Where Does $7 Trillion Go?

U.S. federal outlays, FY2025 — 100 squares represent about $7.0T (≈ $70B each). Total spending: $7.0T | Deficit: $1.8T

A waffle chart breaking down U.S. federal outlays for fiscal year 2025 into 100 equal squares, each representing approximately $70 billion of the $7.0 trillion total. Social Security, Medicare, and Net Interest alone account for nearly half of all spending, while the federal deficit reached $1.8 trillion — the largest categories visualized through a part-to-whole composition. Built with ggplot2 in R using CBO data.

Figure 1: Waffle chart showing U.S. federal spending for fiscal year 2025, with 100 equal squares representing approximately $70 billion each, totaling $7.0 trillion. Social Security is the largest category at $1,560 billion (22 squares), followed by Medicare at $1,090 billion, Net Interest at $1,050 billion, Defense at $933 billion, Other Mandatory at $747 billion, Medicaid at $648 billion, Other Discretionary at $647 billion, and Veterans Affairs at $325 billion. The federal deficit was $1.8 trillion. Data source: Congressional Budget Office, Monthly Budget Review FY2025

Steps to Create this Graphic

1. Load Packages & Setup

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

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext,     
  janitor, scales, glue  
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 7,
  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

# FY2025 actual federal outlays (billions USD)
# Source: CBO Monthly Budget Review, October 2025 (fiscal year end)
# Total outlays: ~$7.0 trillion | Deficit: $1.8 trillion
#
# Key verified figures (CBO + CRFB reconciliation):
#   Social Security: FY2024 $1,439B + $121B increase = ~$1,560B
#   Medicare:        FY2024 ~$973B  + $117B increase = ~$1,090B  (net of offsetting receipts)
#   Net Interest:    Surpassed $1T for first time; FY2024 ~$970B + $80B = ~$1,050B
#   Defense:         FY2024 ~$895B  + $38B  increase = ~$933B
#   Medicaid:        FY2024 ~$596B  + $52B  increase = ~$648B
#   Veterans Affairs:FY2024 ~$284B  + $41B  increase = ~$325B
#   Other Mandatory + Other Discretionary: residual to $7,000B total
#   Residual = 7000 - (1560+1090+1050+933+648+325) = ~$1,394B split below
spending_raw <- tibble(
  category = c(
    "Social Security",
    "Medicare",
    "Net Interest",
    "Defense",
    "Medicaid",
    "Other Mandatory",
    "Other Discretionary",
    "Veterans Affairs"
  ),
  billions = c(1560, 1090, 1050, 933, 648, 747, 647, 325)
)
```

3. Examine the Data

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

glimpse(spending_raw)
```

4. Tidy Data

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

spending <- spending_raw |>
  mutate(
    # Calculate percentage share of total
    pct       = billions / sum(billions),
    pct_label = percent(pct, accuracy = 0.1),
    
    # Waffle units: 1 unit = ~$70B (so 100 squares = ~$7T)
    units = round(billions / 70),
    
    # Spending type — for secondary grouping / color logic
    type = case_when(
      category %in% c("Social Security", "Medicare", "Medicaid", "Other Mandatory") ~ "Mandatory",
      category == "Net Interest" ~ "Net Interest",
      category %in% c("Defense", "Veterans Affairs", "Other Discretionary") ~ "Discretionary"
    ),
    
    category = factor(category, levels = c(
      "Social Security",
      "Medicare",
      "Net Interest",
      "Other Mandatory",
      "Medicaid",
      "Defense",
      "Other Discretionary",
      "Veterans Affairs"
    ))
  )

# Expand for waffle — each row = one square
waffle_data <- spending |>
  arrange(desc(billions)) |>
  uncount(units) |>
  mutate(
    row_id = row_number(),
    x      = (row_id - 1) %% 10 + 1,
    y      = 10 - ((row_id - 1) %/% 10)
  )

