Continent populations (Complete)

Introduction

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

  1. Countries and populations
  2. Countries and continents

Packages

We will use the tidyverse and scales packages for data wrangling and visualization.

library(tidyverse)
library(scales)

Data

Population

These data come from The World Bank and reflect population counts for the years 2000 to 2023. The populations given are mid-year estimates.

population <- read_csv("https://data-science-with-r.github.io/data/population.csv")

Let’s take a look at the data.

population
# A tibble: 217 × 28
   series_name series_code country_name country_code `2000` `2001` `2002` `2003`
   <chr>       <chr>       <chr>        <chr>         <dbl>  <dbl>  <dbl>  <dbl>
 1 Population… SP.POP.TOTL Afghanistan  AFG          1.95e7 1.97e7 2.10e7 2.26e7
 2 Population… SP.POP.TOTL Albania      ALB          3.09e6 3.06e6 3.05e6 3.04e6
 3 Population… SP.POP.TOTL Algeria      DZA          3.08e7 3.12e7 3.16e7 3.21e7
 4 Population… SP.POP.TOTL American Sa… ASM          5.82e4 5.83e4 5.82e4 5.79e4
 5 Population… SP.POP.TOTL Andorra      AND          6.61e4 6.78e4 7.08e4 7.39e4
 6 Population… SP.POP.TOTL Angola       AGO          1.64e7 1.69e7 1.75e7 1.81e7
 7 Population… SP.POP.TOTL Antigua and… ATG          7.51e4 7.62e4 7.72e4 7.81e4
 8 Population… SP.POP.TOTL Argentina    ARG          3.71e7 3.75e7 3.79e7 3.83e7
 9 Population… SP.POP.TOTL Armenia      ARM          3.17e6 3.13e6 3.11e6 3.08e6
10 Population… SP.POP.TOTL Aruba        ABW          8.91e4 9.07e4 9.18e4 9.27e4
# ℹ 207 more rows
# ℹ 20 more variables: `2004` <dbl>, `2005` <dbl>, `2006` <dbl>, `2007` <dbl>,
#   `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>,
#   `2013` <dbl>, `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>,
#   `2018` <dbl>, `2019` <dbl>, `2020` <dbl>, `2021` <dbl>, `2022` <dbl>,
#   `2023` <dbl>

Continents

These data come from Our World in Data.

continents <- read_csv("https://data-science-with-r.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

Analysis

Data prep

  • For this analysis we’ll focus on the latest available population numbers – 2023. Modify the population data frame to only include 2023 population numbers. Then, rename the column containing 2023 population numbers as population.
population <- population |>
  select(series_name:country_code, `2023`) |>
  rename(population = `2023`)
  • Which variable(s) will we use to join the population and continents data frames?

From population country_code

From continents code

  • 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()

  • Join the two data frames and name assign the joined data frame to a new data frame population_continents.
population_continents <- population |>
  left_join(continents |> select(code, continent), by = join_by(country_code == code))
  • 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_continents |>
  filter(is.na(continent))
# A tibble: 2 × 6
  series_name       series_code country_name   country_code population continent
  <chr>             <chr>       <chr>          <chr>             <dbl> <chr>    
1 Population, total SP.POP.TOTL Channel Islan… CHI              175346 <NA>     
2 Population, total SP.POP.TOTL Kosovo         XKX             1756374 <NA>

Kosovo - OWID_KOS

Channel Islands - OWID_CIS

  • 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_continents <- population |>
  mutate(
    country_code = case_when(
      country_name == "Kosovo" ~ "OWID_KOS",
      country_name == "Channel Islands" ~ "OWID_CIS",
      .default = country_code
    )
  ) |>
  left_join(continents |> select(code, continent), by = join_by(country_code == code))

population_continents |>
  filter(is.na(continent))
# A tibble: 0 × 6
# ℹ 6 variables: series_name <chr>, series_code <chr>, country_name <chr>,
#   country_code <chr>, population <dbl>, continent <chr>
  • Which continent do you think has the highest population? Which do you think has the second highest? The lowest?

Add response here.

  • 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_continents |>
  group_by(continent) |>
  summarize(total_population = sum(population))

Visualization

  • 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_population)) +
  geom_col()

  • 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, update the x scale.

ggplot(population_summary) +
  geom_point(aes(y = continent, x = total_population)) +
  geom_segment(
    aes(
      x = 0, xend = total_population, 
      y = continent, yend = continent)
    ) +
  theme_minimal() +
  labs(
    x = "Total population",
    y = "Continent",
    title = "World population",
    subtitle = "As of 2023",
    caption = "Data sources: The World Bank and Our World in Data"
  ) +
  scale_x_continuous(labels = label_number(scale = 1/1000000000, suffix = " bil"))

  • What additional improvements would you like to make to this plot.

Add response here.