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https://github.com/paleolimbot/ggspatial

Enhancing spatial visualization in ggplot2
https://github.com/paleolimbot/ggspatial

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Enhancing spatial visualization in ggplot2

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README

        

---
output: github_document
---

```{r, include = FALSE}
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

[![ggspatial on CRAN](https://cranlogs.r-pkg.org/badges/ggspatial)](https://cran.r-project.org/package=ggspatial)
[![Coverage Status](https://img.shields.io/codecov/c/github/paleolimbot/ggspatial/master.svg)](https://app.codecov.io/github/paleolimbot/ggspatial?branch=master)
[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
[![R-CMD-check](https://github.com/paleolimbot/ggspatial/workflows/R-CMD-check/badge.svg)](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, eval=FALSE}
install.packages("ggspatial")
```

Or for the development version:

```{r, eval=FALSE}
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 fig-layer-spatial-sf, warning=FALSE, message=FALSE}
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")
```