| Title: | Bindings for ArcGIS |
|---|---|
| Description: | This package provides classes for loading, converting and exporting ArcGIS datasets and layers in R. |
| Authors: | Esri |
| Maintainer: | Esri <[email protected]> |
| License: | file LICENSE |
| Version: | 1.0.1.305 |
| Built: | 2024-11-04 21:43:28 UTC |
| Source: | https://github.com/R-ArcGIS/r-bridge |
Collection of classes and functons for loading, converting and exporting ArcGIS datasets and layers in R.
For a complete list of exported functions, use
library(help = "arcgisbinding").
sp package (Classes and Methods for Spatial Data)
raster package (Geographic Data Analysis and Modeling)
Initialize connection to ArcGIS. Any script running directly from R (i.e.
without being called from a Geoprocessing script) should first call
arc.check_product to create a connection with ArcGIS. Provides
installation details on the version of ArcGIS installed that
arcgisbinding is communicating with. Failure to run this function successfuly implies a problem with ArcGIS installation or environment variables for ArcGIS.
arc.check_product()arc.check_product()
a named list is returned with the following components:
appProduct: ArcGIS Desktop (i.e. ArcMap), or ArcGIS Pro. The name of the product connected.
licenseLicense level: Basic, Standard, or Advanced are the three licensing levels available. Each provides progressively more functionality within the software. See the "Desktop Functionality Matrix" link for details.
versionBuild number: The build number of the release being used. Useful in debugging and when creating error reports.
dllDLL: The dynamic linked library (DLL) in use allowing ArcGIS to communicate with R.
ArcGIS Desktop Functionality Matrix
Additional license levels are available on ArcGIS Desktop: Server, EngineGeoDB, and Engine. These license levels are currently unsupported by this package.
info <- arc.check_product() info$license # ArcGIS license level info$version # ArcGIS build number info$app # product name info$dll # binding DLL in useinfo <- arc.check_product() info$license # ArcGIS license level info$version # ArcGIS build number info$app # product name info$dll # binding DLL in use
arc.data class and methods
## S3 method for class 'arc.data' x[i, j, drop] ### dplyr methods: ## S3 method for class 'arc.data' filter(.data, ..., .dots) ## S3 method for class 'arc.data' arrange(.data, ..., .dots) ## S3 method for class 'arc.data' mutate(.data, ..., .dots) ## S3 method for class 'arc.data' group_by(.data, ..., add) ## S3 method for class 'arc.data' ungroup(x, ...)## S3 method for class 'arc.data' x[i, j, drop] ### dplyr methods: ## S3 method for class 'arc.data' filter(.data, ..., .dots) ## S3 method for class 'arc.data' arrange(.data, ..., .dots) ## S3 method for class 'arc.data' mutate(.data, ..., .dots) ## S3 method for class 'arc.data' group_by(.data, ..., add) ## S3 method for class 'arc.data' ungroup(x, ...)
i, j, ...
|
indices specifying elements to subset |
drop |
if |
x |
A |
.data |
A |
.dots |
other arguments (see package dplyr) |
add |
To add to the existing groups, use |
TODO
arc.data object is data.frame with geometry attribute.
To access geometry use arc.shape.
Class data.frame, directly.
filter: Return rows with matching conditions
arrange: Arrange rows by variables
mutate, transmute: Add new variables
select: Select/rename variables by name
group_by: Group by one or more variables
slice: Select rows by position
distinct: Select distinct/unique rows
You can display the arc.data object. Geometry information, first 5 and last 3 row will be showed.
arc.shape,
arc.open,
arc.select
d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding"))) d ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 FID LATITUDE LONGITUDE ELEVATION OZONE X Y text 1 0 39.1447 -123.2065 194 0.04650 -2298092 515557.4 Value_0 2 1 39.4030 -123.3491 420 0.04969 -2301588 546772.7 Value_1 3 2 37.7661 -122.3978 5 0.05000 -2273948 347691.4 Value_2 4 3 37.9508 -122.3569 23 0.05799 -2264847 366623.2 Value_3 5 4 36.6986 -121.6354 36 0.05860 -2241776 214412.1 Value_0 ... ... ... ... ... ... ... ... ... 191 190 34.0598 -117.1462 0 0.16449 -1921585 -170440.0 Value_2 192 191 34.2412 -117.2756 1384 0.16470 -1928645 -148045.5 Value_3 193 192 34.1065 -117.2732 0 0.17360 -1931774 -162775.2 Value_0 ## End(Not run) # subset rows 1,3 and 5 with corresponding features d135 <- d[c(1,3,5),] # dplyr support require("dplyr") filter(d, ELEVATION > 1800) #add new elevation column in meters mutate(d, elevm = ELEVATION * 0.3048)d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding"))) d ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 FID LATITUDE LONGITUDE ELEVATION OZONE X Y text 1 0 39.1447 -123.2065 194 0.04650 -2298092 515557.4 Value_0 2 1 39.4030 -123.3491 420 0.04969 -2301588 546772.7 Value_1 3 2 37.7661 -122.3978 5 0.05000 -2273948 347691.4 Value_2 4 3 37.9508 -122.3569 23 0.05799 -2264847 366623.2 Value_3 5 4 36.6986 -121.6354 36 0.05860 -2241776 214412.1 Value_0 ... ... ... ... ... ... ... ... ... 191 190 34.0598 -117.1462 0 0.16449 -1921585 -170440.0 Value_2 192 191 34.2412 -117.2756 1384 0.16470 -1928645 -148045.5 Value_3 193 192 34.1065 -117.2732 0 0.17360 -1931774 -162775.2 Value_0 ## End(Not run) # subset rows 1,3 and 5 with corresponding features d135 <- d[c(1,3,5),] # dplyr support require("dplyr") filter(d, ELEVATION > 1800) #add new elevation column in meters mutate(d, elevm = ELEVATION * 0.3048)
arc.dataset S4 class
The dataset_type slot possible values are described in the
referenced "dataset properties – data type" documentation. For feature datasets,
extent contains four double values: (xmin, ymin, xmax, ymax).
