# Getting started with qgisprocess

#### Jannes Muenchow & Floris Vanderhaeghe

#### Last updated: 2023-08-16

For a very short introduction to qgisprocess, visit the homepage.

Here you will learn about package configuration, about basic usage illustrated by two examples, and how to pipe results into a next geoprocessing step.

# Setting up the system

qgisprocess is basically a wrapper around the standalone command line tool `qgis_process`. Therefore, you need to have installed QGIS on your system as well as third-party providers such as GRASS and SAGA to access and run all geoalgorithms provided through `qgis_process` from within R.

The package is meant to support current QGIS releases, i.e. both the latest and the long-term release. Although older QGIS releases are not officially supported, qgisprocess might work with QGIS versions >=3.16. Download instructions for all platforms are available at https://download.qgis.org/.

To facilitate using qgisprocess, we have created a docker image that already comes with the needed software packages. You can pull it from Github’s container registry by running:

    docker pull ghcr.io/geocompx/docker:qgis

For a more detailed introduction on how to get started with docker, please refer to https://github.com/geocompx/docker.

# Package configuration

Since qgisprocess depends on the command line tool `qgis_process`, it already tries to detect `qgis_process` on your system when it is being loaded, and complains if it cannot find it.

    library("qgisprocess")

When loading qgisprocess for the first time, it will cache among others the path to `qgis_process`, the QGIS version and the list of known algorithms. When loading qgisprocess in later R sessions, the cache file is read instead for speed-up, on condition that it is still valid. Therefore, usually you don’t have to do any configuration yourself, unless there’s a message telling you to do so.

If you are interested in the details about this process, e.g. how qgisprocess detected `qgis_process`, run `qgis_configure(use_cached_data = TRUE)`.

    qgis_configure(use_cached_data = TRUE)
    #> Checking configuration in cache file (/home/floris/.cache/R-qgisprocess/cache-0.1.0.rds)
    #> Checking cached QGIS version with version reported by 'qgis_process' ...
    #> QGIS versions match! (3.32.1-Lima)
    #> Checking cached QGIS plugins (and state) with those reported by 'qgis_process' ...
    #> QGIS plugins match! (9 processing provider plugin(s) enabled)
    #>
    #> Restoring configuration from '/home/floris/.cache/R-qgisprocess/cache-0.1.0.rds'
    #> QGIS version: 3.32.1-Lima
    #> Using 'qgis_process' in the system PATH.
    #> >>> If you need another installed QGIS instance, run `qgis_configure()`;
    #>     see `?qgis_configure` if you need to preset the path of 'qgis_process'.
    #> Using JSON for output serialization.
    #> Using JSON for input serialization.
    #> 9 out of 9 available processing provider plugins are enabled.
    #> Having access to 1868 algorithms from 13 QGIS processing providers.
    #> Use qgis_algorithms(), qgis_providers(), qgis_plugins(), qgis_using_json_output(),
    #> qgis_path() and qgis_version() to inspect the cache environment.

If needed the cache will be rebuilt automatically upon loading the package. This is the case when the QGIS version or the location of the `qgis_process` command-line utility has changed, user-settings (e.g. the option `qgisprocess.path`) have been altered or a changed state of the processing provider plugins (enabled vs. disabled) has been detected.

Rebuilding the cache can be triggered manually by running `qgis_configure()` (its default is `use_cached_data = FALSE`).

