Two Economies Account for 42.5% of Global GDP

The remaining 57.5% is spread across 190 other economies

MakeoverMonday

2026

The United States and China together account for 42.5% of global GDP, while the remaining 190 economies combined hold 57.5%. Built as a single 100% composition bar after diagnostic testing showed that cumulative and log-distribution geometries both suppressed the scale separation between the top two economies and the rest. R, ggplot2, ggtext.

Author

Steven Ponce

Published

August 24, 2026

Original

The original visualization comes from Global GDP Ranks

Makeover

Figure 1: Dumbbell chart titled ‘Half of Gen Z Investors Have Redirected Investing Dollars to Sports Betting.’ Each row shows two connected dots per generation: an open circle for the share who consider sports betting part of their long-term financial strategy, and a filled circle for the share who have redirected investing funds to sports betting. Gen Z, highlighted in orange, moves from 26% to 52%. Millennials go from 14% to 31%, Gen X from 6% to 10%, and Boomers from 1% to 4%, showing the gap and the overall share both shrink sharply with age. Source: Betterment 2026 Retail Investor Survey, n=250 per generation.

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, ggtext, showtext, scales, glue, janitor, ggview
)
})

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

df_raw <- readxl::read_excel(
  here::here("data/MakeoverMonday/2026/IMF_20GDP.xlsx"))  |>
  clean_names()
```

3. Examine the Data

Show code

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

glimpse(df_raw)
skimr::skim_without_charts(df_raw)
```

4. Tidy Data

Show code

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

gdp_total <- sum(df_raw$value, na.rm = TRUE)

composition_data <- df_raw |>
  mutate(
    segment = case_when(
      country == "United States" ~ "United States",
      country == "China" ~ "China",
      TRUE ~ "All other 190 economies"
    )
  ) |>
  summarise(value = sum(value), .by = segment) |>
  mutate(
    share = value / gdp_total,
    segment = factor(
      segment,
      levels = c("All other 190 economies", "China", "United States")
    )
  ) |>
  arrange(segment)
```

5. Visualization Parameters

Show code

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    us_accent    = "#722F37", 
    china_accent = "#A8626C",  
    neutral_rest = "#D9D2C9" 
  )
)

fill_values <- c(
  "United States"            = colors$palette[["us_accent"]],
  "China"                    = colors$palette[["china_accent"]],
  "All other 190 economies"  = colors$palette[["neutral_rest"]]
)

label_colors <- c(
  "United States"            = "white",
  "China"                    = "white",
  "All other 190 economies"  = "gray20"
)

### |-  titles and caption ----
title_text <- str_glue("Two Economies Account for 42.5% of Global GDP")

subtitle_text <- str_glue(
  "The remaining 57.5% is spread across 190 other economies"
)

caption_text <- create_mm_caption(
  mm_year = 2026,
  mm_week = 34,
  source_text = paste0(
    "IMF World Economic Outlook, 2026<br>",
    "Note: 4 of 192 economies reflect 2024-2025 estimates (most recent available)"
  )
)

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

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

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_textbox_simple(
      family = fonts$title_1, size = rel(1.6), face = "bold", margin = margin(b = 8),
      lineheight = 1.05
    ),
    plot.subtitle = element_textbox_simple(
      family = fonts$subtitle, size = rel(1.05), margin = margin(b = 20)
    ),
    plot.caption = element_markdown(
      family = fonts$caption, size = rel(0.55), color = "gray40", hjust = 0
    ),
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    legend.position = "none",
    plot.margin = margin(t = 20, r = 30, b = 20, l = 30)
  )
)

theme_set(weekly_theme)
```

6. Plot

Show code

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

### |- plot ----
p <- ggplot(composition_data, aes(x = 1, y = share, fill = segment)) +
  geom_col(width = 0.42) +
  geom_text(
    aes(
      label = glue("{segment}\n{percent(share, accuracy = 0.1)}"),
      color = segment
    ),
    position = position_stack(vjust = 0.5),
    family = fonts$caption,
    size = 4.2,
    lineheight = 0.95
  ) +
  coord_flip(expand = FALSE) +
  scale_fill_manual(values = fill_values) +
  scale_color_manual(values = label_colors) +
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text
  )
```

7. Save

Show code

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

### |- save ----
main_path  <- here::here("data_visualizations", "MakeoverMonday", "2026", "mm_2026_34.png")
thumb_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "thumbnails", "mm_2026_34.png")

# Full-size version, for the QMD figure
save_ggplot(
  plot = p,
  file = main_path,
  width = 10,
  height = 6,
  units = "in",
  dpi = 320,
  create.dir = TRUE
)

# Reduced-size thumbnail, for the YAML `image:` field
fs::dir_create(dirname(thumb_path))
magick::image_read(main_path) |>
  magick::image_resize("400") |>
  magick::image_write(thumb_path)
```