The fields slot includes the details of the ArcGIS data types of the
relevant fields, which include data types not directly representable in R.
internal
pathfile path or layer name
dataset_typedataset type
arc.open,
arc.table-class,
arc.feature-class,
arc.datasetraster-class,
arc.datasetrastermosaic-class
ozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) d # print dataset infoozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) d # print dataset info
arc.datasetraster S4 class. Dataset class for raster objects. Creates a dataset object with type = raster.
A raster dataset is any valid raster format organized into one or more bands. Each band consists of an array of pixels (cells), and each pixel has a value. A raster dataset has at least one band. Raster data is a discrete data representation in which space is divided into uniform cells, or pixels.
Class arc.dataset-class, directly.
srSpatial reference.
extentSpatial extent of the dataset. The Extent describes the rectangle (boundary) containing all the raster dataset's data.
pixel_typeThe pixel type of the referenced raster dataset.
compression_typeThe compression type.
nrowThe number of rows.
ncolThe number of columns.
bandsraster dataset bands information.
Create a arc.raster object
retrieves dimensions of a arc.dataset object
return bands names
TODO
arc.datasetrastermosaic S4 class. Dataset class for mosaic objects.
Mosaic datasets are made up of a collection of rasters. Mosaic structure efficiently stores and manages multiple rasters for visualization and analysis. Detailed information about mosaic datasets can be found in ArcGIS reference for mosaic datasets.
R-ArcGIS bridge handles mosaic data I/O using the arc.open() function.
The mosaic dataset opened using arc.open can be processed on the fly by converting it to a raster object within R using the arc.raster function.
Properties of a mosaic dataset such as extent, pixel_type, nrow, ncol and mosaicking rules.
Mosaicking rules determine how a series of potentially intercepting rasters are displayed as a single raster.
Mosaicking rules go beyond only visualization and can be used to stitch together different rasters making up a mosaic.
Mosaicking rules define how intersections between different rasters within the mosaic dataset are handled and are made up of method and operator. Simply put, method defines which raster will be placed on top of the other for visualization in cases where they overlap and operator defines how the intersection between overlapping rasters in the mosaic dataset will be handled. The information on mosaicking rules can be found under ArcGIS reference for mosaicking rules.
Class arc.feature-class, arc.datasetraster-class directly and
arc.table-class by class "arc.feature-class",
arc.dataset-class by class "arc.table-class".
arc.open,
arc.raster,
arc.select
delete dataset
arc.delete(x, ...) ## S4 method for signature 'arc.dataset' arc.delete(x, ...)arc.delete(x, ...) ## S4 method for signature 'arc.dataset' arc.delete(x, ...)
x |
|
... |
reserved |
logical, TRUE on success.
table_path <- file.path(tempdir(), "data.gdb", "mytable") arc.write(table_path, data=list('f1'=c(23,45), 'f2'=c('hello', 'bob'))) # delete table arc.delete(table_path) # delete database arc.delete(dirname(table_path))table_path <- file.path(tempdir(), "data.gdb", "mytable") arc.write(table_path, data=list('f1'=c(23,45), 'f2'=c('hello', 'bob'))) # delete table arc.delete(table_path) # delete database arc.delete(dirname(table_path))
Geoprocessing environment settings are additional parameters that affect a tool's results. Unlike parameters, they are not directly input as values. Instead, they are values configured in a separate dialog box, and then and interrogated and used by the script when run.
arc.env()arc.env()
The geoprocessing environment can control a variety of attributes relating
to where data is stored, the extent and projection of analysis outputs,
tolerances of output values, and parallel processing, among other attributes.
Commonly used environment settings include workspace, which controls
the default location for geoprocessing tool inputs and outputs. See the
topics listed under "References" for details on the full range of
environment settings that Geoprocessing scripts can utilize.
return enviroment list
This function is only available from within an ArcGIS session. Usually, it is used to get local Geoprocessing tool environment settings within the executing tool.
This function can only read current geoprocessing settings. Settings, such as the current workspace, must be configured in the calling Geoprocessing script, not within the body of the R script.
## Not run: tool_exec <- function(in_para, out_params) { env = arc.env() wkspath <- env$workspace ... return(out_params) } ## End(Not run)## Not run: tool_exec <- function(in_para, out_params) { env = arc.env() wkspath <- env$workspace ... return(out_params) } ## End(Not run)
arc.feature S4 class.
Container for shape information pertaining to extent and shape from a table class.
Class arc.table-class, directly and
arc.dataset-class by class "arc.table".
shapeinfogeometry information (see arc.shapeinfo)
extentspatial extent of the dataset
TODO
return names of columns
return geometry information
arc.open,
arc.dataset-class,
arc.table-class,
arc.datasetraster-class,
arc.datasetrastermosaic-class
ozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) names(d@fields) # get all field names arc.shapeinfo(d) # print shape info d # print dataset infoozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) names(d@fields) # get all field names arc.shapeinfo(d) # print shape info d # print dataset info
The arc.fromP4ToWkt command converts a PROJ.4 coordinate
reference system (CRS) string to a well-known text (WKT) representation.
