rmapshaper is a package which is an R wrapper around the awesome mapshaper tool by Matthew Bloch, which has both a Node.js command-line tool as well as an interactive web tool.
The main advantage of the package is the availability of the topologically-aware simplification algorithm in ms_simplify
(provided by the simplify
tool in mapshaper). This means that shared boundaries between adjacent polygons are always kept intact, with no gaps or overlaps, even at high levels of simplification. It uses the Visvalingam simplification method.
At this time, rmapshaper
provides the following functions:
ms_simplify
- simplify polygons or linesms_clip
- clip an area out of a layer using a polygon layer or a bounding box. Works on polygons, lines, and pointsms_erase
- erase an area from a layer using a polygon layer or a bounding box. Works on polygons, lines, and pointsms_dissolve
- aggregate polygon features, optionally specifying a field to aggregate on. If no field is specified, will merge all polygons into one.ms_explode
- convert multipart shapes to single part. Works with polygons, lines, and points.ms_lines
- convert polygons to topological boundaries (lines)ms_innerlines
- convert polygons to shared inner boundaries (lines)ms_points
- create points from a polygon layerms_filter_fields
- Remove fields from the attributesms_filter_islands
- Remove small detached polygonsThis short vignette focuses on simplifying polygons with the ms_simplify
function.
rmapshaper works with sf
objects as well as geojson strings (character objects of class geo_json
). It also works with Spatial
classes from the sp
package, though this will likely be retired in the future; users are encouraged to use the more modern sf
package.
We will use the nc.gpkg
file (North Carolina county boundaries)
from the sf
package and read it in as an sf
object:
library(rmapshaper) library(sf) file <- system.file("gpkg/nc.gpkg", package = "sf") nc_sf <- read_sf(file)
Plot the original:
plot(nc_sf["FIPS"])
Now simplify using default parameters, then plot the simplified North Carolina counties:
nc_simp <- ms_simplify(nc_sf) plot(nc_simp["FIPS"])
You can see that even at very high levels of simplification, the mapshaper
simplification algorithm preserves the topology, including shared boundaries. The keep
parameter specifies what proportion of vertices to keep:
nc_very_simp <- ms_simplify(nc_sf, keep = 0.001) plot(nc_very_simp["FIPS"])
Compare this to the output using sf::st_simplify
, where overlaps and gaps are evident:
nc_stsimp <- st_simplify(nc_sf, preserveTopology = TRUE, dTolerance = 10000) # dTolerance specified in meters plot(nc_stsimp["FIPS"])
This time we'll demonstrate the ms_innerlines
function:
nc_sf_innerlines <- ms_innerlines(nc_sf) plot(nc_sf_innerlines)
All of the functions are quite fast with geojson
character objects. They are slower with the sf
and
Spatial
classes due to internal conversion to/from json. If you are going to do multiple
operations on large sf
objects,
it's recommended to first convert to json using geojsonsf::sf_geojson()
, or geojsonio::geojson_json()
.
All of the functions have the input object as the first argument,
and return the same class of object as the input. As such, they can be chained together.
For a totally contrived example, using nc_sf
as created above:
library(geojsonsf) library(rmapshaper) library(sf) ## First convert 'states' dataframe from geojsonsf pkg to json nc_sf %>% sf_geojson() |> ms_erase(bbox = c(-80, 35, -79, 35.5)) |> # Cut a big hole in the middle ms_dissolve() |> # Dissolve county borders ms_simplify(keep_shapes = TRUE, explode = TRUE) |> # Simplify polygon geojson_sf() |> # Convert to sf object plot(col = "blue") # plot
Sometimes if you are dealing with a very large spatial object in R, rmapshaper
functions will take a very long time or not work at all. As of version 0.4.0
,
you can make use of the system mapshaper
library if you have it installed.
This will allow you to work with very large spatial objects.
First make sure you have mapshaper installed:
check_sys_mapshaper()
If you get an error, you will need to install mapshaper. First install node (https://nodejs.org/en) and then install mapshaper in a command prompt with:
$ npm install -g mapshaper
Then you can use the sys
argument in any rmapshaper function:
nc_simp_sys <- ms_simplify(nc_sf, sys = TRUE) plot(nc_simp_sys[, "FIPS"])
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.