RchivalTag Tutorial | Part 7 - Simulate Depth Time Series Data

In this tutorial we will learn how to simulate depth time series data from a template data set.

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$Serial <- ts_df$DeployID
ts_df$Lon <- 5; ts_df$Lat <- 43
dy_DepthTS(ts_df)

We can use the depth time series illustrated above as a template data set to simulate new depth time series data via the simulate_DepthTS function:

ts_df_sim <- simulate_DepthTS(ts_df) # simulate data

dy_DepthTS(ts_df_sim) # plot simulated data

Note that the simulate_DepthTS function can insert transmission gaps (by default a transmission rate of 90% will be modeled) via the gaps and trate-arguments and introduces further variation in the Depth records (i.e. template depth records of >= 300 will be replaced by values between 100 and 700 m).

ts_df_sim2 <- simulate_DepthTS(ts_df, trate = 70, ref_Depth_lim = 100) # simulate data

dy_DepthTS(ts_df_sim2) # plot simulated data

library(dplyr)
meta <- rbind(ts_df[,names(ts_df_sim)],ts_df_sim,ts_df_sim2) %>%
        group_by(DeployID, Serial, Ptt) %>%
        summarise(dep.date=min(date),pop.date=max(date)) %>%
        as.data.frame()

ts_list <- list(ts_df, ts_df_sim, ts_df_sim2)
names(ts_list) <- meta$Serial

# plot data coverage
plot_data_coverage(x = ts_list,type="ts", meta = meta)