Create interactive 2023 Toronto mayoral election map in R with leaflet

toronto_mayoral_election_map.R

# See the blog post for details: 
# https://asterhu.com/post/2023-07-11-toronto-mayor-by-election-analysis/

library(opendatatoronto)
library(dplyr)
library(tidyr)
library(sf)
library(leaflet)
library(htmltools)


# Importing data from Open Data Toronto -----------------------------------


# Import from open data toronto and convert to data frame
elections <- 
  list_package_resources("6b1a2631-9b12-4242-a76a-1a707b5c00e4") %>%
  tail(1) %>% 
  get_resource() %>% 
  as.data.frame()


# Flattening and cleaning the data ----------------------------------------


# Flattening list-column to regular columns
elections <- elections %>% 
  unnest(office.candidate.ward)

# Remove unnecessary columns and rename ward related columns
elections <- elections %>% 
  select(7:15) %>% 
  rename(wardName = name,
         wardNum = num)

# Change data types to numeric except candidate and ward
elections <- elections %>%
  mutate(across(-c(office.candidate.name, wardName), as.numeric))

# Verify the updated data types
str(elections)


# Data wrangling to prepare for visualization -----------------------------


# Filter the top five candidates
top_candidates <- elections %>%
  group_by(wardName, office.candidate.name) %>% 
  mutate(ward_votes = sum(votesReceived)) %>% 
  group_by(wardName) %>% 
  slice_max(ward_votes, n = 5)

# Get the winner for each ward
ward_winner <- top_candidates %>% 
  group_by(wardName) %>% 
  slice_max(ward_votes)

# Check the number of winners
unique(ward_winner$office.candidate.name)

# Define colours for each winner
ward_winner <- ward_winner %>%
  mutate(winner_colour = if_else(
    office.candidate.name == "Ana Bailão",
    "#9dbd89",
    "#a989bd")) %>%
  select(wardName, winner_colour)

names_label <- top_candidates %>%
  group_by(wardName) %>%
  arrange(desc(ward_votes)) %>%
  mutate(names = ifelse(
    row_number() == 1, 
    paste("<b>", wardName, "</b><br>", paste(office.candidate.name, ":", votesReceived, collapse = "<br>")), 
    paste(office.candidate.name, ":", votesReceived)), 
    collapse = "<br>") %>%
  mutate(names = lapply(names, htmltools::HTML)) %>%
  distinct(wardName, .keep_all = TRUE) %>%
  select(wardName, names)

to_shapes <- read_sf("Input/25-ward-model-december-2018-wgs84-latitude-longitude/WARD_WGS84.shp")

top_sf <- 
  left_join(top_candidates, to_shapes,by = c("wardName" = "AREA_NAME")) %>% 
  left_join(., ward_winner, by = "wardName") %>% 
  left_join(., names_label, by = "wardName") %>% 
  st_as_sf()


# Creating the interactive map with leaflet -------------------------------


# Define the legend
legends <- tibble(lg_labels = c("Olivia Chow",
                                "Ana Bailão"),
                  lg_colours = c("#a989bd",
                                 "#9dbd89"))

# Create the interactive map
map <- leaflet(options = leafletOptions(minZoom = 10.5, maxZoom = 18)) %>% 
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = top_sf, fillColor = ~winner_colour,
              fillOpacity = 0.2, color = "white", weight = 0.5, smoothFactor = 1,
              label = ~names,
              labelOptions = labelOptions(textsize = "12px")) %>%
  addLegend(position = "bottomright", colors = legends$lg_colours,
            labels = legends$lg_labels, title = "The candidate won the most votes")

# Print the map
map