Package 'sdmtools' reference manual

Title:	Utility tools for Species Distribution Modelling
Description:	What the package does (one paragraph).
Authors:	Gerry Ryan [aut, cre] , David Duncan [ctb], Nick Tierney [ctb]
Maintainer:	Gerry Ryan <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.0.9000
Built:	2024-11-18 03:09:04 UTC
Source:	https://github.com/idem-lab/sdmtools

Assign to nearest raster cell on mask

Description

Adapted from seegSDM. Reposition observations to a location within mask if within a specified distance. Useful for when coastal observations drop off jagged mask and similar

Usage

assign_nearest_land(dat_object, mask_object, max_distance, verbose = TRUE)
assign_nearest_land(dat_object, mask_object, max_distance, verbose = TRUE)

Arguments

`dat_object`	data.frame
`mask_object`	'SpatRaster'
`max_distance`	map units if raster is projected
`verbose`	provide verbose output. Default is TRUE

Value

modified data object minus observations not within specified maximum distance of mask

Create an example mask within a given raster

Description

Create an example mask within a given raster

Usage

example_mask(raster, pc_threshold = NULL)
example_mask(raster, pc_threshold = NULL)

Arguments

`raster`	generated from example_raster for example
`pc_threshold`	A threshold percentile to divide mask / non mask elements

Value

A spatRaster

Examples

example_mask(example_raster(), pc_threshold=0.5)
example_mask(example_raster(), pc_threshold=0.5)

Create example raster for use in examples

Description

Create example raster for use in examples

Usage

example_raster(seed = NULL, layername = NULL)
example_raster(seed = NULL, layername = NULL)

Arguments

`seed`	A seed to pass to `set.seed(seed)`
`layername`	`character` for layer name passed to `names`

Value

A SpatRaster.

Examples

example_raster()

example_raster(
  seed = 3.142,
  layername = "jabberwock_density"
)
example_raster()

example_raster(
  seed = 3.142,
  layername = "jabberwock_density"
)

Create an example vector

Description

Create an example vector

Usage

example_vector(seed = NULL)
example_vector(seed = NULL)

Arguments

seed

A seed to pass to set.seed(seed)

Value

A SpatVector

Examples

example_vector()
example_vector()

Extract data from covariate rasters

Description

Extracts data from raster covariate layers for modelling.

Usage

extract_covariates(
  covariates,
  presences = NULL,
  absences = NULL,
  presences_and_absences = NULL
)
extract_covariates(
  covariates,
  presences = NULL,
  absences = NULL,
  presences_and_absences = NULL
)

Arguments

`covariates`	`SpatRaster` object of one or more layers.
`presences`	`data.frame` or `tibble` of presence locations contaning longitude and latitude as variables `x` and `y`.
`absences`	`data.frame` or `tibble` of absence or background locations contaning longitude and latitude as variables `x` and `y`.
`presences_and_absences`	`data.frame` or `tibble` of presence presence and absence locations containing longitude and latitude as variables `x` and `y`.

Details

extract_covariates will run correctly with either presences only, presences and absences, or presences_and_absences only. (If all three are included it will ignore the last one).

Value

A tibble containing values variables presence and each of the covariate layers at each location.

Author(s)

Gerry Ryan

Examples


library(terra)

cov1 <- example_raster(
 seed = -44,
 layername = "cov1"
)
cov2 <- example_raster(
 seed = 15.3,
 layername = "cov2"
)

covs <- c(cov1, cov2)


presences <- example_vector(seed = 68) %>%
 as.data.frame(geom = "xy")
absences <- example_vector(seed = 9.6) %>%
 as.data.frame(geom = "xy")

extract_covariates(
 covariates = covs,
 presences = presences,
 absences = absences
)

library(terra)

cov1 <- example_raster(
 seed = -44,
 layername = "cov1"
)
cov2 <- example_raster(
 seed = 15.3,
 layername = "cov2"
)

covs <- c(cov1, cov2)


presences <- example_vector(seed = 68) %>%
 as.data.frame(geom = "xy")
absences <- example_vector(seed = 9.6) %>%
 as.data.frame(geom = "xy")

extract_covariates(
 covariates = covs,
 presences = presences,
 absences = absences
)

Global regions

Description

A table of showing the United Nations Regional groups of Member States and World Health Organisation regions of nation states and all states with Officially Assigned ISO3166 Alpha-3 country codes.

