Help for package anipaths

Type:

Package

Title:

Animation of Multiple Trajectories with Uncertainty

Version:

0.10.5

Date:

2025-3-1

Maintainer:

Henry Scharf <hscharf@arizona.edu>

Description:

Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.

License:

GPL-3

RoxygenNote:

7.3.2

Depends:

R (≥ 3.5.0)

Imports:

animation, RColorBrewer, sf, crawl, mgcv, grDevices, ggmap, dplyr, ellipse, ggplot2, igraph, lubridate, magrittr, mvtnorm, stringr, tidyr, tidyselect

Suggests:

knitr, rmarkdown, terra, testthat

VignetteBuilder:

knitr

LazyData:

true

Encoding:

UTF-8

NeedsCompilation:

Packaged:

2025-03-01 21:10:14 UTC; hscharf

Author:

Henry Scharf [aut, cre], Kristine Dinh [aut], Rosales Hugo [aut], Rivera Angelica [aut]

Repository:

CRAN

Date/Publication:

2025-03-02 03:50:02 UTC

animate paths

Description

Animates telemetry data for the purposed of EDA using smoothing splines to interpolate the observed locations. The animations are particularly useful when examining multiple simultaneous trajectories. The output of the call to animate_paths() should bring up a browser window that shows the animation. Additionally, the images generated in images/ (or else the value set for imgdir) may be used with ffmpeg, latex, or other presentation software that can build animations directly from a sequence of images.

Usage

animate_paths(
  paths,
  coord = c("x", "y"),
  Time.name = "time",
  background = NULL,
  bg.axes = TRUE,
  bg.misc = NULL,
  bg.opts = NULL,
  blur.size = 8,
  covariate = NULL,
  covariate.colors = c("black", "white"),
  covariate.legend.loc = "bottomright",
  covariate.thresh = NULL,
  crawl.mu.color = "black",
  crawl.plot.type = "point.tail",
  date.col = "black",
  delta.t = NULL,
  dev.opts = list(),
  dimmed = NULL,
  ID.name = NULL,
  interpolation_type = "gam",
  interval = 1/12,
  legend.loc = "topright",
  main = NULL,
  max_refit_attempts = 10,
  method = "html",
  n.frames = NULL,
  network = NULL,
  network.colors = NULL,
  network.thresh = 0.5,
  network.times = NULL,
  network.ring.trans = 1,
  network.ring.wt = 3,
  network.segment.trans = 0.5,
  network.segment.wt = 3,
  override = FALSE,
  par.opts = list(),
  paths.proj = "+proj=longlat",
  paths.tranform.crs = "+proj=aea",
  plot.date = TRUE,
  pt.alpha = 0.4,
  pt.cex = 1,
  pt.colors = NULL,
  pt.wd = 1,
  res = 1.5,
  return.paths = FALSE,
  s_args = NULL,
  simulation = FALSE,
  simulation.iter = 12,
  tail.alpha = 0.6,
  tail.colors = "gray87",
  tail.length = 5,
  tail.wd = 1,
  theme_map = NULL,
  times = NULL,
  uncertainty.level = NA,
  uncertainty.type = 1,
  whole.path = FALSE,
  xlim = NULL,
  ylim = NULL,
  verbose = FALSE,
  ...
)

Arguments

paths

Either a data.frame with longitudes/eastings, latitudes/northings, IDs, and times (see coord, ID.name, and Time.name), a SpatialPointsDataFrame with IDs and times, or a list of data.frames containing the longitudes, latitudes, and times for each individual (with names provided). If all paths are already synchronous, another option for passing the data is to define paths as a list of matrices, all with the same number of rows, and to specify the times separately via the next argument. This situation might arise when, for example, locations the user wishes to animated correspond to realizations/sampler from a discrete-time movement model. Covariates may be provided as named columns of the matrices in paths.

coord

A character vector of length 2 giving the names of the longitude/easting and latitude/northing columns in the paths data.frame (in that order). This is required if paths is not a SpatialPointsDataFrame.

