Package 'map' reference manual

Title:	Defines a meta class of geographical objects, the 'map' class, and associated tools
Description:	The map class is a collection of map objects ('sp', 'raster', 'sf'), with a number of metadata additions to enable powerful methods, e.g., for leaflet, reproducible GIS etc.
Authors:	Eliot J B McIntire [aut, cre] , Alex M Chubaty [aut] , Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer:	Eliot J B McIntire <[email protected]>
License:	GPL-3
Version:	0.0.5.9005
Built:	2024-12-25 06:30:43 UTC
Source:	https://github.com/PredictiveEcology/map

`.rasterToMemory`

Description

.rasterToMemory

Usage

.rasterToMemory(x, ...)
.rasterToMemory(x, ...)

Arguments

`x`	A `⁠Raster*⁠` object
`...`	Additional arguments passed to `raster`

Calculate area of (named) objects the `map` obj

Description

Calculate area of (named) objects the map obj

Usage

## S4 method for signature 'map'
area(x)
## S4 method for signature 'map'
area(x)

Arguments

`x`	Raster* or SpatialPolygons object

`areaAndPolyValue`

Description

Determine the area of each zone in a raster. TODO: improve description

Usage

areaAndPolyValue(ras)
areaAndPolyValue(ras)

Arguments

ras

A ⁠Raster*⁠ object

Value

list containing: sizeInHa, the area; and polyID, the polygon ID.

Build `map` obj metadata table

Description

Build map obj metadata table

Usage

buildMetadata(
  metadata,
  isStudyArea,
  isRasterToMatch,
  layerName,
  obj,
  columnNameForLabels,
  objHash,
  leaflet,
  envir,
  ...
)
buildMetadata(
  metadata,
  isStudyArea,
  isRasterToMatch,
  layerName,
  obj,
  columnNameForLabels,
  objHash,
  leaflet,
  envir,
  ...
)

Arguments

`metadata`	TODO: description needed
`isStudyArea`	TODO: description needed
`isRasterToMatch`	Logical. Is this(these) layer(s) the `rasterToMatch` layers. If `TRUE`, then this layer can be accessed by `rasterToMatch(map)`
`layerName`	TODO: description needed
`obj`	TODO: description needed
`columnNameForLabels`	TODO: description needed
`objHash`	TODO: description needed
`leaflet`	Logical or Character vector of path(s) to write tiles. If `TRUE` or a character vector, then this layer will be added to a leaflet map. For `RasterLayer` object, this will trigger a call to `gdal2tiles`, making tiles. If path is not specified, it will be the current path. The tile base file path will be `paste0(layerName, "_", rndstr(1, 6))`.
`envir`	TODO: description needed
`...`	Additional arguments.

Extract the crs of a `map`

Description

Extract the crs of a map

Usage

## S4 method for signature 'map'
crs(x, ...)
## S4 method for signature 'map'
crs(x, ...)

Arguments

`x`	Raster* or Spatial object
`...`	additional arguments. None implemented

Fasterize with crop & spTransform first

Description

Fasterize with crop & spTransform first

Usage

fasterize2(emptyRaster, polygonToFasterize, field)
fasterize2(emptyRaster, polygonToFasterize, field)

Arguments

`emptyRaster`	An empty raster with res, crs, extent all correct for to pass to `fasterize`
`polygonToFasterize`	passed to `fasterize`, but it will be cropped first if `extent(emptyRaster) < extent(polygonToFasterize)`
`field`	passed to `fasterize`

Polygonize with GDAL

Description

Based on https://johnbaumgartner.wordpress.com/2012/07/26/getting-rasters-into-shape-from-r/.

Usage

gdal_polygonizeR(
  x,
  outshape = NULL,
  gdalformat = "ESRI Shapefile",
  pypath = NULL,
  readpoly = TRUE,
  quiet = TRUE
)
gdal_polygonizeR(
  x,
  outshape = NULL,
  gdalformat = "ESRI Shapefile",
  pypath = NULL,
  readpoly = TRUE,
  quiet = TRUE
)

Arguments

`x`	TODO: description needed
`outshape`	TODO: description needed
`gdalformat`	TODO: description needed
`pypath`	TODO: description needed
`readpoly`	TODO: description needed
`quiet`	TODO: description needed

Extract leaflet tile paths from a `map` obj

Description

Extract leaflet tile paths from a map obj

Usage

leafletTiles(map)
leafletTiles(map)

Arguments

map

A map class obj

Value

A vector of paths indicating the relative paths. Any layers that don't have leaflet tiles will return NA.

