In jmlondon/aleutpvdata: Satellite Telemetry Data: Aleutian Islands harbor seals (2014-2017)

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

Connect to the Data Portal

Wildlife Computers (Redmond, Washington, USA) provides an API for their data portal that allows us to download the latest telemetry data for any deployment. To facilitate connection and use of this API within R, the wcUtils package has been developed.

The data portal is not a public repository. Data access is controlled by the data owner. Thus, only those users who are either owners of the deployment data or users who have been granted access by the owner can access the API.

The wcUtils package is available for install from GitHub:

if (!require('devtools')) install.packages('devtools')
if (!require('wcUtils')) {
  devtools::install_github('jmlondon/wcUtils')
}
if (!require('tidyverse')) install.packages('tidyverse')

Download KotzEB09 Telemetry

All of the deployments have been labeled within the data portal as ProjectID = AleutPV. We will use that information to only download the relevant data.

# first thing, get deployment data from WCDP
r <- wcUtils::wcPOST()
# get KotzEB09 ids and download the data
aleut_ids <- wcUtils::wcGetProjectIDs(r,project = 'AleutPV')

for (i in 1:length(aleut_ids)) {
  zipfile <- wcUtils::wcGetZip(id = aleut_ids[i])
  file.copy(zipfile,'.',overwrite = TRUE)
  file.rename(file.path(basename(zipfile)),
              file.path(paste(aleut_ids[i],"zip",sep = '.'))
  )
}

The zip files downloaded from the data portal are named based on the unique id assigned within the data portal. For our deployments, we have assigned each a unique DeployID. It would be more informative if we could rename all of these zip files to the assigned DeployID. Eventually, this functionality could be implemented within the wcUtils package and the API, but for now, we'll just open each zip file and examine the data files to extract the assigned DeployID.

df <- tibble::as_tibble()
for (zipfile in list.files(pattern = ".zip",full.names = TRUE)) {
  summary_name <- grep('*-Summary.csv',
                      unzip(file.path(
                                      basename(zipfile)),
                            list = TRUE)$Name,
                       value = TRUE)
  deployid <- read.csv(unzip(zipfile, files = summary_name))$DeployID
  deployid <- as.character(deployid)
  instr <- read.csv(unzip(zipfile, files = summary_name))$Instr
  instr <- as.character(instr)
  first_tx <- read.csv(unzip(zipfile, files = summary_name))$LatestXmitTime
  first_tx <- as.character(first_tx)
  last_tx <- read.csv(unzip(zipfile, files = summary_name))$LatestXmitTime
  last_tx <- as.character(last_tx)
  ndays_tx <- read.csv(unzip(zipfile, files = summary_name))$XmitDays
  ndays_tx <- as.character(ndays_tx)

  file.remove(summary_name)
  file.rename(file.path(zipfile),
              file.path(paste(deployid,"zip",sep = '.'))
  )

  df <- df %>% dplyr::bind_rows(list(filename = paste(deployid,"zip",sep = '.'),
                 instrument = instr,
                 "first transmission" = first_tx,
                 "last transmission" = last_tx,
                 "no. days" = ndays_tx
                 ))
}

List of Archive Files

df %>% knitr::kable(booktabs = TRUE,
  caption = 'Files and Associated Key Deployment Statistics')

jmlondon/aleutpvdata documentation built on Aug. 3, 2019, 2:20 p.m.