convert objects into a stars object
Source:R/cubble.R, R/ncdf.R, R/OpenStreetMap.R, and 6 more
st_as_stars.Rdconvert objects into a stars object
Usage
# S3 method for class 'cubble_df'
st_as_stars(.x, ..., check_times = FALSE)
# S3 method for class 'ncdfgeom'
st_as_stars(.x, ..., sf_geometry = NA)
# S3 method for class 'OpenStreetMap'
st_as_stars(.x, ..., as_col = FALSE)
# S3 method for class 'stars_proxy'
st_as_stars(
.x,
...,
downsample = 0,
url = attr(.x, "url"),
envir = parent.frame()
)
# S3 method for class 'data.frame'
st_as_stars(.x, ..., dims = coords, xy, y_decreasing = TRUE, coords = 1:2)
# S3 method for class 'Raster'
st_as_stars(.x, ..., att = 1, ignore_file = FALSE)
# S3 method for class 'SpatRaster'
st_as_stars(
.x,
...,
ignore_file = FALSE,
as_attributes = all(terra::is.factor(.x))
)
# S3 method for class 'sf'
st_as_stars(.x, ..., dims = attr(.x, "sf_column"))
st_as_stars(.x, ...)
# S3 method for class 'list'
st_as_stars(.x, ..., dimensions = NULL)
# Default S3 method
st_as_stars(.x = NULL, ..., raster = NULL)
# S3 method for class 'stars'
st_as_stars(.x, ..., curvilinear = NULL, crs = st_crs("OGC:CRS84"))
# S3 method for class 'bbox'
st_as_stars(
.x,
...,
nx,
ny,
dx = dy,
dy = dx,
xlim = .x[c("xmin", "xmax")],
ylim = .x[c("ymin", "ymax")],
values = 0,
n = 64800,
pretty = FALSE,
inside = FALSE,
nz,
proxy = FALSE
)
# S3 method for class 'xts'
st_as_stars(.x, ..., dimensions, name = "attr")Arguments
- .x
object to convert
- ...
in case
.xis of classbbox, arguments passed on to pretty. In case.xis of classnc_proxy, arguments passed on toread_ncdf.- check_times
logical; should we check that the time stamps of all time series are identical?
- sf_geometry
sf data.frame with geometry and attributes to be added to stars object. Must have same number of rows as timeseries instances.
- as_col
logical; return rgb numbers (FALSE) or (character) color values (TRUE)?
- downsample
integer: if larger than 0, downsample with this rate (number of pixels to skip in every row/column); if length 2, specifies downsampling rate in x and y.
- url
character; URL of the stars endpoint where the data reside
- envir
environment to resolve objects in
- dims
the column names or indices that form the cube dimensions
- xy
the x and y raster dimension names or indices; only takes effect after
dimshas been specified, see details- y_decreasing
logical; if TRUE, (numeric) y values get a negative delta (decrease with increasing index)
- coords
same as dims, for symmetry with st_as_sf
- att
see factorValues; column in the RasterLayer's attribute table
- ignore_file
logical; if
TRUE, ignore the SpatRaster object file name- as_attributes
logical; if
TRUEand.xhas more than one layer, load these as separate attributes rather than as a band or time dimension (only implemented for the case whereignore_fileisTRUE)- dimensions
object of class dimensions
- raster
character; the names of the dimensions that denote raster dimensions
- curvilinear
only for creating curvilinear grids: named length 2 list holding longitude and latitude matrices or stars arrays, or the names of the corresponding attributes in
.x; the names of this vector should correspond to raster dimensions the matrices are associated with; see Details.- crs
object of class
crswith the coordinate reference system of the values incurvilinear; see details- nx
integer; number of cells in x direction; see details
- ny
integer; number of cells in y direction; see details
- dx
numeric or object of class units; cell size in x direction; see details
- dy
numeric or object of class units; cell size in y direction; see details
- xlim
length 2 numeric vector with extent (min, max) in x direction
- ylim
length 2 numeric vector with extent (min, max) in y direction
- values
value(s) to populate the raster values with
- n
the (approximate) target number of grid cells
- pretty
logical; should cell coordinates have pretty values?
- inside
logical; should all cells entirely fall inside the bbox, potentially not covering it completely (
TRUE), or always cover the bbox (FALSE), or find a good approximation (NA, default)?- nz
integer; number of cells in z direction; if missing no z-dimension is created.