Usage

global_regions
global_regions

Format

`global_regions` A tibble with 249 rows and 6 columns:

country: Country name. Given name conflicts between the two data sources, names identified in country are defined as countrycode::countrycode(global_regions$iso3, origin = "iso3c", destination = "country.name")
iso2, iso3: Alpha-2 & Alpha-3 ISO3166 country codes
who_region: World Health Organisation region
un_region: United Nations Regional group of Member States
continent: Continent

Details

Includes all Officially Assigned ISO3166 Alpha-3 country codes.

Not all 'countries' include a continent.

Special cases of UN regional groupings

Israel

In May 2000, Israel became a full member of the Group of Western European and other States on a temporary basis (subject to renewal), thereby enabling it to put forward candidates for election to various bodies of the General Assembly. In 2004, Israel obtained a permanent renewal to its membership.

Kiribati

As of 2010, Kiribati (geographically in Oceania) is not a member of any regional group, despite other Oceania nations belonging to the Group of Asia-Pacific States.

Türkiye

Türkiye participates fully in both the Group of Western European and other States and the Group of Asia-Pacific States, but for electoral purposes is considered a member of the Group of Western European and other States only.

While Türkiye is listed in both groupings in the original dataset; here we remove the duplication and list it only where it is a voting member, i.e., Western European and other States

United States of America

The United States of America is not a member of any regional group, but attends meetings of the Group of Western European and other States as an observer and is considered to be a member of that group for electoral purposes.

Source

Table combined from un-regions.csv, and who-regions.csv, housed in package data-raw directory.

un-regions.csv manually created from tables on "Regional groups of Member States"

https://www.un.org/dgacm/en/content/regional-groups

2024-02-02

who-regions.csv was downloaded from

https://ourworldindata.org/grapher/who-regions

2024-02-02

World Health Organization – processed by Our World in Data & World Health Organization.

continent defined by countrycode::codelist$continent, (largely?) by World Bank.

Import rasters from directory

Description

Imports all rasters of a given extension type from a specified directory using terra::rast. Based on seegSDM::importRasters.

Usage

import_rasts(path, ext = ".grd", as_list = FALSE)
import_rasts(path, ext = ".grd", as_list = FALSE)

Arguments

`path`	Directory path containing rasters
`ext`	Extension type
`as_list`	`logical` Should the `spatRaster` objects be returned as a `list` (`TRUE`) or concatenated as layers in a single `SpatRaster` object (`FALSE`; default)

Value

A SpatRaster if as_list = FALSE, or list of SpatRaster objects.

Examples

## Not run: 
  rasters <- import_rasts("/data/grids/covariates")

## End(Not run)
## Not run: 
  rasters <- import_rasts("/data/grids/covariates")

## End(Not run)

title

Description

Checks whether longitude and latitude coincide with non-missing pixels of a raster. The function takes two arguments: points, a dataframe containing columns named longitude' and 'latitude', and mask is a raster. Returns a dataframe of longitude and latitude only those rows with points falling on non-missing pixels. If all points fall on missing pixels, the function throws an error.

Usage

inside_mask(points, mask)
inside_mask(points, mask)

Arguments

`points`	dataframe containing columns named 'longitude' and 'latitude'
`mask`	a raster

Value

dataframe of longitude and latitude only those rows with points falling on non-missing pixels. If all points fall on missing pixels, the function throws an error.

Make Africa Mask

Description

Makes a SpatRaster or SpatVector mask layer of Africa, based on shapefiles for African nations from the malaraAtlas package.

