Package 'anipaths'

Title: Animation of Multiple Trajectories with Uncertainty
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.
Authors: Henry Scharf [aut, cre], Kristine Dinh [aut], Rosales Hugo [aut], Rivera Angelica [aut]
Maintainer: Henry Scharf <[email protected]>
License: GPL-3
Version: 0.10.3
Built: 2024-10-31 03:04:58 UTC
Source: https://github.com/cran/anipaths

Help Index

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 landcover 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")

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 overwritting 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

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)

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.