8. Session Info

Expand for Session Info

R version 4.6.1 (2026-06-24)
Platform: aarch64-apple-darwin23
Running under: macOS Tahoe 26.5.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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      ggview_0.2.2    janitor_2.2.1   glue_1.8.1     
 [5] scales_1.4.0    showtext_0.9-8  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
[13] dplyr_1.2.1     purrr_1.2.2     readr_2.2.0     tidyr_1.3.2    
[17] tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.60          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.3        tools_4.6.1        generics_0.1.4     curl_7.1.0        
 [9] pkgconfig_2.0.3    RColorBrewer_1.1-3 skimr_2.2.2        S7_0.2.2          
[13] readxl_1.5.0       lifecycle_1.0.5    compiler_4.6.1     farver_2.1.2      
[17] textshaping_1.0.5  repr_1.1.7         codetools_0.2-20   snakecase_0.11.1  
[21] litedown_0.10      htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1     
[25] magick_2.9.1       commonmark_2.0.0   tidyselect_1.2.1   digest_0.6.39     
[29] stringi_1.8.7      labeling_0.4.3     rprojroot_2.1.1    fastmap_1.2.0     
[33] grid_4.6.1         cli_3.6.6          magrittr_2.0.5     base64enc_0.1-6   
[37] withr_3.0.3        timechange_0.4.0   rmarkdown_2.31     otel_0.2.0        
[41] cellranger_1.1.0   ragg_1.5.2         hms_1.1.4          evaluate_1.0.5    
[45] knitr_1.51         markdown_2.0       rlang_1.3.0        gridtext_0.1.6    
[49] Rcpp_1.1.2         xml2_1.6.0         rstudioapi_0.19.0  jsonlite_2.0.0    
[53] R6_2.6.1           fs_2.1.0           systemfonts_1.3.2

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_34.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 34: Global GDP Ranks - CSV: 192 rows × 5 columns (rank, code, country, value, year). Covers all 192 IMF-tracked economies ranked by GDP (USD). 188 of 192 observations reflect 2026; four economies (Pakistan, Sri Lanka, Lebanon, Afghanistan) carry the most recent available estimate from 2024–2025 rather than a 2026 figure. - The original visualization presents a static sortable table of the top 10 economies by GDP and share of world total, requiring the reader to infer concentration from a truncated list — the other 182 economies are simply absent. This makeover abandons rank as the organizing structure entirely and encodes the finding directly: a single 100% composition bar showing the United States (25.9%) and China (16.6%) together account for 42.5% of global GDP, with all 190 remaining economies combined making up the other 57.5%.

Source Data: 2. International Monetary Fund, 2026, World Economic Outlook Database

11. Custom Functions Documentation

📦 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

Citation

BibTeX citation:

@online{ponce2026,
  author = {Ponce, Steven},
  title = {Two {Economies} {Account} for 42.5\% of {Global} {GDP}},
  date = {2026-08-24},
  url = {https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_34.html},
  langid = {en}
}

For attribution, please cite this work as:

Ponce, Steven. 2026. “Two Economies Account for 42.5% of Global GDP.” August 24. https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_34.html.

