Classes for 'GeoJSON'

Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; linting through the 'geojsonlint' package; and serializing to/from 'Geobuf' binary 'GeoJSON' format.

geojson aims to deal only with geojson data, without requiring any of the sp/rgdal/rgeos stack. That means this package is light weight.

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.

Stable CRAN version

install.packages("geojson")

Dev version

devtools::install_github("ropensci/geojson")

library("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 x 2
#>       a b             
#>   <int> <list>        
#> 1     1 <S3: geopoint>
#> 2     2 <S3: geopoint>
#> 3     3 <S3: geopoint>
#> 4     4 <S3: geopoint>
#> 5     5 <S3: geopoint>

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]]]

tibble(a = 1:5, b = list(mls))
#> # A tibble: 5 x 2
#>       a b                       
#>   <int> <list>                  
#> 1     1 <S3: geomultilinestring>
#> 2     2 <S3: geomultilinestring>
#> 3     3 <S3: geomultilinestring>
#> 4     4 <S3: geomultilinestring>
#> 5     5 <S3: geomultilinestring>

tibble(a = 1:5, b = list(pt), c = list(mls))
#> # A tibble: 5 x 3
#>       a b              c                       
#>   <int> <list>         <list>                  
#> 1     1 <S3: geopoint> <S3: geomultilinestring>
#> 2     2 <S3: geopoint> <S3: geomultilinestring>
#> 3     3 <S3: geopoint> <S3: geomultilinestring>
#> 4     4 <S3: geopoint> <S3: geomultilinestring>
#> 5     5 <S3: geopoint> <S3: geomultilinestring>

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
#> [24] 00 00 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://storage.googleapis.com/osm-extracts.interline.io/honolulu_hawaii.geojsonl"
f <- tempfile(fileext = ".geojsonl")
download.file(url, f)
x <- ndgeo_read(f, verbose = FALSE)
x
#> <geojson> 
#>   type:  FeatureCollection 
#>   features (n): 65004 
#>   features (geometry / length) [first 5]:
#>     Point / 2
#>     Point / 2
#>     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

News

geojson 0.2.0

NEW FEATURES

gains new function to_geojson convert GeoJSON character string to the approriate GeoJSON class by detecting GeoJSON type automatically. this makes some other tasks easier (#28) (#29)

MINOR IMPROVEMENTS

Improve as.geojson function to do print summary on all GeoJSON types well, not just GeometryCollection and FeatureCollection (#27)

geojson 0.1.4

MINOR IMPROVEMENTS

Changed properties_add() to give back the same class object as that given to the function. In addition, correctly adds properties to FeatureCollection objects as well. (#22)
Added properties fxns tests

BUG FIXES

Fixed bug in print.featurecollection() that was not calculating and printing number of features correctly (#24)

geojson 0.1.2

BUG FIXES

Fixed bug in internal function that checked for existence of a Suggested package (#17)

geojson 0.1.0

NEW FEATURES

Released to CRAN.

Reference manual

install.packages("geojson")

0.3.2 by Scott Chamberlain, 22 days ago

https://github.com/ropensci/geojson

Report a bug at https://github.com/ropensci/geojson/issues

Browse source code at https://github.com/cran/geojson

Authors: Scott Chamberlain [aut, cre] , Jeroen Ooms [aut]

Documentation: PDF Manual

Task views: Analysis of Spatial Data

MIT + file LICENSE license

Imports methods, sp, jsonlite, protolite, jqr, magrittr, lazyeval

Suggests geojsonlint, tibble, testthat, knitr, rmarkdown, sf, stringi

Imported by geojsonio.

See at CRAN

geojson

Classes for 'GeoJSON'

geojson class

geometrycollection

inspect the object

properties

crs

bbox

geojson in data.frame's

geobuf

newline-delimited GeoJSON

Meta

News

geojson 0.2.0

NEW FEATURES

MINOR IMPROVEMENTS

geojson 0.1.4

MINOR IMPROVEMENTS

BUG FIXES

geojson 0.1.2

BUG FIXES

geojson 0.1.0

NEW FEATURES

Reference manual

R links

R homepage

Download R

Mailing lists

R documentation

R manuals

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The R Journal

CRAN links

CRAN homepage

CRAN repository policy

Submit a package

METACRAN stuff

About METACRAN

At github

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