Time.name

The name of the columns in paths gving the observation times. This column must be of class POSIXt, or numeric.

background

Three possibilities: (1) A single background image over which animation will be overlayed, or a SpatRaster objects with one layers corresponding to each frame. (2) A list with values center (long/lat), zoom, and maptype (see ggmap::get_googlemap()) which will be used to generate a background for the animation based on Google maps tiles. Additional arguments may be added which will be passed to ggmap::get_googlemap(). (3) A logical value of TRUE, which will cue the function to get the best Google Map tile combination it can come up with. Note: ggmap must be installed for (2) and (3). Note: if you are calling animate_paths() several times in a short period of time you may get an error from Google for trying to pull tiles too often (e.g., Error in download.file(url, destfile = tmp, quiet = !messaging, mode = "wb") : cannot open URL 'http://maps.googleapis...'). Waiting a minute or so usually solves this.

bg.axes

logical: should animation place axis labels when using a background image (default is TRUE). If RGoogleMaps is used to produce background, labels will be "northing" and "easting". Otherwise, the strings given to coord will be used.

bg.misc

Character string which will be executed as R code after generating the background, and before adding trajectories, etc.

bg.opts

Options passed to plot() function call that makes background in each frame. For example, this could be used to specify blue ocean and gray land cover if background is a MULTIPOLYGON simple features object and bg.opts = list(bg = "dodgerblue4", col = "gray", border = "gray").

blur.size

a integer of the size for blur points; default is 8

covariate

The name of the column in paths that identifies the covariate to be mapped to a ring of color around each point.

covariate.colors

vector of colors which will be used in their given order to make a color ramp (see colorRamp())

covariate.legend.loc

either the location of the covariate legend, or NA if no legend is desired

covariate.thresh

if changed from its default value of NULL, the interpolated value of the covariate will be binarized based on this numeric value.

crawl.mu.color

color for the main predictions for crawl interpolation; default is black

crawl.plot.type

a character string of what type of the plot you wish to generate when interpolation_type = "crawl". Default is "point.tail" for points with tails; input "point" for point plot and input "blur" for blur point plot; ; input "blur.point" for blur point with tails.

date.col

default is "black"

delta.t

The gap in time between each frame in the animation. Specify one of delta.t or n.frames. If both are specified, delta.t is used.

dev.opts

Options passed to png() before creating each frame.

dimmed

Numeric vector of individuals to "dim" in the animation. Order corresponds to the order of the ID.name variable, or order of paths list.

ID.name

The name of the column in paths that identifies each individual. If left as NULL (default), a single individual is assumed.

interpolation_type

a character string of the type of interpolation. Default is "gam" for a generalized addictive model. Use "crawl" to interpolate using crawl package. Note: due to the ongoing shift in PROJ4/6 standards, warning about CRS comments may appear.

interval

Seconds per frame in animation. Default is 1/12 (or 12 frames per second).

legend.loc

passed to first argument of legend() function. Default is "topright". NA removes legend.

main

Title for each frame.

max_refit_attempts

an integer of number of resampling when the fit for crawl failed to run; default is 10

method

either "html" (default) or "mp4". The latter requires the user has installed ffmpeg (see ?animation::saveVideo()).

n.frames

The number of frames used to animate the complete time domain of the data.

network

Array of dimensions (# individuals, # individuals, n.frames) that gives a dyanmic network structure among the individuals.

network.colors

A symmetric matrix of dimension length(paths) \times length(paths) giving the colors associated with each pairwise relationship.

network.thresh

Network structure is summarized in the animation in a binary way, regardless of whether or not the network is continuously weighted or not. The value of network.thresh determines the level below which no connection is shown, and above which an active connection is shown via colored rings and connecting segments.

network.times

Numeric vector. If network time grid doesn't match n.frames, supply the times at which the network has been evaluated so it can be interpolated using smoothing splines.

network.ring.trans

transparency of network segments (default is 1)

network.ring.wt

thickness of network rings (default is 3)

network.segment.trans

transparency of network segments (default is 0.5)

network.segment.wt

thickness of network segments (default is 3)

override

Logical variable toggling where or not to override warnings about how long the animation procedure will take.

par.opts

Options passed to par() before creating each frame.

paths.proj

PROJ.4 string corresponding to the projection of the data. Default is "+proj=longlat".

