R/get-key-values.R
oe_get_keys.RdThis function returns the OSM keys and (optionally) the values stored in the
other_tags field. See Details. In both cases, the keys are sorted according
to the number of occurrences, which means that the most common keys are
stored first.
oe_get_keys(
zone,
layer = "lines",
values = FALSE,
which_keys = NULL,
download_directory = oe_download_directory()
)
# Default S3 method
oe_get_keys(
zone,
layer = "lines",
values = FALSE,
which_keys = NULL,
download_directory = oe_download_directory()
)
# S3 method for class 'character'
oe_get_keys(
zone,
layer = "lines",
values = FALSE,
which_keys = NULL,
download_directory = oe_download_directory()
)
# S3 method for class 'sf'
oe_get_keys(
zone,
layer = "lines",
values = FALSE,
which_keys = NULL,
download_directory = oe_download_directory()
)
# S3 method for class 'oe_key_values_list'
print(x, n = getOption("oe_max_print_keys", 10L), ...)An sf object with an other_tags field or a character vector
(of length 1) that can be linked to or pointing to a .osm.pbf or .gpkg
file with an other_tags field. Character vectors are linked to .osm.pbf
files using oe_find().
Which layer should be read in? Typically points, lines
(the default), multilinestrings, multipolygons or other_relations. If
you specify an ad-hoc query using the argument query (see introductory
vignette and examples), then oe_get() and oe_read() will read the layer
specified in the query and ignore layer argument. See also
#122.
Logical. If TRUE, then function returns the keys and the
corresponding values, otherwise only the keys. Defaults to FALSE.
Character vector used to subset only some keys and
corresponding values. Ignored if values is FALSE. See examples.
Path of the directory that stores the .osm.pbf
files. Only relevant when zone is as a character vector that must be
matched to a file via oe_find(). Ignored unless zone is a character
vector.
object of class oe_key_values_list
Maximum number of keys (and corresponding values) to print; can be
set globally by options(oe_max_print_keys=...). Default value is 10.
Ignored.
If the argument values is FALSE (the default), then the function
returns a character vector with the names of all keys stored in the
other_tags field. If values is TRUE, then the function returns named
list which stores all keys and the corresponding values. In the latter
case, the returned object has class oe_key_values_list and we defined an
ad-hoc printing method. See Details.
OSM data are typically documented using several
tags, i.e. pairs of two
items, namely a key and a value. The conversion between .osm.pbf and
.gpkg formats is governed by a CONFIG file that lists which tags must
be explicitly added to the .gpkg file. All the other keys are
automatically stored using an other_tags field with a syntax compatible
with the PostgreSQL HSTORE type. See
here for
more details.
When the argument values is TRUE, then the function returns a named
list of class oe_key_values_list that, for each key, summarises the
corresponding values. The key-value pairs are stored using the following
format: list(key1 = c("value1", "value1", "value2", ...), key2 = c("value1", ...) ...). We decided to implement an ad-hoc method for
printing objects of class oe_key_values_list using the following
structure:
key1 = {#value1 = n1; #value2 = n2; #value3 = n3,
...} key2 = {#value1 = n1; #value2 = n2; ...} key3 = {#value1 = n1} ...where n1 denotes the number of times that value1 is repeated, n2
denotes the number of times that value2 is repeated and so on. Also the
values are listed according to the number of occurrences in decreasing
order. By default, the function prints only the ten most common keys, but
the number can be adjusted using the option oe_max_print_keys.
Finally, the hstore_get_value() function can be used inside the query
argument in oe_get() to extract one particular tag from an existing file.
Check the introductory vignette and see examples.
