,
Ian Jonsen [aut, ctb, cre] ,
Vinay Udyawer [aut, ctb] ,
Ross Dwyer [aut, ctb] ,
Kylie Scales [aut, ctb] ,
Francisca Maron [aut, ctb] ,
Xavier Hoenner [aut, ctb] ,
Charlie Huveneers [aut, ctb] ,
Yuri Niella [aut, ctb] | Title: | Rapid Extraction of Marine Observations for Roving Animals |
|---|---|
| Description: | R Package to access and integrate animal tracking data from the IMOS Animal Tracking Facility database and web-app, and environmental data from the AODN database. |
| Authors: | Fabrice Jaine [aut, ctb] ,
Ian Jonsen [aut, ctb, cre] ,
Vinay Udyawer [aut, ctb] ,
Ross Dwyer [aut, ctb] ,
Kylie Scales [aut, ctb] ,
Francisca Maron [aut, ctb] ,
Xavier Hoenner [aut, ctb] ,
Charlie Huveneers [aut, ctb] ,
Yuri Niella [aut, ctb] |
| Maintainer: | Ian Jonsen <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.8-03 |
| Built: | 2024-11-18 14:27:49 UTC |
| Source: | https://github.com/imos-AnimalTracking/remora |
Rapid Extraction of Marine Observations for Roving Animals
Ross Dwyer, Xavier Hoenner, Charlie Huveneers, Fabrice Jaine, Ian Jonsen, Francisca Maron, Kylie Scales, Vinay Udyawer
Hoenner, X et al. (2018) Australia’s continental-scale acoustic tracking database and its automated quality control process. Scientific Data 5, 170206. https://doi.org/10.1038/sdata.2017.206
runQC extractEnv extractMoor shinyReport
Provides a platform-independent way to interactively select files
choose_file(caption)choose_file(caption)
caption |
caption to display in choose file window. If method defaults to base::file.choose then caption is printed to the console (doesn't work reliably due to shiny server issues) |
The data needs to have the detections merged to the receiver metadata. Not to be called by user
det_per_receiver_location(data)det_per_receiver_location(data)
data |
merged detections data and receiver metadata |
a tibble
not to be called by user
det_per_species(data)det_per_species(data)
data |
detections data |
a tibble
not to be called by user
det_per_station(data)det_per_station(data)
data |
merged detections data and receiver metadata |
a tibble
not to be called by user
det_per_tag(data)det_per_tag(data)
data |
detections data |
a tibble
not to be called by user
det_per_tag_location(data)det_per_tag_location(data)
data |
detections data |
a tibble
not to be called by user
det_species_station(data)det_species_station(data)
data |
merged detections data and receiver metadata |
a tibble
Accesses and download environmental data from the Bluelink (CSIRO) server and append variables to detection data based on date of detection
extractBlue( df, X, Y, datetime, env_var, extract_depth = 0, var_name = paste(env_var, extract_depth, sep = "_"), folder_name = "Bluelink", env_buffer = 1, cache_layers = FALSE, full_timeperiod = FALSE, station_name = NULL, export_step = FALSE, export_path = "Processed_data", .parallel = FALSE, .ncores = NULL, verbose = TRUE )extractBlue( df, X, Y, datetime, env_var, extract_depth = 0, var_name = paste(env_var, extract_depth, sep = "_"), folder_name = "Bluelink", env_buffer = 1, cache_layers = FALSE, full_timeperiod = FALSE, station_name = NULL, export_step = FALSE, export_path = "Processed_data", .parallel = FALSE, .ncores = NULL, verbose = TRUE )
df |
detection data source in data frame with at the minimum a X, Y and date time field |
X |
name of column with X coordinate or longitude (EPSG 4326) |
Y |
name of column with Y coordinate or latitude (EPSG 4326) |
datetime |
name of column with date time stamp (Coordinated Universal Time; UTC) |
env_var |
variable needed from Bluelink. Options include ('BRAN_temp', 'BRAN_salt', BRAN_ssh', 'BRAN_mld', 'BRAN_cur', 'BRAN_wind'). |
extract_depth |
Bluelink data is 3D, so data can be obtained either at the water surface or at depth. Please provide the depth of interest (between 0 and 4,509 m) as numeric and the function will automatically obtain the data at the nearest available layer. By default the data will be extracted at the water surface. |
var_name |
name for the column including the extracted environmental data. Can be usefull if the user wants to download the same environmental data at different depths. If not specified, it will be chosen based on the env_var and extract_depth arguments. |
folder_name |
name of folder within the working directory where the downloaded and processed netCDF files should be saved. Default (NULL) produces automatic folder names based on study extent and deletes processed files after processing. |
env_buffer |
distance (in decimal degrees) to expand the study area beyond the coordinates to extract environmental data. Default value is 1°. |
cache_layers |
should the downloaded and processed environmental data be cached within the working directory? If FALSE (default), the Bluelink data will be stored in a temporary folder and discarded after environmental extraction. Otherwise, it will be saved in the "cached" folder within folder_name. |