Make tiles (pyramids) using `gdal2tiles`

Description

Make tiles (pyramids) using gdal2tiles

Usage

makeTiles(tilePath, obj, overwrite = FALSE, ...)
makeTiles(tilePath, obj, overwrite = FALSE, ...)

Arguments

`tilePath`	A director to write tiles
`obj`	A raster objects with or without file-backing
`overwrite`	Logical. If `FALSE`, and the director exists, then it will not overwrite any files.
`...`	Passed to `reproducible::projectInputs` e.g., `useGDAL`

The `map` class

Description

Contains a common system for organzing vector and raster layers, principally for use with leaflet and shiny.

Slots

metadata: data.table with columns describing metadata of map objects in maps slot.
.xData: Named environment of map-type objects (e.g., sf, ⁠Raster*⁠, ⁠Spatial*⁠. Each entry may also be simply an environment, which indicates where to find the object, i.e., via get(layerName, envir = environment).
CRS: The common crs of all layers
paths: File paths. A named list of paths. The default is a list of length 2, dataPath and tilePath
analyses: A data.table or data.frame of the types of analyses to perform.
analysesData: A data.table or data.frame of the results of the analyses.

Append a spatial object to map

Description

If isStudyArea = TRUE, then several things will be triggered:

This layer will be added to metadata with studyArea set to max(studyArea(map)) + 1.
update CRS slot to be the CRS of the study area.

Usage

mapAdd(obj, map, layerName, overwrite = getOption("map.overwrite", FALSE), ...)

## Default S3 method:
mapAdd(
  obj = NULL,
  map = new("map"),
  layerName = NULL,
  overwrite = getOption("map.overwrite"),
  columnNameForLabels = 1,
  leaflet = FALSE,
  isStudyArea = FALSE,
  isRasterToMatch = FALSE,
  envir = NULL,
  useCache = TRUE,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)
mapAdd(obj, map, layerName, overwrite = getOption("map.overwrite", FALSE), ...)

## Default S3 method:
mapAdd(
  obj = NULL,
  map = new("map"),
  layerName = NULL,
  overwrite = getOption("map.overwrite"),
  columnNameForLabels = 1,
  leaflet = FALSE,
  isStudyArea = FALSE,
  isRasterToMatch = FALSE,
  envir = NULL,
  useCache = TRUE,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)

Arguments

`obj`	Optional spatial object, currently `RasterLayer`, `SpatialPolygons`.
`map`	Optional map object. If not provided, then one will be created. If provided, then the present `object` or options passed to `prepInputs` e.g., `url`, will be appended to this `map`.
`layerName`	Required. A label for this map layer. This can be the same as the object name.
`overwrite`	Logical. If `TRUE` and this `layerName` exists in the `map`, then it will replace the existing object. Default is `getOption("map.overwrite")`
`...`	Additonal arguments passed to `reproducible::postProcess()`, `reproducible::projectInputs()`, `reproducible::fixErrors()`, and `reproducible::prepInputs()`.
`columnNameForLabels`	A character string indicating which column to use for labels. This is currently only used if the object is a `SpatialPolygonsDataFrame`.
`leaflet`	Logical or Character vector of path(s) to write tiles. If `TRUE` or a character vector, then this layer will be added to a leaflet map. For `RasterLayer` object, this will trigger a call to `gdal2tiles`, making tiles. If path is not specified, it will be the current path. The tile base file path will be `paste0(layerName, "_", rndstr(1, 6))`.
`isStudyArea`	Logical. If `TRUE`, this will be assigned the label, "StudyArea", and will be passed into `prepInputs` for any future layers added.
`isRasterToMatch`	Logical indicating ... TODO: need description
`envir`	An optional environment. If supplied, then the obj will not be placed "into" the maps slot, rather the environment label will be placed into the maps slot. Upon re
`useCache`	Logical. If `TRUE`, then internal calls to `Cache` will be used. Default is `TRUE`
`useParallel`	Logical. If `TRUE`, then if there is more than one calculation to do at any stage, it will create and use a parallel cluster via `makeOptimalCluster`. If running analyses in parallel, it may be useful to pass a function (via `.clInit`) to be run on each of the nodes immediately upon cluster creation (e.g., to set options).

