Unlocking the power of data visualization with R

This is day 8 - blue for #30DayMapChallenge. I am looking at the ggplot2-book map chapter. In this case I’d like to understand how to make modification of a .tif image positioning it inside a ggplot.

library(tidyverse)

Load the {ozmaps} for the geometry of Australia.

library(ozmaps)
library(sf)

oz_states <- ozmaps::ozmap_states
oz_states

st_crs(oz_states) = st_crs(4283)

oz_states%>%
ggplot()+
  geom_sf()+
  coord_sf()

tmap::tm_shape(oz_states) +
    tmap::tm_polygons()

First sat vis of Australia

Then, select the raster images from source.

See which raster images are available with loading the {bomrang} package and using the syntax below.

Once you have found the file name, you can obtain the file searching on google tab with this string: “ftp://ftp.bom.gov.au/anon/gen/gms/” with the final name of the file

For example google this, it might take a little while: ftp://ftp.bom.gov.au/anon/gen/gms/IDE00422.202001072100.tif

It pops up a window asking you to identify as a “guest”, (it might probably saying that it couldn’t connect you, but then split all the pictures. You need to search for the desired picture, if it didn’t prompt with the one you were searching for, mind it is .tif type of file and you might need to choose a picture from your current year, such as 2021 - as the pictures from 2020 are not available anymore.)

# sf::gdal_read() 
# bomrang::get_available_imagery()

# with - bomrang - find the avalable images
files <- bomrang::get_available_imagery() %>%
  stringr::str_subset("202001072100") 

# use curl_download() to obtain a single file, and purrr to 
# vectorise this operation
purrr::walk2(
  .x = paste0("ftp://ftp.bom.gov.au/anon/gen/gms/", files),
  .y = file.path("raster", files),
  .f = ~ download.file(url = .x, destfile = .y)
)

Set the path of the image that you have downloaded and saved inside a “raster” folder in you project subfolder.

img_vis  <- file.path("/Users/federica/Documents/R/R_general_resources/30DayMapChallenge/day8_blue/raster", "IDE00422.202111072100.tiff")
img_inf <- file.path("/Users/federica/Documents/R/R_general_resources/30DayMapChallenge/day8_blue/raster", "IDE00421.202111072100.tiff")

Read the picture with {stars}, it releases information about the picture but we miss the coordinates??

library(stars)
sat_vis <- read_stars(img_vis, RasterIO = list(nBufXSize = 600, nBufYSize = 600))
sat_inf <- read_stars(img_inf, RasterIO = list(nBufXSize = 600, nBufYSize = 600))

A ggplot can be made with the image using a geom_stars()

blue_world <- ggplot() + 
  geom_stars(data = sat_vis) + 
  coord_equal()

blue_world

The image can be easily modified:

final <- blue_world +
  labs(title="Globe Observer",
       subtitle="Sat View of Australia\n#30DayMapChalleng Day 8 - Blue",
       caption="Datasource:Australian Bureau of Meterorology (BOM)\nInfographic: Federica Gazzelloni")+
  ggthemes::theme_map()+
  theme(legend.position = "none",
        plot.background = element_rect(color="#1a1c3d",fill="#1a1c3d"),
        plot.title = element_text(color="#9a9ddb",size=35),
        plot.subtitle = element_text(color="#9a9ddb",size=15),
        plot.caption = element_text(color="#9a9ddb",size=12))

# save final plot
ragg::agg_png("~/Documents/R/R_general_resources/30DayMapChallenge/day8_blue/blue.png",
              res = 320, width = 11, height = 8, units = "in")
final
dev.off()

Second sat vis of Australia

library(showtext)
library(extrafont)
fonts()
loadfonts()
font_add_google("Gochi Hand", "gochi")
font_add_google("Schoolbell", "bell")
font_add_google('Syncopate', 'Syncopate')
showtext_opts(dpi = 320)
showtext_auto(enable = T)

Choose the image and download it with curl_download()

files <- bomrang::get_available_imagery() %>%
  stringr::str_subset("202001072100") 
curl::curl_download("raster/ftp://ftp.bom.gov.au/anon/gen/gms/IDE00422.202111082100.tif",
                    "sat_vis.tif")
img_vis  <- file.path("/Users/federica/Documents/R/R_general_resources/Maps/raster", "IDE00422.202111082100.tif")
library(stars)
sat_vis <- read_stars(img_vis, RasterIO = list(nBufXSize = 600, nBufYSize = 600))
#sat_inf <- read_stars(img_inf, RasterIO = list(nBufXSize = 600, nBufYSize = 600))
oz_states <- st_transform(oz_states, crs = st_crs(sat_vis))
cities <- oz_capitals %>% 
  st_as_sf(coords = c("lon", "lat"), crs = 4326, remove = FALSE)
cities <- st_transform(cities, st_crs(sat_vis))

final2 <- ggplot() + 
  geom_stars(data = sat_vis, show.legend = FALSE) +
  geom_sf(data = oz_states, fill = "#6354c4", color = "#a7c2cc",size=0.3) + 
  geom_sf(data = cities, color = "#15191f") + 
  coord_sf() + 
  theme_void() + 
  #scale_fill_gradient(low = "black", high = "white")+
  geom_sf_text(data = cities, mapping = aes(label = city),color="#ced9eb",check_overlap = T,family="gochi") +
   labs(title="Globe Observer",
       subtitle="Sat View of Australia\n#30DayMapChalleng Day 8 - Blue",
       caption="Datasource:Australian Bureau of Meterorology (BOM)\nInfographic: Federica Gazzelloni")+
  ggthemes::theme_map()+
  theme(text=element_text(family="gochi"),
        legend.position = "none",
        plot.background = element_rect(color="#1a1c3d",fill="#1a1c3d"),
        plot.title = element_text(color="#9a9ddb",size=35,family="Syncopate",hjust=0.5),
        plot.title.position = "panel",
        plot.subtitle = element_text(color="#9a9ddb",size=15,vjust=-1,hjust=0.07),
        plot.caption = element_text(color="#9a9ddb",size=12,hjust=0.8))

# save final plot
ragg::agg_png("~/Documents/R/R_general_resources/30DayMapChallenge/day8_blue/blue2.png",
              res = 320, width = 11, height = 8, units = "in")
final2
dev.off()