To determine the location of `qgis_process`, `qgis_configure()` first checks if the R option `qgisprocess.path` or the global environment variable `R_QGISPROCESS_PATH` has been set. This already indicates that you can specify one of these settings in case `qgis_process` has not been installed in one of the most common locations or if there are multiple QGIS versions available. If this is the case, set `options(qgisprocess.path = '/path/to/qgis_process')` or set the environment variable (e.g. in `.Renviron`) and run `qgis_configure()`. Under Windows make sure to indicate the path to the `qgis_process-qgis.bat` file, e.g.,

    # specify path to QGIS installation on Windows
    options(qgisprocess.path = "C:/Program Files/QGIS 3.28/bin/qgis_process-qgis.bat")
    # or use the QGIS nightly version (if installed via OSGeo4W)
    # options(qgisprocess.path = "C:/OSGeo4W64/bin/qgis_process-qgis-dev.bat")
    qgis_configure() # or use library(qgisprocess) if package was not loaded yet

Assuming that package loading or `qgis_configure()` ran successfully, we can check which QGIS version our system is running (it takes this from the cache):

    qgis_version()
    #> [1] "3.32.1-Lima"

Next, let’s check which plugins are at our disposal:

    qgis_plugins()
    #> # A tibble: 9 × 2
    #>   name                    enabled
    #>   <chr>                   <lgl>
    #> 1 QNEAT3                  TRUE
    #> 2 ViewshedAnalysis        TRUE
    #> 3 cartography_tools       TRUE
    #> 4 grassprovider           TRUE
    #> 5 latlontools             TRUE
    #> 6 otbprovider             TRUE
    #> 7 processing              TRUE
    #> 8 processing_saga_nextgen TRUE
    #> 9 wbt_for_qgis            TRUE

Since we will use GRASS and SAGA later on, you must have GRASS and SAGA version > 7 installed on your system. You also need to install the third-party plugin ‘SAGA Next Generation’ in the QGIS GUI. The GRASS provider plugin is already built-in in QGIS.

Then, let’s enable both plugins:

    qgis_enable_plugins(c("grassprovider", "processing_saga_nextgen"))

Now, let’s list all available providers including available third-party applications:

    qgis_providers()
    #> # A tibble: 13 × 3
    #>    provider         provider_title                         algorithm_count
    #>    <chr>            <chr>                                            <int>
    #>  1 cartographytools Cartography tools                                    5
    #>  2 gdal             GDAL                                                56
    #>  3 grass7           GRASS                                              306
    #>  4 latlontools      Lat Lon tools                                        8
    #>  5 otb              OTB                                                109
    #>  6 qgis             QGIS                                                50
    #>  7 3d               QGIS (3D)                                            1
    #>  8 pdal             QGIS (PDAL)                                         17
    #>  9 native           QGIS (native c++)                                  243
    #> 10 qneat3           QNEAT3 - Qgis Network Analysis Toolbox              14
    #> 11 sagang           SAGA Next Gen                                      509
    #> 12 visibility       Visibility analysis                                  4
    #> 13 wbt              WhiteboxTools                                      546

This tells us that we can also use the third-party providers GDAL, GRASS and SAGA through the QGIS interface.

# Basic usage

## First example

To find out about the available (cached) geoalgorithms, run:

    algs <- qgis_algorithms()
    algs
    #> # A tibble: 1,868 × 24
    #>    provider         provider_title    algorithm     algorithm_id algorithm_title
    #>    <chr>            <chr>             <chr>         <chr>        <chr>
    #>  1 3d               QGIS (3D)         3d:tessellate tessellate   Tessellate
    #>  2 cartographytools Cartography tools cartographyt… averagelines Average linest…
    #>  3 cartographytools Cartography tools cartographyt… collapsedua… Collapse dual …
    #>  4 cartographytools Cartography tools cartographyt… removecross… Remove cross r…
    #>  5 cartographytools Cartography tools cartographyt… removeculde… Remove cul-de-…
    #>  6 cartographytools Cartography tools cartographyt… removeround… Remove roundab…
    #>  7 gdal             GDAL              gdal:aspect   aspect       Aspect
    #>  8 gdal             GDAL              gdal:assignp… assignproje… Assign project…
    #>  9 gdal             GDAL              gdal:bufferv… buffervecto… Buffer vectors
    #> 10 gdal             GDAL              gdal:buildvi… buildvirtua… Build virtual …
    #> # ℹ 1,858 more rows
    #> # ℹ 19 more variables: provider_can_be_activated <lgl>,
    #> #   provider_is_active <lgl>, provider_long_name <chr>, provider_version <chr>,
    #> #   provider_warning <chr>, can_cancel <lgl>, deprecated <lgl>, group <chr>,
    #> #   has_known_issues <lgl>, help_url <chr>, requires_matching_crs <lgl>,
    #> #   short_description <chr>, tags <list>, default_raster_file_extension <chr>,
    #> #   default_vector_file_extension <chr>, …