# Copy the ITS file to tempdir() to make sure that the examples do not
# require internet connection. You can skip the next 4 lines (and start
# directly with oe_get_keys) when running the examples locally.
its_pbf = file.path(tempdir(), "test_its-example.osm.pbf")
file.copy(
from = system.file("its-example.osm.pbf", package = "osmextract"),
to = its_pbf,
overwrite = TRUE
)
#> [1] TRUE
# Get keys
oe_get_keys("ITS Leeds", download_directory = tempdir())
#> [1] "surface" "lanes" "bicycle"
#> [4] "lit" "access" "oneway"
#> [7] "maxspeed" "ref" "foot"
#> [10] "natural" "lanes:backward" "lanes:forward"
#> [13] "source:name" "step_count" "lanes:psv:backward"
#> [16] "alt_name" "layer" "motor_vehicle"
#> [19] "tunnel" "bridge" "covered"
#> [22] "incline" "lanes:psv" "service"
#> [25] "turn:lanes" "turn:lanes:forward" "frequency"
#> [28] "indoor" "lcn" "level"
#> [31] "maxheight" "operator" "power"
#> [34] "source:geometry" "substation" "turn:lanes:backward"
#> [37] "voltage" "website"
# Get keys and values
oe_get_keys("ITS Leeds", values = TRUE, download_directory = tempdir())
#> Found 38 unique keys, printed in ascending order of % NA values. The first 10 keys are:
#> surface (91% NAs) = {#asphalt = 12; #paved = 3; #cobblestone = 1; #paving_sto...}
#> lanes (91% NAs) = {#2 = 9; #1 = 7}
#> bicycle (92% NAs) = {#yes = 10; #designated = 5}
#> lit (92% NAs) = {#yes = 15}
#> access (92% NAs) = {#permissive = 12; #yes = 2}
#> oneway (93% NAs) = {#yes = 13}
#> maxspeed (93% NAs) = {#30 mph = 12}
#> ref (94% NAs) = {#A660 = 9; #4184 = 1}
#> foot (95% NAs) = {#yes = 5; #designated = 4}
#> natural (96% NAs) = {#tree_row = 7}
#> [Truncated output...]
# Subset some keys
oe_get_keys(
"ITS Leeds", values = TRUE, which_keys = c("surface", "lanes"),
download_directory = tempdir()
)
#> Found 2 unique keys, printed in ascending order of % NA values.
#> surface (91% NAs) = {#asphalt = 12; #paved = 3; #cobblestone = 1; #paving_sto...}
#> lanes (91% NAs) = {#2 = 9; #1 = 7}
# Print all (non-NA) values for a given set of keys
res = oe_get_keys("ITS Leeds", values = TRUE, download_directory = tempdir())
res["surface"]
#> $surface
#> [1] "asphalt" "asphalt" "asphalt" "asphalt"
#> [5] "asphalt" "asphalt" "paved" "cobblestone"
#> [9] "asphalt" "asphalt" "paved" "paved"
#> [13] "paving_stones" "asphalt" "asphalt" "asphalt"
#> [17] "asphalt"
#>
# Get keys from an existing sf object
its = oe_get("ITS Leeds", download_directory = tempdir())
#> The input place was matched with: ITS Leeds
#> The chosen file was already detected in the download directory. Skip downloading.
#> Starting with the vectortranslate operations on the input file!
#> 0...10...20...30...40...50...60...70...80...90...100 - done.
#> Finished the vectortranslate operations on the input file!
#> Reading layer `lines' from data source `/tmp/RtmpkOnDwL/test_its-example.gpkg' using driver `GPKG'
#> Simple feature collection with 189 features and 10 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: -1.562458 ymin: 53.80471 xmax: -1.548076 ymax: 53.81105
#> Geodetic CRS: WGS 84
oe_get_keys(its, values = TRUE)
#> Found 38 unique keys, printed in ascending order of % NA values. The first 10 keys are:
#> surface (91% NAs) = {#asphalt = 12; #paved = 3; #cobblestone = 1; #paving_sto...}
#> lanes (91% NAs) = {#2 = 9; #1 = 7}
#> bicycle (92% NAs) = {#yes = 10; #designated = 5}
#> lit (92% NAs) = {#yes = 15}
#> access (92% NAs) = {#permissive = 12; #yes = 2}
#> oneway (93% NAs) = {#yes = 13}
#> maxspeed (93% NAs) = {#30 mph = 12}
#> ref (94% NAs) = {#A660 = 9; #4184 = 1}
#> foot (95% NAs) = {#yes = 5; #designated = 4}
#> natural (96% NAs) = {#tree_row = 7}
#> [Truncated output...]