paths.tranform.crs

a PROJ.4 string of coordinate projection transformation based on the animals' location; default is "+proj=aea +lat_1=30 +lat_2=70".

plot.date

Logical variable toggling date text at the time center of the animation.

pt.alpha

alpha value for the points

pt.cex

A numeric value giving the character expansion (size) of the points for each individual. Default is 1.

pt.colors

A vector of colors to be used for each individual in the animation. Default values come from Color Brewer palettes. When a network is provided, this is ignored and individuals are all colored black. If NA, no plot colors are chosen to distinguish individuals. This can be useful when making animations involving a covariate. Consider also setting legend.loc to NA in this case.

pt.wd

size of the points; default is 1

res

Resolution of images in animation. Increase this for higher quality (and larger) images.

return.paths

logical. Default is FALSE, but if TRUE then the interpolated paths are returned and no animation is produced.

s_args

Default is NULL, in which case anipaths attempts to select a reasonable number of knots for the GAM interpolation. Alternatively, the user can provide a list of arguments to mgcv::s() the same length and order as number of unique individuals (i.e., unique(paths[, ID.name])). Each entry in the list should be a named list/vector (e.g., s_args = list(list(k = 10), list(k = 12), ...)).

simulation

logical. Generate simulation predictions to have multiple projects for the animal paths; default is FALSE.

simulation.iter

an integer of how many paths the crawl model will generate; default is 5.

tail.alpha

alpha value for the tails

tail.colors

default is "gray87". Can be single color or vector of colors.

tail.length

Length of the tail trailing each individual.

tail.wd

Thickness of tail trailing behind each individual. Default is 1.

theme_map

plot theme for ggplot, default is NULL

times

If all paths are already synchronous, another option for passing the data is to define paths as a list of matrices, all with the same number of rows, and to specify the times separately via this argument.

uncertainty.level

value in (0, 1) corresponding to level at which to draw uncertainty ellipses. NA (default) results in no ellipses.

uncertainty.type

State what type of uncertainty plot 1 is default for tails more than 1 is amount of predicted trajectories for each unique individual and blurs for blur plot

whole.path

logical. If TRUE (default = FALSE), the complete interpolated trajectories will be plotted in the background of the animation. If whole.path = TRUE, consider also setting tail.length = 0.

xlim

Boundaries for plotting. If left undefined, the range of the data will be used.

ylim

Boundaries for plotting. If left undefined, the range of the data will be used.

verbose

logical; TRUE prints messages about fitting details

...

other arguments to be passed to ani.options to animation options such as the time interval between image frames.

Value

video file, possibly a directory containing the individual images, or interpolated paths.

Examples

##
vultures$POSIX <- as.POSIXct(vultures$timestamp, tz = "UTC")
vultures_paths <- vultures[vultures$POSIX > as.POSIXct("2009-03-01", origin = "1970-01-01") &
  vultures$POSIX < as.POSIXct("2009-05-01", origin = "1970-01-01"), ]
animate_paths(
  paths = vultures_paths,
  delta.t = "week",
  coord = c("location.long", "location.lat"),
  Time.name = "POSIX",
  ID.name = "individual.local.identifier"
)
## Not run: 
background <- list(
  center = c(-90, 10),
  zoom = 3,
  maptype = "satellite"
)
library(ggmap)
library(RColorBrewer)
COVARIATE <- cos(as.numeric(vultures_paths$timestamp) /
  diff(range(as.numeric(vultures_paths$timestamp))) * 4 * pi)
animate_paths(
  paths = cbind(vultures_paths, COVARIATE),
  delta.t = "week",
  coord = c("location.long", "location.lat"),
  Time.name = "POSIX", covariate = "COVARIATE",
  covariate.colors = brewer.pal(n = 9, "RdYlGn"),
  ID.name = "individual.local.identifier",
  background = background
)

# animation using crawl interpolation
animate_paths(
  paths = vultures_paths,
  delta.t = "week",
  coord = c("location.long", "location.lat"),
  Time.name = "POSIX",
  ID.name = "individual.local.identifier",
  interpolation_type = "crawl"
)

