geojson
aims to deal only with geojson data in a lightweight way.
We’ve defined classes (S3
) following the GeoJSON spec. These classes sort of overlap with sp
’s classes, but not really. There’s also some overlap in GeoJSON classes with Well-Known Text (WKT) classes, but GeoJSON has a subset of WKT’s classes.
The package geoops supports manipulations on the classes defined in this package. This package is used within geojsonio to make some tasks easier.
Installation
Stable CRAN version
install.packages("geojson")
Dev version
remotes::install_github("ropensci/geojson")
geojson class
Essentially a character string with S3 class geojson
attached to make it easy to perform operations on
x <- "{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Point\",\"coordinates\":[-99.74,32.45]},\"properties\":{}}]}"
as.geojson(x)
#> <geojson>
#> type: FeatureCollection
#> features (n): 1
#> features (geometry / length) [first 5]:
#> Point / 2
geometrycollection
x <- '{
"type": "GeometryCollection",
"geometries": [
{
"type": "Point",
"coordinates": [100.0, 0.0]
},
{
"type": "LineString",
"coordinates": [ [101.0, 0.0], [102.0, 1.0] ]
}
]
}'
(y <- geometrycollection(x))
#> <GeometryCollection>
#> geometries (n): 2
#> geometries (geometry / length):
#> Point / 2
#> LineString / 2
inspect the object
get the string
y[[1]]
#> [1] "{\n \"type\": \"GeometryCollection\",\n \"geometries\": [\n {\n \"type\": \"Point\",\n \"coordinates\": [100.0, 0.0]\n },\n {\n \"type\": \"LineString\",\n \"coordinates\": [ [101.0, 0.0], [102.0, 1.0] ]\n }\n ]\n}"
get the type
geo_type(y)
#> [1] "GeometryCollection"
pretty print the geojson
geo_pretty(y)
#> {
#> "type": "GeometryCollection",
#> "geometries": [
#> {
#> "type": "Point",
#> "coordinates": [
#> 100.0,
#> 0.0
#> ]
#> },
#> {
#> "type": "LineString",
#> "coordinates": [
#> [
#> 101.0,
#> 0.0
#> ],
#> [
#> 102.0,
#> 1.0
#> ]
#> ]
#> }
#> ]
#> }
#>
write to disk
geo_write(y, f <- tempfile(fileext = ".geojson"))
jsonlite::fromJSON(f, FALSE)
#> $type
#> [1] "GeometryCollection"
#>
#> $geometries
#> $geometries[[1]]
#> $geometries[[1]]$type
#> [1] "Point"
#>
#> $geometries[[1]]$coordinates
#> $geometries[[1]]$coordinates[[1]]
#> [1] 100
#>
#> $geometries[[1]]$coordinates[[2]]
#> [1] 0
#>
#>
#>
#> $geometries[[2]]
#> $geometries[[2]]$type
#> [1] "LineString"
#>
#> $geometries[[2]]$coordinates
#> $geometries[[2]]$coordinates[[1]]
#> $geometries[[2]]$coordinates[[1]][[1]]
#> [1] 101
#>
#> $geometries[[2]]$coordinates[[1]][[2]]
#> [1] 0
#>
#>
#> $geometries[[2]]$coordinates[[2]]
#> $geometries[[2]]$coordinates[[2]][[1]]
#> [1] 102
#>
#> $geometries[[2]]$coordinates[[2]][[2]]
#> [1] 1
properties
Add properties
x <- '{ "type": "LineString", "coordinates": [ [100.0, 0.0], [101.0, 1.0] ]}'
res <- linestring(x) %>% feature() %>% properties_add(population = 1000)
res
#> <Feature>
#> type: LineString
#> coordinates: [[100,0],[101,1]]
Get a property
properties_get(res, property = 'population')
#> 1000
crs
Add crs
crs <- '{
"type": "name",
"properties": {
"name": "urn:ogc:def:crs:OGC:1.3:CRS84"
}
}'
z <- x %>% feature() %>% crs_add(crs)
z
#> {
#> "type": "Feature",
#> "properties": {
#>
#> },
#> "geometry": {
#> "type": "LineString",
#> "coordinates": [
#> [
#> 100,
#> 0
#> ],
#> [
#> 101,
#> 1
#> ]
#> ]
#> },
#> "crs": {
#> "type": "name",
#> "properties": {