Since we have also installed GRASS and SAGA, nearly 1000 geoalgorithms are at our disposal. To find out about a specific geoalgorithm and a description of its arguments, use `qgis_show_help()`, e.g.:

    qgis_show_help("native:buffer")
    ## Buffer (native:buffer)
    ##
    ## ----------------
    ## Description
    ## ----------------
    ## This algorithm computes a buffer area for all the features in an input layer, using a fixed or dynamic distance.
    ##
    ## The segments parameter controls the number of line segments to use to approximate a quarter circle when creating rounded offsets.
    ##
    ##...

To find out the arguments of a specific geoalgorithm, run:

    qgis_get_argument_specs("native:buffer")
    #> # A tibble: 9 × 6
    #>   name    description qgis_type default_value available_values acceptable_values
    #>   <chr>   <chr>       <chr>     <list>        <list>           <list>
    #> 1 INPUT   Input layer source    <NULL>        <NULL>           <chr [1]>
    #> 2 DISTAN… Distance    distance  <int [1]>     <NULL>           <chr [3]>
    #> 3 SEGMEN… Segments    number    <int [1]>     <NULL>           <chr [3]>
    #> 4 END_CA… End cap st… enum      <int [1]>     <chr [3]>        <chr [2]>
    #> 5 JOIN_S… Join style  enum      <int [1]>     <chr [3]>        <chr [2]>
    #> 6 MITER_… Miter limit number    <int [1]>     <NULL>           <chr [3]>
    #> 7 DISSOL… Dissolve r… boolean   <lgl [1]>     <NULL>           <chr [4]>
    #> 8 SEPARA… Keep disjo… boolean   <lgl [1]>     <NULL>           <chr [4]>
    #> 9 OUTPUT  Buffered    sink      <NULL>        <NULL>           <chr [1]>

And finally run it with `qgis_run_algorithm()`:

    # if needed, first install spDataLarge:
    # remotes::install_github("Nowosad/spDataLarge")
    data("random_points", package = "spDataLarge")
    result <- qgis_run_algorithm("native:buffer", INPUT = random_points, DISTANCE = 50)
    #> Argument `SEGMENTS` is unspecified (using QGIS default value).
    #> Using `END_CAP_STYLE = "Round"`
    #> Using `JOIN_STYLE = "Round"`
    #> Argument `MITER_LIMIT` is unspecified (using QGIS default value).
    #> Argument `DISSOLVE` is unspecified (using QGIS default value).
    #> Argument `SEPARATE_DISJOINT` is unspecified (using QGIS default value).
    #> Using `OUTPUT = qgis_tmp_vector()`

As a convenience to the user, `qgis_run_algorithm()` reports all unspecified and automatically chosen arguments. If you want to have even more information on what is going on in the background, set `.quiet` to `FALSE`. The `result` object is of class `qgis_result` and contains the path to the output file created by `qgis_process` (when not explicitly setting an output filepath, qgisprocess creates it automatically for you). The output filepath can be extracted with `qgis_extract_output()`. `qgis_result` objects are of type `list` which, aside from the geoprocessing result, also contain debugging information about the used algorithm, input arguments and messages from the processing step. See `?qgis_result_status` for various convenience functions to extract all of this information easily from `qgis_result` objects.