Examples

## Not run: 
library(sp)
library(raster)
library(reproducible)
cwd <- getwd()
setwd(tempdir())
coords <- structure(c(-122.98, -116.1, -99.2, -106, -122.98,
                      59.9, 65.73, 63.58, 54.79, 59.9),
                    .Dim = c(5L, 2L))
Sr1 <- Polygon(coords)
Srs1 <- Polygons(list(Sr1), "s1")
StudyArea <- SpatialPolygons(list(Srs1), 1L)
crs(StudyArea) <- paste("+init=epsg:4326 +proj=longlat +datum=WGS84",
                        "+no_defs +ellps=WGS84 +towgs84=0,0,0")
StudyArea <- SpatialPolygonsDataFrame(StudyArea,
                           data = data.frame(ID = 1, shinyLabel = "zone2"),
                           match.ID = FALSE)

ml <- mapAdd(StudyArea, isStudyArea = TRUE, layerName = "Small Study Area",
             poly = TRUE, analysisGroup2 = "Small Study Area")

if (require("SpaDES.tools", quietly = TRUE)) {
  options(map.useParallel = FALSE)
  smallStudyArea <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea <- SpatialPolygonsDataFrame(smallStudyArea,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  ml <- mapAdd(smallStudyArea, ml, isStudyArea = TRUE, filename2 = NULL,
               analysisGroup2 = "Smaller Study Area",
               poly = TRUE,
               layerName = "Smaller Study Area") # adds a second studyArea within 1st

  rasTemplate <- raster(extent(studyArea(ml)), res = 0.001)
  tsf <- randomPolygons(rasTemplate, numTypes = 8)*30
  crs(tsf) <- crs(ml)
  vtm <- randomPolygons(tsf, numTypes = 4)
  levels(vtm) <- data.frame(ID = sort(unique(vtm[])),
                            Factor = c("black spruce", "white spruce", "aspen", "fir"))
  crs(vtm) <- crs(ml)
  ml <- mapAdd(tsf, ml, layerName = "tsf1",
               filename2 = "tsf1.tif", # to postProcess
               # to map object
               tsf = "tsf1.tif", # to column in map@metadata
               analysisGroup1 = "tsf1_vtm1",  # this is the label for analysisGroup1
               leaflet = TRUE, # to column in map@metadata; used for visualizing in leaflet
               overwrite = TRUE)
  ml <- mapAdd(vtm, ml, filename2 = "vtm1.grd",
               layerName = "vtm1",
               vtm = "vtm1.grd",
               analysisGroup1 = "tsf1_vtm1", leaflet = TRUE, overwrite = TRUE)

  ageClasses <- c("Young", "Immature", "Mature", "Old")
  ageClassCutOffs <- c(0, 40, 80, 120)

  # add an analysis -- this will trigger analyses because there are already objects in the map
  #    This will trigger 2 analyses:
  #    LeadingVegTypeByAgeClass on each raster x polygon combo (only 1 currently)
  #    so there is 1 raster group, 2 polygon groups, 1 analyses - Total 2, 2 run now
  ml <- mapAddAnalysis(ml, functionName ="LeadingVegTypeByAgeClass",
                       ageClasses = ageClasses, ageClassCutOffs = ageClassCutOffs)
  # add an analysis -- this will trigger analyses because there are already objects in the map
  #    This will trigger 2 more analyses:
  #    largePatches on each raster x polygon combo (only 1 currently)
  #    so there is 1 raster group, 2 polygon groups, 2 analyses - Total 4, only 2 run now
  ml <- mapAddAnalysis(ml, functionName = "LargePatches", ageClasses = ageClasses,
                       id = "1", labelColumn = "shinyLabel",
                       ageClassCutOffs = ageClassCutOffs)

