## ----include = F--------------------------------------------------------------
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(eval = F)

## ----fig.show='hold', fig.cap="test"------------------------------------------
# library(anipaths)
# vultures$POSIX <- as.POSIXct(vultures$timestamp, tz = "UTC")
# vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009, ] ## limit attention to 2009
# animate_paths(paths = vultures_paths,
#               delta.t = "day",
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier")

## ----map_logical--------------------------------------------------------------
# library(ggmap)
# animate_paths(paths = vultures_paths,
#               delta.t = 2 * 24 * 60 * 60, ## number of seconds in two days
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = TRUE, img.name = "background_TRUE")

## ----map_google---------------------------------------------------------------
# vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009:2010, ]
# background <- list(center = c(-90, 10),
#                    zoom = 3,
#                    maptype = "satellite")
# animate_paths(paths = vultures_paths,
#               delta.t = 3 * 24 * 60 * 60, ## number of seconds in three days
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = background, img.name = "background_google")

## ----map_simple---------------------------------------------------------------
# background <- geodata::world(path = ".")
# sf::st_crs(background)$proj4string ## matches default projection in animate_paths()
# animate_paths(paths = vultures_paths,
#               delta.t = "week",
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = background, img.name = "background_user")
# system("rm -r gadm") ## remove geodata map from machine

## ----make_covariates----------------------------------------------------------
# behaviors <- c("exploratory", "directed", "stationary")
# set.seed(1)
# vultures_paths$behavior <-
#   unlist(sapply(unique(vultures_paths$individual.local.identifier), function(id){
#     v_id <- vultures_paths[vultures_paths$individual.local.identifier == id, ]
#     switches <- c(0, sort(sample(1:nrow(v_id), 2)), nrow(v_id))
#     rep(behaviors[sample(1:3, 3)], diff(switches))
#   }))

## ----plot_covariates----------------------------------------------------------
# delta.t <- "day"
# background <- geodata::world(path = ".")
# sf::st_crs(background)$proj4string ## matches default projection in animate_paths()
# animate_paths(paths = vultures_paths,
#               delta.t = delta.t,
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               covariate = "behavior", covariate.colors = viridis::viridis(3),
#               ID.name = "individual.local.identifier",
#               background = background, img.name = "covariates")
# system("rm -r gadm") ## remove geodata map from machine

## -----------------------------------------------------------------------------
# interp <- animate_paths(paths = vultures_paths,
#                         delta.t = "day",
#                         coord = c("location.long", "location.lat"),
#                         Time.name = "POSIX",
#                         ID.name = "individual.local.identifier",
#                         s_args = rep(list(list(k = 10)), 10),
#                         return.paths = T)
# plot(interp, i = 2)

## -----------------------------------------------------------------------------
# obs_counts <- merge(data.frame(individual.local.identifier = unique(vultures_paths$individual.local.identifier)),
#                     aggregate(timestamp ~ individual.local.identifier, data = vultures_paths, FUN = length),
#                     by = "individual.local.identifier", sort = F)
# obs_counts
# s_args <- lapply(obs_counts$timestamp, function(x) c(k = floor(min(x / 4, 306))))
# interp <- animate_paths(paths = vultures_paths,
#                         delta.t = "day",
#                         coord = c("location.long", "location.lat"),
#                         Time.name = "POSIX",
#                         ID.name = "individual.local.identifier",
#                         s_args = s_args,
#                         return.paths = T)
# plot(interp, i = 2)

## -----------------------------------------------------------------------------
# interp <- animate_paths(paths = vultures_paths,
#                         delta.t = "day",
#                         coord = c("location.long", "location.lat"),
#                         Time.name = "POSIX",
#                         ID.name = "individual.local.identifier",
#                         return.paths = T, verbose = T)
# plot(interp, i = 2)

## ----whole_path_anim----------------------------------------------------------
# animate_paths(paths = vultures_paths,
#               delta.t = delta.t,
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = background,
#               whole.path = TRUE, tail.length = 0, img.name = "whole_traj")

## ----dim_anim-----------------------------------------------------------------
# vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009 &
#                              vultures$location.lat > 32, ]
# animate_paths(paths = vultures_paths,
#               delta.t = delta.t,
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = background, dimmed = c(1, 3, 5), img.name = "dim")

## ----make_network-------------------------------------------------------------
# vultures_paths <- vultures[format(vultures$POSIX, "%Y") == 2009, ]
# set.seed(1)
# n_indiv <- length(unique(vultures_paths$individual.local.identifier))
# change_pts <- 5
# network.times <- seq(min(vultures_paths$POSIX), max(vultures_paths$POSIX), l = change_pts + 2)
# network <- array(NA, dim = c(n_indiv, n_indiv, length(network.times)))
# for(time_i in 2:length(network.times)){
#   network_mat <- matrix(sample(1:0, n_indiv^2, prob = c(0.1, 0.9), replace = T),
#                         n_indiv, n_indiv)
#   network_mat[lower.tri(network_mat)] <- t(network_mat)[lower.tri(network_mat)]
#   diag(network_mat) <- 1
#   network[, , (time_i - 1):time_i] <- network_mat
# }

## ----network_anim-------------------------------------------------------------
# delta.t <- 3600*24*2
# animate_paths(paths = vultures_paths,
#               delta.t = delta.t,
#               coord = c("location.long", "location.lat"),
#               Time.name = "POSIX",
#               ID.name = "individual.local.identifier",
#               background = background, network = network, network.times = network.times,
#               img.name = "network")

## ----remove files, eval = F---------------------------------------------------
# system("rm -r js; rm -r css; rm -r images; rm index.html")