Importing data from Open Data Toronto

The unofficial by-election data was published by Open Data Toronto and can be found here. JSON files were available for direct download, and there was even an R library library(opendatatoronto) for downloading the data, which I used.

The data package contained two JSON files, but I only needed the second one. To import the data, I used tail(1) to select the second file and get_resource() to download it. The imported JSON file was a list of three lists, so I converted it to a data frame using the base R function as.data.frame().

# 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

The output included a nested list in the column office.candidates.ward, which is common for JSON files. To flatten it into regular columns, I used tidyr::unnest to expand both rows and columns.

Few more things I’ve done during the data cleaning process:

• Remove unnecessary columns
• Renamed the name and num variables to wardName and wardNum respectively, as their names appeared confusing after flattening the list-column
• Converted all variables to numeric data type, except for the candidate name and ward name

# 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))

After cleaning the data, I used str() to verify the data structure.

# Verify the updated data types
str(elections)

tibble [2,550 × 9] (S3: tbl_df/tbl/data.frame)
 $ office.candidate.name         : chr [1:2550] "Olivia Chow" "Olivia Chow" "Olivia Chow" "Olivia Chow" ...
 $ office.candidate.votesReceived: num [1:2550] 269372 269372 269372 269372 269372 ...
 $ wardName                      : chr [1:2550] "Etobicoke North" "Etobicoke Centre" "Etobicoke-Lakeshore" "Parkdale-High Park" ...
 $ wardNum                       : num [1:2550] 1 2 3 4 5 6 7 8 9 10 ...
 $ polls                         : num [1:2550] 52 67 82 65 62 52 46 62 54 90 ...
 $ pollsReceived                 : num [1:2550] 52 67 82 65 62 52 46 62 54 90 ...
 $ totalVoters                   : num [1:2550] 70378 89293 103271 82195 76398 ...
 $ votesCounted                  : num [1:2550] 17822 37925 42189 39170 25024 ...
 $ votesReceived                 : num [1:2550] 4972 8049 12424 19569 7399 ...

Now the data frame looks much cleaner and is ready for some fun data wrangling!

Data wrangling to prepare for visualization

Apparently, it is unrealistic to include all of the 102 candidates on the map. Instead, I wanted to show the top five candidates with the most votes in each ward. This can be achieved using group_by() and slice_max(). I first grouped the data by ward and candidates to create a variable ward_votes to sum the number of votes they received in each ward so that I can use it later for the visuals. Then, I grouped the data by ward again and used slice_max() to select the top five entries within each ward group.

# 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)

I also wanted to know the candidate who received the most votes in each ward, so that I could map the district with a colour representing the candidate¹. Similarly to the previous step, I used slice_max() to find the winner.

# 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)

[1] "Olivia Chow" "Ana Bailão" 

By checking the number of winners using unique(), I confirmed that either Chow or Bailão received the most votes in each of the 25 wards. I then defined their colours as purple and avocado, respectively, as they are the main colours of their websites.

Lastly, I removed all other columns except for the ward and the winner_colour, as this data frame would be merged with the main data frame later.

# 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)

Here comes the hard part. For the text labels in the map, I wanted to display the ward name followed by the top five candidates and their corresponding votes. Needless to say, the information should be presented in multiple lines.

However, when I created the map, it didn’t process the <br> (line break) in the defined label as I expected. After some trial and error and internet search, I discovered that defining the text labels within the data frame and using lapply(names, htmltools::HTML) seemed to be the only feasible way to display the line break in leaflet map.

Here are the steps to make it work.

1. Get and arrange the candidate names. Since we have already identified the top five candidates, I simply grouped the data by ward and arranged the votes in descending order.
2. Create the text labels. I created a variable called names and concatenated the candidate’s name and their votes within each group. To ensure that the ward name appears only on the first line, I used an ifelse conditional statement to identify the first row (row_number() == 1). Simply pasting the ward name with the candidates’ names wouldn’t work, as the ward name would appear on each line.
3. Fine-tune the labels. I added HTML styling such as <b> (bold) and <br> (line break) to improve the aesthetics. As mentioned earlier, we need to apply htmltools::HTML for leaflet to effectively process the HTML tags in the map.
4. Similar to the ward_winner data frame, this data frame will also be merged later, so I only kept the ward name and the text label column. It also makes sense to removed other duplicate rows using distinct(), because the information is only meaningful at the ward level.

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)

Now we can load the shapefile for the geometry. It was my first time working with shapefiles, and it turned out to be quite straightforward. The city wards data can be downloaded here from Open Data Toronto. The model is based on the 2018 election, and I believe there haven’t been any changes since then. I used sf::read_sf to load the shapefile.

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

The last step of data wrangling was to create a merged data frame that included all the information I had collected. I did this by using multiple left_join() operations.

Once I had the merged data frame top_sf, I converted it to an sf object so that the geometry information could be read properly.

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

Finally, it’s time to create the interactive map!

Before creating the map, I defined the legend to indicate the candidate who received the most votes in each ward, representing the community preferences. I could utilize the previous data frames, but I got lazy and created a 2x2 tibble for the two candidates.

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

Creating a leaflet map is not very different from using ggplot2. The official documentation for R is not as detailed compared to ggplot2, but it still provides helpful information, and it also supports piping.

Here are the steps to create the map with leaflet:

1. Set the leafletOptions() to control the zoom level within a specified limit
2. Use addProviderTiles to define the tile style for the map. The complete provider set can be viewed here
3. Use addPolygons to map the appearance based on the data. The fillColor will represent the colour of the winner in each ward, and the labels will be the text labels we created. I also customized the polygons to make it semi-transparent with a smooth white boundary
4. Add the colour legend and its title using addLegend
5. Finally, print the map

# 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