Cannot retrieve contributors at this time

277 lines (198 sloc) 10.7 KB

	library(lubridate)
	library(dplyr)
	library(adehabitatHR)
	library(sf)

	## 27
	data_1 <- read.table("36927.csv", header = T, sep = ";", dec = ",") #
	data_1$UTC.Date <- as.POSIXct(data_1$UTC.Date, format = "%d.%m.%Y")

	data_1$timestamp <- with(data_1, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_1$timestamp <- round_date(data_1$timestamp, "30 mins") # rounding of time to 30 mins

	data_1 <- data_1[data_1$DOP < 6 ,] # DOP < 6
	data_1 <- data_1 %>% filter(timestamp >= "2021-11-02 00:30:00") #removal of the observation before putting on the collar
	data_1 <- data_1 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_1$Longitude <- as.numeric(sub(",", ".", data_1$Longitude, fixed = TRUE)) # converting to numeric
	data_1$Latitude <- as.numeric(sub(",", ".", data_1$Latitude, fixed = TRUE))

	data_1 <- data_1[!is.na(data_1$Longitude),] # removal of NA values
	data_1 <- data_1[!is.na(data_1$Latitude),] # removal of NA values

	data_1sf <- st_as_sf(data_1, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_1sf <- st_set_crs(data_1sf, 4326)
	plot(st_geometry(data_1sf))

	# projecting to ETRS89 European coordinate system
	data_1sf <- st_transform(data_1sf, 3035)
	plot(st_geometry(data_1sf))

	# creating the input data frame
	xy_1 <- st_coordinates(data_1sf)
	data_1 <- data.frame(ID = data_1sf$CollarID,
	x=xy_1[,1],
	y=xy_1[,2],
	timestamp=data_1sf$timestamp,
	Longitude=data_1$Longitude,
	Latitude=data_1$Latitude)

	# adding years, months, hours
	data_1$month <- format(data_1$timestamp, "%m")
	data_1$year <- format(data_1$timestamp, "%Y")
	data_1$hour <- format(data_1$timestamp, "%H:%M")


	data_1$month <- as.numeric((data_1$month))
	data_1$year <- as.numeric((data_1$year))

	## 28
	data_2 <- read.table("36928.csv", header = T, sep = ";", dec = ",")
	data_2$UTC.Date <- as.POSIXct(data_2$UTC.Date, format = "%d.%m.%Y")

	data_2$timestamp <- with(data_2, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_2$timestamp <- round_date(data_2$timestamp, "30 mins") # rounding of time to 30 mins

	data_2 <- data_2[data_2$DOP < 6 ,] # DOP < 6
	data_2 <- data_2 %>% filter(timestamp >= "2022-05-02 03:00:00") #removal of the observation before putting on the collar
	data_2 <- data_2 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_2$Longitude <- as.numeric(sub(",", ".", data_2$Longitude, fixed = TRUE)) # converting to numeric
	data_2$Latitude <- as.numeric(sub(",", ".", data_2$Latitude, fixed = TRUE))

	data_2 <- data_2[!is.na(data_2$Longitude),] # removal of NA values


	data_2sf <- st_as_sf(data_2, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_2sf <- st_set_crs(data_2sf, 4326)
	plot(st_geometry(data_2sf))

	# projecting to ETRS89 European coordinate system
	data_2sf <- st_transform(data_2sf, 5513)
	plot(st_geometry(data_2sf))

	# creating the input data frame
	xy_2 <- st_coordinates(data_2sf)
	data_2 <- data.frame(ID = data_2$CollarID,
	x=xy_2[,1],
	y=xy_2[,2],
	timestamp=data_2$timestamp,
	Longitude=data_2$Longitude,
	Latitude=data_2$Latitude)

	# adding years, months, hours
	data_2$month <- format(data_2$timestamp, "%m")
	data_2$year <- format(data_2$timestamp, "%Y")
	data_2$hour <- format(data_2$timestamp, "%H:%M")


	data_2$month <- as.numeric((data_2$month))
	data_2$year <- as.numeric((data_2$year))


