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Read an ESRI Shapefile into a record batch stream

Source: R/extendr-wrappers.R
read_shapefile.Rd

Reads the .shp geometries together with the .dbf attributes. It takes the CRS from the sibling .prj file when one is present.

Usage

read_shapefile(path)

Arguments

path

path to a .shp file. read_shapefile() resolves the sibling .dbf and .prj files from the same base name.

Value

a nanoarrow_array_stream of a single record batch, holding the .dbf attribute columns followed by a geometry column.

Details

read_shapefile() reads polylines as multilinestrings and polygons as multipolygons, because a shapefile record of either type may contain multiple parts.

It chooses the coordinate dimension once per file, from the shape type and from whether the coordinates actually carry measures. It therefore reads a PolygonZ file with no measures as XYZ, rather than as XYZM full of nulls.

shape typemeasures presentdimension
Point, Polyline, Polygon, Multipointnot applicableXY
PointM, PolylineM, PolygonM, MultipointMnoXY
PointM, PolylineM, PolygonM, MultipointMyesXYM
PointZ, PolylineZ, PolygonZ, MultipointZnoXYZ
PointZ, PolylineZ, PolygonZ, MultipointZyesXYZM

If only some coordinates carry a measure, read_shapefile() drops M and emits a message, because Arrow cannot mix dimensions in one column.

read_shapefile() does not support Multipatch files.

See also

Other io: read_flatgeobuf(), read_geojson()

Examples

path <- system.file("shape/nc.shp", package = "sf")

# read into a nanoarrow array stream
res <- read_shapefile(path)
res
#> <nanoarrow_array_stream struct<AREA: double, PERIMETER: double, CNTY_: double, CNTY_ID: double, NAME: string, FIPS: string, FIPSNO: double, CRESS_ID: double, BIR74: double, SID74: double, NWBIR74: double, BIR79: double, SID79: double, NWBIR79: double, geometry: geoarrow.multipolygon{list<polygons: list<rings: list<vertices: struct<x: double, y: double>>>>}>>
#>  $ get_schema:function ()  
#>  $ get_next  :function (schema = x$get_schema(), validate = TRUE)  
#>  $ release   :function ()  

# convert to a df
df <- as.data.frame(res)
head(df)
#>    AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
#> 1 0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1
#> 2 0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0
#> 3 0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5
#> 4 0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1
#> 5 0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9
#> 6 0.097     1.670  1833    1833    Hertford 37091  37091       46  1452     7
#>   NWBIR74 BIR79 SID79 NWBIR79
#> 1      10  1364     0      19
#> 2      10   542     3      12
#> 3     208  3616     6     260
#> 4     123   830     2     145
#> 5    1066  1606     3    1197
#> 6     954  1838     5    1237
#>                                                                   geometry
#> 1 <MULTIPOLYGON (((-81.4727554 36.2343559, -81.5408401 36.2725067, -81.56>
#> 2 <MULTIPOLYGON (((-81.2398911 36.3653641, -81.2406921 36.3794174, -81.26>
#> 3 <MULTIPOLYGON (((-80.4563446 36.2425575, -80.476387 36.2547264, -80.536>
#> 4 <MULTIPOLYGON (((-76.0089722 36.3195953, -76.0173492 36.3377304, -76.03>
#> 5 <MULTIPOLYGON (((-77.2176666 36.2409821, -77.2346115 36.2145996, -77.29>
#> 6 <MULTIPOLYGON (((-76.7450638 36.2339172, -76.98069 36.2302361, -76.9947>