For QGIS 3.24 and later, `qgis_run_algorithm()` passes the input arguments to QGIS as a JSON string. The JSON input string is also included in `qgis_result` objects. Moreover, the user can specify input arguments directly as JSON in `qgis_run_algorithm()`. That is useful since input parameters can be copied from the QGIS GUI as JSON. This will be demonstrated in a separate tutorial.

    # inspect the result object
    class(result)
    #> [1] "qgis_result"
    names(result)
    #> [1] "OUTPUT"           ".algorithm"       ".args"            ".raw_json_input"
    #> [5] ".processx_result"
    result # only prints the output element(s)
    #> <Result of `qgis_run_algorithm("native:buffer", ...)`>
    #> List of 1
    #>  $ OUTPUT: 'qgis_outputVector' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc83c0f5433.gpkg"

To read in the QGIS output and visualize it, we can run:

    library("sf")
    library("mapview")
    # attach QGIS output
    # either do it "manually":
    buf <- read_sf(qgis_extract_output(result, "OUTPUT"))
    # or use the st_as_sf.qgis_result method:
    buf <- sf::st_as_sf(result)
    # plot your result
    mapview(buf, col.regions = "blue") +
      mapview(random_points, col.regions = "red", cex = 3)

You can convert each qgis-algorithm into an R function with `qgis_function()`. So using our buffer example from above, we could also run:

    # create a function
    qgis_buffer <- qgis_function("native:buffer")
    # run the function
    result <- qgis_buffer(INPUT = random_points, DISTANCE = 50)

This is basically what package qgis is doing for each available QGIS function while also providing an R help file for each function. Hence, if you prefer running QGIS with callable R functions, check it out.

## Second example

As a second example, let’s have a look at how to do raster processing running GRASS in the background. To compute various terrain attributes of a digital elevation model, we can use `grass7:r.slope.aspect`.

`qgis_get_description()` (also included in `qgis_show_help()`) gives us the general description of the algorithm.

    qgis_get_description("grass7:r.slope.aspect")
    #>                                                                                         grass7:r.slope.aspect
    #> "Generates raster layers of slope, aspect, curvatures and partial derivatives from a elevation raster layer."

We can find out about the arguments again with the help of `qgis_get_argument_specs()`.

    qgis_get_argument_specs("grass7:r.slope.aspect")
    #> # A tibble: 21 × 6
    #>    name   description qgis_type default_value available_values acceptable_values
    #>    <chr>  <chr>       <chr>     <list>        <list>           <list>
    #>  1 eleva… Elevation   raster    <NULL>        <NULL>           <chr [1]>
    #>  2 format Format for… enum      <int [1]>     <chr [2]>        <chr [2]>
    #>  3 preci… Type of ou… enum      <int [1]>     <chr [3]>        <chr [2]>
    #>  4 -a     Do not ali… boolean   <lgl [1]>     <NULL>           <chr [4]>
    #>  5 -e     Compute ou… boolean   <lgl [1]>     <NULL>           <chr [4]>
    #>  6 -n     Create asp… boolean   <lgl [1]>     <NULL>           <chr [4]>
    #>  7 zscale Multiplica… number    <dbl [1]>     <NULL>           <chr [3]>
    #>  8 min_s… Minimum sl… number    <dbl [1]>     <NULL>           <chr [3]>
    #>  9 slope  Slope       rasterDe… <NULL>        <NULL>           <chr [1]>
    #> 10 aspect Aspect      rasterDe… <NULL>        <NULL>           <chr [1]>
    #> # ℹ 11 more rows

`qgis_get_output_specs()` shows the different outputs that will be calculated:

    qgis_get_output_specs("grass7:r.slope.aspect")
    #> # A tibble: 9 × 3
    #>   name       description                                   qgis_output_type
    #>   <chr>      <chr>                                         <chr>
    #> 1 aspect     Aspect                                        outputRaster
    #> 2 dx         First order partial derivative dx (E-W slope) outputRaster
    #> 3 dxx        Second order partial derivative dxx           outputRaster
    #> 4 dxy        Second order partial derivative dxy           outputRaster
    #> 5 dy         First order partial derivative dy (N-S slope) outputRaster
    #> 6 dyy        Second order partial derivative dyy           outputRaster
    #> 7 pcurvature Profile curvature                             outputRaster
    #> 8 slope      Slope                                         outputRaster
    #> 9 tcurvature Tangential curvature                          outputRaster

