Title:	'tidyverse' Methods and 'ggplot2' Helpers for 'terra' Objects
Version:	0.7.2
Description:	Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'.
License:	MIT + file LICENSE
URL:	https://dieghernan.github.io/tidyterra/, https://github.com/dieghernan/tidyterra
BugReports:	https://github.com/dieghernan/tidyterra/issues
Depends:	R (≥ 3.6.0)
Imports:	cli (≥ 3.0.0), data.table, dplyr (≥ 1.0.0), ggplot2 (≥ 3.5.0), magrittr, rlang, scales, sf (≥ 1.0.0), terra (≥ 1.8-10), tibble (≥ 3.0.0), tidyr (≥ 1.0.0)
Suggests:	hexbin, isoband, knitr, lifecycle, maptiles, rmarkdown, s2, testthat (≥ 3.0.0), vctrs, vdiffr
VignetteBuilder:	knitr
Config/Needs/coverage:	covr
Config/Needs/website:	geodata, dieghernan/gitdevr, ragg, styler, metR, ggspatial, cpp11, remotes, gganimate, gifski
Config/testthat/edition:	3
Config/testthat/parallel:	false
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
X-schema.org-keywords:	r, terra, ggplot-extension, r-spatial, rspatial, cran, cran-r, r-package, rstats, rstats-package
NeedsCompilation:	no
Packaged:	2025-04-13 19:08:15 UTC; diego
Author:	Diego Hernangómez [aut, cre, cph], Dewey Dunnington [ctb] (for ggspatial code), ggplot2 authors [cph] (for contour code), Andrea Manica [ctb]
Maintainer:	Diego Hernangómez <diego.hernangomezherrero@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-14 10:50:02 UTC

tidyterra: 'tidyverse' Methods and 'ggplot2' Helpers for 'terra' Objects

Description

Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'.

Author(s)

Maintainer: Diego Hernangómez diego.hernangomezherrero@gmail.com (ORCID) [copyright holder]

Other contributors:

• Dewey Dunnington (ORCID) (for ggspatial code) [contributor]

• ggplot2 authors (for contour code) [copyright holder]

• Andrea Manica [contributor]

See Also

Useful links:

• https://dieghernan.github.io/tidyterra/

• https://github.com/dieghernan/tidyterra

• Report bugs at https://github.com/dieghernan/tidyterra/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Order a SpatVector using column values

Description

arrange.SpatVector() orders the geometries of a SpatVector by the values of selected columns.

Usage

## S3 method for class 'SpatVector'
arrange(.data, ..., .by_group = FALSE)

Arguments

Value

A SpatVector object.

terra equivalent

terra::sort()

Methods

Implementation of the generic dplyr::arrange() function for SpatVector class.

See Also

dplyr::arrange()

Other single table verbs: filter.Spat, mutate.Spat, rename.Spat, select.Spat, slice.Spat, summarise.SpatVector()

Other dplyr verbs that operate on rows: distinct.SpatVector(), filter.Spat, slice.Spat

Other dplyr methods: bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
library(dplyr)

v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))


# Single variable

v %>%
  arrange(desc(iso2))


# Two variables
v %>%
  mutate(even = as.double(cpro) %% 2 == 0, ) %>%
  arrange(desc(even), desc(iso2))


# With new variables
v %>%
  mutate(area_geom = terra::expanse(v)) %>%
  arrange(area_geom)

Get cell number, row and column from a SpatRaster

Description

as_coordinates() can be used to obtain the position of each cell on the SpatRaster matrix.

Usage

as_coordinates(x, as.raster = FALSE)

Arguments

Value

A tibble or a SpatRaster (if as.raster = TRUE) with the same number of rows (or cells) than the number of cells in x.

When as.raster = TRUE the resulting SpatRaster has the same crs, extension and resolution than x

See Also

slice.SpatRaster()

Coercing objects: as_sf(), as_spatraster(), as_spatvector(), as_tibble.Spat, fortify.Spat

Examples

library(terra)

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

r <- rast(f)

as_coordinates(r)
as_coordinates(r, as.raster = TRUE)

as_coordinates(r, as.raster = TRUE) %>% plot()

Coerce a SpatVector to a sf object

Description

as_sf() turns a SpatVector to sf object. This is a wrapper of sf::st_as_sf() with the particularity that the groups created with group_by.SpatVector() are preserved.

Usage

as_sf(x, ...)

Arguments

Value

A sf object object with an additional tbl_df class, for pretty printing method.

See Also

Coercing objects: as_coordinates(), as_spatraster(), as_spatvector(), as_tibble.Spat, fortify.Spat

Examples

library(terra)

f <- system.file("extdata/cyl.gpkg", package = "tidyterra")
v <- terra::vect(f)

# This is ungrouped
v
is_grouped_spatvector(v)

# Get an ungrouped data
a_sf <- as_sf(v)

dplyr::is_grouped_df(a_sf)

# Grouped

v$gr <- c("C", "A", "A", "B", "A", "B", "B")
v$gr2 <- rep(c("F", "G", "F"), 3)

gr_v <- group_by(v, gr, gr2)

gr_v
is_grouped_spatvector(gr_v)

group_data(gr_v)

# A sf

a_gr_sf <- as_sf(gr_v)

dplyr::is_grouped_df(a_gr_sf)

group_data(a_gr_sf)

Coerce a data frame to SpatRaster

Description

as_spatraster() turns an existing data frame or tibble into a SpatRaster. This is a wrapper of terra::rast() S4 method for signature data.frame.

Usage

as_spatraster(x, ..., xycols = 1:2, crs = "", digits = 6)

Arguments

Details

If no crs is provided and the tibble has been created with the method as_tibble.SpatRaster(), the crs is inferred from attr(x, "crs").

Value

A SpatRaster.

terra equivalent

terra::rast() (see S4 method for signature data.frame).

See Also

pull_crs() for retrieving crs, and the corresponding utils sf::st_crs() and terra::crs().

Coercing objects: as_coordinates(), as_sf(), as_spatvector(), as_tibble.Spat, fortify.Spat

Examples

library(terra)

r <- rast(matrix(1:90, ncol = 3), crs = "EPSG:3857")

r

# Create tibble
as_tbl <- as_tibble(r, xy = TRUE)

as_tbl

# From tibble
newrast <- as_spatraster(as_tbl, crs = "EPSG:3857")
newrast

Method for coercing objects to SpatVector

Description

as_spatvector() turns an existing object into a SpatVector. This is a wrapper of terra::vect() S4 method for signature data.frame.

Usage

as_spatvector(x, ...)

## S3 method for class 'data.frame'
as_spatvector(x, ..., geom = c("lon", "lat"), crs = "")

## S3 method for class 'sf'
as_spatvector(x, ...)

## S3 method for class 'sfc'
as_spatvector(x, ...)

## S3 method for class 'SpatVector'
as_spatvector(x, ...)

Arguments

Details

This function differs from terra::vect() on the following:

• geometries with NA or "" values are removed prior to conversion

• If x is a grouped data frame (see dplyr::group_by()) the grouping vars are transferred and a "grouped" SpatVector is created (see group_by.SpatVector()).

• If no crs is provided and the tibble has been created with the method as_tibble.SpatVector(), the crs is inferred from attr(x, "crs").

• Handles correctly the conversion of EMPTY geometries between sf and terra.

Value

A SpatVector.

terra equivalent

terra::vect()

See Also

pull_crs() for retrieving crs, and the corresponding utils sf::st_crs() and terra::crs().

Coercing objects: as_coordinates(), as_sf(), as_spatraster(), as_tibble.Spat, fortify.Spat

Examples

library(terra)

v <- vect(matrix(1:80, ncol = 2), crs = "EPSG:3857")

v$cat <- sample(LETTERS[1:4], size = nrow(v), replace = TRUE)

v

# Create tibble
as_tbl <- as_tibble(v, geom = "WKT")

as_tbl

# From tibble
newvect <- as_spatvector(as_tbl, geom = "geometry", crs = "EPSG:3857")
newvect

Coerce a SpatVector or SpatRaster object to data frames

Description

as_tibble() methods for SpatRaster and SpatVector objects.

Usage

## S3 method for class 'SpatRaster'
as_tibble(x, ..., xy = FALSE, na.rm = FALSE, .name_repair = "unique")

## S3 method for class 'SpatVector'
as_tibble(x, ..., geom = NULL, .name_repair = "unique")

Arguments

Value

A tibble.

terra equivalent

terra::as.data.frame()

Methods

Implementation of the generic tibble::as_tibble() function.

SpatRaster and SpatVector

The tibble is returned with an attribute including the crs of the initial object in WKT format (see pull_crs()).

About layer/column names

When coercing SpatRaster objects to data frames, x and y names are reserved for geographic coordinates of each cell of the SpatRaster It should be also noted that terra allows layers with duplicated names.

In the process of coercing a SpatRaster to a tibble, tidyterra may rename the layers of your SpatRaster for overcoming this issue. Specifically, layers may be renamed on the following cases:

• Layers with duplicated names.

• When coercing to a tibble, if xy = TRUE, layers named x or y would be renamed.

• When working with tidyverse methods (i.e. filter.SpatRaster()), the latter would happen as well.

tidyterra would display a message informing of the changes on the names of the layer.

The same issue happens for SpatVector with names geometry (when geom = c("WKT", "HEX")) and x, y (when geom = "XY"). These are reserved names representing the geometry of the SpatVector (see terra::as.data.frame()). If geom is not NULL then the logic described for SpatRaster would apply as well for the columns of the SpatVector.

See Also

tibble::as_tibble(), terra::as.data.frame()

Coercing objects: as_coordinates(), as_sf(), as_spatraster(), as_spatvector(), fortify.Spat

Examples

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

as_tibble(r, na.rm = TRUE)

as_tibble(r, xy = TRUE)

# SpatVector

f <- system.file("extdata/cyl.gpkg", package = "tidyterra")
v <- vect(f)

as_tibble(v)

Create a complete ggplot for ⁠Spat*⁠ objects

Description

autoplot() uses ggplot2 to draw plots as the ones produced by terra::plot()/terra::plotRGB() in a single command.

Usage

## S3 method for class 'SpatRaster'
autoplot(
  object,
  ...,
  rgb = NULL,
  use_coltab = NULL,
  facets = NULL,
  nrow = NULL,
  ncol = 2
)

## S3 method for class 'SpatVector'
autoplot(object, ...)

Arguments

Details

Implementation of ggplot2::autoplot() method.

Value

A ggplot2 layer

Methods

Implementation of the generic ggplot2::autoplot() function.

SpatRaster

Uses geom_spatraster() or geom_spatraster_rgb().

SpatVector

Uses geom_spatvector(). Labels can be placed with geom_spatvector_text() or geom_spatvector_label().

See Also

ggplot2::autoplot()

Other ggplot2 utils: fortify.Spat, geom_spat_contour, geom_spatraster(), geom_spatraster_rgb(), ggspatvector, stat_spat_coordinates()

Other ggplot2 methods: fortify.Spat

Examples

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

library(terra)
temp <- rast(file_path)

library(ggplot2)
autoplot(temp)


# With a tile

tile <- system.file("extdata/cyl_tile.tif", package = "tidyterra") %>%
  rast()

autoplot(tile)

# With coltabs

ctab <- system.file("extdata/cyl_era.tif", package = "tidyterra") %>%
  rast()

autoplot(ctab)

#  With vectors
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
autoplot(v)

v %>% autoplot(aes(fill = cpro)) +
  geom_spatvector_text(aes(label = iso2)) +
  coord_sf(crs = 25829)

Bind multiple SpatVector sf and data frames objects by column

Description

Bind any number of SpatVector, data frames and sf object by column, making a wider result. This is similar to do.call(cbind, dfs).

Where possible prefer using a join to combine SpatVector and data frames objects. bind_spat_cols() binds the rows in order in which they appear so it is easy to create meaningless results without realizing it.

