Using riding_binplot to create
tile grid maps
Description
The riding_binplot() function takes data at the federal
riding level as its input and creates a tile grid map to visualize these
data with ggplot2.
Overview
Although Canada is a very large country, around 80% of its population
is concentrated in urban areas. Because of this, the majority of federal
electoral ridings are small and urban while a much smaller number of
ridings in sparsely-populated regions account for the majority of
Canada’s landmass. Because of this, visualizing statistics at the riding
level with standard cloropeth maps is not ideal. The
riding_binplot() function of the mapcan
package offers an alternative: the tile grid map. Inspired by the
statebins package (Rudis
2017), federal riding tile grid maps are a way of visualizing
riding-level data when it is not neccessary to accurately represent the
ridings geographically.
Using riding_binplot()
Categorical variables
Preparing the data
riding_binplot() requires a data frame that includes a
riding-level variable as well as numeric riding code variable. Specify
the riding-level variable of interest in the value_col
argument. Specify the numeric riding code variable in the
riding_col argument. For instance, we may want to create a
tile plot for the seat distribution of the 2015 federal election.
Luckily, mapcan comes with built-in federal election
data (the federal_election_results data frame) that dates
back to 1996 with variables for riding codes/names, provinces,
population, voter turnout and the winning party for each riding. Let’s
restrict federal_election_results to include only
observations (ridings) from the 2015 election. Because we only need (1)
a riding characteristic variable and (2) a riding code variable, we can
select only these columns from the data.
fed_2015 <- federal_election_results %>%
filter(election_year == 2015) %>%
dplyr::select(riding_code, party)Note: We remove all irrelevant variables from the data frame to
illustrate more clearly how riding_binplot operates. They
do not need to be removed in practice.
This new data frame has the two columns that we need: one to specify
the riding code (riding_code) and another to specify a
riding-level characteristic of interest (party). Let’s look
at a sample:
fed_2015 %>%
sample_n(10)
#> riding_code party
#> 1 47003 NDP
#> 2 13007 Liberal
#> 3 12010 Liberal
#> 4 35003 Liberal
#> 5 35114 Liberal
#> 6 35064 Liberal
#> 7 35002 NDP
#> 8 24039 NDP
#> 9 35095 Liberal
#> 10 35090 LiberalLooking good.
Plotting the data
Use continuous = TRUE for continuous variables and
continuous = FALSE for categorical variables.
party is a categorical variable, so we will specify
continuous = FALSE:
fed_2015_bins <- fed_2015 %>%
riding_binplot(value_col = party,
continuous = FALSE) +
ggtitle("Tile grid map of 2015 federal election results")
fed_2015_bins
You will notice that the axis text has no substantive significance
(it just the coordinates of the tiles). 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.
You will also notice that the colours provided by default from
riding_binplot are not ideal for a tile plot with Canadian
political parties. Because riding_binplot simply creates a
ggplot object, we can override the
riding_binplot scale and add our own:
fed_2015_bins +
scale_fill_manual(name = "Party",
values = c("mediumturquoise", "blue", "springgreen3", "red", "orange"))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
In this tile grid map, tiles are arranged by province yet, because
seats are highly concentrated in urban areas, each tile only roughly
corresponds to geographic location of the riding it represents. The
arrange = TRUE option provides a better representation of
the disribution of variables within provinces when the exact location of
the riding is not relevant.
fed_2015 %>%
riding_binplot(value = party,
continuous = FALSE,
arrange = TRUE) +
theme_mapcan() +
scale_fill_manual(name = "Party",
values = c("mediumturquoise", "blue", "springgreen3", "red", "orange")) +
ggtitle("Tile grid map of 2015 federal election results")
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
Compared to a standard cloropeth map, the tile grid map gives us a better sense of how the 2015 federal election shaped up:
fed_2015_cloropleth <- mapcan(boundaries = ridings, type = standard)
left_join(fed_2015_cloropleth, fed_2015, by = "riding_code") %>%
ggplot(aes(long, lat, group = group, fill = party)) +
geom_polygon() +
coord_fixed() +
scale_fill_manual(name = "Party",
values = c("mediumturquoise", "blue", "springgreen3", "red", "orange")) +
ggtitle("Tile grid map of 2015 federal election results") +
theme_mapcan()
Continuous variables
Let’s make a riding tile plot with a continuous variable: voter
turnout. This variable is included in the
federal_election_results dataset, which is part of the
mapcan package. Specify continuous = TRUE in
riding_binplot so that ggplot uses a continuous fill scale. The default
scale is scale_fill_viridis_c() from the
viridis package. Though, as demonstrated above, this can be
changed easily by overriding the default scale.
Note that the data can be tidied up using dplyr and
piped directly into riding_binplot():
federal_election_results %>%
filter(election_year == 2015) %>%
riding_binplot(value = voter_turnout,
continuous = TRUE) +
ggtitle("Tile grid map of 2015 federal election voter turnout")
Once again, because we care little about the axis grid and axis
values, we can use the theme_mapcan() theme:
federal_election_results %>%
filter(election_year == 2015) %>%
riding_binplot(value = voter_turnout,
continuous = TRUE) +
theme_mapcan() +
ggtitle("Tile grid map of 2015 federal election voter turnout")
Like with the election results that are plotted above, we may want to
arrange the values of voter turnout within provinces. Fortunately, we
can also use the arrange = TRUE argument when the
riding-level variable is continuous.
federal_election_results %>%
filter(election_year == 2015) %>%
riding_binplot(value = voter_turnout,
continuous = TRUE,
arrange = TRUE) +
theme_mapcan() +
ggtitle("Tile grid map of 2015 federal election voter turnout")
If squares are not your jam, you can also use hexagons with the
shape = hexagon argument. Note that the default is
shape = square.
federal_election_results %>%
filter(election_year == 2015) %>%
riding_binplot(value = voter_turnout,
continuous = TRUE,
arrange = TRUE,
shape = "hexagon") +
theme_mapcan() +
ggtitle("Hexagon grid map of 2015 federal election voter turnout")
Provincial ridings
Currently, only Quebec provincial ridings can be plotted with
riding_binplot(). In the near future,
riding_binplot() will be able to create bin plots for the
other provinces.
To create a provincial riding plot, specify
provincial = TRUE (to let riding_binplot know that it is
provincial ridings you wish to plot) and province = QC for
Quebec.
riding_binplot(quebec_provincial_results,
value_col = party,
riding_col = riding_code,
continuous = FALSE,
provincial = TRUE,
province = QC,
shape = "hexagon") +
theme_mapcan() +
scale_fill_manual(name = "Winning party",
values = c("deepskyblue1", "red","royalblue4", "orange")) +
ggtitle("Hexagon grid map of 2018 Quebec provincial election results")
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.