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https://github.com/ateucher/rmapshaper

An R wrapper for the mapshaper javascript library
https://github.com/ateucher/rmapshaper

r rlang rstats

Last synced: 12 days ago
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An R wrapper for the mapshaper javascript library

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README 

        
---

output:

  github_document:

    html_preview: true

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/"

)

options(warnPartialMatchArgs = FALSE)

```



[![Codecov test coverage](https://codecov.io/gh/ateucher/rmapshaper/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ateucher/rmapshaper?branch=master)

[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/rmapshaper)](https://cran.r-project.org/package=rmapshaper)

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# rmapshaper rmapshaper website



An R package providing access to the awesome [mapshaper](https://github.com/mbloch/mapshaper/) tool by Matthew Bloch, which has both a [Node.js command-line tool](https://github.com/mbloch/mapshaper/wiki/Introduction-to-the-Command-Line-Tool) as well as an [interactive web tool](https://mapshaper.org/).



I started this package so that I could use mapshaper's [Visvalingam](https://bost.ocks.org/mike/simplify/) simplification method in R. There is, as far as I know, no other R package that performs topologically-aware multi-polygon simplification. (This means that shared boundaries between adjacent polygons are always kept intact, with no gaps or overlaps, even at high levels of simplification).



But mapshaper does much more than simplification, so I am working on wrapping most of the core functionality of mapshaper into R functions.



So far, `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 in geojson format, but currently only with polygons and lines in the `Spatial` classes (not `SpatialMultiPoints` and `SpatialMultiPointsDataFrame`).

- `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



If you run into any bugs or have any feature requests, please file an [issue](https://github.com/ateucher/rmapshaper/issues/)



### Installation



`rmapshaper` is on CRAN. Install the current version with:



```r

install.packages("rmapshaper")

```



You can install the development version from github with `remotes`:



```r

## install.packages("remotes")

library(remotes)

install_github("ateucher/rmapshaper")

```



### 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:



```{r}

library(rmapshaper)

library(sf)



file <- system.file("gpkg/nc.gpkg", package = "sf")

nc_sf <- read_sf(file)

```



Plot the original: 



```{r}

plot(nc_sf["FIPS"])

```



Now simplify using default parameters, then plot the simplified North Carolina counties:

```{r}

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:



```{r}

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:



```{r}



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:



```{r}

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:



```{r eval=rmapshaper:::v8_version() >= '6'}

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:



```{r eval=nzchar(Sys.which("mapshaper"))}

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:



```{r eval=nzchar(Sys.which("mapshaper"))}

nc_simp_internal <- ms_simplify(nc_sf)

nc_simp_sys <- ms_simplify(nc_sf, sys = TRUE, sys_mem=8) #sys_mem specifies the amount of memory to use in Gb.  It defaults to 8 if omitted. 



par(mfrow = c(1,2))

plot(st_geometry(nc_simp_internal), main = "internal")

plot(st_geometry(nc_simp_sys), main = "system")

```



### Thanks



This package uses the [V8](https://cran.r-project.org/package=V8)

package to provide an environment in which to run mapshaper’s javascript

code in R. It relies heavily on all of the great spatial packages that

already exist (especially `sf`), and the `geojsonio` and the `geojsonsf` packages for

converting between `geojson`, `sf` and `Spatial`

object.



Thanks to [timelyportfolio](https://github.com/timelyportfolio) for helping me wrangle the javascript to the point where it works in V8. He also wrote the [mapshaper htmlwidget](https://github.com/timelyportfolio/mapshaper_htmlwidget), which provides access to the mapshaper web interface, right in your R session. We have plans to combine the two in the future.



### Code of Conduct



Please note that this project is released with a [Contributor Code of Conduct](https://github.com/ateucher/rmapshaper/blob/master/CONDUCT.md). By participating in this project you agree to abide by its terms.



### LICENSE



MIT