  # Add a second polygon, trigger
  smallStudyArea2 <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea2 <- SpatialPolygonsDataFrame(smallStudyArea2,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  # add a new layer -- this will trigger analyses because there are already analyese in the map
  #    This will trigger 2 more analyses ... largePatches on each *new* raster x polygon combo
  #    (now there are 2) -- so there is 1 raster group, 3 polygon groups, 2 analyses - Total 6
  ml <- mapAdd(smallStudyArea2, ml, isStudyArea = FALSE, filename2 = NULL, overwrite = TRUE,
               analysisGroup2 = "Smaller Study Area 2",
               poly = TRUE,
               layerName = "Smaller Study Area 2") # adds a second studyArea within 1st

  # Add a *different* second polygon, via overwrite. This should trigger new analyses
  smallStudyArea2 <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea2 <- SpatialPolygonsDataFrame(smallStudyArea2,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  # add a new layer -- this will trigger analyses because there are already analyses in the map
  #    This will trigger 2 more analyses ... largePatches on each *new* raster x polygon combo
  #    (now there are 2) -- so there is 1 raster group, 3 polygon groups, 2 analyses - Total 6
  ml <- mapAdd(smallStudyArea2, ml, isStudyArea = FALSE, filename2 = NULL, overwrite = TRUE,
               analysisGroup2 = "Smaller Study Area 2",
               poly = TRUE,
               layerName = "Smaller Study Area 2") # adds a second studyArea within 1st

  # Add a 2nd pair of rasters
  rasTemplate <- raster(extent(studyArea(ml)), res = 0.001)
  tsf2 <- randomPolygons(rasTemplate, numTypes = 8)*30
  crs(tsf2) <- crs(ml)
  vtm2 <- randomPolygons(tsf2, numTypes = 4)
  levels(vtm2) <- data.frame(ID = sort(unique(vtm2[])),
                            Factor = c("black spruce", "white spruce", "aspen", "fir"))
  crs(vtm2) <- crs(ml)
  ml <- mapAdd(tsf2, ml, filename2 = "tsf2.tif", layerName = "tsf2",
               tsf = "tsf2.tif",
               analysisGroup1 = "tsf2_vtm2", leaflet = TRUE, overwrite = TRUE)
  ml <- mapAdd(vtm2, ml, filename2 = "vtm2.grd", layerName = "vtm2",
               vtm = "vtm2.grd",
               analysisGroup1 = "tsf2_vtm2", leaflet = TRUE, overwrite = TRUE)

  # post hoc analysis of data
  #  use or create a specialized function that can handle the analysesData slot
  ml <- mapAddPostHocAnalysis(map = ml, functionName = "rbindlistAG",
                              postHocAnalysisGroups = "analysisGroup2",
                              postHocAnalyses = "all")
}

## cleanup
setwd(cwd)
unlink(tempdir(), recursive = TRUE)

## End(Not run)

## Not run: 
library(sp)
library(raster)
library(reproducible)
cwd <- getwd()
setwd(tempdir())
coords <- structure(c(-122.98, -116.1, -99.2, -106, -122.98,
                      59.9, 65.73, 63.58, 54.79, 59.9),
                    .Dim = c(5L, 2L))
Sr1 <- Polygon(coords)
Srs1 <- Polygons(list(Sr1), "s1")
StudyArea <- SpatialPolygons(list(Srs1), 1L)
crs(StudyArea) <- paste("+init=epsg:4326 +proj=longlat +datum=WGS84",
                        "+no_defs +ellps=WGS84 +towgs84=0,0,0")
StudyArea <- SpatialPolygonsDataFrame(StudyArea,
                           data = data.frame(ID = 1, shinyLabel = "zone2"),
                           match.ID = FALSE)

ml <- mapAdd(StudyArea, isStudyArea = TRUE, layerName = "Small Study Area",
             poly = TRUE, analysisGroup2 = "Small Study Area")

if (require("SpaDES.tools", quietly = TRUE)) {
  options(map.useParallel = FALSE)
  smallStudyArea <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea <- SpatialPolygonsDataFrame(smallStudyArea,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  ml <- mapAdd(smallStudyArea, ml, isStudyArea = TRUE, filename2 = NULL,
               analysisGroup2 = "Smaller Study Area",
               poly = TRUE,
               layerName = "Smaller Study Area") # adds a second studyArea within 1st