full_timeperiod |
should environmental variables be extracted for each day across full monitoring period? This option is time and memory consuming for long projects. If this option is selected, the returned dataset will be standardized for the days with/without detections across all stations (station_name column) where animals were detected. For more details please see the package vignettes. |
station_name |
if full_timeperiod = TRUE, please provide the column that identifies the name of the acoustic stations |
export_step |
should the processed dataset be exported to file? This is particularly usefull for large datasets, to avoid function failure due to issues with internet connexion. The rows with missing data will be exported as NAs, and only these will be completed if the function is rerun providing the exported dataset as input (df) |
export_path |
path and name of to export the dataset with appended environmnetal data |
.parallel |
should the function be run in parallel |
.ncores |
number of cores to use if set to parallel. If none provided,
uses |
verbose |
should function provide details of what operation is being conducted.
Set to |
The extractBlue function allows the user to download, process and
append a range of 3D environmental variables (between the water surface to 4,509 m depth)
to each detection within a telemetry data set.
We advocate for users to first undertake a quality control step using
the runQC function before further analysis, however the
functionality to append environmental data will work on any dataset that has
at the minimum spatial coordinates (i.e., latitude, longitude; in EPSG 4326)
and a timestamp (in UTC) for each detection event. Quality controlled
environmental variables housed in the Bluelink (BRAN) CSIRO server will be extracted
for each specific coordinate at the specific timestamp where available. A
summary table of the full range of environmental variables currently
available can be accessed using the imos_variables function.
a dataframe with the environmental variable appended as an extra column based on date of each detection
## Input example detection dataset that have run through the quality control ## workflow (see 'runQC' function) library(tidyverse) data("TownsvilleReefQC") ## simplify & subset data for speed qc_data <- TownsvilleReefQC %>% unnest(cols = c(QC)) %>% ungroup() %>% filter(Detection_QC %in% c(1,2)) %>% filter(filename == unique(filename)[1]) %>% slice(1:20) ## Extract daily interpolated sea surface temperature ## cache_layers & fill_gaps args set to FALSE for speed data_with_temp <- extractBlue(df = qc_data, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime", env_var = "BRAN_temp", extract_depth = 0, verbose = TRUE)## Input example detection dataset that have run through the quality control ## workflow (see 'runQC' function) library(tidyverse) data("TownsvilleReefQC") ## simplify & subset data for speed qc_data <- TownsvilleReefQC %>% unnest(cols = c(QC)) %>% ungroup() %>% filter(Detection_QC %in% c(1,2)) %>% filter(filename == unique(filename)[1]) %>% slice(1:20) ## Extract daily interpolated sea surface temperature ## cache_layers & fill_gaps args set to FALSE for speed data_with_temp <- extractBlue(df = qc_data, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime", env_var = "BRAN_temp", extract_depth = 0, verbose = TRUE)
Accesses and download environmental data from the IMOS THREDDS server and append variables to detection data based on date of detection
extractEnv( df, X = "longitude", Y = "latitude", datetime = "detection_timestamp", env_var, folder_name = NULL, verbose = TRUE, cache_layers = TRUE, crop_layers = TRUE, full_timeperiod = FALSE, fill_gaps = FALSE, buffer = NULL, nrt = FALSE, output_format = ".grd", .parallel = TRUE, .ncores = NULL )extractEnv( df, X = "longitude", Y = "latitude", datetime = "detection_timestamp", env_var, folder_name = NULL, verbose = TRUE, cache_layers = TRUE, crop_layers = TRUE, full_timeperiod = FALSE, fill_gaps = FALSE, buffer = NULL, nrt = FALSE, output_format = ".grd", .parallel = TRUE, .ncores = NULL )
df |
detection data source in data frame with at the minimum a X, Y and date time field |
X |
name of column with X coordinate or longitude (EPSG 4326) |
Y |
name of column with Y coordinate or latitude (EPSG 4326) |
datetime |
name of column with date time stamp (Coordinated Universal Time; UTC) |
env_var |
variable needed options include ('rs_sst', 'rs_sst_interpolated', 'rs_salinity', 'rs_chl', 'rs_turbidity', 'rs_npp', 'bathy', 'dist_to_land', 'rs_current') |
folder_name |
name of folder within 'imos.cache' where downloaded rasters should be saved. default NULL produces automatic folder names based on study extent |
verbose |
should function provide details of what operation is being conducted.