Well-known text is used by ArcGIS and other applications to robustly
describe a coordinate reference system. Converts PROJ.4 stings which
include either the '+proj' fully specified projection parameter, or the
'+init' form that takes well-known IDs (WKIDs), such as EPSG codes,
as input.
arc.fromP4ToWkt(proj4)arc.fromP4ToWkt(proj4)
proj4 |
PROJ.4 projection string |
The produced WKT is equivalent to the ArcPy spatial reference exported string:
arcpy.Describe(layer).SpatialReference.exportToString()
return WKT string
OGC specification 12-063r5
The '+init' method currently only works with ArcGIS Pro.
arc.fromP4ToWkt("+proj=eqc") # Equirectangular arc.fromP4ToWkt("+proj=latlong +datum=wgs84") # WGS 1984 geographic arc.fromP4ToWkt("+init=epsg:2806") # initalize based on EPSG codearc.fromP4ToWkt("+proj=eqc") # Equirectangular arc.fromP4ToWkt("+proj=latlong +datum=wgs84") # WGS 1984 geographic arc.fromP4ToWkt("+init=epsg:2806") # initalize based on EPSG code
Convert a well-known text (WKT) coordinate reference system (CRS) string to a PROJ.4 representation. PROJ.4 strings were created as a convenient way to pass CRS information to the command-line PROJ.4 utilities, and have an expressive format. Alternatively, can accept a well-known ID (WKID), a numeric value that ArcGIS uses to specify projections. See the 'Using spatial references' resource for lookup tables which map between WKIDs and given projection names.
arc.fromWktToP4(wkt)arc.fromWktToP4(wkt)
wkt |
WKT projection string, or a WKID integer |
return PROJ.4 string
OGC specification 12-063r5
d <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) arc.fromWktToP4(arc.shapeinfo(d)$WKT) arc.fromWktToP4(4326) # use a WKID for WGS 1984, a widely # used standard for geographic coordinatesd <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) arc.fromWktToP4(arc.shapeinfo(d)$WKT) arc.fromWktToP4(4326) # use a WKID for WGS 1984, a widely # used standard for geographic coordinates
Open ArcGIS datasets, tables, rasters and layers. Returns a new
arc.dataset-class object which contains details on both the spatial
information and attribute information (data frame) contained within the dataset.
arc.open(path)arc.open(path)
path |
file path (character) or layer name (character) |
An arc.dataset-class object
Feature Class: A collection of geographic features with the
same geometry type (i.e. point, line, polygon) and the same spatial reference,
combined with an attribute table. Feature classes can be stored in a variety
of formats, including: files (e.g. Shapefiles), Geodatabases, components
of feature datasets, and as coverages. All of these types can be accessed
using the full path of the relevant feature class (see note below on how to
specify path names).
Layer: A layer references a feature layer, but also includes
additional information necessary to symbolize and label a dataset appropriately.
arc.open supports active layers in the current ArcGIS session, which
can be addressed simply by referencing the layer name as it is displayed within
the application. Instead of referencing file layers on disk (i.e.
.lyr and .lyrx files), the direct reference to the actual dataset
should be used.
Table: Tables are effectively the same as data frames, containing
a collection of records (or observations) organized in rows, with columns
storing different variables (or fields). Feature classes similarly contain a
table, but include the additional information about geometries lacking in a
standalone table. When a standalone table is queries for its spatial information,
e.g. arc.shape(table), it will return NULL. Table data types include
formats such as text files, Excel spreadsheets, dBASE tables, and INFO tables.
rasters: Rasters represent continuous geographic data in cells, or pixels, of equal size (square or rectangular). Spatial data represented on this rasters are also known as grided data. In contrast to spatial data structures represented in feature classes, rasters contain information on spatially continuous data.
Paths must be properly quoted for the Windows platform. There are two styles of paths that work within R on Windows:
Doubled backslashes, such as:
C:\\Workspace\\archive.gdb\\feature_class.
Forward-slashes such as:
C:/Workspace/archive.gdb/feature_class.
Network paths can be accessed with a leading
\\\\host\share or //host/share path.
To access tables and data within a Feature Dataset, reference the full path to
the dataset, which follows the structure:
<directory>/<Geodatabase Name>/<feature dataset name>/<dataset name>.
So for a table called table1 located in a feature dataset fdataset within
a Geodatabase called data.gdb, the full path might be:
C:/Workspace/data.gdb/fdataset/table1
arc.select,
arc.raster,
arc.write
## open feature filename <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(filename) cat('all fields:', names(d@fields), fill = TRUE) # print all fields ## open raster filename <- system.file("pictures", "logo.jpg", package="rgdal") d <- arc.open(filename) dim(d) # show raster dimension## open feature filename <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(filename) cat('all fields:', names(d@fields), fill = TRUE) # print all fields ## open raster filename <- system.file("pictures", "logo.jpg", package="rgdal") d <- arc.open(filename) dim(d) # show raster dimension
Methods to create a arc.raster object from scratch, extent, arc.open object or a raster file (inside or outside of a file geodatabase).