  rasTemplate <- raster(extent(studyArea(ml)), res = 0.001)
  tsf <- randomPolygons(rasTemplate, numTypes = 8)*30
  crs(tsf) <- crs(ml)
  vtm <- randomPolygons(tsf, numTypes = 4)
  levels(vtm) <- data.frame(ID = sort(unique(vtm[])),
                            Factor = c("black spruce", "white spruce", "aspen", "fir"))
  crs(vtm) <- crs(ml)
  ml <- mapAdd(tsf, ml, layerName = "tsf1",
               filename2 = "tsf1.tif", # to postProcess
               # to map object
               tsf = "tsf1.tif", # to column in map@metadata
               analysisGroup1 = "tsf1_vtm1",  # this is the label for analysisGroup1
               leaflet = TRUE, # to column in map@metadata; used for visualizing in leaflet
               overwrite = TRUE)
  ml <- mapAdd(vtm, ml, filename2 = "vtm1.grd",
               layerName = "vtm1",
               vtm = "vtm1.grd",
               analysisGroup1 = "tsf1_vtm1", leaflet = TRUE, overwrite = TRUE)

  ageClasses <- c("Young", "Immature", "Mature", "Old")
  ageClassCutOffs <- c(0, 40, 80, 120)

  # add an analysis -- this will trigger analyses because there are already objects in the map
  #    This will trigger 2 analyses:
  #    LeadingVegTypeByAgeClass on each raster x polygon combo (only 1 currently)
  #    so there is 1 raster group, 2 polygon groups, 1 analyses - Total 2, 2 run now
  ml <- mapAddAnalysis(ml, functionName ="LeadingVegTypeByAgeClass",
                       ageClasses = ageClasses, ageClassCutOffs = ageClassCutOffs)
  # add an analysis -- this will trigger analyses because there are already objects in the map
  #    This will trigger 2 more analyses:
  #    largePatches on each raster x polygon combo (only 1 currently)
  #    so there is 1 raster group, 2 polygon groups, 2 analyses - Total 4, only 2 run now
  ml <- mapAddAnalysis(ml, functionName = "LargePatches", ageClasses = ageClasses,
                       id = "1", labelColumn = "shinyLabel",
                       ageClassCutOffs = ageClassCutOffs)

  # Add a second polygon, trigger
  smallStudyArea2 <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea2 <- SpatialPolygonsDataFrame(smallStudyArea2,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  # add a new layer -- this will trigger analyses because there are already analyese in the map
  #    This will trigger 2 more analyses ... largePatches on each *new* raster x polygon combo
  #    (now there are 2) -- so there is 1 raster group, 3 polygon groups, 2 analyses - Total 6
  ml <- mapAdd(smallStudyArea2, ml, isStudyArea = FALSE, filename2 = NULL, overwrite = TRUE,
               analysisGroup2 = "Smaller Study Area 2",
               poly = TRUE,
               layerName = "Smaller Study Area 2") # adds a second studyArea within 1st

  # Add a *different* second polygon, via overwrite. This should trigger new analyses
  smallStudyArea2 <- randomPolygon(studyArea(ml), 1e5)
  smallStudyArea2 <- SpatialPolygonsDataFrame(smallStudyArea2,
                           data = data.frame(ID = 1, shinyLabel = "zone1"),
                           match.ID = FALSE)
  # add a new layer -- this will trigger analyses because there are already analyses in the map
  #    This will trigger 2 more analyses ... largePatches on each *new* raster x polygon combo
  #    (now there are 2) -- so there is 1 raster group, 3 polygon groups, 2 analyses - Total 6
  ml <- mapAdd(smallStudyArea2, ml, isStudyArea = FALSE, filename2 = NULL, overwrite = TRUE,
               analysisGroup2 = "Smaller Study Area 2",
               poly = TRUE,
               layerName = "Smaller Study Area 2") # adds a second studyArea within 1st

