We'll have a look at the principles of ggplot2 in general, which we can later apply to making maps.

We'll make a plot for historical election results from four randomly selected states in the US (dataset presidentialElections from package pscl):

## # A tibble: 88 x 4
##    state      demVote  year south
##    <chr>        <dbl> <int> <lgl>
##  1 California    58.4  1932 FALSE
##  2 Florida       74.5  1932 TRUE 
##  3 Illinois      55.2  1932 FALSE
##  4 Tennessee     66.5  1932 TRUE 
##  5 California    67.0  1936 FALSE
##  6 Florida       76.1  1936 TRUE 
##  7 Illinois      57.7  1936 FALSE
##  8 Tennessee     68.8  1936 TRUE 
##  9 California    57.4  1940 FALSE
## 10 Florida       74.0  1940 TRUE 
## # … with 78 more rows

Step 1: Load ggplot2 and pass the dataset.

library(ggplot2)

ggplot(sampled_pres)

Step 2: Specify an aesthetic mapping via aes().
This describes how variables of your data are mapped to visual properties: "year" is plotted on the x-axis and the share of votes for Democrats on the y-axis.

ggplot(sampled_pres, aes(x = year, y = demVote))

Step 3: Adding a geom.
geoms are geometric objects; they describe which graphical primitives to use (e.g. points in a scatter plot or bars in a bar plot). Notice that this plot looks wrong. We'll fix that next.

ggplot(sampled_pres, aes(x = year, y = demVote)) + geom_line()

Step 4: Fixing the aesthetic mapping.
The previous plot looked wrong because several y-values appeared for the same years and they were all connected by a single line: For each year, we have a y-value (vote share) for each state! We adjust the aesthetic mapping by making the line color dependent on state, hence we have a line (of different color) per state:

ggplot(sampled_pres, aes(x = year, y = demVote, color = state)) + geom_line()

Step 5: Adding another layer.
We can add more geometric objects by adding another geom_ layer. For better readability, I put the layer functions on separate lines:

ggplot(sampled_pres, aes(x = year, y = demVote, color = state)) +
    geom_line() +
    geom_point()

• when you pass a dataset and aesthetic mapping to ggplot() it used by all layers as default
• you can override it in each geom_... layer by setting aes(...) and data = ...
• each layer may actually use a different dataset; when plotting maps, this is often used (e.g. one layer with data for a "background map", another for points on it)

random_dots <- data.frame(x = sample(sampled_pres$year, 10),
                          y = rnorm(10, mean = 50, sd = 10))
ggplot() +
    geom_line(aes(x = year, y = demVote, color = state), data = sampled_pres) +
    geom_point(aes(x = x, y = y), data = random_dots) # use a different dataset here

We usually work with two-dimensional vector data: Points located within a coordinate reference system (CRS).

• the point -73.94°, 40.6° is represented in the WGS84 CRS: the world geodetic system (e.g. used by GPS)
• points describe location on a sphere¹; its components are given in degrees:
  • -73.94° is the longitude: about 74° West from the meridian
  • 40.6° is the latitude: about 41° North from the equator

¹ WGS84 actually models the Earth as ellipsoid

Let's put this point into context:

• points can be connected to form other geometric shapes such as lines or polygons
• points and shapes can represent numerous geographic entities: cities, buildings, countries, rivers, lakes, etc.

Most packages for working with geo-data in R rely on the Simple Features standard (→ sf package).

Simple features describe how objects in the real world can be represented in computers in terms of their spatial geometry:

Features have a geometry describing where on Earth the feature is located, and they have attributes, which describe other properties.
– sf package vignette

Examples:

First, we load the packages that we need:

library(ggplot2)
library(maps)
library(sf)

The function map can be used to load the "world" data. We need to convert it to a "simple features" object via st_as_sf():

worldmap_data <- st_as_sf(map('world', plot = FALSE, fill = TRUE))
head(worldmap_data, n = 3)  # to have a look inside

## Simple feature collection with 3 features and 1 field
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -8.68335 ymin: 18.98662 xmax: 74.89131 ymax: 42.64795
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##                         geometry          ID
## 1 MULTIPOLYGON (((74.89131 37... Afghanistan
## 2 MULTIPOLYGON (((20.06396 42...     Albania
## 3 MULTIPOLYGON (((8.576563 36...     Algeria

• spatial dataset with a "header" giving some general information
• two columns: geometry (spatial data) and ID (attribute)

What do the numbers in the geometry column mean? What's their CRS?

We have some cities along with their longitude (lng) and latitude (lat):

##       name    lng    lat
## 1   Berlin  13.38  52.52
## 2 New York -73.94  40.60
## 3   Sydney 151.21 -33.87

We add a "point geom" layer to our map:

ggplot(some_cities) +                                # pass the cities data to plot
    geom_sf(data = worldmap_data) +                  # layer 1: world countries
    geom_point(aes(x = lng, y = lat), color = 'red') # layer 2: points at city coord.

You may also add text labels for the cities. ggplot2 provides geom_text and geom_label (draws box around text):

ggplot(some_cities) +                                  # pass the cities data to plot
    geom_sf(data = worldmap_data) +                    # layer 1: world countries
    geom_point(aes(x = lng, y = lat), color = 'red') + # layer 2: points
    geom_label(aes(x = lng, y = lat, label = name),    # layer 3: labels
               hjust = 0, vjust = 1, nudge_x = 3) +    # labels appear next to point
    theme(axis.title = element_blank(),
          axis.text = element_blank())                 # disable the axis labels

In order to …

• link your data with existing information on geographic entities e.g. country GDP, city population, poverty rate in the neighboorhood
• visualize your data geographically

… you need to:

1. agree on (administrative) level for matching (e.g. country, state, municipality)
2. find identifiers to match the observations w/ respective geographic entities

• different names may refer to the same country (e.g. Germany / Federal Republic of Germany, BRD, etc.) → often not a good identifier
• ISO 3166-1 designates every country a two- or three-letter code (e.g. DE / DEU)
• often used in datasets (e.g. from the UN)

The Nomenclature of Territorial Units for Statistics (NUTS) divides the EU territory into regions at 3 different levels for socio-economic analyses of the regions.

Candidate countries and countries inside EFTA also have NUTS regions (Norway, Switzerland, Albania, Turkey, etc.).

In Germany, regions are hierarchically structured and identified by "Amtlicher Gemeindeschlüssel" (AGS ~ "municipality identificator"). The Gemeindeverzeichnis contains all regions with their name, AGS, area, population and other features.

Since 2006, Berlin uses "Lebensweltlich orientierte Räume" (LOR) to structure the city area into sub-regions at different levels, each of them identified by "LOR" code:

Level 1: Prognoseräume (60 regions); Level 2: Bezirksregionen (138 regions); Level 3: Planungsräume (447 regions)

There are numerous file formats that store data for geographic information system (GIS) software. The most popular include:

• ESRI shapefile ("SHP"):
  • consists of at least three files in the same directory with filename extensions .shp, .shx and .dbf
• GeoJSON (.geojson, .json) and TopoJSON (.topojson, .json)
• KML (.kml, .kmz): Keyhole Markup Language, used by Google Earth
• GML (.gml): Geography Markup Language
• AutoCAD DXF (.dxf)

You may also encounter other formats such as vector data files (.svg, .poly) or databases (.sql, .sqlite).

Good news: sf can handle all popular file formats.