### Visualizing data

# Loading the caribou data frame and looking at locations per caribou
load("./_data/caribou.daily.df.rda")
head(caribou.daily.df)
table(caribou.daily.df$id)

# Subsetting to one caribou and making some plots of it's movement
FY1607 <- subset(caribou.daily.df, id == "FY1607")
with(FY1607, plot(x,y,asp=1, type="o"))


par(mar = c(0,4,0,0), oma = c(4,0,5,0), xpd=NA)
layout(rbind(c(1,2), c(1,3)))
with(FY1607, {
     plot(x, y, asp = 1, type="o")
     plot(time, x, type="o", xaxt="n", xlab="") 
     plot(time, y, type="o")
     title(FY1607$id[1], outer=TRUE)
})

# Creating the scan.track funciton
scan.track <- function(x,y,time, ...){
  par(mar = c(0,4,0,0), oma = c(4,0,5,4), xpd=NA)
  layout(rbind(c(1,2), c(1,3)))
     plot(x, y, asp = 1, type="o", ...)
     plot(time, x, type="o", xaxt="n", xlab="", ...)
     plot(time, y, type="o", ...)
}

# Trying it with another caribou
with(subset(caribou.daily.df, id == "FA1403"), 
  scan.track(x,y,time,pch=19, col=rgb(1,0,0,.5), cex=0.5))

# Making some PDFs (don't need to run this in the workshop, but the code is here in case you need it in the future!)

# pdf("caribouscans.pdf", width = 6, height = 3)
# for(myname in levels(caribou.daily.df$id)){
# 	me <- subset(caribou.daily.df, id == myname)
#   scan.track(me$x, me$y, me$time, pch=19, col=rgb(0,0,0,.5), cex=0.5)
#   title(myname, outer=TRUE)
# }
# dev.off()


# more PDF creation

# pdf("myanimation.pdf")
# for(i in 1:nrow(FY1607)){
#   with(FY1607, {
#   		 plot(x,y, asp=1, type="l", col="grey")
#        lines(x[1:i], y[1:i], col="blue", cex=0.5, lwd=2)
#        points(x[i], y[i], pch=21, cex=2, col="lightblue")
#        title(paste(id[1], day.date[i]))
# 
#   })
#   box()
# }
# dev.off()
# 

# A quick ggplot
require(ggplot2)
ggplot(caribou.daily.df, aes(x=x, y=y, col=id)) + geom_path(alpha = 0.5) + geom_point(alpha = 0.5) + coord_fixed()

# now incorporting a Google basemap
require(ggmap)
latrange <- range(caribou.daily.df$lat) + c(-.5,.5)
longrange <- range(caribou.daily.df$lon) + c(-.5,.5)
caribou.map <- get_map(location = c(longrange[1], latrange[1], longrange[2], latrange[2]), maptype = "terrain", source="google")

# You could also get this using the code below, modifying the zoom argument as necessary
## caribou.map <- get_map(bbox(caribou.move), source="google", zoom=8, maptype="terrain")

# plotting the empty basemap
ggmap(caribou.map)

# And the version with the caribou data overlay
ggmap(caribou.map) + 
	geom_point(data = caribou.daily.df, mapping = aes(x = lon, y = lat, col=id), alpha = 0.6, size=1) +
	geom_path(data = caribou.daily.df, mapping = aes(x = lon, y = lat, col=id), alpha = 0.6, size=1) +
	coord_map() + labs(x = "Longitude", y = "Latitude") + 
	scale_color_discrete(l = 65) + 
	guides(color=guide_legend(ncol=2))

# And now for an interactive map!  
require(mapview)
require(sp)
caribou.sp <- caribou.daily.df
coordinates(caribou.sp)<- ~lon + lat
proj4string(caribou.sp)<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
mapview(caribou.sp, zcol="id", legend = TRUE, cex=5, lwd=2, map.type = "Esri.DeLorme")

# This is code for creating individual Google Earth kml files.  
# Again, don't need to run this during the workshop, unless you want to!

# require(maptools)
# require(gplots)
# palette(rich.colors(4))
# for(myid in unique(caribou.daily.df$id)){
#   m <- subset(caribou.daily.df, id == myid)[c("lon", "lat")]
#   m.lines <- Lines(Line(m), ID = myid)
#   kmlLine(m.lines, kmlfile = paste0(myid,".kml"),
#           kmlname = myid, description = myid,
#           lwd=4,
#           col=match(myid, unique(caribou.daily.df$id)))
# }


load("./_data/caribou.df.rda")

# Plotting a caribou with the track colored by elevation
ggplot(subset(caribou.df, id == "FY1607"), aes(x = lon, y = lat, color=elevation)) + 
  geom_path() + 
  geom_point() +
  scale_color_continuous(na.value="transparent")#, limits = c(1400, 2500))

# Same thing but with multiple caribou:
ggmap(caribou.map, darken = c(0.5, "white")) + 
  geom_point(data = caribou.df, aes(x = lon, y = lat, color = elevation), size =2) +
  scale_color_gradientn(colors=terrain.colors(10), na.value="transparent") +
  geom_point(shape=1, size = 2, color = "black") +
  coord_map() + 
  labs(x = "Longitude", y = "Latitude")