Now let us calculate the terrain attributes.

    library("terra")
    # attach digital elevation model from Mt. Mongón (Peru)
    dem <- rast(system.file("raster/dem.tif", package = "spDataLarge"))
    # if not already done, enable the grass plugin
    # qgis_enable_plugins("grassprovider")
    info <- qgis_run_algorithm(alg = "grass7:r.slope.aspect", elevation = dem)

Just printing the `info` object shows which output files have been made:

    info
    #> <Result of `qgis_run_algorithm("grass7:r.slope.aspect", ...)`>
    #> List of 9
    #>  $ aspect    : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc827ed191d.tif"
    #>  $ dx        : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc85fbc2679.tif"
    #>  $ dxx       : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc8341e5d5c.tif"
    #>  $ dxy       : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc811b3a326.tif"
    #>  $ dy        : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc81ee2c977.tif"
    #>  $ dyy       : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc859456cd.tif"
    #>  $ pcurvature: 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc8348b1ffc.tif"
    #>  $ slope     : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc824030ff2.tif"
    #>  $ tcurvature: 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc85035c9d8.tif"

Combine these output rasters as a multi-layered `SpatRaster` object and plot it:

    # just keep the names of output rasters
    nms <- qgis_get_output_specs("grass7:r.slope.aspect")$name
    # read in the output rasters
    r <- info[nms] |>
      unlist() |>
      rast() |>
      as.numeric()
    names(r) <- nms
    # plot the output
    plot(r)

An alternative way to combine the rasters is given below.

    r <- lapply(info[nms], \(x) as.numeric(qgis_as_terra(x))) |>
      rast()

Since we have now many terrain attributes at our disposal, let us take the opportunity to add their values to points laying on top of them with the help of the SAGA algorithm `sagang:addrastervaluestopoints`.

    qgis_get_argument_specs("sagang:addrastervaluestopoints")
    #> # A tibble: 4 × 6
    #>   name    description qgis_type default_value available_values acceptable_values
    #>   <chr>   <chr>       <chr>     <list>        <list>           <list>
    #> 1 SHAPES  Points      source    <NULL>        <NULL>           <chr [1]>
    #> 2 GRIDS   Grids       multilay… <NULL>        <NULL>           <list [0]>
    #> 3 RESULT  Result      vectorDe… <NULL>        <NULL>           <chr [1]>
    #> 4 RESAMP… Resampling  enum      <int [1]>     <chr [4]>        <chr [2]>

The `GRIDS` argument is of type multilayer. To pass multiple layers to one argument, you can either repeat the corresponding argument as often as needed …

    rp_tp <- qgis_run_algorithm(
      "sagang:addrastervaluestopoints",
      SHAPES = random_points,
      GRIDS = qgis_extract_output(info, "aspect"),
      GRIDS = qgis_extract_output(info, "slope"),
      GRIDS = qgis_extract_output(info, "tcurvature"),
      RESAMPLING = 0)
    #> Using `RESULT = qgis_tmp_vector()`

… or you can pass to it all needed layers in one list. One could use the `list()` command but it is recommendended to use the `qgis_list_input()` function which is more robust, and therefore will also support non-JSON-input configurations (e.g. QGIS < 3.24).

    rp_tp <- qgis_run_algorithm(
      "sagang:addrastervaluestopoints",
      SHAPES = random_points,
      GRIDS = qgis_list_input(
        qgis_extract_output(info, "aspect"),
        qgis_extract_output(info, "slope"),
        qgis_extract_output(info, "tcurvature")
      ),
      RESAMPLING = 0)