  # Add a 2nd pair of rasters
  rasTemplate <- raster(extent(studyArea(ml)), res = 0.001)
  tsf2 <- randomPolygons(rasTemplate, numTypes = 8)*30
  crs(tsf2) <- crs(ml)
  vtm2 <- randomPolygons(tsf2, numTypes = 4)
  levels(vtm2) <- data.frame(ID = sort(unique(vtm2[])),
                            Factor = c("black spruce", "white spruce", "aspen", "fir"))
  crs(vtm2) <- crs(ml)
  ml <- mapAdd(tsf2, ml, filename2 = "tsf2.tif", layerName = "tsf2",
               tsf = "tsf2.tif",
               analysisGroup1 = "tsf2_vtm2", leaflet = TRUE, overwrite = TRUE)
  ml <- mapAdd(vtm2, ml, filename2 = "vtm2.grd", layerName = "vtm2",
               vtm = "vtm2.grd",
               analysisGroup1 = "tsf2_vtm2", leaflet = TRUE, overwrite = TRUE)

  # post hoc analysis of data
  #  use or create a specialized function that can handle the analysesData slot
  ml <- mapAddPostHocAnalysis(map = ml, functionName = "rbindlistAG",
                              postHocAnalysisGroups = "analysisGroup2",
                              postHocAnalyses = "all")
}

## cleanup
setwd(cwd)
unlink(tempdir(), recursive = TRUE)

## End(Not run)

Add an analysis to a `map` object

Description

TODO: description needed

Usage

mapAddAnalysis(
  map,
  functionName,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)
mapAddAnalysis(
  map,
  functionName,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)

Arguments

`map`	A `map` object
`functionName`	The name of the analysis function to add
`useParallel`	Logical indicating whether to use multiple threads. Defaults to `getOption("map.useParallel", FALSE)`.
`...`	Additional arguments passed to `functionName`.

Add a post hoc analysis function to a `map` object

Description

Add a post hoc analysis function to a map object

Usage

mapAddPostHocAnalysis(
  map,
  functionName,
  postHocAnalysisGroups = NULL,
  postHocAnalyses = "all",
  useParallel = getOption("map.useParallel", FALSE),
  ...
)
mapAddPostHocAnalysis(
  map,
  functionName,
  postHocAnalysisGroups = NULL,
  postHocAnalyses = "all",
  useParallel = getOption("map.useParallel", FALSE),
  ...
)

Arguments

`map`	Optional map object. If not provided, then one will be created. If provided, then the present `object` or options passed to `prepInputs` e.g., `url`, will be appended to this `map`.
`functionName`	A function that is designed for post hoc analysis of map class objects, e.g., `rbindlistAG`.
`postHocAnalysisGroups`	Character string with one `analysisGroups`, i.e., `"analysisGroup1"` or `"analysisGroup2"`.
`postHocAnalyses`	Character vector with `"all"`, (which will do all `analysisGroups`; default), or 1 or more of the the `functionName`s that are in the analyses slot.
`useParallel`	Logical. If `TRUE`, then if there is more than one calculation to do at any stage, it will create and use a parallel cluster via `makeOptimalCluster`. If running analyses in parallel, it may be useful to pass a function (via `.clInit`) to be run on each of the nodes immediately upon cluster creation (e.g., to set options).
`...`	Optional arguments to pass into `functionName`

Generic analysis for map objects

Description

This is the workhorse function that runs any analyses described in map@analyses. It uses hashing, and will not rerun any analysis that already ran on identical inputs.

Usage

mapAnalysis(
  map,
  functionName = NULL,
  purgeAnalyses = NULL,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)
mapAnalysis(
  map,
  functionName = NULL,
  purgeAnalyses = NULL,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)

Arguments

`map`	Optional map object. If not provided, then one will be created. If provided, then the present `object` or options passed to `prepInputs` e.g., `url`, will be appended to this `map`.
`functionName`	A function name that will be run on combinations of inputs in the map object. See details.
`purgeAnalyses`	A character string indicating which analysis group combination or part thereof (e.g., the name entered into the row under `analysisGroup2` column of the `map@metadata` or a `functionName`.
`useParallel`	Logical. If `TRUE`, then if there is more than one calculation to do at any stage, it will create and use a parallel cluster via `makeOptimalCluster`. If running analyses in parallel, it may be useful to pass a function (via `.clInit`) to be run on each of the nodes immediately upon cluster creation (e.g., to set options).
`...`	Additonal arguments passed to `reproducible::postProcess()`, `reproducible::projectInputs()`, `reproducible::fixErrors()`, and `reproducible::prepInputs()`.