#> "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
#> }
#> }
#> }
Get crs
crs_get(z)
#> $type
#> [1] "name"
#>
#> $properties
#> $properties$name
#> [1] "urn:ogc:def:crs:OGC:1.3:CRS84"
bbox
Add bbox
tt <- x %>% feature() %>% bbox_add()
tt
#> {
#> "type": "Feature",
#> "properties": {
#>
#> },
#> "geometry": {
#> "type": "LineString",
#> "coordinates": [
#> [
#> 100,
#> 0
#> ],
#> [
#> 101,
#> 1
#> ]
#> ]
#> },
#> "bbox": [
#> 100,
#> 0,
#> 101,
#> 1
#> ]
#> }
Get bbox
bbox_get(tt)
#> [1] 100 0 101 1
geojson in data.frame’s
x <- '{ "type": "Point", "coordinates": [100.0, 0.0] }'
(pt <- point(x))
#> <Point>
#> coordinates: [100,0]
library("tibble")
tibble(a = 1:5, b = list(pt))
#> # A tibble: 5 × 2
#> a b
#> <int> <list>
#> 1 1 <geopoint [1]>
#> 2 2 <geopoint [1]>
#> 3 3 <geopoint [1]>
#> 4 4 <geopoint [1]>
#> 5 5 <geopoint [1]>
x <- '{ "type": "MultiLineString",
"coordinates": [ [ [100.0, 0.0], [101.0, 1.0] ], [ [102.0, 2.0], [103.0, 3.0] ] ] }'
(mls <- multilinestring(x))
#> <MultiLineString>
#> no. lines: 2
#> no. nodes / line: 2, 2
#> coordinates: [[[100,0],[101,1]],[[102,2],[103,3]]]
geobuf
Geobuf is a compact binary encoding for geographic data using protocol buffers https://github.com/mapbox/geobuf (via the protolite) package.
file <- system.file("examples/test.pb", package = "geojson")
(json <- from_geobuf(file))
#> {"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[102,0.5]},"id":999,"properties":{"prop0":"value0","double":0.0123,"negative_int":-100,"positive_int":100,"negative_double":-1.2345e+16,"positive_double":1.2345e+16,"null":null,"array":[1,2,3.1],"object":{"foo":[5,6,7]}},"blabla":{"foo":[1,1,1]}},{"type":"Feature","geometry":{"type":"LineString","coordinates":[[102,0],[103,-1.1],[104,-3],[105,1]]},"id":123,"properties":{"custom1":"test","prop0":"value0","prop1":0}},{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[99,10],[101,0],[100,1],[99,10]]]},"id":"test-id","properties":{"prop0":"value0","prop1":{"this":"that"}},"custom1":"jeroen"},{"type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[[[[102,2],[103,2],[103,3],[102,2]]],[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[100.2,0.2],[100.2,0.8],[100.2,0.2]]]]}},{"type":"Feature","geometry":{"type":"GeometryCollection","geometries":[{"type":"Point","coordinates":[100,0]},{"type":"LineString","coordinates":[[101,0],[102,1]]},{"type":"MultiPoint","coordinates":[[100,0],[101,1]]},{"type":"MultiLineString","coordinates":[[[100,0],[101,1]],[[102,2],[103,3]]]},{"type":"MultiPolygon","coordinates":[[[[102,2],[103,2],[103,3],[102,3],[102,2]]],[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[100.2,0.2],[100.8,0.2],[100.8,0.8],[100.2,0.8],[100.2,0.2]]]]}]}}]}
pb <- to_geobuf(json)
class(pb)
#> [1] "raw"
f <- tempfile(fileext = ".pb")
to_geobuf(json, f)
from_geobuf(f)
#> {"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[102,0.5]},"id":999,"properties":{"prop0":"value0","double":0.0123,"negative_int":-100,"positive_int":100,"negative_double":-1.2345e+16,"positive_double":1.2345e+16,"null":null,"array":[1,2,3.1],"object":{"foo":[5,6,7]}},"blabla":{"foo":[1,1,1]}},{"type":"Feature","geometry":{"type":"LineString","coordinates":[[102,0],[103,-1.1],[104,-3],[105,1]]},"id":123,"properties":{"custom1":"test","prop0":"value0","prop1":0}},{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