## End(Not run)

# Run to remove files generated by this function
system("rm -r js; rm -r css; rm -r images; rm index.html")

Animation Expression

Description

Animation Expression

Usage

animation_expression(
  bg,
  bg.axes,
  bg.misc,
  bg.opts,
  blur.size,
  cliques,
  color_covariate_function,
  coord,
  covariate,
  covariate.factors,
  covariate.interp,
  covariate.legend.loc,
  covariate.name,
  covariate.range,
  covariate.thresh,
  covariate.ticks,
  crawl.mu.color,
  crawl.plot.type,
  cur.time,
  date.col,
  delta.t,
  dev.opts,
  dimmed,
  ID_names,
  interpolation_type,
  interval,
  legend.loc,
  main,
  method,
  n.frames,
  network,
  network.interp,
  network.ring.trans,
  network.ring.wt,
  network.segment.trans,
  network.thresh,
  network.segment.wt,
  par.opts,
  paths,
  paths.interp,
  plot.date,
  pt.alpha,
  pt.cex,
  pt.colors,
  pt.wd,
  res,
  scale,
  simulation,
  simulation.iter,
  tail.alpha,
  tail.colors,
  tail.length,
  tail.wd,
  theme_map,
  time.grid,
  Time.name,
  uncertainty.level,
  uncertainty.type,
  whole.path,
  xlim,
  ylim,
  ...
)

Arguments

bg

a list object for the ggmap background if background object is supplied

bg.axes

bg.misc

Character string which will be executed as R code after generating the background, and before adding trajectories, etc.

bg.opts

Options passed to plot() function call that makes background in each frame. For example, this could be used to specify blue ocean and gray landcover if background is a SpatialPolygonsDataFrame and bg.opts = list(bg = "dodgerblue4", col = "gray", border = "gray").

blur.size

a integer of the size for blur points; default is 8

cliques

A list of colors for network projections

color_covariate_function

a function to generate color for covariate interpolation

coord

covariate

The name of the column in paths that identifies the covariate to be mapped to a ring of color around each point.

covariate.factors

factor levels for covariate interpolation

covariate.interp

interpolation for covariate projections

covariate.legend.loc

either the location of the covariate legend, or NA if no legend is desired

covariate.name

name of covariate interpolation

covariate.range

the range of covariate interpolation

covariate.thresh

if changed from its default value of NULL, the interpolated value of the covariate will be binarized based on this numeric value.

covariate.ticks

crawl.mu.color

color for the main predictions for crawl interpolation; default is black

crawl.plot.type

cur.time

start time of animation

date.col

default is "black"

delta.t

The gap in time between each frame in the animation. Specify one of delta.t or n.frames. If both are specified, delta.t is used.

dev.opts

Options passed to png() before creating each frame.

dimmed

Numeric vector of individuals to "dim" in the animation. Order corresponds to the order of the ID.name variable, or order of paths list.

ID_names

a list of names for each animal in the data

interpolation_type

a character string of the type of interpolation. Default is "gam" for a generalized addictive model. Input "crawl" to interpolate using crawl package

interval

Seconds per frame in animation. Default is 1/12 (or 12 frames per second).

legend.loc

passed to first argument of legend() function. Default is "topright". NA removes legend.

main

Title for each frame. SOON: support for changing titles to allow for, say, dates.

method

either "html" (default) or "mp4". The latter requires the user has installed ffmpeg (see ?animation::saveVideo()).

n.frames

The number of frames used to animate the complete time domain of the data.

network

Array of dimensions (# individuals, # individuals, n.frames) that gives a dyanmic network structure among the individuals.

network.interp

interpolated network of dimension (n.indiv, n.indiv, n.frames)

network.ring.trans

transparency of network segments (default is 1)

network.ring.wt

thickness of network rings (default is 3)

network.segment.trans

network.thresh

network.segment.wt

thickness of network segments (default is 3)

par.opts

Options passed to par() before creating each frame.

paths

A list of all paths from each animals stored in a data.frame or SpatialPointsDataFrame object.

paths.interp

a path animation object that contains all predicted and simulated paths for all animals

plot.date

Logical variable toggling date text at the time center of the animation.

