00	91	Head_of_Old_River_Rel	156.00	240.2	136.8
DurhamFerry_Rel	2024-04-17 08:46:00	2024-04-19 08:59:00	140	DurhamFerry_Rel	183.58	242.0	142.2
ALL	2024-04-17 08:00:00	2024-04-19 09:16:00	335	NA	166.30	240.8	140.1

---

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)
   release_stats_agg <- aggregate(cbind(Release_lon, Release_lat) ~ Release_location, data = release_stats[release_stats$Release != "ALL",], FUN = mean)
   release_stats_agg <- merge(release_stats_agg, aggregate(Number_fish_released ~ Release_location, data = release_stats[release_stats$Release != "ALL",], FUN = sum))

   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(data = fish_per_site[seq(from = 1, to = nrow(fish_per_site), by = 2),], lng = ~longitude, lat = ~latitude, label = ~fish_count, 
                       labelOptions = labelOptions(noHide = T, direction = "left", textsize = "15px"), group = "Receiver Sites",
                       popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
       addPulseMarkers(data = fish_per_site[seq(from = 2, to = nrow(fish_per_site), by = 2),], lng = ~longitude, lat = ~latitude, label = ~fish_count, 
                       labelOptions = labelOptions(noHide = T, direction = "right", textsize = "15px"), group = "Receiver Sites",
                       popup = ~general_location, icon = makePulseIcon(heartbeat = 1.3)) %>%
       addCircleMarkers(data = release_stats_agg, ~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, 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$first_detect) == F,]
   releases <- aggregate(list(first_detect = detects_study$release_time), by = list(TagCode = detects_study$TagCode, release_time = detects_study$release_time, release_location=detects_study$release_location,release_rkm = detects_study$release_rkm), min)
   releases$river_km <- releases$release_rkm
   releases$mov_counter <- NA
   releases$general_location <- NA
   releases$general_location <- releases$release_location
   releases$release_location <- NULL

   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, x = ~first_detect, y = ~river_km, color = ~TagCode, width = 900, height = 600, dynamicTicks = TRUE, connectgaps = TRUE, mode = "lines+markers", type = "scatter",hoverinfo = "text",
      text = ~paste("</br> TagCode: ", TagCode,
                    "</br> Arrival: ", first_detect,
                    "</br> Location: ", general_location)) %>%
      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(dplyr)
library(cder)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

recv_locs <- gen_locs

detects_omr_cor <- detects_study[detects_study$general_location %in% c("Old_River_Quimby", "Holland_Cut_Quimby", "Old River", "MiddleRiver", "Clifton_Court_US_Radial_Gates", "SWP_radial_gates_DS", "SWP_radial_gates_US", "CVP_Trash_Rack_1", "CVP_Tank", "SWP_intake", "Clifton_Court_Intake_Canal"),]

if (nrow(detects_omr_cor)>0) {
  # Save the last detection at each location
  detects_omr_cor <- merge(detects_omr_cor,aggregate(list(last_detect = detects_omr_cor$DateTime_PST), by = list(TagCode= detects_omr_cor$TagCode, general_location = detects_omr_cor$general_location), FUN = max))
  
  # Convert last detection to day
  detects_omr_cor$Day <- as.Date(detects_omr_cor$last_detect, "Etc/GMT+8")
  
  starttime <- as.Date(min(detects_omr_cor$release_time), "Etc/GMT+8")
  ## Endtime should be either now or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
             max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
  
  daterange <- data.frame(Date = seq.Date(from = starttime, to = endtime, by = "day"))
  
  rels <- unique(study_tagcodes$Release)
  rel_num <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_omr_cor$Release))])
  
  tagcount <- aggregate(list(unique_tags = detects_omr_cor$TagCode), 
                        by = list(Date = detects_omr_cor$Day, Location = detects_omr_cor$general_location), 
                        FUN = function(x){length(unique(x))})
  tagcount$Location <- factor(tagcount$Location, levels = c("Old_River_Quimby", "Holland_Cut_Quimby",
                                                            "MiddleRiver", "Clifton_Court_US_Radial_Gates",
                                                            "CVP_Trash_Rack_1", "CVP_Tank",
                                                            "Clifton_Court_Intake_Canal"))
  tagcount1 <- reshape2::dcast(tagcount, Date ~ Location, drop = FALSE)
  daterange1 <- merge(daterange, tagcount1, all.x=T)
  