## S4 method for signature 'arc.datasetraster' arc.raster(object, bands, ...) ## S4 method for signature 'arc.datasetrastermosaic' arc.raster(object, bands, ...) ## S4 method for signature 'NULL' arc.raster(object, path, dim, nrow, ncol, nband, extent, origin_x, origin_y, cellsize_x, cellsize_y, pixel_type, nodata, sr, ...)## S4 method for signature 'arc.datasetraster' arc.raster(object, bands, ...) ## S4 method for signature 'arc.datasetrastermosaic' arc.raster(object, bands, ...) ## S4 method for signature 'NULL' arc.raster(object, path, dim, nrow, ncol, nband, extent, origin_x, origin_y, cellsize_x, cellsize_y, pixel_type, nodata, sr, ...)
object |
codearc.datasetraster-class object. |
bands |
optional, integer. List of bands to read (default: all bands). |
... |
optional additional arguments such as |
path |
file path (character) or layer name (character). |
dim |
optional. List for number of rows and columns of the raster. |
nrow |
optional, integer > 0. Number of rows for the raster or mosaic dataset. The default is |
ncol |
optional, integer > 0. Number of columns for the raster or mosaic dataset. The default is |
nband |
integer > 0. Number of bands to create. |
extent |
optional, list. extent of raster to be read. The default is |
origin_x |
optional. Minimum x coordinate. |
origin_y |
optional. Minimum y coordinate. |
cellsize_x |
optional. Size of pixel in x-axis. |
cellsize_y |
optional. Size of pixel in y-axis. |
pixel_type |
optional. Type of raster pixels. For details about different pixel types see |
nodata |
numeric, value for no data values. |
sr |
optional transform raster to spatial reference. The default is |
arc.raster returns a raster object (type of arc.raster-class.).
arc.open,
arc.write,
arc.raster-class
## resample raster r.file <- system.file("pictures", "cea.tif", package="rgdal") r <- arc.raster(arc.open(r.file), nrow=200, ncol=200, resample_type="CubicConvolution") stopifnot(r$nrow == 200 && r$resample_type == "CubicConvolution") ## Not run: > r type : Raster pixel_type : U8 (8bit) nrow : 200 ncol : 200 resample_type : CubicConvolution cellsize : 154.256892046808, 154.557002731725 nodata : NA extent : xmin=-28493.17, ymin=4224973, xmax=2358.212, ymax=4255885 WKT : PROJCS["North_American_1927_Cylindrical_Equal_Area",GEOGCS["... band : Band_1 ## End(Not run) ## create an empty raster r = arc.raster(NULL, path=tempfile("new_raster", fileext=".img"), extent=c(0, 0, 100, 100), nrow=100, ncol=100, nband=5, pixel_type="F32") stopifnot(all(dim(r) == c(100, 100, 5))) ## Not run: > dim(r) nrow ncol nband 100 100 5 ## End(Not run)## resample raster r.file <- system.file("pictures", "cea.tif", package="rgdal") r <- arc.raster(arc.open(r.file), nrow=200, ncol=200, resample_type="CubicConvolution") stopifnot(r$nrow == 200 && r$resample_type == "CubicConvolution") ## Not run: > r type : Raster pixel_type : U8 (8bit) nrow : 200 ncol : 200 resample_type : CubicConvolution cellsize : 154.256892046808, 154.557002731725 nodata : NA extent : xmin=-28493.17, ymin=4224973, xmax=2358.212, ymax=4255885 WKT : PROJCS["North_American_1927_Cylindrical_Equal_Area",GEOGCS["... band : Band_1 ## End(Not run) ## create an empty raster r = arc.raster(NULL, path=tempfile("new_raster", fileext=".img"), extent=c(0, 0, 100, 100), nrow=100, ncol=100, nband=5, pixel_type="F32") stopifnot(all(dim(r) == c(100, 100, 5))) ## Not run: > dim(r) nrow ncol nband 100 100 5 ## End(Not run)
A raster dataset is any valid raster format organized into one or more bands. Each band consists of an array of pixels (cells), and each pixel has a value. A raster dataset has at least one band. Raster data is a discrete data representation in which space is divided into uniform cells, or pixels.
srGet or set spacial reference
extentGet or set extent. Use it to read a portion of the raster.
nrowGet or set number of rows.
ncolGet or set number of columns.
cellsizeGet pixel size.
pixel_typeGet or set pixel type. For details see ArcGIS help on pixel types.
pixel_depthGet pixel depth. Pixel depth/Bit depth (1, 2, 4, 8, 16, 32, 64). For details see ArcGIS help on pixel types.
nodataGet or set nodata value
resample_typeGet or set resampling type. For details see ArcGIS help on rasampling.
colormapGet or set color map table. Return is a vector of 256 colors in the RGB format.
bandsGet list of raster bands
bandGet a single raster band
namesreturn bands names
dimretrieves dimensions
$show()show object
$pixel_block(ul_x, ul_y, nrow, ncol, bands)Read pixel values.
ul_x, ul_y - optional, upper left corner in pixels
nrow, ncol - optional, size in pixels
bands - optional, select band(s).
The values returned are always a matrix, with the rows representing cells,
and the columns representing band(s), c(nrow*ncol, length(bands))
(see Example #1)
$write_pixel_block(values, ul_x, ul_y, ncol, nrow)Write pixel values. (see Example #2)
ul_x, ul_y - optional, upper left corner in pixels
nrow, ncol - optional, size in pixels
$has_colormap()logical, return TRUE if raster has colormap
$attribute_table()Query raster attribute table.
Return data.frame object.
Raster datasets that contain attribute tables typically have cell values that represent or define a class, group, category, or membership.
