| Title: | Acoustic Telemetry Data Analysis |
|---|---|
| Description: | Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>. |
| Authors: | Hugo Flávio [aut, cre] |
| Maintainer: | Hugo Flávio <[email protected]> |
| License: | GPL-3 |
| Version: | 1.3.0.9007 |
| Built: | 2024-08-23 05:49:12 UTC |
| Source: | https://github.com/hugomflavio/actel |
advEfficiency estimates efficiency ranges by fitting a beta distribution
with parameters = number of detected tags and = number of missed
tags. The desired quantiles (argument q) are then calculated from distribution.
Plots are also drawn showing the distribution, the median point (dashed red line) and
the range between the lowest and largest quantile requested (red shaded section).
advEfficiency( x, labels = NULL, q = c(0.025, 0.5, 0.975), force.grid = NULL, paired = TRUE, title = "" )advEfficiency( x, labels = NULL, q = c(0.025, 0.5, 0.975), force.grid = NULL, paired = TRUE, title = "" )
x |
An efficiency object from actel ( |
labels |
a vector of strings to substitute default plot labels |
q |
The quantile values to be calculated. Defaults to |
force.grid |
A vector of format c(nrow, ncol) that allows the user to define the number of rows and columns to distribute the plots in. |
paired |
Logical: For efficiency derived from residency analyses, should min. and max. estimates for an array be displayed next to each other? |
title |
A title for the plot (feeds into title parameter of ggplot's labs function). |
Examples for inclusion in a paper:
If advEfficiency was run on an overall.CJS object (i.e. migration analysis):
"Array efficiency was estimated by fitting a beta distribution
( = number of tags detected subsequently and at the array,
= number of tags detected subsequently but not at the array)
and calculating the median estimated efficiency value using the R package actel [citation]."
If advEfficiency was run on an efficiency object (i.e. residency analysis):
If you are using maximum efficiency estimates:
"Array efficiency was estimated by fitting a beta distribution
( = number of events recorded by the array,
= number of events known to have been missed by the array).
and calculating the median estimated efficiency value using the R package actel [citation]."
If you are using minimum efficiency estimates:
"Array efficiency was estimated by fitting a beta distribution
( = number of events recorded by the array,
= number of events both known to have been missed and potentially missed by the array).
and calculating the median estimated efficiency value using the R package actel [citation]."
If advEfficiency was run on an intra.array.CJS object:
"Intra-array efficiency was estimated by comparing the tags detected at each of the
two replicates. For each replicate, a beta distribution was fitted
( = number of tags detected at both replicates, = number
of tags detected at the opposite replicate but not at the one for which efficiency
is being calculated) and the median estimated efficiency value was calculated. The overall
efficiency of the array was then estimated as 1-((1-R1)*(1-R2)), where R1 and R2 are
the median efficiency estimates for each replicate. These calculations were performed
using the R package actel [citation]."
Replace [citation] with the output of citation('actel')
A data frame with the required quantile values and a plot of the efficiency distributions.
# Example using the output of simpleCJS. x <- matrix( c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE), ncol = 5, byrow = TRUE) colnames(x) <- c("Release", "A1", "A2", "A3", "A4") cjs.results <- simpleCJS(x) # These cjs results can be used in advEfficiency advEfficiency(cjs.results) # Example using the output of dualArrayCJS. x <- matrix( c( TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE), ncol = 2, byrow = TRUE) colnames(x) <- c("A1.1", "A1.2") cjs.results <- dualArrayCJS(x) # These cjs results can be used in advEfficiency advEfficiency(cjs.results) # advEfficiency can also be run with the output from the main analyses. # the example.results dataset is the output of a migration analysis advEfficiency(example.results$overall.CJS)# Example using the output of simpleCJS. x <- matrix( c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE), ncol = 5, byrow = TRUE) colnames(x) <- c("Release", "A1", "A2", "A3", "A4") cjs.results <- simpleCJS(x) # These cjs results can be used in advEfficiency advEfficiency(cjs.results) # Example using the output of dualArrayCJS. x <- matrix( c( TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE), ncol = 2, byrow = TRUE) colnames(x) <- c("A1.1", "A1.2") cjs.results <- dualArrayCJS(x) # These cjs results can be used in advEfficiency advEfficiency(cjs.results) # advEfficiency can also be run with the output from the main analyses. # the example.results dataset is the output of a migration analysis advEfficiency(example.results$overall.CJS)
Produces template files and folders required to run the explore,
migration and residency functions.
blankWorkspace(dir, force = FALSE)blankWorkspace(dir, force = FALSE)
dir |
The name of the target directory. Will be created if not present. |
force |
logical. Defaults to FALSE. Prevents deploying files in a directory that already exists without explicit permission. |
No return value, called for side effects
# running blankWorkspace deploys template # files to a directory specified by the user blankWorkspace(paste0(tempdir(), "/blankWorkspace_example"))# running blankWorkspace deploys template # files to a directory specified by the user blankWorkspace(paste0(tempdir(), "/blankWorkspace_example"))
Completes the bottom diagonal of a matrix with the same number of rows and columns.
completeMatrix(x)completeMatrix(x)
x |
A distances matrix to be completed. |
It is highly recommended to read the manual page regarding distances matrices before running this function. You can find it here: https://hugomflavio.github.io/actel-website/manual-distances.html
A matrix of distances between pairs of points.
# Create dummy matrix with upper diagonal filled. x <- matrix( c( 0, 1, 2, 3, 4, 5, NA, 0, 1, 2, 3, 4, NA, NA, 0, 1, 2, 3, NA, NA, NA, 0, 1, 2, NA, NA, NA, NA, 0, 1, NA, NA, NA, NA, NA, 0), ncol = 6, byrow = TRUE) # inspect x x # run completeMatrix completeMatrix(x)# Create dummy matrix with upper diagonal filled. x <- matrix( c( 0, 1, 2, 3, 4, 5, NA, 0, 1, 2, 3, 4, NA, NA, 0, 1, 2, 3, NA, NA, NA, 0, 1, 2, NA, NA, NA, NA, 0, 1, NA, NA, NA, NA, NA, 0), ncol = 6, byrow = TRUE) # inspect x x # run completeMatrix completeMatrix(x)
Use blankWorkspace instead.
createWorkspace(dir, force = FALSE)createWorkspace(dir, force = FALSE)
dir |
The name of the target directory. Will be created if not present. |
force |
logical. Defaults to FALSE. Prevents deploying files in a directory that already exists without explicit permission. |
No return value, called for side effects
# createWorkspace is deprecated. Use blankWorkspace instead.# createWorkspace is deprecated. Use blankWorkspace instead.
Import RData in a list format
dataToList(source)dataToList(source)
source |
A RData file. |
A list containing the objects present in the source RData file.
# Dummy example: # Create two objects: object_1 <- "This" object_2 <- "Worked!" # Save them as an RData file in R's temporary directory save(object_1, object_2, file = paste0(tempdir(), "/dataToList_example.RData")) # Remove the dummy objects as we don't need them any more rm(object_1, object_2) # Load the RData file as a single object x <- dataToList(paste0(tempdir(), "/dataToList_example.RData")) # inspect x x# Dummy example: # Create two objects: object_1 <- "This" object_2 <- "Worked!" # Save them as an RData file in R's temporary directory save(object_1, object_2, file = paste0(tempdir(), "/dataToList_example.RData")) # Remove the dummy objects as we don't need them any more rm(object_1, object_2) # Load the RData file as a single object x <- dataToList(paste0(tempdir(), "/dataToList_example.RData")) # inspect x x
Using a previously created transition layer (see transitionLayer), calculates the distances
between spatial points. Adapted from Grant Adams' script "distance to closest mpa". if the argument 'actel'
is set to TRUE (default), an actel-compatible matrix is generated, and the user will be asked if they would
like to save the matrix as 'distances.csv' in the current directory.
distancesMatrix( t.layer, starters = NULL, targets = starters, coord.x = "x", coord.y = "y", id.col = NULL, actel = TRUE )distancesMatrix( t.layer, starters = NULL, targets = starters, coord.x = "x", coord.y = "y", id.col = NULL, actel = TRUE )
t.layer |
A TransitionLayer object, generated by |
starters |
A data frame with the points from which to start measuring the distance. Ignored if actel = TRUE (default), as the 'spatial.csv' is loaded as starters. |
targets |
A data frame with the points to which a way must be found. Ignored if actel = TRUE (default), as the 'spatial.csv' is loaded as targets. |
coord.x, coord.y
|
The names of the columns containing the x and y coordinates in the starters and targets. Must be identical in the starters and targets. |
id.col |
The name of the column containing the IDs of the points to be used as starters and targets. Must be identical in both files. Ignored if actel = TRUE (default), as the stations' standard names are used. |
actel |
Logical: Should the distance matrix be optimized for actel? Defaults to TRUE. |
It is highly recommended to read the manual page regarding distances matrices before running this function. You can find it here: https://hugomflavio.github.io/actel-website/manual-distances.html
A matrix with the distances between each pair of points.
# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # move to a temporary directory old.wd <- getwd() setwd(tempdir()) # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # create a temporary spatial.csv file file.copy(paste0(aux, "/example_spatial.csv"), "spatial.csv") # import the shape file and use the spatial.csv # to check the extents. x <- shapeToRaster(shape = paste0(aux, "/example_shapefile.shp"), coord.x = "x", coord.y = "y", size = 20) raster::plot(x) # Build the transition layer t.layer <- transitionLayer(x) # compile the distances matrix. Columns x and y in the spatial dataframe # contain the coordinates of the stations and release sites. distancesMatrix(t.layer, coord.x = 'x', coord.y = 'y') # return to original directory setwd(old.wd) rm(old.wd) } rm(aux, missing.packages)# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # move to a temporary directory old.wd <- getwd() setwd(tempdir()) # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # create a temporary spatial.csv file file.copy(paste0(aux, "/example_spatial.csv"), "spatial.csv") # import the shape file and use the spatial.csv # to check the extents. x <- shapeToRaster(shape = paste0(aux, "/example_shapefile.shp"), coord.x = "x", coord.y = "y", size = 20) raster::plot(x) # Build the transition layer t.layer <- transitionLayer(x) # compile the distances matrix. Columns x and y in the spatial dataframe # contain the coordinates of the stations and release sites. distancesMatrix(t.layer, coord.x = 'x', coord.y = 'y') # return to original directory setwd(old.wd) rm(old.wd) } rm(aux, missing.packages)
Calculate estimated last-array efficiency
dualArrayCJS(input)dualArrayCJS(input)
input |
A presence/absence matrix. |
A list containing:
absolutes A matrix with the absolute number of tags detected at each replicate and at both,
single.efficiency A vector of calculated array detection efficiencies for each of the replicates,
combined.efficiency The value of the combined detection efficiency for the array.
Perry et al (2012), 'Using mark-recapture models to estimate survival from telemetry data'. url: https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data
# prepare a dummy presence/absence matrix x <- matrix(c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE), ncol = 2) colnames(x) <- c("R1", "R2") # run CJS dualArrayCJS(x)# prepare a dummy presence/absence matrix x <- matrix(c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE), ncol = 2) colnames(x) <- c("R1", "R2") # run CJS dualArrayCJS(x)
Creates an empty matrix based on the local 'spatial.csv' file and saves it to 'distances.csv' so the user can manually fill it.
emptyMatrix(input = "spatial.csv")emptyMatrix(input = "spatial.csv")
input |
Either a data frame with spatial data or the path to the file containing the spatial information. |
It is highly recommended to read the manual page regarding distances matrices before running this function. You can find it here: https://hugomflavio.github.io/actel-website/manual-distances.html
An empty matrix with the rows and columns required to operate with the target spatial file.
# This function requires a file with spatial information # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # run emptyMatrix on the temporary spatial.csv file emptyMatrix(paste0(aux, "/example_spatial.csv"))# This function requires a file with spatial information # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # run emptyMatrix on the temporary spatial.csv file emptyMatrix(paste0(aux, "/example_spatial.csv"))
Creates a ready-to-run workspace with example data.
exampleWorkspace(dir, force = FALSE)exampleWorkspace(dir, force = FALSE)
dir |
The name of the target directory. Will be created if not present. |
force |
logical. Defaults to FALSE. Prevents deploying files in a directory that already exists without explicit permission. |
No return value, called for side effects.
# deploy a minimal dataset to try actel! exampleWorkspace(paste0(tempdir(), "/exampleWorkspace_example"))# deploy a minimal dataset to try actel! exampleWorkspace(paste0(tempdir(), "/exampleWorkspace_example"))
explore allows you to quickly get a summary of your data. You can use
explore to get a general feel for the study results, and check if the
input files are behaving as expected. It is also a good candidate if you just
want to validate your detections for later use in other analyses.
explore( tz = NULL, datapack = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )explore( tz = NULL, datapack = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )
tz |
The time zone of the study area. Must match one of the values
present in |
datapack |
A data bundle pre-compiled through the function |
max.interval |
The number of minutes that must pass between detections for a new event to be created. Defaults to 60. |
minimum.detections |
DEPRECATED. Please use the arguments min.total.detections and min.per.event instead. |
min.total.detections |
Minimum number of times a tag must have been detected during the study period for the detections to be considered true and not just random noise. Defaults to 2. |
min.per.event |
Minimum number of detections an event must have to be deemed valid. For analyses with both array and section events, a vector of two values can be provided. If only one value is provided, the same threshold applies for both types of events. Defaults to 1. |
start.time |
Detection data prior to the timestamp set in
|
stop.time |
Detection data posterior to the timestamp set in
|
speed.method |
Can take two forms: 'last to first' or 'last to last'. If 'last to first' (default), the last detection on the previous array is matched to the first detection on the target array to perform the calculations. If 'last to last', the last detection on the previous array is matched to the last detection on the target array to perform the calculations. If 'first to first', the first detection on the previous array is matched to the first detection on the target array to perform the calculations. |
speed.warning |
If a tag moves at a speed equal or greater than
|
speed.error |
If a tag moves at a speed equal or greater than
|
jump.warning |
If a tag crosses a number of arrays equal or greater
than |
jump.error |
If a tag crosses a number of arrays equal or greater than
|
inactive.warning |
If a tag spends a number of days equal or greater
than |
inactive.error |
If a tag spends a number of days equal or greater than
|
exclude.tags |
A vector of tags that should be excluded from the detection data before any analyses are performed. Intended to be used if stray tags from a different code space but with the same signal as a target tag are detected in the study area. |
override |
A vector of signals for which the user intends to manually define which movement events are valid and invalid. |
report |
Logical. Should an HTML report be created at the end of the analysis? NOTE: Setting report to TRUE will generate an HTML file in the current directory. Additionally, if auto.open = TRUE (default), the web browser will automatically be launched to open the report once the function terminates. |
auto.open |
Logical: Should the report be automatically opened once the analysis is over? Defaults to TRUE. NOTE: If report = TRUE and auto.open = TRUE, the web browser will automatically be launched to open the report once the function terminates. |
discard.orphans |
Logical: Should actel automatically discard detections that do not fall within receiver deployment periods, or that were recorded before the respective animals were released? |
discard.first |
A threshold amount of time (in hours) that must pass after release for the respective detections to be valid. Set to 0 to discard only the release-to-first-detection calculations. |
save.detections |
Logical: Should the processed detections be saved for future runs? |
GUI |
One of "needed", "always" or "never". If "needed", a new window is opened to inspect the movements only when the movements table is too big to be displayed in R's console. If "always", a graphical interface is always created when the possibility to invalidate events emerges. If "never", a graphical interface is never invoked. In this case, if the table to be displayed does not fit in R's console, a temporary file will be saved and the user will be prompted to open that file and examine it. Defaults to "needed". |
save.tables.locally |
Logical: If a table must be temporarily stored into a file for user inspection, should it be saved in the current working directory, or in R's temporary folder? |
print.releases |
Logical: Should the release sites be printed in the study area diagrams? |
detections.y.axis |
The type of y axis desired for the individual detection plots. While the argument defaults to "auto", it can be hard-set to one of "stations" or "arrays". |
A list containing:
bio: A copy of the biometrics input;
detections: A list containing all detections for each target tag;
valid.detections: A list containing the valid detections for each target tag;
spatial: A list containing the spatial information used during the analysis;
deployments: A data frame containing the deployments of each receiver;
arrays: A list containing the array details used during the analysis;
movements: A list containing all movement events for each target tag;
valid.movements: A list containing the valid movement events for each target tag;
times: A data frame containing all arrival times (per tag) at each array;
rsp.info: A list containing containing appendix information for the RSP package;
dist.mat: A matrix containing the distance matrix used in the analysis (if a valid
distance matrix was supplied)
# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("explore_example") # Move your R session into the example workspace setwd("explore_example") # run the explore analysis. Ensure the tz argument # matches the time zone of the study area. For the # example dataset, tz = "Europe/Copenhagen" results <- explore(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("explore_example") # Move your R session into the example workspace setwd("explore_example") # run the explore analysis. Ensure the tz argument # matches the time zone of the study area. For the # example dataset, tz = "Europe/Copenhagen" results <- explore(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)
Extract Code Spaces from transmitter names
extractCodeSpaces(input)extractCodeSpaces(input)
input |
A vector of transmitter names |
A vector of transmitter signals
# create dummy string x <- c("R64K-1234", "A69-1303-12") # run extractCodeSpaces extractCodeSpaces(x)# create dummy string x <- c("R64K-1234", "A69-1303-12") # run extractCodeSpaces extractCodeSpaces(x)
Extract signals from transmitter names
extractSignals(input)extractSignals(input)
input |
A vector of transmitter names |
A vector of transmitter signals
# create dummy string x <- c("R64K-1234", "A69-1303-12") # run extractSignals extractSignals(x)# create dummy string x <- c("R64K-1234", "A69-1303-12") # run extractSignals extractSignals(x)
Extract speeds from the analysis results.