- proxy
logical; should a
stars_proxyobject be created? (requires gdal_create binary when sf < 1.0-6)- name
character; attribute name for array from an
xtsobject
Details
For the ncdfgeom method: objects are point-timeseries with optional line or polygon geometry for each timeseries specified with the sf_geometry parameter. See ncdfgeom for more about this NetCDF-based format for geometry and timeseries.
If xy is not specified and the first two dimensions in dims are both numeric,
then it is set to these two dimensions.
The st_as_stars method for sf objects without any additional
arguments returns a one-dimensional data cube with a dimension for the simple features
geometries, and all remaining attributes as data cube attributes. When used with
further arguments, the method for data.frames is called.
if curvilinear is a list with stars objects with longitude and latitude values, its coordinate reference system is typically not that of the latitude and longitude values. If curvilinear contains the names of two arrays in .x, then these are removed from the returned object.
For the bbox method: if pretty is TRUE, raster cells may extend the coordinate range of .x on all sides. If in addition to nx and ny, dx and dy are also missing, these are set to a single value computed as sqrt(diff(xlim)*diff(ylim)/n).
If nx and ny are missing and values is a matrix, the number of columns and rows of the matrix are taken.
Otherwise, if nx and ny are missing, they are computed as the (ceiling, floor, or rounded to integer value) of the ratio of the (x or y) range divided by (dx or dy), depending on the value of inside. Positive dy will be made negative. Further named arguments (...) are passed on to pretty. If dx or dy are units objects, their value is converted to the units of st_crs(.x) (only when sf >= 1.0-7).
for the xts methods, if dimensions are provided, time has to be the first dimension.
Examples
if (require(plm, quietly = TRUE)) {
data(Produc, package = "plm")
st_as_stars(Produc)
}
#>
#> Attaching package: ‘plm’
#> The following objects are masked from ‘package:dplyr’:
#>
#> between, lag, lead
#> stars object with 2 dimensions and 9 attributes
#> attribute(s):
#> region pcap hwy water
#> 5 :136 Min. : 2627 Min. : 1827 Min. : 228.5
#> 8 :136 1st Qu.: 7097 1st Qu.: 3858 1st Qu.: 764.5
#> 4 :119 Median : 17572 Median : 7556 Median : 2266.5
#> 1 :102 Mean : 25037 Mean :10218 Mean : 3618.8
#> 3 : 85 3rd Qu.: 27692 3rd Qu.:11267 3rd Qu.: 4318.7
#> 6 : 68 Max. :140217 Max. :47699 Max. :24592.3
#> (Other):170
#> util pc gsp emp
#> Min. : 538.5 Min. : 4053 Min. : 4354 Min. : 108.3
#> 1st Qu.: 2488.3 1st Qu.: 21651 1st Qu.: 16502 1st Qu.: 475.0
#> Median : 7008.8 Median : 40671 Median : 39987 Median : 1164.8
#> Mean :11199.4 Mean : 58188 Mean : 61014 Mean : 1747.1
#> 3rd Qu.:11598.5 3rd Qu.: 64796 3rd Qu.: 68126 3rd Qu.: 2114.1
#> Max. :80728.1 Max. :375342 Max. :464550 Max. :11258.0
#>
#> unemp
#> Min. : 2.800
#> 1st Qu.: 5.000
#> Median : 6.200
#> Mean : 6.602
#> 3rd Qu.: 7.900
#> Max. :18.000
#>
#> dimension(s):
#> from to offset delta values
#> state 1 48 NA NA ALABAMA,...,WYOMING
#> year 1 17 1970 1 NULL
if (require(dplyr, quietly = TRUE)) {
# https://stackoverflow.com/questions/77368957/
spatial_dim <- st_sf(
ID = 1:3,
geometry = list(
st_polygon(list(
cbind(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0))
)),
st_polygon(list(
cbind(c(1, 2, 2, 1, 1), c(0, 0, 1, 1, 0))
)),
st_polygon(list(
cbind(c(2, 3, 3, 2, 2), c(0, 0, 1, 1, 0))
))
)
)
weekdays_dim <- data.frame(weekdays = c("Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday", "Sunday"))
hours_dim <- data.frame(hours = c("8am", "11am", "4pm", "11pm"))
sf_dta <- spatial_dim |>
cross_join(weekdays_dim)|>
cross_join(hours_dim) |>
mutate(population = rnorm(n(), mean = 1000, sd = 200)) |>
select(everything(), geometry)
st_as_stars(sf_dta, dims = c("weekdays", "hours", "geometry"))
}
#> stars object with 3 dimensions and 2 attributes
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> ID 1.0000 1.0000 2.000 2.000 3.000 3.000
#> population 594.2642 903.1372 1013.056 1003.978 1106.926 1324.842
#> dimension(s):
#> from to point
#> weekdays 1 7 NA
#> hours 1 4 NA
#> geometry 1 3 FALSE
#> values
#> weekdays Monday,...,Sunday
#> hours 8am,...,11pm
#> geometry POLYGON ((0 0, 1 0, 1 1, ...,...,POLYGON ((2 0, 3 0, 3 1, ...