pt.alpha

alpha value for the points

pt.cex

A numeric value giving the character expansion (size) of the points for each individual. Default is 1.

pt.colors

pt.wd

size of the points; default is 1

res

Resolution of images in animation. Increase this for higher quality (and larger) images.

scale

simulation

logical. Generate simulation predictions to have multiple projects for the animal paths

simulation.iter

an integer of how many paths the crawl model will generate

tail.alpha

alpha value for the tails

tail.colors

default is "gray87". Can be single color or vector of colors.

tail.length

Length of the tail trailing each individual.

tail.wd

Thickness of tail trailing behind each individual. Default is 1.

theme_map

plot theme for ggplot, default is NULL

time.grid

A vector of time interval.

Time.name

The name of the columns in paths gving the observation times. This column must be of class POSIXt, or numeric.

uncertainty.level

value in (0, 1) corresponding to level at which to draw uncertainty ellipses. NA (default) results in no ellipses.

uncertainty.type

State what type of uncertainty plot 1 is default for tails more than 1 is amount of predicted trajectories for each unique individual and blurs for blur plot

whole.path

logical. If TRUE (default = FALSE), the complete interpolated trajectories will be plotted in the background of the animation. If whole.path = TRUE, consider also setting tail.length = 0.

xlim

Boundaries for plotting. If left undefined, the range of the data will be used.

ylim

Boundaries for plotting. If left undefined, the range of the data will be used.

...

other arguments to be passed to ani.options to animation options such as the time interval between image frames.

Value

animation for different methods and different interpolation types

blur ellipses function

Description

blur ellipses function

Usage

blur_point(
  x,
  levels = seq(0.001, 1 - 0.1, l = 15),
  alpha_mult,
  col = "black",
  center
)

Arguments

x

An object. In the default method the parameter x should be a correlation between -1 and 1 or a square positive definite matrix at least 2x2 in size. It will be treated as the correlation or covariance of a multivariate normal distribution.

levels

contour levels

alpha_mult

multiplier on transparency level

col

default is black

center

two-vector giving center of ellipse

Check overwrite

Description

Check overwrite

Usage

check_overwrite(method, return.paths, ...)

Arguments

method

passed from animate_paths()

return.paths

passed from animate_paths()

...

passed from animate_paths(); used to check for user-specified value for img.name

Value

NULL, unless there is risk of over-writting and the user interrupts animation (FALSE)

Synchronous interpolation of covariate using either GAM (same as paths) or piece-wise constant if covariate is a factor

Description

Synchronous interpolation of covariate using either GAM (same as paths) or piece-wise constant if covariate is a factor

Usage

covariate_interp(paths, covariate = NULL, Time.name, time.grid, s_args)

Arguments

paths

lists of data.frames containing positions, times, and covariate for each individual

covariate

character string giving name of covariate variable in data.frames

Time.name

character string giving name of time variable in data.frames

time.grid

grid of possible times to use for interpolation (individuals will only be interpolated to times within the range of observation times)

s_args

arguments to mgcv::s() for GAM interpolation method

Value

list of interpolated covariate by individual

Single and Multiple Realizations Using crawl Package

Description

Single and Multiple Realizations Using crawl Package

Usage

crawl_interpolation(
  coord,
  delta.t,
  ID_names,
  max_refit_attempts,
  n.indiv,
  paths,
  paths.proj,
  paths.tranform.crs,
  simulation,
  simulation.iter,
  time.grid,
  Time.name
)

Arguments

coord

delta.t

The gap in time between each frame in the animation. Specify one of delta.t or n.frames. If both are specified, delta.t is used.

ID_names

a list of names for each animal in the data

max_refit_attempts

an integer of number of attempts per individual to fit crawl model

n.indiv

an integer of the total number of unique animals in the data

paths

A list of all paths from each animals stored in a data.frame or SpatialPointsDataFrame object.

paths.proj

PROJ.4 string corresponding to the projection of the data

paths.tranform.crs

a character string of coordinate projection transformation based on the animals' location.

simulation

logical. Generate simulation predictions to have multiple projects for the animal paths

simulation.iter

an integer of how many paths the crawl model will generate

time.grid

grid for synchronized interpolations

Time.name

The name of the columns in paths gving the observation times. This column must be of class POSIXt, or numeric.