Set to |
cache_layers |
should the extracted environmental data be cached within the working directory? if FALSE stored in temporary folder and discarded after environmental extraction |
crop_layers |
should the extracted environmental data be cropped to within the study site |
full_timeperiod |
should environmental variables extracted for each day across full monitoring period, time and memory consuming for long projects |
fill_gaps |
should the function use a spatial buffer to estimate
environmental variables for detections where there is missing data. Default
is |
buffer |
radius of buffer (in m) around each detection from which
environmental variables should be extracted from. A median value of pixels
that fall within the buffer will be used if |
nrt |
should Near Real-Time current data be used if Delayed-Mode current
data is missing. Default is |
output_format |
File format for cached environmental layers. You can use
|
.parallel |
should the function be run in parallel |
.ncores |
number of cores to use if set to parallel. If none provided,
uses |
The extractEnv function allows the user to access, download and
append a range of environmental variables to each detection within a telemetry
data set. We advocate for users to first undertake a quality control step using
the runQC function before further analysis, however the
functionality to append environmental data will work on any dataset that has
at the minimum spatial coordinates (i.e., latitude, longitude; in EPSG 4326)
and a timestamp (in UTC) for each detection event. Quality controlled
environmental variables housed in the IMOS Thredds server will be extracted
for each specific coordinate at the specific timestamp where available. A
summary table of the full range of environmental variables currently
available can be accessed using the imos_variables function.
a dataframe with the environmental variable appended as an extra column based on date of each detection
## Input example detection dataset that have run through the quality control ## workflow (see 'runQC' function) library(tidyverse) data("TownsvilleReefQC") ## simplify & subset data for example speed-up qc_data <- TownsvilleReefQC %>% unnest(cols = c(QC)) %>% ungroup() %>% filter(Detection_QC %in% c(1,2)) %>% filter(filename == unique(filename)[1]) %>% slice(5:8) ## Extract daily interpolated sea surface temperature ## cache_layers & fill_gaps args set to FALSE for speed data_with_sst <- extractEnv(df = qc_data, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime", env_var = "rs_sst_interpolated", cache_layers = FALSE, crop_layers = TRUE, full_timeperiod = FALSE, fill_gaps = TRUE, folder_name = "test", .parallel = FALSE)## Input example detection dataset that have run through the quality control ## workflow (see 'runQC' function) library(tidyverse) data("TownsvilleReefQC") ## simplify & subset data for example speed-up qc_data <- TownsvilleReefQC %>% unnest(cols = c(QC)) %>% ungroup() %>% filter(Detection_QC %in% c(1,2)) %>% filter(filename == unique(filename)[1]) %>% slice(5:8) ## Extract daily interpolated sea surface temperature ## cache_layers & fill_gaps args set to FALSE for speed data_with_sst <- extractEnv(df = qc_data, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime", env_var = "rs_sst_interpolated", cache_layers = FALSE, crop_layers = TRUE, full_timeperiod = FALSE, fill_gaps = TRUE, folder_name = "test", .parallel = FALSE)
Extracts specified sensor value(s) observed by the IMOS mooring nearest to the supplied QC'd detection location(s)
extractMoor( trackingData, file_loc, sensorType = "temperature", timeMaxh = Inf, distMaxkm = Inf, targetDepthm = NA, scalc = c("min", "max", "mean") )extractMoor( trackingData, file_loc, sensorType = "temperature", timeMaxh = Inf, distMaxkm = Inf, targetDepthm = NA, scalc = c("min", "max", "mean") )
trackingData |
dataframe containing acoustic detection data in IMOS QC format with the |
file_loc |
