Welcome to tidyterra

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}

Load {tidyterra} with additional libraries of the {tidyverse}:

library(tidyterra)
#> 
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
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()`	✔️	✔️
`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 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).

SpatVectors

tidyterra >= 0.4.0 provides support to SpatVectors for most of the {dplyr} 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"
  )

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

rgb_plot

plot of chunk rgb

# Change CRS automatically

rgb_plot +
  coord_sf(crs = 3857)