Life Expectancy for Selected Countries

Data

This plot uses the life_ex and regions data frames of the gcubed package. This data was originally obtained from Our World in Data.

First we join the two tables and select only the life expectancy values for 1950 and 2015.

library(gcubed)
library(dplyr)
le <- inner_join(life_ex, regions, by = c("Entity" = "country")) %>%
  filter(Year == 1950 | Year == 2015) 

head(le)

## # A tibble: 6 x 5
##   Entity    Code   Year    LE incomegroup        
##   <chr>     <chr> <dbl> <dbl> <chr>              
## 1 Argentina ARG    1950  61.4 High income        
## 2 Argentina ARG    2015  76.4 High income        
## 3 Armenia   ARM    1950  62.0 Upper middle income
## 4 Armenia   ARM    2015  74.4 Upper middle income
## 5 Australia AUS    1950  68.8 High income        
## 6 Australia AUS    2015  82.7 High income

Then create a factor variable for the income levels and also order the countries alphabetically within each income group.

library(dplyr)
library(tidyr)

income_levels <- c("Low income", "Lower middle income",
                            "Upper middle income", "High income")
le$incomegroup <- factor(le$incomegroup, levels = income_levels)


le <- le %>%  spread(key = Year, value = LE) %>%
  arrange(incomegroup, Entity) 

country_levels <- le$Entity
le$country <- factor(le$Entity, levels = country_levels)

head(le)

## # A tibble: 6 x 6
##   Entity     Code  incomegroup `1950` `2015` country   
##   <chr>      <chr> <fct>        <dbl>  <dbl> <fct>     
## 1 Benin      BEN   Low income    32.7   60.6 Benin     
## 2 Ethiopia   ETH   Low income    33.3   65.0 Ethiopia  
## 3 Haiti      HTI   Low income    36.2   63.1 Haiti     
## 4 Mozambique MOZ   Low income    30.3   57.7 Mozambique
## 5 Tajikistan TJK   Low income    52.2   70.9 Tajikistan
## 6 Togo       TGO   Low income    33.9   59.9 Togo

Code for plot

library(ggplot2)


le_plt <- ggplot() +
  geom_segment(data=le, mapping=aes(x=country, xend=country,
                                    y = `1950`, yend=`2015`) ) +
  geom_point(data = le, aes(x = country, y = `1950`, colour = "1950")) +
  geom_point(data = le, aes(x = country, y = `2015`, colour = "2015")) +
  theme_bw() +
  scale_x_discrete(labels=country_levels)+
  ylab("Life Expectancy (years)") +
  ggtitle("Global Life Expectancy (1950 - 2015)") +
  scale_colour_manual(values = c("grey", "royalblue"), name = "") 
  
le_plt

Cleaning up the theme elements a little:

le_plt <- le_plt + 
    theme(axis.title.x=element_blank(),
        axis.text.x = element_text(hjust = 1, angle = 90, vjust=0.1),
        axis.ticks.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        legend.title = element_blank(),
        plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

le_plt

Figuring out where the income groups end (along the x-axis).

cumsum(table(le$incomegroup))

##          Low income Lower middle income Upper middle income 
##                   7                  26                  50 
##         High income 
##                  90

Adding dividing lines between the income groups using geom_vline:

le_plt <- le_plt +
  geom_vline(xintercept = 7.5, linetype = "dashed", colour = "grey") +
  geom_vline(xintercept = 26.5, linetype = "dashed", colour = "grey") +
  geom_vline(xintercept = 50.5, linetype = "dashed", colour = "grey")

le_plt 

Adding the text for the income groups using geom_text:

le_plt <- le_plt + 
  geom_text(aes(4,87,label = "Low Income"), colour = "red") +
  geom_text(aes(15,87,label = "Lower middle income"), colour = "red") +
  geom_text(aes(35,87,label = "Upper middle income"), colour = "red") +
  geom_text(aes(60,87,label = "High income"), colour = "red")

le_plt