  daterange1[is.na(daterange1)] <- 0
  daterange2 <- daterange1
  rownames(daterange2) <- daterange2$Date
  
  par(mar=c(6, 5, 2, 5) + 0.1)
 
  daterange3 <- melt(daterange2, id.vars = "Date", variable.name = ".", )
  
  # Add latitude to daterange df
  locs <- data.frame(general_location = unique(daterange3$.)) %>% 
          left_join(., recv_locs, by = 'general_location') %>% filter(is.na(stop) | stop > min(detects_omr_cor$RelDT)) %>% 
          select(location, general_location, latitude) %>% group_by(general_location) %>% 
          summarise(latitude = mean(latitude))
  locs <- locs[order(locs$latitude, decreasing = FALSE),]
  locs$loc_num <- seq.int(nrow(locs))
  daterange4 <- daterange3 %>% left_join(., locs, by = c("." = "general_location"))

  # Get flow data from CDEC
  BTC_flow <- cdec_query("OMR", "20", "H", starttime, endtime)

  # Fish movement
  move_df <- detects_omr_cor %>%
             distinct(., TagCode, last_detect, general_location,
                      .keep_all = TRUE) # find last detection and remove duplicate value for that column
  dup_codes <- move_df$TagCode[which(duplicated(move_df$TagCode))] # then get the tag codes that were detected at multiple locations
  move_df <- move_df[(move_df$TagCode %in% dup_codes),] # save only the fish that were detected at multiple locations
  
  if(nrow(move_df)>1){
    move_df$Day <- as.Date(move_df$last_detect, "Etc/GMT+8")
    new_move_df <- NULL
    for(i in 1:length(unique(move_df$TagCode))){
      tmp_code    <- unique(move_df$TagCode)[i]
      tmp_move_df <- move_df %>% filter(TagCode == tmp_code) # subset data
      tmp_move_df <- tmp_move_df[order(tmp_move_df$last_detect, decreasing = FALSE),] # order data
      for(j in 1:(length(tmp_move_df$TagCode) - 1)){
        tmp_new_move_df <- data.frame(TagCode = tmp_code, location1 = tmp_move_df$general_location[j],
                                     day1 = tmp_move_df$Day[j], location2 = tmp_move_df$general_location[j+1],
                                     day2 = tmp_move_df$Day[j+1])
        tmp_new_move_df$loc_num1 <- locs$loc_num[which(locs$general_location == tmp_new_move_df$location1)]
        tmp_new_move_df$loc_num2 <- locs$loc_num[which(locs$general_location == tmp_new_move_df$location2)]
        new_move_df <- rbind(new_move_df, tmp_new_move_df)
      }
    }
  
