# ggspatial [](https://cran.r-project.org/package=ggspatial) [](https://app.codecov.io/github/paleolimbot/ggspatial?branch=master) [](https://lifecycle.r-lib.org/articles/stages.html#stable) [](https://github.com/paleolimbot/ggspatial/actions) Spatial data plus the power of the `ggplot2` framework means easier mapping. ## Installation The package is available on CRAN, and can be installed using `install.packages("ggspatial")`. The development version can be installed via **remotes**. ``` r install.packages("ggspatial") ``` Or for the development version: ``` r install.packages("remotes") # if remotes isn't installed remotes::install_github("paleolimbot/ggspatial") ``` ## Introduction This package is a framework for interacting with spatial data using **ggplot2** as a plotting backend. The package supports **sf** package objects, **sp** package objects, and **raster** package objects, and uses `geom_sf()` and `coord_sf()` to do most of the heavy lifting with respect to coordinate transformation. ``` r library(ggplot2) library(ggspatial) load_longlake_data() ggplot() + # loads background map tiles from a tile source annotation_map_tile(zoomin = -1) + # annotation_spatial() layers don't train the scales, so data stays central annotation_spatial(longlake_roadsdf, size = 2, col = "black") + annotation_spatial(longlake_roadsdf, size = 1.6, col = "white") + # raster layers train scales and get projected automatically layer_spatial(longlake_depth_raster, aes(colour = after_stat(band1))) + # make no data values transparent scale_fill_viridis_c(na.value = NA) + # layer_spatial trains the scales layer_spatial(longlake_depthdf, aes(fill = DEPTH_M)) + # spatial-aware automagic scale bar annotation_scale(location = "tl") + # spatial-aware automagic north arrow annotation_north_arrow(location = "br", which_north = "true") ```