Usage

bind_spat_cols(
  ...,
  .name_repair = c("unique", "universal", "check_unique", "minimal")
)

Arguments

Value

A SpatVector with the corresponding columns. The geometry and CRS would correspond to the the first SpatVector of ....

terra equivalent

cbind() method

Methods

Implementation of the dplyr::bind_rows() function for SpatVector objects. Note that for the second and subsequent arguments on ... the geometry would not be cbinded, and only the data frame (-ish) columns would be kept.

See Also

dplyr::bind_cols()

Other dplyr verbs that operate on pairs Spat*/data.frame: bind_rows.SpatVector, filter-joins.SpatVector, mutate-joins.SpatVector

Other dplyr methods: arrange.SpatVector(), bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
sv <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
df2 <- data.frame(letters = letters[seq_len(nrow(sv))])

# Data frame
bind_spat_cols(sv, df2)


# Another SpatVector
bind_spat_cols(sv[1:2, ], sv[3:4, ])

# sf objects
sfobj <- sf::read_sf(system.file("shape/nc.shp", package = "sf"))

bind_spat_cols(sv[1:9, ], sfobj[1:9, ])

# Mixed

end <- bind_spat_cols(sv, sfobj[seq_len(nrow(sv)), 1:2], df2)

end
glimpse(end)

# Row sizes must be compatible when column-binding
try(bind_spat_cols(sv, sfobj))

Bind multiple SpatVector, sf/sfc and data frames objects by row

Description

Bind any number of SpatVector, data frames and sf/sfc objects by row, making a longer result. This is similar to do.call(rbind, dfs), but the output will contain all columns that appear in any of the inputs.

Usage

bind_spat_rows(..., .id = NULL)

Arguments

Value

A SpatVector of the same type as the first element of ....

terra equivalent

rbind() method

Methods

Implementation of the dplyr::bind_rows() function for SpatVector objects.

The first element of ... should be a SpatVector. Subsequent elements may be SpatVector, sf/sfc objects or data frames:

• If subsequent SpatVector/sf/sfc objects present a different CRS than the first element, those elements would be reprojected to the CRS of the first element with a message.

• If any element of ... is a tibble/data frame the rows would be cbinded with empty geometries with a message.

See Also

dplyr::bind_rows()

Other dplyr verbs that operate on pairs Spat*/data.frame: bind_cols.SpatVector, filter-joins.SpatVector, mutate-joins.SpatVector

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

v1 <- v[1, "cpro"]
v2 <- v[3:5, c("name", "iso2")]

# You can supply individual SpatVector as arguments:
bind_spat_rows(v1, v2)

# When you supply a column name with the `.id` argument, a new
# column is created to link each row to its original data frame
bind_spat_rows(v1, v2, .id = "id")


# Use with sf
sfobj <- sf::st_as_sf(v2[1, ])

sfobj

bind_spat_rows(v1, sfobj)

# Would reproject with a message on different CRS
sfobj_3857 <- as_spatvector(sfobj) %>% project("EPSG:3857")

bind_spat_rows(v1, sfobj_3857)

# And with data frames with a message
data("mtcars")
bind_spat_rows(v1, sfobj, mtcars, .id = "id2")


# Use lists
bind_spat_rows(list(v1[1, ], sfobj[1:2, ]))

# Or named list combined with .id
bind_spat_rows(list(
  SpatVector = v1[1, ], sf = sfobj[1, ],
  mtcars = mtcars[1, ]
), .id = "source")

Compare attributes of two SpatRaster objects

Description

Two SpatRaster objects are compatible (in terms of combining layers) if the crs, extent and resolution are similar. In those cases you can combine the objects simply as c(x, y).

This function compares those attributes informing of the results. See Solving issues section for minimal guidance.

Usage

compare_spatrasters(x, y, digits = 6)

Arguments

Value

A invisible logical TRUE/FALSE indicating if the SpatRaster objects are compatible, plus an informative message flagging the issues found (if any).

terra equivalent

terra::identical()

Solving issues

• On non-equal crs, try terra::project().

• On non-equal extent try terra::resample().

• On non-equal resolution you can try terra::resample(), terra::aggregate() or terra::disagg().

See Also

terra::identical()

Other helpers: is_grouped_spatvector(), is_regular_grid(), pull_crs()

Examples

library(terra)

x <- rast(matrix(1:90, ncol = 3), crs = "EPSG:3857")

# Nothing
compare_spatrasters(x, x)

# Different crs
y_nocrs <- x
crs(y_nocrs) <- NA

compare_spatrasters(x, y_nocrs)

# Different extent
compare_spatrasters(x, x[1:10, , drop = FALSE])

# Different resolution
y_newres <- x

res(y_newres) <- res(x) / 2
compare_spatrasters(x, y_newres)

# Everything

compare_spatrasters(x, project(x, "epsg:3035"))

Count the observations in each SpatVector group

Description

count() lets you quickly count the unique values of one or more variables:

• df %>% count(a, b) is roughly equivalent to df %>% group_by(a, b) %>% summarise(n = n()).

• count() is paired with tally(), a lower-level helper that is equivalent to df %>% summarise(n = n()).

Usage

## S3 method for class 'SpatVector'
count(
  x,
  ...,
  wt = NULL,
  sort = FALSE,
  name = NULL,
  .drop = group_by_drop_default(x),
  .dissolve = TRUE
)

## S3 method for class 'SpatVector'
tally(x, wt = NULL, sort = FALSE, name = NULL)

Arguments

Value

A SpatVector object with an additional attribute.

terra equivalent

terra::aggregate()

Methods

Implementation of the generic dplyr::count() family functions for SpatVector objects.

tally() will always return a disaggregated geometry while count() can handle this. See also summarise.SpatVector().

See Also

dplyr::count(), dplyr::tally()

Other dplyr verbs that operate on group of rows: group-by.SpatVector, rowwise.SpatVector(), summarise.SpatVector()

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
f <- system.file("ex/lux.shp", package = "terra")
p <- vect(f)


p %>% count(NAME_1, sort = TRUE)

p %>% count(NAME_1, sort = TRUE)

p %>% count(pop = ifelse(POP < 20000, "A", "B"))

# tally() is a lower-level function that assumes you've done the grouping
p %>% tally()

p %>%
  group_by(NAME_1) %>%
  tally()

# Dissolve geometries by default

library(ggplot2)
p %>%
  count(NAME_1) %>%
  ggplot() +
  geom_spatvector(aes(fill = n))

# Opt out
p %>%
  count(NAME_1, .dissolve = FALSE, sort = TRUE) %>%
  ggplot() +
  geom_spatvector(aes(fill = n))

Cross-blended hypsometric tints

Description

A tibble including the color map of 4 gradient palettes. All the palettes includes also a definition of colors limits in terms of elevation (meters), that can be used with ggplot2::scale_fill_gradientn().

Format

A tibble of 41 rows and 6 columns. with the following fields:

pal

Name of the palette.

limit

Recommended elevation limit (in meters) for each color.

r

Value of the red channel (RGB color mode).

g

Value of the green channel (RGB color mode).

b

Value of the blue channel (RGB color mode).

hex

Hex code of the color.

Details

From Patterson & Jenny (2011):

More recently, the role and design of hypsometric tints have come under scrutiny. One reason for this is the concern that people misread elevation colors as climate or vegetation information. Cross-blended hypsometric tints, introduced in 2009, are a partial solution to this problem. They use variable lowland colors customized to match the differing natural environments of world regions, which merge into one another.

Source

Derived from:

• Patterson, T., & Jenny, B. (2011). The Development and Rationale of Cross-blended Hypsometric Tints. Cartographic Perspectives, (69), 31 - 46. doi:10.14714/CP69.20.

See Also

scale_fill_cross_blended_c()

Other datasets: grass_db, hypsometric_tints_db, princess_db, volcano2

Examples

data("cross_blended_hypsometric_tints_db")

cross_blended_hypsometric_tints_db

# Select a palette
warm <- cross_blended_hypsometric_tints_db %>%
  filter(pal == "warm_humid")

f <- system.file("extdata/asia.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = warm$hex)

# Use with limits
p +
  scale_fill_gradientn(
    colors = warm$hex,
    values = scales::rescale(warm$limit),
    limit = range(warm$limit),
    na.value = "lightblue"
  )

Keep distinct/unique rows and geometries of SpatVector objects

Description

Keep only unique/distinct rows and geometries from a SpatVector.

Usage

## S3 method for class 'SpatVector'
distinct(.data, ..., .keep_all = FALSE)

Arguments

Value

A SpatVector object.

terra equivalent

terra::unique()

Methods

Implementation of the generic dplyr::distinct() function.

SpatVector

It is possible to remove duplicate geometries including the geometry variable explicitly in the ... call. See Examples.

See Also

dplyr::distinct(), terra::unique()

Other dplyr verbs that operate on rows: arrange.SpatVector(), filter.Spat, slice.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)

v <- vect(system.file("ex/lux.shp", package = "terra"))

# Create a vector with dups
v <- v[sample(seq_len(nrow(v)), 100, replace = TRUE), ]
v$gr <- sample(LETTERS[1:3], 100, replace = TRUE)

# All duplicates
ex1 <- distinct(v)
ex1

nrow(ex1)

# Duplicates by NAME_1
ex2 <- distinct(v, gr)
ex2
nrow(ex2)

# Same but keeping all cols
ex2b <- distinct(v, gr, .keep_all = TRUE)
ex2b
nrow(ex2b)


# Unique geometries
ex3 <- distinct(v, geometry)

ex3
nrow(ex3)
# Same as terra::unique()
terra::unique(ex3)

# Unique keeping info
distinct(v, geometry, .keep_all = TRUE)

Drop attributes of ⁠Spat*⁠ objects containing missing values

Description

• SpatVector: drop_na() method drops geometries where any attribute specified by ... contains a missing value.

• SpatRaster: drop_na() method drops cells where any layer specified by ... contains a missing value.

Usage

## S3 method for class 'SpatVector'
drop_na(data, ...)

## S3 method for class 'SpatRaster'
drop_na(data, ...)

Arguments

Value

A ⁠Spat*⁠ object of the same class than data. See Methods.

terra equivalent

terra::trim()

Methods

Implementation of the generic tidyr::drop_na() function.

SpatVector

The implementation of this method is performed on a by-attribute basis, meaning that NAs are assessed on the attributes (columns) of each vector (rows). The result is a SpatVector with potentially less geometries than the input.

SpatRaster

Actual implementation of ⁠drop_na().SpatRaster⁠ can be understood as a masking method based on the values of the layers (see terra::mask()).

SpatRaster layers are considered as columns and SpatRaster cells as rows, so rows (cells) with any NA value on any layer would get a NA value. It is possible also to mask the cells (rows) based on the values of specific layers (columns).

drop_na() would effectively remove outer cells that are NA (see terra::trim()), so the extent of the resulting object may differ of the extent of the input (see terra::resample() for more info).

Check the Examples to have a better understanding of this method.

Feedback needed!

Visit https://github.com/dieghernan/tidyterra/issues. The implementation of this method for SpatRaster may change in the future.

See Also

tidyr::drop_na()

Other tidyr verbs for handling missing values: fill.SpatVector(), replace_na.Spat

Other tidyr methods: fill.SpatVector(), pivot_longer.SpatVector(), pivot_wider.SpatVector(), replace_na.Spat

Examples

library(terra)

f <- system.file("extdata/cyl.gpkg", package = "tidyterra")

v <- terra::vect(f)

# Add NAs
v <- v %>% mutate(iso2 = ifelse(cpro <= "09", NA, cpro))

# Init
plot(v, col = "red")

# Mask with lyr.1
v %>%
  drop_na(iso2) %>%
  plot(col = "red")
# SpatRaster method


r <- rast(
  crs = "EPSG:3857",
  extent = c(0, 10, 0, 10),
  nlyr = 3,
  resolution = c(2.5, 2.5)
)
terra::values(r) <- seq_len(ncell(r) * nlyr(r))



# Add NAs
r[r > 13 & r < 22 | r > 31 & r < 45] <- NA

# Init
plot(r, nc = 3)

# Mask with lyr.1
r %>%
  drop_na(lyr.1) %>%
  plot(nc = 3)

# Mask with lyr.2
r %>%
  drop_na(lyr.2) %>%
  plot(nc = 3)

# Mask with lyr.3
r %>%
  drop_na(lyr.3) %>%
  plot(nc = 3)

# Auto-mask all layers
r %>%
  drop_na() %>%
  plot(nc = 3)

Fill in missing values with previous or next value on a SpatVector

Description

Fills missing values in selected columns using the next or previous entry. This is useful in the common output format where values are not repeated, and are only recorded when they change.

