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.
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.
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
).
Load tidyterra with additional libraries of the tidyverse:
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> <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.
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"
)
# 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 filled
ggplot() +
geom_spatraster_contour_filled(data = volcano2) +
scale_fill_whitebox_d(palette = "atlas") +
labs(fill = "elevation")
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
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"
)
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)
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)
# 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)