[99,10],[101,0],[100,1],[99,10]]]},"id":"test-id","properties":{"prop0":"value0","prop1":{"this":"that"}},"custom1":"jeroen"},{"type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[[[[102,2],[103,2],[103,3],[102,2]]],[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[100.2,0.2],[100.2,0.8],[100.2,0.2]]]]}},{"type":"Feature","geometry":{"type":"GeometryCollection","geometries":[{"type":"Point","coordinates":[100,0]},{"type":"LineString","coordinates":[[101,0],[102,1]]},{"type":"MultiPoint","coordinates":[[100,0],[101,1]]},{"type":"MultiLineString","coordinates":[[[100,0],[101,1]],[[102,2],[103,3]]]},{"type":"MultiPolygon","coordinates":[[[[102,2],[103,2],[103,3],[102,3],[102,2]]],[[[100,0],[101,0],[101,1],[100,1],[100,0]],[[100.2,0.2],[100.8,0.2],[100.8,0.8],[100.2,0.8],[100.2,0.2]]]]}]}}]}
x <- '{ "type": "Polygon",
"coordinates": [
[ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ]
]
}'
y <- polygon(x)
to_geobuf(y)
#> [1] 10 02 18 06 2a 1a 0a 18 08 04 12 01 04 1a 11 80 84 af 5f 00 80 89 7a 00 00
#> [26] 80 89 7a ff 88 7a 00
x <- '{"type": "MultiPoint", "coordinates": [ [100.0, 0.0], [101.0, 1.0] ] }'
y <- multipoint(x)
to_geobuf(y)
#> [1] 10 02 18 06 2a 11 0a 0f 08 01 1a 0b 80 84 af 5f 00 80 89 7a 80 89 7a
newline-delimited GeoJSON
read nd-GeoJSON
url <- "https://raw.githubusercontent.com/ropensci/geojson/main/inst/examples/ndgeojson1.json"
f <- tempfile(fileext = ".geojsonl")
download.file(url, f)
x <- ndgeo_read(f, verbose = FALSE)
x
#> <geojson>
#> type: FeatureCollection
#> features (n): 3
#> features (geometry / length) [first 5]:
#> Point / 2
#> Point / 2
#> Point / 2
Write nd-GeoJSON
One would think we could take the output of ndego_read()
above and pass to ndgeo_write()
. However, in this example, the json is too big for jqr
to handle, the underlying parser tool. So here’s a smaller example:
file <- system.file("examples", "featurecollection2.geojson",
package = "geojson")
str <- paste0(readLines(file), collapse = " ")
(x <- featurecollection(str))
#> <FeatureCollection>
#> type: FeatureCollection
#> no. features: 3
#> features (1st 5): Point, Point, Point
outfile <- tempfile(fileext = ".geojson")
ndgeo_write(x, outfile)
jsonlite::stream_in(file(outfile))
#> Found 3 records... Imported 3 records. Simplifying...
#> type id properties.NOME
#> 1 Feature 0 Sec de segunrança
#> 2 Feature 1 Teste
#> 3 Feature 3 Delacorte Theater
#> properties.URL
#> 1 http://www.theatermania.com/new-york/theaters/45th-street-theatre_2278/
#> 2 http://www.bestofoffbroadway.com/theaters/47streettheatre.html
#> 3 http://www.centralpark.com/pages/attractions/delacorte-theatre.html
#> properties.ADDRESS1 properties.CIDADE properties.CEP
#> 1 354 West 45th Street Goiânia 74250010
#> 2 304 West 47th Street New York NA
#> 3 Central Park - Mid-Park at 80th Street New York NA
#> properties.ZIP geometry.type geometry.coordinates
#> 1 NA Point -49.25624, -16.68961
#> 2 74250010 Point -49.27624, -16.65561
#> 3 74250010 Point -49.27726, -16.67906
Meta
- Please report any issues or bugs.
- License: MIT
- Get citation information for
geojson
in R doingcitation(package = 'geojson')
- Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
(This was originally setup without requiring any of the GEOS/GDAL
stack but now the package sp depends on sf it can’t be avoided without overhaul).