Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 1: Basics

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Beautiful maps in a beautiful world

Maps are used in a variety of fields to express data in an appealing and

interpretive way. Data can be expressed into simplified patterns, and

this data interpretation is generally lost if the data is only seen

through a spread sheet. Maps can add vital context by incorporating many

variables into an easy to read and applicable context. Maps are also

very important in the information world because they can quickly allow

the public to gain better insight so that they can stay informed. It’s

critical to have maps be effective, which means creating maps that can

be easily understood by a given audience. For instance, maps that need

to be understood by children would be very different from maps intended

to be shown to geographers.

Knowing what elements are required to enhance your data is key into

making effective maps. Basic elements of a map that should be considered

are polygon, points, lines, and text. Polygons, on a map, are closed

shapes such as country borders. Lines are considered to be linear shapes

that are not filled with any aspect, such as highways, streams, or

roads. Finally, points are used to specify specific positions, such as

city or landmark locations. With that in mind, one need to think about

what elements are required in the map to really make an impact, and

convey the information for the intended audience. Layout and formatting

are the second critical aspect to enhance data visually. The arrangement

of these map elements and how they will be drawn can be adjusted to make

a maximum impact.

A solution using R and its ecosystem of packages

Current solutions for creating maps usually involves GIS software, such

as ArcGIS, QGIS, eSpatial, etc., which allow to visually prepare a map,

in the same approach as one would prepare a poster or a document layout.

On the other hand, R, a free and open-source software development

environment (IDE) that is used for computing statistical data and

graphic in a programmable language, has developed advanced spatial

capabilities over the years, and can be used to draw maps

programmatically.

R is a powerful and flexible tool. R can be used from calculating data

sets to creating graphs and maps with the same data set. R is also free,

which makes it easily accessible to anyone. Some other advantages of

using R is that it has an interactive language, data structures,

graphics availability, a developed community, and the advantage of

adding more functionalities through an entire ecosystem of packages. R

is a scriptable language that allows the user to write out a code in

which it will execute the commands specified.

Using R to create maps brings these benefits to mapping. Elements of a

map can be added or removed with ease — R code can be tweaked to make

major enhancements with a stroke of a key. It is also easy to reproduce

the same maps for different data sets. It is important to be able to

script the elements of a map, so that it can be re-used and interpreted

by any user. In essence, comparing typical GIS software and R for

drawing maps is similar to comparing word processing software (e.g.

Microsoft Office or LibreOffice) and a programmatic typesetting system

such as LaTeX, in that typical GIS software implement a WYSIWIG approach

(“What You See Is What You Get”), while R implements a WYSIWYM approach

(“What You See Is What You Mean”).

The package ggplot2 implements the grammar of graphics in R, as a way

to create code that make sense to the user: The grammar of graphics is a

term used to breaks up graphs into semantic components, such as

geometries and layers. Practically speaking, it allows (and forces!) the

user to focus on graph elements at a higher level of abstraction, and

how the data must be structured to achieve the expected outcome. While

ggplot2 is becoming the de facto standard for R graphs, it does not

handle spatial data specifically. The current state-of-the-art of

spatial objects in R relies on Spatial classes defined in the package

sp , but the new package

sf has recently implemented

the “simple feature” standard, and is steadily taking over sp .

Recently, the package ggplot2 has allowed the use of simple features

from the package sf as layers in a graph 1 . The combination of

ggplot2 and sf therefore enables to programmatically create maps,

using the grammar of graphics, just as informative or visually appealing

as traditional GIS software.

Drawing beautiful maps programmatically with R, sf and ggplot2

This tutorial is the first part in a series of three:

• General concepts illustrated with the world Map (this document)

• Adding additional layers: an example with points and

  polygons

• Positioning and layout for complex maps

Getting started

Many R packages are available from CRAN ,

the Comprehensive R Archive Network, which is the primary repository of

R packages. The full list of packages necessary for this series of

tutorials can be installed with:

install.packages(c("cowplot", "googleway", "ggplot2", "ggrepel", 
    "ggspatial", "libwgeom", "sf", "rworldmap", "rworldxtra"))

We start by loading the basic packages necessary for all maps, i.e.

ggplot2 and sf . We also suggest to use the classic dark-on-light

theme for ggplot2 ( theme_bw ), which is appropriate for maps:

library("ggplot2")
theme_set(theme_bw())
library("sf")

The package rworldmap provides a map of countries of the entire world;

a map with higher resolution is available in the package rworldxtra .

We use the function getMap to extract the world map (the resolution

can be set to "low" , if preferred):

library("rworldmap")
library("rworldxtra")
world <- getMap(resolution = "high")
class(world)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

world <- st_as_sf(world)
class(world)

## [1] "sf"         "data.frame"

General concepts illustrated with the world map

Data and basic plot ( ggplot and geom_sf )

First, let us start with creating a base map of the world using

ggplot2 . This base map will then be extended with different map

elements, as well as zoomed in to an area of interest. We can check that

the world map was properly retrieved and converted into an sf object,

and plot it with ggplot2 :

ggplot(data = world) +
    geom_sf()

This call nicely introduces the structure of a ggplot call: The first

part ggplot(data = world) initiates the ggplot graph, and indicates

that the main data is stored in the world object. The line ends up

with a + sign, which indicates that the call is not complete yet, and

each subsequent line correspond to another layer or scale. In this case,

we use the geom_sf function, which simply adds a geometry stored in a

sf object. By default, all geometry functions use the main data

defined in ggplot() , but we will see later how to provide additional

data.

