AI’s Job Market Impact: Transformation with Net Growth

Analysis of synthetic job market data modeling AI adoption scenarios (2024-2030)

MakeoverMonday
Data Visualization
R Programming
2025
Visualizing AI’s impact on job markets using synthetic data. While some industries face disruption, the analysis reveals net job creation through strategic slope and waterfall charts built with R and ggplot2.
Published

September 9, 2025

Makeover

Figure 1: Two-panel chart showing AI’s job market impact from 2024 to 2030. The top panel displays a slope chart with IT jobs growing from 18.3M to 18.8M (green line) and Transportation declining from 18.8M to 18.3M (red line), with other industries in gray background lines. Bottom panel shows waterfall chart demonstrating cumulative job changes by sector, starting with Transportation’s -444K loss, followed by smaller losses in Education (-93K) and Manufacturing (-12K), then gains in Finance (+39K), Retail (+221K), Entertainment (+387K), Healthcare (+403K), and IT (+512K), resulting in net positive job creation. The chart indicates that the data is synthetic, modeling AI adoption scenarios.

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  = 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
#|
ai_job_trends <- read_csv(
  here::here('data/MakeoverMonday/2025/ai_job_trends_dataset.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

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

glimpse(ai_job_trends)
skimr::skim_without_charts(ai_job_trends)
```

4. Tidy Data

Show code 
```{r}
#| label: tidy
#| warning: false
# Helpers
fmt_km <- function(x) {
  case_when(
    abs(x) >= 1e6 ~ paste0(number(x / 1e6, accuracy = 0.1), "M"),
    abs(x) >= 1e3 ~ paste0(number(x / 1e3, accuracy = 1), "K"),
    TRUE ~ number(x, accuracy = 1)
  )
}

ai_data_clean <- ai_job_trends |>
  filter(!is.na(job_openings_2024), !is.na(projected_openings_2030)) |>
  mutate(job_change = projected_openings_2030 - job_openings_2024)

### |- P1: Slope chart data ----
slope_data <- ai_data_clean |>
  group_by(industry) |>
  summarise(
    openings_2024 = sum(job_openings_2024),
    openings_2030 = sum(projected_openings_2030),
    net_change = sum(job_change),
    abs_change = abs(sum(job_change)),
    .groups = "drop"
  ) |>
  arrange(desc(abs_change)) |>
  mutate(
    rank_by_change = row_number(),
    is_top_change = rank_by_change <= 2,
    story_type = case_when(
      is_top_change & net_change > 0 ~ "biggest_growth",
      is_top_change & net_change < 0 ~ "biggest_decline",
      TRUE ~ "background"
    )
  ) |>
  pivot_longer(c(openings_2024, openings_2030), names_to = "year", values_to = "openings") |>
  mutate(year_clean = if_else(year == "openings_2024", 2024, 2030))

# labels for P1
labels_2030 <- slope_data |>
  filter(story_type != "background", year_clean == 2030) |>
  mutate(x_lab = year_clean + 0.25)

### |- P2: Waterfall chart data ----
wf_core <- ai_data_clean |>
  group_by(industry) |>
  summarise(change = sum(job_change), .groups = "drop") |>
  arrange(change) |>
  mutate(idx = row_number())

waterfall_data <- wf_core |>
  arrange(idx) |>
  mutate(
    step_start = lag(cumsum(change), default = 0),
    step_end = cumsum(change),
    type = case_when(
      industry == "Total" ~ "Total",
      change >= 0 ~ "Job Gains",
      TRUE ~ "Job Losses"
    ),
    label_y = pmax(step_start, step_end) + max(abs(change)) * 0.035,
    lbl = if_else(industry == "Total",
      paste0(if_else(change >= 0, "+", ""), fmt_km(change)),
      fmt_km(change)
    ),
    lab_wrapped = str_wrap(industry, width = 10)
  )
```

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(
    
  "biggest_growth"  = "#2E6F77",  
  "biggest_decline" = "#D97B66",
  
  "Job Gains" = "#3F5E9A",    
  "Job Losses" = "#A7B6CF",   
  "Total"     = "#2C4476"  
))   

### |-  titles and caption ----
title_text <- str_glue("Ranking Drug Harms in the UK")

subtitle_text <- str_glue(
  "**Harm scores** based on a multi-criteria decision analysis by a panel of experts,<br>",
  "ranking drugs from 0 to 100 on **16 distinct criteria**.<br><br>",
  "Harm to others (left) vs harm to users (right) • weighted scores • UK, 2010 • small panel shows total harm"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("Nutt et al. (2010),<br>The Lancet – Drug harms in the UK: a multicriteria decision analysis")
)

### |-  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(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    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),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_blank(), #element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

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

# Set theme
theme_set(weekly_theme)   
```

