The Stakes for U.S. Truck Manufacturing Under Shifting Federal Policy

Employment impact analysis across clean vehicle scenarios and policy retreat alternatives

MakeoverMonday
Data Visualization
R Programming
2025
Analysis of how federal clean vehicle policies could create up to 19,122 manufacturing jobs or eliminate 3,332 jobs under policy retreat. Using EPI economic modeling data, this dual-chart visualization reveals the employment consequences of different policy scenarios across assembly and supply chain sectors in America’s truck manufacturing industry.
Author

Steven Ponce

Published

June 2, 2025

Original

The original visualization Figure B: Change in employment from baseline scenario, Job-years, 2024–2032 comes from Economic Policy Institute+

Original visualization

Makeover

Figure 1: Dual-chart visualization showing U.S. truck manufacturing employment impacts. The top heatmap displays five policy scenarios (rows) versus three job categories (columns), with green indicating job gains and red showing losses. The policy Retreat row shows red losses (-596 to -3,332 jobs), while other scenarios show green gains. The bottom waterfall chart shows progressive job creation from baseline through four positive scenarios, building from +11,182 to +19,122 total jobs, with a separate red bar showing -3,332 job loss under the Policy Retreat alternative.

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
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    lubridate,      # Make Dealing with Dates a Little Easier
    patchwork       # The Composer of Plots
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  12,
    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

## Original Chart
# Change in employment from baseline scenario, Job-years, 2024–2032, Figure B
# https://www.epi.org/publication/future-clean-trucks-buses/

## Article
# What future will U.S. truck manufacturing have under Trump?
# https://www.epi.org/publication/future-clean-trucks-buses/

## Data
# What future will U.S. truck manufacturing have under Trump?
# https://data.world/makeovermonday/what-future-will-us-truck-manufacturing-have-under-trump

truck_manufacturing_raw <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/US truck manufacturing under Trump.xlsx')) |> 
  janitor::clean_names()
```

3. Examine the Data

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

glimpse(truck_manufacturing_raw)
skimr::skim(truck_manufacturing_raw)
```

4. Tidy Data

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

# P1. heatmap data----
heatmap_data <- truck_manufacturing_raw |>
  mutate(
    scenario_clean = case_when(
      str_detect(scenario, "policy retreat") ~ "Policy Retreat",
      str_detect(scenario, "union wages") ~ "Clean + Market + Union",
      str_detect(scenario, "50% clean.*10% market") ~ "Clean + Market Share",
      str_detect(scenario, "Baseline.*10%") ~ "Baseline + Market",
      str_detect(scenario, "50% clean") ~ "50% Clean Vehicles",
      TRUE ~ scenario
    ),
    # Order scenarios logically
    scenario_factor = factor(scenario_clean,
      levels = c(
        "50% Clean Vehicles",
        "Baseline + Market",
        "Clean + Market Share",
        "Clean + Market + Union",
        "Policy Retreat"
      )
    )
  ) |>
  # Transform to long format
  select(scenario_factor, assembly, supply_chain, total) |>
  pivot_longer(
    cols = -scenario_factor,
    names_to = "job_category",
    values_to = "jobs"
  ) |>
  # Create clean category labels
  mutate(
    job_category_clean = case_when(
      job_category == "assembly" ~ "Assembly",
      job_category == "supply_chain" ~ "Supply Chain",
      job_category == "total" ~ "Total"
    ),
    job_category_factor = factor(job_category_clean,
      levels = c("Assembly", "Supply Chain", "Total")
    ),
    # Create scaled values for color intensity (-1 to 1 scale)
    jobs_scaled = case_when(
      jobs < 0 ~ pmax(jobs / abs(min(jobs)), -1), # negative values
      jobs > 0 ~ pmin(jobs / max(jobs), 1), # positive values
      TRUE ~ 0
    ),
    # Text color based on intensity
    text_color = ifelse(abs(jobs_scaled) > 0.6, "white", "black"),
    # Formatted labels
    jobs_label = case_when(
      jobs == 0 ~ "0",
      jobs > 0 ~ paste0("+", comma(jobs, accuracy = 1)),
      jobs < 0 ~ comma(jobs, accuracy = 1)
    )
  )

