# Spatial data example

In this example we illustrate the capabilities of the PieGlyph package to overlay pie-chart glyphs on a map to illustrate spatial patterns in attributes.

We show an example where the proportion of votes received by the Republic and Democratic parties in each US state is show for a hypothetical election.

These plots can be created easily with existing techniques too. However, due to the pie-charts being tied with the plot dimensions it isnâ€™t possible to visualise the map in different geographical projections or change the aspect ratio of the plot without converting the pie-charts into ellipses. PieGlyph offers a solution to this problem.

library(PieGlyph)
library(ggplot2)
library(dplyr)

### Create data

Load the geographical information including the latitude and longitude for the states in USA.

states_boundaries <- map_data("state")

The dataset contains 15537 rows describing the geographical boundaries each state (except Alaska and Hawaii) in USA. The long, lat and region are the columns of interest to us. long and lat describe the longitude and latitudes respectively of the boundaries of the states, while region contains the names of each state.

head(states_boundaries)
#>        long      lat group order  region subregion
#> 1 -87.46201 30.38968     1     1 alabama      <NA>
#> 2 -87.48493 30.37249     1     2 alabama      <NA>
#> 3 -87.52503 30.37249     1     3 alabama      <NA>
#> 4 -87.53076 30.33239     1     4 alabama      <NA>
#> 5 -87.57087 30.32665     1     5 alabama      <NA>
#> 6 -87.58806 30.32665     1     6 alabama      <NA>

Create fake elections results each state in states_boundaries data

set.seed(123)

# Get names of state names from map data

# Simulate percentage of votes received in each state by the Democratic, Republic and other parties
mutate('Democratic' = round(runif(50, 1, 100)),
'Republic' = round(runif(50, 1, (100 - Democratic))),
'Other' = 100 - Democratic - Republic)

# Add the latitude and longitude of the geographical centers of the states to place the pies
mutate('pie_lat' = state.center$y, 'pie_long' = state.center$x)

# Filter out any states that weren't present in the map_data
votes_data <- votes_data %>% filter(State %in% unique(states_boundaries\$region))

The dataset contains 48 rows describing the percentage of votes different parties got in the respective state. State describes the state name, Democaratic, Republic and Other describe the percent of votes the parties got in the state. pie_lat and pie_long describe the geographical centre of each state (this is where the pie will be placed on the plot).

head(votes_data)
#>         State Democratic Republic Other pie_lat  pie_long
#> 1     alabama         29        4    67 32.5901  -86.7509
#> 2     arizona         41       47    12 34.2192 -111.6250
#> 3    arkansas         88        2    10 34.7336  -92.2992
#> 4  california         94        4     2 36.5341 -119.7730
#> 5    colorado          6       20    74 38.6777 -105.5130
#> 6 connecticut         53        7    40 41.5928  -72.3573

### Create plot

##### Create map
map <- ggplot(states_boundaries, aes(x = long, y = lat)) +
# Add states and their borders
geom_polygon(aes(group = group),
fill = 'darkseagreen', colour = 'black')+
# Axis titles
labs(x = 'Longitude', y ='Latitude')+
# Blue background for the sea behind
theme(panel.background = element_rect(fill = 'lightsteelblue2'))+
# Coordinate system for maps
coord_map()
map

##### Add pie charts showing proportion of votes for different in each states
plot <- map +
# Add pie-charts for each state
geom_pie_glyph(aes(y = pie_lat, x = pie_long),
data = votes_data, colour = 'black',
slices = c('Democratic','Republic','Other'))+
# Colours of the pie sectors
scale_fill_manual(values = c('#047db7','#c52d25', 'grey'), name = 'Party')+
# Place legend on top of the plot
theme(legend.position = 'top')
plot

As the pie-charts are created independent of the axes and plot dimensions in PieGlyph, they are unaffected by any change in the map projection

plot +
# Different map projection
coord_map('albers', lat0 = 45.5, lat1 = 29.5)

plot +
# Different map projection
coord_map('gnomonic')