	## 30
	data_3 <- read.table("36930.csv", header = T, sep = ";", dec = ",")
	data_3$UTC.Date <- as.POSIXct(data_3$UTC.Date, format = "%d.%m.%Y")

	data_3$timestamp <- with(data_3, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_3$timestamp <- round_date(data_3$timestamp, "30 mins") # rounding of time to 30 mins

	data_3 <- data_3[data_3$DOP < 6 ,] # DOP < 6
	data_3 <- data_3 %>% filter(timestamp >= "2020-11-30 18:00:00") #removal of the observation before putting on the collar
	data_3 <- data_3 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_3$Longitude <- as.numeric(sub(",", ".", data_3$Longitude, fixed = TRUE)) # converting to numeric
	data_3$Latitude <- as.numeric(sub(",", ".", data_3$Latitude, fixed = TRUE))

	data_3 <- data_3[!is.na(data_3$Longitude),] # removal of NA values

	data_3sf <- st_as_sf(data_3, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_3sf <- st_set_crs(data_3sf, 4326)
	plot(st_geometry(data_3sf))

	# projecting to ETRS89 European coordinate system
	data_3sf <- st_transform(data_3sf, 3035)
	plot(st_geometry(data_3sf))

	# creating the input data frame
	xy_3 <- st_coordinates(data_3sf)
	data_3 <- data.frame(ID = data_3$CollarID,
	x=xy_3[,1],
	y=xy_3[,2],
	timestamp=data_3$timestamp,
	Longitude=data_3$Longitude,
	Latitude=data_3$Latitude)

	# adding years, months, hours
	data_3$month <- format(data_3$timestamp, "%m")
	data_3$year <- format(data_3$timestamp, "%Y")
	data_3$hour <- format(data_3$timestamp, "%H:%M")


	data_3$month <- as.numeric((data_3$month))
	data_3$year <- as.numeric((data_3$year))

	## 31
	data_4 <- read.table("36931.csv", header = T, sep = ";", dec = ",")
	data_4$UTC.Date <- as.POSIXct(data_4$UTC.Date, format = "%d.%m.%Y")

	data_4$timestamp <- with(data_4, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_4$timestamp <- round_date(data_4$timestamp, "30 mins") # rounding of time to 30 mins

	data_4 <- data_4[data_4$DOP < 6 ,] # DOP < 6
	data_4 <- data_4 %>% filter(timestamp >= "2021-10-24 04:30:00") #removal of the observation before putting on the collar
	data_4 <- data_4 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_4$Longitude <- as.numeric(sub(",", ".", data_4$Longitude, fixed = TRUE)) # converting to numeric
	data_4$Latitude <- as.numeric(sub(",", ".", data_4$Latitude, fixed = TRUE))

	data_4 <- data_4[!is.na(data_4$Longitude),] # removal of NA values


	data_4sf <- st_as_sf(data_4, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_4sf <- st_set_crs(data_4sf, 4326)
	plot(st_geometry(data_4sf))

	# projecting to ETRS89 European coordinate system

	data_4sf <- st_transform(data_4sf, 3035)
	plot(st_geometry(data_4sf))

	# creating the input data frame
	xy_4 <- st_coordinates(data_4sf)
	data_4 <- data.frame(ID = data_4$CollarID,
	x=xy_4[,1],
	y=xy_4[,2],
	timestamp=data_4$timestamp,
	Longitude=data_4$Longitude,
	Latitude=data_4$Latitude)

	# adding years, months, hours
	data_4$month <- format(data_4$timestamp, "%m")
	data_4$year <- format(data_4$timestamp, "%Y")
	data_4$hour <- format(data_4$timestamp, "%H:%M")


	data_4$month <- as.numeric((data_4$month))
	data_4$year <- as.numeric((data_4$year))