getSpeeds( input, type = c("all", "forward", "backward"), direct = FALSE, n.events = c("first", "all", "last") )getSpeeds( input, type = c("all", "forward", "backward"), direct = FALSE, n.events = c("first", "all", "last") )
input |
An actel results object generated by |
type |
The type of movements to record. One of "all", "forward", or "backward". In the two last options, only the forward or backwards (relatively to the study area structure) movement speeds are returned. |
direct |
Logical: Extract only speeds between arrays that are directly connected (i.e. neighbouring arrays)? |
n.events |
The events to record. One of "first", "all", or "last". |
A data frame with the following columns:
Tag: The tag of the animal who performed the recorded speed
Event: The valid event where the speed was recorded
From.array: The array from which the tags left
From.station: The station from which the tags left
To.array: The array to which the tags arrived
To.station: The station to which the tags arrived
Speed: The speed recorded in the described movement
# using the example results loaded with actel getSpeeds(example.results) # You can specify which direction of movement to extract with 'type' getSpeeds(example.results, type = "forward") # or getSpeeds(example.results, type = "backward") # and also how many events per tag (this won't change the output # with the example.results, only because these results are minimal). getSpeeds(example.results, n.events = "first") # or getSpeeds(example.results, n.events = "all") # or getSpeeds(example.results, n.events = "last")# using the example results loaded with actel getSpeeds(example.results) # You can specify which direction of movement to extract with 'type' getSpeeds(example.results, type = "forward") # or getSpeeds(example.results, type = "backward") # and also how many events per tag (this won't change the output # with the example.results, only because these results are minimal). getSpeeds(example.results, n.events = "first") # or getSpeeds(example.results, n.events = "all") # or getSpeeds(example.results, n.events = "last")
Extract timestamps from the analysis results.
getTimes( input, locations = NULL, move.type = c("array", "section"), event.type = c("arrival", "departure"), n.events = c("first", "all", "last") )getTimes( input, locations = NULL, move.type = c("array", "section"), event.type = c("arrival", "departure"), n.events = c("first", "all", "last") )
input |
An actel results object generated by |
locations |
The names of the arrays or sections to be included. If left NULL, information for all arrays/sections is extracted. |
move.type |
The type of events to record: one of "array" or "section". |
event.type |
The point to be recorded: one of "arrival" or "departure". |
n.events |
The events to record. One of "first", "all", or "last". |
A data frame with the timestamps for each tag (rows) and array (columns)
# using the example results loaded with actel getTimes(example.results) # You can specify which events to extract with 'event.type' getTimes(example.results, event.type = "arrival") # or getTimes(example.results, event.type = "departure") # and also how many events per tag. getTimes(example.results, n.events = "first") # or getTimes(example.results, n.events = "all") # or getTimes(example.results, n.events = "last")# using the example results loaded with actel getTimes(example.results) # You can specify which events to extract with 'event.type' getTimes(example.results, event.type = "arrival") # or getTimes(example.results, event.type = "departure") # and also how many events per tag. getTimes(example.results, n.events = "first") # or getTimes(example.results, n.events = "all") # or getTimes(example.results, n.events = "last")
Please use shapeToRaster instead.
loadShape( shape, size, spatial = "spatial.csv", coord.x = NULL, coord.y = NULL, buffer = NULL, type = c("land", "water") )loadShape( shape, size, spatial = "spatial.csv", coord.x = NULL, coord.y = NULL, buffer = NULL, type = c("land", "water") )
shape |
The path to a shapefile containing land polygons of the study area. |
size |
The pixel size, in metres. |
spatial |
Either a character string specifying the path to a spatial.csv file or a spatial data frame. This argument is not mandatory, but can be used to perform an early check of the shape file's compatibility with the study stations and release sites. |
coord.x, coord.y
|
The names of the columns containing the x and y positions of the stations in the spatial.csv file. these coordinates MUST BE in the same coordinate system as the shape file. |
buffer |
Artificially expand the map edges. Can be a single value (applied to all edges) or four values (xmin, xmax, ymin, ymax). The unit of the buffer depends on the shape file's coordinate system. |
type |
The type of shapefile being loaded. One of "land", if the shapefile's polygons represent landmasses, or "water", if the shapefile's polygons represent water bodies. |
A raster object.
message("This function is deprecated, please use shapeToRaster instead.")message("This function is deprecated, please use shapeToRaster instead.")
Loads a spatial file prepared for actel and appends the Standard.name column. Additionally, performs a series of quality checks on the contents of the target file.
loadSpatial(input = "spatial.csv", section.order = NULL)loadSpatial(input = "spatial.csv", section.order = NULL)
input |
Either a data frame or the name of an input file with spatial data in the actel format. |
section.order |
A vector containing the order by which sections should be aligned in the results. |
A data frame with the spatial information present in 'spatial.csv' and the Standard.name column.
# This function requires the presence of a file with spatial information # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # run loadSpatial on the temporary spatial.csv file loadSpatial(input = paste0(aux, '/example_spatial.csv'))# This function requires the presence of a file with spatial information # Fetch location of actel's example files aux <- system.file(package = "actel")[1] # run loadSpatial on the temporary spatial.csv file loadSpatial(input = paste0(aux, '/example_spatial.csv'))
The migration analysis runs the same initial checks as explore,
but on top of it, it analyses the animal behaviour. By selecting the arrays
that lead to success, you can define whether or not your animals survived the
migration. Additional plots help you find out if some animal/tag has been acting
odd. Multiple options allow you to tweak the analysis to fit your study
perfectly.
