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Welcome to {tidyterra}

tidyterra is a package that add common methods from the tidyverse for SpatRaster and SpatVectors objects created with the {terra} package. It also adds specific geom_spat*() functions for plotting these kind of objects with {ggplot2}.

Why {tidyterra}?

Spat* objects are not like regular data frames. They are a different type of objects, implemented via the S4 object system, and have their own syntax and computation methods, implemented on the {terra} package.

By implementing tidyverse methods for these objects, and more specifically {dplyr} and {tidyr} methods, a useR can now work more easily with Spat*, just like (s)he would do with tabular data.

Note that in terms of performance, {terra} is much more optimized for working for this kind of objects, so it is recommended also to learn a bit of {terra} syntax. Each function of {tidyterra} refers (when possible) to the corresponding equivalent on {terra}.

A note for advanced {terra} users

As previously mentioned, {tidyterra} is not optimized in terms of performance. Specially when working with filter() and mutate() methods, it can be slow.

As a rule of thumb, {tidyterra} can handle objects with less than 10.000.000 slots of information(i.e., terra::ncell(a_rast) * terra::nlyr(a_rast) < 10e6).

Get started with {tidyterra}

First thing you’ll notice when loading {tidyterra} is that a set of packages would be also attached.

Currently, the following methods are available:

tidyverse method SpatVector SpatRaster
tibble::as_tibble() ✔️ ✔️
dplyr::filter() ✔️ ✔️
dplyr::mutate() ✔️ ✔️
dplyr::pull() ✔️ ✔️
dplyr::relocate() ✔️ ✔️
dplyr::rename() ✔️ ✔️
dplyr::select() ✔️ ✔️
dplyr::slice() ✔️ ✔️
dplyr::transmute() ✔️ ✔️
tidyr::drop_na() ✔️ ✔️ (questioned)
tidyr::replace_na() ✔️ ✔️

Let’s see some of them in action:

library(terra)
f <- system.file("extdata/cyl_temp.tif", package = "tidyterra")

temp <- rast(f)

temp
#> class       : SpatRaster 
#> dimensions  : 89, 116, 3  (nrow, ncol, nlyr)
#> resolution  : 3856.617, 3856.617  (x, y)
#> extent      : 2893583, 3340950, 2019451, 2362690  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
#> source      : cyl_temp.tif 
#> names       :   tavg_04,   tavg_05,   tavg_06 
#> min values  :  0.565614,  4.294102,  8.817221 
#> max values  : 13.283829, 16.740898, 21.113781

mod <- temp %>%
  select(-1) %>%
  mutate(newcol = tavg_06 - tavg_05) %>%
  relocate(newcol, .before = 1) %>%
  replace_na(list(newcol = 3)) %>%
  rename(difference = newcol)

mod
#> class       : SpatRaster 
#> dimensions  : 89, 116, 3  (nrow, ncol, nlyr)
#> resolution  : 3856.617, 3856.617  (x, y)
#> extent      : 2893583, 3340950, 2019451, 2362690  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
#> sources     : memory  
#>               memory  
#>               memory  
#> names       : difference,   tavg_05,   tavg_06 
#> min values  :   2.786910,  4.294102,  8.817221 
#> max values  :   5.408157, 16.740898, 21.113781

On the previous example, we had:

  • Eliminated the first layer of the raster tavg_04.

  • Created a new layer newcol as the difference of the layers tavg_05 and tavg_06.

  • Relocated newcolas the first layer of the SpatRaster

  • Replaced the NA cells on newcol with 3.

  • Renamed newcol to difference.

In all the process, the essential properties of the SpatRaster (number of cells, columns and rows, extent, resolution and coordinate reference system) have not been modified. Other methods as filter(), slice() or drop_na() can modify these properties, as they would do when applied to a data frame (number of rows would be modified on that case).

Plotting with {ggplot2}

SpatRasters

{tidyterra} provides several geom_* for SpatRasters. When the SpatRaster has the CRS informed (i.e. terra::crs(a_rast) != ""), the geom uses ggplot2::coord_sf(), and may be also reprojected for adjusting the coordinates to other spatial layers:


library(ggplot2)

# A faceted SpatRaster

ggplot() +
  geom_spatraster(data = temp) +
  facet_wrap(~lyr) +
  scale_fill_whitebox_c(
    palette = "muted",
    na.value = "white"
  )

plot of chunk terraplots



# Contour lines for a specific layer

f_volcano <- system.file("extdata/volcano2.tif", package = "tidyterra")
volcano2 <- rast(f_volcano)

ggplot() +
  geom_spatraster(data = volcano2) +
  geom_spatraster_contour(data = volcano2, breaks = seq(80, 200, 5)) +
  scale_fill_whitebox_c() +
  coord_sf(expand = FALSE) +
  labs(fill = "elevation")

plot of chunk terraplots



# Contour filled

ggplot() +
  geom_spatraster_contour_filled(data = volcano2) +
  scale_fill_whitebox_d(palette = "atlas") +
  labs(fill = "elevation")

plot of chunk terraplots

With {tidyterra} you can also plot RGB SpatRasters to add imagery to your plots:


# Read a vector

f_v <- system.file("extdata/cyl.gpkg", package = "tidyterra")
v <- vect(f_v)