	##32
	data_5 <- read.table("36932.csv", header = T, sep = ";", dec = ",")
	data_5$UTC.Date <- as.POSIXct(data_5$UTC.Date, format = "%d.%m.%Y")

	data_5$timestamp <- with(data_5, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_5$timestamp <- round_date(data_5$timestamp, "30 mins") # rounding of time to 30 mins

	data_5 <- data_5[data_5$DOP < 6 ,] # DOP < 6
	data_5 <- data_5 %>% filter(timestamp >= "2022-05-08 23:30:00") #removal of the observation before putting on the collar
	data_5 <- data_5 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_5$Longitude <- as.numeric(sub(",", ".", data_5$Longitude, fixed = TRUE)) # converting to numeric
	data_5$Latitude <- as.numeric(sub(",", ".", data_5$Latitude, fixed = TRUE))

	data_5 <- data_5[!is.na(data_5$Longitude),] # removal of NA values


	data_5sf <- st_as_sf(data_5, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_5sf <- st_set_crs(data_5sf, 4326)
	plot(st_geometry(data_5sf))

	# projecting to ETRS89 European coordinate system
	data_5sf <- st_transform(data_5sf, 3035)
	plot(st_geometry(data_5sf))

	# creating the input data frame
	xy_5 <- st_coordinates(data_5sf)
	data_5 <- data.frame(ID = data_5$CollarID,
	x=xy_5[,1],
	y=xy_5[,2],
	timestamp=data_5$timestamp,
	Longitude=data_5$Longitude,
	Latitude=data_5$Latitude)

	# adding years, months, hours
	data_5$month <- format(data_5$timestamp, "%m")
	data_5$year <- format(data_5$timestamp, "%Y")
	data_5$hour <- format(data_5$timestamp, "%H:%M")


	data_5$month <- as.numeric((data_5$month))
	data_5$year <- as.numeric((data_5$year))

	## 29456
	data_6 <- read.table("29456 .csv", header = T, sep =";", dec =".")
	data_6$UTC.Date <- as.POSIXct(data_6$UTC.Date, format = "%d.%m.%Y")
	data_6$timestamp <- with(data_6, ymd(UTC.Date) + hms(UTC.Time)) # merge date and time
	data_6$timestamp <- round_date(data_6$timestamp, "30 mins") # rounding of time to 30 mins

	data_6 <- data_6 [,c("CollarID", "timestamp", "Longitude", "Latitude")]

	data_6$Longitude <- as.numeric(sub(",", ".", data_6$Longitude, fixed = TRUE)) # converting to numeric
	data_6$Latitude <- as.numeric(sub(",", ".", data_6$Latitude, fixed = TRUE))

	data_6 <- data_6[!is.na(data_6$Longitude),] # removal of NA values


	data_6sf <- st_as_sf(data_6, coords=c("Longitude","Latitude")) # converting to a sf object

	# defining the coordinate system (4326 is a code of WGS84 coordinate system, in which GPS is measuring)
	data_6sf <- st_set_crs(data_6sf, 4326)
	plot(st_geometry(data_6sf))

	# projecting to ETRS89 European coordinate system
	data_6sf <- st_transform(data_6sf, 3035)
	plot(st_geometry(data_6sf))

	# creating the input data frame
	xy_6 <- st_coordinates(data_6sf)
	data_6 <- data.frame(ID = data_6$CollarID,
	x=xy_6[,1],
	y=xy_6[,2],
	timestamp=data_6$timestamp,
	Longitude=data_6$Longitude,
	Latitude=data_6$Latitude)

	# adding years, months, hours
	data_6$month <- format(data_6$timestamp, "%m")
	data_6$year <- format(data_6$timestamp, "%Y")
	data_6$hour <- format(data_6$timestamp, "%H:%M")


	data_6$month <- as.numeric((data_6$month))
	data_6$year <- as.numeric((data_6$year))


	## spojení do listu
	wolves <- list(data_1, data_2, data_3,data_4,data_5, data_6)
	save(wolves, file = "wolves.RData")

Give feedback

Unstar this repository?

Provide feedback

Unstar this repository?

AKDE-wolves/Data_preparation.R

Provide feedback