The World Started Tracking Severe Food Insecurity in 2016

Countries reporting each indicator annually. Severe Food Insecurity adoption jumps post-2016

TidyTuesday
2025
FAO data reveals global tracking of Severe Food Insecurity surged 70% after 2016.
Published

October 13, 2025

Figure 1: Line chart showing five food security indicators tracked by countries from 2005 to 2025. Four gray lines (Average dietary energy requirements, Undernourishment, Child Overweight, Child Stunting) remain steady at 200-240 countries throughout. One purple line (Moderate/Severe Food Insecurity) dramatically surges from 40 countries in 2015 to 139 countries in 2016 (3.5x increase), then continues rising to ~190 countries by 2022, with a gray shaded box highlighting the post-2016 period of expanded tracking.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{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
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  scales,        # Scale Functions for Visualization
  glue,          # Interpreted String Literals
  geomtextpath,  # Curved Text in 'ggplot2' # Curved Text in 'ggplot2'
  ggrepel        # Automatically Position Non-Overlapping Text Labels with 'ggplot2' 
  )})

### |- 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

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

tt <- tidytuesdayR::tt_load(2025, week = 41)

food_security <- tt$food_security |> clean_names()

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

3. Examine the Data

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

glimpse(food_security)
```

4. Tidy Data

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

fs_clean <- food_security |>
  mutate(
    year = year_end,
    item_short = case_when(
      str_detect(item, "children under 5.*overweight") ~ "Child Overweight",
      str_detect(item, "children under 5.*stunted") ~ "Child Stunting",
      str_detect(item, "Prevalence of undernourishment.*3-year") ~ "Undernourishment (3y)",
      str_detect(item, "Prevalence of moderate or severe food insecurity") ~ "Moderate/Severe Food Insecurity",
      TRUE ~ str_trunc(item, 35)
    )
  )

# Manually select items that tell the story
selected_items <- c(
  "Moderate/Severe Food Insecurity",
  "Child Overweight",
  "Child Stunting",
  "Undernourishment (3y)",
  "Average dietary energy requireme..."
)

# Count countries per year/indicator
participation <- fs_clean |>
  filter(!is.na(value), item_short %in% selected_items) |>
  distinct(area, year, item_short) |>
  count(item_short, year, name = "n_countries") |>
  mutate(is_highlight = item_short == "Moderate/Severe Food Insecurity") |>
  arrange(item_short, year)
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette = c("TRUE" = "#7209b7", "FALSE" = "gray70")
)

### |- titles and caption ----
title_text <- str_glue(
    "The World Started Tracking Severe Food Insecurity in 2016"
    )

subtitle_text <- str_glue(
  "Countries reporting each indicator annually. Severe Food Insecurity adoption jumps **post-2016**.<br>",
  "**Note**: Most values are modeled estimates rather than direct measurements.<br>"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 41,
  source_text = "FAO Suite of Food Security Indicators"
)

### |-  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)),
    plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.9), margin = margin(b = 20)),

    ## Grid
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
    
    # Axes
    axis.title = element_text(size = rel(0.9), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.95)),
    axis.ticks = element_blank(),
    
    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
        face = "bold", 
        color = "gray20", 
        size = rel(1),
        margin = margin(t = 8, b = 8)
    ),
    panel.spacing = unit(2, "lines"),
    
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, 
                                color = colors$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

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

### |- final plot ----
p <- ggplot(participation, aes(year, n_countries, group = item_short)) +
  # Annotate
    annotate("rect",
             xmin = 2016 - 0.5, xmax = 2024.5, 
             ymin = 0, ymax = 250, alpha = 0.06  
    ) +
  # Geoms
    # Gray lines (background)
    geom_line(
        data = filter(participation, !is_highlight),
        color = colors$palette[2],
        linewidth = 0.6,
        alpha = 0.5
    ) +
    # Gray line labels
    geom_text_repel(
        data = filter(participation, !is_highlight) |>
            group_by(item_short) |>
            slice_max(year, n = 1) |>  
            ungroup(),
        aes(label = item_short),
        family = "text",
        size = 2.8,
        color = colors$palette[2],
        alpha = 0.7,
        hjust = 0,
        nudge_x = 0.15,
        direction = "y",
        segment.size = 0.3,
        segment.alpha = 0.3,
        segment.color = colors$palette[2],
        max.overlaps = 20, 
        seed = 456
    ) +
    # Purple highlight line
    geom_textline(
        data = filter(participation, is_highlight),
        aes(label = item_short),
        color = colors$palette[1],
        linewidth = 1.8,         
        size = 3.6,             
        vjust = -0.25,
        hjust = 0.35,
        # straight = TRUE,
        text_smoothing = 30,
        na.rm = TRUE
    ) +
  # Scales
    scale_x_continuous(
        limits = c(2005, 2025),
        breaks = seq(2005, 2025, 5),
    ) +
    scale_y_continuous(
        limits = c(0, 260),      
        breaks = seq(0, 250, 50)
    ) +
    coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Year",
    y = "Countries Reporting"
  ) +
  # Theme
  theme(
    panel.grid.minor  = element_blank(),
    panel.grid.major.x= element_blank(),
    panel.grid.major.y= element_line(colour = "gray90", linewidth = 0.2),
    plot.title = element_text(
      size = rel(1.55),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.75),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 0, b = 0)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    )
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 41, 
  width  = 8,
  height = 6
  )
```

8. 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         ggrepel_0.9.6      geomtextpath_0.1.4 glue_1.8.0        
 [5] scales_1.3.0       janitor_2.2.0      showtext_0.9-7     showtextdb_3.0    
 [9] sysfonts_0.8.9     ggtext_0.1.2       lubridate_1.9.3    forcats_1.0.0     
[13] stringr_1.5.1      dplyr_1.1.4        purrr_1.0.2        readr_2.1.5       
[17] tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1      tidyverse_2.0.0   
[21] 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

The complete code for this analysis is available in tt_2025_41.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 41: [World Food Day](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-10-14

11. Custom Functions Documentation

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 โ†’ GitHub: R/utils

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