$save_as(path, opt)TODO (see Example #3)
$commit(opt)End writing. (see Example #2.3)
opt - additional parameter(s):
(default: "build-stats"), ("build-pyramid")
Write to an ArcGIS raster dataset
arc.raster,
arc.write,
arc.datasetraster-class
## Example #1. read 5x5 pixel block with 10,10 offset r.file <- system.file("pictures", "cea.tif", package="rgdal") r <- arc.raster(arc.open(r.file)) v <- r$pixel_block(ul_x = 10L, ul_y = 10L, nrow = 5L, ncol= 5L) dim(v) == c(25, 1) #[1] TRUE TRUE stopifnot(length(v) == 25) ## Example #2. process big raster ## 2.1 create new arc.raster r2 = arc.raster(NULL, path=tempfile("r2", fileext=".img"), dim=dim(r), pixel_type=r$pixel_type, nodata=r$nodata, extent=r$extent,sr=r$sr) ## 2.2 loop by rows, process pixels for (i in 1L:r$nrow) { v <- r$pixel_block(ul_y = i - 1L, nrow = 1L) r2$write_pixel_block(v * 1.5, ul_y = i - 1L, nrow = 1L, ncol = r$ncol) } ## 2.3 stop all writings and crete raster file r2$commit() ## Example #3. resample raster r <- arc.raster(arc.open(r.file), nrow=200L, ncol=200L, resample_type="BilinearGaussBlur") ## save to a different format r$save_as(tempfile("new_raster", fileext=".img")) ## Example #4. get and compare all pixel values r.file <- system.file("pictures", "logo.jpg", package="rgdal") rx <- raster::brick(r.file) r <- arc.raster(arc.open(r.file)) stopifnot(all(raster::values(rx) == r$pixel_block()))## Example #1. read 5x5 pixel block with 10,10 offset r.file <- system.file("pictures", "cea.tif", package="rgdal") r <- arc.raster(arc.open(r.file)) v <- r$pixel_block(ul_x = 10L, ul_y = 10L, nrow = 5L, ncol= 5L) dim(v) == c(25, 1) #[1] TRUE TRUE stopifnot(length(v) == 25) ## Example #2. process big raster ## 2.1 create new arc.raster r2 = arc.raster(NULL, path=tempfile("r2", fileext=".img"), dim=dim(r), pixel_type=r$pixel_type, nodata=r$nodata, extent=r$extent,sr=r$sr) ## 2.2 loop by rows, process pixels for (i in 1L:r$nrow) { v <- r$pixel_block(ul_y = i - 1L, nrow = 1L) r2$write_pixel_block(v * 1.5, ul_y = i - 1L, nrow = 1L, ncol = r$ncol) } ## 2.3 stop all writings and crete raster file r2$commit() ## Example #3. resample raster r <- arc.raster(arc.open(r.file), nrow=200L, ncol=200L, resample_type="BilinearGaussBlur") ## save to a different format r$save_as(tempfile("new_raster", fileext=".img")) ## Example #4. get and compare all pixel values r.file <- system.file("pictures", "logo.jpg", package="rgdal") rx <- raster::brick(r.file) r <- arc.raster(arc.open(r.file)) stopifnot(all(raster::values(rx) == r$pixel_block()))
Load dataset to a standard data frame.
## S4 method for signature 'arc.table' arc.select(object, fields, where_clause, selected, sr, ...)## S4 method for signature 'arc.table' arc.select(object, fields, where_clause, selected, sr, ...)
object |
arc.dataset-class object |
fields |
string, or list of strings, containing fields to include (default: all) |
where_clause |
SQL where clause |
selected |
use only selected records (if any) when dataset is a layer or standalone table |
sr |
transform geometry to Spatial Reference (default: object@sr) |
... |
optional additional arguments such as |
arc.select returns a data.frame object (type of arc.data).
If object is arc.feature-class, the "shape" of class
arc.shape-class will be attached to the resulting
arc.data object.
## read all fields ozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) df <- arc.select(d, names(d@fields)) head(df, n=3) ## read 'name', 'fid' and geometry df <- arc.select(d, c('fid', 'ozone'), where_clause="fid < 5") nrow(df) ## transform points to "+proj=eqc" df <- arc.select(d,"fid", where_clause="fid<5", sr="+proj=eqc") arc.shape(df) ## datum transformation, from NAD_1983 to WGS_1984 ## prepare dataset x <- c(1455535.149968639, 1446183.62438038, 1447950.6349539757) y <- c(478067.64943164587, 484500.4190463871, 479746.6336064786) data_path <- file.path(tempdir(), "data.gdb", "test_datum") ## save as NAD_1983 arc.write(data_path, coords=cbind(x, y), shape_info=list(type="Point", WKID=2893)) ## read and transform to WGS_1984 df <- arc.select(arc.open(data_path), sr=4326, transformation='NAD_1983_HARN_To_WGS_1984_2') x <- arc.shape(df)$x stopifnot(sprintf('%.8f', x[1]) == '-76.49626388')## read all fields ozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) df <- arc.select(d, names(d@fields)) head(df, n=3) ## read 'name', 'fid' and geometry df <- arc.select(d, c('fid', 'ozone'), where_clause="fid < 5") nrow(df) ## transform points to "+proj=eqc" df <- arc.select(d,"fid", where_clause="fid<5", sr="+proj=eqc") arc.shape(df) ## datum transformation, from NAD_1983 to WGS_1984 ## prepare dataset x <- c(1455535.149968639, 1446183.62438038, 1447950.6349539757) y <- c(478067.64943164587, 484500.4190463871, 479746.6336064786) data_path <- file.path(tempdir(), "data.gdb", "test_datum") ## save as NAD_1983 arc.write(data_path, coords=cbind(x, y), shape_info=list(type="Point", WKID=2893)) ## read and transform to WGS_1984 df <- arc.select(arc.open(data_path), sr=4326, transformation='NAD_1983_HARN_To_WGS_1984_2') x <- arc.shape(df)$x stopifnot(sprintf('%.8f', x[1]) == '-76.49626388')
Get geometry object of arc.shape-class from arc.data object.