migration( tz = NULL, section.order = NULL, datapack = NULL, success.arrays = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, if.last.skip.section = TRUE, replicates = NULL, disregard.parallels = TRUE, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )migration( tz = NULL, section.order = NULL, datapack = NULL, success.arrays = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, if.last.skip.section = TRUE, replicates = NULL, disregard.parallels = TRUE, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )
tz |
The time zone of the study area. Must match one of the values
present in |
section.order |
A vector containing the order by which sections should be aligned in the results. |
datapack |
A data bundle pre-compiled through the function |
success.arrays |
The arrays that mark the end of the study area. If a tag crosses one of these arrays, the respective animal is considered to have successfully migrated through the study area. |
max.interval |
The number of minutes that must pass between detections for a new event to be created. Defaults to 60. |
minimum.detections |
DEPRECATED. Please use the arguments min.total.detections and min.per.event instead. |
min.total.detections |
Minimum number of times a tag must have been detected during the study period for the detections to be considered true and not just random noise. Defaults to 2. |
min.per.event |
Minimum number of detections an event must have to be deemed valid. For analyses with both array and section events, a vector of two values can be provided. If only one value is provided, the same threshold applies for both types of events. Defaults to 1. |
start.time |
Detection data prior to the timestamp set in
|
stop.time |
Detection data posterior to the timestamp set in
|
speed.method |
Can take two forms: 'last to first' or 'last to last'. If 'last to first' (default), the last detection on the previous array is matched to the first detection on the target array to perform the calculations. If 'last to last', the last detection on the previous array is matched to the last detection on the target array to perform the calculations. If 'first to first', the first detection on the previous array is matched to the first detection on the target array to perform the calculations. |
speed.warning |
If a tag moves at a speed equal or greater than
|
speed.error |
If a tag moves at a speed equal or greater than
|
jump.warning |
If a tag crosses a number of arrays equal or greater
than |
jump.error |
If a tag crosses a number of arrays equal or greater than
|
inactive.warning |
If a tag spends a number of days equal or greater
than |
inactive.error |
If a tag spends a number of days equal or greater than
|
exclude.tags |
A vector of tags that should be excluded from the detection data before any analyses are performed. Intended to be used if stray tags from a different code space but with the same signal as a target tag are detected in the study area. |
override |
A vector of signals for which the user intends to manually define which movement events are valid and invalid. |
report |
Logical. Should an HTML report be created at the end of the analysis? NOTE: Setting report to TRUE will generate an HTML file in the current directory. Additionally, if auto.open = TRUE (default), the web browser will automatically be launched to open the report once the function terminates. |
auto.open |
Logical: Should the report be automatically opened once the analysis is over? Defaults to TRUE. NOTE: If report = TRUE and auto.open = TRUE, the web browser will automatically be launched to open the report once the function terminates. |
discard.orphans |
Logical: Should actel automatically discard detections that do not fall within receiver deployment periods, or that were recorded before the respective animals were released? |
discard.first |
A threshold amount of time (in hours) that must pass after release for the respective detections to be valid. Set to 0 to discard only the release-to-first-detection calculations. |
save.detections |
Logical: Should the processed detections be saved for future runs? |
if.last.skip.section |
Logical: Should a tag detected at the last array of a given section be considered to have disappeared in the next section? |
replicates |
A list containing, for each array to which intra-array efficiency is to be calculated: The standard names of the stations to be used as a replicate. See the vignettes for more details. |
disregard.parallels |
Logical: Should the presence of parallel arrays invalidate potential efficiency peers? See the vignettes for more details. |
GUI |
One of "needed", "always" or "never". If "needed", a new window is opened to inspect the movements only when the movements table is too big to be displayed in R's console. If "always", a graphical interface is always created when the possibility to invalidate events emerges. If "never", a graphical interface is never invoked. In this case, if the table to be displayed does not fit in R's console, a temporary file will be saved and the user will be prompted to open that file and examine it. Defaults to "needed". |
save.tables.locally |
Logical: If a table must be temporarily stored into a file for user inspection, should it be saved in the current working directory, or in R's temporary folder? |
print.releases |
Logical: Should the release sites be printed in the study area diagrams? |
detections.y.axis |
The type of y axis desired for the individual detection plots. While the argument defaults to "auto", it can be hard-set to one of "stations" or "arrays". |
A list containing:
detections: A list containing all detections for each target tag;
valid.detections: A list containing the valid detections for each target tag;
spatial: A list containing the spatial information used during the analysis;
deployments: A data frame containing the deployments of each receiver;
arrays: A list containing the array details used during the analysis;
movements: A list containing all movement events for each target tag;
valid.movements: A list containing the valid movement events for each target tag;
section.movements: A list containing the valid section shifts for each target tag;
status.df: A data.frame containing summary information for each tag, including the
following columns:
Times.entered.[section]: Number of times the tag was recorded entering a given section.
Average.time.until.[section]: Time spent between release or leaving another section and reaching at the given section.
Average.speed.to.[section]: Average speed from release or leaving one section and reaching the given section (if speed.method = "last to first"), or from release/leaving one section and leaving the given section (if speed.method = "last to last").
First.array.[section]: Array in which the tag was first detected in a given section
First.station.[section]: Standard name of the first station where the tag was detected in a given section
First.arrived.[section]: Very first arrival time at a given section
Average.time.in.[section]: Average time spent within a given section at each stay.
Average.speed.in.[section]: Average speed within a given section at each stay (only displayed if speed.method = "last to first").
Last.array.[section]: Array in which the tag was last detected in a given section
Last.station.[section]: Standard name of the last station where the tag was detected in a given section
Last.left.[section]: Very last departure time from a given section
Total.time.in[section]: Total time spent in a given section
Very.last.array: Last array where the tag was detected
Status: Fate assigned to the tag
Valid.detections: Number of valid detections
Invalid.detections: Number of invalid detections
Backwards.movements: Number of backward movement events
Max.cons.back.moves: Longest successive backwards movements
P.type: Type of processing:
'Skipped' if no data was found for the tag,
'Auto' if no user interaction was required,
'Manual' if user interaction was suggested and the user made changes to the validity of the events,
'Overridden' if the user listed the tag in the
override argument.
Comments: Comments left by the user during the analysis
section.overview: A data frame containing the number of tags that
disappeared in each section;
group.overview: A list containing the number of known and
estimated tags to have passed through each array, divided by group;
release.overview: A list containing the number of known and
estimated tags to have passed through each array, divided by group and release sites;
matrices: A list of CJS matrices used for the efficiency calculations;
overall.CJS: A list of CJS results of the inter-array CJS calculations;
intra.array.CJS: A list of CJS results of the intra-array CJS calculations;
times: A data frame containing all arrival times (per tag) at each array;
rsp.info: A list containing appendix information for the RSP package;
dist.mat: The distance matrix used in the analysis (if a valid
distance matrix was supplied)
# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("migration_example") # Move your R session into the example workspace setwd("migration_example") # run the migration analysis. Ensure the tz argument # matches the time zone of the study area and that the # sections match your array names. The line below works # for the example data. results <- migration(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("migration_example") # Move your R session into the example workspace setwd("migration_example") # run the migration analysis. Ensure the tz argument # matches the time zone of the study area and that the # sections match your array names. The line below works # for the example data. results <- migration(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)
Plot simultaneous/cumulative presences at a give array or set of arrays
plotArray( input, arrays, title, xlab, ylab, lwd = 1, col, by.group = TRUE, y.style = c("absolute", "relative"), type = c("default", "bars", "lines"), timestep = c("days", "hours", "mins"), cumulative = FALSE, ladder.type = c("arrival", "departure") )plotArray( input, arrays, title, xlab, ylab, lwd = 1, col, by.group = TRUE, y.style = c("absolute", "relative"), type = c("default", "bars", "lines"), timestep = c("days", "hours", "mins"), cumulative = FALSE, ladder.type = c("arrival", "departure") )
input |
The results of an actel analysis (either explore, migration or residency). |
arrays |
One or more arrays to be analysed. If multiple arrays are provided, data will be grouped. |
title |
An optional title for the plot. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
lwd |
The line width, only relevant for line plots. |
col |
The colour of the lines or bars. |
by.group |
Logical: Should the data be presented separately for each group? |
y.style |
The style of the y axis. Either "absolute", for the number of animals that arrive in each day, or "relative", for the proportion of animals over the total number of animals that arrived. |
type |
The type of plot to be drawn. By default, a line is plotted if cumulative = TRUE, and bars are plotted otherwise. |
timestep |
The time resolution for the grouping of the results. Defaults to "days", but can be set to "hours" and "mins" (at the expense of computing time). |
cumulative |
Logical. If TRUE, a cumulative plot of arrivals is drawn, otherwise the number of tags simultaneously present at the array(s) is drawn. |
ladder.type |
Type of cumulative plot to show. "arrival" to plot the moments of arrival, or "departure" to plot the moments of departure. Not applicable for non-cumulative plots. |
A ggplot object.
