#'=================================================================================================#
#' Using R to Analyse Ecological and Spatial Data                                                  #                                                                                           #
#' "Calculating movement Metrics"             #
#'=================================================================================================#

#' NB!!!! Set the working directory
setwd("C:/Shambala/data/")

#' MAKE SURE THE WORKING DIRECTORY IS CORRECT i.e. [1] "C:/R_course/Day3/data"
getwd()

#'----------------------------------------- Load Packages ---------------------------------------#### 
library(maptools)
library(adehabitatHR)
library(move)
library(rgdal)
library(lubridate)
library(plyr)
library(stringr)
library(ggplot2)
library(ggmap)


####------------------------------------- 1- Import Data ---------------------------------------####

#' Import cleaned .csv data 
#'  thanda_cheetah_clean.csv or kruger_buffalo_clean.csv
tracking_data <- read.csv("data_clean.csv")

#' Import the study area boundry .shp file
#'  The dsn must not end in '/'
#'  Thanda_boundary or kruger
#study_area <- readOGR(dsn = '.', layer = 'kruger')

####------------------------------ 2- Introduction to 'move' -----------------------------------####

#' Define the timezone, format is default POSIXct after cleaning
tracking_data$timestamp <- as.POSIXct(tracking_data$timestamp,   tz="Africa/Johannesburg")

#' Takes the Longitude and Latitude from ele and saves it as a SpatialPoints data.frame with a projection
sp_latlong<- SpatialPoints(tracking_data[, c("lon", "lat")],proj4string=CRS("+proj=longlat +ellps=WGS84"))

#' Project the coordinates, this is UTM36 for Kruger
sp_proj<- spTransform(sp_latlong, CRS("+init=epsg:32736")) 

#' Project other layers that you may want to plot
#study_area_proj<-spTransform(study_area, projection(sp_proj))

#' Add projected locations to data frame
locs<-as.data.frame(sp_proj@coords)
tracking_data$x<-locs$lon
tracking_data$y<-locs$lat

#' Define the data frame as a move object:
#'   1) If there is one animal, then a move object is made
#'   2) If there are multiple animals - like this example - then a move stack object is made
move_obj <- move(x=tracking_data$x,             # Longitude
                 y=tracking_data$y,             # Latitude
                 time=tracking_data$timestamp,  # Timestamp i.e. date and time
                 data=tracking_data,            # The data source
                 proj=CRS("+init=epsg:32736"),  # The projection of the data source
                 animal=tracking_data$animal)   # The column containing the animal unique identifier

#' Summary overview 
move_obj

#' Move object structure 
str(move_obj)

#' Plot a move object, the type "l" means line
plot(move_obj, type="l")

#' Examples of accessing information from the move object you have created 
timestamps(move_obj)[1:5] # Extract the timestamp for the first five points i.e. 1:5
coordinates(move_obj)[1:10,] # Extract the coordinates for the first ten points i.e 1:10
idData(move_obj) # Extract the animals that make up the move object
n.locs(move_obj) # Extract the number of locations for each of the animals

#####-------------------------------- 3- Calculate Movement Metrics ------------------------------####

#' Extract properties
#' Add Speed to the move object, typically in m.s
move_obj$speed <-unlist(lapply(speed(move_obj), c, NA)) # adds an NA to the first row
summary(move_obj$speed)


#' Add Distance to the move object, typically in m
move_obj$distance <-unlist(lapply(distance(move_obj), c, NA))  # adds an NA to the first row
summary(move_obj$distance)

#' Add Angle to the move object (Angles in degrees (between -180 and 180)
move_obj$angle <-unlist(lapply(angle(move_obj), c, NA))  # adds an NA to the first row
summary(move_obj$angle)

 
#####--------------------------------- 4- Export the Results -----------------------------------####


#' Convert move object to a data frame, only select columns of interest
export <- as.data.frame(move_obj) # Creates a data frame called export from the move object

#' Neaten the table up and only select columns you are interested in
#' If you get this error "arguments imply differing number of rows:..." it means one of the 
#'  columns does not exist - try re-run all the code in section 3 above
export <- data.frame(animal=export$animal, 
                     timestamp=export$timestamp, 
                     lon=export$lon, 
                     lat=export$lat, 
                     #utm.easting=export$utm.easting,
                     #utm.northing=export$utm.northing, 
                     speed=export$speed, 
                     distance=export$distance 
                     #angle=export$angle
                     )

#' Prefer speed in km/h
export$speed <- export$speed*3.6

# write the dataset to CSV
write.csv(as.data.frame(export), "data_metrics.csv")

# ...or create a shapefile from the points
#' Convert the data points into spatialPointsDataFrame
data_spat<- SpatialPointsDataFrame(export[, c("lon", "lat")],data=export)

#' Define the projection of the points
proj4string(data_spat)<-CRS("+proj=longlat +ellps=WGS84")

#' Write to .shp
writeOGR(data_spat, ".", "data_metrics", driver="ESRI Shapefile", overwrite_layer = T)