10 Mapping Global Life Expectancy

10.1 Set up

library(tidyverse)

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✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.2.0     
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✖ dplyr::lag()    masks stats::lag()
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library(sf) # For spatial data operations

Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

library(rnaturalearth) # For world map data

load("out/expectancy_merged.RData")

10.2 Overview of Life Expectancy

Here let’s start by looking at the summary statistics of worldwide life expectancy in the most recent year available (2022).

# Summary statistics of worldwide life expectancy in 2022
df_merge |>
  st_drop_geometry() |>
  filter(year == 2022) |>
  summarise(mean_life_expectancy = mean(life_expectancy),
            median_life_expectancy = median(life_expectancy),
            min_life_expectancy = min(life_expectancy),
            max_life_expectancy = max(life_expectancy))

Top 10 Countries/Regions with Highest Life Expectancy

# Top 10 countries with the highest life expectancy in 2022
df_merge |>
  st_drop_geometry() |>
  filter(year == 2022) |>
  arrange(desc(life_expectancy)) |>
  head(10)

10.3 Data Visualization

Bar Plot: Life Expectancy in 2022 by 10 Major Countries

# List of 10 major countries (with highest GDP)
country_list <- c("United States", "China", "Japan", "Germany", "India", 
                  "United Kingdom", "France", "Brazil", "Italy", "Canada")

df_merge |>
  st_drop_geometry() |>
  filter(year == 2022) |>
  filter(country %in% country_list) |>
  ggplot(aes(x = reorder(country, life_expectancy), y = life_expectancy)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(title = "Life Expectancy in 2022 by 10 Major Countries",
       x = "Country",
       y = "Life Expectancy at Birth",
       fill = "Country",
       caption = "Source: World Bank | Author: Bin Chen") +
  geom_text(aes(label = round(life_expectancy, 1)), hjust = -0.1, size = 3) +
  theme_minimal()

Ignoring unknown labels:
• fill : "Country"

Line Plot: Life expectancy trend in China

# Life expectancy trend in China
df_merge |>
  st_drop_geometry() |>
  filter(country == "China") |>
  ggplot(aes(x = year, y = life_expectancy)) +
  geom_line(color = "steelblue") +
  labs(title = "Life Expectancy Trend in China",
       x = "Year",
       y = "Life Expectancy at Birth",
       caption = "Source: World Bank | Author: Bin Chen") +
  theme_minimal()

Multiple Lines

# Life expectancy trend in the 10 major countries
df_merge |>
  st_drop_geometry() |>
  filter(country %in% country_list) |>
  ggplot(aes(x = year, y = life_expectancy, color = country)) +
  geom_line() +
  labs(title = "Life Expectancy Trend in 10 Major Countries",
       x = " ",
       y = " ",
       caption = "Source: World Bank | Author: Bin Chen") +
  theme_minimal() +
  facet_wrap(~country, scales = "free_y") +
  theme(legend.position = "none")

Map Plot: Life Expectancy by Country in 2022

# Map of life expectancy by country in 2022
df_merge |>
  filter(year == 2022) |>
1  ggplot(aes()) +
2  geom_sf(aes(geometry = geometry, fill = life_expectancy)) +
  scale_fill_gradient(low = "white",
                      high = "#3182bd",
                      na.value = "grey90",
                      name = "Life Expectancy",
                      guide = guide_colorbar(
                        barwidth = 10,
                        barheight = 0.5,
                        title.position = "top",
                        title.hjust = 0.5
3                      )) +
  labs(title = "Life Expectancy at Birth by Country in 2022",
       fill = "Life Expectancy at Birth",
4       caption = "Source: World Bank | By: Bin Chen") +
5  theme_void() +
  theme(legend.position = "bottom",
        legend.title = element_text(size = 10),
6        plot.title = element_text(hjust = 0.5, size = 11))

1: Set up the plot.
2: Create a spatial feature plot using the world map geometry and fill color based on life expectancy.
3: Set the color gradient for life expectancy.
4: Add titles and captions.
5: Remove the background.
6: Adjust the legend position and title size.

10.4 Key Functions Recap

Function	Package	Purpose	Example Use
`st_drop_geometry()`	`sf`	Remove spatial geometry from sf object	`df_merge \\| st_drop_geometry()`
`geom_sf()`	`ggplot2`	Plot spatial features (map geometries)	`geom_sf(aes(geometry = geometry, fill = life_expectancy))`
`scale_fill_gradient()`	`ggplot2`	Create gradient color scale for fill	`scale_fill_gradient(low = "white", high = "#3182bd", na.value = "grey90")`
`guide_colorbar()`	`ggplot2`	Customize the colorbar legend	`guide_colorbar(barwidth = 10, barheight = 0.5, title.position = "top")`
`theme_void()`	`ggplot2`	Remove background, gridlines, and axes	`theme_void()`
`element_text()`	`ggplot2`	Customize text elements in themes	`element_text(size = 10, hjust = 0.5)`
`filter()`	`dplyr`	Filter rows based on conditions	`filter(year == 2022)`
`arrange()`	`dplyr`	Sort rows by one or more columns	`arrange(desc(life_expectancy))`
`summarise()`	`dplyr`	Compute summary statistics for groups	`summarise(mean_life_expectancy = mean(life_expectancy))`
`head()`	Base R	Get first n rows of data	`head(10)`
`ggplot()`	`ggplot2`	Create a new ggplot object	`ggplot(aes(x = country, y = life_expectancy))`
`aes()`	`ggplot2`	Specify aesthetic mappings (x, y, color, fill)	`aes(x = year, y = life_expectancy, color = country)`
`geom_col()`	`ggplot2`	Create a bar chart	`geom_col(fill = "steelblue")`
`geom_line()`	`ggplot2`	Create a line plot	`geom_line(color = "steelblue")`
`geom_text()`	`ggplot2`	Add text labels to a plot	`geom_text(aes(label = round(life_expectancy, 1)), hjust = -0.1, size = 3)`
`labs()`	`ggplot2`	Add titles, labels, and captions	`labs(title = "...", x = "Country", y = "Life Expectancy at Birth")`
`coord_flip()`	`ggplot2`	Flip x and y axes	`coord_flip()`
`facet_wrap()`	`ggplot2`	Create multiple plots based on categorical var	`facet_wrap(~country, scales = "free_y")`
`theme_minimal()`	`ggplot2`	Apply minimal theme	`theme_minimal()`
`theme()`	`ggplot2`	Customize plot appearance	`theme(legend.position = "bottom", plot.title = element_text())`
`reorder()`	Base R	Reorder levels of a variable	`reorder(country, life_expectancy)`