Details

This function will do a sequence of things. First, it will run expand.grid on any columns whose names start with analysisGroup, creating a factorial set of analyses as described by these columns. It will assess the combinations against the arguments used by the functionName. For any analysisGroup that does not provide the correct arguments for the functionName, these analysisGroups will be omitted for that particular function. For efficiency, the function will then assess if any of these has already been run. For those that have not been run, it will then run the functionName on arguments that it finds in the metadata slot of the map object, as well as any arguments passed in here in the .... In general, the arguments being passed in here should be fixed across all analyses, while any that vary by analysis should be entered into the metadata table at the time of adding the layer to the map, via mapAdd.

Value

TODO

Remove objects from a `map`

Description

Remove objects from a map

Usage

mapRm(map, layer, ask = TRUE, ...)

## Default S3 method:
mapRm(map = NULL, layer = NULL, ask = TRUE, ...)
mapRm(map, layer, ask = TRUE, ...)

## Default S3 method:
mapRm(map = NULL, layer = NULL, ask = TRUE, ...)

Arguments

`map`	TODO: document this
`layer`	TODO: document this
`ask`	TODO: document this
`...`	TODO: document this

Examples

if (require("SpaDES.tools", quietly = TRUE)) {
  p <- terra::vect(cbind(-120, 60), crs = "epsg:4326") |>
    SpaDES.tools::randomPolygon(area = 1e5) |>
    sf::st_as_sf() |>
    sf::as_Spatial()
  m <- mapAdd(p, layerName = "p")
  m

  m <- mapRm(m, "p")
  m
}
if (require("SpaDES.tools", quietly = TRUE)) {
  p <- terra::vect(cbind(-120, 60), crs = "epsg:4326") |>
    SpaDES.tools::randomPolygon(area = 1e5) |>
    sf::st_as_sf() |>
    sf::as_Spatial()
  m <- mapAdd(p, layerName = "p")
  m

  m <- mapRm(m, "p")
  m
}

Extract the metadata obj

Description

Methods for specific classes exist.

Usage

metadata(x)

## S3 method for class 'Raster'
metadata(x)

## S3 method for class 'map'
metadata(x)
metadata(x)

## S3 method for class 'Raster'
metadata(x)

## S3 method for class 'map'
metadata(x)

Arguments

`x`	TODO: description needed

Extract rasters in the `map` object

Description

This will extract all objects in or pointed to within the map.

Usage

rasters(map)

## S3 method for class 'map'
rasters(map)

sp(map)

## S3 method for class 'map'
sp(map)

sf(map)

## S3 method for class 'map'
sf(map)

spatialPolygons(map)

spatialPoints(map)

maps(map, class = NULL, layerName = NULL)
rasters(map)

## S3 method for class 'map'
rasters(map)

sp(map)

## S3 method for class 'map'
sp(map)

sf(map)

## S3 method for class 'map'
sf(map)

spatialPolygons(map)

spatialPoints(map)

maps(map, class = NULL, layerName = NULL)

Arguments

`map`	A `map` class obj
`class`	If supplied, this will be the class of objects returned. Default is `NULL` which is "all", meaning all objects in the `map` object.
`layerName`	TODO: description needed

Value

A list of maps (i.e., sp, raster, or sf objects) of class class

Extract the rasterToMatch(s) from a `x`

Description

If layer is not provided and there is more than one studyArea, then this will extract the last one added.

Usage

## S4 method for signature 'map'
rasterToMatch(x, layer = 1)
## S4 method for signature 'map'
rasterToMatch(x, layer = 1)

Arguments

`x`	TODO: describe this
`layer`	TODO: describe this

Utility functions for grouping analyses in a `map` object

Description

Utility functions for grouping analyses in a map object

Usage

rbindlistAG(map, functionName, analysisGroups)
rbindlistAG(map, functionName, analysisGroups)

Arguments

`map`	Optional map object. If not provided, then one will be created. If provided, then the present `object` or options passed to `prepInputs` e.g., `url`, will be appended to this `map`.
`functionName`	TODO: description needed
`analysisGroups`	A character (length 1 currently), indicating which analysis group (e.g., "analysisGroup1") should be used to `rbindlist`. Can also specify `"all"` which will `rbindlist` all outputs.