Value

interpolation values from crawl package

GAM interpolation using `mgcv:gam()`.

Description

GAM interpolation using mgcv:gam().

Usage

gam_interp(
  formula = NULL,
  y,
  time,
  pred_times,
  se.fit = T,
  s_args = NULL,
  uncertainty.type,
  verbose = F
)

Arguments

formula

optionally specify formula for mgcv::gam() using y as response and time as predictor.

y

observations

time

times for observations

pred_times

prediction times

se.fit

logical default is TRUE; should standard pointwise errors be computed for interpolation

s_args

Arguments to mgcv::s() can be passed using a named list/vector.

uncertainty.type

State what type of uncertainty plot 1 is default for tails more than 1 is amount of predicted trajectories for each unique individual and blurs for blur plot

verbose

logical; TRUE prints messages about fitting details

Value

interpolated values

Figure out scale and centering of google map by transforming reported lat long bounding box back to web mercator

Description

Figure out scale and centering of google map by transforming reported lat long bounding box back to web mercator

Usage

get_googlemap_min_scale(map)

Arguments

map

ggmap object

Value

scale (factor by which web mercator has been shrunk) and min (leftmost, bottom most coordinate of rectangle)

get_network_colors() Finds all maximal cliques in the network at each time point and tries to assign them a useful coloring

Description

get_network_colors() Finds all maximal cliques in the network at each time point and tries to assign them a useful coloring

Usage

get_network_colors(binary.network, network.color.options = NULL)

Arguments

binary.network

a 3D array giving the time-varying adjecency matrix of a dynamic network.

network.color.options

vector of colors

Value

a list of two elements: a list of the maximal cliques at each time, and c list with colors for each clique at each time

adjust center + scale for google map plotting

Description

adjust center + scale for google map plotting

Usage

googlemap_proj(x, map)

Arguments

x

sf object

map

ggmap object

Value

two-column matrix of locations from x projected to match map

Synchronous interpolation of network using piece-wise constant interpolation

Description

Synchronous interpolation of network using piece-wise constant interpolation

Usage

network_interp(network = NULL, network.times, time.grid)

Arguments

network

array of network observations of dimension (n.indiv, n.indiv, length(network.times))

network.times

vector of times at which network observations are made

time.grid

times at which network will be interpolated

Value

array of dimension n.indiv, n.indiv, length(time.grid))

Get good alpha_mult

Description

Get good alpha_mult

Usage

new_alpha(sd1, sd2)

Arguments

sd1

standard deviation of longitude

sd2

standard deviation of latitude

Value

scalar value to be used for alpha_mult in blur_point()

Synchronous GAM interpolation of all paths

Description

Synchronous GAM interpolation of all paths

Usage

paths_gam_interp(
  paths,
  coord,
  Time.name,
  time.grid,
  s_args = NULL,
  uncertainty.type,
  verbose = F
)

Arguments

paths

lists of data.frames containing positions, times, and covariate for each individual

coord

two-vector of character strings giving names of x and y coordinates in data.frames

Time.name

character string giving name of time variable in data.frames

time.grid

grid of possible times to use for interpolation (individuals will only be interpolated to times within the range of observation times)

s_args

List of arguments to mgcv::s() the same length as number of unique individuals. Each entry in the list should be a named list/vector.

uncertainty.type

State what type of uncertainty plot 1 is default for tails more than 1 is amount of predicted trajectories for each unique individual and blurs for blur plot

verbose

logical; TRUE prints messages about fitting details

Value

list of interpolated paths by individual

Plot animation path interpolation

Description

This is mainly intended as a way to check that the interpolations used in the animation are working as expected.

Usage

## S3 method for class 'paths_animation'
plot(x, ..., i = 1, level = 0.05, type = "path", ylim_x = NULL, ylim_y = NULL)

Arguments

x

paths_animation object as created through a call to animate_paths().

...

additional arguments passed to plot.

i

index of individual to plot (corresponds to index in unique(paths[, 'ID.name')).

level

confidence level for error bands. NA removes bands.

type

either "path" (default) for two marginal interpolation plots, or "covariate" for a single interpolation plot

ylim_x

y-axis limits for marginal plots (x, easting, etc.)

ylim_y

y-axis limits for marginal plots (y, northing, etc.)

