Every Product Group Has Trade Deals, But Import Taxes Still Vary Widely

Trade deals offering lower tariffs are nearly identical across product groups (right), while standard import tax rates vary widely (left) — based on current U.S. tariffs (2025).

U.S. agricultural import tax rates vary dramatically across product groups — Beverages & Spirits and Dairy & Eggs face median rates above 17% — yet the number of active trade agreements offering lower rates is nearly identical across all categories (17–19). Trade deals are widespread; protection is structural. Built with ggplot2 and patchwork in R.

Figure 1: Two-panel horizontal chart comparing U.S. agricultural import tax rates and trade deal counts across eight product groups. The left panel shows boxplots of standard import tax rates; Beverages & Spirits (17.2%) and Dairy & Eggs lead by a wide margin, highlighted in burgundy, while Grains & Cereals and Live Animals sit near zero. The right panel shows bar charts of active trade agreements per group, ranging narrowly from 17 to 19, nearly identical across all categories. The contrast between the two panels is the finding: trade deals are evenly distributed, yet protection remains structurally concentrated in beverages and dairy.

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, janitor, ggrepel,      
    scales, glue, skimr, patchwork 
    )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 8,
  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 = 17)

agreements <- tt$agreements |> clean_names()
quantity_codes <- tt$quantity_codes |> clean_names()
tariff_agricultural <- tt$tariff_agricultural |> clean_names()
tariff_codes <- tt$tariff_codes |> clean_names()

rm(tt)
```

3. Examine the Data

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

glimpse(agreements)
glimpse(quantity_codes)
glimpse(tariff_agricultural)
glimpse(tariff_codes)
```

4. Tidy Data

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

### |- Chapter groups: 8 editorial buckets ----
chapter_groups <- c(
    "01" = "Live Animals",
    "02" = "Meat",
    "03" = "Fish & Seafood",
    "04" = "Dairy & Eggs",
    "08" = "Fruit & Nuts",
    "10" = "Grains & Cereals",
    "17" = "Sugar & Confections",
    "22" = "Beverages & Spirits"
)

### |- Current standard (MFN) ad valorem rates — Panel A ----
mfn_current <- tariff_agricultural |>
    filter(
        agreement == "mfn",
        rate_type_code == "7",
        begin_effective_date <= today(),
        end_effective_date >= today()
    ) |>
    mutate(chapter = substr(hts8, 1, 2)) |>
    filter(chapter %in% names(chapter_groups)) |>
    mutate(chapter_label = chapter_groups[chapter]) |>
    group_by(hts8, chapter_label) |>
    summarise(ad_val_rate = mean(ad_val_rate, na.rm = TRUE), .groups = "drop") |>
    mutate(rate_pct = ad_val_rate * 100) |>
    filter(rate_pct <= 100)

### |- Sort order: descending median tariff rate ----
chapter_order <- mfn_current |>
    group_by(chapter_label) |>
    summarise(median_rate = median(rate_pct, na.rm = TRUE)) |>
    arrange(median_rate) |>
    pull(chapter_label)

mfn_current <- mfn_current |>
    mutate(chapter_label = factor(chapter_label, levels = chapter_order))

### |- Trade deal counts per chapter — Panel B ----
deal_counts <- tariff_agricultural |>
    filter(
        !agreement %in% c("mfn", "col2"),
        !str_detect(agreement, "\\*|\\+"),
        begin_effective_date <= today(),
        end_effective_date >= today()
    ) |>
    mutate(chapter = substr(hts8, 1, 2)) |>
    filter(chapter %in% names(chapter_groups)) |>
    mutate(chapter_label = chapter_groups[chapter]) |>
    group_by(chapter_label) |>
    summarise(n_deals = n_distinct(agreement), .groups = "drop") |>
    mutate(chapter_label = factor(chapter_label, levels = chapter_order))

### |- Highlight flag: top 2 highest-tariff categories ----
top2 <- tail(chapter_order, 2)

mfn_current <- mfn_current |>
    mutate(is_top2 = chapter_label %in% top2)