Usage

## S3 method for class 'SpatVector'
fill(data, ..., .direction = c("down", "up", "downup", "updown"))

Arguments

Value

A SpatVector object.

Methods

Implementation of the generic tidyr::fill() function for SpatVector.

Grouped SpatVector

With grouped SpatVector created by group_by.SpatVector(), fill() will be applied within each group, meaning that it won't fill across group boundaries.

See Also

tidyr::fill()

Other tidyr verbs for handling missing values: drop_na.Spat, replace_na.Spat

Other tidyr methods: drop_na.Spat, pivot_longer.SpatVector(), pivot_wider.SpatVector(), replace_na.Spat

Examples

library(dplyr)

lux <- terra::vect(system.file("ex/lux.shp", package = "terra"))

# Leave some blanks for demo purporses

lux_blnk <- lux %>%
  mutate(NAME_1 = if_else(NAME_1 != NAME_2, NA, NAME_2))


as_tibble(lux_blnk)

# `fill()` defaults to replacing missing data from top to bottom
lux_blnk %>%
  fill(NAME_1) %>%
  as_tibble()


# direction = "up"
lux_blnk %>%
  fill(NAME_1, .direction = "up") %>%
  as_tibble()

# Grouping and downup - will restore the initial state
lux_blnk %>%
  group_by(ID_1) %>%
  fill(NAME_1, .direction = "downup") %>%
  as_tibble()

Filtering joins for SpatVector objects

Description

Filtering joins filter rows from x based on the presence or absence of matches in y:

• semi_join() return all rows from x with a match in y.

• anti_join() return all rows from x without a match in y.

See dplyr::semi_join() for details.

Usage

## S3 method for class 'SpatVector'
semi_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'SpatVector'
anti_join(x, y, by = NULL, copy = FALSE, ...)

Arguments

Value

A SpatVector object.

terra equivalent

terra::merge()

Methods

Implementation of the generic dplyr::semi_join() family

SpatVector

The geometry column has a sticky behavior. This means that the result would have always the geometry of x for the records that matches the join conditions.

See Also

dplyr::semi_join(), dplyr::anti_join(), terra::merge()

Other dplyr verbs that operate on pairs Spat*/data.frame: bind_cols.SpatVector, bind_rows.SpatVector, mutate-joins.SpatVector

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
library(ggplot2)

# Vector
v <- terra::vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

# A data frame
df <- data.frame(
  cpro = sprintf("%02d", 1:10),
  x = runif(10),
  y = runif(10),
  letter = rep_len(LETTERS[1:3], length.out = 10)
)

v

# Semi join
semi <- v %>% semi_join(df)

semi

autoplot(semi, aes(fill = iso2)) + ggtitle("Semi Join")


# Anti join

anti <- v %>% anti_join(df)

anti

autoplot(anti, aes(fill = iso2)) + ggtitle("Anti Join")

Subset cells/geometries of ⁠Spat*⁠ objects

Description

The filter() function is used to subset ⁠Spat*⁠ objects, retaining all cells/geometries that satisfy your conditions. To be retained, the cell/geometry must produce a value of TRUE for all conditions.

It is possible to filter a SpatRaster by its geographic coordinates. You need to use filter(.data, x > 42). Note that x and y are reserved names on terra, since they refer to the geographic coordinates of the layer.

See Examples and section About layer names on as_tibble.Spat().

Usage

## S3 method for class 'SpatRaster'
filter(.data, ..., .preserve = FALSE, .keep_extent = TRUE)

## S3 method for class 'SpatVector'
filter(.data, ..., .preserve = FALSE)

Arguments

Value

A ⁠Spat*⁠ object of the same class than .data. See Methods.

Methods

Implementation of the generic dplyr::filter() function.

SpatRaster

Cells that do not fulfill the conditions on ... are returned with value NA. On a multi-layer SpatRaster the NA is propagated across all the layers.

If .keep_extent = TRUE the returning SpatRaster has the same crs, extent, resolution and hence the same number of cells than .data. If .keep_extent = FALSE the outer NA cells are trimmed with terra::trim(), so the extent and number of cells may differ. The output would present in any case the same crs and resolution than .data.

x and y variables (i.e. the longitude and latitude of the SpatRaster) are also available internally for filtering. See Examples.

SpatVector

The result is a SpatVector with all the geometries that produce a value of TRUE for all conditions.

See Also

dplyr::filter()

Other single table verbs: arrange.SpatVector(), mutate.Spat, rename.Spat, select.Spat, slice.Spat, summarise.SpatVector()

Other dplyr verbs that operate on rows: arrange.SpatVector(), distinct.SpatVector(), slice.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

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

r <- rast(f) %>% select(tavg_04)

plot(r)


# Filter temps
r_f <- r %>% filter(tavg_04 > 11.5)

# Extent is kept
plot(r_f)


# Filter temps and extent
r_f2 <- r %>% filter(tavg_04 > 11.5, .keep_extent = FALSE)

# Extent has changed
plot(r_f2)


# Filter by geographic coordinates
r2 <- project(r, "epsg:4326")

r2 %>% plot()

r2 %>%
  filter(
    x > -4,
    x < -2,
    y > 42
  ) %>%
  plot()

Fortify ⁠Spat*⁠ Objects

Description

Fortify SpatRaster and SpatVector objects to data frames. This provide native compatibility with ggplot2::ggplot().

Usage

## S3 method for class 'SpatRaster'
fortify(
  model,
  data,
  ...,
  .name_repair = "unique",
  maxcell = terra::ncell(model) * 1.1,
  pivot = FALSE
)

## S3 method for class 'SpatVector'
fortify(model, data, ...)

## S3 method for class 'SpatGraticule'
fortify(model, data, ...)

Arguments

Value

fortify.SpatVector() returns a sf object and fortify.SpatRaster() returns a tibble. See Methods.

Methods

Implementation of the generic ggplot2::fortify() method.

SpatRaster

Return a tibble than can be used with ⁠ggplot2::geom_*⁠ like ggplot2::geom_point(), ggplot2::geom_raster(), etc.

The resulting tibble includes the coordinates on the columns ⁠x, y⁠. The values of each layer are included as additional columns named as per the name of the layer on the SpatRaster.

The CRS of the SpatRaster can be retrieved with attr(fortifiedSpatRaster, "crs").

It is possible to convert the fortified object onto a SpatRaster again with as_spatraster().

When pivot = TRUE the SpatRaster is fortified in a "long" format (see tidyr::pivot_longer()). The fortified object would have the following columns:

• ⁠x,y⁠: Coordinates (center) of the cell on the corresponding CRS.

• lyr: Indicating the name of the SpatRaster layer of value.

• value: The value of the SpatRaster in the corresponding lyr.

This option may be useful when using several ⁠geom_*⁠ and for faceting, see Examples.

SpatVector and SpatGraticule

Return a sf object than can be used with ggplot2::geom_sf().

See Also

sf::st_as_sf(), as_tibble.Spat, as_spatraster(), ggplot2::fortify().

Other ggplot2 utils: autoplot.Spat, geom_spat_contour, geom_spatraster(), geom_spatraster_rgb(), ggspatvector, stat_spat_coordinates()

Other ggplot2 methods: autoplot.Spat

Coercing objects: as_coordinates(), as_sf(), as_spatraster(), as_spatvector(), as_tibble.Spat

Examples

# Get a SpatRaster
r <- system.file("extdata/volcano2.tif", package = "tidyterra") %>%
  terra::rast() %>%
  terra::project("EPSG:4326")

fortified <- ggplot2::fortify(r)

fortified

# The crs is an attribute of the fortified SpatRaster

attr(fortified, "crs")

# Back to a SpatRaster with
as_spatraster(fortified)

# You can now use a SpatRaster with any geom
library(ggplot2)

ggplot(r) +
  geom_histogram(aes(x = elevation),
    bins = 20, fill = "lightblue",
    color = "black"
  )


# Create a SpatVector
extfile <- system.file("extdata/cyl.gpkg", package = "tidyterra")
cyl <- terra::vect(extfile)

cyl

# To sf
ggplot2::fortify(cyl)

# Now you can use geom_sf() straight away thanks to fortify::SpatVector()

library(ggplot2)

ggplot(cyl) +
  geom_sf()

Plot SpatRaster contours

Description

These geoms create contours of SpatRaster objects. To specify a valid surface, you should specify the layer on aes(z = layer_name), otherwise all the layers would be consider for creating contours. See also Facets section.

The underlying implementation is based on ggplot2::geom_contour().

geom_spatraster_contour_text() creates labeled contours and it is implemented on top of isoband::isolines_grob().

Usage

geom_spatraster_contour(
  mapping = NULL,
  data,
  ...,
  maxcell = 5e+05,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  mask_projection = FALSE
)

geom_spatraster_contour_text(
  mapping = NULL,
  data,
  ...,
  maxcell = 5e+05,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  size.unit = "mm",
  label_format = scales::label_number(),
  label_placer = isoband::label_placer_minmax(),
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  mask_projection = FALSE
)

geom_spatraster_contour_filled(
  mapping = NULL,
  data,
  ...,
  maxcell = 5e+05,
  bins = NULL,
  binwidth = NULL,
  breaks = NULL,
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  mask_projection = FALSE
)

Arguments

Value

A ggplot2 layer

terra equivalent

terra::contour()

Aesthetics

geom_spatraster_contour() / geom_spatraster_contour_text() understands the following aesthetics:

• alpha

• colour

• group

• linetype

• linewidth geom_spatraster_contour_text() understands also:

• size

• label

• family

• fontface

Additionally, geom_spatraster_contour_filled() understands also the following aesthetics, as well as the ones listed above:

• fill

• subgroup

Check ggplot2::geom_contour() for more info on contours and vignette("ggplot2-specs", package = "ggplot2") for an overview of the aesthetics.

Computed variables

These geom computes internally some variables that are available for use as aesthetics, using (for example) ⁠aes(color = after_stat(<computed>))⁠ (see ggplot2::after_stat()).

• after_stat(lyr): Name of the layer.

• after_stat(level): Height of contour. For contour lines, this is numeric vector that represents bin boundaries. For contour bands, this is an ordered factor that represents bin ranges.

• after_stat(nlevel): Height of contour, scaled to maximum of 1.

• after_stat(level_low), after_stat(level_high), after_stat(level_mid): (contour bands only) Lower and upper bin boundaries for each band, as well the mid point between the boundaries.

Dropped variables

• z: After contouring, the z values of individual data points are no longer available.

Coords

When the SpatRaster does not present a crs (i.e., terra::crs(rast) == "") the geom does not make any assumption on the scales.

On SpatRaster that have a crs, the geom uses ggplot2::coord_sf() to adjust the scales. That means that also the SpatRaster may be reprojected.

Facets

You can use facet_wrap(~lyr) for creating a faceted plot by each layer of the SpatRaster object. See ggplot2::facet_wrap() for details.

See Also

ggplot2::geom_contour().

The metR package also provides a set of alternative functions:

• metR::geom_contour2().

• metR::geom_text_contour() and metR::geom_label_contour().

• metR::geom_contour_tanaka().

