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)
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::filter() ✔️ ✔️
dplyr::mutate() ✔️ ✔️
dplyr::pull() ✔️ ✔️
dplyr::relocate() ✔️ ✔️
dplyr::rename() ✔️ ✔️
dplyr::select() ✔️ ✔️
dplyr::slice() ✔️ ✔️
dplyr::transmute() ✔️ ✔️
tidyr::drop_na() ✔️ ✔️ (questioned)
tidyr::replace_na() ✔️ ✔️
ggplot2::autoplot() ✔️ ✔️
ggplot2::fortify() ✔️ to sf with sf::st_as_sf() ✔️ To tibble with coordinates

Let’s see some of them in action:

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

temp <- rast(f)

temp
#> class       : SpatRaster 
#> dimensions  : 89, 116, 3  (nrow, ncol, nlyr)
#> resolution  : 3856.617, 3856.617  (x, y)
#> extent      : 2893583, 3340950, 2019451, 2362690  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
#> source      : cyl_temp.tif 
#> names       :   tavg_04,   tavg_05,   tavg_06 
#> min values  :  0.565614,  4.294102,  8.817221 
#> max values  : 13.283829, 16.740898, 21.113781

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  : 89, 116, 3  (nrow, ncol, nlyr)
#> resolution  : 3856.617, 3856.617  (x, y)
#> extent      : 2893583, 3340950, 2019451, 2362690  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
#> sources     : memory  
#>               memory  
#>               memory  
#> names       : difference,   tavg_05,   tavg_06 
#> min values  :   2.786910,  4.294102,  8.817221 
#> max values  :   5.408157, 16.740898, 21.113781

On the previous example, we had:

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).

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 = 3035)

plot of chunk rgb

{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  : 232, 432, 1  (nrow, ncol, nlyr)
#> resolution  : 22550.66, 22512.94  (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   :        -10071.50 
#> max value   :          6064.73

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_hypso_tint_c(
    palette = "gmt_globe",
    labels = scales::label_number(),
    breaks = c(-10000, -5000, 0, 2500, 5000, 8000),
    guide = guide_colorbar(
      direction = "horizontal",
      title.position = "top",
      barwidth = 20
    )
  ) +
  labs(
    fill = "elevation (m)",
    title = "Hypsometric map of Asia"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")