arc.shape(x)arc.shape(x)
x |
a |
returns arc.shape-class
arc.shapeinfo,
arc.select,
arc.data
d <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) df <- arc.select(d, 'ozone') shp <- arc.shape(df) stopifnot(length(shp) == nrow(df)) shp ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 length : 193 ## End(Not run)d <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) df <- arc.select(d, 'ozone') shp <- arc.shape(df) stopifnot(length(shp) == nrow(df)) shp ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 length : 193 ## End(Not run)
arc.shape S4 class. Object arc.shape is a geometry collection.
arc.shape is attached to an ArcGIS data.frame as the
attribute "shape". Each element corresponds to one record in
the input data frame. Points are presented as an array of lists, with
each list containing (x, y, Z, M), where
Class list, directly.
.Datainternal
shapeinfogeometry information, for mode details see arc.shapeinfo
signature(x = "arc.shape", i=numeric) select geometry subset
return geometry information
length of collection
d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), "FID") shape <- arc.shape(d) shape ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 length : 193 ## End(Not run) # access X and Y values xy <- list(X=shape$x, Y=shape$y)d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), "FID") shape <- arc.shape(d) shape ## Not run: geometry type : Point WKT : PROJCS["USA_Contiguous_Albers_Equal_Area_Conic",GEOGCS["GCS_... WKID : 102003 length : 193 ## End(Not run) # access X and Y values xy <- list(X=shape$x, Y=shape$y)
arc.shapeinfo provides details on what type of geometry is stored
within the dataset, and the spatial reference of the geometry. The
well-known text, WKT, allows interoperable transfer of the spatial
reference system (CRS) between environments. The WKID is a numeric
value that ArcGIS uses to precisely specify a projection.
## S4 method for signature 'arc.shape' arc.shapeinfo(object) ## S4 method for signature 'arc.feature' arc.shapeinfo(object)## S4 method for signature 'arc.shape' arc.shapeinfo(object) ## S4 method for signature 'arc.feature' arc.shapeinfo(object)
object |
arc.feature-class or arc.shape-class object |
returns named list of :
type |
geometry type: "Point", "Polyline", or "Polygon" |
hasZ |
TRUE if geometry includes Z-values |
hasM |
TRUE if geometry includes M-values |
WKT |
well-known text representation of the shape's spatial reference |
WKID |
well-known ID of the shape's spatial reference |
d <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) # from arc.feature info <- arc.shapeinfo(d) info$WKT # print dataset spatial reference # from arc.shape df <- arc.select(d, 'ozone') info <- arc.shapeinfo(arc.shape(df))d <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) # from arc.feature info <- arc.shapeinfo(d) info$WKT # print dataset spatial reference # from arc.shape df <- arc.select(d, 'ozone') info <- arc.shapeinfo(arc.shape(df))
arc.table S4 class
The fields slot includes the details of the ArcGIS data types of the
relevant fields, which include data types not directly representable in R.
Class arc.dataset-class, directly.
fieldsnamed list of field types.
return data.frame. TODO
return names of columns
arc.open,
arc.dataset-class,
arc.feature-class
ozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) names(d@fields) # get all field names arc.shapeinfo(d) # print shape info d # print dataset infoozone.file <- system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding") d <- arc.open(ozone.file) names(d@fields) # get all field names arc.shapeinfo(d) # print shape info d # print dataset info
Export a data object to an ArcGIS dataset. If the data frame
includes a spatial attribute, this function writes a feature dataset. If no
spatial attribute is found, a table is instead written. If data is raster-like object,
this function writes a raster dataset. See ‘Details’ section for more information.
arc.write(path, data, ..., overwrite = FALSE)arc.write(path, data, ..., overwrite = FALSE)
path |
full output path |
data |
Accepts input source objects (see ‘Details’ for the types of objects allowed). |
... |
Additional arguments:
|
overwrite |
overwrite existing dataset. default |
Export to a new table dataset when data type is:
named list of vectors (see Example #4)
data.frame
Export to a new feature dataset when data type is:
arc.data result of arc.select
named list of vectors, parameters coords and shape_info are required (see Example #5)
data.frame, parameters coords and shape_info are required (see Example #2)
SpatialPointsDataFrame in package sp
SpatialLinesDataFrame in package sp
SpatialPolygonsDataFrame in package sp
Export to a new raster dataset when data type is:
arc.raster result of arc.raster
SpatialPixels, SpatialPixelsDataFrame in package sp (see Example #6)
SpatialGrid in package sp
RasterLayer in package raster (see Example #7)
RasterBrick in package raster
Below are pairs of example paths and the resulting data types:
C:/place.gdb/fc: File Geodatabase Feature Class
C:/place.gdb/fdataset/fc: File Geodatabase Feature Dataset
in_memory\logreg: In-memory workspace (must be run in ArcGIS Session)
C:/place.shp: Esri Shapefile
C:/place.dbf: Table
C:/place.gdb/raster: File Geodatabase Raster when data parameter is arc.raster or Raster* object
C:/image.img: ERDAS Imaging
C:/image.tif: Geo TIFF
To write Date column type corresponding data column must have
POSIXct type (see Example #4).