Other ggplot2 utils: autoplot.Spat, fortify.Spat, geom_spatraster(), geom_spatraster_rgb(), ggspatvector, stat_spat_coordinates()

Examples

library(terra)

# Raster
f <- system.file("extdata/volcano2.tif", package = "tidyterra")
r <- rast(f)

library(ggplot2)

ggplot() +
  geom_spatraster_contour(data = r)


# Labelled
ggplot() +
  geom_spatraster_contour_text(
    data = r, breaks = c(110, 130, 160, 190),
    color = "grey10", family = "serif"
  )


ggplot() +
  geom_spatraster_contour(
    data = r, aes(color = after_stat(level)),
    binwidth = 1,
    linewidth = 0.4
  ) +
  scale_color_gradientn(
    colours = hcl.colors(20, "Inferno"),
    guide = guide_coloursteps()
  ) +
  theme_minimal()

# Filled with breaks
ggplot() +
  geom_spatraster_contour_filled(data = r, breaks = seq(80, 200, 10)) +
  scale_fill_hypso_d()

# Both lines and contours
ggplot() +
  geom_spatraster_contour_filled(
    data = r, breaks = seq(80, 200, 10),
    alpha = .7
  ) +
  geom_spatraster_contour(
    data = r, breaks = seq(80, 200, 2.5),
    color = "grey30",
    linewidth = 0.1
  ) +
  scale_fill_hypso_d()

Visualise SpatRaster objects

Description

This geom is used to visualise SpatRaster objects (see terra::rast()). The geom is designed for visualise the object by layers, as terra::plot() does.

For plotting SpatRaster objects as map tiles (i.e. RGB SpatRaster), use geom_spatraster_rgb().

The underlying implementation is based on ggplot2::geom_raster().

stat_spatraster() is provided as a complementary function, so the geom can be modified.

Usage

geom_spatraster(
  mapping = aes(),
  data,
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = FALSE,
  interpolate = FALSE,
  maxcell = 5e+05,
  use_coltab = TRUE,
  mask_projection = FALSE,
  ...
)

stat_spatraster(
  mapping = aes(),
  data,
  geom = "raster",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = FALSE,
  maxcell = 5e+05,
  ...
)

Arguments

Value

A ggplot2 layer

terra equivalent

terra::plot()

Coords

When the SpatRaster does not present a crs (i.e., terra::crs(rast) == "") the geom does not make any assumption on the scales.

On SpatRaster that have a crs, the geom uses ggplot2::coord_sf() to adjust the scales. That means that also the SpatRaster may be reprojected.

Aesthetics

geom_spatraster() understands the following aesthetics:

• fill

• alpha

If fill is not provided, geom_spatraster() creates a ggplot2 layer with all the layers of the SpatRaster object. Use facet_wrap(~lyr) to display properly the SpatRaster layers.

If fill is used, it should contain the name of one layer that is present on the SpatRaster (i.e. ⁠geom_spatraster(data = rast, aes(fill = <name_of_lyr>)⁠). Names of the layers can be retrieved using names(rast).

Using geom_spatraster(..., mapping = aes(fill = NULL)) or ⁠geom_spatraster(..., fill = <color value(s)>)⁠ would create a layer with no mapped fill aesthetic.

fill can use computed variables.

For alpha use computed variable. See section Computed variables.

stat_spatraster()

stat_spatraster() understands the same aesthetics than geom_spatraster() when using geom = "raster" (the default):

• fill

• alpha

When geom = "raster" the fill parameter would behave as in geom_spatraster(). If another geom is used stat_spatraster() would understand the aesthetics of the required geom and ⁠aes(fill = <name_of_lyr>)⁠ would not be applicable.

Note also that mapping of aesthetics x and y is provided by default, so the user does not need to add those aesthetics on aes(). In all the cases the aesthetics should be mapped by using computed variables. See section Computed variables and Examples.

Facets

You can use facet_wrap(~lyr) for creating a faceted plot by each layer of the SpatRaster object. See ggplot2::facet_wrap() for details.

Computed variables

This geom computes internally some variables that are available for use as aesthetics, using (for example) aes(alpha = after_stat(value)) (see ggplot2::after_stat()).

• after_stat(value): Values of the SpatRaster.

• after_stat(lyr): Name of the layer.

Source

Based on the layer_spatial() implementation on ggspatial package. Thanks to Dewey Dunnington and ggspatial contributors.

See Also

ggplot2::geom_raster(), ggplot2::coord_sf(), ggplot2::facet_wrap()

Recommended geoms:

• ggplot2::geom_point().

• ggplot2::geom_label().

• ggplot2::geom_text().

Other ggplot2 utils: autoplot.Spat, fortify.Spat, geom_spat_contour, geom_spatraster_rgb(), ggspatvector, stat_spat_coordinates()

Examples

# Avg temperature on spring in Castille and Leon (Spain)
file_path <- system.file("extdata/cyl_temp.tif", package = "tidyterra")

library(terra)
temp_rast <- rast(file_path)

library(ggplot2)

# Display a single layer
names(temp_rast)

ggplot() +
  geom_spatraster(data = temp_rast, aes(fill = tavg_04)) +
  # You can use coord_sf
  coord_sf(crs = 3857) +
  scale_fill_grass_c(palette = "celsius")

# Display facets
ggplot() +
  geom_spatraster(data = temp_rast) +
  facet_wrap(~lyr, ncol = 2) +
  scale_fill_grass_b(palette = "celsius", breaks = seq(0, 20, 2.5))

# Non spatial rasters

no_crs <- rast(crs = NA, extent = c(0, 100, 0, 100), nlyr = 1)
values(no_crs) <- seq_len(ncell(no_crs))


ggplot() +
  geom_spatraster(data = no_crs)

# Downsample

ggplot() +
  geom_spatraster(data = no_crs, maxcell = 25)



# Using stat_spatraster
# Default
ggplot() +
  stat_spatraster(data = temp_rast) +
  facet_wrap(~lyr)

# Using points
ggplot() +
  stat_spatraster(
    data = temp_rast,
    aes(color = after_stat(value)),
    geom = "point", maxcell = 250
  ) +
  scale_colour_viridis_c(na.value = "transparent") +
  facet_wrap(~lyr)

# Using points and labels

r_single <- temp_rast %>% select(1)

ggplot() +
  stat_spatraster(
    data = r_single,
    aes(color = after_stat(value)),
    geom = "point",
    maxcell = 2000
  ) +
  stat_spatraster(
    data = r_single,
    aes(label = after_stat(round(value, 2))),
    geom = "label",
    alpha = 0.85,
    maxcell = 20
  ) +
  scale_colour_viridis_c(na.value = "transparent")

Visualise SpatRaster objects as images

Description

This geom is used to visualise SpatRaster objects (see terra::rast()) as RGB images. The layers are combined such that they represent the red, green and blue channel.

For plotting SpatRaster objects by layer values use geom_spatraster().

The underlying implementation is based on ggplot2::geom_raster().

Usage

geom_spatraster_rgb(
  mapping = aes(),
  data,
  interpolate = TRUE,
  r = 1,
  g = 2,
  b = 3,
  alpha = 1,
  maxcell = 5e+05,
  max_col_value = 255,
  ...,
  stretch = NULL,
  zlim = NULL,
  mask_projection = FALSE
)

Arguments

Value

A ggplot2 layer

terra equivalent

terra::plotRGB()

Aesthetics

No aes() is required. In fact, aes() will be ignored.

Coords

When the SpatRaster does not present a crs (i.e., terra::crs(rast) == "") the geom does not make any assumption on the scales.

On SpatRaster that have a crs, the geom uses ggplot2::coord_sf() to adjust the scales. That means that also the SpatRaster may be reprojected.

Source

Based on the layer_spatial() implementation on ggspatial package. Thanks to Dewey Dunnington and ggspatial contributors.

See Also

ggplot2::geom_raster(), ggplot2::coord_sf(), grDevices::rgb().

You can get also RGB tiles from the maptiles package, see maptiles::get_tiles().

Other ggplot2 utils: autoplot.Spat, fortify.Spat, geom_spat_contour, geom_spatraster(), ggspatvector, stat_spat_coordinates()

Examples

# Tile of Castille and Leon (Spain) from OpenStreetMap
file_path <- system.file("extdata/cyl_tile.tif", package = "tidyterra")

library(terra)
tile <- rast(file_path)

library(ggplot2)


ggplot() +
  geom_spatraster_rgb(data = tile) +
  # You can use coord_sf
  coord_sf(crs = 3035)

# Combine with sf objects
vect_path <- system.file("extdata/cyl.gpkg", package = "tidyterra")

cyl_sf <- sf::st_read(vect_path)

ggplot(cyl_sf) +
  geom_spatraster_rgb(data = tile) +
  geom_sf(aes(fill = iso2)) +
  coord_sf(crs = 3857) +
  scale_fill_viridis_d(alpha = 0.7)

Visualise SpatVector objects

Description

Wrappers of ggplot2::geom_sf() family used to visualise SpatVector objects (see terra::vect()).

Usage

geom_spatvector(
  mapping = aes(),
  data = NULL,
  na.rm = FALSE,
  show.legend = NA,
  ...
)

geom_spatvector_label(
  mapping = aes(),
  data = NULL,
  na.rm = FALSE,
  show.legend = NA,
  ...,
  nudge_x = 0,
  nudge_y = 0,
  label.size = 0.25,
  inherit.aes = TRUE
)

geom_spatvector_text(
  mapping = aes(),
  data = NULL,
  na.rm = FALSE,
  show.legend = NA,
  ...,
  nudge_x = 0,
  nudge_y = 0,
  check_overlap = FALSE,
  inherit.aes = TRUE
)

stat_spatvector(
  mapping = NULL,
  data = NULL,
  geom = "rect",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Details

These functions are wrappers of ggplot2::geom_sf() functions. Since a fortify.SpatVector() method is provided, ggplot2 treat a SpatVector in the same way that a sf object. A side effect is that you can use ggplot2::geom_sf() directly with SpatVector objects.

See ggplot2::geom_sf() for details on aesthetics, etc.

Value

A ggplot2 layer

terra equivalent

terra::plot()

See Also

ggplot2::geom_sf()

Other ggplot2 utils: autoplot.Spat, fortify.Spat, geom_spat_contour, geom_spatraster(), geom_spatraster_rgb(), stat_spat_coordinates()

Examples

# Create a SpatVector
extfile <- system.file("extdata/cyl.gpkg", package = "tidyterra")

cyl <- terra::vect(extfile)
class(cyl)

library(ggplot2)

ggplot(cyl) +
  geom_spatvector()


# With params

ggplot(cyl) +
  geom_spatvector(aes(fill = name), color = NA) +
  scale_fill_viridis_d() +
  coord_sf(crs = 3857)

# Add labels
ggplot(cyl) +
  geom_spatvector(aes(fill = name), color = NA) +
  geom_spatvector_text(aes(label = iso2),
    fontface = "bold",
    color = "red"
  ) +
  scale_fill_viridis_d(alpha = 0.4) +
  coord_sf(crs = 3857)

# You can use now geom_sf with SpatVectors!



ggplot(cyl) +
  geom_sf() +
  labs(
    title = paste("cyl is", as.character(class(cyl))),
    subtitle = "With geom_sf()"
  )

Get a nice glimpse of your ⁠Spat*⁠ objects

Description

glimpse() is like a transposed version of print(): layers/columns run down the page, and data runs across. This makes it possible to see every layer/column in a ⁠Spat*⁠ object.

Usage

## S3 method for class 'SpatRaster'
glimpse(x, width = NULL, ..., n = 10, max_extra_cols = 20)

## S3 method for class 'SpatVector'
glimpse(x, width = NULL, ..., n = 10, max_extra_cols = 20)

Arguments

Value

original x is (invisibly) returned, allowing glimpse() to be used within a data pipeline.

terra equivalent

print()

Methods

Implementation of the generic dplyr::glimpse() function for ⁠Spat*⁠. objects.

See Also

tibble::print.tbl_df()

Other dplyr verbs that operate on columns: mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, select.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)

# SpatVector
v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

v %>% glimpse(n = 2)

# Use on a pipeline
v %>%
  glimpse() %>%
  mutate(a = 30) %>%
  # with options
  glimpse(geom = "WKT")

# SpatRaster
r <- rast(system.file("extdata/cyl_elev.tif", package = "tidyterra"))

r %>% glimpse()

# Use on a pipeline
r %>%
  glimpse() %>%
  mutate(b = elevation_m / 100) %>%
  # With options
  glimpse(xy = TRUE)

GRASS color tables

Description

A tibble including the color map of 51 gradient palettes. Some palettes includes also a definition of colors limits that can be used with ggplot2::scale_fill_gradientn().

