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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# sdmtools
<!-- badges: start -->
[](https://github.com/idem-lab/sdmtools/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/idem-lab/sdmtools?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://app.codecov.io/gh/idem-lab/sdmtools)
<!-- badges: end -->
A set of helper functions to facilitate species distribution modelling.
## Installation
You can install `sdmtools` with:
``` r
install.packages(
"sdmtools",
repos = "https://idem-lab.r-universe.dev"
)
```
## Data
```{r}
library(sdmtools)
```
`raster_to_terra` — an annotated equivalence table of functions from the `raster` and `terra`. First 5 lines:
```{r, echo=FALSE}
knitr::kable(
raster_to_terra[1:5,],
format = "html",
col.names = c("raster", "terra", "comment for terra use"),
column.spec = c("c", "c", ">{\\raggedright}p{8cm}")
)
```
`global_regions` — a tibble showing the WHO region, UN region, and continent for for 249 countries and country-like things. First 5 lines:
```{r, echo=FALSE}
knitr::kable(
global_regions[1:5,],
format = "html",
#col.names = c("raster", "terra", "comment for terra use"),
column.spec = c("c", "c", ">{\\raggedright}p{8cm}")
)
```
### Data-generating functions
The package `terra` is fiddly about storing its `spat...` objects in packages, so we chose to generate example spatial data on-demand using functions, rather than storing it.
`example_raster` — an example `spatRaster`.
```{r}
library(terra)
r <- example_raster()
r
plot(r)
```
`example_vector` — an example `spatVector`.
```{r}
library(terra)
v <- example_vector()
v
plot(v)
```
`make_africa_mask` — makes a mask layer of Africa based on shapefiles from `malariaAtlas::getShp`.
Can produce either a `SpatRaster` or `SpatVector`.
```{r}
library(terra)
africa_mask <- make_africa_mask(type = "vector")
plot(africa_mask)
```
## Function examples
`rastpointplot` — simple utility to plot a raster with points over it.
```{r}
rastpointplot(r,v)
```
`source_R` — source all R files in a target directory
```r
source_R("/Users/frankenstein/project/R") # do not run
```
`import_rasts` — import all rasters from a directory into a single object
```r
rasters <- import_rasts("/data/grids/covariates") # do not run
```
`split_rast` — split a raster.
```{r}
r <- example_raster()
s <- split_rast(r, grain = 2)
s
```
```{r}
ps <- lapply(
s,
FUN = extend,
y = r
) |>
rast()
c(
r,
ps
) |>
plot()
```
### Functions for a species distribution modelling workflow
We have some covariate layers: `cov1` and `cov2`
```{r}
library(terra)
cov1 <- example_raster(
seed = -44,
layername = "cov1"
)
cov2 <- example_raster(
seed = 15.3,
layername = "cov2"
)
covs <- c(cov1, cov2)
```
`std_rast` — standardise a `spatRaster` by transforming it to have a range of 0—1
```{r}
cov1_st <- std_rast(cov1)
plot(cov1_st)
```
We have some presences and absences
```{r}
presences <- example_vector(seed = 68) %>%
as.data.frame(geom = "xy")
absences <- example_vector(seed = 9.6) %>%
as.data.frame(geom = "xy")
presences
```
`extract_covariates` — extract covariate values from `spatRaster` or `raster` layers for a given set of points
Pass in either `presences` and `absences` as a `data.frame` or `tibble` of with , or `presences_and_absences` as a single data frame points with a presence or ID column(s)
```{r}
sdm_data <- extract_covariates(
covariates = covs,
presences = presences,
absences = absences
)
```
We can then make a spatial prediction of our model using `predict_sdm` and write and read it out in a single step with `writereadrast`, and write it to a temporary file with `temptif`:
```{r}
# first we make a simple model, using data from above
m <- glm(presence ~ cov1 + cov2, data = sdm_data)
prediction_rast <- predict_sdm(m, covs) |>
writereadrast(filename = temptif())
plot(prediction_rast)
```