Large U.S. Services Surpluses Mask Overall Trade Deficits

Only 3 of 15 U.S. free trade partners have a services surplus but an overall trade deficit (2024): Canada, South Korea, and Mexico.

Compares U.S. services-only and overall (goods + services) trade balances for the three free-trade partners where the two diverge in sign in 2024. Canada, South Korea, and Mexico each show a services surplus that becomes an overall deficit once goods are included — a gap the original services-only ranking obscured. Built in R with ggplot2, ggtext, and scales.

Original

The original visualization comes from America’s Services Trade Balances with Its Free Trade Partners

Original visualization

Makeover

Figure 1: Diverging column chart comparing the U.S. services trade balance to the overall (goods + services) trade balance for three countries in 2024. Canada: services +$34.9B, overall −$35.7B. South Korea: services +$10.7B, overall −$55.5B. Mexico: services +$2.5B, overall −$179.0B. All three show a positive services balance but a negative overall balance — the only 3 of 15 U.S. free-trade partners where this pattern holds in 2024.

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

# 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

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

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

3. Examine the Data

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

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

4. Tidy Data

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

### |- verify the central claim ----
# "Canada, South Korea, and Mexico are the only 3 of 15 partners with a
# services surplus AND an overall deficit"
n_total <- nrow(df_raw)

df_selected <- df_raw |>
  filter(services_b > 0, goods_and_services_b < 0)

n_selected <- nrow(df_selected)

### |- scope to the three hero countries ----
hero_countries <- c("Canada", "South Korea", "Mexico")

df3 <- df_raw |>
  filter(country %in% hero_countries) |>
  mutate(country = factor(country, levels = hero_countries))

### |- long format, sign-coded ----
# "Overall" -> "Goods + services"
df_sign <- df3 |>
  select(country, Services = services_b, `Goods + services` = goods_and_services_b) |>
  pivot_longer(cols = -country, names_to = "metric", values_to = "value") |>
  mutate(
    metric = factor(metric, levels = c("Services", "Goods + services")),
    sign = if_else(value >= 0, "Positive", "Negative"),
    label = label_dollar(accuracy = 0.1, style_positive = "plus")(value) |>
      str_replace("^-", "\u2212") |>
      paste0("B")
  )
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    positive  = "#1E3A5F",
    negative  = "#B5532F",
    zero_line = "gray20",
    grid      = "gray92"
  )
)
clrs <- colors$palette
col_positive <- clrs[["positive"]]
col_negative <- clrs[["negative"]]
col_zero     <- clrs[["zero_line"]]
col_grid     <- clrs[["grid"]]

### |- titles and caption ----
title_text <- "Large U.S. Services Surpluses Mask Overall Trade Deficits"

subtitle_text <- str_glue(
  "Only {n_selected} of {n_total} U.S. free trade partners have a services ",
  "surplus but an overall trade deficit (2024):<br>",
  "{glue_collapse(hero_countries, sep = ', ', last = ', and ')}."
)

caption_text <- create_mm_caption(
  mm_year = 2026,
  mm_week = 31,
  source_text = paste0(
    "Positive values indicate a U.S. surplus; negative values indicate a ",
    "U.S. deficit.<br>",
    "Source: U.S. Bureau of Economic Analysis (2024), via Visual Capitalist ",
    "and the Hinrich Foundation."
  )
)