6. Plot

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

### |- P1: Slope chart ----
p1 <- slope_data |>
  ggplot(aes(x = year_clean, y = openings, group = industry)) +
  # Geoms
  geom_line(
    data = slope_data |> filter(story_type == "background"), # background lines
    color = "gray", linewidth = 1.5, alpha = 0.6
  ) +
  geom_line(
    data = slope_data |> filter(story_type != "background"), # highlights lines
    aes(color = story_type), linewidth = 3
  ) +
  geom_point(
    data = slope_data |> filter(story_type == "background"), # background points
    color = "gray", size = 3, alpha = 0.6
  ) +
  geom_point(
    data = slope_data |> filter(story_type != "background"), # highlights points
    aes(color = story_type), size = 5
  ) +
  geom_text(
    data = slope_data |> filter(story_type != "background"),
    aes(label = fmt_km(openings), colour = story_type),
    vjust = -1.8, fontface = "bold", size = 4.0, show.legend = FALSE
  ) +
  geom_text(
    data = labels_2030,
    aes(x = x_lab, y = openings, label = industry, color = story_type),
    hjust = 0, fontface = "bold", size = 4.2, show.legend = FALSE
  ) +
  # Scales
  scale_x_continuous(breaks = c(2024, 2030), limits = c(2023.6, 2031.7)) +
  scale_y_continuous(labels = function(x) fmt_km(x)) +
  scale_colour_manual(values = colors$palette) +
  # Labs
  labs(
    title = "AI Creates Winners and Losers",
    subtitle = "Industries with the largest absolute change in job openings (2024 - 2030)",
    x = NULL, y = "Total Job Openings"
  ) +
  # Theme
  theme(
    plot.margin = margin(t = 10, r = 72, b = 10, l = 10),
    legend.position = "none"
  )

### |- P2: Waterfall chart ----
p2 <- ggplot(waterfall_data) +
  # Geoms
  geom_hline(yintercept = 0, linewidth = 0.6, colour = "gray") +
  geom_rect(
    aes(
      xmin = idx - 0.38, xmax = idx + 0.38,
      ymin = step_start, ymax = step_end,
      fill = type
    ),
    colour = "white", linewidth = 1.1, alpha = 0.96
  ) +
  geom_segment(
    data = filter(waterfall_data, idx < max(idx)),
    aes(
      x = idx + 0.38, xend = (idx + 1) - 0.38,
      y = step_end, yend = step_end
    ),
    linetype = "dashed", colour = "black", alpha = 0.7
  ) +
  geom_text(aes(x = idx, y = label_y, label = lbl),
    fontface = "bold", size = 4.0, color = colors$text,
    vjust = 0
  ) +
  # Scales
  scale_fill_manual(values = colors$palette, name = "Net Impact", drop = FALSE) +
  scale_x_continuous(
    breaks = waterfall_data$idx,
    labels = waterfall_data$lab_wrapped,
    expand = expansion(mult = c(0.02, 0.06))
  ) +
  scale_y_continuous(labels = label_number(accuracy = 1, scale_cut = cut_short_scale())) +
  # Labs
  labs(
    title = "Net Result: AI Creates Jobs",
    subtitle = "Cumulative impact across industries",
    x = NULL, y = "Cumulative Job Change"
  ) +
  # Theme +
  theme(
    axis.text.x = element_text(angle = 0, hjust = 0.5), 
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    plot.margin = margin(10, 10, 10, 10)
  )

### |- final combined plot ----
combined_plots <- p1 / p2 +
  plot_layout(heights = c(1.5, 1))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        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 = 05)
      )
    )
  )
```

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 = 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         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        pillar_1.9.0       crayon_1.5.3       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[61] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_37.qmd.

For the full repository, click here.

10. References

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