# Read a tile
f_rgb <- system.file("extdata/cyl_tile.tif", package = "tidyterra")

r_rgb <- rast(f_rgb)

rgb_plot <- ggplot() +
  geom_spatraster_rgb(data = r_rgb) +
  geom_spatvector(data = v, fill = NA, size = 1)

rgb_plot

plot of chunk rgb

# Change CRS automatically

rgb_plot +
  coord_sf(crs = 3035)

plot of chunk rgb

{tidyterra} provides selected scales that are suitable for creating hypsometric and bathymetric maps:


asia <- rast(system.file("extdata/asia.tif", package = "tidyterra"))

asia
#> class       : SpatRaster 
#> dimensions  : 367, 683, 1  (nrow, ncol, nlyr)
#> resolution  : 14263.38, 14231.61  (x, y)
#> extent      : 7619120, 17361007, -1304745, 3918256  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857) 
#> source      : asia.tif 
#> name        : file44bc291153f2 
#> min value   :        -9991.472 
#> max value   :         6156.079

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_hypso_tint_c(
    palette = "gmt_globe",
    labels = scales::label_number(),
    breaks = c(-10000, -5000, 0, 2500, 5000, 8000),
    guide = guide_colorbar(
      direction = "horizontal",
      title.position = "top",
      barwidth = 20
    )
  ) +
  labs(
    fill = "elevation (m)",
    title = "Hypsometric map of Asia"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

plot of chunk hypso

SpatVectors

{tidyterra} allows you to plot SpatVectors with {ggplot2} using the geom_spatvector() functions:

lux <- system.file("ex/lux.shp", package = "terra")

spatvector <- terra::vect(lux)

ggplot() +
  geom_spatvector(data = spatvector, aes(fill = POP)) +
  geom_spatvector_text(
    data = spatvector, aes(label = NAME_2),
    color = "grey90"
  ) +
  scale_fill_binned(labels = scales::number_format()) +
  coord_sf(crs = 3857)

plot of chunk lux_ggplot

The underlying implementation is to take advantage of the conversion terra::vect()/sf::st_as_sf() (see About SpatVectors) and use ggplot2::geom_sf() as an endpoint for creating the layer.

About SpatVectors

SpatVector objects are vector data. This means that they are a set of individual points with geographic information (i.e. location of restaurants), that can be also grouped to form lines (i.e. a river) and, when these lines forms a closed polygon, a spatial polygon (i.e. a country).

{terra} can handle vector files on the S4 system. There is other alternative, the {sf} package, that represents the same information on a tabular way. You can convert easily between the two packages like this:

lux <- system.file("ex/lux.shp", package = "terra")

spatvector <- terra::vect(lux)
spatvector
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 12, 6  (geometries, attributes)
#>  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
#>  source      : lux.shp
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#>  names       :  ID_1   NAME_1  ID_2   NAME_2  AREA   POP
#>  type        : <num>    <chr> <num>    <chr> <num> <int>
#>  values      :     1 Diekirch     1 Clervaux   312 18081
#>                    1 Diekirch     2 Diekirch   218 32543
#>                    1 Diekirch     3  Redange   259 18664

terra::plot(spatvector, main = "SpatVector", axes = TRUE)

plot of chunk terra_sf



# To sf
sfobj <- sf::st_as_sf(spatvector)
head(sfobj, 3)
#> Simple feature collection with 3 features and 6 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 5.746118 ymin: 49.69933 xmax: 6.315773 ymax: 50.18162
#> Geodetic CRS:  WGS 84
#>   ID_1   NAME_1 ID_2   NAME_2 AREA   POP                       geometry
#> 1    1 Diekirch    1 Clervaux  312 18081 POLYGON ((6.026519 50.17767...
#> 2    1 Diekirch    2 Diekirch  218 32543 POLYGON ((6.178368 49.87682...
#> 3    1 Diekirch    3  Redange  259 18664 POLYGON ((5.881378 49.87015...

plot(sfobj$geometry, main = "sf", axes = TRUE)

plot of chunk terra_sf


# Back to terra

spatvector2 <- terra::vect(sfobj)

spatvector2
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 12, 6  (geometries, attributes)
#>  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#>  names       :  ID_1   NAME_1  ID_2   NAME_2  AREA   POP
#>  type        : <num>    <chr> <num>    <chr> <num> <int>
#>  values      :     1 Diekirch     1 Clervaux   312 18081
#>                    1 Diekirch     2 Diekirch   218 32543
#>                    1 Diekirch     3  Redange   259 18664

On that sense, {sf} already has its own implementation of {tidyverse} methods. Since converting SpatVector to {sf} it is straightforward with no loss of information, {tidyterra} is specially focused on SpatRasters. See an example of how to work with {terra} + {sf} + {dplyr}:

library(dplyr)
library(terra)
library(sf)

spat_summ <- spatvector2 %>%
  # to sf
  st_as_sf() %>%
  # dplyr
  group_by(ID_1) %>%
  summarise(TOTPOP = sum(POP)) %>%
  # back to terra
  vect()

spat_summ
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 3, 2  (geometries, attributes)
#>  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#>  names       :  ID_1 TOTPOP
#>  type        : <num>  <int>
#>  values      :     1  91186
#>                    2  71093
#>                    3 439726

terra::plot(spat_summ, "TOTPOP")

plot of chunk full_spatvector