AE 06: Joining country populations with continents

Goal

Our ultimate goal in this application exercise is to create a bar plot of total populations of continents, where the input data are:

Countries and populations
Countries and continents

library(tidyverse) # for data wrangling and visualization
library(scales)    # for pretty axis breaks

Data

Countries and populations

These data come from The World Bank and reflect population counts as of 2022.

population <- read_csv("https://sta199-s24.github.io/data/world-pop-2022.csv")

Let’s take a look at the data.

population

# A tibble: 217 × 3
   country              year population
   <chr>               <dbl>      <dbl>
 1 Afghanistan          2022    41129. 
 2 Albania              2022     2778. 
 3 Algeria              2022    44903. 
 4 American Samoa       2022       44.3
 5 Andorra              2022       79.8
 6 Angola               2022    35589. 
 7 Antigua and Barbuda  2022       93.8
 8 Argentina            2022    46235. 
 9 Armenia              2022     2780. 
10 Aruba                2022      106. 
# ℹ 207 more rows

Continents

These data come from Our World in Data.

continents <- read_csv("https://sta199-s24.github.io/data/continents.csv")

Let’s take a look at the data.

continents

# A tibble: 285 × 4
   entity                code      year continent    
   <chr>                 <chr>    <dbl> <chr>        
 1 Abkhazia              OWID_ABK  2015 Asia         
 2 Afghanistan           AFG       2015 Asia         
 3 Akrotiri and Dhekelia OWID_AKD  2015 Asia         
 4 Aland Islands         ALA       2015 Europe       
 5 Albania               ALB       2015 Europe       
 6 Algeria               DZA       2015 Africa       
 7 American Samoa        ASM       2015 Oceania      
 8 Andorra               AND       2015 Europe       
 9 Angola                AGO       2015 Africa       
10 Anguilla              AIA       2015 North America
# ℹ 275 more rows

Exercises

Think out loud:
- Which variable(s) will we use to join the population and continents data frames?
country from population and entity from continents
- We want to create a new data frame that keeps all rows and columns from population and brings in the corresponding information from continents. Which join function should we use?
left_join() with population on the left.
Demo: Join the two data frames and name assign the joined data frame to a new data frame population_continents.

population_continent <- population |>
  left_join(continents, by = join_by(country == entity))

Demo: Take a look at the newly created population_continent data frame. There are some countries that were not in continents. First, identify which countries these are (they will have NA values for continent).

population_continent |>
  filter(is.na(continent))

# A tibble: 6 × 6
  country                   year.x population code  year.y continent
  <chr>                      <dbl>      <dbl> <chr>  <dbl> <chr>    
1 Congo, Dem. Rep.            2022     99010. <NA>      NA <NA>     
2 Congo, Rep.                 2022      5970. <NA>      NA <NA>     
3 Hong Kong SAR, China        2022      7346. <NA>      NA <NA>     
4 Korea, Dem. People's Rep.   2022     26069. <NA>      NA <NA>     
5 Korea, Rep.                 2022     51628. <NA>      NA <NA>     
6 Kyrgyz Republic             2022      6975. <NA>      NA <NA>

Demo: All of these countries are actually in the continents data frame, but under different names. So, let’s clean that data first by updating the country names in the population data frame in a way they will match the continents data frame, and then joining them, using a case_when() statement in mutate(). At the end, check that all countries now have continent information.

population_continent <- population |>
  mutate(country = case_when(
    country == "Congo, Dem. Rep." ~ "Democratic Republic of Congo",
    country == "Congo, Rep." ~ "Congo",
    country == "Hong Kong SAR, China" ~ "Hong Kong",
    country == "Korea, Dem. People's Rep." ~ "North Korea",
    country == "Korea, Rep." ~ "South Korea",
    country == "Kyrgyz Republic" ~ "Kyrgyzstan",
    .default = country
    )
  ) |>
  left_join(continents, by = join_by(country == entity))

population_continent |>
  filter(is.na(continent))

# A tibble: 0 × 6
# ℹ 6 variables: country <chr>, year.x <dbl>, population <dbl>, code <chr>,
#   year.y <dbl>, continent <chr>

Think out loud: Which continent do you think has the highest population? Which do you think has the second highest? The lowest?

Add your response here.

Demo: Create a new data frame called population_summary that contains a row for each continent and a column for the total population for that continent, in descending order of population. Note that the function for calculating totals in R is sum().

population_summary <- population_continent |>
  group_by(continent) |>
  summarize(total_pop = sum(population)) |>
  arrange(desc(total_pop))

Your turn: Make a bar plot with total population on the y-axis and continent on the x-axis, where the height of each bar represents the total population in that continent.

ggplot(population_summary, aes(x = continent, y = total_pop)) +
  geom_col()

Your turn: Recreate the following plot, which is commonly referred to as a lollipop plot. Hint: Start with the points, then try adding the segments, then add axis labels and caption, and finally, as a stretch goal, update the x scale (which will require a function we haven’t introduced in lectures or labs yet!).

ggplot(population_summary, aes()) +
  geom_point(aes(x = total_pop, y = continent)) +
  geom_segment(aes(y = continent, yend = continent, x = 0, xend = total_pop)) +
  scale_x_continuous(labels = label_number(scale = 1/1000000, suffix = " bil")) +
  theme_minimal() +
  labs(
    x = "Total population",
    y = "Continent",
    title = "World population",
    subtitle = "As of 2022",
    caption = "Data sources: The World Bank and Our World in Data"
  )

Think out loud: What additional improvements would you like to make to this plot.

Answers may vary. Ordering the continents in decreasing order of population.