CalFishTrack
Central Valley Enhanced
Acoustic Tagging Project
Putah Creek Sacramento pikeminnow
2019-2020 Season (PROVISIONAL DATA)
1. Project Status
Study is complete, all tags are no longer active. All times in Pacific Standard Time.
Telemetry Study Template for this study can be found here
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
tagcodes <- as.data.frame(fread("qry_HexCodes.txt", stringsAsFactors = F))
tagcodes$RelDT <- as.POSIXct(tagcodes$RelDT, format = "%m/%d/%Y %I:%M:%S %p", tz = "Etc/GMT+8")
latest <- read.csv("latest_download.csv", stringsAsFactors = F)
study_tagcodes <- tagcodes[tagcodes$StudyID == "Putah_Creek_PKM_2020",]
if (nrow(study_tagcodes) == 0){
cat("Project has not yet begun")
}else{
cat(paste("Project began on ", min(study_tagcodes$RelDT), ", see tagging details below:", sep = ""))
study_tagcodes$Release <- "Release 1"
release_stats <- aggregate(list(First_release_time = study_tagcodes$RelDT),
by= list(Release = study_tagcodes$Release),
FUN = min)
release_stats <- merge(release_stats,
aggregate(list(Last_release_time = study_tagcodes$RelDT),
by= list(Release = study_tagcodes$Release),
FUN = max),
by = c("Release"))
release_stats <- merge(release_stats, aggregate(list(Number_fish_released =
study_tagcodes$TagID_Hex),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {length(unique(x))}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Release_location = study_tagcodes$Rel_loc),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {head(x,1)}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Release_rkm = study_tagcodes$Rel_rkm),
by= list(Release = study_tagcodes$Release),
FUN = function(x) {head(x,1)}),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Mean_length = study_tagcodes$Length),
by= list(Release = study_tagcodes$Release),
FUN = mean, na.rm = T),
by = c("Release"))
release_stats <- merge(release_stats,
aggregate(list(Mean_weight = study_tagcodes$Weight),
by= list(Release = study_tagcodes$Release),
FUN = mean, na.rm = T),
by = c("Release"))
release_stats2<-release_stats[,-3]
colnames(release_stats2)[2]<-"Release time"
release_stats[,c("Mean_length", "Mean_weight")] <- round(release_stats[,c("Mean_length", "Mean_weight")],1)
release_stats$First_release_time <- format(release_stats$First_release_time, tz = "Etc/GMT+8")
release_stats$Last_release_time <- format(release_stats$Last_release_time, tz = "Etc/GMT+8")
kable(release_stats, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left")
} | Release | First_release_time | Last_release_time | Number_fish_released | Release_location | Release_rkm | Mean_length | Mean_weight |
|---|---|---|---|---|---|---|---|
| Release 1 | 2020-04-22 11:30:00 | 2020-05-20 11:50:00 | 62 | Russell Ranch | 168.9 | 88.5 | 7.2 |
2. Detections statistics at all realtime receivers
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
library(cder)
library(reshape2)
detects_study <- fread(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products\\Study_detection_files\\detects_Putah_Creek_PKM_2020.csv", sep = ""), colClasses = c(DateTime_PST = "character", RelDT = "character"))
if (nrow(detects_study) == 0){
"No detections yet"
} else {
study_count <- nrow(study_tagcodes)
gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
arrivals <- aggregate(list(DateTime_PST = detects_study$DateTime_PST), by = list(general_location = detects_study$general_location, TagCode = detects_study$TagCode), FUN = min)
tag_stats <- aggregate(list(First_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = min)
tag_stats <- merge(tag_stats,
aggregate(list(Mean_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = mean),
by = c("general_location"))
tag_stats <- merge(tag_stats,
aggregate(list(Last_arrival = arrivals$DateTime_PST),
by= list(general_location = arrivals$general_location),
FUN = max),
by = c("general_location"))
tag_stats <- merge(tag_stats,
aggregate(list(Fish_count = arrivals$TagCode),
by= list(general_location = arrivals$general_location),
FUN = function(x) {length(unique(x))}),
by = c("general_location"))
tag_stats$Percent_arrived <- round(tag_stats$Fish_count/study_count * 100,2)