Usage

make_africa_mask(
  filename = NULL,
  type = c("raster", "vector"),
  res = c("high", "low"),
  countries = NULL
)
make_africa_mask(
  filename = NULL,
  type = c("raster", "vector"),
  res = c("high", "low"),
  countries = NULL
)

Arguments

`filename`	Character of file path and name if mask is to be written to disc.
`type`	Character `raster` or `vector`; to return mask as either `SpatRaster` or `SpatVector`.
`res`	Character `"high"` or `"low"`; corresponding to resolution of 0.008333333 or 0.04166667 decimal degrees
`countries`	Character of ISO3 country names. If `NULL` returns all countries in Africa.

Details

Raster layers creates with extent of terra::ext(-18.0000019073486, 52.0416647593181, -34.9999987284343, 37.5416679382324)

Value

SpatRaster or SpatVector in WGS 84 (EPSG:4326).

Examples

## Not run: 
# Create an object in workspace
africa_mask_v <- make_africa_mask(type = "vector")

# Save to disk
make_africa_mask(filename = "africa_mask.tif", type = "raster")

# or do both at once
africa_mask_r <- make_africa_mask("africa_mask.tif")

## End(Not run)
## Not run: 
# Create an object in workspace
africa_mask_v <- make_africa_mask(type = "vector")

# Save to disk
make_africa_mask(filename = "africa_mask.tif", type = "raster")

# or do both at once
africa_mask_r <- make_africa_mask("africa_mask.tif")

## End(Not run)

Make presence-only list for `multispeciesPP`

Description

Make presence-only list for multispeciesPP

Usage

make_mpp_list(x, id)
make_mpp_list(x, id)

Arguments

`x`	A `data.frame` containing covariate and bias values for locations, and an identity (species) column
`id`	The name of the identity column in `tidyselect` form. `id` must be a column name in `x`.

Details

The package multispeciesPP requires a very annoying named list format for presence-only data. This function takes a table of covariate / bias values and an ID column (i.e. species), and returns a named list where each element is the values corresponding to that identity only. Ugh.

Value

A named list or data frames.

Examples

## Not run: 
make_mpp_list(presence_only_data, species)

## End(Not run)
## Not run: 
make_mpp_list(presence_only_data, species)

## End(Not run)

Mask all

Description

Masks all NA cells across all layers, such that returned layers have matching NA cells.

Usage

mask_all(rasts, filename = NULL, overwrite = FALSE)
mask_all(rasts, filename = NULL, overwrite = FALSE)

Arguments

`rasts`	`SpatRaster` with `nlyr(rasts)` > 1 to mask
`filename`	`character`to save output
`overwrite`	`logical` overwrite existing `filename`?

Details

Uses a ton of RAM and will break for larger rasters.

Value

SpatRaster

Examples


# Create some SpatRaster layers with non-matching NA cells
library(terra)
library(sdmtools)

r <- c(
  example_raster(seed = 1),
  example_raster(seed = 2),
  example_raster(seed = 3)
)
rvals <- terra::values(r)
nas <- c(1:10, 105:120, 215:240)
rvals[nas] <- NA
r[] <- rvals

# check if it pleases you to do so
# plot(r)
# mask out non-overlapping `NA` values in all layers
s <- mask_all(r)
s

# plot(s)
# Create some SpatRaster layers with non-matching NA cells
library(terra)
library(sdmtools)

r <- c(
  example_raster(seed = 1),
  example_raster(seed = 2),
  example_raster(seed = 3)
)
rvals <- terra::values(r)
nas <- c(1:10, 105:120, 215:240)
rvals[nas] <- NA
r[] <- rvals

# check if it pleases you to do so
# plot(r)
# mask out non-overlapping `NA` values in all layers
s <- mask_all(r)
s

# plot(s)

Create mask from raster layers

Description

Creates a mask where a cell in any layer of r that is NA will be returned as NA.

