Welcome to tidyterra

Welcome to {tidyterra}

tidyterra is a package that adds 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* objects, 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

Load tidyterra with additional libraries of the tidyverse:

library(tidyterra)
library(dplyr)
library(tidyr)

Currently, the following methods are available:

tidyverse method	`SpatVector`	`SpatRaster`
`tibble::as_tibble()`	✔️	✔️
`dplyr::select()`	✔️	✔️ Select layers
`dplyr::mutate()`	✔️	✔️ Create /modify layers
`dplyr::transmute()`	✔️	✔️
`dplyr::filter()`	✔️	✔️ Modify cells values and (additionally) remove outer cells.
`dplyr::slice()`	✔️	✔️ Additional methods for slicing by row and column.
`dplyr::pull()`	✔️	✔️
`dplyr::rename()`	✔️	✔️
`dplyr::relocate()`	✔️	✔️
`dplyr::distinct()`	✔️
`dplyr::arrange()`	✔️
`dplyr::glimpse()`	✔️	✔️
`dplyr::inner_join()` family	✔️
`dplyr::summarise()`	✔️
`dplyr::group_by()` family	✔️
`dplyr::rowwise()`	✔️
`dplyr::count()`, `tally()`	✔️
`dplyr::bind_cols()` / `dplyr::bind_rows()`	✔️ as `bind_spat_cols()` / `bind_spat_rows()`
`tidyr::drop_na()`	✔️	✔️ Remove cell values with `NA` on any layer. Additionally, outer cells with `NA` are removed.
`tidyr::replace_na()`	✔️	✔️
`tidyr::fill()`	✔️
`tidyr::pivot_longer()`	✔️
`tidyr::pivot_wider()`	✔️
`ggplot2::autoplot()`	✔️	✔️
`ggplot2::fortify()`	✔️ to sf via `sf::st_as_sf()`	To a tibble with coordinates.
`ggplot2::geom_*()`	✔️ `geom_spatvector()`	✔️ `geom_spatraster()` and `geom_spatraster_rgb()`.

Let’s see some of them in action:

`SpatRasters`

See an example with SpatRaster objects:

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

temp <- rast(f)

temp
#> class       : SpatRaster 
#> dimensions  : 87, 118, 3  (nrow, ncol, nlyr)
#> resolution  : 3881.255, 3881.255  (x, y)
#> extent      : -612335.4, -154347.3, 4283018, 4620687  (xmin, xmax, ymin, ymax)
#> coord. ref. : World_Robinson 
#> source      : cyl_temp.tif 
#> names       :   tavg_04,   tavg_05,  tavg_06 
#> min values  :  1.885463,  5.817587, 10.46338 
#> max values  : 13.283829, 16.740898, 21.11378

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  : 87, 118, 3  (nrow, ncol, nlyr)
#> resolution  : 3881.255, 3881.255  (x, y)
#> extent      : -612335.4, -154347.3, 4283018, 4620687  (xmin, xmax, ymin, ymax)
#> coord. ref. : World_Robinson 
#> source(s)   : memory
#> names       : difference,   tavg_05,  tavg_06 
#> min values  :   2.817647,  5.817587, 10.46338 
#> max values  :   5.307511, 16.740898, 21.11378

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 newcol as 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).

`SpatVectors`

tidyterra >= 0.4.0 provides support to SpatVectors for most of the dplyr and tidyr methods, so it is possible to arrange, group and summarise information of SpatVectors.

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

v_lux <- vect(lux)

v_lux %>%
  # Create categories
  mutate(gr = cut(POP / 1000, 5)) %>%
  group_by(gr) %>%
  # Summary
  summarise(
    n = n(),
    tot_pop = sum(POP),
    mean_area = mean(AREA)
  ) %>%
  # Arrange
  arrange(desc(gr))
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 3, 4  (geometries, attributes)
#>  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#>  names       :          gr     n tot_pop mean_area
#>  type        :      <fact> <int>   <int>     <num>
#>  values      :   (147,183]     2  359427       244
#>                (40.7,76.1]     1   48187       185
#>                (4.99,40.7]     9  194391     209.8

As in the case of SpatRaster, basic properties as the geometry and the CRS are preserved.

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"
  )

A faceted SpatRaster

# 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")

Contour lines plot for a SpatRaster

# Contour filled

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

Contour filled plot for a SpatRaster

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(v) +
  geom_spatraster_rgb(data = r_rgb) +
  geom_spatvector(fill = NA, size = 1)

rgb_plot

Plotting a RGB SpatRaster

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  : 164, 306, 1  (nrow, ncol, nlyr)
#> resolution  : 31836.23, 31847.57  (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   :        -9558.468 
#> max value   :         5801.927

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_hypso_tint_c(
    palette = "gmt_globe",
    labels = scales::label_number(),
    # Further refinements
    breaks = c(-10000, -5000, 0, 2000, 5000, 8000),
    guide = guide_colorbar(reverse = TRUE)
  ) +
  labs(
    fill = "elevation (m)",
    title = "Hypsometric map of Asia"
  ) +
  theme(
    legend.position = "bottom",
    legend.title.position = "top",
    legend.key.width = rel(3),
    legend.ticks = element_line(colour = "black", linewidth = 0.3),
    legend.direction = "horizontal"
  )

Hypsometric tints

`SpatVectors`

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

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

v_lux <- terra::vect(lux)

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

Plotting SpatVectors

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

With tidyterra we can also aggregate SpatVectors at our convenience:

# Dissolving
v_lux %>%
  # Create categories
  mutate(gr = cut(POP / 1000, 5)) %>%
  group_by(gr) %>%
  # Summary
  summarise(
    n = n(),
    tot_pop = sum(POP),
    mean_area = mean(AREA)
  ) %>%
  ggplot() +
  geom_spatvector(aes(fill = tot_pop), color = "black") +
  geom_spatvector_label(aes(label = gr)) +
  coord_sf(crs = 3857)

Union of SpatVectors



# Same but keeping internal boundaries
v_lux %>%
  # Create categories
  mutate(gr = cut(POP / 1000, 5)) %>%
  group_by(gr) %>%
  # Summary without dissolving
  summarise(
    n = n(),
    tot_pop = sum(POP),
    mean_area = mean(AREA),
    .dissolve = FALSE
  ) %>%
  ggplot() +
  geom_spatvector(aes(fill = tot_pop), color = "black") +
  geom_spatvector_label(aes(label = gr)) +
  coord_sf(crs = 3857)

Union of SpatVector keeping the inner borders

First steps with the tidyterra package