--- title: "Two Economies Account for 42.5% of Global GDP" subtitle: "The remaining 57.5% is spread across 190 other economies" description: "The United States and China together account for 42.5% of global GDP, while the remaining 190 economies combined hold 57.5%. Built as a single 100% composition bar after diagnostic testing showed that cumulative and log-distribution geometries both suppressed the scale separation between the top two economies and the rest. R, ggplot2, ggtext." date: "2026-08-24" author: - name: "Steven Ponce" url: "https://stevenponce.netlify.app" citation: url: "https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_34.html" categories: ["MakeoverMonday", "2026"] tags: [ "makeover-monday", "composition-chart", "100-percent-bar", "gdp", "global-economy", "concentration", "economics", "ggtext", "data-storytelling", "imf-data", "2026" ] image: "thumbnails/mm_2026_34.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2026 current_week <- 34 project_file <- "mm_2026_34.qmd" project_image <- "mm_2026_34.png" ## Data Sources data_main <- "https://www.imf.org/en/data" data_secondary <- "https://www.imf.org/en/data" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_34_original_chart.png" ## Organization/Platform Links org_primary <- "https://statisticsoftheworld.com/gdp-by-country" org_secondary <- "https://statisticsoftheworld.com/gdp-by-country" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization comes from `r create_link("Global GDP Ranks", org_primary)` ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2026/Week_34/original_chart.png) ### Makeover ![Dumbbell chart titled 'Half of Gen Z Investors Have Redirected Investing Dollars to Sports Betting.' Each row shows two connected dots per generation: an open circle for the share who consider sports betting part of their long-term financial strategy, and a filled circle for the share who have redirected investing funds to sports betting. Gen Z, highlighted in orange, moves from 26% to 52%. Millennials go from 14% to 31%, Gen X from 6% to 10%, and Boomers from 1% to 4%, showing the gap and the overall share both shrink sharply with age. Source: Betterment 2026 Retail Investor Survey, n=250 per generation.](mm_2026_34.png){#fig-1} ### [**Steps to Create this Graphic**]{.mark} #### [1. Load Packages & Setup]{.smallcaps} ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, ggtext, showtext, scales, glue, janitor, ggview ) }) # Source utility functions suppressMessages( source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/themes/base_theme.R")) ``` #### [2. Read in the Data]{.smallcaps} ```{r} #| label: read #| include: true #| eval: true #| warning: false #| df_raw <- readxl::read_excel( here::here("data/MakeoverMonday/2026/IMF_20GDP.xlsx")) |> clean_names() ``` #### [3. Examine the Data]{.smallcaps} ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(df_raw) skimr::skim_without_charts(df_raw) ``` #### [4. Tidy Data]{.smallcaps} ```{r} #| label: tidy #| warning: false gdp_total <- sum(df_raw$value, na.rm = TRUE) composition_data <- df_raw |> mutate( segment = case_when( country == "United States" ~ "United States", country == "China" ~ "China", TRUE ~ "All other 190 economies" ) ) |> summarise(value = sum(value), .by = segment) |> mutate( share = value / gdp_total, segment = factor( segment, levels = c("All other 190 economies", "China", "United States") ) ) |> arrange(segment) ``` #### [5. Visualization Parameters]{.smallcaps} ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = list( us_accent = "#722F37", china_accent = "#A8626C", neutral_rest = "#D9D2C9" ) ) fill_values <- c( "United States" = colors$palette[["us_accent"]], "China" = colors$palette[["china_accent"]], "All other 190 economies" = colors$palette[["neutral_rest"]] ) label_colors <- c( "United States" = "white", "China" = "white", "All other 190 economies" = "gray20" ) ### |- titles and caption ---- title_text <- str_glue("Two Economies Account for 42.5% of Global GDP") subtitle_text <- str_glue( "The remaining 57.5% is spread across 190 other economies" ) caption_text <- create_mm_caption( mm_year = 2026, mm_week = 34, source_text = paste0( "IMF World Economic Outlook, 2026<br>", "Note: 4 of 192 economies reflect 2024-2025 estimates (most recent available)" ) ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- base_theme <- create_base_theme(colors) weekly_theme <- extend_weekly_theme( base_theme, theme( plot.title = element_textbox_simple( family = fonts$title_1, size = rel(1.6), face = "bold", margin = margin(b = 8), lineheight = 1.05 ), plot.subtitle = element_textbox_simple( family = fonts$subtitle, size = rel(1.05), margin = margin(b = 20) ), plot.caption = element_markdown( family = fonts$caption, size = rel(0.55), color = "gray40", hjust = 0 ), axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), panel.grid = element_blank(), legend.position = "none", plot.margin = margin(t = 20, r = 30, b = 20, l = 30) ) ) theme_set(weekly_theme) ``` #### [6. Plot]{.smallcaps} ```{r} #| label: plot #| warning: false ### |- plot ---- p <- ggplot(composition_data, aes(x = 1, y = share, fill = segment)) + geom_col(width = 0.42) + geom_text( aes( label = glue("{segment}\n{percent(share, accuracy = 0.1)}"), color = segment ), position = position_stack(vjust = 0.5), family = fonts$caption, size = 4.2, lineheight = 0.95 ) + coord_flip(expand = FALSE) + scale_fill_manual(values = fill_values) + scale_color_manual(values = label_colors) + labs( title = title_text, subtitle = subtitle_text, caption = caption_text ) ``` #### [7. Save]{.smallcaps} ```{r} #| label: save #| warning: false ### |- save ---- main_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "mm_2026_34.png") thumb_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "thumbnails", "mm_2026_34.png") # Full-size version, for the QMD figure save_ggplot( plot = p, file = main_path, width = 10, height = 6, units = "in", dpi = 320, create.dir = TRUE ) # Reduced-size thumbnail, for the YAML `image:` field fs::dir_create(dirname(thumb_path)) magick::image_read(main_path) |> magick::image_resize("400") |> magick::image_write(thumb_path) ``` #### [8. Session Info]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### [9. GitHub Repository]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### [10. References]{.smallcaps} ::: {.callout-tip collapse="true"} ##### Expand for References **Primary Data (Makeover Monday):** 1. Makeover Monday 2026 Week 34: `r create_link("Global GDP Ranks", "https://statisticsoftheworld.com/gdp-by-country")` - CSV: 192 rows × 5 columns (`rank`, `code`, `country`, `value`, `year`). Covers all 192 IMF-tracked economies ranked by GDP (USD). 188 of 192 observations reflect 2026; four economies (Pakistan, Sri Lanka, Lebanon, Afghanistan) carry the most recent available estimate from 2024–2025 rather than a 2026 figure. - The original visualization presents a static sortable table of the top 10 economies by GDP and share of world total, requiring the reader to infer concentration from a truncated list — the other 182 economies are simply absent. This makeover abandons rank as the organizing structure entirely and encodes the finding directly: a single 100% composition bar showing the United States (25.9%) and China (16.6%) together account for 42.5% of global GDP, with all 190 remaining economies combined making up the other 57.5%. **Source Data:** 2. International Monetary Fund, 2026, `r create_link("World Economic Outlook Database", "https://www.imf.org/en/data")` ::: #### [11. Custom Functions Documentation]{.smallcaps} ::: {.callout-note collapse="true"} ##### 📦 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](https://github.com/poncest/personal-website/tree/master/R) :::