    fig1 <- plot_ly(data = daterange4, type = 'scatter', mode = "markers", 
                    marker = list(color = ~ value, size = ~value*5, 
                                  colorbar = list(title = "Num. of arrivals", len = 0.35, x = 1.06, y = 0.73), 
                                  colorscale = 'Viridis', line = list(width = 0)),
                     hoverinfo = 'text', text = ~paste('Date:', Date,'<br># of arrivals:', value),
                     x = ~Date, y = ~loc_num) %>%
            add_annotations(x = new_move_df$day2, y = new_move_df$loc_num2, axref="x", ayref="y", text="", showarrow=TRUE,
                            ax = new_move_df$day1, ay = new_move_df$loc_num1, arrowcolor = 'darkgrey',
                            opacity = 0.5, standoff = 5, startstandoff = 5) %>%
            layout(xaxis = list(range = c(min(daterange$Date) - 1, max(daterange$Date) + 1), showgrid = FALSE, showline = TRUE),
                   yaxis = list(range = c(min(locs$loc_num) - 1, max(locs$loc_num) + 1), showline = TRUE,
                   title = "", ticktext = locs$general_location, tickvals = locs$loc_num), showlegend = FALSE) %>% 
            add_annotations(text = sprintf("<b>North<b>"), xref = 'paper', yref = 'paper', x = -0.13, xanchor = 'left',
                            y = 1.05, yanchor = 'top', showarrow = FALSE, font = list(size = 20)) %>%
            add_annotations(text = sprintf("<b>South<b>"), xref = 'paper', yref = 'paper', x = -0.13, xanchor = 'left',
                            y = 0.04, yanchor = 'top', showarrow = FALSE, font = list(size = 20))
    fig2 <- plot_ly(data = BTC_flow, type = 'scatter', mode = 'lines') %>%
            add_trace(x = ~DateTime, y = ~Value) %>%
            layout(xaxis = list(range = c(min(daterange$Date) - 1, max(daterange$Date) + 1), showgrid = FALSE, showline = TRUE),
                   yaxis = list(title = "Flow (cfs) at OMR"))
    subplot(fig1, fig2, nrows = 2, margin = 0.04, heights = c(0.7, 0.3), titleY = TRUE) 
  }else{
      plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
  text(1.5,1.5, labels = "NOT ENOUGH DETECTIONS", cex = 2)
  }
}else{
  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)
}

2.3 (BETA) Detections in the Old and Middle rivers (OMR) for duration of tag life (top) and flow at OMR (bottom). Arrows indicate fish movement.

_______________________________________________________________________________________________________

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 = "")))

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 = "3.1 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),]

  # 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	11.4	1.7	8.4	15.3	96.8	13.3	7.0	38
Dos_Reis_Rel	14.5	3.5	8.9	22.7	NA	11.2	6.6	15
DurhamFerry_Rel	7.9	2.3	4.4	13.8	NA	15.4	6.5	11
Head_of_Old_River_Rel	13.3	3.6	7.7	21.9	NA	14.1	7.8	12

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
MiddleRiver	2024-04-20 02:18:02	2024-04-27 20:38:14	2024-05-21 13:32:14	17	5.07	150.000
Clifton_Court_US_Radial_Gates	2024-04-18 18:58:11	2024-04-27 17:53:50	2024-06-03 20:40:24	31	9.25	146.000
Holland_Cut_Quimby	2024-04-18 22:45:07	2024-04-29 01:05:41	2024-05-10 09:20:25	16	4.78	145.000
CVP_Tank	2024-04-21 00:50:08	2024-05-04 17:05:45	2024-05-23 08:29:03	9	2.69	144.531
CVP_Trash_Rack_1	2024-04-18 11:52:42	2024-04-28 12:01:55	2024-10-27 04:58:09	57	17.01	144.500
Clifton_Court_Intake_Canal	2024-04-19 07:15:07	2024-05-02 11:03:55	2024-05-24 21:03:27	12	3.58	142.721
Old_River_Quimby	2024-04-23 10:34:02	2024-05-18 08:27:39	2024-09-18 11:06:58	7	2.09	141.000
SacRiverWalnutGrove_2	2024-04-21 16:51:17	2024-04-28 19:44:11	2024-05-05 22:37:05	2	0.60	120.300
Georg_Sl_1	2024-04-21 16:37:55	2024-04-28 19:21:09	2024-05-05 22:04:23	2	0.60	119.600
Benicia_east	2024-04-22 05:50:39	2024-05-01 08:40:56	2024-05-19 15:50:53	37	11.04	52.240
Benicia_west	2024-04-22 05:53:09	2024-05-07 04:12:35	2024-10-06 06:25:16	31	9.25	52.040

