Examples
Creating a choropleth map with provincial boundaries
mapcan(boundaries = province,
type = standard) %>%
head()
#> long lat order hole piece pr_sgc_code group
#> 1 2278204 1261783 1 FALSE 1 10 10.1
#> 2 2294272 1251745 2 FALSE 1 10 10.1
#> 3 2300435 1250534 3 FALSE 1 10 10.1
#> 4 2306656 1243299 4 FALSE 1 10 10.1
#> 5 2315548 1241630 5 FALSE 1 10 10.1
#> 6 2313319 1237013 6 FALSE 1 10 10.1
#> pr_english pr_french pr_alpha
#> 1 Newfoundland and Labrador Terre-Neuve-et-Labrador NL
#> 2 Newfoundland and Labrador Terre-Neuve-et-Labrador NL
#> 3 Newfoundland and Labrador Terre-Neuve-et-Labrador NL
#> 4 Newfoundland and Labrador Terre-Neuve-et-Labrador NL
#> 5 Newfoundland and Labrador Terre-Neuve-et-Labrador NL
#> 6 Newfoundland and Labrador Terre-Neuve-et-Labrador NLmapcan() gives us a data frame with necessary components (longitude, latitude, order, hole, piece, and group) for use with geom_polygon() in the ggplot package. It also provides four different variables to describe the province that make it easy to merge this data with provincial data of your choice.
Basic ingredients for ggplot
To create a plot with data from mapcan(), the following aesthetic mappings are required: x = long (longitude), y = lat (latitude), group = group (this tells geom_polygon() how to group observations—in this case, provinces). Let’s initialize the plot:
pr_map <- mapcan(boundaries = province,
type = standard) %>%
ggplot(aes(x = long, y = lat, group = group))
pr_map
This doesn’t tell us much. We need to add a geom to visualize the map.
Using geom_polygon to plot the coordinates
To visualize the geographic data with ggplot, use geom_polygon(). It is important to also specify coord_fixed()—this fixes the relationship between longitude (the x-axis) and latitude (the y-axis):
You will notice that the axis text has no substantive significance. You can remove it, along with the axis ticks and background grid using theme_mapcan function, a ggplot theme that is part of the mapcan package:
pr_map +
theme_mapcan() +
## Add a title
ggtitle("Map of Canada with Provincial/Territorial Boundaries")
Though beautiful, this map is not very informative (unless you are unfamiliar with the shape of Canada). Let’s add some province-level data.
Incorporate province-level statistics
It is relatively straightforward to merge your own province-level statistics into the geographic data that mapcan() provides. To illustrate, we will work with the province_pop_annual data frame that is included in the mapcan package. This dataset provides annual provincial/territorial population estimates dating back to 1971. Let’s use the most recent population data, from 2017:
pop_2017 <- mapcan::province_pop_annual %>%
filter(year == 2017)
head(pop_2017)
#> year province population
#> 1 2017 Canada 36708083
#> 2 2017 Newfoundland and Labrador 528817
#> 3 2017 Prince Edward Island 152021
#> 4 2017 Nova Scotia 953869
#> 5 2017 New Brunswick 759655
#> 6 2017 Quebec 8394034The next step is to attach these numbers to every point on the polygons of the provinces. To do this, we first create the required geographic with mapcan(), then we use inner_join() from the dplyr package to merge in the pop_2017 data:
pr_geographic <- mapcan(boundaries = province,
type = standard)
pr_geographic <- inner_join(pr_geographic,
pop_2017,
by = c("pr_english" = "province"))To colour the provinces according to their population size, set the population variable as a fill aesthetic. Because population is a continuous variable (and because I don’t like the default colours), I will use scale_fill_viridis_c() colour scale to colour the map.
pr_geographic %>%
ggplot(aes(x = long, y = lat, group = group, fill = population)) +
geom_polygon() +
coord_fixed() +
theme_mapcan() +
scale_fill_viridis_c(name = "Population") +
ggtitle("Canadian Population by Province")
### Creating a choropleth map with federal riding boundaries
Generate geographic data with riding boundaries
To create a map with federal riding boundaries, we specify boundaries = ridings. For the sake of illustration, let’s also look at just one province: British Columbia.
