In allisonglider/seabiRds: Prepares biologging data for analysis and upload to the lab database

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The first step in processing logging data is to get the metadata about each deployment into a standard format. Often the data recorded when putting on and removing a device will be recorded in a different way between sites and across years, so it is helpful to standardize that data. First we will read in an example of a deployment file from some technosmart logger deployments on thick-billed murres.

Load example technomsart deployment data and format it

library(seabiRds)

# Get path to the example deployment data
fn <- system.file('extdata/technosmart', "technosmart_ex.csv", package = "seabiRds", mustWork = T)

# Read in data from this file
raw_dep <- read.csv(fn, stringsAsFactors = F)

# Look at head of the data
head(raw_dep)

This example data was missing a few things we need for the formatDeployments() function, so we will do some pre-processing. Each deployment needs to have a unique ID that will match the file name of your raw data. I have used deployment ids that comabine the GPS id with the band number and the date of recapture. This ensures that across all of my data each deployment ID is unique, even if I happen to put the same GPS unit on the same bird at some other time. We will paste these fields together to generate the deployment IDs.These deployment IDs must appear in the file names (although the file name can be longer) of the raw data, so that we can match each deployment to the data files. We will also add some columns for species, colony, colour band, deployment longitude, and deployment latitude; these data were not recorded in the field because they were the same for all deployments, but it is required information.

raw_dep$Species <- 'TBMU' # add species column
raw_dep$Colony <- 'Coats' # add colony column
raw_dep$Colour <- NA # Colour isa required field so add an empty column
raw_dep$Dep_lon <- -82.01 # longitude of deployment
raw_dep$Dep_lat <- 62.95 # latitude of deployment

head(raw_dep)

Now run the formatDeployments() function to standardize the data. The function requires the data frame of your raw deployments, a date format, deployment timezone, and column names in your data for any relevant fields. Because Technosmart loggers record up to 3 data types (GPS, TDR, and ACC), we will map the logger ID to gps_id, tdr_id, and acc_id so that we know there are three types of data available from this deployment. If we had also deployed an additional logger on this bird, such as a camera logger, we could include that now.

# Run formatDeployments
dep_data <- formatDeployments(
  deployments = raw_dep, # dataframe with raw data
  dateFormat = "%Y-%m-%d %H:%M", #POSIXct format of dates
  dep_tz = "US/Eastern", # deployment time zone
  species = 'Species', # column name containing species
  metal_band = 'Band', # column name containing band number
  colour_band = 'Colour', # column name containing band colours
  dep_id = 'FileName', # column name containing deployment id
  fill_dep_id = F, # If function 
  site = 'Colony',
  dep_lon = 'Dep_lon', # column name containing deployment longitude
  dep_lat = 'Dep_lat', # column name containing deployment latitude
  time_released = 'DeploymentEDT', # column name containing initial release time
  time_recaptured = 'RecaptureEDT', # column name containing recapture time
  status_on = 'StatusOn', # column name containing breeding status at release
  status_off = 'StatusOff', # column name containing breeding status at recapture
  mass_on = 'MassOn', # column name containing mass at release
  mass_off = 'MassOff', # column name containing mass at recapture
  gps_id = 'GPS', # column name containing logger id
  tdr_id = 'GPS', # column name containing logger id
  acc_id = 'GPS' # column name containing logger id
)

Now we have our metadata in a standard format. There are some extra fields that are needed for our biologging database (e.g. exclude and fed_unfed). If you aren't enetering your data into the database, you could get rid of any fields you don't need.

# Look at the results
head(dep_data)

The next step is to read in your raw GPS data. All of the converted data files from your loggers should be stored in a single folder. We will get the path to example data stored withi this package, then use the readGPSData() function to read that data and convert it to a standard format.

If we compare the file names in our folder to our deployment ids, we can see that each deployment id is part of the file name. Sometimes technosmart units will create multiple files from the same deployment and each file will have a suffix like "_S1", "_S2", etc. The function will match all of those files to the same deployment.

# Get the path to the example data stored within this package
folder <- paste0(find.package('seabiRds'), "/extdata/technosmart/gps_data")


sort(list.files(folder)) # look at file names

sort(dep_data$dep_id) # look at deployment IDs

Now read in the these files with readGPSData().

# Run the readGPSData function for Technosmart data
ts_data <- readGPSData(
  inputFolder = folder, # path to the colder storing the raw GPS files
  deployments = dep_data, # formatted deployment data
  tagType = "Technosmart"
)

# Look at the output
head(ts_data)

Now we have loaded all the GPS data and matched each file to a deployment. If there are any deployments with no matching data or data with no matching deployments, the function would have printed a message warning us. This is useful for checking if there are any errors in your data or file names.

The next step will clip your GPS data to the start and end times of your deployment. This removes any data that may have been collected before the logger went on the bird or after removing the logger from the bird. The function will produce two plots for each deployment, showing distance from the deployment location over time and a map of the deployment. Any locations that are removed will show in red and locations that are retained are shown in black.This will help you identify any data issues with therelease and recapture times, or if you have an issue with timezones.

ts_data <- cleanGPSData(data = ts_data,
                            deployments = dep_data,
                            speedThreshold = 150, # remove locations that require the bird to travel faster than 150 km/hr
                            plot = F)

allisonglider/seabiRds documentation built on Feb. 14, 2025, 7:37 a.m.