library(raster)
library(ncdf4)
library(foreach)
library(doSNOW)

cl <- makeCluster(8, type="SOCK") # for 4 cores machine
registerDoSNOW (cl)

setwd("MY/WORKING/DIRECTORY/")
temp <- nc_open("ppt_1958.nc")

temp <- raster("ppt_1958.nc")
temp <- stack("ppt_1958.nc")

URL <- "https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_ppt_1959.nc"

a <- "/tmp/RtmpTF076t/file1906f2e84c4f4"

download.file(url = URL, destfile = a)

rasternc <- function(nc, array = FALSE, var, z = 1,
                     xmin = -180, xmax = 180,
                     ymin = -90, ymax = 90,
                     dx = 1/24, dy = 1/24){
  f <- ncdf4::nc_open(filename = nc)
  cat(paste("The file has",f$nvars,"variables:n"))
  cat(names(f$var))
  if(missing(var)){
    choice <- utils::menu(names(f$var), title="Choose var")
    nvar <- names(f$var)[choice]
    u <- ncdf4::ncvar_get(f, nvar)
    cat(paste0("nThe '", nvar, "' array is ",
               format(object.size(u), units = "Mb"), "n"))
  } else {
    u <- ncdf4::ncvar_get(f, var)
    cat(paste0("nThe '", var, "' array is ",
               format(object.size(u), units = "Mb"), "n"))
  }
  if(array){
    return(u)
  }
  lon <- seq(xmin, xmax, by = dx)
  lat <- seq(ymin, ymax, by = dy)

  ru1 <- raster::flip(raster::raster(t(u[1:dim(u)[1],
                                         dim(u)[2]:1,
                                         z]),
                                     xmn = min(lon),xmx = max(lon),
                                     ymn = min(lat),ymx = max(lat),
                                     crs="+init=epsg:4326"), "y")

  cat(paste0("nThis raster is ", format(object.size(ru1), units = "Mb")))
  return(ru1)
}

library(raster)
library(cptcity)
r <- rasternc(a)
#The 'ppt' array is 3417.2 Mb
#This raster is 284.8 Mb

# boring plot
#spplot(r, scales = list(draw = T), main = "TerraClimate_ppt_1959")
# still boring plot
#spplot(r, scales = list(draw = T), main = "TerraClimate_ppt_1959",
 #      col.regions = cpt())
# perfection
spplot(r, scales = list(draw = T), main = "TerraClimate_ppt_1959",
       col.regions = lucky())

rr <- stars::st_as_stars(r)
plot(rr, col = cpt(), axes = T)