Well, what do these packages have in common?
And on 22nd August 2017 on the r-spatial github page it was requested if there could be a common package which could be shared by all the interactive web-plotting libraries
Currently there is code in the leaflet package that extracts data from sp and sf objects and converts it into a dataframe that is then passed to the Javascript side (by converting it into a JSON). This code is fairly generic and not really dependent on anything leaflet specific. It makes a lot of sense to take out this code and make it a package of its own. That way we can build other web plotting R packages to wrap say d3.geo or mapboxGL or cesium and reuse a major chunk of the code that takes data from spatial objects and passes it to Javascript.
so spatialwidget is my attempt at this library.
It takes a simple feature object (sf), plus some user-supplied arguments, and converts the data into JSON, ready for plotting/ parsing in whatever javascript library you chose.
Sure. In this example I’m using the capitals data, which is an sf object of all the capital cities.
library(spatialwidget)
library(sf)
# Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
sf <- spatialwidget::widget_capitals
sf
# Simple feature collection with 200 features and 2 fields
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: -174 ymin: -53 xmax: 179.13 ymax: 64.1
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# CRS: NA
# First 10 features:
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# country capital geometry
# 1 Afghanistan Kabul POINT (69.11 34.28)
# 2 Albania Tirane POINT (19.49 41.18)
# 3 Algeria Algiers POINT (3.08 36.42)
# 4 American Samoa Pago Pago POINT (-170.43 -14.16)
# 5 Andorra Andorra la Vella POINT (1.32 42.31)
# 6 Angola Luanda POINT (13.15 -8.5)
# 7 Antigua and Barbuda West Indies POINT (-61.48 17.2)
# 8 Argentina Buenos Aires POINT (-60 -36.3)
# 9 Armenia Yerevan POINT (44.31 40.1)
# 10 Aruba Oranjestad POINT (-70.02 12.32)As each capital is a POINT, you can use widget_point() to conver it to JSON.
l <- widget_point( data = sf[1:2, ], fill_colour = "country" , legend = F)
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# old-style crs object detected; please recreate object with a recent sf::st_crs()Each row of capitals has been converted into a JSON object. And all these objects are within an array.
Look, here are the first two rows of capitals as JSON
sf[1:2, ]
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# Simple feature collection with 2 features and 2 fields
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: 19.49 ymin: 34.28 xmax: 69.11 ymax: 41.18
# CRS: NA
# country capital geometry
# 1 Afghanistan Kabul POINT (69.11 34.28)
# 2 Albania Tirane POINT (19.49 41.18)
jsonify::pretty_json( l$data )
# [
# {
# "type": "Feature",
# "properties": {
# "fill_colour": "#440154FF"
# },
# "geometry": {
# "geometry": {
# "type": "Point",
# "coordinates": [
# 69.11,
# 34.28
# ]
# }
# }
# },
# {
# "type": "Feature",
# "properties": {
# "fill_colour": "#FDE725FF"
# },
# "geometry": {
# "geometry": {
# "type": "Point",
# "coordinates": [
# 19.49,
# 41.18
# ]
# }
# }
# }
# ]You can see that the coordinates are inside a geometry object, and the user-defined fill_colour is within the properties object.
Well spotted. But it’s not quite GeoJSON for a very good reason.
Some plotting libraries can use more than one geometry, such as mapdeck::add_arc(), which uses an origin and destination. So spatialwidget needs to handle multiple geometries.
Typical GeoJSON will take the form
Whereas I’ve nested the geometries one-level deeper, so the pseudo-GeoJSON i’m using takes the form
Where the myGeometry object is defined on a per-application bases. You are free to call this whatever you want inside your library.
Yep.
The arcs data is an sf object with two POINT geometry columns. So say we want to generate an arc-map showing an arc between Sydney and all the other capitals cities. Just call widget_od, supplying the origin and destination columns.
l <- widget_od( widget_arcs[1:2, ], origin = "origin", destination = "destination")
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# old-style crs object detected; please recreate object with a recent sf::st_crs()
# old-style crs object detected; please recreate object with a recent sf::st_crs()
jsonify::pretty_json( l$data )
# [
# {
# "type": "Feature",
# "properties": {
# "fill_colour": "#440154FF"
# },
# "geometry": {
# "origin": {
# "type": "Point",
# "coordinates": [
# 149.08,
# -35.15
# ]
# },
# "destination": {
# "type": "Point",
# "coordinates": [
# 69.11,
# 34.28
# ]
# }
# }
# },
# {
# "type": "Feature",
# "properties": {
# "fill_colour": "#440154FF"
# },
# "geometry": {
# "origin": {
# "type": "Point",
# "coordinates": [
# 149.08,
# -35.15
# ]
# },
# "destination": {
# "type": "Point",
# "coordinates": [
# 19.49,
# 41.18
# ]
# }
# }
# }
# ]Notice now the geometry object has within it an origin and a destination. This is why I’ve nested the geometries one level deeper within the GeoJSON
You can use these R functions, but they have limited scope. This package has been designed so you use the C++ functions directly. I’ve gone into more detail in the vignette, so it’s probably best you read that to understand how to call the C++ functions.