arc.open,
arc.select,
arc.raster
## Example #1. write a shapefile fc <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) d <- arc.select(fc, 'ozone') d[1,] <- 0.6 arc.write(tempfile("ca_new", fileext=".shp"), d) ## create and write to a new file geodatabase fgdb_path <- file.path(tempdir(), "data.gdb") data(meuse, package="sp") ## Example #2. create feature dataset 'meuse' arc.write(file.path(fgdb_path, "meuse\\pts"), data=meuse, coords=c("x", "y", "elev"), shape_info=list(type='Point',hasZ=TRUE,WKID=28992)) data(meuse.riv, package="sp") riv <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon(meuse.riv)),"meuse.riv"))) ## Example #3. write only geometry arc.write(file.path(fgdb_path, "meuse\\riv"), coords=riv) ## Example #4. write a table t <- Sys.time() # now arc.write(file.path(fgdb_path, "tlb"), data=list( 'f_double'=c(23,45), 'f_string'=c('hello', 'bob'), 'f_datetime'=as.POSIXct(c(t, t - 3600)) # now and an hour ago )) ## Example #5. from scratch as feature class arc.write(file.path(fgdb_path, "fc_pts"), data=list('data'=rnorm(100)), coords=list(x=runif(100,min=0,max=10),y=runif(100,min=0,max=10)), shape_info=list(type='Point')) ## Example #6. write Raster # make SpatialPixelsDataFrame data(meuse.grid, package="sp") sp::coordinates(meuse.grid) = c("x", "y") sp::gridded(meuse.grid) <- TRUE meuse.grid@proj4string=sp::CRS(arc.fromWktToP4(28992)) arc.write(file.path(fgdb_path, "meuse_grid"), meuse.grid) ## Example #7. write using a RasterLayer object r <- raster::raster(ncol=10, nrow=10) raster::values(r) <- runif(raster::ncell(r)) arc.write(file.path(fgdb_path, "raster"), r)## Example #1. write a shapefile fc <- arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")) d <- arc.select(fc, 'ozone') d[1,] <- 0.6 arc.write(tempfile("ca_new", fileext=".shp"), d) ## create and write to a new file geodatabase fgdb_path <- file.path(tempdir(), "data.gdb") data(meuse, package="sp") ## Example #2. create feature dataset 'meuse' arc.write(file.path(fgdb_path, "meuse\\pts"), data=meuse, coords=c("x", "y", "elev"), shape_info=list(type='Point',hasZ=TRUE,WKID=28992)) data(meuse.riv, package="sp") riv <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon(meuse.riv)),"meuse.riv"))) ## Example #3. write only geometry arc.write(file.path(fgdb_path, "meuse\\riv"), coords=riv) ## Example #4. write a table t <- Sys.time() # now arc.write(file.path(fgdb_path, "tlb"), data=list( 'f_double'=c(23,45), 'f_string'=c('hello', 'bob'), 'f_datetime'=as.POSIXct(c(t, t - 3600)) # now and an hour ago )) ## Example #5. from scratch as feature class arc.write(file.path(fgdb_path, "fc_pts"), data=list('data'=rnorm(100)), coords=list(x=runif(100,min=0,max=10),y=runif(100,min=0,max=10)), shape_info=list(type='Point')) ## Example #6. write Raster # make SpatialPixelsDataFrame data(meuse.grid, package="sp") sp::coordinates(meuse.grid) = c("x", "y") sp::gridded(meuse.grid) <- TRUE meuse.grid@proj4string=sp::CRS(arc.fromWktToP4(28992)) arc.write(file.path(fgdb_path, "meuse_grid"), meuse.grid) ## Example #7. write using a RasterLayer object r <- raster::raster(ncol=10, nrow=10) raster::values(r) <- runif(raster::ncell(r)) arc.write(file.path(fgdb_path, "raster"), r)
Create Raster* object from arc.raster TODO
## S4 method for signature 'arc.raster' as.raster(x, kind ,...)## S4 method for signature 'arc.raster' as.raster(x, kind ,...)
x |
arc.raster-class object |
kind |
internal parameter |
... |
. |
return RasterLayer for single band source or RasterBrick
## convert arc.raster to Rasterlayer object r.file <- system.file("pictures", "logo.jpg", package="rgdal") r <- arc.raster(arc.open(r.file)) rx <- as.raster(r)## convert arc.raster to Rasterlayer object r.file <- system.file("pictures", "logo.jpg", package="rgdal") r <- arc.raster(arc.open(r.file)) rx <- as.raster(r)
Convert an ArcGIS arc.data to the equivalent sp data frame
type. The output types that can be generated: SpatialPointsDataFrame,
SpatialLinesDataFrame, or SpatialPolygonsDataFrame.
Convert an arc.raster object to a SpatialGridDataFrame object.
Convert an ArcGIS arc.data to the equivalent sfc object
type. The output types that can be generated:
POINT, MULTIPOINT, POLYGON, MULTIPOLYGON, LINESTRING, MULTILINESTRING.
arc.data2sp(x, ...) arc.data2sf(x, ...)arc.data2sp(x, ...) arc.data2sf(x, ...)
x |
|
... |
optional additional argument such |
sp::Spatial*DataFrame object.
sf::sfc object.
arc.open,
arc.select
arc.raster
d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), 'ozone') require("sp") df.sp <- arc.data2sp(d) ## Not run: spplot(df.sp) require("sf") df.sf <- arc.data2sf(d) ## Not run: plot(df.sf)d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), 'ozone') require("sp") df.sp <- arc.data2sp(d) ## Not run: spplot(df.sp) require("sf") df.sf <- arc.data2sf(d) ## Not run: plot(df.sf)
Convert arc.shape-class to sp spatial geometry:
SpatialPoints, SpatialLines, or SpatialPolygons.
Similar to arc.data2sp.
Convert arc.shape-class to sfc simple feature geometry:
POINT, MULTIPOINT, POLYGON, MULTIPOLYGON, LINESTRING, MULTILINESTRING.
