Africa’s Indigenous Language Families Span the Continentr

Niger-Congo, Afroasiatic, and Nilo-Saharan are spoken natively across 20–40 countries with speakers evenly distributed. Colonial languages cluster in fewer regions.

A scatter plot exploring the geographic spread of African language families. Using the Herfindahl-Hirschman Index to measure speaker concentration, this visualization reveals that indigenous families like Niger-Congo span 40+ countries with evenly distributed speakers, while colonial languages (English, French) cluster in specific regions.

Figure 1: Scatter plot showing African language families by geographic concentration versus number of countries. Indigenous families (Niger-Congo, Afroasiatic, Nilo-Saharan) cluster in the upper-left with low concentration and 20-40 countries. Colonial languages (English, French) appear in the lower-right with high concentration in only 3-4 countries.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
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
    ggrepel,       # Non-overlapping Text Labels
    janitor,       # Simple Tools for Examining and Cleaning Dirty Data
    scales,        # Scale Functions for Visualization
    glue           # Interpreted String Literals
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 8,
  height = 6,
  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

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2026, week = 02)

africa_raw <- tt$africa |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(africa_raw)
```

4. Tidy Data

```{r}
#| label: tidy-fixed
#| warning: false

#' Note:
#' HHI (Herfindahl-Hirschman Index) measures market concentration - originally used 
#' in economics to see if one company dominates a market. We're adapting it to see 
#' if one country dominates a language family's speaker base.
#' 

### |-  calculate HHI concentration + country coverage ----
family_summary <- africa_raw |>
    filter(!is.na(native_speakers), native_speakers > 0) |>
    group_by(family, country) |>
    summarise(
        speakers = sum(native_speakers), 
        .groups = "drop"
        ) |>
    group_by(family) |>
    mutate(
        total = sum(speakers),
        share = speakers / total
    ) |>
    summarise(
        hhi_concentration = sum(share^2),
        n_countries = n_distinct(country),
        .groups = "drop"
    ) |>
    filter(n_countries > 1) |>
    group_by(family) |>
    slice_max(n_countries, n = 1) |>
    ungroup() |>
    mutate(
        family_group = if_else(family %in% c("English", "French"),
                               "Colonial",
                               "Indigenous")
    )

### |-  labels for key points ----
label_df <- family_summary |>
    filter(
        str_detect(family, regex("^English$|^French$|Niger|Afroasiatic|Nilo", ignore_case = TRUE))
    )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
      "Colonial"   = "#E76F51",
      "Indigenous" = "gray40"
  )
)

### |- titles and caption ----
title_text <- str_glue("Africa's Indigenous Language Families Span the Continent")

subtitle_text <- str_glue(
    "Niger-Congo, Afroasiatic, and Nilo-Saharan are spoken natively across 20–40 countries<br>with speakers evenly distributed. ", 
    "<b style='color:#E76F51'>Colonial languages</b> cluster in fewer regions.",
)

caption_text <- create_social_caption(
    tt_year = 2026,
    tt_week = 02,
    source_text = "Wikipedia 'Languages of Africa' | Concentration = Herfindahl-Hirschman Index"
)

### |-  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(
    # Text styling
    plot.title = element_text(
      face = "bold", family = fonts$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_markdown(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.25),

    # Axes
    axis.title.x = element_text(size = 9.5, color = "gray30", margin = margin(t = 8)),
    axis.title.y = element_text(size = 9.5, color = "gray30", margin = margin(r = 8)),
    # axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$subtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20),
    
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- Plot ----
p <- ggplot(family_summary, aes(x = hhi_concentration, y = n_countries)) +
    # Geoms
    geom_point(
        aes(color = family_group),
        size = 3,
        alpha = 0.85
    ) +
    geom_text_repel(
        data = label_df,
        aes(label = family, color = family_group),
        size = 3.0,
        fontface = "bold",
        box.padding = 0.4,
        point.padding = 0.3,
        segment.color = "gray70",
        segment.size = 0.3,
        max.overlaps = Inf,
        seed = 123
    ) +
    # Scales
    scale_color_manual(
        values = colors$palette,
        guide = "none"
    ) +
    scale_x_continuous(
        limits = c(0, 1.1),
        breaks = c(0, 0.25, 0.5, 0.75, 1)
    ) +
    scale_y_continuous(
        breaks = seq(0, 45, by = 10)
    ) +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = "Speaker Concentration\n(0 = evenly spread across countries, 1 = single country dominates)",
        y = "Number of Countries"
    ) + 
    # Theme
    theme(
        axis.title.y = element_text(
            angle = 0,           # Makes it horizontal
            vjust = 1.02,        # Pushes it to the top of the axis
            hjust = 0.5,
            margin = margin(r = -50) # Adjusts distance from the axis
        ),
    plot.title = element_markdown(
        size = rel(1.4),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 0, b = 5)
    ),
    plot.subtitle = element_markdown(
        size = rel(0.8),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
        size = rel(0.5),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
        margin = margin(t = 20, b = 5)
    )
)
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2026, 
  week = 02, 
  width  = 8,
  height = 6,
  )
```

8. Session Info

TipExpand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

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] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] ggrepel_0.9.6   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1      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       camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     utf8_1.2.4         withr_3.0.2       
[41] rappdirs_0.3.3     bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29    
[45] tidytuesdayR_1.1.2 gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3        
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[61] jsonlite_1.8.9     R6_2.5.1           systemfonts_1.1.0 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_02.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 02: The Languages of Africa

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils