8.8 Exercises
E1. List and describe three types of vector, raster, and geodatabase formats.
ESRI Shapefile .shp is a very popular proprietary vector format
KML .kml is a Google Earth open Vector format. When zipped it is .kmz
FlatGeobuf .fgb is a single file format for quick reading and streaming vector data
GeoTIFF .tif/.tiff is a popular open layered raster format
ArcASCII .asc is an open text format kind of like csv with a six line header
Geospatial .pdf is a raster and vector format
GeoPackage is an OGC standard for a SQLite container
E2. Name at least two differences between the sf functions read_sf() and st_read().
st_read(
dsn,
layer,
...,
query = NA,
options = NULL,
quiet = FALSE,
geometry_column = 1L,
type = 0,
promote_to_multi = TRUE,
stringsAsFactors = sf_stringsAsFactors(),
int64_as_string = FALSE,
check_ring_dir = FALSE,
fid_column_name = character(0),
drivers = character(0),
wkt_filter = character(0),
optional = FALSE
)
read_sf(..., quiet = TRUE, stringsAsFactors = FALSE, as_tibble = TRUE)
E3. Read the cycle_hire_xy.csv file from the spData package as a spatial object (Hint: it is located in the misc folder). What is a geometry type of the loaded object?
These are points
cycle_hire <- system.file("misc/cycle_hire_xy.csv", package = "spData")
cycle_hire_csv <- readr::read_csv(cycle_hire)## Rows: 742 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, area
## dbl (5): X, Y, id, nbikes, nempty
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.cycle_hire_csv## # A tibble: 742 × 7
## X Y id name area nbikes nempty
## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
## 1 -0.110 51.5 1 River Street Clerkenwell 4 14
## 2 -0.198 51.5 2 Phillimore Gardens Kensington 2 34
## 3 -0.0846 51.5 3 Christopher Street Liverpool Street 0 32
## 4 -0.121 51.5 4 St. Chad's Street King's Cross 4 19
## 5 -0.157 51.5 5 Sedding Street Sloane Square 15 12
## 6 -0.144 51.5 6 Broadcasting House Marylebone 0 18
## 7 -0.168 51.5 7 Charlbert Street St. John's Wood 15 0
## 8 -0.170 51.5 8 Lodge Road St. John's Wood 5 13
## 9 -0.0964 51.5 9 New Globe Walk Bankside 3 16
## 10 -0.0928 51.5 10 Park Street Bankside 1 17
## # ℹ 732 more rowsE4. Download the borders of Germany using rnaturalearth, and create a new object called germany_borders. Write this new object to a file of the GeoPackage format.
germany_borders <- rnaturalearth::ne_countries(country = "Germany")
write_sf(obj = germany_borders, dsn = "germany.gpkg")E5. Download the global monthly minimum temperature with a spatial resolution of five minutes using the geodata package. Extract the June values, and save them to a file named tmin_june.tif file (hint: use terra::subset()).
worldclim_tmin <- geodata::worldclim_global("tmin", res = 5, path = tempdir())
worldclim_tmin_june <- terra::subset(worldclim_prec, subset = "wc2.1_5m_tmin_06")
terra::writeRaster(worldclim_tmin_june, "tmin_june.tif")E6. Create a static map of Germany’s borders, and save it to a PNG file.
germany_borders <- rnaturalearth::ne_countries(country = "Germany")
png(filename = "germany.png", width = 500, height = 350)
plot(germany_borders)
dev.off()E7. Create an interactive map using data from the cycle_hire_xy.csv file. Export this map to a file called cycle_hire.html.
cycle_hire <- system.file("misc/cycle_hire_xy.csv", package = "spData")
cycle_hire_csv <- readr::read_csv(cycle_hire)
library(mapview)
mapview_obj <- mapview(cycle_hire_csv, xcol = "X", ycol = "Y", zcol = "nbikes", legend = TRUE)
mapview_obj
mapshot(mapview_obj, file = "cycle_hire.html")