File:January Temperature Middle East 1.png

1. Asenna tarvittavat paketit vain, jos install_packages_yes on TRUE

install_packages_yes <- FALSE

if (install_packages_yes) {

 install.packages("terra")
 install.packages("ggplot2")
 install.packages("sf")
 install.packages("rnaturalearth")
 install.packages("rnaturalearthdata")
 install.packages("rayshader")
 install.packages("viridis")
 install.packages("ggtext")
 install.packages("metR")

}

1. Lataa tarvittavat kirjastot

library(terra) library(ggplot2) library(sf) library(rnaturalearth) library(rnaturalearthdata) library(viridis) library(ggtext) library(metR) library(rayshader)

1. Lataa ja prosessoi WorldClim-data
2. Define the WorldClim data URL for the desired resolution (10 minutes)

url <- "https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_tavg.zip"

1. Define the local file path

temp_file <- tempfile(fileext = ".zip")

1. Download the file

download.file(url, temp_file, mode = "wb")

1. Unzip the file

temp_dir <- tempdir() unzip(temp_file, exdir = temp_dir)

1. List the files in the unzipped directory

files <- list.files(temp_dir, pattern = "\\.tif$", full.names = TRUE)

1. Load the raster files as a SpatRaster object

worldclim_raster <- rast(files)

1. Define the extent for the Middle East region (approximate coordinates)

extent_me <- ext(30, 55, 25, 45)

1. Lataa Lähi-idän rajaus

bbox <- ext(30, 55, 25, 45)

1. Select January and July layers

jan_raster <- rast(file.path(temp_dir, "wc2.1_10m_tavg_01.tif")) jul_raster <- rast(file.path(temp_dir, "wc2.1_10m_tavg_07.tif"))

1. Crop the layers to the Middle East region

jan_me_raster <- crop(jan_raster, extent_me) jul_me_raster <- crop(jul_raster, extent_me)

1. Convert to data frames for ggplot2

jan_df <- as.data.frame(jan_me_raster, xy = TRUE) jul_df <- as.data.frame(jul_me_raster, xy = TRUE)

1. Varmista, että sarakkeen nimi on 'layer'

names(jan_df)[3] <- "layer" names(jul_df)[3] <- "layer"

1. Lataa DEM-data ja prosessoi se

dem_url <- "https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_elev.zip" dem_temp_file <- tempfile(fileext = ".zip") download.file(dem_url, dem_temp_file, mode = "wb") unzip(dem_temp_file, exdir = temp_dir) dem_raster <- rast(file.path(temp_dir, "wc2.1_10m_elev.tif"))

1. Crop the DEM layer to the Middle East region

dem_me_raster <- crop(dem_raster, extent_me)

1. Convert DEM to a data frame for ggplot2

dem_df <- as.data.frame(dem_me_raster, xy = TRUE)

1. Varmista, että DEM-sarakkeen nimi on 'layer'

names(dem_df)[3] <- "layer"

1. Lataa luonnonmaan tiedot
2. Load natural earth data for countries, rivers, and lakes
3. world <- ne_countries(scale = "medium", returnclass = "sf")

rivers <- ne_download(scale = "medium", type = "rivers_lake_centerlines", category = "physical", returnclass = "sf") lakes <- ne_download(scale = "medium", type = "lakes", category = "physical", returnclass = "sf") coastlines <- ne_download(scale = "medium", type = "coastline", category = "physical", returnclass = "sf")

rivers_vect <- vect(rivers) middle_east_rivers <- crop(rivers_vect, bbox)

lakes_vect <- vect(lakes) middle_east_lakes <- crop(lakes_vect, bbox)

coastlines_vect <- vect(coastlines) middle_east_coastlines <- crop(coastlines_vect, bbox)

1. ocean <- ne_download(scale = "medium", type = "ocean", category = "physical", returnclass = "sf")

1. Define a base plot function with contours, transparency, and DEM overlay

base_plot <- function(data, title) {

 ggplot() +
  #geom_raster(data = dem_df, aes(x = x, y = y, fill = layer), alpha = 0.3) +
   scale_fill_gradient(low = "white", high = "black", name = "Elevation", guide = "none") +
   geom_raster(data = data, aes(x = x, y = y, fill = layer), alpha = 1.0, interpolate = TRUE) +
   #geom_raster(data = dem_df, aes(x = x, y = y, fill = layer), alpha = 0.1) +
   geom_contour(data = data, aes(x = x, y = y, z = layer), color = "#5f0000", linewidth=0.5, alpha=0.5, breaks = seq(-20, 50, by = 5)) +

1. metR::geom_text_contour(aes(z = layer, label = after_stat(level))) +

   geom_text_contour(data = data, aes(x = x, y = y, z = layer), breaks = seq(-50, 50, by = 5), check_overlap = TRUE) +
   scale_fill_viridis(option = "turbo", name = "Temp (°C)") +
   #geom_sf(data = middle_east_world, fill = NA, color = "black") +
   #geom_sf(data = middle_east_rivers, color = "blue") +
   #geom_sf(data = middle_east_lakes, fill = "blue", color = "blue") +
   #geom_sf(data = middle_east_coastlines, fill = "blue", color = "blue") +
   geom_sf(data = rivers, color = "blue", alpha=0.5) +
   geom_sf(data = lakes, fill = "blue", color = "blue", alpha=0.5) +
   geom_sf(data = coastlines, fill = "blue", color = "blue", alpha=0.5) + 
  #geom_sf(data = ocean, fill = "blue", color = "blue") +
   coord_sf(xlim = c(30, 55), ylim = c(25, 45)) +
   theme_minimal() +
   labs(title = title, x = "Longitude", y = "Latitude")

}

1. print("TEOUK")
2. stop(-1)

1. Plot January temperature

p1 <- base_plot(jan_df, "Average Temperature in January of Middle East ")

1. Plot July temperature

p2 <- base_plot(jul_df, "Average Temperature in July of Middle East ")

1. Print the plots

print(p1) print(p2)

1. Tallenna kuvat PNG-tiedostoina

ggsave("January_Temperature_Middle_East.png", plot = p1, width = 10, height = 8, bg="white", dpi = 300) ggsave("July_Temperature_Middle_East.png", plot = p2, width = 10, height = 8, bg="white", dpi = 300)