demo(nc, echo=FALSE,ask=FALSE)
st_as_stars(nc)
#> stars object with 1 dimensions and 14 attributes
#> attribute(s):
#> AREA PERIMETER CNTY_ CNTY_ID
#> Min. :0.0420 Min. :0.999 Min. :1825 Min. :1825
#> 1st Qu.:0.0910 1st Qu.:1.324 1st Qu.:1902 1st Qu.:1902
#> Median :0.1205 Median :1.609 Median :1982 Median :1982
#> Mean :0.1263 Mean :1.673 Mean :1986 Mean :1986
#> 3rd Qu.:0.1542 3rd Qu.:1.859 3rd Qu.:2067 3rd Qu.:2067
#> Max. :0.2410 Max. :3.640 Max. :2241 Max. :2241
#> NAME FIPS FIPSNO CRESS_ID
#> Length:100 Length:100 Min. :37001 Min. : 1.00
#> Class :character Class :character 1st Qu.:37050 1st Qu.: 25.75
#> Mode :character Mode :character Median :37100 Median : 50.50
#> Mean :37100 Mean : 50.50
#> 3rd Qu.:37150 3rd Qu.: 75.25
#> Max. :37199 Max. :100.00
#> BIR74 SID74 NWBIR74 BIR79
#> Min. : 248 Min. : 0.00 Min. : 1.0 Min. : 319
#> 1st Qu.: 1077 1st Qu.: 2.00 1st Qu.: 190.0 1st Qu.: 1336
#> Median : 2180 Median : 4.00 Median : 697.5 Median : 2636
#> Mean : 3300 Mean : 6.67 Mean :1050.8 Mean : 4224
#> 3rd Qu.: 3936 3rd Qu.: 8.25 3rd Qu.:1168.5 3rd Qu.: 4889
#> Max. :21588 Max. :44.00 Max. :8027.0 Max. :30757
#> SID79 NWBIR79
#> Min. : 0.00 Min. : 3.0
#> 1st Qu.: 2.00 1st Qu.: 250.5
#> Median : 5.00 Median : 874.5
#> Mean : 8.36 Mean : 1352.8
#> 3rd Qu.:10.25 3rd Qu.: 1406.8
#> Max. :57.00 Max. :11631.0
#> dimension(s):
#> from to refsys point
#> geom 1 100 NAD27 FALSE
#> values
#> geom MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572...