Usage

mask_from_all(r)
mask_from_all(r)

Arguments

`r`	`SpatRaster` with >1 layer.

Details

Similar in intention to mask_all, but (a) will work on larger rasters because it only holds the values of a single layer in memory at a time, and (b) returns a mask layer, rather than masking each layer in r. Can be very slow

Value

SpatRaster with values NA or 1.

Examples

r <- example_raster(seed = 10)
s <- example_raster(seed = 11)

r[10:20] <- NA

s[5:15] <- NA

q <- mask_from_all(c(r,s))

library("terra")
plot(c(r,s,q))

r <- example_raster(seed = 10)
s <- example_raster(seed = 11)

r[10:20] <- NA

s[5:15] <- NA

q <- mask_from_all(c(r,s))

library("terra")
plot(c(r,s,q))

Title

Description

Title

Usage

maskpointsdf(df, msk)
maskpointsdf(df, msk)

Arguments

`df`	data.frame
`msk`	mask

Value

tibble of two columsn, lon and lat

match ref

Description

Crop, resample, and mask x against ref, and optionally replace any missing values.

Usage

match_ref(x, ref, missing_val = NULL, filename = NULL, overwrite = TRUE)
match_ref(x, ref, missing_val = NULL, filename = NULL, overwrite = TRUE)

Arguments

`x`	`SpatRaster` object to bash into shape
`ref`	`SpatRaster` reference object
`missing_val`	If `NULL` missing value are left as is. Otherwise any `NA` or `NaN` values in `x` that are not `NA` in ref will be replaced by `missing_val`
`filename`	If not `NULL` output will be written to filename
`overwrite`	`logical`. If `filename` not `NULL`, then if `TRUE`, `filename` will be overwritten.

Value

SpatRaster of nlyrs(x) trimmed to extent and resolution of ref.

Examples

#placeholder example
#placeholder example

Identify nearest land

Description

Adapted from seegSDM Identify closest neighbouring cell that does not return NA on raster mask

Usage

nearest_land(points, raster, max_distance)
nearest_land(points, raster, max_distance)

Arguments

`points`	anything `terra::extract()` accepts as the y argument
`raster`	raster
`max_distance`	the map units if raster is projected

Value

matrix of XY coordinates of nearest cell on mask, or NAs

`long_tibble` constructor function

Description

long_tibble constructor function

Usage

new_long_tibble(x)
new_long_tibble(x)

Arguments

`x`	A `tbl_df` object.

Value

long_tibble.

Raster predictions from `multispeciesPP`

Description

Make spatial predictions from multispeciesPP::predict.multispeciesPP by passing in data as terra::SpatRaster layers and returning a SpatRaster.

Usage

predict_mpp_rast(
  model,
  data,
  sp,
  type = c("response", "link"),
  filename = NULL,
  overwrite = FALSE
)
predict_mpp_rast(
  model,
  data,
  sp,
  type = c("response", "link"),
  filename = NULL,
  overwrite = FALSE
)

Arguments

`model`	A `multispeciesPP` model object
`data`	`SpatRaster` object with covariate and bias layers
`sp`	`character` Species name to predict
`type`	`character`. Prediction scale — `"response"` or `"link"`.
`filename`	`character`to save output
`overwrite`	`logical` overwrite existing `filename`?

Value

SpatRaster

Raster predictions from `multispeciesPP` for all species

Description

Calls sdmtools::predict_mpp_rast over all species in model

Usage

predict_mpp_rast_all(
  model,
  data,
  type = c("response", "link"),
  filename = NULL,
  overwrite = FALSE
)
predict_mpp_rast_all(
  model,
  data,
  type = c("response", "link"),
  filename = NULL,
  overwrite = FALSE
)

Arguments

`model`	A `multispeciesPP` model object
`data`	`SpatRaster` object with covariate and bias layers
`type`	`character`. Prediction scale — `"response"` or `"link"`.
`filename`	`character`to save output
`overwrite`	`logical` overwrite existing `filename`?