Note that layers are added one at a time in a ggplot call, so the

order of each layer is very important. All data will have to be in an

sf format to be used by ggplot2 ; data in other formats (e.g. classes

from sp ) will be manually converted to sf classes if necessary.

Title, subtitle, and axis labels ( ggtitle , xlab , ylab )

A title and a subtitle can be added to the map using the function

ggtitle , passing any valid character string (e.g. with quotation

marks) as arguments. Axis names are absent by default on a map, but can

be changed to something more suitable (e.g. “Longitude” and “Latitude”),

depending on the map:

ggplot(data = world) +
    geom_sf() +
    xlab("Longitude") + ylab("Latitude") +
    ggtitle("World map", subtitle = paste0("(", length(unique(world$NAME)), " countries)"))

Map color ( geom_sf )

In many ways, sf geometries are no different than regular geometries,

and can be displayed with the same level of control on their attributes.

Here is an example with the polygons of the countries filled with a

green color (argument fill ), using black for the outline of the

countries (argument color ):

ggplot(data = world) + 
    geom_sf(color = "black", fill = "lightgreen")

ggplot(data = world) +
    geom_sf(aes(fill = POP_EST)) +
    scale_fill_viridis_c(option = "plasma", trans = "sqrt")

Projection and extent ( coord_sf )

The function coord_sf allows to deal with the coordinate system, which

includes both projection and extent of the map. By default, the map will

use the coordinate system of the first layer that defines one (i.e.

scanned in the order provided), or if none, fall back on WGS84

(latitude/longitude, the reference system used in GPS). Using the

argument crs , it is possible to override this setting, and project on

the fly to any projection. This can be achieved using any valid PROJ4

string (here, the European-centric ETRS89 Lambert Azimuthal Equal-Area

projection):

ggplot(data = world) +
    geom_sf() +
    coord_sf(crs = "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs ")

Spatial Reference System Identifier (SRID) or an European Petroleum

Survey Group (EPSG) code are available for the projection of interest,

they can be used directly instead of the full PROJ4 string. The two

following calls are equivalent for the ETRS89 Lambert Azimuthal

Equal-Area projection, which is EPSG code 3035:

ggplot(data = world) +
    geom_sf() +
    coord_sf(crs = "+init=epsg:3035")

ggplot(data = world) +
    geom_sf() +
    coord_sf(crs = st_crs(3035))

The extent of the map can also be set in coord_sf , in practice

allowing to “zoom” in the area of interest, provided by limits on the

x-axis ( xlim ), and on the y-axis ( ylim ). Note that the limits are

automatically expanded by a fraction to ensure that data and axes don’t

overlap; it can also be turned off to exactly match the limits provided

with expand = FALSE :

ggplot(data = world) +
    geom_sf() +
    coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97), expand = FALSE)

Scale bar and North arrow (package ggspatial )

Several packages are available to create a scale bar on a map (e.g.

prettymapr , vcd , ggsn , or legendMap ). We introduce here the

package ggspatial , which provides easy-to-use functions…

scale_bar that allows to add simultaneously the north symbol and a

scale bar into the ggplot map. Five arguments need to be set manually:

lon , lat , distance_lon , distance_lat , and distance_legend . The

location of the scale bar has to be specified in longitude/latitude in

the lon and lat arguments. The shaded distance inside the scale bar

is controlled by the distance_lon argument. while its width is

determined by distance_lat . Additionally, it is possible to change the

font size for the legend of the scale bar (argument legend_size , which

defaults to 3). The North arrow behind the “N” north symbol can also be

adjusted for its length ( arrow_length ), its distance to the scale

( arrow_distance ), or the size the N north symbol itself

( arrow_north_size , which defaults to 6). Note that all distances

( distance_lon , distance_lat , distance_legend , arrow_length ,

arrow_distance ) are set to "km" by default in distance_unit ; they

can also be set to nautical miles with “nm”, or miles with “mi”.

library("ggspatial")
ggplot(data = world) +
    geom_sf() +
    annotation_scale(location = "bl", width_hint = 0.5) +
    annotation_north_arrow(location = "bl", which_north = "true", 
        pad_x = unit(0.75, "in"), pad_y = unit(0.5, "in"),
        style = north_arrow_fancy_orienteering) +
    coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97))

## Scale on map varies by more than 10%, scale bar may be inaccurate

Note the warning of the inaccurate scale bar: since the map use

unprojected data in longitude/latitude (WGS84) on an equidistant

cylindrical projection (all meridians being parallel), length in

(kilo)meters on the map directly depends mathematically on the degree of

latitude. Plots of small regions or projected data will often allow for

more accurate scale bars.