# P2. waterfall chart data -----
waterfall_data <- tibble(
  category = c(
    "Baseline", "50% Clean Vehicles", "Add Market Share",
    "Add Union Wages", "Policy Retreat Alternative"
  ),
  # Calculate incremental values
  value = c(
    0, # Baseline starting point
    truck_manufacturing_raw$total[1], # 50% clean vehicles: 11,182
    truck_manufacturing_raw$total[2] - truck_manufacturing_raw$total[1], # Market share increment: 13,272 - 11,182 = 2,090
    truck_manufacturing_raw$total[4] - truck_manufacturing_raw$total[3], # Union wages increment: 19,122 - 15,441 = 3,681
    truck_manufacturing_raw$total[5] # Policy retreat: -3,332 (standalone negative scenario)
  ),
  type = c("baseline", "positive", "positive", "positive", "negative")
) |>
  # Calculate cumulative totals and bar positioning
  mutate(
    cumulative = case_when(
      category == "Policy Retreat Alternative" ~ value,
      TRUE ~ cumsum(value)
    ),
    # Calculate where each bar should start and end
    ymin = case_when(
      category == "Baseline" ~ 0,
      category == "Policy Retreat Alternative" ~ 0,
      TRUE ~ lag(cumulative, default = 0)
    ),
    ymax = case_when(
      category == "Policy Retreat Alternative" ~ value,
      TRUE ~ cumulative
    ),
    # Labels
    category_label = case_when(
      category == "Baseline" ~ "Baseline\n(Starting Point)",
      category == "50% Clean Vehicles" ~ "50% Clean\nVehicles",
      category == "Add Market Share" ~ "Increase\nMarket Share",
      category == "Add Union Wages" ~ "Add Union\nWages",
      category == "Policy Retreat Alternative" ~ "Policy Retreat\n(Alternative Path)"
    ),
    category = factor(category, levels = category),
    category_label = factor(category_label, levels = category_label)
  )
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "#DC143C", "#FFFFFF",  "#2E8B57",                                         # heatmap    
  "baseline" = "#708090", "positive" = "#2E8B57", "negative" = "#DC143C"    # waterfall chart
))
  
### |-  titles and caption ----
title_text <- str_glue("The Stakes for U.S. Truck Manufacturing Under Shifting Federal Policy")
subtitle_text <- str_glue("Employment impact analysis across clean vehicle scenarios and policy retreat alternatives")


# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 23,
    source_text = "<br>EPI analysis of S&P Global (2024), IMPLAN (2024), and FRED data | Job-years, 2024-2032"
)

### |-  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(

    # Legend formatting
    legend.position = "plot",
    legend.title = element_text(face = "bold"),

    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 

    # Axis formatting
    axis.title.x = element_text(face = "bold", size = rel(0.85)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text.y = element_text(face = "bold", size = rel(0.85)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

# P1. Heatmap ----
p1 <- heatmap_data |>
  ggplot(aes(x = job_category_factor, y = scenario_factor)) +

  # Geoms
  geom_tile(aes(fill = jobs_scaled),
    color = "white",
    linewidth = 1.5,
    alpha = 0.9
  ) +
  geom_text(aes(label = jobs_label, color = text_color),
    fontface = "bold",
    size = 4.2
  ) +

  # Scales
  scale_fill_gradient2(
    low = colors$palette[1],
    mid = colors$palette[2],
    high = colors$palette[3],
    midpoint = 0,
    name = "Job Impact\nIntensity",
    labels = c("High Loss", "Neutral", "High Gain"),
    breaks = c(-1, 0, 1),
    guide = guide_colorbar(
      title.position = "top",
      title.hjust = 0.5,
      barwidth = 1.2,
      barheight = 8,
      frame.colour = "gray70",
      ticks.colour = "gray70"
    )
  ) +
  scale_color_identity() +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +

  # Labs
  labs(
    subtitle = "Job breakdown shows potential gains and losses across Assembly and Supply Chain sectors",
    x = "Job Category",
    y = "Policy Scenario"
  ) +

  # Theme
  theme_minimal(base_size = 11) +
  theme(
    plot.subtitle = element_text(
      size = 12,
      color = "gray40",
      margin = margin(b = 15)
    ),
    axis.title = element_text(
      size = 10,
      face = "bold",
      color = "gray30"
    ),
    axis.title.x = element_text(margin = margin(t = 10)),
    axis.title.y = element_text(margin = margin(r = 10)),
    axis.text = element_text(
      size = 9,
      color = "gray20",
      face = "bold"
    ),
    legend.position = "right",
    legend.title = element_text(
      size = 9,
      face = "bold",
      color = "gray30"
    ),
    legend.text = element_text(
      size = 8,
      color = "gray40"
    ),
    legend.margin = margin(l = 15),
    panel.grid = element_blank(),
    plot.margin = margin(10, 15, 15, 15),
    plot.background = element_rect(fill = colors$background, color = NA),
    panel.background = element_rect(fill = colors$background, color = NA)
  )

# P2. waterfall chart -----
p2 <- waterfall_data |>   
  ggplot(aes(x = category_label)) +

  # Geoms
  geom_segment(
    data = waterfall_data[2:4, ],
    aes(
      x = as.numeric(category_label) + 0.45,
      xend = as.numeric(category_label) + 1 - 0.45,
      y = ymax,
      yend = ymax
    ),
    linetype = "dashed", alpha = 0.6, linewidth = 0.8, color = "gray50"
  ) +
  geom_rect(
    aes(
      xmin = as.numeric(category_label) - 0.35,
      xmax = as.numeric(category_label) + 0.35,
      ymin = ymin,
      ymax = ymax,
      fill = type
    ),
    alpha = 0.9, color = "white", linewidth = 0.5
  ) +
  geom_text(
    aes(
      y = (ymin + ymax) / 2,
      label = ifelse(value == 0, "0",
        paste0(ifelse(value > 0, "+", ""), comma(value, accuracy = 1))
      )
    ),
    fontface = "bold", size = 4, color = "white"
  ) +
  geom_text(
    data = filter(waterfall_data, type != "baseline" & type != "negative"),
    aes(
      y = ymax + 500,
      label = paste("Total:", comma(cumulative, accuracy = 1))
    ),
    fontface = "bold", size = 3.5, color = "gray30"
  ) +
  geom_hline(yintercept = 0, color = "black", linewidth = 1, alpha = 0.8) +

  # Scales
  scale_fill_manual(values = colors$palette, guide = "none") +
  scale_y_continuous(
    labels = function(x) {
      case_when(
        x >= 0 ~ paste0("+", comma(x)),
        x < 0 ~ comma(x),
        TRUE ~ as.character(x)
      )
    },
    breaks = pretty_breaks(n = 6),
    expand = expansion(mult = c(0.15, 0.1))
  ) +
  scale_x_discrete() +

  # Labs
  labs(
    subtitle = "Policy scenarios build progressively from baseline to maximum employment potential",
    x = NULL,
    y = "Change in Employment (Job-years)"
  ) +

  # Theme
  theme_minimal(base_size = 11) +
  theme(
    plot.subtitle = element_text(
      size = 12,
      color = "gray40",
      margin = margin(b = 15)
    ),
    axis.title.y = element_text(
      size = 10,
      face = "bold",
      color = "gray30",
      margin = margin(r = 15)
    ),
    axis.text.x = element_text(
      size = 9,
      face = "bold",
      color = "gray20",
      lineheight = 1.1
    ),
    axis.text.y = element_text(
      size = 9,
      color = "gray30"
    ),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.5),
    plot.margin = margin(15, 15, 10, 15),
    plot.background = element_rect(fill = colors$background, color = NA),
    panel.background = element_rect(fill = colors$background, color = NA)
  )

# Combined Plot ----
combined_plot <- p1 / p2 +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_markdown(
        size = rel(1.6),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(1),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.6),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      ),
     plot.margin = margin(25, 25, 25, 25),
    )
  )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "makeovermonday", 
  year = 2025,
  week = 23,
  width = 10, 
  height = 12
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

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      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3   
[13] ggplotify_0.1.2    skimr_2.1.5        readxl_1.4.3       lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      janitor_2.2.0     
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        timechange_0.3.0   rmarkdown_2.29    
[45] cellranger_1.1.0   hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[49] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5    
[53] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         svglite_2.1.3     
[57] rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[61] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_23.qmd.

For the full repository, click here.

10. References

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