Country Populations Over Time

Load Packages

Data

Import data from Github repository.

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>

Tidying

• What are the aesthetic mappings needed?

• Reshape population data such that it can be used to generate the desired visualization.

x: year y: population color, shape: country_name

population |>
  pivot_longer(
    cols = `2000`:`2023`,
    names_to = "year",
    values_to = "population"
  )

# A tibble: 5,208 × 6
   series_name       series_code country_name country_code year  population
   <chr>             <chr>       <chr>        <chr>        <chr>      <dbl>
 1 Population, total SP.POP.TOTL Afghanistan  AFG          2000    19542982
 2 Population, total SP.POP.TOTL Afghanistan  AFG          2001    19688632
 3 Population, total SP.POP.TOTL Afghanistan  AFG          2002    21000256
 4 Population, total SP.POP.TOTL Afghanistan  AFG          2003    22645130
 5 Population, total SP.POP.TOTL Afghanistan  AFG          2004    23553551
 6 Population, total SP.POP.TOTL Afghanistan  AFG          2005    24411191
 7 Population, total SP.POP.TOTL Afghanistan  AFG          2006    25442944
 8 Population, total SP.POP.TOTL Afghanistan  AFG          2007    25903301
 9 Population, total SP.POP.TOTL Afghanistan  AFG          2008    26427199
10 Population, total SP.POP.TOTL Afghanistan  AFG          2009    27385307
# ℹ 5,198 more rows

The first pivot creates year as a string(chr) variable. Let’s convert it to a numeric value.

population_longer <- population |> #create new df by passing the pivot to a new name
  pivot_longer(
    cols = `2000`:`2023`,
    names_to = "year",
    values_to = "population",
    names_transform = as.numeric
  )

Visualization

• Now we are able to begin to to visualize the transformed data.

population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name)) +
  geom_line() +
  geom_point()

Fixing the visualization

• Update x-axis scales

• Update y-axis so it’s scaled to millions and uses the same breaks as the goal plot.

• Theme

• Labels

• Placement of legend

#fix shapes first-- each country now gets its own shape
population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point()

#fix the x-axis

population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(limits = c(2000, 2024), breaks = seq(2000, 2024, 4))

#fix the y-axis

population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(limits = c(2000, 2024), breaks = seq(2000, 2024, 4))+
  scale_y_continuous(
    breaks = seq(250000000, 1250000000, 250000000),
    labels = label_number(scale = 1/1000000, suffix = "mil")
  ) +
  scale_color_manual(
    values = c(
      "United States" = "#0A3161",
      "China" = "#EE1C25",
      "India" = "#FF671F"
    )
  ) +
  theme_minimal() +
  labs(
    title = "Country Populations Over Time",
    subtitle = "2000 to 2023",
    caption = "Data Source: World Bank", 
    x = "Year",
    y = "Population (millions)",
    color = "Country",
    shape = "Country"
  )

#fix the color scheme by adding specific colors US = #0A3161
#China = #EE1C25, India = #FF671F

#fix shapes first-- each country now gets its own shape
population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point()

#fix the x-axis

population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(limits = c(2000, 2024), breaks = seq(2000, 2024, 4))

#fix the y-axis

population_longer |>
  filter(country_name %in% c("China", "India", "United States")) |>
  ggplot(aes(x = year, y = population, color = country_name, shape = country_name)) +
  geom_line() +
  geom_point() +
  scale_x_continuous(limits = c(2000, 2024), breaks = seq(2000, 2024, 4))+
  scale_y_continuous(
    breaks = seq(250000000, 1250000000, 250000000),
    labels = label_number(scale = 1/1000000, suffix = "mil")
  ) +
  scale_color_manual(
    values = c(
      "United States" = "#0A3161",
      "China" = "#EE1C25",
      "India" = "#FF671F"
    )
  ) +
  labs(
    title = "Country Populations Over Time",
    subtitle = "2000 to 2023",
    caption = "Data Source: World Bank", 
    x = "Year",
    y = "Population (millions)",
    color = NULL,
    shape = NULL
  ) +
    theme_minimal() +
    theme(legend.position = "top")