# Using the example results that come with actel plotArray(example.results, arrays = "A9") # Because plotArray returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotArray(example.results, arrays = "A9") p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# Using the example results that come with actel plotArray(example.results, arrays = "A9") # Because plotArray returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotArray(example.results, arrays = "A9") p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
The output of plotDetections is a ggplot object, which means you can then use it in combination with other ggplot functions, or even together with other packages such as patchwork.
plotDetections( input, tag, type, y.axis = c("auto", "stations", "arrays"), title, xlab, ylab, col, array.alias, section.alias, frame.warning = TRUE, x.label.format, only.valid = FALSE, like.migration = TRUE )plotDetections( input, tag, type, y.axis = c("auto", "stations", "arrays"), title, xlab, ylab, col, array.alias, section.alias, frame.warning = TRUE, x.label.format, only.valid = FALSE, like.migration = TRUE )
input |
The results of an actel analysis (either explore, migration or residency). |
tag |
The transmitter to be plotted. |
type |
DEPRECATED. Please use the argument y.axis instead. |
y.axis |
The type of y axis desired. One of "stations" (default) or "arrays". |
title |
An optional title for the plot. If left empty, a default title will be added. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
col |
An optional colour scheme for the detections. If left empty, default colours will be added. |
array.alias |
A named vector of format c("old_array_name" = "new_array_name") to replace default array names with user defined ones. |
section.alias |
A named vector of format c("old_section_name" = "new_section_name") to replace default section names with user defined ones. |
frame.warning |
Logical. By default, actel highlights manually changed or overridden tags in yellow and red plot frames, respectively. Set to FALSE to deactivate this behaviour. |
x.label.format |
A character string giving a date-time format for the x labels. If missing, ggplot's default labels are used. |
only.valid |
Logical. Should only valid detections be printed? |
like.migration |
Logical. For plots originating from migration analyses, should the additional grey vertical bars be included? Defaults to TRUE, and only has a visible effect if the input stems from a migration analysis. |
A ggplot object.
# Using the example results that come with actel plotDetections(example.results, 'R64K-4451') # Because plotDetections returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotDetections(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# Using the example results that come with actel plotDetections(example.results, 'R64K-4451') # Because plotDetections returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotDetections(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
Plot array live times
plotLive( input, arrays, show.stations = FALSE, array.size = 2, station.size = 1, show.caps = TRUE, cap.prop = 2, title = "", xlab = "", ylab = "", col )plotLive( input, arrays, show.stations = FALSE, array.size = 2, station.size = 1, show.caps = TRUE, cap.prop = 2, title = "", xlab = "", ylab = "", col )
input |
An actel results object, or a preload object |
arrays |
Optional: A subset of arrays to be plotted |
show.stations |
Logical: Should the live times of each station be shown under the array bars? |
array.size |
The size of the array bars (defaults to 2) |
station.size |
The size of the station bars (defaults to 1) |
show.caps |
Logical: Should cap lines be shown at the end of each live period? |
cap.prop |
The relative size of the caps, as compared to the respective bars (defaults to 2). |
title |
An optional title for the plot. |
xlab, ylab
|
Optional axis names for the plot. |
col |
An optional colour scheme for the array bars. If left empty, default colours will be added. Note: Station bars are 40% lighter than the array bars. |
A ggplot object.
# Using the example results that come with actel plotLive(example.results) # Because plotLive returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotLive(example.results) p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# Using the example results that come with actel plotLive(example.results) # Because plotLive returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotLive(example.results) p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
The output of plotMoves is a ggplot object, which means you can then use it in combination with other ggplot functions, or even together with other packages such as patchwork.
plotMoves( input, tags, title, xlab, ylab, col, array.alias, show.release = TRUE )plotMoves( input, tags, title, xlab, ylab, col, array.alias, show.release = TRUE )
input |
The results of an actel analysis (either explore, migration or residency). |
tags |
The transmitters to be plotted (optional). |
title |
An optional title for the plot. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
col |
An optional colour scheme for the detections. If left empty, default colours will be added. |
array.alias |
A named vector of format c("old_array_name" = "new_array_name") to replace default array names with user defined ones. |
show.release |
Logical: Should the line from release to first detection be displayed? |
A ggplot object.
# Using the example results that come with actel plotMoves(example.results, 'R64K-4451') # Because plotMoves returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotMoves(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# Using the example results that come with actel plotMoves(example.results, 'R64K-4451') # Because plotMoves returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotMoves(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
By default, this function plots the global residency. However, you can use the argument 'group' to plot the results only from a specific animal group. Lastly, you can also use 'sections', rather than 'group', to compare the residency at a specific section (or group of sections) between the different groups.
plotRatios( input, groups, sections, type = c("absolutes", "percentages"), title, xlab, ylab, col, col.by = c("default", "section", "group") )plotRatios( input, groups, sections, type = c("absolutes", "percentages"), title, xlab, ylab, col, col.by = c("default", "section", "group") )
input |
The results of an actel analysis (either explore, migration or residency). |
groups |
An optional argument to plot only the data corresponding to some groups. |
sections |
An optional argument to plot the residency of the multiple groups for a specific subset of sections. |
type |
The type of residency to be displayed. One of 'absolutes' (the default) or 'percentages'. |
title |
An optional title for the plot. If left empty, a default title will be added. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
col |
An optional colour scheme for the detections. If left empty, default colours will be added. |
col.by |
Colour scheme to use. One of 'section' or 'group'. By default, plots are coloured by section if all
sections are displayed, and by group if only a subset of the sections is required using the argument |
The output of plotRatios is a ggplot object, which means you can then use it in combination with other ggplot functions, or even together with other packages such as patchwork.
A ggplot object.
# For this example, I have modified the example.results that come with actel, # so they resemble a residency output plotRatios(example.residency.results) # Because plotRatios returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotRatios(example.residency.results, groups = "A") p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# For this example, I have modified the example.results that come with actel, # so they resemble a residency output plotRatios(example.residency.results) # Because plotRatios returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotRatios(example.residency.results, groups = "A") p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
The output of plotResidency is a ggplot object, which means you can then use it in combination with other ggplot functions, or even together with other packages such as patchwork.
plotResidency(input, tag, title, xlab, ylab, col)plotResidency(input, tag, title, xlab, ylab, col)
input |
The results of an actel analysis (either explore, migration or residency). |
tag |
The transmitter to be plotted. |
title |
An optional title for the plot. If left empty, a default title will be added. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
col |
An optional colour scheme for the detections. If left empty, default colours will be added. |
A ggplot object.
# For this example, I have modified the example.results that come with actel, # so they resemble a residency output plotResidency(example.residency.results, 'R64K-4451') # Because plotResidency returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotResidency(example.residency.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# For this example, I have modified the example.results that come with actel, # so they resemble a residency output plotResidency(example.residency.results, 'R64K-4451') # Because plotResidency returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotResidency(example.residency.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
The output of plotSensors is a ggplot object, which means you can then use it in combination with other ggplot functions, or even together with other packages such as patchwork.
plotSensors( input, tag, sensor, title = tag, xlab, ylab, pcol, psize = 1, lsize = 0.5, colour.by = c("array", "section"), array.alias, lcol = "grey40", verbose = TRUE )plotSensors( input, tag, sensor, title = tag, xlab, ylab, pcol, psize = 1, lsize = 0.5, colour.by = c("array", "section"), array.alias, lcol = "grey40", verbose = TRUE )
input |
The results of an actel analysis (either explore, migration or residency). |
tag |
The transmitter to be plotted. |
sensor |
The sensors to be plotted. If left empty, all available sensors are plotted |
title |
An optional title for the plot. If left empty, a default title will be added. |
xlab, ylab
|
Optional axis names for the plot. If left empty, default axis names will be added. |
pcol |
The colour for the points. If unset, a default palette is used. |
psize |
The size of the points. Defaults to 1. |
lsize |
The width of the line. Defaults to 0.5 (same as standard ggplots) |
colour.by |
One of "arrays" or "sections", defines how the points should be coloured. |
array.alias |
A named vector of format c("old_array_name" = "new_array_name") to replace default array names with user defined ones. Only relevant if colour.by = "arrays". |
lcol |
The colour for the line. Defaults to grey. |
verbose |
Logical: Should warning messages be printed, if necessary? |
A ggplot object.