4.2.1 Detections for Dos_Reis_Rel release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
MiddleRiver	2024-04-20 02:18:02	2024-04-28 14:13:51	2024-05-21 13:32:14	10	9.62	150.000
Clifton_Court_US_Radial_Gates	2024-04-29 16:32:58	2024-05-17 06:36:41	2024-06-03 20:40:24	2	1.92	146.000
Holland_Cut_Quimby	2024-04-23 00:02:55	2024-05-01 18:12:22	2024-05-10 09:20:25	4	3.85	145.000
CVP_Tank	2024-04-29 05:48:46	2024-04-30 00:41:21	2024-04-30 19:33:56	2	1.92	144.531
CVP_Trash_Rack_1	2024-04-22 17:28:52	2024-04-28 06:26:10	2024-05-05 14:17:56	8	7.69	144.500
Clifton_Court_Intake_Canal	2024-05-24 21:03:27	2024-05-24 21:03:27	2024-05-24 21:03:27	1	0.96	142.721
Old_River_Quimby	2024-04-23 12:12:03	2024-04-27 04:08:02	2024-05-08 03:50:30	4	3.85	141.000
Benicia_east	2024-04-22 18:40:06	2024-04-29 09:35:11	2024-05-15 13:08:43	15	14.42	52.240
Benicia_west	2024-04-22 18:40:58	2024-05-12 09:55:28	2024-10-06 06:25:16	13	12.50	52.040

4.2.2 Detections for DurhamFerry_Rel release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
MiddleRiver	2024-04-26 21:11:05	2024-04-27 03:02:03	2024-04-27 08:55:52	3	2.14	150.000
Clifton_Court_US_Radial_Gates	2024-04-23 05:24:51	2024-05-02 06:42:08	2024-05-12 18:35:08	6	4.29	146.000
Holland_Cut_Quimby	2024-04-27 09:22:08	2024-05-02 01:01:36	2024-05-06 16:41:05	2	1.43	145.000
CVP_Tank	2024-04-29 10:32:23	2024-05-11 09:30:43	2024-05-23 08:29:03	2	1.43	144.531
CVP_Trash_Rack_1	2024-04-20 06:50:20	2024-04-29 10:55:58	2024-05-23 04:57:27	14	10.00	144.500
Clifton_Court_Intake_Canal	2024-04-30 12:49:33	2024-05-07 22:23:33	2024-05-17 14:04:01	3	2.14	142.721
Old_River_Quimby	2024-04-23 10:34:02	2024-07-06 10:50:30	2024-09-18 11:06:58	2	1.43	141.000
Benicia_east	2024-04-26 06:52:15	2024-05-03 10:33:51	2024-05-13 12:37:17	10	7.14	52.240
Benicia_west	2024-04-26 06:55:36	2024-05-04 11:37:27	2024-05-13 19:01:45	8	5.71	52.040

4.2.3 Detections for Head_of_Old_River_Rel release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
MiddleRiver	2024-04-20 16:12:12	2024-04-26 13:51:21	2024-05-04 15:32:55	4	4.40	150.000
Clifton_Court_US_Radial_Gates	2024-04-18 18:58:11	2024-04-24 20:45:20	2024-05-19 16:58:36	23	25.27	146.000
Holland_Cut_Quimby	2024-04-18 22:45:07	2024-04-27 08:39:49	2024-05-04 21:34:55	10	10.99	145.000
CVP_Tank	2024-04-21 00:50:08	2024-05-03 21:53:32	2024-05-17 02:38:43	5	5.49	144.531
CVP_Trash_Rack_1	2024-04-18 11:52:42	2024-04-28 04:09:03	2024-10-27 04:58:09	35	38.46	144.500
Clifton_Court_Intake_Canal	2024-04-19 07:15:07	2024-04-27 14:34:07	2024-05-21 07:44:04	8	8.79	142.721
Old_River_Quimby	2024-05-04 21:00:24	2024-05-04 21:00:24	2024-05-04 21:00:24	1	1.10	141.000
SacRiverWalnutGrove_2	2024-04-21 16:51:17	2024-04-28 19:44:11	2024-05-05 22:37:05	2	2.20	120.300
Georg_Sl_1	2024-04-21 16:37:55	2024-04-28 19:21:09	2024-05-05 22:04:23	2	2.20	119.600
Benicia_east	2024-04-22 05:50:39	2024-05-02 01:59:02	2024-05-19 15:50:53	12	13.19	52.240
Benicia_west	2024-04-22 05:53:09	2024-05-02 12:26:56	2024-05-19 15:54:36	10	10.99	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")
}