st_as_stars(st_drop_geometry(nc), dims = "NAME")
#> stars object with 1 dimensions and 13 attributes
#> attribute(s):
#> AREA PERIMETER CNTY_ CNTY_ID
#> Min. :0.0420 Min. :0.999 Min. :1825 Min. :1825
#> 1st Qu.:0.0910 1st Qu.:1.324 1st Qu.:1902 1st Qu.:1902
#> Median :0.1205 Median :1.609 Median :1982 Median :1982
#> Mean :0.1263 Mean :1.673 Mean :1986 Mean :1986
#> 3rd Qu.:0.1542 3rd Qu.:1.859 3rd Qu.:2067 3rd Qu.:2067
#> Max. :0.2410 Max. :3.640 Max. :2241 Max. :2241
#> FIPS FIPSNO CRESS_ID BIR74
#> Length:100 Min. :37001 Min. : 1.00 Min. : 248
#> Class :character 1st Qu.:37050 1st Qu.: 25.75 1st Qu.: 1077
#> Mode :character Median :37100 Median : 50.50 Median : 2180
#> Mean :37100 Mean : 50.50 Mean : 3300
#> 3rd Qu.:37150 3rd Qu.: 75.25 3rd Qu.: 3936
#> Max. :37199 Max. :100.00 Max. :21588
#> SID74 NWBIR74 BIR79 SID79
#> Min. : 0.00 Min. : 1.0 Min. : 319 Min. : 0.00
#> 1st Qu.: 2.00 1st Qu.: 190.0 1st Qu.: 1336 1st Qu.: 2.00
#> Median : 4.00 Median : 697.5 Median : 2636 Median : 5.00
#> Mean : 6.67 Mean :1050.8 Mean : 4224 Mean : 8.36
#> 3rd Qu.: 8.25 3rd Qu.:1168.5 3rd Qu.: 4889 3rd Qu.:10.25
#> Max. :44.00 Max. :8027.0 Max. :30757 Max. :57.00
#> NWBIR79
#> Min. : 3.0
#> 1st Qu.: 250.5
#> Median : 874.5
#> Mean : 1352.8
#> 3rd Qu.: 1406.8
#> Max. :11631.0
#> dimension(s):
#> from to values
#> NAME 1 100 Ashe,...,Brunswick
data.frame(expand.grid(x=1:5, y = 1:5), z = rnorm(25)) |> st_as_stars()
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> z -2.194688 -1.452389 -0.5143981 -0.4837413 0.2901747 1.627052
#> dimension(s):
#> from to offset delta x/y
#> x 1 5 0.5 1 [x]
#> y 1 5 5.5 -1 [y]
nc = st_read(system.file("gpkg/nc.gpkg", package="sf"))
#> Reading layer `nc.gpkg' from data source
#> `/home/runner/work/_temp/Library/sf/gpkg/nc.gpkg' using driver `GPKG'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
st_as_stars(nc)
#> stars object with 1 dimensions and 14 attributes
#> attribute(s):
#> AREA PERIMETER CNTY_ CNTY_ID
#> Min. :0.0420 Min. :0.999 Min. :1825 Min. :1825
#> 1st Qu.:0.0910 1st Qu.:1.324 1st Qu.:1902 1st Qu.:1902
#> Median :0.1205 Median :1.609 Median :1982 Median :1982
#> Mean :0.1263 Mean :1.673 Mean :1986 Mean :1986
#> 3rd Qu.:0.1542 3rd Qu.:1.859 3rd Qu.:2067 3rd Qu.:2067
#> Max. :0.2410 Max. :3.640 Max. :2241 Max. :2241
#> NAME FIPS FIPSNO CRESS_ID
#> Length:100 Length:100 Min. :37001 Min. : 1.00
#> Class :character Class :character 1st Qu.:37050 1st Qu.: 25.75
#> Mode :character Mode :character Median :37100 Median : 50.50
#> Mean :37100 Mean : 50.50
#> 3rd Qu.:37150 3rd Qu.: 75.25
#> Max. :37199 Max. :100.00
#> BIR74 SID74 NWBIR74 BIR79
#> Min. : 248 Min. : 0.00 Min. : 1.0 Min. : 319
#> 1st Qu.: 1077 1st Qu.: 2.00 1st Qu.: 190.0 1st Qu.: 1336
#> Median : 2180 Median : 4.00 Median : 697.5 Median : 2636
#> Mean : 3300 Mean : 6.67 Mean :1050.8 Mean : 4224
#> 3rd Qu.: 3936 3rd Qu.: 8.25 3rd Qu.:1168.5 3rd Qu.: 4889
#> Max. :21588 Max. :44.00 Max. :8027.0 Max. :30757
#> SID79 NWBIR79
#> Min. : 0.00 Min. : 3.0
#> 1st Qu.: 2.00 1st Qu.: 250.5
#> Median : 5.00 Median : 874.5
#> Mean : 8.36 Mean : 1352.8
#> 3rd Qu.:10.25 3rd Qu.: 1406.8
#> Max. :57.00 Max. :11631.0
#> dimension(s):
#> from to refsys point
#> geom 1 100 NAD27 FALSE
#> values
#> geom MULTIPOLYGON (((-81.47276...,...,MULTIPOLYGON (((-78.65572...