RchivalTag Tutorial | Part 6 - Quantifying Deep Dives

In this tutorial we will learn how to quantify deep dives from depth temperature time series data.

Getting started | Requirements

To run this tutorial we will need RchivalTag version >= 0.1.5 and oceanmap version >= 0.13. You can find the newest versions of both packages on github

## install or load package
# install.packages("RchivalTag") # from CRAN
# library(xfun)
# install_github("rkbauer/oceanmap") # newest version from github
# install_github("rkbauer/RchivalTag") # newest version from github
library("RchivalTag")

## Package overview and version:
?RchivalTag 
help(package="RchivalTag") ## list of functions

ts_file <- system.file("example_files/104659-Series.csv",package="RchivalTag")
ts_df <- read_TS(ts_file)
ts_df$Lon <- 5; ts_df$Lat <- 43
dy_DepthTS(ts_df)

depth_lim <- 100
ts_list <- list(ts_df)
sm <- c()
idive <- 1
for(n in 1:length(ts_list)){
  dat <- classify_DayTime(ts_list[[n]])
  dat$idive <- NA
  # sm <- dat %>% summarise(nperc=100-round(100*length(which(is.na(Depth)))/n(),1))
  # if(sm$nperc >= 99) stop()
  # dat$ii <- 1:nrow(dat)
  ii <- which(dat$Depth[2:nrow(dat)] > depth_lim & dat$Depth[1:(nrow(dat)-1)] < depth_lim)+1
  for(i in ii){
    j <- which(dat$Depth[i:nrow(dat)] < depth_lim)[1]-1
    if(all(!is.na(dat$Depth[i:(i+j)]))){ # do not include gaps
      add <- data.frame(idive=idive,
                        DeployID=dat$DeployID[1],
                        tstep=diff(as.numeric(dat$datetime[1:2])),
                        dive_duration=as.numeric(dat$datetime[i+j])-as.numeric(dat$datetime[i]),
                        tstart=dat$datetime[i],
                        tend=dat$datetime[i+j],
                        sunrise=dat$sunrise[i],
                        sunset=dat$sunset[i],
                        dawn=dat$dawn.ast[i],
                        dusk=dat$dusk.ast[i],
                        daytime=dat$daytime.long[i],
                        maxDepth=max(dat$Depth[i:(i+j)]))
      sm <- rbind(sm,add)
      dat$idive[(i-1):(i+j)] <- idive
      idive <- idive + 1
    }
  }
}
sm$tstart_hour <- RchivalTag:::.datetime2hour(sm$tstart)
par(xaxs="i",yaxs="i",las=1)
h <- hist(sm$tstart_hour,col="grey",breaks = 0:24,
          xlab="Hour (UTC)",main="",ylab=paste0("Deep Dives > ",depth_lim," m (n)"),axes=F)
axis(2)
axis(1,at=h$mids,labels = h$breaks[1:length(h$mids)])
box()

library(DT)
library(dplyr)
sm %>% mutate(tstart=as.character(tstart),tend=as.character(tend),
              sunrise=as.character(sunrise),sunset=as.character(sunset),
              dawn=as.character(dawn),dusk=as.character(dusk)) %>% 
    datatable() %>%
    DT::formatStyle(columns = 1:13, fontSize = '70%')

The following code will illustrate the raw dive profiles. This can be interesting in case there are reoccuring patterns (i.e. of dive depth, duration and timing).

library(dplyr)
library(ggplot2)
input <- dat %>% 
  filter(!is.na(idive)) %>%
  mutate(Time = RchivalTag:::.datetime2hour.dc(datetime),
         dawn=mean(RchivalTag:::.datetime2hour.dc(dawn.ast)),
         dusk=mean(RchivalTag:::.datetime2hour.dc(dusk.ast)),
         sunrise=mean(RchivalTag:::.datetime2hour.dc(sunrise)),
         sunset=mean(RchivalTag:::.datetime2hour.dc(sunset)))
ylim <- c(500,0)

input %>%
  ggplot() + 
  annotate("rect", xmin=-Inf,xmax=Inf,ymin=ylim[1],ymax=ylim[2],fill="grey") +
  annotate("rect", xmin=input$dawn,xmax=input$sunrise,ymin=ylim[1],ymax=ylim[2],fill="grey90") +
  annotate("rect", xmin=input$sunset,xmax=input$dusk,ymin=ylim[1],ymax=ylim[2],fill="grey90") +
  annotate("rect", xmin=input$sunrise,xmax=input$sunset,ymin=ylim[1],ymax=ylim[2],fill="white") +
  geom_line(aes(x=Time,y=Depth,group=idive)) + coord_cartesian(xlim = c(0,24), ylim = ylim) + 
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        axis.ticks.length = unit(1,"cm")) + 
  geom_hline(yintercept=100, linetype="dashed") +
  theme_minimal()