# Using the example results that come with actel plotSensors(example.results, 'R64K-4451') # Because plotSensors returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotSensors(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!# Using the example results that come with actel plotSensors(example.results, 'R64K-4451') # Because plotSensors returns a ggplot object, you can store # it and edit it manually, e.g.: library(ggplot2) p <- plotSensors(example.results, 'R64K-4451') p <- p + xlab("changed the x axis label a posteriori") p # You can also save the plot using ggsave!
Wraps functions adapted from the circular R package.
plotTimes( times, night = NULL, circular.scale = c("area", "linear"), col, alpha = 0.8, title = "", mean.dash = TRUE, mean.range = TRUE, mean.range.darken.factor = 1.4, rings = TRUE, file, width, height, bg = "transparent", ncol, legend.pos = c("auto", "corner", "bottom"), ylegend, xlegend, xjust = c("auto", "centre", "left", "right"), expand = 0.95, cex = 1 )plotTimes( times, night = NULL, circular.scale = c("area", "linear"), col, alpha = 0.8, title = "", mean.dash = TRUE, mean.range = TRUE, mean.range.darken.factor = 1.4, rings = TRUE, file, width, height, bg = "transparent", ncol, legend.pos = c("auto", "corner", "bottom"), ylegend, xlegend, xjust = c("auto", "centre", "left", "right"), expand = 0.95, cex = 1 )
times |
A list of of time vectors (each vector will be plotted as a series). |
night |
A vector of two times defining the start and stop of the night period (in HH:MM format). |
circular.scale |
Allows the user to decide between using an area-adjusted scale ("area"), or a linear scale ("linear"). Defaults to "area", which better represents the proportion differences in the dataset. |
col |
A vector of colour names to paint each time series (colours will be added transparency). |
alpha |
A value between 0 and 1 for the opacity of each layer (defaults to 0.8). |
title |
A title for the plot. |
mean.dash |
Logical: Should the mean value be displayed on the plot's edge? |
mean.range |
Logical: Should the SEM be displayed? (only relevant if mean.dash = TRUE) |
mean.range.darken.factor |
A numeric factor to darken the mean range edges for each group. Values greater than 1 darken the colour, and values lower than 1 lighten the colour. |
rings |
Logical: Should inner plot rings be displayed? |
file |
A file name to save the plot to. Leave NULL to plot on active graphics device. Available file extensions: .svg, .pdf, .png and .tiff. |
height, width
|
The height and width of the output file. Use inches for .pdf and .svg files or pixels for .png and .tiff files. |
bg |
The colour of the plot background. Defaults to "transparent". |
ncol |
The number of columns in which to set the legend items. By default, actel decides the number of columns based on the number of data series to be plotted. |
legend.pos |
Where should the legend be drawn? By default, actel decides whether to plot the legend in the corner of the plot at the bottom the plot depending on the number of data series to plot. Possible values: 'auto', 'corner', 'bottom'. |
ylegend |
Adjustment to the vertical positioning of the legend. Only relevant if the legend is being drawn in the corner of the plot. |
xlegend |
Adjustment to the horizontal positioning of the legend. |
xjust |
How the legend is to be justified when the legend is drawn at the bottom a the plot. One of 'auto' (i.e. let actel decide the best adjustment), 'left', 'centre', or 'right'. |
expand |
Parameter that controls the size of the plotted circle. Defaults to 0.95. Larger values expand the circle, while smaller values shrink the circle. |
cex |
A numerical vector giving the amount by which plotting characters and symbols should be scaled relative to the default. When saving the plot in a vectorial form, it is recommended to change the height and width arguments rather than the cex. |
For more details about the original functions, visit the circular package homepage at https://github.com/cran/circular
A circular plot
# The output of timesToCircular can be used as an input to plotTimes. x <- getTimes(example.results, location = "A1", n.events = "first", event.type = "arrival") times <- timesToCircular(x) # plot times plotTimes(times) # A night period can be added with 'night' plotTimes(times, night = c("20:00", "06:00"))# The output of timesToCircular can be used as an input to plotTimes. x <- getTimes(example.results, location = "A1", n.events = "first", event.type = "arrival") times <- timesToCircular(x) # plot times plotTimes(times) # A night period can be added with 'night' plotTimes(times, night = c("20:00", "06:00"))
This function allows the user to prepare a set of R objects to be run through
an explore, migration or residency
analysis.
preload( biometrics, spatial, deployments, detections, dot = NULL, distances = NULL, tz, start.time = NULL, stop.time = NULL, section.order = NULL, exclude.tags = NULL, disregard.parallels = FALSE, discard.orphans = FALSE )preload( biometrics, spatial, deployments, detections, dot = NULL, distances = NULL, tz, start.time = NULL, stop.time = NULL, section.order = NULL, exclude.tags = NULL, disregard.parallels = FALSE, discard.orphans = FALSE )
biometrics |
A data frame containing biometric information. |
spatial |
A data frame containing spatial information. |
deployments |
A data frame containing deployment information. |
detections |
A data frame containing the detections. |
dot |
A DOT string of the array configuration. |
distances |
A distances matrix between arrays. See |
tz |
The time zone of the study area. Must match one of the values
present in |
start.time |
Detection data prior to the timestamp set in
|
stop.time |
Detection data posterior to the timestamp set in
|
section.order |
A vector containing the order by which sections should be aligned in the results. |
exclude.tags |
A vector of tags that should be excluded from the detection data before any analyses are performed. Intended to be used if stray tags from a different code space but with the same signal as a target tag are detected in the study area. |
disregard.parallels |
Logical: Should the presence of parallel arrays invalidate potential efficiency peers? See the vignettes for more details. |
discard.orphans |
Logical: Should actel automatically discard detections that do not fall within receiver deployment periods, or that were recorded before the respective animals were released? |
A dataset that can be used as an input for actel's main analyses. This dataset contains:
bio: The biometric data
sections: The sections of the study area, if set using the argument sections (required to run residency and migration analyses)
deployments: The deployment data
spatial: The spatial data, split in stations and release sites.
dot: A table of array connections.
arrays: A list with the details of each array
dotmat: A matrix of distances between arrays (in number of arrays).
dist.mat: The distances matrix.
detections.list: A processed list of detections for each tag.
paths: A list of the possible paths between each pair of arrays.
disregard.parallels: Logical: Should parallel arrays invalidate efficiency peers? (required to run residency and migration analyses)
tz: The time zone of the study area
# This function requires a series of pre-created R objects. # We can create them by loading the example files from actel: aux <- system.file(package = "actel")[1] bio <- read.csv(paste0(aux, "/example_biometrics.csv")) deployments <- read.csv(paste0(aux, "/example_deployments.csv")) spatial <- read.csv(paste0(aux, "/example_spatial.csv")) detections <- read.csv(paste0(aux, "/example_detections.csv")) dot <- "A0--A1--A2--A3--A4--A5--A6--A7--A8--A9" # Now that we have the R objects created, we can run preload: x <- preload(biometrics = bio, deployments = deployments, spatial = spatial, detections = detections, dot = dot, tz = "Europe/Copenhagen")# This function requires a series of pre-created R objects. # We can create them by loading the example files from actel: aux <- system.file(package = "actel")[1] bio <- read.csv(paste0(aux, "/example_biometrics.csv")) deployments <- read.csv(paste0(aux, "/example_deployments.csv")) spatial <- read.csv(paste0(aux, "/example_spatial.csv")) detections <- read.csv(paste0(aux, "/example_detections.csv")) dot <- "A0--A1--A2--A3--A4--A5--A6--A7--A8--A9" # Now that we have the R objects created, we can run preload: x <- preload(biometrics = bio, deployments = deployments, spatial = spatial, detections = detections, dot = dot, tz = "Europe/Copenhagen")
Read dot file or string
readDot(input = NULL, string = NULL, silent = FALSE)readDot(input = NULL, string = NULL, silent = FALSE)
input |
The name of a file containing dot connections. |
string |
A string of dot connections. |
silent |
Logical: Should warnings be suppressed? |
A data frame with the connections present in the input.
