R/map_pos_data.R

In khufkens/swiftr: Process bird (swift) GPS position tracker data and meta-data.

#' Plot position data as processed by merge_pos_file()
#'
#' @param data a *.pos data logger / tracker file location
#' @param vi a tag number (if set this defaults to unknown)
#' @param window_size number of trailing or leading days over which to
#' integrate remote sensing (vegetation index) data
#' @return plots data processed by merge_pos_file()
#'
#' @keywords ancillary data, remote sensing, google earth engine
#' @export
#' @examples
#' \dontrun{
#'  map_pos_data(data = ...)
#' }

map_pos_data = function(data = NULL,
                        vi = "evi",
                        window_size = 60){

  # read the data if necessary
  if(!is.data.frame(data)){
    if(file.exists(data)){
      data = readRDS(data)
    }else{
      stop("file is not present...")
    }
  }

  # split out the components
  raw_modis_data = data[[1]]
  summary_data = data[[2]]

  ggplot() +
    geom_tile(data = raw_modis_data, aes(x = date, y = site, fill = vi)) +
    scale_fill_viridis() +
    geom_line(data = unique(location),
              aes(x = date + window_size,
                  y = sample), col = "white") +
    geom_line(data = unique(location),
              aes(x = date,
                  y = sample), col = "red") +
    geom_line(data = unique(location),
              aes(x = date - window_size,
                  y = sample), col = "white")

  # trailing values
  world <- map_data("world")
  gg <- ggplot() + xlim(c(0,120)) +
    ylim(c(-40,50)) #+ coord_map("ortho", orientation = c(10, 60, 0))
  gg <- gg + geom_map(data=world, map=world,
                      aes(x=long, y=lat, map_id=region),
                      color="white", fill="#7f7f7f", size=0.05, alpha=1/4)
  trailing <- gg + geom_point(data=d,
                              aes(x=longitude, y=latitude, colour=evi_leading)) +
    scale_colour_viridis()

  # compile the bird data
  date = as.Date(sprintf("%s-%s-%s",df$year,df$month,df$day),"%Y-%m-%d")
  sample = 1:nrow(df)
  location = unique(data.frame(sample,date))

  vi_matrix$vi[vi_matrix$vi < 0 ] = NA

  start = data.frame(df$longitude,df$latitude)[-nrow(df),]
  end = data.frame(df$longitude,df$latitude)[-1,]
  diff = cbind(start,end)

  travel_distance = apply(diff,1,function(x){
    distm(c(x[1],x[2]),c(x[3],x[4]))/1000
  })

  }