Examples

vultures$POSIX <- as.POSIXct(vultures$timestamp, tz = "UTC")
vultures_paths <- vultures[vultures$POSIX > as.POSIXct("2009-03-22", origin = "1970-01-01") &
  vultures$POSIX < as.POSIXct("2009-04-05", origin = "1970-01-01"), ]
interpolated_paths <-
  animate_paths(
    paths = vultures_paths,
    delta.t = 3600 * 6,
    coord = c("location.long", "location.lat"),
    Time.name = "POSIX",
    ID.name = "individual.local.identifier",
    s_args = rep(list(list(k = 10)), 6),
    return.paths = TRUE
  )
plot(interpolated_paths, i = 2)

GPS locations of turkey vultures.

Description

A dataset containing a subset of the locations of turkey vultures (2003–2006), with time stamps, from:

Usage

vultures

Format

A data frame with 215719 rows and 11 variables:

timestamp: time of observation
location.long: logitude
location.lat: latitude
individual.local.identifier: identifier for each individual

...

Details

Dodge S, Bohrer G, Bildstein K, Davidson SC, Weinzierl R, Mechard MJ, Barber D, Kays R, Brandes D, Han J (2014) Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Philosophical Transactions of the Royal Society B 20130195. doi:10.1098/rstb.2013.0195

Bildstein K, Barber D, Bechard MJ (2014) Data from: Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Movebank Data Repository. doi:10.5441/001/1.46ft1k05

Source

doi:10.5441/001/1.46ft1k05 Bildstein K, Barber D, Bechard MJ (2014) Data from: Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Movebank Data Repository.

GPS locations of three species of whales.

Description

A dataset containing locations of whales, with time stamps, from:

Usage

whales

Format

A data frame with 4303 rows and 4 variables:

timestamp: time of observation
location.long: logitude
location.lat: latitude
individual.local.identifier: identifier for each individual

...

Details

Irvine LM, Winsor MH, Follett TM, Mate BR, Palacios DM (2020) An at-sea assessment of Argos location accuracy for three species of large whales, and the effect of deep-diving behavior on location error. Animal Biotelemetry 8:20.

Irvine LM, Follett TM, Winsor MH, Mate BR, Palacios DM (2020) Data from: Study "Blue and fin whales Southern California 2014-2015 - Argos data". Movebank Data Repository. doi:10.5441/001/1.98f5r6d0

Source

doi:10.5441/001/1.98f5r6d0 Irvine LM, Follett TM, Winsor MH, Mate BR, Palacios DM (2020) Data from: Study "Blue and fin whales Southern California 2014-2015 - Argos data". Movebank Data Repository.

animate paths

Description

Usage

Arguments

Value

Examples

Animation Expression

Description

Usage

Arguments

Value

blur ellipses function

Description

Usage

Arguments

Check overwrite

Description

Usage

Arguments

Value

Synchronous interpolation of covariate using either GAM (same as paths) or piece-wise constant if covariate is a factor

Description

Usage

Arguments

Value

Single and Multiple Realizations Using crawl Package

Description

Usage

Arguments

Value

GAM interpolation using mgcv:gam().

Description

Usage

Arguments

Value

Figure out scale and centering of google map by transforming reported lat long bounding box back to web mercator

Description

Usage

Arguments

Value

get_network_colors() Finds all maximal cliques in the network at each time point and tries to assign them a useful coloring

Description

Usage

Arguments

Value

adjust center + scale for google map plotting

Description

Usage

Arguments

Value

Synchronous interpolation of network using piece-wise constant interpolation

Description

Usage

Arguments

Value

Get good alpha_mult

Description

Usage

Arguments

Value

Synchronous GAM interpolation of all paths

Description

Usage

Arguments

Value

Plot animation path interpolation

Description

Usage

Arguments

Examples

GPS locations of turkey vultures.

Description

Usage

Format

Details

Source

GPS locations of three species of whales.

Description

Usage

Format

GAM interpolation using `mgcv:gam()`.