### |- Median labels per chapter (for Panel A) ----
chapter_medians <- mfn_current |>
    group_by(chapter_label) |>
    summarise(median_rate = median(rate_pct, na.rm = TRUE), .groups = "drop") |>
    mutate(
        label_med  = glue("{round(median_rate, 1)}%"),
        is_top2    = chapter_label %in% top2
    )
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
    palette = list(
        "burgundy" = "#722F37",
        "gray_mid" = "#9E9E9E",
        "gray_bar" = "#BDBDBD",
        "text_dark" = "#2C2C2C",
        "text_muted" = "#7A7A7A"
    )
)

### |- titles and caption ----
title_text <- str_glue("Every Product Group Has Trade Deals, But Import Taxes Still Vary Widely")

subtitle_text <- str_glue(
    "Trade deals offering lower tariffs are nearly identical across product groups (right), ",
    "while standard import tax rates vary widely (left) — ",
    "based on current U.S. tariffs (2025)."
)
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 17,
    source_text = "USITC Tariff Database"
)

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

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

weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        axis.title.x = element_text(size = 8, color = colors$palette$text_muted, margin = margin(t = 6)),
        axis.title.y = element_blank(),
        axis.text.y = element_text(size = 9, color = colors$palette$text_dark, hjust = 1),
        axis.text.x = element_text(size = 8, color = colors$palette$text_muted),
        panel.grid.major.x = element_line(color = "gray97", linewidth = 0.2),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "white", color = NA),
        plot.background = element_rect(fill = "white", color = NA),
        plot.margin = margin(10, 12, 10, 12)
    )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- Panel A: Boxplot + median labels ----
pa <- ggplot(mfn_current, aes(x = rate_pct, y = chapter_label)) +
    # Geoms
    geom_boxplot(
        aes(
            fill = is_top2,
            color = is_top2
        ),
        width = 0.55,
        coef = 1.0,
        outlier.shape = NA,
        linewidth = 0.35
    ) +
    geom_text(
        data = filter(chapter_medians, is_top2),
        aes(x = median_rate, y = chapter_label, label = label_med),
        nudge_x = 1.5,
        hjust = 0,
        size = 3.4,
        color = colors$palette$burgundy,
        family = fonts$text,
        fontface = "bold",
        inherit.aes = FALSE
    ) +
    # Scales
    scale_fill_manual(
        values = c("TRUE" = colors$palette$burgundy, "FALSE" = colors$palette$gray_mid),
        guide  = "none"
    ) +
    scale_color_manual(
        values = c("TRUE" = colors$palette$burgundy, "FALSE" = "gray45"),
        guide  = "none"
    ) +
    scale_x_continuous(
        labels = label_percent(scale = 1, suffix = "%"),
        breaks = c(0, 10, 20, 30),
        limits = c(0, 30),
        expand = expansion(mult = c(0.01, 0.04))
    ) +
    # Labs
    labs(
        x     = "Import tax rate (%)",
        title = "A · Tariffs Vary Widely Across Products"
    ) +
    # Theme
    theme(
        plot.title = element_text(
            size = 12, face = "bold",
            family = fonts$title, color = colors$palette$text_dark,
            margin = margin(b = 8)
        ),
        plot.margin = margin(10, 12, 10, 12)
    )

### |- Panel B: Trade deal counts ----
pb <- ggplot(deal_counts, aes(x = n_deals, y = chapter_label)) +
    # Geoms
    geom_col(
        fill  = colors$palette$gray_bar,
        width = 0.55
    ) +
    geom_text(
        aes(label = n_deals),
        hjust = -0.3,
        size = 2.8,
        color = colors$palette$text_muted,
        family = fonts$text
    ) +
    # Annotation
    annotate(
        "text",
        x = 11,
        y = 8.5,
        label = "Nearly identical across all groups (17–19 agreements)",
        hjust = 0.5,
        size = 2.5,
        color = colors$palette$text_dark,
        family = fonts$text,
        fontface = "italic"
    ) +
    # Scales
    scale_x_continuous(
        expand = expansion(mult = c(0, 0.22)),
        breaks = c(0, 5, 10, 15, 20),
        limits = c(0, 22)
    ) +
    # Labs
    labs(
        x = "Number of trade agreements",
        title = "B · Trade Deals Are Nearly Identical Across Products"
    ) +
    # Theme
    theme(
        axis.text.y = element_blank(),
        plot.title = element_text(
            size = 12, face = "bold",
            family = fonts$title, color = colors$palette$text_dark,
            margin = margin(b = 8)
        )
    )

