# Welcome to tidyterra

### First steps with the tidyterra package

## 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>     <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 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