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

	Blw_Salt_RT	MeridianBr	Stan_Valley_Oak	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	SacRiverWalnutGrove_2	Georg_Sl_1	Sac_BlwGeorgiana	Sac_BlwGeorgiana2	Benicia_east	Benicia_west
2024-04-17
2024-04-18							1	1		2
2024-04-19							1			10	1
2024-04-20						2	5			11	1
2024-04-21							3		1	5			1	1
2024-04-22						2	1	1		4							2	2
2024-04-23						1	6	3		2	1	4
2024-04-24						2				2	2						1	1
2024-04-25							1	1									3	1
2024-04-26						2	2	1	1								6	5
2024-04-27						3		2	1	3	1						2	2
2024-04-28						1	2			6							3	2
2024-04-29							3		2	2	1						3	2
2024-04-30						1		1	1	2	1						3	3
2024-05-01								1									3	2
2024-05-02										1							1	1
2024-05-03								1									1
2024-05-04						1		1		1		1
2024-05-05						1	1			1	1		1	1
2024-05-06								1
2024-05-07							1										1	1
2024-05-08								1				1					1	1
2024-05-09							1										1
2024-05-10								1									3	3
2024-05-11																		1
2024-05-12							1			1
2024-05-13																	1	1
2024-05-14
2024-05-15																	1	1
2024-05-16										2
2024-05-17									2		1
2024-05-18
2024-05-19							1										1	1
2024-05-20
2024-05-21						1					1
2024-05-22
2024-05-23									1	1
2024-05-24											1
2024-05-25
2024-05-26
2024-05-27
2024-05-28
2024-05-29
2024-05-30
2024-05-31
2024-06-01
2024-06-02
2024-06-03							1
2024-06-04
2024-06-05
2024-06-06
2024-06-07
2024-06-08
2024-06-09
2024-06-10
2024-06-11
2024-06-12
2024-06-13
2024-06-14
2024-06-15
2024-06-16
2024-06-17
2024-06-18
2024-06-19
2024-06-20
2024-06-21
2024-06-22
2024-06-23
2024-06-24
2024-06-25
2024-06-26
2024-06-27
2024-06-28
2024-06-29
2024-06-30
2024-07-01
2024-07-02
2024-07-03
2024-07-04
2024-07-05
2024-07-06
2024-07-07
2024-07-08
2024-07-09
2024-07-10
2024-07-11
2024-07-12
2024-07-13
2024-07-14
2024-07-15
2024-07-16
2024-07-17
2024-07-18
2024-07-19
2024-07-20
2024-07-21
2024-07-22
2024-07-23
2024-07-24
2024-07-25
2024-07-26
2024-07-27
2024-07-28
2024-07-29
2024-07-30
2024-07-31
2024-08-01
2024-08-02
2024-08-03
2024-08-04
2024-08-05
2024-08-06
2024-08-07
2024-08-08
2024-08-09
2024-08-10
2024-08-11
2024-08-12
2024-08-13
2024-08-14	NA
2024-08-15
2024-08-16	NA
2024-08-17	NA
2024-08-18	NA
2024-08-19	NA
2024-08-20	NA
2024-08-21	NA
2024-08-22	NA
2024-08-23	NA
2024-08-24	NA
2024-08-25	NA
2024-08-26	NA
2024-08-27	NA
2024-08-28	NA
2024-08-29	NA
2024-08-30	NA
2024-08-31	NA
2024-09-01	NA
2024-09-02	NA
2024-09-03	NA
2024-09-04	NA
2024-09-05	NA
2024-09-06	NA
2024-09-07	NA
2024-09-08	NA
2024-09-09	NA
2024-09-10	NA
2024-09-11
2024-09-12
2024-09-13
2024-09-14
2024-09-15
2024-09-16
2024-09-17
2024-09-18												1
2024-09-19
2024-09-20
2024-09-21
2024-09-22
2024-09-23
2024-09-24
2024-09-25

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

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