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Showing content from https://github.com/pachadotdev/canadamaps/ below:

pachadotdev/canadamaps: Maps of the Political and Administrative Divisions of Canada.

The idea is to avoid “duplications”, for example instead of adding a provinces map or others, we provide functions to sum Census Divisions in all possible cases.

library(ggplot2)
library(canadamaps)

ggplot(data = census_divisions) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Census Divisions")

The same idea can be applied to other maps with different levels of aggregation.

ggplot(data = get_agricultural_divisions()) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Census Agricultural Regions")

ggplot(data = get_economic_regions()) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Economic Regions")

ggplot(data = federal_electoral_districts) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Federal Electoral Districts")

ggplot(data = get_provinces()) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Provinces")

We can change the CRS with the sf package but please read the explanation from Stats Canada.

# shortcut function to change the CRS
census_divisions <- lambert_projection(census_divisions)

ggplot(data = census_divisions) +
  geom_sf(aes(geometry = geometry)) +
  labs(title = "Canada's Census Divisions")

Let’s say I want to replicate the map from Health Canada, which was checked on 2023-08-02 and was updated up to 2024-02-25. To do this, I need to download the CSV file from Health Canada and then combine it with the provinces map from canadamaps.

library(readr)
library(dplyr)
library(sf)

url <- "https://health-infobase.canada.ca/src/data/covidLive/vaccination-coverage-map.csv"
csv <- paste0("data_processing/", gsub(".*/", "", url))
if (!file.exists(csv)) download.file(url, csv)

vaccination <- read_csv(csv, col_types = cols(prop5plus_atleast1dose = col_character())) %>%
  filter(week_end == as.Date("2024-02-25"), pruid != 1) %>%
  select(pruid, proptotal_atleast1dose) %>%
  mutate(
    proptotal_atleast1dose = as.numeric(case_when(
      proptotal_atleast1dose == ">=99" ~ 99,
      TRUE ~ proptotal_atleast1dose
    ))
  )

vaccination <- vaccination %>%
  inner_join(get_provinces(), by = "pruid") %>% # canadamaps in action
  mutate(
    label = paste(gsub(" /.*", "", prname),
      paste0(proptotal_atleast1dose, "%"),
      sep = "\n"
    ),
  )

An initial plot can be done with the following code.

# colours obtained with Chromium's inspector
colours <- c("#efefa2", "#c2e699", "#78c679", "#31a354", "#006837")

ggplot(vaccination) +
  geom_sf(aes(fill = proptotal_atleast1dose, geometry = geometry)) +
  geom_sf_label(aes(label = label, geometry = geometry)) +
  scale_fill_gradientn(colours = colours, name = "Cumulative percent") +
  labs(title = "Cumulative percent of the population who have received at least 1 dose of a COVID-19 vaccine") +
  theme_minimal(base_size = 13)

We can use different ggplot themes.

ggplot(vaccination) +
  geom_sf(aes(fill = proptotal_atleast1dose, geometry = geometry)) +
  geom_sf_label(aes(label = label, geometry = geometry)) +
  scale_fill_gradientn(colours = colours, name = "Cumulative percent") +
  labs(title = "Cumulative percent of the population who have received at least 1 dose of a COVID-19 vaccine") +
  theme_void() +
  theme(
    legend.position = "top",
    plot.title = element_text(hjust = 0.5)
  )

If we want to fill the information for Alberta, which is not seen in the original map, we can fill and then filter.

library(tidyr)

vaccination <- read_csv(csv, col_types = cols(prop5plus_atleast1dose = col_character())) %>%
  arrange(pruid, week_end) %>%
  group_by(pruid) %>%
  fill(proptotal_atleast1dose, .direction = "down") %>% # Alberta is filled with an older value
  filter(week_end == as.Date("2024-02-25"), pruid != 1) %>%
  select(pruid, proptotal_atleast1dose) %>%
  mutate(
    proptotal_atleast1dose = as.numeric(case_when(
      proptotal_atleast1dose == ">=99" ~ 99,
      TRUE ~ proptotal_atleast1dose
    ))
  ) %>%
  inner_join(get_provinces(), by = "pruid") %>% # canadamaps in action
  mutate(
    label = paste(gsub(" /.*", "", prname),
      paste0(proptotal_atleast1dose, "%"),
      sep = "\n"
    ),
  ) %>%
  lambert_projection()

ggplot(vaccination) +
  geom_sf(aes(fill = proptotal_atleast1dose, geometry = geometry)) +
  geom_sf_label(aes(label = label, geometry = geometry)) +
  scale_fill_gradientn(colours = colours, name = "Cumulative percent") +
  labs(title = "Cumulative percent of the population who have received at least 1 dose of a COVID-19 vaccine") +
  theme_minimal(base_size = 13)

The finest division in this package is the Census Division (CDs) which can be of the next types (reference: https://www.statcan.gc.ca/en/subjects/standard/sgc/2011/sgc-tab-d).

The division type is specified in the census_divisions table.

Census Agricultural Regions (CARs) can be obtained as sums of CDs. Excluding some special cases for Northwestern Territories, Nunavur and Yukon that we clarified over email communication, the source to match CDs to CARs was obtained from Census of Agriculture Reference Maps and manually organized in a spreadsheet (https://github.com/pachadotdev/canadamaps/tree/main/data_xlsx).

Economic Regions (ERs) can be obtained as sums of CDs. The only special case is the Halton, which belongs to two economic zones and it’s the only CD that has to be carefully separated (i.e. see https://github.com/pachadotdev/canadamaps/blob/main/data_processing/02_census_divisions_and_derivatives.R).

These cannot be obtained as sums of CDs, therefore these are stored in their own table federal_electoral_districts.

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