character string describing folder location in which NetCDF files of mooring data have been saved after running mooringDownload function |
sensorType |
character string containing name of mooring sensor to query. Can be "temperature", "velocity" or "salinity". |
timeMaxh |
optional numeric string containing the maximum time threshold to merge detection and mooring sensor values. |
distMaxkm |
optional numeric string containing the maximum distance threshold in kilometers to include in output. |
targetDepthm |
extracts the nearest sensor to this depth value. if set as NA then all sensors returned for this timestamp at this mooring |
scalc |
select the lower or higher depth value when there are 2 options for sensors nearest the targetDepth provided. Users can also specify a mean or |
the trackingData dataframe as a nested tibble object with the sensor values from the nearest moorLocations that fall within the specified time, distance and depth thresholds.
Get expert distribution shapefile, if available, from CSIRO's Geoserver. Full CAAB code list: http://www.marine.csiro.au/datacentre/caab/caab_dump_latest.xlsx
get_expert_distribution_shp_CAAB(CAAB_species_id, spe)get_expert_distribution_shp_CAAB(CAAB_species_id, spe)
CAAB_species_id |
CAAB id of species for which a distribution shapefile is required |
spe |
species scientific name |
For a few species acoustically tagged no shapefile exists.
shp is a multipolygon sf data.frame object of the species' distribution
# example code x <- TownsvilleReefQC$QC[[1]] expert_shp <- get_expert_distribution_shp_CAAB(CAAB_species_id = x$CAAB_species_id[1], spe = x$species_scientific_name[1])# example code x <- TownsvilleReefQC$QC[[1]] expert_shp <- get_expert_distribution_shp_CAAB(CAAB_species_id = x$CAAB_species_id[1], spe = x$species_scientific_name[1])
Links the QC Detection Dataset to the Nearest IMOS Mooring ID
getDistance( trackingData, moorLocations, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime" )getDistance( trackingData, moorLocations, X = "receiver_deployment_longitude", Y = "receiver_deployment_latitude", datetime = "detection_datetime" )
trackingData |
dataframe containing acoustic detection data in IMOS QC format |
moorLocations |
dataframe containing the locations of IMOS moorings |
X |
name of column with X coordinate or longitude (EPSG 4326) |
Y |
name of column with Y coordinate or latitude (EPSG 4326) |
datetime |
name of column with date time stamp (Coordinated Universal Time; UTC) |
The trackingData dataframe with the nearest moor_site_code to each acoustic detection
Extract number of tag detections that fall within the duration of sensor coverage at each mooring
getOverlap(x)getOverlap(x)
x |
The |
a grouped tibble describing proportion of temporal overlap between mooring coverage and detections dataset
grabQC() lets you obtain subsets of the QC output
grabQC( x, what = c("dQC", "detections", "QCflags", "tag_meta", "rec_meta", "meas"), flag = "all" )grabQC( x, what = c("dQC", "detections", "QCflags", "tag_meta", "rec_meta", "meas"), flag = "all" )
x |
a QC output object (a nested tibble with class |
what |
defined subset of the QC output is to be grabbed; either
|
flag |
specifies which quality controlled detections to return
(see examples): any combination of: |
a data frame with the requested subset of the QC output
## grab detections and QCflags from example QC output & return only the ## `valid` and `likely valid` detections data(TownsvilleReefQC) d.qc <- grabQC(TownsvilleReefQC, what = "dQC", flag = c("valid", "likely valid")) ## return all detections d.qc <- grabQC(TownsvilleReefQC, what = "dQC")## grab detections and QCflags from example QC output & return only the ## `valid` and `likely valid` detections data(TownsvilleReefQC) d.qc <- grabQC(TownsvilleReefQC, what = "dQC", flag = c("valid", "likely valid")) ## return all detections d.qc <- grabQC(TownsvilleReefQC, what = "dQC")
remora packageSummary of available IMOS environmental variable to append to detection data.