# Get keys from a character vector pointing to a file (might be faster than
# reading the complete file and then filter it)
its_path = oe_get(
"ITS Leeds", download_only = TRUE,
download_directory = tempdir(), quiet = TRUE
)
oe_get_keys(its_path, values = TRUE)
#> Found 38 unique keys, printed in ascending order of % NA values. The first 10 keys are:
#> surface (91% NAs) = {#asphalt = 12; #paved = 3; #cobblestone = 1; #paving_sto...}
#> lanes (91% NAs) = {#2 = 9; #1 = 7}
#> bicycle (92% NAs) = {#yes = 10; #designated = 5}
#> lit (92% NAs) = {#yes = 15}
#> access (92% NAs) = {#permissive = 12; #yes = 2}
#> oneway (93% NAs) = {#yes = 13}
#> maxspeed (93% NAs) = {#30 mph = 12}
#> ref (94% NAs) = {#A660 = 9; #4184 = 1}
#> foot (95% NAs) = {#yes = 5; #designated = 4}
#> natural (96% NAs) = {#tree_row = 7}
#> [Truncated output...]
# Add a key to an existing .gpkg file without repeating the
# vectortranslate operations
its = oe_get("ITS Leeds", download_directory = tempdir())
#> The input place was matched with: ITS Leeds
#> The chosen file was already detected in the download directory. Skip downloading.
#> The corresponding gpkg file was already detected. Skip vectortranslate operations.
#> Reading layer `lines' from data source `/tmp/RtmpkOnDwL/test_its-example.gpkg' using driver `GPKG'
#> Simple feature collection with 189 features and 10 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: -1.562458 ymin: 53.80471 xmax: -1.548076 ymax: 53.81105
#> Geodetic CRS: WGS 84
colnames(its)
#> [1] "osm_id" "name" "highway" "waterway" "aerialway"
#> [6] "barrier" "man_made" "railway" "z_order" "other_tags"
#> [11] "geometry"
its_extra = oe_read(
its_path,
query = "SELECT *, hstore_get_value(other_tags, 'oneway') AS oneway FROM lines",
quiet = TRUE
)
colnames(its_extra)
#> [1] "osm_id" "name" "highway" "waterway" "aerialway"
#> [6] "barrier" "man_made" "railway" "z_order" "other_tags"
#> [11] "oneway" "geometry"
# The following fails since there is no points layer in the .gpkg file
if (FALSE) { # \dontrun{
oe_get_keys(its_path, layer = "points")} # }
# Add layer and read keys
its_path = oe_get(
"ITS Leeds", layer = "points", download_only = TRUE,
download_directory = tempdir(), quiet = TRUE
)
oe_get_keys(its_path, layer = "points")
#> [1] "amenity" "addr:postcode"
#> [3] "addr:street" "addr:city"
#> [5] "fhrs:id" "capacity"
#> [7] "covered" "addr:housenumber"
#> [9] "operator" "bicycle_parking"
#> [11] "addr:suburb" "natural"
#> [13] "shop" "crossing"
#> [15] "naptan:AtcoCode" "naptan:Bearing"
#> [17] "naptan:CommonName" "naptan:PlusbusZoneRef"
#> [19] "naptan:ShortCommonName" "naptan:Street"
#> [21] "naptan:verified" "addr:housename"
#> [23] "bus" "collection_times"
#> [25] "local_ref" "naptan:Crossing"
#> [27] "naptan:Indicator" "naptan:Landmark"
#> [29] "public_transport" "condition"
#> [31] "entrance" "ref:UK:leedscc:bin"
#> [33] "shelter" "waste_basket:model"
#> [35] "crossing_ref" "wheelchair"
#> [37] "brand" "brand:wikidata"
#> [39] "brand:wikipedia" "noexit"
#> [41] "booth" "old_name"
#> [43] "opening_hours" "advertising"
#> [45] "foot" "kerb"
#> [47] "post_box:type" "tactile_paving"
#> [49] "takeaway" "toilets:wheelchair"
#> [51] "addr:unit" "cuisine"
#> [53] "level" "naptan:Notes"
#> [55] "royal_cypher" "source:addr"
#> [57] "timetable" "tourism"
#> [59] "website" "access"
#> [61] "addr:source" "artist_name"
#> [63] "artwork_type" "atm"
#> [65] "bicycle" "building"
#> [67] "contact:website" "direction"
#> [69] "fee" "healthcare"
#> [71] "historic" "horse"
#> [73] "live_display" "loc_name"
#> [75] "material" "motor_vehicle"
#> [77] "naptan:BusStopType" "not:addr:postcode"
#> [79] "phone" "post_box:design"
#> [81] "recycling:glass_bottles" "recycling:paper"
#> [83] "traffic_signals" "url"
#> [85] "wikidata"
# Remove .pbf and .gpkg files in tempdir
rm(its_pbf, res, its_path, its, its_extra)
oe_clean(tempdir())