Value

A list of data.tables.

`runMapAnalyses`

Description

TODO: description needed

Usage

runMapAnalyses(
  map,
  purgeAnalyses = NULL,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)
runMapAnalyses(
  map,
  purgeAnalyses = NULL,
  useParallel = getOption("map.useParallel", FALSE),
  ...
)

Arguments

`map`	TODO
`purgeAnalyses`	TODO
`useParallel`	TODO
`...`	TODO

Value

TODO

Show method for map class objects

Description

Show method for map class objects

Usage

## S4 method for signature 'map'
show(object)
## S4 method for signature 'map'
show(object)

Arguments

object

TODO: describe this

Extract the studyArea(s) from a `map`

Description

If layer is not provided and there is more than one studyArea, then this will extract the last one added.

Usage

studyArea(map, layer = NA, sorted = FALSE)

## S4 method for signature 'ANY'
studyArea(map, layer = NA, sorted = FALSE)

## S4 method for signature 'map'
studyArea(map, layer = NA, sorted = FALSE)

studyArea(map, layer = NA) <- value

## S4 replacement method for signature 'map'
studyArea(map, layer = NA) <- value
studyArea(map, layer = NA, sorted = FALSE)

## S4 method for signature 'ANY'
studyArea(map, layer = NA, sorted = FALSE)

## S4 method for signature 'map'
studyArea(map, layer = NA, sorted = FALSE)

studyArea(map, layer = NA) <- value

## S4 replacement method for signature 'map'
studyArea(map, layer = NA) <- value

Arguments

`map`	TODO: document this
`layer`	TODO: document this
`sorted`	Logical. Should the numeric `layer` be referring to geographic area of the `area` or the order that the `studyArea` were placed into map object
`value`	The value to assign to the object.

Map class methods

Description

Tools for getting objects and metadata in and out of a map class.

Usage

studyAreaName(x, layer)

## S3 method for class 'map'
studyAreaName(x, layer = 1)

## S3 method for class 'data.table'
studyAreaName(x, layer = 1)
studyAreaName(x, layer)

## S3 method for class 'map'
studyAreaName(x, layer = 1)

## S3 method for class 'data.table'
studyAreaName(x, layer = 1)

Arguments

`x`	TODO: document this
`layer`	TODO: document this

Package 'map'

Help Index

.rasterToMemory

Description

Usage

Arguments

Calculate area of (named) objects the map obj

Description

Usage

Arguments

See Also

areaAndPolyValue

Description

Usage

Arguments

Value

Build map obj metadata table

Description

Usage

Arguments

Extract the crs of a map

Description

Usage

Arguments

See Also

Fasterize with crop & spTransform first

Description

Usage

Arguments

Polygonize with GDAL

Description

Usage

Arguments

Extract leaflet tile paths from a map obj

Description

Usage

Arguments

Value

Make tiles (pyramids) using gdal2tiles

Description

Usage

Arguments

The map class

Description

Slots

Append a spatial object to map

Description

Usage

Arguments

Examples

Add an analysis to a map object

Description

Usage

Arguments

Add a post hoc analysis function to a map object

Description

Usage

Arguments

Generic analysis for map objects

Description

Usage

Arguments

Details

Value

Remove objects from a map

Description

Usage

Arguments

See Also

Examples

Extract the metadata obj

Description

Usage

Arguments

Extract rasters in the map object

Description

Usage

Arguments

Value

See Also

`.rasterToMemory`

Calculate area of (named) objects the `map` obj

`areaAndPolyValue`

Build `map` obj metadata table

Extract the crs of a `map`

Extract leaflet tile paths from a `map` obj

Make tiles (pyramids) using `gdal2tiles`

The `map` class

Add an analysis to a `map` object

Add a post hoc analysis function to a `map` object

Remove objects from a `map`

Extract rasters in the `map` object

Extract the rasterToMatch(s) from a `x`

Utility functions for grouping analyses in a `map` object

`runMapAnalyses`

Extract the studyArea(s) from a `map`