To verify that it worked, read in the output.

    sf::st_as_sf(rp_tp)
    #> Simple feature collection with 100 features and 5 fields
    #> Geometry type: POINT
    #> Dimension:     XYZ
    #> Bounding box:  xmin: 795551.4 ymin: 8932370 xmax: 797242.3 ymax: 8934800
    #> z_range:       zmin: 0 zmax: 0
    #> Projected CRS: +proj=utm +zone=17 +south +ellps=WGS84 +units=m +no_defs
    #> # A tibble: 100 × 6
    #>       id  spri file5dc827ed191d file5dc824030ff2 file5dc85035c9d8
    #>    <int> <int>            <dbl>            <dbl>            <dbl>
    #>  1     1     4            246.              4.85        -0.000425
    #>  2     2     4            126.              4.23        -0.00246
    #>  3     3     3            301.              8.57        -0.00111
    #>  4     4     2             96.9             6.63         0.00100
    #>  5     5     4            337.             14.3         -0.000145
    #>  6     6     5            245.             10.6         -0.000484
    #>  7     7     6            272.              9.47        -0.000532
    #>  8     8     2            307.              6.21        -0.000236
    #>  9     9     3             67.8            11.5         -0.00135
    #> 10    10     3            107.             12.9          0.00134
    #> # ℹ 90 more rows
    #> # ℹ 1 more variable: geom <POINT [m]>

# Piping

`qgis_process` does not lend itself naturally to piping because its first argument is the name of a geoalgorithm instead of a data object. `qgis_run_algorithm_p()` circumvents this by accepting a `.data` object as its first argument, and pipes this data object into the the first argument of a geoalgorithm assuming that the specified geoalgorithm needs a data input object as its first argument.

    system.file("longlake/longlake_depth.gpkg", package = "qgisprocess") |>
      qgis_run_algorithm_p("native:buffer", DISTANCE = 50)
    #> Argument `SEGMENTS` is unspecified (using QGIS default value).
    #> Using `END_CAP_STYLE = "Round"`
    #> Using `JOIN_STYLE = "Round"`
    #> Argument `MITER_LIMIT` is unspecified (using QGIS default value).
    #> Argument `DISSOLVE` is unspecified (using QGIS default value).
    #> Argument `SEPARATE_DISJOINT` is unspecified (using QGIS default value).
    #> Using `OUTPUT = qgis_tmp_vector()`
    #> <Result of `qgis_run_algorithm("native:buffer", ...)`>
    #> List of 1
    #>  $ OUTPUT: 'qgis_outputVector' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc835617bba.gpkg"

If `.data` is a `qgis_result` object, `qgis_run_algorithm_p()` automatically tries to select an element named `OUTPUT`. However, if the output has another name (e.g., `DEM_PREPROC` as in the example below) or if there are multiple output elements to choose from (e.g., `sagang:sagawetnessindex` has four output rasters, check with `qgis_outputs("sagang:sagawetnessindex")`), you can specify the wanted output object via the `.select` argument. Please note that we make sure that temporary output raster files, i.e., all output rasters we do not specifically name ourselves, should use SAGA’s native raster file format by setting the `qgisprocess.tmp_raster_ext` option to `.sdat`. Using the default raster output format `.tif` might lead to trouble depending on the installed versions of third-party packages (GDAL, SAGA, etc.).