Format

A tibble of 2920 rows and 6 columns. with the following fields:

pal

Name of the palette.

limit

(Optional) limit for each color.

r

Value of the red channel (RGB color mode).

g

Value of the green channel (RGB color mode).

b

Value of the blue channel (RGB color mode).

hex

Hex code of the color.

Details

Summary of palettes provided, description and recommended use:

terra equivalent

terra::map.pal()

Source

Derived from https://github.com/OSGeo/grass/tree/main/lib/gis/colors. See also r.color - GRASS GIS Manual.

References

GRASS Development Team (2024). Geographic Resources Analysis Support System (GRASS) Software, Version 8.3.2. Open Source Geospatial Foundation, USA. https://grass.osgeo.org.

See Also

scale_fill_grass_c()

Other datasets: cross_blended_hypsometric_tints_db, hypsometric_tints_db, princess_db, volcano2

Examples

data("grass_db")

grass_db
# Select a palette

srtm_plus <- grass_db %>%
  filter(pal == "srtm_plus")

f <- system.file("extdata/asia.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = srtm_plus$hex)

# Use with limits
p +
  scale_fill_gradientn(
    colors = srtm_plus$hex,
    values = scales::rescale(srtm_plus$limit),
    limit = range(srtm_plus$limit),
    na.value = "lightblue"
  )

Group a SpatVector by one or more variables

Description

Most data operations are done on groups defined by variables. group_by.SpatVector() adds new attributes to an existing SpatVector indicating the corresponding groups. See Methods.

Usage

## S3 method for class 'SpatVector'
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))

## S3 method for class 'SpatVector'
ungroup(x, ...)

Arguments

Details

See Details on dplyr::group_by().

Value

A SpatVector object with an additional attribute.

Methods

Implementation of the generic dplyr::group_by() family functions for SpatVector objects.

When mixing terra and dplyr syntax on a grouped SpatVector (i.e, subsetting a SpatVector like v[1:3,1:2]) the groups attribute can be corrupted. tidyterra would try to re-group the SpatVector. This would be triggered the next time you use a dplyr verb on your SpatVector.

Note also that some operations (as terra::spatSample()) would create a new SpatVector. In these cases, the result won't preserve the groups attribute. Use group_by() to re-group.

See Also

dplyr::group_by(), dplyr::ungroup()

Other dplyr verbs that operate on group of rows: count.SpatVector(), rowwise.SpatVector(), summarise.SpatVector()

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
f <- system.file("ex/lux.shp", package = "terra")
p <- vect(f)


by_name1 <- p %>% group_by(NAME_1)

# grouping doesn't change how the SpatVector looks
by_name1

# But add metadata for grouping: See the coercion to tibble

# Not grouped
p_tbl <- as_tibble(p)
class(p_tbl)
head(p_tbl, 3)

# Grouped
by_name1_tbl <- as_tibble(by_name1)
class(by_name1_tbl)
head(by_name1_tbl, 3)


# It changes how it acts with the other dplyr verbs:
by_name1 %>% summarise(
  pop = mean(POP),
  area = sum(AREA)
)

# Each call to summarise() removes a layer of grouping
by_name2_name1 <- p %>% group_by(NAME_2, NAME_1)

by_name2_name1
group_data(by_name2_name1)

by_name2 <- by_name2_name1 %>% summarise(n = dplyr::n())
by_name2
group_data(by_name2)

# To removing grouping, use ungroup
by_name2 %>%
  ungroup() %>%
  summarise(n = sum(n))

# By default, group_by() overrides existing grouping
by_name2_name1 %>%
  group_by(ID_1, ID_2) %>%
  group_vars()


# Use add = TRUE to instead append
by_name2_name1 %>%
  group_by(ID_1, ID_2, .add = TRUE) %>%
  group_vars()

# You can group by expressions: this is a short-hand
# for a mutate() followed by a group_by()
p %>%
  group_by(ID_COMB = ID_1 * 100 / ID_2) %>%
  relocate(ID_COMB, .before = 1)

Grouping metadata for SpatVector objects

Description

This collection of functions accesses data about grouped SpatVector objects in various ways:

• group_data() returns a tibble that defines the grouping structure. The columns give the values of the grouping variables. The last column, always called .rows, is a list of integer vectors that gives the location of the rows in each group.

• group_keys() returns a tibble describing the groups.

• group_rows() returns a list of integer vectors giving the rows that each group contains.

• group_indices() returns an integer vector the same length as .data that gives the group that each row belongs to.

• group_vars() gives names of grouping variables as character vector.

• groups() gives the names of the grouping variables as a list of symbols.

• group_size() gives the size of each group.

• n_groups() gives the total number of groups.

See dplyr::group_data().

Usage

## S3 method for class 'SpatVector'
group_data(.data)

## S3 method for class 'SpatVector'
group_keys(.tbl, ...)

## S3 method for class 'SpatVector'
group_indices(.data, ...)

## S3 method for class 'SpatVector'
group_vars(x)

## S3 method for class 'SpatVector'
groups(x)

## S3 method for class 'SpatVector'
group_size(x)

## S3 method for class 'SpatVector'
n_groups(x)

Arguments

Value

See the description of the function. The results are usually tibbles, lists or vectors. These functions does not return SpatVector objects.

Examples

library(terra)

v <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))
v$gr_1 <- rep_len(c("A", "A", "B"), length.out = nrow(v))
v$gr_2 <- rep_len(c("C", "D"), length.out = nrow(v))

# Ungrouped

n_groups(v)

group_vars(v)

group_keys(v)

group_size(v)

groups(v)

group_rows(v)

group_data(v)

group_indices(v)

# Grouped by one var
gv <- group_by(v, gr_1)

n_groups(gv)

group_vars(gv)

group_keys(gv)

group_size(gv)

groups(gv)

group_rows(gv)

group_data(gv)

group_indices(gv)

# Grouped by several vars

gv2 <- group_by(v, gr_1, gr_2)

n_groups(gv2)

group_vars(gv2)

group_keys(gv2)

group_size(gv2)

groups(gv2)

group_rows(gv2)

group_data(gv2)

group_indices(gv2)

Hypsometric palettes database

Description

A tibble including the color map of 33 gradient palettes. All the palettes includes also a definition of colors limits in terms of elevation (meters), that can be used with ggplot2::scale_fill_gradientn().

Format

A tibble of 1102 rows and 6 columns. with the following fields:

pal

Name of the palette.

limit

Recommended elevation limit (in meters) for each color.

r

Value of the red channel (RGB color mode).

g

Value of the green channel (RGB color mode).

b

Value of the blue channel (RGB color mode).

hex

Hex code of the color.

Source

cpt-city: http://seaviewsensing.com/pub/cpt-city/.

See Also

scale_fill_hypso_c()

Other datasets: cross_blended_hypsometric_tints_db, grass_db, princess_db, volcano2

Examples

data("hypsometric_tints_db")

hypsometric_tints_db

# Select a palette
wikicols <- hypsometric_tints_db %>%
  filter(pal == "wiki-2.0")

f <- system.file("extdata/asia.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = wikicols$hex)

# Use with limits
p +
  scale_fill_gradientn(
    colors = wikicols$hex,
    values = scales::rescale(wikicols$limit),
    limit = range(wikicols$limit)
  )

A grouped SpatVector

Description

The easiest way to create a grouped SpatVector is to call the group_by() method on a SpatVector: this will take care of capturing the unevaluated expressions for you. See group_by.SpatVector() for details.

This function is the adapted version of dplyr::is_grouped_df().

See also group_data.SpatVector() for the accessory functions that retrieve various metadata from a grouped SpatVector.

Usage

is_grouped_spatvector(x)

Arguments

See Also

Other helpers: compare_spatrasters(), is_regular_grid(), pull_crs()

Check if x and y positions conforms a regular grid

Description

Assess if the coordinates x,y of an object conforms a regular grid. This function is called by its side effects.

This function is internally called by as_spatraster().

Usage

is_regular_grid(xy, digits = 6)

Arguments

Value

invisible() if is regular or an error message otherwise

See Also

as_spatraster()

Other helpers: compare_spatrasters(), is_grouped_spatvector(), pull_crs()

Examples

p <- matrix(1:90, nrow = 45, ncol = 2)

is_regular_grid(p)


# Jitter location
set.seed(1234)
jitter <- runif(length(p)) / 10e4
p_jitter <- p + jitter

# Need to adjust digits
is_regular_grid(p_jitter, digits = 4)

Mutating joins for SpatVector objects

Description

Mutating joins add columns from y to x, matching observations based on the keys. There are four mutating joins: the inner join, and the three outer joins.

See dplyr::inner_join() for details.

Usage

## S3 method for class 'SpatVector'
inner_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'SpatVector'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'SpatVector'
right_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

## S3 method for class 'SpatVector'
full_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  ...,
  keep = NULL
)

Arguments

Value

A SpatVector object.

terra equivalent

terra::merge()

Methods

Implementation of the generic dplyr::inner_join() family

SpatVector

The geometry column has a sticky behavior. This means that the result would have always the geometry of x for the records that matches the join conditions.

Note that for right_join() and full_join() it is possible to return empty geometries (since y is expected to be a data frame with no geometries). Although this kind of joining operations may not be common on spatial manipulation, it is possible that the function crashes, since handling of EMPTY geometries differs on terra and sf.

See Also

dplyr::inner_join(), dplyr::left_join(), dplyr::right_join(), dplyr::full_join(), terra::merge()

Other dplyr verbs that operate on pairs Spat*/data.frame: bind_cols.SpatVector, bind_rows.SpatVector, filter-joins.SpatVector

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
library(ggplot2)
# Vector
v <- terra::vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

# A data frame
df <- data.frame(
  cpro = sprintf("%02d", 1:10),
  x = runif(10),
  y = runif(10),
  letter = rep_len(LETTERS[1:3], length.out = 10)
)

# Inner join
inner <- v %>% inner_join(df)

nrow(inner)
autoplot(inner, aes(fill = letter)) + ggtitle("Inner Join")


# Left join

left <- v %>% left_join(df)
nrow(left)

autoplot(left, aes(fill = letter)) + ggtitle("Left Join")


# Right join
right <- v %>% right_join(df)
nrow(right)

autoplot(right, aes(fill = letter)) + ggtitle("Right Join")

# There are empty geometries, check with data from df
ggplot(right, aes(x, y)) +
  geom_point(aes(color = letter))


# Full join
full <- v %>% full_join(df)
nrow(full)

autoplot(full, aes(fill = letter)) + ggtitle("Full Join")

# Check with data from df
ggplot(full, aes(x, y)) +
  geom_point(aes(color = letter))

Create, modify, and delete cell values/layers/attributes of ⁠Spat*⁠ objects

Description

mutate() adds new layers/attributes and preserves existing ones on a ⁠Spat*⁠ object. transmute() adds new layers/attributes and drops existing ones. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to NULL.

Usage

## S3 method for class 'SpatRaster'
mutate(.data, ...)

## S3 method for class 'SpatVector'
mutate(.data, ...)

## S3 method for class 'SpatRaster'
transmute(.data, ...)

## S3 method for class 'SpatVector'
transmute(.data, ...)

Arguments

Value

A ⁠Spat*⁠ object of the same class than .data. See Methods.

terra equivalent

Some terra methods for modifying cell values: terra::ifel(), terra::classify(), terra::clamp(), terra::app(), terra::lapp(), terra::tapp()

Methods

Implementation of the generic dplyr::mutate(), dplyr::transmute() functions.

SpatRaster

Add new layers and preserves existing ones. The result is a SpatRaster with the same extent, resolution and crs than .data. Only the values (and possibly the number) of layers is modified.

transmute() would keep only the layers created with ....

SpatVector

The result is a SpatVector with the modified (and possibly renamed) attributes on the function call.

transmute() would keep only the attributes created with ....

See Also

dplyr::mutate(), dplyr::transmute() methods.

terra provides several ways to modify ⁠Spat*⁠ objects:

• terra::ifel().

• terra::classify().

• terra::clamp().

• terra::app(), terra::lapp(), terra::tapp().