# create dummy dot string x1 <- c("A--B--C--D--E--F") # run readDot readDot(string = x1) # more complex strings are acceptable: x2 <- c( "A--B--C--D--E--F A--G--H--I--E H--C") readDot(string = x2) # Alternatively, connections can be read from a file # let's create a dummy file in R's temporary directory: write("A--B--C--D--E--F\nA--G--H--I--E\nH--C\n", file = paste0(tempdir(), "/dummy_dot.txt")) # now we can read it using readDot readDot(input = paste0(tempdir(), "/dummy_dot.txt"))# create dummy dot string x1 <- c("A--B--C--D--E--F") # run readDot readDot(string = x1) # more complex strings are acceptable: x2 <- c( "A--B--C--D--E--F A--G--H--I--E H--C") readDot(string = x2) # Alternatively, connections can be read from a file # let's create a dummy file in R's temporary directory: write("A--B--C--D--E--F\nA--G--H--I--E\nH--C\n", file = paste0(tempdir(), "/dummy_dot.txt")) # now we can read it using readDot readDot(input = paste0(tempdir(), "/dummy_dot.txt"))
Recover latest actel crash log
recoverLog(file, overwrite = FALSE)recoverLog(file, overwrite = FALSE)
file |
Name of the file to which the log should be saved. |
overwrite |
Logical: If 'file' already exists, should its content be overwritten? |
No return value, called for side effects.
recoverLog(file = paste0(tempdir(), "/new_log.txt"))recoverLog(file = paste0(tempdir(), "/new_log.txt"))
The residency analysis runs the same initial checks as
explore, but, similarly to migration, explores
particular points of the animal behaviour. If you want to know where your animals
were in each day of the study, how many animals were in each section each day,
and other residency-focused variables, this is the analysis you are looking
for!
residency( tz = NULL, section.order = NULL, datapack = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, section.warning = 1, section.error = 1, section.minimum, timestep = c("days", "hours"), replicates = NULL, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )residency( tz = NULL, section.order = NULL, datapack = NULL, max.interval = 60, minimum.detections, min.total.detections = 2, min.per.event = 1, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "last to last", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, discard.first = NULL, save.detections = FALSE, section.warning = 1, section.error = 1, section.minimum, timestep = c("days", "hours"), replicates = NULL, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, print.releases = TRUE, detections.y.axis = c("auto", "stations", "arrays") )
tz |
The time zone of the study area. Must match one of the values
present in |
section.order |
A vector containing the order by which sections should be aligned in the results. |
datapack |
A data bundle pre-compiled through the function |
max.interval |
The number of minutes that must pass between detections for a new event to be created. Defaults to 60. |
minimum.detections |
DEPRECATED. Please use the arguments min.total.detections and min.per.event instead. |
min.total.detections |
Minimum number of times a tag must have been detected during the study period for the detections to be considered true and not just random noise. Defaults to 2. |
min.per.event |
Minimum number of detections an event must have to be deemed valid. For analyses with both array and section events, a vector of two values can be provided. If only one value is provided, the same threshold applies for both types of events. Defaults to 1. |
start.time |
Detection data prior to the timestamp set in
|
stop.time |
Detection data posterior to the timestamp set in
|
speed.method |
Can take two forms: 'last to first' or 'last to last'. If 'last to first' (default), the last detection on the previous array is matched to the first detection on the target array to perform the calculations. If 'last to last', the last detection on the previous array is matched to the last detection on the target array to perform the calculations. If 'first to first', the first detection on the previous array is matched to the first detection on the target array to perform the calculations. |
speed.warning |
If a tag moves at a speed equal or greater than
|
speed.error |
If a tag moves at a speed equal or greater than
|
jump.warning |
If a tag crosses a number of arrays equal or greater
than |
jump.error |
If a tag crosses a number of arrays equal or greater than
|
inactive.warning |
If a tag spends a number of days equal or greater
than |
inactive.error |
If a tag spends a number of days equal or greater than
|
exclude.tags |
A vector of tags that should be excluded from the detection data before any analyses are performed. Intended to be used if stray tags from a different code space but with the same signal as a target tag are detected in the study area. |
override |
A vector of signals for which the user intends to manually define which movement events are valid and invalid. |
report |
Logical. Should an HTML report be created at the end of the analysis? NOTE: Setting report to TRUE will generate an HTML file in the current directory. Additionally, if auto.open = TRUE (default), the web browser will automatically be launched to open the report once the function terminates. |
auto.open |
Logical: Should the report be automatically opened once the analysis is over? Defaults to TRUE. NOTE: If report = TRUE and auto.open = TRUE, the web browser will automatically be launched to open the report once the function terminates. |
discard.orphans |
Logical: Should actel automatically discard detections that do not fall within receiver deployment periods, or that were recorded before the respective animals were released? |
discard.first |
A threshold amount of time (in hours) that must pass after release for the respective detections to be valid. Set to 0 to discard only the release-to-first-detection calculations. |
save.detections |
Logical: Should the processed detections be saved for future runs? |
section.warning |
If a tag has section movement events with less or equal to
|
section.error |
If a tag has section movement events with less or equal to
|
section.minimum |
DEPRECATED: Please use section.warning and section.error instead. |
timestep |
The resolution desired for the residency calculations. One of "days" (default) or "hours". |
replicates |
A list containing, for each array to which intra-array efficiency is to be calculated: The standard names of the stations to be used as a replicate. See the vignettes for more details. |
GUI |
One of "needed", "always" or "never". If "needed", a new window is opened to inspect the movements only when the movements table is too big to be displayed in R's console. If "always", a graphical interface is always created when the possibility to invalidate events emerges. If "never", a graphical interface is never invoked. In this case, if the table to be displayed does not fit in R's console, a temporary file will be saved and the user will be prompted to open that file and examine it. Defaults to "needed". |
save.tables.locally |
Logical: If a table must be temporarily stored into a file for user inspection, should it be saved in the current working directory, or in R's temporary folder? |
print.releases |
Logical: Should the release sites be printed in the study area diagrams? |
detections.y.axis |
The type of y axis desired for the individual detection plots. While the argument defaults to "auto", it can be hard-set to one of "stations" or "arrays". |
A list containing:
detections: A list containing all detections for each target tag;
valid.detections: A list containing the valid detections for each target tag;
spatial: A list containing the spatial information used during the analysis;
deployments: A data frame containing the deployments of each receiver;
arrays: A list containing the array details used during the analysis;
movements: A list containing all movement events for each target tag;
valid.movements: A list containing the valid movement events for each target tag;
section.movements: A list containing the valid section shifts for each target tag;
status.df: A data frame containing summary information for each tag, including the
following columns:
Times.entered.[section]: Total number of times the tag entered a given section
Average.entry.[section]: Average entry time at a given section
Average.time.[section]: Average time the tag spent in a given section during each visit
Average.departure.[section]: Average departure time from a given section
Total.time.[section]: Total time spent in a given section
Very.last.array: Last array where the tag was detected
Very.last.time: Time of the last valid detection
Status: Fate assigned to the animal
Valid.detections: Number of valid detections
Invalid.detections: Number of invalid detections
Valid.events: Number of valid events
Invalid.events: Number of invalid events
P.type: Type of processing:
'Skipped' if no data was found for the tag,
'Auto' if no user interaction was required,
'Manual' if user interaction was suggested and the user made changes to the validity of the events,
'Overridden' if the user listed the tag in the
override argument.