# Total squares
total_squares <- sum(spending$units)  # ~100

### |- legend labels ----
legend_labels <- spending |>
  arrange(category) |>
  mutate(
    label = glue("{category}  ${scales::comma(billions)}B")
  ) |>
  pull(label, name = category)
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
   palette = c(
    "Social Security"     = "#2F6C8E",
    "Medicare"            = "#4B9FC0",
    "Net Interest"        = "#D97941",
    "Defense"             = "#4A6FA5",   
    "Medicaid"            = "#76B8C8",
    "Other Mandatory"     = "#9CC9D5",
    "Other Discretionary" = "#B98AA0",
    "Veterans Affairs"    = "#9A5874"
  )
)

### |- titles and caption ----
title_text <- str_glue("Where Does $7 Trillion Go?")

subtitle_text <- str_glue(
  "U.S. federal outlays, FY2025 — {total_squares} squares represent about $7.0T (≈ $70B each)<br>",
  "Total spending: **$7.0T** | Deficit: **$1.8T** | Source: **CBO**"
)

caption_text <- create_dcc_caption(
  dcc_year = 2026,
  dcc_day = 01,
  source_text = "Congressional Budget Office (CBO), Monthly Budget Review FY2025"
)

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

### |-  plot theme ----
base_theme <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Axes
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),

    # Grid — none needed for waffle
    panel.grid = element_blank(),

    # Legend
    legend.position = "right",
    legend.direction = "vertical",
    legend.title = element_text(
      size = 9, face = "bold", family = fonts$text, color = colors$text
    ),
    legend.text = element_text(
      size = 8, family = fonts$text, color = colors$text
    ),
    legend.key.size = unit(0.9, "lines"),
    legend.key.spacing.y = unit(2, "pt"),
    plot.margin = margin(20, 20, 16, 20)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----
p <-
  ggplot(waffle_data, aes(x = x, y = y, fill = category)) +
  # Geoms
  geom_tile(
    color     = "white",
    linewidth = 0.5,
    width     = 0.92,
    height    = 0.92
  ) +
  # Scales
  scale_fill_manual(
    values = unlist(colors$palette),
    labels = legend_labels,
    name   = NULL
  ) +
  # Scales
  scale_x_continuous(expand = expansion(add = 0.5)) +
  scale_y_continuous(expand = expansion(add = 0.5)) +
  coord_equal() +
  # Labs
  labs(
    title    = title_text,
    subtitle = subtitle_text,
    caption  = caption_text
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.75),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.75),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 1.1,
      hjust = 0,
      halign = 0,
      margin = margin(t = 10, b = 5)
    ),
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 01, 
  width = 7, 
  height = 6
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      glue_1.8.0      scales_1.4.0    janitor_2.2.1  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.0    
[13] purrr_1.2.1     readr_2.2.0     tidyr_1.3.2     tibble_3.2.1   
[17] ggplot2_4.0.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.56          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.1        tools_4.3.1        generics_0.1.4     curl_7.0.0        
 [9] gifski_1.32.0-2    pacman_0.5.1       pkgconfig_2.0.3    RColorBrewer_1.1-3
[13] S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1     farver_2.1.2      
[17] textshaping_1.0.4  codetools_0.2-19   snakecase_0.11.1   litedown_0.9      
[21] htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1      camcorder_0.1.0   
[25] magick_2.8.6       commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39     
[29] stringi_1.8.7      labeling_0.4.3     rsvg_2.6.2         rprojroot_2.1.1   
[33] fastmap_1.2.0      grid_4.3.1         cli_3.6.5          magrittr_2.0.3    
[37] withr_3.0.2        timechange_0.4.0   rmarkdown_2.30     otel_0.2.0        
[41] ragg_1.5.0         hms_1.1.4          evaluate_1.0.5     knitr_1.51        
[45] markdown_2.0       rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1        
[49] xml2_1.5.2         svglite_2.1.3      rstudioapi_0.18.0  jsonlite_2.0.0    
[53] R6_2.6.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_01.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • Congressional Budget Office (CBO). (2025). Monthly Budget Review: Summary for Fiscal Year 2025. Retrieved from https://www.cbo.gov/publication/61307

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