## 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)