imos_variables(variable = NULL)imos_variables(variable = NULL)
variable |
(optional) name of a specific variable you are interested in. Default of |
This function helps users identify what quality controlled environmental variables
can be accessed and appended to their detection data using the extractEnv of mooringTable functions.
a formatted table with details of available variables accessible through the remora package
## Identify all available variables imos_variables() ## If there is a specific variable you are interested in imos_variables(variable = "rs_sst_interpolated")## Identify all available variables imos_variables() ## If there is a specific variable you are interested in imos_variables(variable = "rs_sst_interpolated")
Downloads all IMOS mooring data for a specified, single mooring site
mooringDownload( moor_site_codes, fromWeb, sensorType = "temperature", itimeout = 240, file_loc )mooringDownload( moor_site_codes, fromWeb, sensorType = "temperature", itimeout = 240, file_loc )
moor_site_codes |
character vector of the name(s) of the desired mooring(s) (updated from moorID to match download file) |
fromWeb |
logical string detailing whether or no the netCDF should be downloaded from the web or uploaded from a saved file location |
sensorType |
character string detailing which sensor value we are interested in. Can be "temperature", "velocity", "salinity or "oxygen"." |
itimeout |
integer value for number of seconds we are willing to wait before timeout to download netcdf from the web. Defaults to 60 |
file_loc |
character string for the location of the saved files |
The moorData dataframe with the sensor information time series for a given site_code
Uses a Web link to send a Web Feature Service (WFS) query directly to AODN geoserver
mooringTable(sensorType = "temperature")mooringTable(sensorType = "temperature")
sensorType |
Character string containing name of mooring sensor to query. Can be "temperature" or "velocity" |
dataframe containing the locations of IMOS moorings as csv tibble
not to be called by user
n_detections(data)n_detections(data)
data |
detections data |
not to be called by user
n_receivers(data)n_receivers(data)
data |
merged detections data and receiver metadata |
not to be called by user
n_species(data)n_species(data)
data |
detections data |
not to be called by user
n_tags(data)n_tags(data)
data |
detections data |
Creates an interactive depth-time plot from a sensor dataset from a specified IMOS mooring, with an option to overlay species detection records from tracking dataset from closest receiver
plotDT( moorData, moorName, dateStart = NULL, dateEnd = NULL, varName = c("temperature", "vcur", "ucur", "psal"), trackingData = NULL, speciesID, IDtype = "CAAB", detStart = NULL, detEnd = NULL )plotDT( moorData, moorName, dateStart = NULL, dateEnd = NULL, varName = c("temperature", "vcur", "ucur", "psal"), trackingData = NULL, speciesID, IDtype = "CAAB", detStart = NULL, detEnd = NULL )
moorData |
Dataframe containing the sensor data from a single IMOS mooring. |
moorName |
Character string specifying mooring name using IMOS ID code. |
dateStart |
Optional character string of start date of date range of mooring data to plot, as "YYYY-MM-DD" |
dateEnd |
Optional character string of end date of date range of mooring data to plot, as "YYYY-MM-DD" |
varName |
Character string of mooring sensor variable of interest. Can be "temperature", "ucur" for meridional (u) velocity, "vcur" for zonal (v) velocity |
trackingData |
Dataframe containing tag detections data to plot. Takes output from getDistance() function as input. Set as NULL for a base plot with no detections. |
speciesID |
Character string describing species or tag identifier. |
IDtype |
Character string describing type of species or tag identifier. Can be CAAB code, tag ID, common name or scientific name. Must match identifier in detections dataset. |
detStart |
Optional character string of start date of date range of detections data to overlay on depth-time plot. |
detEnd |
Optional character string of end date of date range of detections data to overlay on depth-time plot. |
plotQCint() QC'd detections colour-coded by their
assessed validity status, overlaid on species expert distribution extent
plotQC(x, path = NULL, pal = "PuOr", revpal = TRUE)plotQC(x, path = NULL, pal = "PuOr", revpal = TRUE)
x |
a remora output object with |
path |
path to save map(s). Options are: |
pal |
a |
revpal |
reverse order of colour palette. |
produces interactive leaflet maps of species expert distribution and location of QC'd detections
## example QC'd data data(TownsvilleReefQC) ## save plot as an .html file to the working directory plotQC(TownsvilleReefQC, path = ".") ## clean up system("rm *_QCmap.html")## example QC'd data data(TownsvilleReefQC) ## save plot as an .html file to the working directory plotQC(TownsvilleReefQC, path = ".") ## clean up system("rm *_QCmap.html")
Open netCDF files from BRAN and converts them into data frame format.