Similar to arc.data2sf.
arc.shape2sp(shape, ...) arc.shape2sf(shape, ...)arc.shape2sp(shape, ...) arc.shape2sf(shape, ...)
shape |
|
... |
optional |
an object of class sp::Spatial*.
an object of class sf::sfc, which is a classed list-column with simple feature geometries.
arc.shape,
arc.data2sp
arc.data2sf
d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), 'ozone') x <- arc.shape(d) geom <- arc.shape2sp(x) ## Not run: plot(geom) geom <- arc.shape2sf(x) ## Not run: plot(geom)d <- arc.select(arc.open(system.file("extdata", "ca_ozone_pts.shp", package="arcgisbinding")), 'ozone') x <- arc.shape(d) geom <- arc.shape2sp(x) ## Not run: plot(geom) geom <- arc.shape2sf(x) ## Not run: plot(geom)
The arc.portal_connect() function to sign in to a portal. To check available portals call arc.check_portal().
Functions returns a list that contains active info and available portals.
arc.portal_connect(url, user, password) arc.check_portal()arc.portal_connect(url, user, password) arc.check_portal()
url |
The URL of the portal to be signed in to. (character) |
user |
The user name of the user signing in to the portal. (character) |
password |
The password of the user signing in to the portal. (character) |
If url already in active list of portals connections then user and password parameters are optional
An named list of portal connections.
url - The URL of the current portal.
user - The user name.
version - The portal version.
organization - The organization name.
session - TODO.
token - This is the Enterprise token for built-in logins.
portals - list of active portals.
offlines - list of offline portals.
The following table shows the pixel_type value and the range of values stored for different bit depths:
| Pixel type | Bit depth | Range of values that each cell can contain |
"U1" |
1 bit | 0 to 1 |
"U2" |
2 bits | 0 to 3 |
"U4" |
4 bits | 0 to 15 |
"U8" |
Unsigned 8 bit integers | 0 to 255 |
"S8" |
8 bit integers | -128 to 128 |
"U16" |
Unsigned 16 bit integers | 0 to 65535 |
"S16" |
16 bit integers | -32768 to 32767 |
"U32" |
Unsigned 32 bit integers | 0 to 4294967295 |
"S32" |
32 bit integers | -2147483648 to 2147483647 |
"F32" |
32 bit Single precision floating point | -3.402823466e+38 to 3.402823466e+38 |
"F64" |
64 bit Double precision floating point | 0 to 18446744073709551616 |
The following table shows the resamp_type value:
| Resample type | Definition |
"NearestNeighbor" |
- Performs a nearest neighbor assignment and is the fastest of the interpolation methods. This is the default. |
"BilinearInterpolation" |
- Performs a bilinear interpolation and determines the new value of a cell based on a weighted distance average of the four nearest input cell centers. |
"CubicConvolution" |
- Performs a cubic convolution and determines the new value of a cell based on fitting a smooth curve through the 16 nearest input cell centers. |
"Majority" |
- Performs a majority algorithm and determines the new value of the cell based on the most popular values within the filter window. |
"BilinearInterpolationPlus" |
TODO |
"BilinearGaussBlur" |
TODO |
"BilinearGaussBlurPlus" |
TODO |
"Average" |
TODO |
"Minimum" |
TODO |
"Average" |
TODO |
"VectorAverage" |
TODO |
Note The Bilinear and Cubic options should not be used with categorical data, since the cell values may be altered.
The following table shows the compression_type value:
| Compression type | Lossy or lossless | Notes |
"LZ77" |
Lossless | |
"JPEG" |
Lossy | Can define a compression quality |
"JPEG 2000" |
Lossy or lossless | Can define a compression quality |
"PackBits" |
Lossless | Applies to TIFF only |
"LZW" |
Lossless | |
"RLE" |
Lossless | |
"CCITT GROUP 3" |
Lossless | Applies to TIFF only |
"CCITT GROUP 4" |
Lossless | Applies to TIFF only |
"CCITT (1D)" |
Lossless | Applies to TIFF only |
"None" |
No data compression | |
Geoprocessing tools have a progressor, which includes both a progress
label and a progress bar. The default progressor continuously moves back
and forth to indicate the script is running. Using
arc.progress_label and arc.progress_pos
allows fine control over the script progress. Updating the progressor
isn't necessary, but is useful in situations where solely outputting messages
to the dialog is insufficient to communicate script progress.
arc.progress_label(label) arc.progress_pos(pos = -1)arc.progress_label(label) arc.progress_pos(pos = -1)
label |
Progress Label |
pos |
Progress position (in percent) |
Using arc.progress_label allows control over the label that is
displayed at the top of the running script. For example, it might be used
to display the current step of the analysis taking place.
Using arc.progress_pos allows control over the progrssor position
displayed at the top of the running script. The position is an integer percentage,
0 to 100, that the progress bar should be set to, with 100 indicating
the script has completed (100%).
Setting the position to -1 resets the progressor to the default progressor, which continuously moves to indicate the script is running.
Understanding the progressor in script tools
Currently only functions in ArcGIS Pro, and has no effect in ArcGIS Desktop.
This function is only available from within an ArcGIS session, and has no effect when run from the command line or in background geoprocessing.
arc.progress_pos,
"Progress Messages" example Geoprocessing script
## Not run: arc.progress_label("Calculating bootstrap samples...") arc.progress_pos(55) ## End(Not run)## Not run: arc.progress_label("Calculating bootstrap samples...") arc.progress_pos(55) ## End(Not run)