Value

SpatRaster

Predict Species Distribution Model

Description

Produces raster prediction from SDM based on model and covariate layers.

Usage

predict_sdm(
  model,
  covariates,
  type = NULL,
  layer_name = "predicted_distribution"
)
predict_sdm(
  model,
  covariates,
  type = NULL,
  layer_name = "predicted_distribution"
)

Arguments

`model`	A model object.
`covariates`	`SpatRaster` covariate layers.
`type`	Scale of prediction (response, model, etc.).
`layer_name`	Name for predicted layer.

Value

SpatRaster prediction from model

Examples

## Not run: 
  m <- glm(z ~ cov1, cov2, data = sdm_data)

  prediction <- predict_sdm(m, covs)


## End(Not run)
## Not run: 
  m <- glm(z ~ cov1, cov2, data = sdm_data)

  prediction <- predict_sdm(m, covs)


## End(Not run)

Print method for class `long_tibble`

Description

Prints data tables stored in sdmtools for their entire length*, but maintains other nice print features of tbl_df, i.e., tibbles.

Usage

## S3 method for class 'long_tibble'
print(x, ...)
## S3 method for class 'long_tibble'
print(x, ...)

Arguments

`x`	An object of class `long_tibble`.
`...`	extra arguments for printing

Details

*"entire length" — well really, up to 999 lines, which none currently are.

Examples

print(raster_to_terra)

print(raster_to_terra)

`raster` to `terra` equivalence table

Description

A table of equivalent functions (or near equivalent) from the raster and terra packages.

Usage

raster_to_terra
raster_to_terra

Format

`global_regions` A tibble with 42 rows and 3 columns:

raster: function name in raster
terra: function name in terra
comment: Annotations about differences in arguments or usage, etc.

Details

Some argument names may differ.

Source

Initially produced by Gerry Ryan and supplemented with "New method names" section in https://cran.r-project.org/web/packages/terra/terra.pdf, annotations added by IDEM members

Raster plot with points

Description

Simple convenience function to plot points over a raster. Useful for quick data checks.

Usage

rastpointplot(r, v, pch = 16, cex = 0.5)
rastpointplot(r, v, pch = 16, cex = 0.5)

Arguments

`r`	`spatRaster` object
`v`	`spatVector` object
`pch`	`integer` point symbol. See `?par`
`cex`	`numeric` point size multiplier. See `?par`

Value

graphical plot

Examples

r <- example_raster()
v <- example_vector()
rastpointplot(r,v)
r <- example_raster()
v <- example_vector()
rastpointplot(r,v)

Save plot

Description

Single line wrapper to save plot as png

Usage

save_plot(
  p,
  filename,
  width = 2400,
  height,
  units = c("px", "cm", "mm"),
  res = 300
)
save_plot(
  p,
  filename,
  width = 2400,
  height,
  units = c("px", "cm", "mm"),
  res = 300
)

Arguments

`p`	A plot object
`filename`	`character` to save plot
`width`	In `units`
`height`	In `units`. If missing, `height` will be scaled to `width` at the golden ratio.
`units`	Units of `width` and `height`. `"px"` — pixels, the default, `"cm"`, or `"mm"`. `"in"` are not allowed on principle.
`res`	resolution, default 300px

Value

nothing. Writes a plot to filename

Examples

## Not run: 

lovely_plot <- plot(1:10)
# why use three lines
png("lovely_plot.png")
lovely_plot
dev.off()

#when you could use one
save_plot(p = lovely_plot, "lovely_plot.png")


## End(Not run)
## Not run: 

lovely_plot <- plot(1:10)
# why use three lines
png("lovely_plot.png")
lovely_plot
dev.off()

#when you could use one
save_plot(p = lovely_plot, "lovely_plot.png")


## End(Not run)

set layer names

Description

Convenience function for setting SpatRaster layer names, enables setting in piped workflows.

