Multi-Page Dashboard

Code

co2_percap <- read_csv("C:/Users/jason/OneDrive/SAIT/SAIT Instructor/2025-2026/SPRING 2025 - COURSE - DATA420/data/per-capita-co-emissions.csv")

co2_percap <- co2_percap %>%
  rename(Country = Entity)

p <- co2_percap %>% 
  ggplot(aes(Year, `Annual CO2 emissions (per capita)`, color = Country)) +
  geom_line(linewidth=0.5) +
  scale_y_continuous(labels = function(x) paste0(x, " t")) +
  labs(title = "Annual CO2 Emissions Per Capita by Country",
       x = "Year",
       y = "CO2 Emissions (per capita)",
       color = "Country") +
  theme_minimal()

ggplotly(p)

Code

co2_industry <- read_csv("C:/Users/jason/OneDrive/SAIT/SAIT Instructor/2025-2026/SPRING 2025 - COURSE - DATA420/data/per-capita-co2-fuel.csv")

co2_industry %>%
  ggplot(aes(x = reorder(Industry, CO2Emissions), y = CO2Emissions)) +
  geom_col(fill = "steelblue") +
  geom_text(aes(label = round(CO2Emissions, 2)), 
            hjust = -0.1, color = "black", size = 3.5) +
  coord_flip() +
  labs(title = "CO2 Emissions by Industry (2023)",
       x = "",
       y = "Emissions (tonnes per capita)") +
  scale_y_continuous(labels = scales::label_number(suffix = " t"),
                     expand = expansion(mult = c(0, 0.1))) +
  theme_minimal()

Code

co2_country <- read_csv("C:/Users/jason/OneDrive/SAIT/SAIT Instructor/2025-2026/SPRING 2025 - COURSE - DATA420/data/per-capita-co2-country.csv")
# Quick fix: replace country names for compatibility
co2_country_fixed <- co2_country %>%
  mutate(Country = case_when(
    Country == "United States" ~ "United States of America",
    Country == "Democratic Republic of Congo" ~ "Democratic Republic of the Congo",
    Country == "Czechia" ~ "Czech Republic",
    Country == "Ivory Coast" ~ "Côte d'Ivoire",
    TRUE ~ Country
  ))

world <- ne_countries(scale = "medium", returnclass = "sf")
# Join the CO2 data to the map
world_co2 <- world %>%
  left_join(co2_country_fixed, by = c("name" = "Country"))

ggplot(world_co2) +
  geom_sf(aes(fill = `Annual CO2 Emissions per Capita`), color = "gray60", size = 0.1) +
  scale_fill_viridis_c(
    option = "plasma",
    na.value = "black",
    name = "CO2 per capita\n(tonnes)",
    limits = c(0, 25)
  ) +
  labs(title = "Annual CO2 Emissions per Capita by Country (2023)",
       caption = "Data Source: per-capita-co2-country.csv") +
  theme_minimal()

Code

df <- read_csv("C:/Users/jason/OneDrive/SAIT/SAIT Instructor/2025-2026/SPRING 2025 - COURSE - DATA420/data/per-capita-co-transport.csv")


# Pivot and arrange factor levels so Road is at the bottom
df_long <- df %>%
  pivot_longer(cols = -Year, names_to = "Transport", values_to = "Emissions") %>%
  mutate(
    Transport = factor(Transport, levels = c("Road", "Rail", "Shipping", "Aviation", "Pipeline transport"))
  )

p3 <- df_long %>%
  ggplot(aes(Year, Emissions, fill = Transport)) +
  geom_area(color = "white", alpha = 0.70) +
  scale_fill_brewer(palette = "Set2") +
  labs(
    title = "Per Capita CO2 Emissions by Transport Mode (1990–2022)",
    x = "Year",
    y = "Emissions (tonnes per capita)",
    fill = "Transport"
  ) +
  theme_minimal()

ggplotly(p3)