output: github_document

rosm::set_default_cachedir(system.file("rosm.cache", package = "ggspatial"))
knitr::opts_chunk$set(
  collapse = TRUE,
  dpi = 150,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

ggspatial

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.

install.packages("ggspatial")

Or for the development version:

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.

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")