ggmap rasters

[quantifish]

# https://stackoverflow.com/questions/47749078/how-to-put-a-geom-sf-produced-map-on-top-of-a-ggmap-produced-raster
library(ggplot2)
library(ggmap)
library(sf)
nc_map <- get_map("north carolina", maptype = "satellite", zoom = 6, source = "google")
st_crs(nc_map)
# Coordinate Reference System: NA
# assume the coordinate refence system is 3857
plot(st_transform(nc_shp, crs = 3857)[1], bgMap = nc_map)

[quantifish]

register_google(key = key)

ggmap_bbox <- function(map, crs = 3857) {
  if (!inherits(map, "ggmap")) stop("map must be a ggmap object")
  # Extract the bounding box (in lat/lon) from the ggmap to a numeric vector,
  # and set the names to what sf::st_bbox expects:
  map_bbox <- setNames(unlist(attr(map, "bb")), c("ymin", "xmin", "ymax", "xmax"))

  # Coonvert the bbox to an sf polygon, transform it to 3857,
  # and convert back to a bbox (convoluted, but it works)
  bbox_3857 <- st_bbox(map_bbox, crs = 4326) %>%
    st_as_sfc() %>%
    st_transform(crs = crs) %>%
    st_bbox()

  # Overwrite the bbox of the ggmap object with the transformed coordinates
  attr(map, "bb")$ll.lat <- bbox_3857["ymin"]
  attr(map, "bb")$ll.lon <- bbox_3857["xmin"]
  attr(map, "bb")$ur.lat <- bbox_3857["ymax"]
  attr(map, "bb")$ur.lon <- bbox_3857["xmax"]
  map
}


library(ggmap)
get_map("Japan", zoom = 5, source = "stamen") %>% ggmap()

st_bbox(CCSBT %>% st_transform(crs = 4326))
# gm <- get_map(location = "New Zealand", zoom = 2, source = "google", maptype = "satellite")
gm <- get_map(location = c(lon = 0, lat = -30), zoom = 1, source = "google", maptype = "satellite")
ggmap(gm)


map <- ggmap_bbox(gm, crs = proj_ccsbt())
# map <- ggmap_bbox(gm)
# p <- ggmap(map) +
p <- ggplot() +
  annotation_map_tile(zoom = 7) +
  geom_sf(data = CCSBT, fill = NA)
  # coord_ccsbt()
p

[quantifish]

library(ncdf4)
library(ncdf.tools)
library(ecmwfr)

wf_set_key(user = "youremail@gmail.com", key = "secretkey", service = "webapi")

request <- list(dataset      = "reanalysis-era5-single-levels-monthly-means",
                product_type = "reanalysis",
                variable     = "sea_surface_temperature",
                year         = 1979:2020,
                month        = 1:12,
                area         = "-30/160/-55/185",
                format       = "netcdf",
                target       = "era5-nz_sst_to_2020.nc")

ncfile <- wf_request(user = "41999", request = request, transfer = TRUE, path = "", verbose = FALSE)



library(raster)
# should add code to get this direct from web
data(era5_nz_sst)
prj <- "+proj=merc +lon_0=100 +lat_ts=-41 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"

suppressWarnings({
  sst1 <- era5_nz_sst %>%
    projectRaster(crs = prj) %>%
    rasterToPoints()
})

sst <- sst1 %>%
  data.frame() %>%
  pivot_longer(cols = c(-x, -y)) %>%
  separate(col = name, into = c("year", "month", "day", "hr", "min", "sec"), sep = "\\.") %>%
  mutate(year = gsub("X", "", year), value = value - 273.15) %>%
  filter(year %in% c("1979", "1989", "2009", "2019"), month %in% c("01"))
nz <- get_coast(proj = prj, resolution = "med")

ggplot() +
  geom_tile(data = sst, aes(x = x, y = y, fill = value), alpha = 0.95) +
  facet_wrap(year ~ month) +
  geom_sf(data = nz, fill = "black", colour = NA, size = 0.3) +
  coord_sf() +
  scale_fill_viridis("SST (°C)", alpha = 0.8, option = "plasma")

[quantifish]

f <- "../data-raw/mfe-average-seasurface-temperature-19932012-GTiff.zip"
fz <- unzip(zipfile = f, list = TRUE)
fz
unzip(zipfile = f)
mfe_average_sst <- raster::raster(x = "average-seasurface-temperature-19932012.tif", values = TRUE)# %>%
projectRaster(crs = proj_nzsf)
names(mfe_average_sst) <- "layer"
mfe_average_sst[mfe_average_sst[] < -10 | mfe_average_sst[] > 35] <- NA
range(mfe_average_sst[], na.rm = TRUE)
plot(mfe_average_sst)
file.remove(fz$Name)
use_data(mfe_average_sst, overwrite = TRUE)
st_transform(3994) %>%
data(mfe_average_sst)
rpts <- mfe_average_sst %>%
  rasterToPoints() %>%
  data.frame()
ggplot() +
  geom_raster(data = rpts, aes(x = x, y = y, fill = layer)) +
  # plot_statistical_areas(area = "EEZ", fill = NA) +
  # plot_coast(proj = projection(mfe_average_sst), resolution = "med", fill = "black", colour = NA, size = 0.3) +
  coord_sf() +
  scale_fill_viridis(alpha = 0.8, option = "magma") +
  labs(fill = "SST (°C)")

[quantifish]

data(gebco_depth_raster)

rpts <- gebco_depth_raster %>%
  rasterToPoints() %>%
  data.frame()

ggplot() +
  geom_raster(data = rpts, aes(x = x, y = y, fill = layer)) +
  plot_statistical_areas(area = "EEZ", fill = NA) +
  plot_coast(resolution = "med", fill = "black", colour = NA, size = 0.3) +
  coord_sf() +
  scale_fill_viridis(alpha = 0.8, option = "magma") +
  labs(fill = "Depth (m)")

[quantifish]

x <- gebco_CCAMLR %>%
  st_as_stars() %>%
  st_transform(crs = proj_ccamlr()) %>%
  as.data.frame()
as("raster") %>%
as("SpatialPixelsDataFrame")
x <- gebco_CCAMLR %>%
  as("SpatialPixelsDataFrame") %>%
  st_transform(crs = proj_ccamlr())