bc_ridings <- mapcan(boundaries = ridings,
type = standard,
province = BC)
head(bc_ridings)
#> long lat order hole piece riding_code group
#> 11743 -1917601 428903.3 11743 FALSE 1 59001 59001.1
#> 11744 -1917311 426212.4 11744 FALSE 1 59001 59001.1
#> 11745 -1922364 417586.0 11745 FALSE 1 59001 59001.1
#> 11746 -1924519 416810.5 11746 FALSE 1 59001 59001.1
#> 11747 -1942173 424886.9 11747 FALSE 1 59001 59001.1
#> 11748 -1939550 430489.9 11748 FALSE 1 59001 59001.1
#> riding_name_english riding_name_french pr_sgc_code pr_english
#> 11743 Abbotsford Abbotsford 59 British Columbia
#> 11744 Abbotsford Abbotsford 59 British Columbia
#> 11745 Abbotsford Abbotsford 59 British Columbia
#> 11746 Abbotsford Abbotsford 59 British Columbia
#> 11747 Abbotsford Abbotsford 59 British Columbia
#> 11748 Abbotsford Abbotsford 59 British Columbia
#> pr_french pr_alpha centroid_long centroid_lat
#> 11743 Colombie-Britannique BC -1929376 423992.3
#> 11744 Colombie-Britannique BC NA NA
#> 11745 Colombie-Britannique BC NA NA
#> 11746 Colombie-Britannique BC NA NA
#> 11747 Colombie-Britannique BC NA NA
#> 11748 Colombie-Britannique BC NA NAPlot geographic data with riding boundaries
ggplot(bc_ridings, aes(x = long, y = lat, group = group)) +
geom_polygon() +
coord_fixed() +
theme_mapcan() +
ggtitle("British Columbia \nFederal Electoral Ridings")
Incorporate riding-level statistics
Like with province-level statistics above, we can also merge our own riding-level statistics into the riding-level geographic data that mapcan() has produced. We will work with the federal_election_results data frame that is included in the mapcan package. This dataset provides federal election results for all elections dating back to 1997. We will use the results of 2015 federal election to colour the ridings in British Columbia.
Note: At the moment, mapcan() only provides geographic data for the electoral boundaries (2013 Representation Order) of the 2015 federal election.
bc_results <- mapcan::federal_election_results %>%
# Restrict data to include just 2015 election results from BC
filter(election_year == 2015 & pr_alpha == "BC")
head(bc_results)
#> riding_name_english
#> 1 Abbotsford
#> 2 Burnaby North--Seymour
#> 3 Burnaby South
#> 4 Cariboo--Prince George
#> 5 Central Okanagan--Similkameen--Nicola
#> 6 Chilliwack--Hope
#> riding_name_french riding_code population
#> 1 Abbotsford 59001 96819
#> 2 Burnaby North--Seymour 59002 100632
#> 3 Burnaby South 59003 105037
#> 4 Cariboo--Prince George 59004 108252
#> 5 Central Okanagan--Similkameen--Nicola 59005 104398
#> 6 Chilliwack--Hope 59006 92734
#> voter_turnout candidate election_year party pr_alpha
#> 1 69.7 Fast, Ed 2015 Conservative BC
#> 2 70.3 Beech, Terry 2015 Liberal BC
#> 3 60.8 Stewart, Kennedy 2015 NDP BC
#> 4 67.8 Doherty, Todd 2015 Conservative BC
#> 5 71.0 Albas, Dan 2015 Conservative BC
#> 6 69.7 Strahl, Mark 2015 Conservative BC
#> pr_french pr_sgc_code pr_english
#> 1 Colombie-Britannique 59 British Columbia
#> 2 Colombie-Britannique 59 British Columbia
#> 3 Colombie-Britannique 59 British Columbia
#> 4 Colombie-Britannique 59 British Columbia
#> 5 Colombie-Britannique 59 British Columbia
#> 6 Colombie-Britannique 59 British ColumbiaNext, we merge the two data frames (i.e. the geographic data and the election results data):
To colour the ridings according the winning party of the 2015 election, set the party variable as a fill aesthetic:
bc_riding_map <- bc_ridings %>%
ggplot(aes(x = long, y = lat, group = group, fill = party)) +
geom_polygon() +
coord_fixed() +
theme_mapcan() +
ggtitle("British Columbia \n2015 Federal Electoral Results")The colours are not ideal. We can easily provide our own custom colours that correspond to the colours associated with the different parties with ggplot’s scale_fill_manual():
bc_riding_map +
scale_fill_manual(name = "Winning party",
values = c("blue", "springgreen3", "red", "Orange")) 