Other single table verbs: arrange.SpatVector(), filter.Spat, rename.Spat, select.Spat, slice.Spat, summarise.SpatVector()

Other dplyr verbs that operate on columns: glimpse.Spat, pull.Spat, relocate.Spat, rename.Spat, select.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, pull.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)

# SpatRaster method
f <- system.file("extdata/cyl_temp.tif", package = "tidyterra")
spatrast <- rast(f)

mod <- spatrast %>%
  mutate(exp_lyr1 = exp(tavg_04 / 10)) %>%
  select(tavg_04, exp_lyr1)

mod
plot(mod)

# SpatVector method
f <- system.file("extdata/cyl.gpkg", package = "tidyterra")
v <- vect(f)

v %>%
  mutate(cpro2 = paste0(cpro, "-CyL")) %>%
  select(cpro, cpro2)

Pivot SpatVector from wide to long

Description

pivot_longer() "lengthens" data, increasing the number of rows and decreasing the number of columns. The inverse transformation is pivot_wider.SpatVector()

Learn more in tidyr::pivot_wider().

Usage

## S3 method for class 'SpatVector'
pivot_longer(
  data,
  cols,
  ...,
  cols_vary = "fastest",
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL
)

Arguments

Value

A SpatVector object.

Methods

Implementation of the generic tidyr::pivot_longer() function.

SpatVector

The geometry column has a sticky behavior. This means that the result would have always the geometry of data.

See Also

tidyr::pivot_longer()

Other tidyr verbs for pivoting: pivot_wider.SpatVector()

Other tidyr methods: drop_na.Spat, fill.SpatVector(), pivot_wider.SpatVector(), replace_na.Spat

Examples

library(dplyr)
library(tidyr)
library(ggplot2)
library(terra)

temp <- rast((system.file("extdata/cyl_temp.tif", package = "tidyterra")))
cyl <- vect(system.file("extdata/cyl.gpkg", package = "tidyterra")) %>%
  project(temp)

# Add average temp

temps <- terra::extract(temp, cyl, fun = "mean", na.rm = TRUE, xy = TRUE)
cyl_temp <- cbind(cyl, temps) %>%
  glimpse()

# And pivot long for plot
cyl_temp %>%
  pivot_longer(
    cols = tavg_04:tavg_06,
    names_to = "label",
    values_to = "temp"
  ) %>%
  ggplot() +
  geom_spatvector(aes(fill = temp)) +
  facet_wrap(~label, ncol = 1) +
  scale_fill_whitebox_c(palette = "muted")

Pivot SpatVector from long to wide

Description

pivot_wider() "widens" a SpatVector, increasing the number of columns and decreasing the number of rows. The inverse transformation is pivot_longer.SpatVector().

Usage

## S3 method for class 'SpatVector'
pivot_wider(
  data,
  ...,
  id_cols = NULL,
  id_expand = FALSE,
  names_from = "name",
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_vary = "fastest",
  names_expand = FALSE,
  names_repair = "check_unique",
  values_from = "value",
  values_fill = NULL,
  values_fn = NULL,
  unused_fn = NULL
)

Arguments

Value

A SpatVector object.

Methods

Implementation of the generic tidyr::pivot_wider() function.

SpatVector

The geometry column has a sticky behavior. This means that the result would have always the geometry of data.

See Also

tidyr::pivot_wider()

Other tidyr verbs for pivoting: pivot_longer.SpatVector()

Other tidyr methods: drop_na.Spat, fill.SpatVector(), pivot_longer.SpatVector(), replace_na.Spat

Examples

library(dplyr)
library(tidyr)
library(ggplot2)

cyl <- terra::vect(system.file("extdata/cyl.gpkg", package = "tidyterra"))

# Add extra row with info
xtra <- cyl %>%
  slice(c(2, 3)) %>%
  mutate(
    label = "extra",
    value = TRUE
  ) %>%
  rbind(cyl, .) %>%
  glimpse()

# Pivot by geom
xtra %>%
  pivot_wider(
    id_cols = iso2:name, values_from = value,
    names_from = label
  )

Princess palettes database

Description

A tibble including the color map of 15 gradient palettes.

Format

A tibble of 75 rows and 5 columns. with the following fields:

pal

Name of the palette.

r

Value of the red channel (RGB color mode).

g

Value of the green channel (RGB color mode).

b

Value of the blue channel (RGB color mode).

hex

Hex code of the color.

Source

https://leahsmyth.github.io/Princess-Colour-Schemes/index.html.

See Also

scale_fill_princess_c()

Other datasets: cross_blended_hypsometric_tints_db, grass_db, hypsometric_tints_db, volcano2

Examples

data("princess_db")

princess_db

# Select a palette
maori <- princess_db %>%
  filter(pal == "maori")

f <- system.file("extdata/volcano2.tif", package = "tidyterra")
r <- terra::rast(f)

library(ggplot2)

p <- ggplot() +
  geom_spatraster(data = r) +
  labs(fill = "elevation")

p +
  scale_fill_gradientn(colors = maori$hex)

Extract a single layer/attribute

Description

pull() is similar to $ on a data frame. It's mostly useful because it looks a little nicer in pipes and it can optionally name the output.

It is possible to extract the geographic coordinates of a SpatRaster. You need to use pull(.data, x, xy = TRUE). x and y are reserved names on terra, since they refer to the geographic coordinates of the layer.

See Examples and section About layer names on as_tibble.Spat().

Usage

## S3 method for class 'SpatRaster'
pull(.data, var = -1, name = NULL, ...)

## S3 method for class 'SpatVector'
pull(.data, var = -1, name = NULL, ...)

Arguments

Value

A vector the same number of cells/geometries as .data.

On SpatRaster objects, note that the default (na.rm = FALSE) would remove empty cells, so you may need to pass (na.rm = FALSE) to .... See terra::as.data.frame().

terra equivalent

terra::values()

Methods

Implementation of the generic dplyr::pull() function. This is done by coercing the ⁠Spat*⁠ object to a tibble first (see as_tibble.Spat) and then using dplyr::pull() method over the tibble.

SpatRaster

When passing option na.rm = TRUE to ..., only cells with a value distinct to NA are extracted. See terra::as.data.frame().

If xy = TRUE option is passed to ..., two columns names x and y (corresponding to the geographic coordinates of each cell) are available in position 1 and 2. Hence, pull(.data, 1) and pull(.data, 1, xy = TRUE) return different result.

SpatVector

When passing geom = "WKT"/geom = "HEX" to ..., the geometry of the SpatVector can be pulled passing var = geometry. Similarly to SpatRaster method, when using geom = "XY" the ⁠x,y⁠ coordinates can be pulled with var = x/var = y. See terra::as.data.frame() options.

See Also

dplyr::pull()

Other dplyr verbs that operate on columns: glimpse.Spat, mutate.Spat, relocate.Spat, rename.Spat, select.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, relocate.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

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

# Extract second layer
r %>%
  pull(2) %>%
  head()

# With xy the first two cols are `x` (longitude) and `y` (latitude)

r %>%
  pull(2, xy = TRUE) %>%
  head()

# With renaming

r %>%
  mutate(cat = cut(cyl_tile_3, c(0, 100, 300))) %>%
  pull(cyl_tile_3, name = cat) %>%
  head()

Extract CRS on WKT format

Description

Extract the WKT version of the CRS associated to a string, number of sf/Spat* object.

The Well-known text (WKT) representation of coordinate reference systems (CRS) is a character string that identifies precisely the parameters of each CRS. This is the current standard used on sf and terra packages.

Usage

pull_crs(.data, ...)

Arguments

Details

Although the WKT representation is the same, sf and terra API slightly differs. For example, sf can do:

sf::st_transform(x, 25830)

While sf equivalent is:

terra::project(bb, "epsg:25830")

Knowing the WKT would help to smooth workflows when working with different packages and object types.

Value

A WKT representation of the corresponding CRS.

Internals

This is a thin wrapper of sf::st_crs() and terra::crs().

See Also

terra::crs(), sf::st_crs() for knowing how these packages handle CRS definitions.

Other helpers: compare_spatrasters(), is_grouped_spatvector(), is_regular_grid()

Examples

# sf objects

sfobj <- sf::st_as_sfc("MULTIPOINT ((0 0), (1 1))", crs = 4326)

fromsf1 <- pull_crs(sfobj)
fromsf2 <- pull_crs(sf::st_crs(sfobj))

# terra

v <- terra::vect(sfobj)
r <- terra::rast(v)

fromterra1 <- pull_crs(v)
fromterra2 <- pull_crs(r)

# integers
fromint <- pull_crs(4326)

# Characters
fromchar <- pull_crs("epsg:4326")


all(
  fromsf1 == fromsf2,
  fromsf2 == fromterra1,
  fromterra1 == fromterra2,
  fromterra2 == fromint,
  fromint == fromchar
)

cat(fromsf1)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

anti_join, arrange, count, distinct, filter, full_join, glimpse, group_by, group_by_drop_default, group_data, group_indices, group_keys, group_rows, group_size, group_vars, groups, groups, inner_join, left_join, mutate, n_groups, pull, relocate, rename, rename_with, right_join, rowwise, select, semi_join, slice, slice_head, slice_max, slice_min, slice_sample, slice_tail, summarise, summarize, tally, transmute, ungroup

ggplot2

aes, after_stat, autoplot, fortify

tibble

as_tibble

tidyr

drop_na, fill, pivot_longer, pivot_wider, replace_na

Change layer/attribute order

Description

Use relocate() to change layer/attribute positions, using the same syntax as select.Spat to make it easy to move blocks of layers/attributes at once.

Usage

## S3 method for class 'SpatRaster'
relocate(.data, ..., .before = NULL, .after = NULL)

## S3 method for class 'SpatVector'
relocate(.data, ..., .before = NULL, .after = NULL)

Arguments

Value

A ⁠Spat*⁠ object of the same class than .data. See Methods.

terra equivalent

terra::subset(data, c("name_layer", "name_other_layer"))

Methods

Implementation of the generic dplyr::relocate() function.

SpatRaster

Relocate layers of a SpatRaster.

SpatVector

The result is a SpatVector with the attributes on a different order.

See Also

dplyr::relocate()

Other dplyr verbs that operate on columns: glimpse.Spat, mutate.Spat, pull.Spat, rename.Spat, select.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, rename.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)


f <- system.file("extdata/cyl_tile.tif", package = "tidyterra")
spatrast <- rast(f) %>% mutate(aa = 1, bb = 2, cc = 3)

names(spatrast)


spatrast %>%
  relocate(bb, .before = cyl_tile_3) %>%
  relocate(cyl_tile_1, .after = last_col())

Rename layers/attributes

Description

rename() changes the names of individual layers/attributes using new_name = old_name syntax; rename_with() renames layers/attributes using a function.

Usage

## S3 method for class 'SpatRaster'
rename(.data, ...)

## S3 method for class 'SpatRaster'
rename_with(.data, .fn, .cols = everything(), ...)

## S3 method for class 'SpatVector'
rename(.data, ...)

## S3 method for class 'SpatVector'
rename_with(.data, .fn, .cols = everything(), ...)

Arguments

Value

A ⁠Spat*⁠ object of the same class than .data. See Methods.

terra equivalent

⁠names(Spat*) <- c("a", "b", "c")⁠

Methods

Implementation of the generic dplyr::rename() function.

SpatRaster

Rename layers of a SpatRaster.

SpatVector

The result is a SpatVector with the renamed attributes on the function call.