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

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

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

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

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    legend.position = "none",
    axis.text = element_text(size = 9, family = fonts$text),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_line(color = col_grid, linewidth = 0.35),
    panel.grid.major.x = element_blank(),
    strip.text = element_text(face = "bold", size = 11.5, margin = margin(b = 4), family = fonts$title_2),
    plot.title.position = "plot",
    plot.title = element_text(
      face = "bold", size = 22, family = fonts$title_1, color = colors$title,
      margin = margin(b = 4), lineheight = 1.15
    ),
    plot.subtitle = element_textbox_simple(
      color = colors$subtitle, size = 10, family = fonts$subtitle,
      lineheight = 1.25, margin = margin(b = 8)
    ),
    plot.caption = element_textbox_simple(
      hjust = 0, size = 6, color = colors$caption,
      family = fonts$caption, margin = margin(t = 8)
    ),
    panel.spacing.x = unit(1.1, "lines"),
    plot.margin = margin(t = 10, r = 16, b = 6, l = 12)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- plot ----
p <- ggplot(df_sign, aes(x = metric, y = value, fill = sign)) +
  geom_col(
    data = df_sign |> filter(sign == "Positive"),
    aes(fill = sign), width = 0.70
  ) +
  geom_col(
    data = df_sign |> filter(sign == "Negative"),
    aes(fill = sign), width = 0.62
  ) +
  geom_hline(yintercept = 0, color = col_zero, linewidth = 0.6) +
  geom_text(
    data = df_sign |> filter(sign == "Positive"),
    aes(label = label), vjust = -0.5, size = 3.6, fontface = "bold", color = "gray15"
  ) +
  geom_text(
    data = df_sign |> filter(sign == "Negative"),
    aes(label = label), vjust = 1.5, size = 3.6, fontface = "bold", color = "gray15"
  ) +
  facet_wrap(~country, nrow = 1) +
  scale_fill_manual(values = c(Positive = col_positive, Negative = col_negative)) +
  scale_y_continuous(
    labels = label_dollar(suffix = "B"),
    breaks = c(-200, -100, 0, 50),
    expand = expansion(mult = 0.13)
  ) +
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL, y = NULL
  ) +
  canvas(width = 10, height = 5.6, units = "in")
```

7. Save

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

### |- save ----
main_path  <- here::here("data_visualizations", "MakeoverMonday", "2026", "mm_2026_31.png")
thumb_path <- here::here("data_visualizations", "MakeoverMonday", "2026", "thumbnails", "mm_2026_31.png")

# Full-size version, for the QMD figure
save_ggplot(
  plot = p,
  file = main_path,
  width = 10,
  height = 5.6,
  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

TipExpand 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      patchwork_1.3.2 ggview_0.2.2    janitor_2.2.1  
 [5] glue_1.8.1      scales_1.4.0    showtext_0.9-8  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.1     purrr_1.2.2     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0
[21] 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      rprojroot_2.1.1    fastmap_1.2.0      grid_4.6.1        
[33] cli_3.6.6          magrittr_2.0.5     base64enc_0.1-6    withr_3.0.3       
[37] timechange_0.4.0   rmarkdown_2.31     otel_0.2.0         cellranger_1.1.0  
[41] ragg_1.5.2         hms_1.1.4          evaluate_1.0.5     knitr_1.51        
[45] markdown_2.0       rlang_1.3.0        gridtext_0.1.6     Rcpp_1.1.2        
[49] xml2_1.6.0         rstudioapi_0.19.0  jsonlite_2.0.0     R6_2.6.1          
[53] fs_2.1.0           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in mm_2026_31.qmd.

For the full repository, click here.

10. References

TipExpand for References

Primary Data (Makeover Monday): 1. Makeover Monday 2026 Week 31: America’s Services Trade Balances with Its Free Trade Partners - XLSX: 15 rows × 4 columns (country, goods_and_services_b, goods_b, services_b). Each row is one U.S. free-trade partner, with 2024 trade balance figures in USD billions. - The original visualization shows only the services balance, ranked across all 15 partners in a radial layout. This makeover restores the omitted goods balance and total (goods + services) balance the original left out.

Source Data: 2. U.S. Bureau of Economic Analysis, 2024, via Visual Capitalist and the Hinrich Foundation

Methodological Notes: 3. The three highlighted partners (Canada, South Korea, Mexico) were selected with an objective rule computed directly from the data — positive services balance and negative overall balance — not chosen by hand. This is enforced in code with stopifnot(n_selected == 3), so the chart’s central claim (“only 3 of 15”) fails loudly rather than silently going stale if the underlying data is ever refreshed. 4. CAFTA-DR, one of the 15 partners in the denominator, is a six-country regional trade bloc, not a single nation. It is retained in the count consistent with how the source treats it; all copy in this piece says “partners,” never “countries,” so the claim stays accurate without needing a separate exclusion or footnote. 5. The shared y-axis scale across all three country panels is intentional. Mexico’s overall balance (−$179.0B) dominating the shared scale is part of the finding, not a distortion to correct with independent per-panel scales.

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