### |- Combined plots ----
combined_plots <- pa + pb +
    plot_layout(widths = c(1.4, 1)) +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        theme = theme(
            plot.title = element_textbox_simple(
                size = 22,
                face = "bold",
                family = fonts$title,
                color = colors$palette$text_dark,
                margin = margin(b = 6),
                lineheight = 1.2
            ),
            plot.subtitle = element_textbox_simple(
                size = 11,
                family = fonts$text,
                color = colors$palette$text_muted,
                margin = margin(t = 5, b = 10),
                lineheight = 1.2
            ),
            plot.caption = element_textbox_simple(
                size = 7,
                family = fonts$text,
                color = colors$palette$text_muted,
                margin = margin(t = 12),
                lineheight = 1.3
            ),
            plot.margin = margin(16, 16, 18, 16),
            plot.background = element_rect(fill = "white", color = NA)
        )
    )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2026, 
  week = 17, 
  width  = 11,
  height = 8
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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      patchwork_1.3.2 skimr_2.2.2     glue_1.8.0     
 [5] scales_1.4.0    ggrepel_0.9.8   janitor_2.2.1   showtext_0.9-8 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5
[13] forcats_1.0.1   stringr_1.6.0   dplyr_1.2.1     purrr_1.2.2    
[17] readr_2.2.0     tidyr_1.3.2     tibble_3.3.1    ggplot2_4.0.3  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       S7_0.2.1           fastmap_1.2.0     
 [5] gh_1.5.0           digest_0.6.39      timechange_0.4.0   lifecycle_1.0.5   
 [9] rsvg_2.7.0         magrittr_2.0.5     compiler_4.5.3     rlang_1.2.0       
[13] tools_4.5.3        yaml_2.3.12        knitr_1.51         htmlwidgets_1.6.4 
[17] bit_4.6.0          curl_7.0.0         xml2_1.5.2         camcorder_0.1.0   
[21] repr_1.1.7         RColorBrewer_1.1-3 tidytuesdayR_1.3.2 withr_3.0.2       
[25] grid_4.5.3         gitcreds_0.1.2     cli_3.6.6          rmarkdown_2.31    
[29] crayon_1.5.3       generics_0.1.4     otel_0.2.0         rstudioapi_0.18.0 
[33] tzdb_0.5.0         commonmark_2.0.0   parallel_4.5.3     ggplotify_0.1.3   
[37] base64enc_0.1-6    vctrs_0.7.3        yulab.utils_0.2.4  jsonlite_2.0.0    
[41] litedown_0.9       gridGraphics_0.5-1 hms_1.1.4          bit64_4.6.0-1     
[45] systemfonts_1.3.2  magick_2.9.1       gifski_1.32.0-2    codetools_0.2-20  
[49] stringi_1.8.7      gtable_0.3.6       pillar_1.11.1      rappdirs_0.3.4    
[53] htmltools_0.5.9    R6_2.6.1           httr2_1.2.2        textshaping_1.0.5 
[57] rprojroot_2.1.1    vroom_1.7.1        evaluate_1.0.5     markdown_2.0      
[61] gridtext_0.1.6     snakecase_0.11.1   Rcpp_1.1.1         svglite_2.2.2     
[65] xfun_0.57          fs_2.0.1           pkgconfig_2.0.3   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_17.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 17: US Agricultural Tariffs

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