README.md

In till-tietz/rrelief: Visualize Data Associated with Simple Feature Geometries over a Shaded Relief Background

rrelief

rrelief allows you to conveniently build visually appealing and informative maps with ggplot. Using this package you can now easily visualize data over a shaded relief background without masking the relief. This package heavily relies on the amazing raster (http://CRAN.R-project.org/package=raster), sf (https://CRAN.R-project.org/package=sf), exactextractr (https://CRAN.R-project.org/package=exactextractr) and elevatr (https://cran.r-project.org/web/packages/elevatr/index.html) packages and simply provides some convenient wrappers to implement the routine task of mapping with minimal coding on your end.

Installation

You can install the development version of rrelief from GitHub with:

install.packages("devtools")
devtools::install_github("till-tietz/rrelief")

Usage

This quick example will hopefully help to illustrate the general rrelief workflow, show what the package can do and get you started on making some nice maps. For this example we’ll be using some voting district data for the state of Washington from (https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html), as well as some elevation data for the north western United States from (http://srtm.csi.cgiar.org/srtmdata/).

Generating a map in rrelief is split into three successive core steps:

1.generate a DEM 2. generate a relief and overlay your data 3. generate the map

We’ll look at each step in turn.

Generating a DEM

Fundamentally all you need to get started is a sf data.frame with polygon geometries and the variables you wish to map. rrelief utilizes elevatr to pull DEM raster data from Terrain Tiles on AWS (https://registry.opendata.aws/terrain-tiles/) based on the bounding box of your sf, so you don’t have to deal with finding suitable elevation data.

library(rrelief)

#loading census_tract shape file as an sf object and adding a random variable
voting_districts <- sf::st_read("cb_2018_53_sldu_500k.shp")
voting_districts <- sf::st_transform(voting_districts, crs = 4326)
voting_districts[,"var"] <- runif(nrow(voting_districts), 0,100)

#generating DEM 
raster <- rrelief::generate.dem(map.data = voting_districts, aggregate = 3)

Should you already have suitable elevation data raster data in .tif format you can generate a DEM using rrelief as follows.

#generating DEM 
raster <- rrelief::generate.dem(map.data = voting_districts, 
                                raster.files = c("dem_1.tif","dem_2.tif"), aggregate = 3)

Generating a relief and overlaying Data

#running rrelief keeping the crs projection at 4326
rrelief_output <- rrelief::overlay.relief(map.data = voting_districts, variables = c("var"),
                                          elevation.raster = raster, make.hillshade = TRUE, 
                                          coordinate.system = NULL, altitude = 45, azimuth = 270,
                                          z.factor = 10)

overlay.relief returns a data.frame of points with their respective hill-shade and variable values.

| value | x | y | coverage_fraction | var | |----------:|----------:|---------:|-------------------:|---------:| | 0.6933837 | -122.3413 | 47.58625 | 0.0065928 | 60.99251 | | 0.7028063 | -122.3388 | 47.58625 | 0.4054028 | 60.99251 | | 0.7341834 | -122.3363 | 47.58625 | 0.4298274 | 60.99251 | | 0.7347732 | -122.3337 | 47.58625 | 0.4629008 | 60.99251 | | 0.7044837 | -122.3312 | 47.58625 | 0.4805375 | 60.99251 | | 0.7518519 | -122.3287 | 47.58625 | 0.4751625 | 60.99251 | | 0.7388729 | -122.3263 | 47.58625 | 0.4679143 | 60.99251 | | 0.7167771 | -122.3238 | 47.58625 | 0.4451203 | 60.99251 | | 0.9926822 | -122.3213 | 47.58625 | 0.4399785 | 60.99251 | | 0.9323923 | -122.3187 | 47.58625 | 0.1329148 | 60.99251 |

Generating the Map

Now you can simply create your base map with generate.map.

map <- generate.map(data = rrelief_output, x = "x", y = "y", 
                    hillshade = "value", variable = "var")

You can further customize your base map with all the ggplot options you know and love. Let’s add a slightly more compelling colour scheme and nicer legend.

map +
  scale_fill_viridis_c(alpha = 0.8) +
  guides(fill = guide_colourbar(title = "Random Variable",
                                label.position = "bottom",
                                frame.colour = "black",
                                barwidth = 10,
                                barheight = 1,
                                title.vjust = 1))

till-tietz/rrelief documentation built on Sept. 5, 2022, 7:41 p.m.