See Also

dplyr::rename()

Other single table verbs: arrange.SpatVector(), filter.Spat, mutate.Spat, select.Spat, slice.Spat, summarise.SpatVector()

Other dplyr verbs that operate on columns: glimpse.Spat, mutate.Spat, pull.Spat, relocate.Spat, select.Spat

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rowwise.SpatVector(), select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
f <- system.file("extdata/cyl_tile.tif", package = "tidyterra")
spatrast <- rast(f) %>% mutate(aa = 1, bb = 2, cc = 3)

spatrast

spatrast %>% rename(
  this_first = cyl_tile_1,
  this_second = cyl_tile_2
)

spatrast %>% rename_with(
  toupper,
  .cols = starts_with("c")
)

Replace NAs with specified values

Description

Replace NA values on layers/attributes with specified values

Usage

## S3 method for class 'SpatRaster'
replace_na(data, replace = list(), ...)

## S3 method for class 'SpatVector'
replace_na(data, replace, ...)

Arguments

Value

A ⁠Spat*⁠ object of the same class than data. Geometries and spatial attributes are preserved.

terra equivalent

Use ⁠r[is.na(r)] <- <replacement>⁠

See Also

tidyr::replace_na()

Other tidyr verbs for handling missing values: drop_na.Spat, fill.SpatVector()

Other tidyr methods: drop_na.Spat, fill.SpatVector(), pivot_longer.SpatVector(), pivot_wider.SpatVector()

Examples

library(terra)

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

r %>% plot()

r %>%
  replace_na(list(tavg_04 = 6, tavg_06 = 20)) %>%
  plot()

Group SpatVector objects by rows

Description

rowwise() allows you to compute on a SpatVector a row-at-a-time. This is most useful when a vectorised function doesn't exist.

Most dplyr verbs implementation in tidyterra preserve row-wise grouping, with the exception of summarise.SpatVector(). You can explicitly ungroup with ungroup.SpatVector() or as_tibble(), or convert to a grouped SpatVector with group_by.SpatVector().

Usage

## S3 method for class 'SpatVector'
rowwise(data, ...)

Arguments

Details

See Details on dplyr::rowwise().

Value

The same SpatVector object with an additional attribute.

Methods

Implementation of the generic dplyr::rowwise() function for SpatVector objects.

When mixing terra and dplyr syntax on a row-wise SpatVector (i.e, subsetting a SpatVector like v[1:3,1:2]) the groups attribute can be corrupted. tidyterra would try to re-generate the SpatVector. This would be triggered the next time you use a dplyr verb on your SpatVector.

Note also that some operations (as terra::spatSample()) would create a new SpatVector. In these cases, the result won't preserve the groups attribute. Use rowwise.SpatVector() to re-group.

See Also

dplyr::rowwise()

Other dplyr verbs that operate on group of rows: count.SpatVector(), group-by.SpatVector, summarise.SpatVector()

Other dplyr methods: arrange.SpatVector(), bind_cols.SpatVector, bind_rows.SpatVector, count.SpatVector(), distinct.SpatVector(), filter-joins.SpatVector, filter.Spat, glimpse.Spat, group-by.SpatVector, mutate-joins.SpatVector, mutate.Spat, pull.Spat, relocate.Spat, rename.Spat, select.Spat, slice.Spat, summarise.SpatVector()

Examples

library(terra)
library(dplyr)

v <- terra::vect(system.file("shape/nc.shp", package = "sf"))

# Select new births
nb <- v %>%
  select(starts_with("NWBIR")) %>%
  glimpse()

# Compute the mean of NWBIR on each geometry
nb %>%
  rowwise() %>%
  mutate(nb_mean = mean(c(NWBIR74, NWBIR79)))

# Additional examples

# use c_across() to more easily select many variables
nb %>%
  rowwise() %>%
  mutate(m = mean(c_across(NWBIR74:NWBIR79)))

# Compute the minimum of x and y in each row

nb %>%
  rowwise() %>%
  mutate(min = min(c_across(NWBIR74:NWBIR79)))

# Summarising
v %>%
  rowwise() %>%
  summarise(mean_bir = mean(BIR74, BIR79)) %>%
  glimpse() %>%
  autoplot(aes(fill = mean_bir))

# Supply a variable to be kept
v %>%
  mutate(id2 = as.integer(CNTY_ID / 100)) %>%
  rowwise(id2) %>%
  summarise(mean_bir = mean(BIR74, BIR79)) %>%
  glimpse() %>%
  autoplot(aes(fill = as.factor(id2)))

Gradient scales from Wikipedia color schemes

Description

Implementation based on the Wikipedia Colorimetric conventions for topographic maps.

Three scales are provided:

• ⁠scale_*_wiki_d()⁠: For discrete values.

• ⁠scale_*_wiki_c()⁠: For continuous values.

• ⁠scale_*_wiki_b()⁠: For binning continuous values.

Additionally, a color palette wiki.colors() is provided. See also grDevices::terrain.colors() for details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_wiki_d(
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_wiki_d(
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_wiki_c(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_wiki_c(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_wiki_b(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_wiki_b(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

wiki.colors(n, alpha = 1, rev = FALSE)

Arguments

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

See Also

terra::plot(), ggplot2::scale_fill_viridis_c()

See also ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_cross_blended, scale_grass, scale_hypso, scale_princess, scale_terrain, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = wiki.colors(100))

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_wiki_c()

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_wiki_b(breaks = seq(70, 200, 10))

# With discrete values
factor <- volcano2_rast %>% mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))


ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_wiki_d(na.value = "gray10")

Discrete scales based in the color table of a SpatRaster

Description

Some categorical SpatRaster objects may have an associated color table. This function extract those values. These functions generates scales and vector of colors based on the color table terra::coltab() associated to a SpatRaster.

You can also get a vector of colors named with the corresponding factor with get_coltab_pal().

Additional parameters ... would be passed on to ggplot2::discrete_scale().

Note that tidyterra just documents a selection of these additional parameters, check ggplot2::discrete_scale() to see the full range of parameters accepted.

Usage

scale_fill_coltab(
  data,
  ...,
  alpha = 1,
  na.translate = FALSE,
  na.value = "transparent",
  drop = TRUE
)

scale_colour_coltab(
  data,
  ...,
  alpha = 1,
  na.translate = FALSE,
  na.value = "transparent",
  drop = TRUE
)

get_coltab_pal(x)

Arguments

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

See Also

terra::coltab(), ggplot2::discrete_scale(), ggplot2::scale_fill_manual(),

Examples

library(terra)
# Geological Eras
# Spanish Geological Survey (IGME)

r <- rast(system.file("extdata/cyl_era.tif", package = "tidyterra"))

plot(r)

# Get coltab
coltab_pal <- get_coltab_pal(r)

coltab_pal


# With ggplot2 + tidyterra
library(ggplot2)

gg <- ggplot() +
  geom_spatraster(data = r)


# Default plot
gg

# With coltabs
gg +
  scale_fill_coltab(data = r)

Cross blended hypsometric tints scales

Description

Implementation of the cross blended hypsometric gradients presented on doi:10.14714/CP69.20. The following fill scales and palettes are provided:

• ⁠scale_*_cross_blended_d()⁠: For discrete values.

• ⁠scale_*_cross_blended_c()⁠: For continuous values.

• ⁠scale_*_cross_blended_b()⁠: For binning continuous values.

• cross_blended.colors(): A gradient color palette. See also grDevices::terrain.colors() for details.

An additional set of scales is provided. These scales can act as hypsometric (or bathymetric) tints.

• ⁠scale_*_cross_blended_tint_d()⁠: For discrete values.

• ⁠scale_*_cross_blended_tint_c()⁠: For continuous values.

• ⁠scale_*_cross_blended_tint_b()⁠: For binning continuous values.

• cross_blended.colors2(): A gradient color palette. See also grDevices::terrain.colors() for details.

See Details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_cross_blended_d(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_cross_blended_d(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_cross_blended_c(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_cross_blended_c(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_cross_blended_b(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_cross_blended_b(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

cross_blended.colors(n, palette = "cold_humid", alpha = 1, rev = FALSE)

scale_fill_cross_blended_tint_d(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_cross_blended_tint_d(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_cross_blended_tint_c(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_cross_blended_tint_c(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_cross_blended_tint_b(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_cross_blended_tint_b(
  palette = "cold_humid",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "coloursteps"
)

cross_blended.colors2(n, palette = "cold_humid", alpha = 1, rev = FALSE)

Arguments

Details

On ⁠scale_*_cross_blended_tint_*⁠ palettes, the position of the gradients and the limits of the palette are redefined. Instead of treating the color palette as a continuous gradient, they are rescaled to act as a hypsometric tint. A rough description of these tints are:

• Blue colors: Negative values.

• Green colors: 0 to 1.000 values.

• Browns: 1000 to 4.000 values.

• Whites: Values higher than 4.000.

The following orientation would vary depending on the palette definition (see cross_blended_hypsometric_tints_db for an example on how this could be achieved).

Note that the setup of the palette may not be always suitable for your specific data. For example, a SpatRaster of small parts of the globe (and with a limited range of elevations) may not be well represented. As an example, a SpatRaster with a range of values on ⁠[100, 200]⁠ would appear almost as an uniform color. This could be adjusted using the limits/values parameters.

When passing limits parameter to ⁠scale_*_cross_blended_tint_*⁠ the colors would be restricted of those specified by this parameter, keeping the distribution of the tint. You can combine this with oob (i.e. oob = scales::oob_squish) to avoid blank pixels in the plot.

cross_blended.colors2() provides a gradient color palette where the distance between colors is different depending of the type of color. In contrast, cross_blended.colors() provides an uniform gradient across colors. See Examples.

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

Source

• Patterson, T., & Jenny, B. (2011). The Development and Rationale of Cross-blended Hypsometric Tints. Cartographic Perspectives, (69), 31 - 46. doi:10.14714/CP69.20.

• Patterson, T. (2004). Using Cross-blended Hypsometric Tints for Generalized Environmental Mapping. Online, Accessed June 10, 2022.

See Also

cross_blended_hypsometric_tints_db, terra::plot(), terra::minmax(), ggplot2::scale_fill_viridis_c().

See also ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_grass, scale_hypso, scale_princess, scale_terrain, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = cross_blended.colors(100, palette = "arid"))

# Palette with uneven colors
plot(volcano2_rast, col = cross_blended.colors2(100, palette = "arid"))


library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_cross_blended_c(palette = "cold_humid")


# Full map with true tints

f_asia <- system.file("extdata/asia.tif", package = "tidyterra")
asia <- rast(f_asia)

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_cross_blended_tint_c(
    palette = "warm_humid",
    labels = scales::label_number(),
    breaks = c(-10000, 0, 5000, 8000),
    guide = guide_colorbar(reverse = TRUE)
  ) +
  labs(fill = "elevation (m)") +
  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"
  )

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_cross_blended_b(breaks = seq(70, 200, 25), palette = "arid")

# With breaks
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_cross_blended_b(
    breaks = seq(75, 200, 25),
    palette = "arid"
  )

# With discrete values
factor <- volcano2_rast %>%
  mutate(cats = cut(elevation,
    breaks = c(100, 120, 130, 150, 170, 200),
    labels = c(
      "Very Low", "Low", "Average", "High",
      "Very High"
    )
  ))

ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_cross_blended_d(na.value = "gray10", palette = "cold_humid")


# Tint version
ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_cross_blended_tint_d(
    na.value = "gray10",
    palette = "cold_humid"
  )

# Display all the cross-blended palettes

pals <- unique(cross_blended_hypsometric_tints_db$pal)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = cross_blended.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)
# Display all the cross-blended palettes on version 2

pals <- unique(cross_blended_hypsometric_tints_db$pal)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = cross_blended.colors2(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

GRASS scales

Description

Implementation of GRASS color tables. The following fill scales and palettes are provided:

• ⁠scale_*_grass_d()⁠: For discrete values.

• ⁠scale_*_grass_c()⁠: For continuous values.

• ⁠scale_*_grass_b()⁠: For binning continuous values.

• grass.colors(): Gradient color palette. See also grDevices::terrain.colors() for details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

These palettes are an implementation of terra::map.pal(), that is the default color palettes provided by terra::plot() (terra ⁠> 1.7.78⁠).

Usage

scale_fill_grass_d(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_grass_d(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_grass_c(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  use_grass_range = TRUE,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_grass_c(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  use_grass_range = TRUE,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_grass_b(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  use_grass_range = TRUE,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_grass_b(
  palette = "viridis",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  use_grass_range = TRUE,
  na.value = "transparent",
  guide = "coloursteps"
)

grass.colors(n, palette = "viridis", alpha = 1, rev = FALSE)

Arguments

Details

Some palettes are mapped by default to a specific range of values (see grass_db). However, it is possible to modify this behaviour with the use_grass_range argument, When FALSE the color scales would be mapped to the range of values of the color/fill aesthethics, See Examples.