tag_stats <- merge(tag_stats, unique(gen_locs[,c("general_location", "rkm")]))
tag_stats <- tag_stats[order(tag_stats$rkm, decreasing = T),]
tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
print(kable(tag_stats, row.names = F,
caption = "4.1 Detections for all releases combined",
"html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
for (j in sort(unique(study_tagcodes$Release))) {
if(nrow(detects_study[detects_study$Release == j,]) > 0 ) {
temp <- detects_study[detects_study$Release == j,]
arrivals1 <- aggregate(list(DateTime_PST = temp$DateTime_PST), by = list(general_location = temp$general_location, TagCode = temp$TagCode), FUN = min)
rel_count <- nrow(study_tagcodes[study_tagcodes$Release == j,])
tag_stats1 <- aggregate(list(First_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = min)
tag_stats1 <- merge(tag_stats1,
aggregate(list(Mean_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = mean),
by = c("general_location"))
tag_stats1 <- merge(tag_stats1,
aggregate(list(Last_arrival = arrivals1$DateTime_PST),
by= list(general_location = arrivals1$general_location),
FUN = max),
by = c("general_location"))
tag_stats1 <- merge(tag_stats1,
aggregate(list(Fish_count = arrivals1$TagCode),
by= list(general_location = arrivals1$general_location),
FUN = function(x) {length(unique(x))}),
by = c("general_location"))
tag_stats1$Percent_arrived <- round(tag_stats1$Fish_count/rel_count * 100,2)
tag_stats1 <- merge(tag_stats1, unique(gen_locs[,c("general_location", "rkm")]))
tag_stats1 <- tag_stats1[order(tag_stats1$rkm, decreasing = T),]
tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
final_stats <- kable(tag_stats1, row.names = F,
caption = paste("4.2 Detections for",j,"release groups", sep = " "),
"html")
print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
cat("\n\n\\pagebreak\n")
print(paste("No detections for",j,"release group yet", sep=" "), quote = F)
cat("\n\n\\pagebreak\n")
}
}
}[1] “No detections yet”
## Set fig height for next plot here, based on how long fish have been at large
figheight <- min(10,max(c(3,as.numeric(difftime(Sys.Date(), min(study_tagcodes$RelDT), units = "days")) / 4)))2.2 Fish arrivals per day
setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = ""))
if (nrow(detects_study) == 0){
"No detections yet"
} else {
beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F)
beacon_by_day$day <- as.Date(beacon_by_day$day)
arrivals$day <- as.Date(format(arrivals$DateTime_PST, "%Y-%m-%d"))
arrivals_per_day <- aggregate(list(New_arrivals = arrivals$TagCode), by = list(day = arrivals$day, general_location = arrivals$general_location), length)
arrivals_per_day$day <- as.Date(arrivals_per_day$day)
## Now subset to only look at data for the correct beacon for that day
beacon_by_day <- as.data.frame(beacon_by_day[which(beacon_by_day$TagCode == beacon_by_day$beacon),])
## Now only keep beacon by day for days since fish were released
beacon_by_day <- beacon_by_day[beacon_by_day$day >= as.Date(min(study_tagcodes$RelDT)) & beacon_by_day$day <= endtime,]
beacon_by_day <- merge(beacon_by_day, gen_locs[,c("location", "general_location","rkm")], by = "location", all.x = T)
arrivals_per_day <- merge(beacon_by_day, arrivals_per_day, all.x = T, by = c("general_location", "day"))
arrivals_per_day$day <- factor(arrivals_per_day$day)
## Remove bench test and other NA locations
arrivals_per_day <- arrivals_per_day[!arrivals_per_day$general_location == "Bench_test",]
arrivals_per_day <- arrivals_per_day[is.na(arrivals_per_day$general_location) == F,]
## Change order of data to plot decreasing rkm
arrivals_per_day <- arrivals_per_day[order(arrivals_per_day$rkm, decreasing = T),]
arrivals_per_day$general_location <- factor(arrivals_per_day$general_location, unique(arrivals_per_day$general_location))
ggplot(data=arrivals_per_day, aes(x=general_location, y=fct_rev(as_factor(day)))) +
geom_tile(fill = "lightgray", color = "black") +
geom_text(aes(label=New_arrivals)) +
labs(x="General Location", y = "Date") +
theme(panel.background = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1))
}[1] “No detections yet”
rm(list = ls())