AI’s Job Market Impact: Transformation with Net Growth

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

MakeoverMonday

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

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] 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

Expand for GitHub Repo

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

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 37: AI Impact on Job Market: (2024–2030)

Article

AI Impact on Job Market: (2024–2030)

--- title: "AI's Job Market Impact: Transformation with Net Growth" subtitle: "Analysis of synthetic job market data modeling AI adoption scenarios (2024-2030)" description: "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." date: "2025-09-09" categories: ["MakeoverMonday", "2025"] tags: [ "artificial-intelligence", "job-market-analysis", "employment-trends", "data-storytelling", "ggplot2", "slope-charts", "waterfall-charts", "economic-visualization", "synthetic-data", "kaggle-dataset", "patchwork", "professional-charts", "automation-impact", "workforce-transformation" ] image: "thumbnails/mm_2025_37.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 <- 37 project_file <- "mm_2025_37.qmd" project_image <- "mm_2025_37.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025week-37" data_secondary <- "https://data.world/makeovermonday/2025week-37" ## 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_37/original_chart.png" ## Organization/Platform Links org_primary <- "https://www.kaggle.com/datasets/sahilislam007/ai-impact-on-job-market-20242030?" org_secondary <- "https://www.kaggle.com/" # 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, '")') } } ``` ### Makeover ![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.](mm_2025_37.png){#fig-1} ### **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, # 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 ```{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 ```{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 ```{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 ```{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, ", "ranking drugs from 0 to 100 on **16 distinct criteria**. ", "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), 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 ```{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 ```{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 ::: {.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("AI Impact on Job Market: (2024–2030)", data_main)` 2. Article - `r create_link("AI Impact on Job Market: (2024–2030)", data_secondary)` :::