When passing limits parameter the colors would be restricted of those specified by this parameter, keeping the distribution of the palette. You can combine this with oob (i.e. oob = scales::oob_squish) to avoid blank pixels in the plot.

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aes().

terra equivalent

terra::map.pal()

Source

Derived from https://github.com/OSGeo/grass/tree/main/lib/gis/colors. See also r.color - GRASS GIS Manual.

References

GRASS Development Team (2024). Geographic Resources Analysis Support System (GRASS) Software, Version 8.3.2. Open Source Geospatial Foundation, USA. https://grass.osgeo.org.

See Also

grass_db, terra::plot(), terra::minmax(), ggplot2::scale_fill_viridis_c().

See also ggplot2 docs on additional ... parameters:

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_cross_blended, scale_hypso, scale_princess, scale_terrain, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = grass.colors(100, palette = "haxby"))

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_grass_c(palette = "terrain")

# Use with no default limits
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_grass_c(palette = "terrain", use_grass_range = FALSE)

# Full map with true tints

f_asia <- system.file("extdata/asia.tif", package = "tidyterra")
asia <- rast(f_asia)

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_grass_c(
    palette = "srtm_plus",
    labels = scales::label_number(),
    breaks = c(-10000, 0, 5000, 8000),
    guide = guide_colorbar(reverse = FALSE)
  ) +
  labs(fill = "elevation (m)") +
  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"
  )

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_grass_b(breaks = seq(70, 200, 25), palette = "sepia")


# With discrete values
factor <- volcano2_rast %>%
  mutate(cats = cut(elevation,
    breaks = c(100, 120, 130, 150, 170, 200),
    labels = c(
      "Very Low", "Low", "Average", "High",
      "Very High"
    )
  ))

ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_grass_d(palette = "soilmoisture")


# Display all the GRASS palettes
data("grass_db")

pals_all <- unique(grass_db$pal)

# In batches
pals <- pals_all[c(1:25)]
# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = grass.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

# Second batch
pals <- pals_all[-c(1:25)]

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = grass.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

Gradient scales for representing hypsometry and bathymetry

Description

Implementation of a selection of gradient palettes available in cpt-city.

The following scales and palettes are provided:

• ⁠scale_*_hypso_d()⁠: For discrete values.

• ⁠scale_*_hypso_c()⁠: For continuous values.

• ⁠scale_*_hypso_b()⁠: For binning continuous values.

• hypso.colors(): A gradient color palette. See also grDevices::terrain.colors() for details.

An additional set of scales is provided. These scales can act as hypsometric (or bathymetric) tints.

• ⁠scale_*_hypso_tint_d()⁠: For discrete values.

• ⁠scale_*_hypso_tint_c()⁠: For continuous values.

• ⁠scale_*_hypso_tint_b()⁠: For binning continuous values.

• hypso.colors2(): A gradient color palette. See also grDevices::terrain.colors() for details.

See Details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_hypso_d(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_hypso_d(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_hypso_c(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_hypso_c(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_hypso_b(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_hypso_b(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

hypso.colors(n, palette = "etopo1_hypso", alpha = 1, rev = FALSE)

scale_fill_hypso_tint_d(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_hypso_tint_d(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_hypso_tint_c(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_hypso_tint_c(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_hypso_tint_b(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_hypso_tint_b(
  palette = "etopo1_hypso",
  ...,
  alpha = 1,
  direction = 1,
  values = NULL,
  limits = NULL,
  na.value = "transparent",
  guide = "coloursteps"
)

hypso.colors2(n, palette = "etopo1_hypso", alpha = 1, rev = FALSE)

Arguments

Details

On ⁠scale_*_hypso_tint_*⁠ palettes, the position of the gradients and the limits of the palette are redefined. Instead of treating the color palette as a continuous gradient, they are rescaled to act as a hypsometric tint. A rough description of these tints are:

• Blue colors: Negative values.

• Green colors: 0 to 1.000 values.

• Browns: 1000 to 4.000 values.

• Whites: Values higher than 4.000.

The following orientation would vary depending on the palette definition (see hypsometric_tints_db for an example on how this could be achieved).

Note that the setup of the palette may not be always suitable for your specific data. For example, a SpatRaster of small parts of the globe (and with a limited range of elevations) may not be well represented. As an example, a SpatRaster with a range of values on ⁠[100, 200]⁠ would appear almost as an uniform color. This could be adjusted using the limits/values parameters.

When passing limits parameter to ⁠scale_*_hypso_tint_*⁠ the colors would be restricted of those specified by this parameter, keeping the distribution of the tint. You can combine this with oob (i.e. oob = scales::oob_squish) to avoid blank pixels in the plot.

hypso.colors2() provides a gradient color palette where the distance between colors is different depending of the type of color. In contrast, hypso.colors() provides an uniform gradient across colors. See Examples.

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

Source

cpt-city: http://seaviewsensing.com/pub/cpt-city/.

See Also

hypsometric_tints_db, terra::plot(), terra::minmax(), ggplot2::scale_fill_viridis_c()

See also ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_cross_blended, scale_grass, scale_princess, scale_terrain, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = hypso.colors(100, palette = "wiki-2.0_hypso"))

# Palette with uneven colors
plot(volcano2_rast, col = hypso.colors2(100, palette = "wiki-2.0_hypso"))


library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_hypso_c(palette = "colombia_hypso")


# Full map with true tints

f_asia <- system.file("extdata/asia.tif", package = "tidyterra")
asia <- rast(f_asia)

ggplot() +
  geom_spatraster(data = asia) +
  scale_fill_hypso_tint_c(
    palette = "etopo1",
    labels = scales::label_number(),
    breaks = c(-10000, 0, 5000, 8000),
    guide = guide_colorbar(reverse = TRUE)
  ) +
  labs(fill = "elevation (m)") +
  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"
  )

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_hypso_b(breaks = seq(70, 200, 25), palette = "wiki-2.0_hypso")

# With breaks
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_hypso_b(
    breaks = seq(75, 200, 25),
    palette = "wiki-2.0_hypso"
  )

# With discrete values
factor <- volcano2_rast %>% mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))

ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_hypso_d(na.value = "gray10", palette = "dem_poster")


# Tint version
ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_hypso_tint_d(na.value = "gray10", palette = "dem_poster")

# Display all the cpl_city palettes

pals <- unique(hypsometric_tints_db$pal)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = hypso.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)
# Display all the cpl_city palettes on version 2

pals <- unique(hypsometric_tints_db$pal)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = hypso.colors2(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

Gradient scales from princess color schemes

Description

Implementation of the gradient palettes presented in https://leahsmyth.github.io/Princess-Colour-Schemes/index.html. Three scales are provided:

• ⁠scale_*_princess_d()⁠: For discrete values.

• ⁠scale_*_princess_c()⁠: For continuous values.

• ⁠scale_*_princess_b()⁠: For binning continuous values.

Additionally, a color palette princess.colors() is provided. See also grDevices::terrain.colors() for details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_princess_d(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_princess_d(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_princess_c(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_princess_c(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_princess_b(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_princess_b(
  palette = "snow",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

princess.colors(n, palette = "snow", alpha = 1, rev = FALSE)

Arguments

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

Source

https://github.com/LeahSmyth/Princess-Colour-Schemes.

See Also

terra::plot(), ggplot2::scale_fill_viridis_c()

See also ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_cross_blended, scale_grass, scale_hypso, scale_terrain, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = princess.colors(100))

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_princess_c()

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_princess_b(breaks = seq(70, 200, 10), palette = "denmark")

# With discrete values
factor <- volcano2_rast %>% mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))


ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_princess_d(na.value = "gray10", palette = "maori")


# Display all the princess palettes

pals <- unique(princess_db$pal)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = princess.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

Terrain colour scales from grDevices

Description

Implementation of the classic color palette terrain.colors():

• ⁠scale_*_terrain_d()⁠: For discrete values.

• ⁠scale_*_terrain_c()⁠: For continuous values.

• ⁠scale_*_terrain_b()⁠: For binning continuous values.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_terrain_d(
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_terrain_d(
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_terrain_c(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_terrain_c(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_terrain_b(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_terrain_b(
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

Arguments

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

See Also

terra::plot(), ggplot2::scale_fill_viridis_c() and ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_cross_blended, scale_grass, scale_hypso, scale_princess, scale_whitebox

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_terrain_c()

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_terrain_b(breaks = seq(70, 200, 10))

# With discrete values
factor <- volcano2_rast %>% mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))


ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_terrain_d(na.value = "gray10")

Gradient scales from WhiteboxTools color schemes

Description

Implementation of the gradient palettes provided by WhiteboxTools. Three scales are provided:

• ⁠scale_*_whitebox_d()⁠: For discrete values.

• ⁠scale_*_whitebox_c()⁠: For continuous values.

• ⁠scale_*_whitebox_b()⁠: For binning continuous values.

Additionally, a color palette whitebox.colors() is provided. See also grDevices::terrain.colors() for details.

Additional parameters ... would be passed on to:

• Discrete values: ggplot2::discrete_scale().

• Continuous values: ggplot2::continuous_scale().

• Binned continuous values: ggplot2::binned_scale().

Note that tidyterra just documents a selection of these additional parameters, check the ggplot2 functions listed above to see the full range of parameters accepted by these scales.

Usage

scale_fill_whitebox_d(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_colour_whitebox_d(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.translate = FALSE,
  drop = TRUE
)

scale_fill_whitebox_c(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_colour_whitebox_c(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "colourbar"
)

scale_fill_whitebox_b(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

scale_colour_whitebox_b(
  palette = "high_relief",
  ...,
  alpha = 1,
  direction = 1,
  na.value = "transparent",
  guide = "coloursteps"
)

whitebox.colors(n, palette = "high_relief", alpha = 1, rev = FALSE)

Arguments

Value

The corresponding ggplot2 layer with the values applied to the fill/colour aesthetics.

Source

https://github.com/jblindsay/whitebox-tools, under MIT License. Copyright (c) 2017-2021 John Lindsay.

See Also

terra::plot(), ggplot2::scale_fill_viridis_c()

See also ggplot2 docs on additional ... parameters.

Other gradient scales and palettes for hypsometry: scale_color_coltab(), scale_cross_blended, scale_grass, scale_hypso, scale_princess, scale_terrain

Examples

filepath <- system.file("extdata/volcano2.tif", package = "tidyterra")

library(terra)
volcano2_rast <- rast(filepath)

# Palette
plot(volcano2_rast, col = whitebox.colors(100))

library(ggplot2)
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_whitebox_c()

# Binned
ggplot() +
  geom_spatraster(data = volcano2_rast) +
  scale_fill_whitebox_b(breaks = seq(70, 200, 10), palette = "atlas")

# With discrete values
factor <- volcano2_rast %>% mutate(cats = cut(elevation,
  breaks = c(100, 120, 130, 150, 170, 200),
  labels = c(
    "Very Low", "Low", "Average", "High",
    "Very High"
  )
))


ggplot() +
  geom_spatraster(data = factor, aes(fill = cats)) +
  scale_fill_whitebox_d(na.value = "gray10", palette = "soft")


# Display all the whitebox palettes

pals <- c(
  "atlas", "high_relief", "arid", "soft", "muted", "purple",
  "viridi", "gn_yl", "pi_y_g", "bl_yl_rd", "deep"
)

# Helper fun for plotting

ncols <- 128
rowcol <- grDevices::n2mfrow(length(pals))

opar <- par(no.readonly = TRUE)
par(mfrow = rowcol, mar = rep(1, 4))

for (i in pals) {
  image(
    x = seq(1, ncols), y = 1, z = as.matrix(seq(1, ncols)),
    col = whitebox.colors(ncols, i), main = i,
    ylab = "", xaxt = "n", yaxt = "n", bty = "n"
  )
}
par(opar)

Subset layers/attributes of ⁠Spat*⁠ objects

Description

Select (and optionally rename) attributes/layers in ⁠Spat*⁠ objects, using a concise mini-language. See Methods.

Usage