# rmapshaper Basics

#### Andy Teucher

#### 2025-12-03

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 lines
- `ms_clip` - clip an area out of a layer using a polygon layer or a bounding box. Works on polygons, lines, and points
- `ms_erase` - erase an area from a layer using a polygon layer or a bounding box. Works on polygons, lines, and points
- `ms_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 layer
- `ms_filter_fields` - Remove fields from the attributes
- `ms_filter_islands` - Remove small detached polygons

This short vignette focuses on simplifying polygons with the `ms_simplify` function.

### Usage

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)

    ## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

    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

### Using the system mapshaper

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()

    ## mapshaper version 0.6.113 is installed and on your PATH

    ##                     mapshaper-xl
    ## "/opt/homebrew/bin/mapshaper-xl"

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"])