Country names and other names ( geom_text and annotate )

The world data set already contains country names and the coordinates

of the centroid of each country (among more information). We can use

this information to plot country names, using world as a regular

data.frame in ggplot2 . We first check the country name information:

head(world[, c("NAME", "LON", "LAT")])

## Simple feature collection with 6 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -70.06164 ymin: -18.0314 xmax: 74.89231 ymax: 60.48075
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##          NAME       LON       LAT                       geometry
## 1       Aruba -69.97345  12.51678 MULTIPOLYGON (((-69.87609 1...
## 2 Afghanistan  66.00845  33.83627 MULTIPOLYGON (((71.02458 38...
## 3      Angola  17.56405 -12.32934 MULTIPOLYGON (((13.98233 -5...
## 4    Anguilla -63.05667  18.22432 MULTIPOLYGON (((-63.0369 18...
## 5     Albania  20.05399  41.14258 MULTIPOLYGON (((20.06496 42...
## 6       Aland  19.94429  60.22851 MULTIPOLYGON (((19.91892 60...

The function geom_text can be used to add a layer of text to a map

using geographic coordinates. The function requires the data needed to

enter the country names, which is the same data as the world map. Again,

we have a very flexible control to adjust the text at will on many

aspects:

• The size (argument size );
• The alignment, which is centered by default on the coordinates
  provided. The text can be adjusted horizontally or vertically using
  the arguments hjust and vjust , which can either be a number
  between 0 (right/bottom) and 1 (top/left) or a character (“left”,
  “middle”, “right”, “bottom”, “center”, “top”). The text can also be
  offset horizontally or vertically with the argument nudge_x and
  nudge_y ;
• The font of the text, for instance its color (argument color ) or
  the type of font ( fontface );
• The overlap of labels, using the argument check_overlap , which
  removes overlapping text. Alternatively, when there is a lot of
  overlapping labels, the package ggrepel provides a
  geom_text_repel function that moves label around so that they do
  not overlap.

Additionally, the annotate function can be used to add a single

character string at a specific location, as demonstrated here to add the

Gulf of Mexico:

ggplot(data = world) +
    geom_sf() +
    geom_text(aes(LON, LAT, label = NAME), size = 4, hjust = "left", 
        color = "darkblue", fontface = "bold", check_overlap = TRUE) +
    annotate(geom = "text", x = -90, y = 26, label = "Gulf of Mexico", 
        fontface = "italic", color = "grey22", size = 6) +
    coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97), expand = FALSE)

Final map

Now to make the final touches, the theme of the map can be edited to

make it more appealing. We suggested the use of theme_bw for a

standard theme, but there are many other themes that can be selected

from (see for instance ?ggtheme in ggplot2 , or the package

ggthemes which provide

several useful themes). Moreover, specific theme elements can be tweaked

to get to the final outcome:

• Position of the legend: Although not used in this example, the
  argument legend.position allows to automatically place the legend
  at a specific location (e.g. "topright" , "bottomleft" , etc.);
• Grid lines (graticules) on the map: by using panel.grid.major and
  panel.grid.minor , grid lines can be adjusted. Here we set them to
  a gray color and dashed line type to clearly distinguish them from
  country borders lines;
• Map background: the argument panel.background can be used to color
  the background, which is the ocean essentially, with a light blue;
• Many more elements of a theme can be adjusted, which would be too
  long to cover here. We refer the reader to the documentation for the
  function theme .

ggplot(data = world) +
    geom_sf(fill = "antiquewhite1") +
    geom_text(aes(LON, LAT, label = NAME), size = 4, hjust = "left",
        color = "darkblue", fontface = "bold", check_overlap = TRUE) +
    annotate(geom = "text", x = -90, y = 26, label = "Gulf of Mexico", 
        fontface = "italic", color = "grey22", size = 6) +
    annotation_scale(location = "bl", width_hint = 0.5) +
    annotation_north_arrow(location = "bl", which_north = "true", 
        pad_x = unit(0.75, "in"), pad_y = unit(0.5, "in"),
        style = north_arrow_fancy_orienteering) +
    coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97), expand = FALSE) +
    xlab("Longitude") + ylab("Latitude") +
    ggtitle("Map of the Gulf of Mexico and the Caribbean Sea") +
    theme(panel.grid.major = element_line(color = gray(.5),
        linetype = "dashed", size = 0.5),
        panel.background = element_rect(fill = "aliceblue"))

Saving the map with ggsave

The final map now ready, it is very easy to save it using ggsave . This

function allows a graphic (typically the last plot displayed) to be

saved in a variety of formats, including the most common PNG (raster

bitmap) and PDF (vector graphics), with control over the size and

resolution of the outcome. For instance here, we save a PDF version of

the map, which keeps the best quality, and a PNG version of it for web

purposes:

ggsave("map.pdf")
ggsave("map_web.png", width = 6, height = 6, dpi = "screen")