queries based on first example blog post
When inserting data using the geomongo R6 class the user has the option (via the TYPE_DATA parameter) to either give a character string (or vector), a list, a file or a folder of files as input. To start with, I’ll use the following character strings ( they appear in the first example blog post , the "_id" ’s were removed),
library(GeoMongo)
# important : the property-names of each geojson object should be of type character string
loc1 = '{
"name" : "Squaw Valley",
"location" : {
"type" : "Point",
"coordinates" : [
-120.24,
39.21
]
}
}'
loc2 = '{
"name" : "Mammoth Lakes",
"location" : {
"type" : "Point",
"coordinates" : [
-118.9,
37.61
]
}
}'
loc3 = '{
"name" : "Aspen",
"location" : {
"type" : "Point",
"coordinates" : [
-106.82,
39.18
]
}
}'
loc4 = '{
"name" : "Whistler",
"location" : {
"type" : "Point",
"coordinates" : [
-122.95,
50.12
]
}
}'
# create a vector of character strings
char_FILES = c(loc1, loc2, loc3, loc4) Before inserting the data one should make sure that MongoDB is running on the Operating System. Information on how to install MongoDB can be found here.
The geomongo R6 class will be initialized and a database and collection will be created,
init = geomongo$new(host = 'localhost', port = 27017) # assuming MongoDB runs locally
getter_client = init$getClient() # get MongoClient()
init_db = getter_client[["example_db"]] # create a new database
init_col = init_db$create_collection("example_col") # create a new collectionAfter the preliminary steps, one can continue by inserting the char_FILES object to the relevant database / collection using the geoInsert method. The TYPE_DATA parameter equals here to dict_many meaning it can take either a list of lists (nested list) or a character vector of strings,
init$geoInsert(DATA = char_FILES, # input data
TYPE_DATA = 'dict_many', # character vector of strings as input
COLLECTION = init_col, # specify the relevant collection
GEOMETRY_NAME = "location") # give the 'geometry name' of each geo-objectOne can now run various commands to check the correctness of the inserted data,
init_db$collection_names() # prints out the collection names of the relevant database"example_col"init_col$find_one() # prints one of the inserted geometry objects$`_id`
5984a0b742b2563fb5838f6a
$location
$location$type
[1] "Point"
$location$coordinates
[1] -120.24 39.21
$name
[1] "Squaw Valley"init_col$count() # prints the number of the inserted geometry objects[1] 4I’ll continue reproducing some of the geo-queries of the first example blog post from within an R-session.
The first query is about the number of locations in the state of Colorado, where Colorado is approximated as the below GeoJson square,
{
"type": "Polygon",
"coordinates": [[
[-109, 41],
[-102, 41],
[-102, 37],
[-109, 37],
[-109, 41]
]]
}and the corresponding MongoDB query would be,
db.locations.find({
... location: {
... $geoIntersects: {
... $geometry: {
... type: "Polygon",
... coordinates: [[
... [-109, 41],
... [-102, 41],
... [-102, 37],
... [-109, 37],
... [-109, 41]
... ]]
... }
... }
... }
... })This query can be translated in R in the following way:
- curly braces correspond to R-lists
- arrays (of size 2) to R-vectors,
query_geoIntersects = list('location' =
list('$geoIntersects' =
list('$geometry' =
list(
type = "Polygon",
coordinates =
list(
list(
c(-109, 41),
c(-102, 41),
c(-102, 37),
c(-109, 37),
c(-109, 41)
)
)
)
)
)
)and the find METHOD of geoQuery function will be used to return locations which are within the boundaries of Colorado,
loc_intersect = init$geoQuery(QUERY = query_geoIntersects, # query from previous chunk
METHOD = "find", # the method to use
COLLECTION = init_col, # the collection to use
GEOMETRY_NAME = "location", # the geometry name to use
TO_LIST = FALSE) # returns a data.table
loc_intersectThe output can be returned either as a list or as a data.table,
# data.table format
location.type location.coordinates1 location.coordinates2 name id
1: Point -106.82 39.18 Aspen 5984a0b742b2563fb5838f6cThe next few code chunks will show how to return documents that are within a certain distance of a given point using the geoWithin and centerSphere operators (locations with a square of circumradius 300 miles centered on San Francisco, approximately latitude 37.7, longitude -122.5).
# MongoDB query
db.locations.find({
... location: {
... $geoWithin: {
... $centerSphere: [[-122.5, 37.7], 300 / 3963.2]
... }
... }
... })and the corresponding query in R,
geoWithin_sph = list('location' =
list('$geoWithin' =
list('$centerSphere' =
list(
c(-122.5, 37.7), 300 / 3963.2)
)
)
)
# no need to specify again the "COLLECTION" and "GEOMETRY_NAME" parameters
# as we use the same initialization of the R6 class with the previous query
res_geoWithin_sph = init$geoQuery(QUERY = geoWithin_sph,
METHOD = "find")
res_geoWithin_sph# example output
location.type location.coordinates1 location.coordinates2
1: Point -118.90 37.61
2: Point -120.24 39.21
name id
Mammoth Lakes 5984a0b742b2563fb5838f6b
Squaw Valley 5984a0b742b2563fb5838f6aOne can read more about the magic number 3963.2 (radius of the Earth) either in the first example blog post or in the MongoDB documentation.
Here one can also plot the output locations using the leaflet package,
map_dat <- leaflet::leaflet()
map_dat <- leaflet::addTiles(map_dat)
map_dat <- leaflet::addMarkers(map_dat,
lng = unlist(res_geoWithin_sph$location.coordinates1),
lat = unlist(res_geoWithin_sph$location.coordinates2))
map_dat
The next query utilizes the aggregate method to return the locations sorted by distance from a given point,
# MongoDB query
db.locations.aggregate([{
... $geoNear: {
... near: {
... type: 'Point',
... coordinates: [-122.5, 37.1]
... },
... spherical: true,
... maxDistance: 900 * 1609.34,
... distanceMultiplier: 1 / 1609.34,
... distanceField: 'distanceFromSF'
... }
... }])and the corresponding query in R,
query_geonear = list('$geoNear' =
list(near =
list(
type = "Point",
coordinates =
c(-122.5, 37.1)
),
distanceField = "distanceFromSF",
maxDistance = 900 * 1609.34,
distanceMultiplier = 1 / 1609.34,
spherical = TRUE)
)
func_quer_geonear = init$geoQuery(QUERY = query_geonear,
METHOD = "aggregate")
func_quer_geonear# example output
distanceFromSF location.type location.coordinates1 location.coordinates2
1: 190.8044 Point -120.24 39.21
2: 201.0443 Point -118.90 37.61
3: 863.9478 Point -106.82 39.18
name id
Squaw Valley 5984a0b742b2563fb5838f6a
Mammoth Lakes 5984a0b742b2563fb5838f6b
Aspen 5984a0b742b2563fb5838f6c