Usage

set_layer_names(x, layernames)
set_layer_names(x, layernames)

Arguments

`x`	`SpatRaster`
`layernames`	`character` of length `nlyr(x)`

Value

SpatRaster

Examples

example_raster() |>
 set_layer_names("Charlie Watts")
example_raster() |>
 set_layer_names("Charlie Watts")

set levels

Description

Convenience function for setting SpatRaster levels names to enable setting in piped workflows.

Usage

set_levels(x, levs)
set_levels(x, levs)

Arguments

`x`	`SpatRaster`
`levs`	`data.frame`

Value

SpatRaster

Examples

## Not run: 
categorical_raster |>
 set_levels(
  levs = tribble(
    ~value, ~category,
    30, "URBAN CENTRE",
    23, "DENSE URBAN CLUSTER",
    22, "SEMI-DENSE URBAN CLUSTER",
    21, "SUBURBAN OR PERI-URBAN",
    13, "RURAL CLUSTER",
    12, "LOW DENSITY RURAL",
    11, "VERY LOW DENSITY RURAL",
    10, "WATER"
   )
  ) %>%
    as.data.frame()

## End(Not run)

## Not run: 
categorical_raster |>
 set_levels(
  levs = tribble(
    ~value, ~category,
    30, "URBAN CENTRE",
    23, "DENSE URBAN CLUSTER",
    22, "SEMI-DENSE URBAN CLUSTER",
    21, "SUBURBAN OR PERI-URBAN",
    13, "RURAL CLUSTER",
    12, "LOW DENSITY RURAL",
    11, "VERY LOW DENSITY RURAL",
    10, "WATER"
   )
  ) %>%
    as.data.frame()

## End(Not run)

Source all R scripts in a directory

Description

Sources all scripts in a specified directory and optionally prints paths.

Usage

source_R(path = "R", print.names = TRUE)
source_R(path = "R", print.names = TRUE)

Arguments

`path`	Path of directory; default is `⁠/R⁠`.
`print.names`	Print path and name of sourced scripts. Default `TRUE`.

Value

Scripts are sourced to local environment. NULL returned.

Examples

## Not run: 
  source_R("/Users/frankenstein/project/R")

## End(Not run)
## Not run: 
  source_R("/Users/frankenstein/project/R")

## End(Not run)

Split rasters

Description

Split rasters

Usage

split_rast(x, grain = 4, write_temp = FALSE)
split_rast(x, grain = 4, write_temp = FALSE)

Arguments

`x`	A `SpatRaster`
`grain`	Grain of splitting.
`write_temp`	write to a tempfile. Default is FALSE

Details

Splits a SpatRaster up into a grain^2 list of approximately equal geographic sized rasters covering the extent of x

Value

A list of SpatRasters of length grain^2.

Examples


# Split a raster into four
library(terra)
r <- example_raster()
s <- split_rast(r, grain = 2)
s

# plot with original
 ps <- lapply(
   s,
   FUN = extend,
   y = r
 ) |>
   rast()

 c(r, ps) |>  plot()

# Split a raster into four
library(terra)
r <- example_raster()
s <- split_rast(r, grain = 2)
s

# plot with original
 ps <- lapply(
   s,
   FUN = extend,
   y = r
 ) |>
   rast()

 c(r, ps) |>  plot()

Standardise Raster

Description

Defunct.

DO NOT USE THIS FUNCTION; use terra::scale instead

Usage

standardise_rast(x)
standardise_rast(x)

Arguments

`x`	A `SpatRaster` object.

Value

A SpatRaster objects with mean of zero and standard deviation of one.

