# sgsR

## Algorithm structure

`sgsR` is scripted using the `terra` package to handle raster processing, and `sf` package for vector manipulation.

Four primary function verbs exist:

### `strat_*`

Stratification algorithms within `sgsR`. These algorithms use auxiliary raster data (e.g. ALS metric populations) as inputs and provide stratified areas of interest as outputs. Algorithms are either supervised (e.g. `strat_breaks()`), where the user provides quantitative values that drive stratifications, or unsupervised (e.g. `strat_quantiles()`), where the user specifies the desired number of output strata (`nStrata`) and stratification is handled by the algorithm.

### `sample_*`

Sampling algorithms in `sgsR`. Depending on the sampling algorithm, users are able to provide either auxiliary metrics or stratifications derived from `strat_*` functions as inputs. A number of customizable parameters can be set including the sample size (`nSamp`), a minimum distance threshold (`mindist`) between allocated sample units, and the ability for the user to define an access network (`access`) and assign minimum (`buff_inner`) and maximum (`buff_outer`) buffer distances to constrain sampling extents.

### `calculate_*` & `extract_*`

Additional helper functions that `calculate` varying descriptive / summary statistics and values to use in processing. `extract_*` functions derive metric values from co-located sample units.

## Parameters

`sgsR` uses common words that define algorithm parameters:

Parameter | Description
--- | ---
`mraster` | Metric raster(s)
`sraster` | Stratified raster
`access` | Linear vectors representing access routes
`existing` | Existing sample units
`plot` | Visually displays raster and samples

### `mraster`

`mraster` are input raster(s). All raster data used by `sgsR` must be must be a `terra SpatRaster` class.

    library(sgsR)
    library(terra)
    library(sf)

    #--- Load mraster from internal data ---#
    r <- system.file("extdata", "mraster.tif", package = "sgsR")

    #--- load mraster using the terra package ---#
    mraster <- terra::rast(r)

### `sraster`

`sraster` are derived from `strat_*` algorithms (e.g. see `strat_quantiles()` below). The function below used the distribution of `mraster$zq90` and stratified data into 4 equally sized strata.

    #--- apply kmeans algorithm to metrics raster ---#
    sraster <- strat_quantiles(
      mraster = mraster$zq90, # use mraster as input for sampling
      nStrata = 4, # algorithm will produce 4 strata
      plot = TRUE
    ) # algorithm will plot output

The `sraster` output can then become an input parameter (`sraster`) for the `sample_strat()` algorithm.

    #--- apply stratified sampling ---#
    existing <- sample_strat(
      sraster = sraster, # use mraster as input for sampling
      nSamp = 200, # request 200 samples be taken
      mindist = 100, # define that samples must be 100 m apart
      plot = TRUE
    ) # algorithm will plot output

### `access`

One key feature of using some `sample_*` functions is its ability to define `access` corridors. Users can supply a road `access` network (must be `sf` line objects) and define buffers around `access` where samples should be excluded and included.

Relevant and applicable parameters when `access` is defined are:

- `buff_inner` - Can be left as `NULL` (default). Inner buffer parameter that defines the distance from `access` where samples cannot be taken (i.e. if you don’t want samples within 50 m of your `access` layer set `buff_inner = 50`).

- `buff_outer` - Outer buffer parameter that defines the maximum distance that the samples can be located from `access` (i.e. if you don’t want samples more than 200 meters from your `access` layer set `buff_inner = 200`).

    a <- system.file("extdata", "access.shp", package = "sgsR")

    #--- load the access vector using the sf package ---#
    access <- sf::st_read(a)
    #> Reading layer `access' from data source
    #>   `C:\Users\goodb\AppData\Local\Temp\Rtmp8iA5Id\Rinst214c547c7592\sgsR\extdata\access.shp'
    #>   using driver `ESRI Shapefile'
    #> Simple feature collection with 167 features and 2 fields
    #> Geometry type: MULTILINESTRING
    #> Dimension:     XY
    #> Bounding box:  xmin: 431100 ymin: 5337700 xmax: 438560 ymax: 5343240
    #> Projected CRS: UTM_Zone_17_Northern_Hemisphere

    terra::plot(mraster$zq90)
    terra::plot(access, add = TRUE, col = "black")

From the plot output we see the first band (`zq90`) of the `mraster` with the `access` vector overlaid.

## `%>%`

The sgsR package leverages the `%>%` operator from the `magrittr package`.

    #--- non piped ---#
    sraster <- strat_quantiles(
      mraster = mraster$zq90, # use mraster as input for sampling
      nStrata = 4
    ) # algorithm will produce 4 strata

    existing <- sample_strat(
      sraster = sraster, # use mraster as input for sampling
      nSamp = 200, # request 200 samples be taken
      mindist = 100
    ) # define that samples must be 100 m apart

    extract_metrics(
      mraster = mraster,
      existing = existing
    )

    #--- piped ---#
    strat_quantiles(mraster = mraster$zq90, nStrata = 4) %>%
      sample_strat(., nSamp = 200, mindist = 100) %>%
      extract_metrics(mraster = mraster, existing = .)