tidyterra is an R (R Core Team 2023) package that allows
manipulation of spatial data objects as provided by the
terra package (Hijmans 2023), using the verbs of the
packages included in the tidyverse (Wickham et al. 2019), such as
dplyr (Wickham et al. 2023), tidyr
(Wickham, Vaughan, and
Girlich 2023), or tibble (Müller and Wickham
2023). This addition enables users that are already familiar
with the tidyverse to approach spatial data manipulation and analysis
more easily and much faster.
Furthermore, tidyterra extends the functionality of the
ggplot2 package (Wickham 2016) by providing additional
geoms and stats 1 like geom_spatraster() and
geom_spatvector(), as well as carefully chosen scales and
color palettes specifically designed for map production.
tidyterra can manipulate the following classes of
terra objects:
SpatVector objects, which represent vector data such
as points, lines, or polygon geometries.
SpatRaster objects, which represent raster data in
the form of a grid consisting of equally sized rectangles. Each
rectangle can contain one or more values.
The first stable version of tidyterra was included on
CRAN on April 24, 2022, and has been actively used by other packages
(such as ebvcuve (Quoss et al. 2021),
biomod2 (Thuiller et al. 2023),
inlabru (Bachl et al. 2019),
RCzechia (Lacko 2023), and
sparrpowR (Buller et al. 2021)) and cited in
academic research and publications (Bahlburg et
al. (2023), Moraga (2024),
Leonardi, Colucci, and Manica (2023), Meister et al. (2023)) ever since.
The tidyverse is a compilation of R packages that share an underlying design philosophy, grammar, and data structures. The packages within the tidyverse are widely used by R users for tidying, transforming, and visualizing data.
The tidyverse is designed to work with tidy data (“every column
is a variable, every row is an observation, every cell is a single
value”), represented in the form of data frames or tibbles.
However, it is possible to extend the functionality of tidyverse
packages to work with new R object classes by registering the
corresponding S3 methods (Wickham 2019). This means that
dplyr::mutate() can be adapted to work with any object of
class foo by creating the corresponding S3 method
mutate.foo().
While other popular packages designed for spatial data handling, such
as sf (Pebesma
2018) or stars (Pebesma and Bivand 2023), already provide
integration with the tidyverse as part of their infrastructure,
terra objects lack this integration natively. Although
terra offers a wide set of functions for transforming and
visualizing SpatRaster and SpatVector objects,
some users who are not familiar with this package would need to make an
additional effort to learn that syntax. This may imply an additional
challenge during their initial steps in the field of spatial
analysis.
The tidyterra package was developed to address this
integration gap. By providing the corresponding S3 methods, data
analysts can apply the same syntax and functions they are already
familiar with for rectangular data to the objects provided by
terra. This enables users who are not familiar with spatial
data analysis to approach this area more easily.
In addition, tidyterra also offers functions for
plotting terra objects using the ggplot2
syntax. Although packages like rasterVis (Perpiñán and Hijmans
2023) and ggspatial (Dunnington 2023) already allow the
representation of SpatRaster objects via
ggplot2, tidyterra functions provide
additional support for advanced mapping. This support includes the
integration of faceted maps, contours, and the automatic conversion of
spatial layers to the same CRS2 via ggplot2::coord_sf().
Furthermore, tidyterra also provides support for
SpatVector objects, similar to the native support of
sf objects in the ggplot2 package.
Lastly, tidyterra provides a collection of color
palettes specifically designed for representing spatial phenomena (Lindsay 2018).
Additionally, it implements the cross-blended hypsometric tints
described by Patterson and Jenny (2011).
The development philosophy of tidyterra consists on
adapting terra objects to data frame-like structures by
performing different data transformations, that ultimately may impact in
the performance of the package.
When manipulating large raster files (i.e. more than 10.000.000
cells), it is recommended to use the native terra syntax,
that is specifically designed for handling this type of files. In the
case of plotting, the default behavior of the geoms provided is to
resample SpatRaster that presents more than 500.000 cells
to speed up the process (as the terra::plot() does),
however this upper limit can be modified using the maxcell
parameter of the geom function.
Note also that when possible, the help page of each function of
tidyterra references its equivalent in
terra.
tidyterra is available on CRAN, so it can
be easily installed using the following commands in R:
The latest developing version is hosted in GitHub and can be installed using the following command in R:
The following example demonstrates how to manipulate a
SpatRaster object using the dplyr syntax.
Additionally, it illustrates how to seamlessly plot a
SpatRaster object with ggplot2 using the
geom_spatraster() function:
library(tidyterra)
library(tidyverse) # Load all the packages of tidyverse at once
library(scales) # Additional library for labels
# Temperatures in Castille and Leon (selected months)
rastertemp <- terra::rast(system.file("extdata/cyl_temp.tif",
package = "tidyterra"
))
# Rename with the tidyverse
rastertemp <- rastertemp %>%
rename(April = tavg_04, May = tavg_05, June = tavg_06)
# Plot with facets
ggplot() +
geom_spatraster(data = rastertemp) +
facet_wrap(~lyr, ncol = 2) +
scale_fill_whitebox_c(
palette = "muted",
labels = 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"
)
Faceted map with multi-layer raster file.
In the following example we combine a common dplyr
workflow (mutate + select) and we plot the
result. In this case the plot is a contour plot of the original
SpatRaster using
geom_spatraster_contour_filled()and it also includes an
overlay of a SpatVector for reference:
# Compute the variation between April and June and apply a different palette
incr_temp <- rastertemp %>%
mutate(var = June - April) %>%
select(Variation = var)
# Overlay an SpatVector
cyl_vect <- terra::vect(system.file("extdata/cyl.gpkg",
package = "tidyterra"
))
# Contour map with overlay
ggplot() +
geom_spatraster_contour_filled(data = incr_temp) +
geom_spatvector(data = cyl_vect, fill = NA) +
scale_fill_whitebox_d(palette = "bl_yl_rd") +
theme_grey() +
labs(
fill = "º Celsius",
title = "Variation of temperature in Castille and Leon (Spain)",
subtitle = "Difference between April and June"
)
Contour map of temperature variation with a SpatVector overlay
The package includes extensive documentation available online at https://dieghernan.github.io/tidyterra/ including:
terra function, in case users prefer to include those on
their workflows.I would like to thank Robert J. Hijmans for his advice and support in
adapting some of the methods, as well as for the suggestions that helped
us improve the functionalities of the package. I am also thankful to
Dewey Dunnington, Brent Thorne, and the rest of contributors of the
ggspatial package, which served as a key reference during
the initial stages of the development of tidyterra.
tidyterra also incorporates some pieces of code adapted
from ggplot2 for computing contours, which relies on the
package isoband (Wickham, Wilke, and Pedersen 2022)
developed by Claus O. Wilke.