The goal of tidyterra is to provide common methods of the
tidyverse packages for objects
created with the terra
package: SpatRaster and SpatVector. It also provides geoms for
plotting these objects with
ggplot2.
Please cite tidyterra as:
Hernangómez, D., (2023). Using the tidyverse with terra objects: the tidyterra package. Journal of Open Source Software, 8(91), 5751, https://doi.org/10.21105/joss.05751.
A BibTeX entry for LaTeX users is:
@article{Hernangómez2023,
doi = {10.21105/joss.05751},
url = {https://doi.org/10.21105/joss.05751},
year = {2023},
publisher = {The Open Journal},
volume = {8},
number = {91},
pages = {5751},
author = {Diego Hernangómez},
title = {Using the {tidyverse} with {terra} objects: the {tidyterra} package},
journal = {Journal of Open Source Software}
}The full manual of the most recent release of tidyterra on CRAN is online: https://dieghernan.github.io/tidyterra/
tidyverse methods implemented on tidyterra work differently
depending on the type of Spat* object:
-
SpatVector: the methods are implemented usingterra::as.data.frame()coercion. Rows correspond to geometries and columns correspond to attributes of each geometry. -
SpatRaster: The implementation forSpatRasterobjects differs because methods can be applied to layers or cells. tidyterra’s overall approach is to treat the layers as columns of a tibble and the cells as rows (i.e.select(SpatRaster, 1)would select the first layer of aSpatRaster).
The implemented methods return the same type of object as the input,
unless the expected behavior of the method is to return another type of
object (for example, as_tibble() returns a tibble).
Current methods and functions provided by tidyterra are:
| tidyverse method | SpatVector |
SpatRaster |
|---|---|---|
tibble::as_tibble() |
✔️ | ✔️ |
dplyr::select() |
✔️ | ✔️ Select layers |
dplyr::mutate() |
✔️ | ✔️ Create/modify layers |
dplyr::transmute() |
✔️ | ✔️ |
dplyr::filter() |
✔️ | ✔️ Modify cell values and 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() |
✔️ | ✔️ |
Important
tidyterra is a user-friendly wrapper around terra that uses tidyverse methods and verbs. This approach has a performance cost.
If you frequently use terra or work with large raster files, terra is much more performant. Whenever possible, each tidyterra function refers to its equivalent on terra.
As a rule of thumb, if your raster has fewer than 10,000,000 data
slots (i.e. terra::ncell(your_rast) * terra::nlyr(your_rast) < 1e7),
tidyterra is a good fit.
When plotting rasters, resampling is performed automatically (as
terra::plot() does, see the help page). You can adjust this with the
maxcell argument.
Install tidyterra from CRAN:
install.packages("tidyterra")Check the docs of the developing version in https://dieghernan.github.io/tidyterra/dev/
You can install the developing version of tidyterra like so:
# install.packages("pak")
pak::pak("dieghernan/tidyterra")Alternatively, you can install tidyterra using the r-universe:
# Enable this universe
install.packages(
"tidyterra",
repos = c(
"https://dieghernan.r-universe.dev",
"https://cloud.r-project.org"
)
)This basic example shows how to manipulate and plot SpatRaster
objects:
library(tidyterra)
library(terra)
# Temperatures
rastertemp <- rast(system.file("extdata/cyl_temp.tif", package = "tidyterra"))
rastertemp
#> 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 (ESRI:54030)
#> 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
# Rename
rastertemp <- rastertemp |>
rename(April = tavg_04, May = tavg_05, June = tavg_06)
# Facet all layers
library(ggplot2)
ggplot() +
geom_spatraster(data = rastertemp) +
facet_wrap(~lyr, ncol = 2) +
scale_fill_whitebox_c(
palette = "muted",
labels = scales::label_number(suffix = "º"),
n.breaks = 12,
guide = guide_legend(reverse = TRUE)
) +
labs(
fill = "",
title = "Average temperature in Castille and Leon (Spain)",
subtitle = "Months of April, May and June"
)
# Create maximum differences of two months
variation <- rastertemp |>
mutate(diff = June - May) |>
select(variation = diff)
# Add also a overlay of a SpatVector
prov <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
ggplot(prov) +
geom_spatraster(data = variation) +
geom_spatvector(fill = NA) +
scale_fill_whitebox_c(
palette = "deep",
direction = -1,
labels = scales::label_number(suffix = "º"),
n.breaks = 5
) +
theme_minimal() +
coord_sf(crs = 25830) +
labs(
fill = "Variation",
title = "Variation of Temperature in Castile and León (Spain)",
subtitle = "Average Temperatures: June vs. May"
)
tidyterra also provides a geom for plotting RGB SpatRaster tiles
with ggplot2:
rgb_tile <- rast(system.file("extdata/cyl_tile.tif", package = "tidyterra"))
ggplot(prov) +
geom_spatraster_rgb(data = rgb_tile) +
geom_spatvector(fill = NA) +
theme_light() +
# Change the CRS and datum (useful for relabeling graticules).
coord_sf(crs = 3857, datum = 3857)
tidyterra provides specific scales for plotting hypsometric maps with ggplot2:
asia <- rast(system.file("extdata/asia.tif", package = "tidyterra"))
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"
)
This basic example shows how to manipulate and plot SpatVector
objects:
vect(system.file("ex/lux.shp", package = "terra")) |>
mutate(pop_dens = POP / AREA) |>
glimpse() |>
autoplot(aes(fill = pop_dens)) +
scale_fill_whitebox_c(palette = "pi_y_g") +
labs(
fill = "population per km2",
title = "Population density of Luxembourg",
subtitle = "By canton"
)
#> # A SpatVector 12 x 7
#> # Geometry type: Polygons
#> # Geodetic CRS: lon/lat WGS 84 (EPSG:4326)
#> # Extent (x / y) : ([5° 44' 38.9" E / 6° 31' 41.71" E] , [49° 26' 52.11" N / 50° 10' 53.84" N])
#>
#> $ ID_1 <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3
#> $ NAME_1 <chr> "Diekirch", "Diekirch", "Diekirch", "Diekirch", "Diekirch", "…
#> $ ID_2 <dbl> 1, 2, 3, 4, 5, 6, 7, 12, 8, 9, 10, 11
#> $ NAME_2 <chr> "Clervaux", "Diekirch", "Redange", "Vianden", "Wiltz", "Echte…
#> $ AREA <dbl> 312, 218, 259, 76, 263, 188, 129, 210, 185, 251, 237, 233
#> $ POP <dbl> 18081, 32543, 18664, 5163, 16735, 18899, 22366, 29828, 48187,…
#> $ pop_dens <dbl> 57.95192, 149.27982, 72.06178, 67.93421, 63.63118, 100.52660,…
Please leave your feedback or open an issue on https://github.com/dieghernan/tidyterra/issues.
Check our FAQs or open a new issue!
You can also ask in Stack Overflow using the tag [tidyterra].
tidyterra’s ggplot2 geoms are based on ggspatial implementation, by Dewey Dunnington and ggspatial contributors.