Comments: Comments left by the user during the analysis
last.seen: A data frame containing the number of tags last seen in
each study area section;
array.times: A data frame containing ALL the entry times of each tag
in each array;
section.times: A data frame containing all the entry times of each
tag in each section;
residency.list: A list containing the places of residency between first and last
valid detection for each tag;
time.ratios: A list containing the daily location per section (both in seconds spent
and in percentage of day) for each tag;
time.positions: A data frame showing the location where each
tag spent the most time per day;
global.ratios: A list containing summary tables showing the number of active tag
(and respective percentages) present at each location per day;
efficiency: A list containing the results of the inter-array Multi-way efficiency
calculations (see vignettes for more details);
intra.array.CJS: A list containing the results of the intra-array CJS calculations;
rsp.info: A list containing appendix information for the RSP package;
dist.mat: The distance matrix used in the analysis (if a valid
distance matrix was supplied)
# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("residency_example") # Move your R session into the example workspace setwd("residency_example") # run the residency analysis. Ensure the tz argument # matches the time zone of the study area and that the # sections match your array names. The line below works # for the example data. results <- residency(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)# Start by moving to a temporary directory old.wd <- getwd() setwd(tempdir()) # Deploy the example workspace exampleWorkspace("residency_example") # Move your R session into the example workspace setwd("residency_example") # run the residency analysis. Ensure the tz argument # matches the time zone of the study area and that the # sections match your array names. The line below works # for the example data. results <- residency(tz = "Europe/Copenhagen") # to obtain an HTML report, run the analysis with report = TRUE # return to original working directory setwd(old.wd) rm(old.wd)
shapeToRaster can also perform early quality checks on the shape file, to ensure it is compatible with the remaining study data. To activate these, set the names of the columns in the spatial.csv file that contain the x and y coordinates of the stations using coord.x and coord.y. By default, shapeToRaster looks for a spatial.csv file in the current working directory, but this can be personalized using the spatial argument.
shapeToRaster( shape, size, spatial = "spatial.csv", coord.x = NULL, coord.y = NULL, buffer = NULL, type = c("land", "water") )shapeToRaster( shape, size, spatial = "spatial.csv", coord.x = NULL, coord.y = NULL, buffer = NULL, type = c("land", "water") )
shape |
The path to a shapefile containing land polygons of the study area. |
size |
The pixel size, in metres. |
spatial |
Either a character string specifying the path to a spatial.csv file or a spatial data frame. This argument is not mandatory, but can be used to perform an early check of the shape file's compatibility with the study stations and release sites. |
coord.x, coord.y
|
The names of the columns containing the x and y positions of the stations in the spatial.csv file. these coordinates MUST BE in the same coordinate system as the shape file. |
buffer |
Artificially expand the map edges. Can be a single value (applied to all edges) or four values (xmin, xmax, ymin, ymax). The unit of the buffer depends on the shape file's coordinate system. |
type |
The type of shapefile being loaded. One of "land", if the shapefile's polygons represent landmasses, or "water", if the shapefile's polygons represent water bodies. |
It is highly recommended to read the manual page regarding distances matrices before running this function. You can find it here: https://hugomflavio.github.io/actel-website/manual-distances.html
A raster object.
# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # Fetch actel's example shapefile example.shape <- paste0(system.file(package = "actel")[1], "/example_shapefile.shp") # import the shape file x <- shapeToRaster(shape = example.shape, size = 20) # have a look at the resulting raster, # where the blank spaces are the land areas terra::plot(x) } rm(aux, missing.packages)# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # Fetch actel's example shapefile example.shape <- paste0(system.file(package = "actel")[1], "/example_shapefile.shp") # import the shape file x <- shapeToRaster(shape = example.shape, size = 20) # have a look at the resulting raster, # where the blank spaces are the land areas terra::plot(x) } rm(aux, missing.packages)
Computes an analytical CJS model for a presence/absence matrix.
simpleCJS(input, estimate = NULL, fixed.efficiency = NULL, silent = TRUE)simpleCJS(input, estimate = NULL, fixed.efficiency = NULL, silent = TRUE)
input |
A presence/absence matrix. |
estimate |
An estimate of the last array's efficiency, between 0 and 1. |
fixed.efficiency |
A vector of fixed efficiency estimates [0, 1]. |
silent |
Logical: Should messages be printed? This argument is mainly intended for function calls running within actel's analyses. |
A list containing:
absolutes A data frame with the absolute number of tags detected and missed,
efficiency A vector of calculated array detection efficiencies,
survival A matrix of calculated survivals,
lambda A combined detection efficiency * survival estimate for the last array.
Perry et al (2012), 'Using mark-recapture models to estimate survival from telemetry data'. url: https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data
# prepare a dummy presence/absence matrix x <- matrix(c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE), ncol = 3) colnames(x) <- c("Release", "Array1", "Array2") # run CJS simpleCJS(x)# prepare a dummy presence/absence matrix x <- matrix(c(TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE), ncol = 3) colnames(x) <- c("Release", "Array1", "Array2") # run CJS simpleCJS(x)
Find original station name
stationName(input, station)stationName(input, station)
input |
The results of an actel analysis (either explore, migration or residency). |
station |
The station standard name or number. |
The original station name
stationName(example.results, 1) # or stationName(example.results, "St.2")stationName(example.results, 1) # or stationName(example.results, "St.2")
Convert a data frame with timestamps into a list of circular objects
timesToCircular(x, by.group = FALSE)timesToCircular(x, by.group = FALSE)
x |
A data frame where the first column is an identifier, the second column
is a grouping structure, and columns three and onwards are timestamps at different
locations. Can be generated automatically by |
by.group |
Logical: Should the times at each location be divided by the group column (second column of x)? |
A list of circular objects for each data column and, optionally, for each group.
# create dummy input data frame. # Note: the names of the columns are irrelevant. x <- data.frame(ID = c(1:5), Group = c("A", "A", "B", "B", "B"), A1 = as.POSIXct( c("2019-01-03 11:21:12", "2019-01-04 12:22:21", "2019-01-05 13:31:34", "2019-01-06 14:32:43", "2019-01-07 15:23:52")), A2 = as.POSIXct( c("2019-01-08 16:51:55", "2019-01-09 17:42:42", "2019-01-10 18:33:33", "2019-01-11 19:24:32", "2019-01-12 20:15:22")), stringsAsFactors = TRUE) # run timesToCircular timesToCircular(x) # optionally, split results by group: timesToCircular(x, by.group = TRUE)# create dummy input data frame. # Note: the names of the columns are irrelevant. x <- data.frame(ID = c(1:5), Group = c("A", "A", "B", "B", "B"), A1 = as.POSIXct( c("2019-01-03 11:21:12", "2019-01-04 12:22:21", "2019-01-05 13:31:34", "2019-01-06 14:32:43", "2019-01-07 15:23:52")), A2 = as.POSIXct( c("2019-01-08 16:51:55", "2019-01-09 17:42:42", "2019-01-10 18:33:33", "2019-01-11 19:24:32", "2019-01-12 20:15:22")), stringsAsFactors = TRUE) # run timesToCircular timesToCircular(x) # optionally, split results by group: timesToCircular(x, by.group = TRUE)
Using a previously imported shape file that has been converted to a raster (see shapeToRaster),
Prepares a TransitionLayer object to be used in distance
estimations (see distancesMatrix). Adapted from Grant Adams' script "distance to closest mpa".
transitionLayer(x, directions = c(16, 8, 4))transitionLayer(x, directions = c(16, 8, 4))
x |
A water raster; for example the output of |
directions |
The number of directions considered for every movement situation during cost calculation. See the manual page linked above for more details. |
It is highly recommended to read the manual page regarding distances matrices before running this function. You can find it here: https://hugomflavio.github.io/actel-website/manual-distances.html
A TransitionLayer object.
# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # Fetch actel's example shapefile example.shape <- paste0(system.file(package = "actel")[1], "/example_shapefile.shp") # import the shape file x <- shapeToRaster(shape = example.shape, size = 20) # Build the transition layer t.layer <- transitionLayer(x) # inspect the output t.layer } rm(aux, missing.packages)# check if R can run the distance functions aux <- c( length(suppressWarnings(packageDescription("raster"))), length(suppressWarnings(packageDescription("gdistance"))), length(suppressWarnings(packageDescription("sp"))), length(suppressWarnings(packageDescription("terra")))) missing.packages <- sapply(aux, function(x) x == 1) if (any(missing.packages)) { message("Sorry, this function requires packages '", paste(c("raster", "gdistance", "sp", "terra")[missing.packages], collapse = "', '"), "' to operate. Please install ", ifelse(sum(missing.packages) > 1, "them", "it"), " before proceeding.") } else { # Fetch actel's example shapefile example.shape <- paste0(system.file(package = "actel")[1], "/example_shapefile.shp") # import the shape file x <- shapeToRaster(shape = example.shape, size = 20) # Build the transition layer t.layer <- transitionLayer(x) # inspect the output t.layer } rm(aux, missing.packages)