Welcome to tidyterra

The tidyterra package

tidyterra adds common tidyverse methods for SpatRaster and SpatVector objects from the terra package, and provides geom_spat*() geoms for plotting these objects with ggplot2.

Why tidyterra?

Spat* objects differ from regular data frames: they are S4 objects with their own syntax and computational methods (implemented in terra). By providing tidyverse verbs—especially dplyr and tidyr methods—tidyterra lets users manipulate Spat* objects in a style similar to working with tabular data.

Note: terra is generally more performant. Learning some terra syntax is recommended because tidyterra functions call, where possible, the corresponding terra equivalents.

A note for advanced terra users

tidyterra is not optimized for performance. Operations such as filter() and mutate() can be slower than their terra counterparts.

As a rule of thumb, tidyterra is most suitable for objects with fewer than 1e7 “slots” (i.e., terra::ncell(a_rast) * terra::nlyr(a_rast) < 1e7).

Get started with tidyterra

Load tidyterra together with core tidyverse packages:

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 cell values and (optionally) remove outer cells.
`dplyr::filter_out()`	✔️
`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::add_count()`	✔️
`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()`.
`generics::tidy()`	✔️	✔️
`generics::glance()`	✔️	✔️
`generics::required_pkgs()`	✔️	✔️

Let’s see some of these methods in action.

`SpatRasters`

Example using a SpatRaster:

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

temp <- rast(f)

temp
#> class       : SpatRaster 
#> size        : 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 
#> size        : 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

In this example we:

Removed the first layer (tavg_04).
Created a new layer newcol as the difference between tavg_06 and tavg_05.
Relocated newcol to be the first layer.
Replaced NA values in newcol with 3.
Renamed newcol to difference.

Throughout these steps, core properties of the SpatRaster (number of cells, rows and columns, extent, resolution, and CRS) remain unchanged. Other verbs such as filter(), slice(), or drop_na() may alter these properties in a manner analogous to how row operations affect data frames.

`SpatVectors`

Since tidyterra 0.4.0, most dplyr and tidyr verbs work with SpatVector objects, so you can arrange, group, and summarise their attributes.

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>     <num>     <num>
#>  values      :   (147,183]     2 3.594e+05       244
#>                (40.7,76.1]     1 4.819e+04       185
#>                (4.99,40.7]     9 1.944e+05     209.8

As with SpatRaster, essential properties such as geometry and CRS are preserved during these operations.

Plotting with ggplot2

`SpatRasters`

When a SpatRaster has a CRS defined (terra::crs(a_rast) != ""), the geom uses ggplot2::coord_sf() and can be reprojected to match 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 map using 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

tidyterra also supports RGB SpatRasters for imagery:

# 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

A map combining a RGB SpatRaster and a SpatVector

tidyterra includes color scales suitable for hypsometric and bathymetric maps:

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

asia
#> class       : SpatRaster 
#> size        : 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"
  )

Map of Asia including hypsometric tints

`SpatVectors`

Plot SpatVectors with geom_spatvector():

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)

Choropleth map with a SpatVector object

Implementation-wise, tidyterra converts terra::vect() output to sf via sf::st_as_sf() and then uses ggplot2::geom_sf() to render the layer.

You can also aggregate SpatVectors easily:

# 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)

Dissolving SpatVectors by group


# 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)

Dissolving SpatVectors by group (keeping internal boundaries)