Population Growth Patterns Across Regions

Visualizing growth rate trends from 1960 to 2050. Darker shades represent stronger growth (green) or decline (red), with the horizon line indicating zero growth.

MakeoverMonday
Data Visualization
R Programming
2025
An exploration of global population growth patterns using horizon plots to visualize regional trends from 1960 to 2050. The visualization reveals striking contrasts between high-growth regions like Africa and declining populations in developed regions, highlighting demographic transitions across different parts of the world.
Author

Steven Ponce

Published

February 23, 2025

Original

The original visualization All Countries and Economies comes from data.world.org

Original visualization

Makeover

Figure 1: A horizon plot showing population growth rates across different regions from 1960 to 2050. The visualization uses green shades for positive growth and red shades for decline. Eastern and Southern Africa show consistently high growth rates (green), while Europe and North America trend towards decline (red) in later years.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    readxl,         # Read Excel Files
    ggHoriPlot,     # Horizon Plots for 'ggplot2'
    camcorder       # Record Your Plot History 
)
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code 
#' The raw data for the week MakeoverMonday challenge can be downloaded 
#' here: https://data.world/makeovermonday/2025w9-world-population-estimates-projections
#' 
#' Due to GitHub file size limitation, filter the initial dataset for
#' the `Population growth (annual %)` indicator only.
#' The working csv file was saved as `population_growth_raw.csv`

# Initial raw data
# population_estimates_raw <- read_excel(
#   'data/2025/Population Estimates.xlsx') |> 
#   clean_names()

# Subset for population_growth only
# population_estimates_raw$indicator_name |> unique() |> sort()

# populatio_growth_raw <- population_estimates_raw |> 
#   filter(indicator_name == "Population growth (annual %)") 

# write_csv(x = population_growth_raw, file = 'data/population_growth_raw.csv')

population_growth_raw <- read_csv(
  here::here('data/population_growth_raw.csv')) |> 
  clean_names()

3. Examine the Data

Show code 
glimpse(population_growth_raw)
skim(population_growth_raw)

4. Tidy Data

Show code 
### |-  tidy data ----
population_growth_tidy <- population_growth_raw |> 
  pivot_longer(
    cols = starts_with("x"),     # Select all columns starting with "x"
    names_to = "year",           # Create a new column called "year"
    names_prefix = "x",          # Remove the "x" from the column names
    values_to = "growth_rate"
    ) |>  
  filter(str_detect(country_name, "^(Africa|Asia|Europe|Latin America|North America)")) |> 
  mutate(
    year = as.numeric(year),
    country_name = case_when(
      country_name == "Africa Eastern and Southern" ~ "Eastern & Southern Africa",
      country_name == "Africa Western and Central" ~ "Western & Central Africa",
      country_name == "Latin America & Caribbean" ~ "Latin America & Caribbean",
      TRUE ~ country_name
    ),
    country_name = str_wrap(country_name, width = 30),
    # Order regions for better visual flow
    country_name = factor(country_name, levels = rev(unique(country_name)))
  )

5. Visualization Parameters

Show code 
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "#31a354",  # High Growth 
  "#78c679",  # Medium Growth 
  "#d9f0a3",  # Low Growth 
  "#fdae61",  # Low Decline 
  "#f16913",  # Medium Decline 
  "#d73027"   # High Decline 
  )
)

### |-  titles and caption ----
title_text <- str_glue("Population Growth Patterns Across Regions")
subtitle_text <- str_wrap("Visualizing growth rate trends from 1960 to 2050. Darker shades represent stronger growth (green) or decline (red), with the horizon line indicating zero growth.", width = 90)

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 09,
    source_text = "World Population Estimates & Projections"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Weekly-specific modifications
    legend.position = "top",
    legend.title = element_text(size = rel(0.79)),
    legend.text = element_text(size = rel(0.71)),
    
    axis.title = element_text(size = rel(1.14)),  
    axis.text = element_text(size = rel(0.86)),  
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", size = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 
    
    strip.text.y = element_text(size = rel(0.7), angle = 0), 
    
    panel.border = element_blank(),
    panel.grid = element_blank(),
    panel.spacing = unit(1, "lines"),  
    panel.spacing.y = unit(0, "lines"),
    
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code 
### |-  Plot  ----
p <- ggplot(data = population_growth_tidy) + 
  
  # Geoms
  geom_horizon(
    aes(x = year, y = growth_rate),
    origin = "midpoint", 
    horizonscale = 6, 
    show.legend = TRUE
  ) +
  
  # Scales
  scale_x_continuous(
    breaks = seq(1960, 2050, by = 20),
    expand = c(0.02, 0.02)
  ) +
  scale_y_continuous() +
  scale_fill_manual(
    values = colors$palette,
    name = "Growth Rate",
    labels = c("High Growth", "Medium Growth", "Low Growth",
               "Low Decline", "Medium Decline", "High Decline")
  ) +
  
  # Labs
  labs(
    x = "Year",
    y = NULL,
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text
  ) +
  
  # Facet
  facet_grid(country_name ~ ., scales = "free_y", space = "free_y") +
  
  # Theme
  theme(
    plot.title = element_text(
      size   = rel(2.5),
      family = fonts$title,
      face   = "bold",
      color  = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size   = rel(1.0),
      family = fonts$subtitle,
      color  = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size   = rel(0.6),
      family = fonts$caption,
      color  = colors$caption,
      hjust  = 0.5,
      margin = margin(t = 10)
    )
  )

7. Save

Show code 
### |-  plot image ----  
save_plot(
  p, 
  type = "makeovermonday", 
  year = 2025,
  week = 09,
  width = 10, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0  ggHoriPlot_1.0.1 readxl_1.4.3     here_1.0.1      
 [5] glue_1.8.0       scales_1.3.0     skimr_2.1.5      janitor_2.2.0   
 [9] showtext_0.9-7   showtextdb_3.0   sysfonts_0.8.9   ggtext_0.1.2    
[13] lubridate_1.9.3  forcats_1.0.0    stringr_1.5.1    dplyr_1.1.4     
[17] purrr_1.0.2      readr_2.1.5      tidyr_1.3.1      tibble_3.2.1    
[21] ggplot2_3.5.1    tidyverse_2.0.0 

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1       
[33] rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1 
[37] cli_3.6.3         magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4       
[41] withr_3.0.2       bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29   
[45] bit_4.5.0         cellranger_1.1.0  ragg_1.3.3        hms_1.1.3        
[49] evaluate_1.0.1    knitr_1.49        markdown_1.13     rlang_1.1.4      
[53] gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3       
[57] vroom_1.6.5       svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9   
[61] R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_09.qmd.

For the full repository, click here.

10. References

  1. Article:
  2. Data:
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