Examples

## Not run: 
r <- example_raster()

standardise_rast(r)

## End(Not run)
## Not run: 
r <- example_raster()

standardise_rast(r)

## End(Not run)

Standardise raster

Description

Standardises all layers in a SpatRaster to a scale of 0-1, by dividing by the maximum value in each layer. Only operates by layer

Usage

std_rast(x, reverse, filename = NULL, overwrite = TRUE)
std_rast(x, reverse, filename = NULL, overwrite = TRUE)

Arguments

`x`	`SpatRaster` to standardise
`reverse`	`logical` if `TRUE` will subtract standardised values from 1
`filename`	Optional `character` path and filename to write output
`overwrite`	`logical` if `TRUE` will overwrite `filename`

Details

Will break for non-numeric rasters

Value

A SpatRaster with range 0-1

Examples

example_raster(seed = 3010) |>
  std_rast(reverse = TRUE)
example_raster(seed = 3010) |>
  std_rast(reverse = TRUE)

temptif

Description

Returns a temporary file with a ⁠*.tif⁠ extenstion

Usage

temptif()
temptif()

Value

character

Examples

temptif()
temptif()

Write and read back `spatRaster`

Description

When a new terra::SpatRaster is created it is stored in memory. Writing it to disc and reading the object back from file requires two steps: terra::writeRaster then reading and re-assigning via terra::rast. writereadrast wraps these into a single step.

Usage

writereadrast(x, filename, overwrite = TRUE, layernames = NULL)
writereadrast(x, filename, overwrite = TRUE, layernames = NULL)

Arguments

`x`	A `terra::SpatRaster`
`filename`	A `character` file path and name to save `x` to disc.
`overwrite`	`logical`; overwrite existing raster. NB: by default, `overwrite = TRUE`, this is the opposite of the default behaviour of `terra::writeRaster`
`layernames`	`character` of length `nlyr(x)`

Value

A terra::SpatRaster object reading from disc at filename.

Examples

## Not run: 
# create raster then assign
r <- sdmtools::example_raster()

# usual workflow in two slow tedious boring steps
terra::writeRaster(r, "LowerSpringvale.tif")
s <- terra::rast("LowerSpringvale.tif")

Better workflow in one fast enjoyable lithesome step with `sdmtools`
r <- writereadrast(r, "tootgarook.tif")

# or roll into single step with pipe
q <- sdmtools::example_raster() |>
  writereadrast("frankstonfreeway.tif")

## End(Not run)
## Not run: 
# create raster then assign
r <- sdmtools::example_raster()

# usual workflow in two slow tedious boring steps
terra::writeRaster(r, "LowerSpringvale.tif")
s <- terra::rast("LowerSpringvale.tif")

Better workflow in one fast enjoyable lithesome step with `sdmtools`
r <- writereadrast(r, "tootgarook.tif")

# or roll into single step with pipe
q <- sdmtools::example_raster() |>
  writereadrast("frankstonfreeway.tif")

## End(Not run)

Package 'sdmtools'

Help Index

Assign to nearest raster cell on mask

Description

Usage

Arguments

Value

Create an example mask within a given raster

Description

Usage

Arguments

Value

Examples

Create example raster for use in examples

Description

Usage

Arguments

Value

Examples

Create an example vector

Description

Usage

Arguments

Value

Examples

Extract data from covariate rasters

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Global regions

Description

Usage

Format

global_regions A tibble with 249 rows and 6 columns:

Details

Source

Import rasters from directory

Description

Usage

Arguments

Value

Examples

title

Description

Usage

Arguments

Value

Make Africa Mask

Description

Usage

Arguments

Details

Value

Examples

Make presence-only list for multispeciesPP

Description

Usage

Arguments

Details

Value

Examples

Mask all

Description

Usage

Arguments

Details

Value

Examples

Create mask from raster layers

Description

Usage

Arguments

Details

Value

Examples

Title

`global_regions` A tibble with 249 rows and 6 columns:

Make presence-only list for `multispeciesPP`

`long_tibble` constructor function

Raster predictions from `multispeciesPP`

Raster predictions from `multispeciesPP` for all species

Print method for class `long_tibble`

`raster` to `terra` equivalence table

`global_regions` A tibble with 42 rows and 3 columns: