Mass Deportations Would Devastate Employment Across Multiple States

Projected employment impacts 2025-2029 vs. 2024 baseline

MakeoverMonday

2025

A MakeoverMonday data visualization reimagining the Economic Policy Institute’s analysis of projected employment impacts from mass deportations. Using R and ggplot2, I transformed a basic heat map into a compelling two-chart story showing both the scale of job losses by state and the impact on different worker types. Features include color-coded impact tiers, strategic labeling, and unified design system for publication-ready visualizations.

Author

Steven Ponce

Published

July 15, 2025

Original

The original visualization Figure B, Trump’s deportations will reduce employment in every state. Overall and construction employment losses for 4 million national deportations over four years, by state comes from Economic Policy Institute

Makeover

Figure 1: Two side-by-side charts showing projected employment impacts from mass deportations 2025-2029. Left chart: horizontal bar chart ranking top 20 states by total job losses, with California leading at 1.1M jobs, followed by Texas at 865K. Color coding indicates impact tiers, ranging from 500K+ jobs (red) to under 100K (gray). Right chart: Dumbbell plot showing the same 20 states, with blue and red dots representing job losses for U.S.-born and immigrant workers, respectively. This demonstrates that both worker types are affected in every state. Data source: Economic Policy Institute.

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
  patchwork       # The Composer of Plots
  )
})

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

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
deportations_raw <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/Trump Deportations.xlsx')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

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

glimpse(deportations_raw)
skimr::skim(deportations_raw)
```

4. Tidy Data

Show code

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

deportations <- deportations_raw |>
  filter(state != "United States") |> # Remove national totals
  mutate(
    impact_tier = case_when(
      total_level >= 500000 ~ "mega",
      total_level >= 200000 ~ "high",
      total_level >= 100000 ~ "medium",
      TRUE ~ "lower"
    )
  )

### |-  Plot 1 Data ----
barchart_data <- deportations |>
  top_n(20, total_level) |>
  mutate(state = fct_reorder(state, total_level))

### |-  Plot 2 Data ----
# Get the same top 20 states as P1 in the same order
p1_states_ordered <- deportations |>
  top_n(20, total_level) |>
  arrange(desc(total_level)) |>
  pull(state)

dumbbell_data <- deportations |>
  filter(state %in% p1_states_ordered) |>
  mutate(state = factor(state, levels = rev(p1_states_ordered))) |> # rev() for ggplot ordering
  pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |>
  mutate(
    worker_type = case_when(
      worker_type == "u_s_born" ~ "U.S.-Born Workers",
      worker_type == "immigrant" ~ "Immigrant Workers"
    )
  )
```

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 = list(
  # Scale tiers for bar chart
  mega = "#c0392b", # mega impact (500K+)
  high = "#f39c12", # high impact (200-500K)
  medium = "#3498db", # medium impact (100-200K)
  lower = "#95a5a6",

  # Worker types for dumbbell
  immigrant = "#e74c3c", # immigrant workers
  us_born = "#3498db", # US-born workers
  connector = "#bdc3c7" # connecting lines
))

### |-  titles and caption ----
title_text <- paste0("Mass Deportations Would Devastate Employment Across Multiple States")
subtitle_text <- paste0("Projected employment impacts ", current_year, "-", current_year + 4, " vs. ", current_year - 1, " baseline")

# Create caption
caption_text <- create_mm_caption(
   mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("<br>Economic Policy Institute (July ", current_year, ") | Projections based on 1M annual deportations vs. 330K baseline")
)


### |-  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(
    plot.title = element_text(
      size = rel(1.2), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), hjust = 0.5, family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9), lineheight = 0.9,
      margin = margin(t = 5, b = 20)
    ),

    # Legend formatting
    legend.position = "top",
    legend.direction = "horizontal",
    legend.box.margin = margin(b = 10),
    legend.margin = margin(b = 5),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(face = "bold", size = rel(0.85)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text = element_text(size = rel(0.85), family = fonts$subtitle, color = colors$text),

    # Legends

    # Grid lines
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    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

### |-  Plot 1  bar chart ----
p1 <- barchart_data |>
  ggplot(aes(x = total_level, y = state, fill = impact_tier)) +
  # Geoms
  geom_col(alpha = 0.9, width = 0.7) +
  geom_text(
    aes(label = case_when(
      total_level >= 1000000 ~ paste0(round(total_level / 1000000, 1), "M"),
      total_level >= 1000 ~ paste0(round(total_level / 1000, 0), "K"),
      TRUE ~ scales::comma(total_level)
    )),
    hjust = -0.1,
    size = 3,
    color = "gray30"
  ) +
  # Scales
  scale_x_continuous(
    labels = comma_format(suffix = "K", scale = 1 / 1000),
    expand = c(0, 0, 0.15, 0), # More space for labels
    breaks = scales::pretty_breaks(n = 5)
  ) +
  scale_fill_manual(
    values = colors$palette,
    labels = c(
      "mega" = "500K+ jobs",
      "high" = "200-500K jobs",
      "medium" = "100-200K jobs",
      "lower" = "<100K jobs"
    ),
    name = NULL
  ) +
  # Labs
  labs(
    title = "Scale of Impact: CA and TX Dominate Job Losses",
    subtitle = NULL,
    x = "Projected Job Losses (thousands)",
    y = NULL
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
  ) +
  guides(fill = guide_legend(
    title.position = "top",
    title.hjust = 0.5,
    nrow = 1,
    override.aes = list(alpha = 0.9)
  ))

### |-  Plot 2  dumbbell chart ----
p2 <- dumbbell_data |>
  ggplot(aes(y = state)) +
  # Geoms
  geom_segment(
    data = deportations |>
      filter(state %in% p1_states_ordered) |>
      mutate(state = factor(state, levels = rev(p1_states_ordered))),
    aes(x = u_s_born, xend = immigrant, y = state, yend = state),
    color = colors$palette[7],
    linewidth = 1.5,
    alpha = 0.8,
    inherit.aes = FALSE
  ) +
  geom_point(aes(x = jobs_lost, color = worker_type), size = 3.2, alpha = 0.9) +
  geom_text(
    data = deportations |>
      filter(state %in% c("California", "Texas")) |>
      select(state, u_s_born, immigrant) |>
      pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |>
      mutate(
        state = factor(state, levels = rev(p1_states_ordered)),
        label = paste0(round(jobs_lost / 1000), "K")
      ),
    aes(x = jobs_lost, y = state, label = label),
    hjust = -0.3,
    vjust = 1.4,
    size = 2.5,
    color = "gray30"
  ) +
  # Scales
  scale_x_continuous(
    labels = comma_format(suffix = "K", scale = 1 / 1000),
    expand = c(0.05, 0, 0.2, 0),
    breaks = scales::pretty_breaks(n = 5)
  ) +
  scale_color_manual(
    values = c(
      "U.S.-Born Workers" = colors$palette$us_born,
      "Immigrant Workers" = colors$palette$immigrant
    ),
    breaks = c("U.S.-Born Workers", "Immigrant Workers"),
    name = NULL
  ) +
  # Labs
  labs(
    title = "Both Worker Types Affected: No One is Spared",
    subtitle = NULL,
    x = "Jobs Lost (thousands)",
    y = NULL,
    caption = "Top 20 states by total job losses shown"
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
  ) +
  guides(color = guide_legend(
    title.position = "top",
    title.hjust = 0.5,
    nrow = 1,
    override.aes = list(size = 4)
  ))

### |-  combined plot ----
# Create an invisible spacer plot
spacer <- ggplot() +
  theme_void()

# Use it between charts
combined_plots <- (p1 + spacer + p2) +
  plot_layout(widths = c(1, 0.2, 1.1)) 

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.6),
        family = fonts$title,
        face = "bold",
        hjust = 0.,
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(1.1),
        hjust = 0,
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12, 
  height = 8
  )
```

8. Session Info

Expand for 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] yulab.utils_0.1.8  utf8_1.2.4         generics_0.1.3     renv_1.0.3        
 [5] ggplotify_0.1.2    xml2_1.3.6         stringi_1.8.4      hms_1.1.3         
 [9] digest_0.6.37      magrittr_2.0.3     evaluate_1.0.1     grid_4.4.0        
[13] timechange_0.3.0   fastmap_1.2.0      rprojroot_2.0.4    jsonlite_1.8.9    
[17] fansi_1.0.6        codetools_0.2-20   cli_3.6.4          rlang_1.1.6       
[21] commonmark_1.9.2   munsell_0.5.1      withr_3.0.2        yaml_2.3.10       
[25] tools_4.4.0        tzdb_0.5.0         colorspace_2.1-1   curl_6.0.0        
[29] gridGraphics_0.5-1 vctrs_0.6.5        R6_2.5.1           magick_2.8.5      
[33] lifecycle_1.0.4    fs_1.6.5           htmlwidgets_1.6.4  pkgconfig_2.0.3   
[37] pillar_1.9.0       gtable_0.3.6       Rcpp_1.0.13-1      xfun_0.49         
[41] tidyselect_1.2.1   rstudioapi_0.17.1  knitr_1.49         farver_2.1.2      
[45] htmltools_0.5.8.1  rmarkdown_2.29     compiler_4.4.0     markdown_1.13     
[49] gridtext_0.1.5

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_29.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 29: Figure B, Trump’s deportations will reduce employment in every state

Article

Economic Policy Institute: Trump’s deportation agenda will destroy millions of jobs

--- title: "Mass Deportations Would Devastate Employment Across Multiple States" subtitle: "Projected employment impacts 2025-2029 vs. 2024 baseline" description: "A MakeoverMonday data visualization reimagining the Economic Policy Institute's analysis of projected employment impacts from mass deportations. Using R and ggplot2, I transformed a basic heat map into a compelling two-chart story showing both the scale of job losses by state and the impact on different worker types. Features include color-coded impact tiers, strategic labeling, and unified design system for publication-ready visualizations." author: "Steven Ponce" date: "2025-07-15" categories: ["MakeoverMonday", "2025"] tags: [ "employment", "deportations", "immigration policy", "economic analysis", "ggplot2", "patchwork", "data storytelling", "bar charts", "dumbbell charts", "choropleth alternative", "Economic Policy Institute", "labor market", "policy visualization", "state-level data", "worker demographics" ] image: "thumbnails/mm_2025_29.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 <- 2025 current_week <- 29 project_file <- "mm_2025_29.qmd" project_image <- "mm_2025_29.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025wk-29-trumps-deportations-will-reduce-employment" data_secondary <- "https://www.epi.org/publication/trumps-deportation-agenda-will-destroy-millions-of-jobs-both-immigrants-and-u-s-born-workers-would-suffer-job-losses-particularly-in-construction-and-child-care/" ## 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/2025/Week_29/original_chart.png" ## Organization/Platform Links org_primary <- "https://www.epi.org" org_secondary <- "https://data.world/makeovermonday" # 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 Figure B, **Trump's deportations will reduce employment in every state. Overall and construction employment losses for 4 million national deportations over four years, by state** comes from `r create_link("Economic Policy Institute", data_secondary)` ![Original visualization](`r chart_original`) ### Makeover ![Two side-by-side charts showing projected employment impacts from mass deportations 2025-2029. Left chart: horizontal bar chart ranking top 20 states by total job losses, with California leading at 1.1M jobs, followed by Texas at 865K. Color coding indicates impact tiers, ranging from 500K+ jobs (red) to under 100K (gray). Right chart: Dumbbell plot showing the same 20 states, with blue and red dots representing job losses for U.S.-born and immigrant workers, respectively. This demonstrates that both worker types are affected in every state. Data source: Economic Policy Institute.](`r project_image`){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 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, # 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 patchwork # The Composer of Plots ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 12, 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 #| deportations_raw <- readxl::read_excel( here::here('data/MakeoverMonday/2025/Trump Deportations.xlsx')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(deportations_raw) skimr::skim(deportations_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false deportations <- deportations_raw |> filter(state != "United States") |> # Remove national totals mutate( impact_tier = case_when( total_level >= 500000 ~ "mega", total_level >= 200000 ~ "high", total_level >= 100000 ~ "medium", TRUE ~ "lower" ) ) ### |- Plot 1 Data ---- barchart_data <- deportations |> top_n(20, total_level) |> mutate(state = fct_reorder(state, total_level)) ### |- Plot 2 Data ---- # Get the same top 20 states as P1 in the same order p1_states_ordered <- deportations |> top_n(20, total_level) |> arrange(desc(total_level)) |> pull(state) dumbbell_data <- deportations |> filter(state %in% p1_states_ordered) |> mutate(state = factor(state, levels = rev(p1_states_ordered))) |> # rev() for ggplot ordering pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |> mutate( worker_type = case_when( worker_type == "u_s_born" ~ "U.S.-Born Workers", worker_type == "immigrant" ~ "Immigrant Workers" ) ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list( # Scale tiers for bar chart mega = "#c0392b", # mega impact (500K+) high = "#f39c12", # high impact (200-500K) medium = "#3498db", # medium impact (100-200K) lower = "#95a5a6", # Worker types for dumbbell immigrant = "#e74c3c", # immigrant workers us_born = "#3498db", # US-born workers connector = "#bdc3c7" # connecting lines )) ### |- titles and caption ---- title_text <- paste0("Mass Deportations Would Devastate Employment Across Multiple States") subtitle_text <- paste0("Projected employment impacts ", current_year, "-", current_year + 4, " vs. ", current_year - 1, " baseline") # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("<br>Economic Policy Institute (July ", current_year, ") | Projections based on 1M annual deportations vs. 330K baseline") ) ### |- 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( plot.title = element_text( size = rel(1.2), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 10) ), plot.subtitle = element_markdown( size = rel(0.9), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 20) ), # Legend formatting legend.position = "top", legend.direction = "horizontal", legend.box.margin = margin(b = 10), legend.margin = margin(b = 5), legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.ticks.length = unit(0.2, "cm"), axis.title.x = element_text(face = "bold", size = rel(0.85)), axis.title.y = element_text(face = "bold", size = rel(0.85)), axis.text = element_text(size = rel(0.85), family = fonts$subtitle, color = colors$text), # Legends # Grid lines panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), panel.grid.minor = element_blank(), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot 1 bar chart ---- p1 <- barchart_data |> ggplot(aes(x = total_level, y = state, fill = impact_tier)) + # Geoms geom_col(alpha = 0.9, width = 0.7) + geom_text( aes(label = case_when( total_level >= 1000000 ~ paste0(round(total_level / 1000000, 1), "M"), total_level >= 1000 ~ paste0(round(total_level / 1000, 0), "K"), TRUE ~ scales::comma(total_level) )), hjust = -0.1, size = 3, color = "gray30" ) + # Scales scale_x_continuous( labels = comma_format(suffix = "K", scale = 1 / 1000), expand = c(0, 0, 0.15, 0), # More space for labels breaks = scales::pretty_breaks(n = 5) ) + scale_fill_manual( values = colors$palette, labels = c( "mega" = "500K+ jobs", "high" = "200-500K jobs", "medium" = "100-200K jobs", "lower" = "<100K jobs" ), name = NULL ) + # Labs labs( title = "Scale of Impact: CA and TX Dominate Job Losses", subtitle = NULL, x = "Projected Job Losses (thousands)", y = NULL ) + # Theme theme( panel.grid.major.y = element_blank(), ) + guides(fill = guide_legend( title.position = "top", title.hjust = 0.5, nrow = 1, override.aes = list(alpha = 0.9) )) ### |- Plot 2 dumbbell chart ---- p2 <- dumbbell_data |> ggplot(aes(y = state)) + # Geoms geom_segment( data = deportations |> filter(state %in% p1_states_ordered) |> mutate(state = factor(state, levels = rev(p1_states_ordered))), aes(x = u_s_born, xend = immigrant, y = state, yend = state), color = colors$palette[7], linewidth = 1.5, alpha = 0.8, inherit.aes = FALSE ) + geom_point(aes(x = jobs_lost, color = worker_type), size = 3.2, alpha = 0.9) + geom_text( data = deportations |> filter(state %in% c("California", "Texas")) |> select(state, u_s_born, immigrant) |> pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |> mutate( state = factor(state, levels = rev(p1_states_ordered)), label = paste0(round(jobs_lost / 1000), "K") ), aes(x = jobs_lost, y = state, label = label), hjust = -0.3, vjust = 1.4, size = 2.5, color = "gray30" ) + # Scales scale_x_continuous( labels = comma_format(suffix = "K", scale = 1 / 1000), expand = c(0.05, 0, 0.2, 0), breaks = scales::pretty_breaks(n = 5) ) + scale_color_manual( values = c( "U.S.-Born Workers" = colors$palette$us_born, "Immigrant Workers" = colors$palette$immigrant ), breaks = c("U.S.-Born Workers", "Immigrant Workers"), name = NULL ) + # Labs labs( title = "Both Worker Types Affected: No One is Spared", subtitle = NULL, x = "Jobs Lost (thousands)", y = NULL, caption = "Top 20 states by total job losses shown" ) + # Theme theme( panel.grid.major.y = element_blank(), ) + guides(color = guide_legend( title.position = "top", title.hjust = 0.5, nrow = 1, override.aes = list(size = 4) )) ### |- combined plot ---- # Create an invisible spacer plot spacer <- ggplot() + theme_void() # Use it between charts combined_plots <- (p1 + spacer + p2) + plot_layout(widths = c(1, 0.2, 1.1)) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", hjust = 0., color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.1), hjust = 0, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 0) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 12, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.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 ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Figure B, Trump's deportations will reduce employment in every state", data_main)` 2. Article - `r create_link("Economic Policy Institute: Trump's deportation agenda will destroy millions of jobs", data_secondary)` :::