    dem <- system.file("raster/dem.tif", package = "spDataLarge")
    # in case you need to enable the SAGA next generation algorithms, run the following line:
    # qgis_enable_plugins("processing_saga_nextgen")

    oldopt <- options(qgisprocess.tmp_raster_ext = ".sdat")
    qgis_run_algorithm(algorithm = "sagang:sinkremoval", DEM = dem,
                       METHOD = 1) |>
      qgis_run_algorithm_p("sagang:sagawetnessindex", .select = "DEM_PREPROC")
    #> Argument `SINKROUTE` is unspecified (using QGIS default value).
    #> Using `DEM_PREPROC = qgis_tmp_raster()`
    #> Argument `THRESHOLD` is unspecified (using QGIS default value).
    #> Argument `THRSHEIGHT` is unspecified (using QGIS default value).
    #> Argument `WEIGHT` is unspecified (using QGIS default value).
    #> Using `AREA = qgis_tmp_raster()`
    #> Using `SLOPE = qgis_tmp_raster()`
    #> Using `AREA_MOD = qgis_tmp_raster()`
    #> Using `TWI = qgis_tmp_raster()`
    #> Argument `SUCTION` is unspecified (using QGIS default value).
    #> Using `AREA_TYPE = "[0] total catchment area"`
    #> Using `SLOPE_TYPE = "[0] local slope"`
    #> Argument `SLOPE_MIN` is unspecified (using QGIS default value).
    #> Argument `SLOPE_OFF` is unspecified (using QGIS default value).
    #> Argument `SLOPE_WEIGHT` is unspecified (using QGIS default value).
    #> <Result of `qgis_run_algorithm("sagang:sagawetnessindex", ...)`>
    #> List of 4
    #>  $ AREA    : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc8128371b5.sdat"
    #>  $ AREA_MOD: 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc87fd47cd7.sdat"
    #>  $ SLOPE   : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc84d112e01.sdat"
    #>  $ TWI     : 'qgis_outputRaster' chr "/tmp/RtmpMGzbyG/file5dc85c8cb720/file5dc846a1cf11.sdat"

When piping, `qgis_run_algorithm_p()` automatically cleans up after you by deleting intermediate results. This avoids cluttering your system when running geoalgorithms on large spatial data files. To turn off this behavior, set `.clean` to `FALSE`.

Of course, you can also pipe to `qgis_run_algorithm()` by manually extracting the `OUTPUT` object and redirecting it to the appropriate input argument of the next processing step. This avoids ambiguity and allows for greater flexibility though it might not be as convenient as `qgis_run_algorithm_p()`. For example, intermediate results remain on disk for the duration of your R session, unless you manually call `qgis_clean_result()` on a result object.

    result <- qgis_run_algorithm(algorithm = "sagang:sinkremoval", DEM = dem,
                             METHOD = 1) |>
      qgis_extract_output("DEM_PREPROC") |>
      qgis_run_algorithm(algorithm = "sagang:sagawetnessindex",
                         DEM = _)
    #> Argument `SINKROUTE` is unspecified (using QGIS default value).
    #> Using `DEM_PREPROC = qgis_tmp_raster()`
    #> Argument `THRESHOLD` is unspecified (using QGIS default value).
    #> Argument `THRSHEIGHT` is unspecified (using QGIS default value).
    #> Argument `WEIGHT` is unspecified (using QGIS default value).
    #> Using `AREA = qgis_tmp_raster()`
    #> Using `SLOPE = qgis_tmp_raster()`
    #> Using `AREA_MOD = qgis_tmp_raster()`
    #> Using `TWI = qgis_tmp_raster()`
    #> Argument `SUCTION` is unspecified (using QGIS default value).
    #> Using `AREA_TYPE = "[0] total catchment area"`
    #> Using `SLOPE_TYPE = "[0] local slope"`
    #> Argument `SLOPE_MIN` is unspecified (using QGIS default value).
    #> Argument `SLOPE_OFF` is unspecified (using QGIS default value).
    #> Argument `SLOPE_WEIGHT` is unspecified (using QGIS default value).

    # or using an anonymous function
    # result <- qgis_run_algorithm(algorithm = "sagang:sinkremoval", DEM = dem,
    #                          METHOD = 1) |>
    #   (\(x) qgis_run_algorithm(algorithm = "sagang:sagawetnessindex",
    #                            DEM = x$DEM_PREPROC[1])) ()

    # set the default output raster format to .tif again
    options(oldopt)