remoteNCDF(year, month, var, depth, lon.min, lon.max, lat.min, lat.max)remoteNCDF(year, month, var, depth, lon.min, lon.max, lat.min, lat.max)
year |
Year of interest for data download |
month |
Month of interest for data download |
var |
Variable of interest for data download |
depth |
Depth of interest for data download |
lon.min |
Minimum longitude for data download |
lon.max |
Maximum longitude for data download |
lat.min |
Minimum latitude for data download |
lat.max |
Maximum latitude for data download |
Returns a dataframe with environmental data
conduct quality control (QC) on IMOS-ATF acoustic detections data
runQC( x, lat.check = TRUE, .parallel = FALSE, .ncores = detectCores() - 2, .progress = TRUE )runQC( x, lat.check = TRUE, .parallel = FALSE, .ncores = detectCores() - 2, .progress = TRUE )
x |
a 4-element list of paths to detections, receiver and transmitter deployment metadata, and animal measurements data files. These data must be downloaded from the IMOS-ATF Web App (URL), or have exactly the same structure and variable names as the Web App data. |
lat.check |
(logical; default TRUE) test for receiver_deployment_latitudes in N hemisphere at correct to S hemisphere. Set to FALSE if QC'ing N hemisphere data |
.parallel |
logical; run QC tests in parallel across multiple processors (default is FALSE) |
.ncores |
integer; number of cores to run in parallel. If NULL and
|
.progress |
logical; display QC progress (default is TRUE). |
The QC process merges data from the supplied files downloaded via
the IMOS-ATF Web App (URL): IMOS_detections.csv;
IMOS_receiver_deployment_metadata.csv;
IMOS_transmitter_deployment_metadata.csv;
and IMOS_animal_measurements.csv. Eight quality control tests are
performed on the detections, as outlined in Hoenner et al. (2018), and
QC flags are appended to the merged data for each of these 8 tests.
The QC flags are values ranging between 1 and 4, representing valid,
likely valid, likely invalid, and invalid detections, respectively.
The user can then employ these flags, in various combinations, to filter the
merged data (see examples & vignette).
Utility functions are provided to subset the merged data in various ways
from the nested tibble and to visualise the QC results (see examples
& vignette).
A QC log is written to QC_logfile.txt in the working directory. The
logfile documents potential data issues discovered during the QC process:
e.g., transmitter_deployment_id's present in the detections
file but not in the transmitter metadata file (if supplied);
receiver_deployment_id's present in the detections file but not in the
receiver metadata file (if supplied); NA's present in transmitter deployment
locations. Generally, these issues can not be corrected automatically and
require the user to investigate the cause and take appropriate steps to
correct the data.
the QC output is returned to the parent frame as a nested tibble
with class remora_QC
Hoenner, X et al (2018) Australia’s continental-scale acoustic tracking database and its automated quality control process. Scientific Data 5, 170206. https://doi.org/10.1038/sdata.2017.206
## specify files to QC - use supplied example .csv data files <- list(det = system.file(file.path("test_data","IMOS_detections.csv"), package = "remora"), rmeta = system.file(file.path("test_data", "IMOS_receiver_deployment_metadata.csv"), package = "remora"), tmeta = system.file(file.path("test_data", "IMOS_transmitter_deployment_metadata.csv"), package = "remora"), meas = system.file(file.path("test_data", "IMOS_animal_measurements.csv"), package = "remora")) qc.out <- runQC(files) plotQC(qc.out, path = ".") # saves .html file to working directory ## get detections with QC flags d.qc <- grabQC(qc.out, what = "dQC") ## clean up system("rm QC_logfile.txt *_QCmap.html")## specify files to QC - use supplied example .csv data files <- list(det = system.file(file.path("test_data","IMOS_detections.csv"), package = "remora"), rmeta = system.file(file.path("test_data", "IMOS_receiver_deployment_metadata.csv"), package = "remora"), tmeta = system.file(file.path("test_data", "IMOS_transmitter_deployment_metadata.csv"), package = "remora"), meas = system.file(file.path("test_data", "IMOS_animal_measurements.csv"), package = "remora")) qc.out <- runQC(files) plotQC(qc.out, path = ".") # saves .html file to working directory ## get detections with QC flags d.qc <- grabQC(qc.out, what = "dQC") ## clean up system("rm QC_logfile.txt *_QCmap.html")
not to be called by user
sei(data, date1, date2)sei(data, date1, date2)
data |
merged detections data and receiver metadata |
date1 |
start date for SEI calculation |
date2 |
end date for SEI calculation |
a tibble
Renders the Shiny App for transmitter and receiver reports.
shinyReport(type = "transmitters")shinyReport(type = "transmitters")
type |
"transmitters" or "receivers" to produce the corresponding report |
Shiny app with Transmitter or Receiver Project visualisations and statistics
## Not run: shinyReport(type = "transmitters") ## End(Not run)## Not run: shinyReport(type = "transmitters") ## End(Not run)
Example bull shark acoustic tracking data. Data were sourced from the Integrated Marine Observing System (IMOS) - IMOS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy and the Super Science Initiative.
.RData
helper function to calculate wind direction
windDir(u, v)windDir(u, v)
u |
horizontal (u) wind speed |
v |
vertical (v) wind speed |
Wind direction in degrees clockwise
helper function to calculate wind speed
windSpd(u, v)windSpd(u, v)
u |
horizontal (u) wind speed |
v |
vertical (v) wind speed |
Wind speed in meters per second
.csv files for each tag deploymentwrites a single QCsummary.csv file and/or tag-deployment-specific temporal_outcome's
of quality-controlled detections to .csv files
writeQC(x, path = NULL, summary = TRUE, csv = TRUE)writeQC(x, path = NULL, summary = TRUE, csv = TRUE)
x |
a nested tibble with class |
path |
path to write QC'd files to. If NULL the files are written to the working directory |
summary |
whether to generate a summary |
csv |
whether to generate the individual |
takes a remora_QC nested tibble and writes .csv files
corresponding to each row of the nested tibble. A summary of the QC process
is written to QCsummary.csv. The summary contains the following information for each QC'd tag
(see references for more details):
total_detectionsthe number of raw detections
detections_before_deploymentthe number of detections recorded prior to deployment date in metadata
invalid_deployment_locationlogical indicating whether deployment longitude,latitude is valid
detections_outside_species_rangethe number of detections that occurred outside the species expert distribution
valid_detectionsthe number of detections that passed the QC process (QC flags 1 and 2)
tracking_duration_daysthe number of days between deployment and the last valid detection
invalid_velocitythe number of detections associated with implausible travel speeds
.csv files and/or a QCsummary.csv file are written to the specified path
Hoenner X et al. (2018) Australia’s continental-scale acoustic tracking database and its automated quality control process. Sci Data 5, 170206 https://doi.org/10.1038/sdata.2017.206
## Not run: ## example QC'd data data(TownsvilleReefQC) ## write QC output writeQC(TownsvilleReefQC, summary = TRUE) ## End(Not run)## Not run: ## example QC'd data data(TownsvilleReefQC) ## write QC output writeQC(TownsvilleReefQC, summary = TRUE) ## End(Not run)